diff --git a/Python/Module1_GettingStartedWithPython/GettingStartedWithPython.md b/Python/Module1_GettingStartedWithPython/GettingStartedWithPython.md
index ed1f2f1a..08c6ce40 100644
--- a/Python/Module1_GettingStartedWithPython/GettingStartedWithPython.md
+++ b/Python/Module1_GettingStartedWithPython/GettingStartedWithPython.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -15,7 +15,7 @@ jupyter:
.. meta::
:description: Topic: Basic description of the Python programming language, Difficulty: Easy, Category: Background
- :keywords: python, install, basics, scripts, interpreter, foundations
+ :keywords: python, install, basics, scripts, interpreter, foundations, versions
@@ -144,6 +144,62 @@ As such, Python is a language that is conducive to rapidly prototyping and testi
We will be relying heavily on Python and a library for doing optimized numerical computations, called NumPy, throughout this course.
+
+## Understanding Different Versions of Python
+
+New versions of Python come out periodically, bringing new features and fixes.
+You can keep track of what's new in Python by [bookmarking and checking this page every few months](https://docs.python.org/3/whatsnew/index.html).
+As we take on coding projects in Python, whether it be for work, school, or a personal hobby, it is important that we remain aware of
+the ways in which the language is changing, and that we take care to write code that will remain functional in the future.
+
+All Python version numbers use the `A.B.C` format, in accordance with [semantic versioning](https://semver.org/).
+The three numbers denote major releases, minor releases, and patches.
+For example, as of writing this, the current release of Python is version `3.9.1`.
+
+The first number denotes major releases to the language.
+When a major release comes out, it means that older code will not necessarily work with the new release, and vice versa.
+For example, the following code worked in Python 2 but no longer works because the `xrange` function was removed from the language
+upon the release of Python 3.
+
+```python
+# `xrange` was a frequently-used function in Python 2, but
+# was removed from the language in Python 3 in favor of `range`.
+# Thus the following code does not work in any version of Python 3.
+count = 0
+for i in xrange(10):
+ count = count + 1
+```
+
+The most current major release is Python 3; Python 2 is no longer supported by any bug or security fixes and should not be used.
+All releases in the near future will be improvements to Python 3, and thus they will come in the form of minor releases and patches.
+
+The second number denotes a minor release.
+A minor release will be compatible with code from the preceding release, but it might add new features to the language that are not backwards-compatible.
+For example, Python 3.6 introduced [formatted string literals](https://docs.python.org/3/whatsnew/3.6.html#pep-498-formatted-string-literals), which are
+commonly referred to as "f-strings".
+Thus as of Python 3.6 you could write code like
+
+```python
+# Python 3.6 introduced the "f-string" feature, which is not
+# backwards compatible with Python 3.5
+>>> f"one plus two is {1 + 2}"
+'one plus two is 3'
+```
+
+but this code would not run in Python 3.5.X.
+
+The third and final number denotes a patch, which generally means bug fixes and performance improvements.
+All code within the same minor release will run on all other patches within that minor release
+For example, all Python 3.7.8 code is compatible with a Python 3.7.1 interpreter, and vice versa.
+Patches are released fairly often, and their changes only occur 'under the hood'.
+[Here is a list of changes](https://docs.python.org/3/whatsnew/changelog.html#python-3-9-1-final) were introduced by the patch level increment from Python 3.9.0 to Python 3.9.1;
+few of these would affect our day-to-day experience with using Python (which isn't to say that they aren't important!).
+
+In simpler terms, major releases are neither backward nor forward compatible.
+Minor releases are forward compatible but not necessarily fully backward compatible, and patches are both forward and backward compatible.
+
+
+
## Summary
- Python is a programming language - it provides us with a simple set of grammatical rules that allow us to write human-readable text, which can be translated unambiguously to instruct a computer to perform tasks.
diff --git a/Python/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.md b/Python/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.md
index 2390a6b2..d780d32f 100644
--- a/Python/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.md
+++ b/Python/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -39,41 +39,51 @@ First and foremost, a good IDE will provide a text editor that will:
An IDE also often provides debugging tools so that you can test your code; it will also typically interface with version-control software, like Git, so that you can keep track of versions of your code as you modify it. We will not discuss these useful, but more advanced features here.
-### Recommended IDEs
+## Recommended IDEs
There are many excellent IDEs that can be configured to work well with Python. Two IDEs that we endorse are:
-[PyCharm](https://www.jetbrains.com/pycharm/download): A powerful IDE dedicated to Python.
+### PyCharm
+
+[PyCharm](https://www.jetbrains.com/pycharm/download) is a powerful and highly-polished IDE dedicated to developing Python code.
**Pros**
-- works well out-of-the-box
-- long-supported by professionals and thus is very reliable
-- highly configurable
-- fully-featured, with an excellent debugger, context-dependent "intellisense", type-inference, and more
-- the free "community version" is extremely robust and feature-rich.
+- Works well out-of-the-box.
+- Long-supported by professionals and thus is very reliable.
+- Highly configurable.
+- Fully-featured, with an excellent debugger, context-dependent "intellisense", type-inference, and more.
+- The free "community version" is extremely robust and feature-rich.
+- Generally provides an extremely high-quality and responsive experience for doing Python development.
**Cons**
- - can be resource-heavy, especially for a laptop
- - may be overwhelming to new users (but has good documentation and tutorials)
- - Jupyter notebook support requires the premium version of PyCharm, making it inaccessible to newcomers
+ - Can be resource-heavy, especially for a laptop.
+ - May be overwhelming to new users (but has good documentation and tutorials).
+ - Jupyter notebook support requires the premium version of PyCharm, making it inaccessible to newcomers.
-[Visual Studio Code](https://code.visualstudio.com/) with the [Python extension](https://code.visualstudio.com/docs/languages/python): A lightweight, highly customizable IDE.
+### Visual Studio Code
+
+[Visual Studio Code](https://code.visualstudio.com/) (with the [Python extension](https://code.visualstudio.com/docs/languages/python)) is a lightweight, highly customizable IDE that works with many different languages.
+
+Note: if you decide to use VSCode to do Python development, it is highly recommended that you install Microsoft's [PyLance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
+extension.
+This adds many useful features to the IDE that will make writing Python code a more delightful experience.
**Pros**
-- lightweight and elegant
-- completely free
-- works with many different languages, so you only need to familiarize yourself with one IDE if you are a polyglot programmer
-- a huge number of extensions can be downloaded to add functionality to the editor; these are created by a large community of open-source developers.
-- [has native support for Jupyter notebooks](https://code.visualstudio.com/docs/python/jupyter-support), meaning that you get VSCode's intellisense, debugger, and ability to inspect variables, all in a notebook.
+- Lightweight and elegant.
+- Completely free.
+- Works with many different languages, so you only need to familiarize yourself with one IDE if you are a polyglot programmer.
+- Offers a huge number of extensions that can be downloaded to add functionality to the editor; these are created by a large community of open-source developers.
+- [Has native support for Jupyter notebooks](https://code.visualstudio.com/docs/python/jupyter-support), meaning that you get VSCode's intellisense, debugger, and ability to inspect variables, all in a notebook.
+- Provides incredibly robust [remote coding](https://code.visualstudio.com/docs/remote/remote-overview) and [collaborative coding](https://visualstudio.microsoft.com/services/live-share/) capabilities.
**Cons**
-- configuring VSCode for python development can have a moderate learning curve for newcomers
-- many features, like context-aware intellisense and type-inference, are simply more polished and powerful in PyCharm
+- Configuring VSCode for Python development can have a moderate learning curve for newcomers.
+- Some features, like context-aware intellisense and type-inference, are simply more polished and powerful in PyCharm.
+
-
+
+**Before You Start This Section**:
+
+In the following section we will be using IPython or a Jupyter notebook to run our code.
+Presently, there is an incompatibility with these programs and a Python package called `jedi`, which typically is responsible for performing auto-completions in our code (when prompted by hitting ``, which we will be doing below).
+It is really useful!
+
+First, let's check to see if we have an incompatible version of `jedi` installed.
+In your terminal (before starting a Python/IPython/Jupyter session), run
+
+```
+conda list
+```
+
+And look for the line that starts with `jedi`
+
+```
+jedi 0.18.0
+```
+
+If you see that you have version `0.18.0` installed (as above), then you will want to downgrade it.
+In the same terminal, run the following command
+
+```
+conda install jedi=0.17.2
+```
+You should be all set once you have followed the prompts and the installation has completed!
+
+Note that you will need to repeat this process if you [create a new conda environment](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Installing_Python.html#A-Brief-Introduction-to-Conda-Environments) with IPython/Jupter installed in it.
+
+
Now that you have the Anaconda distribution of Python installed on your machine, let's write some simple Python code! We are going to forego writing a full Python script for now, and instead make use of a convenient tool for doing quick code scratchwork. The IPython console was installed as a part of Anaconda; it will allow us to build incrementally off of snippets of code, instead of having to execute an entire script all at once.
Let's open an IPython console. Open your terminal if you are a Mac/Linux user, or start `cmd.exe` if you are a Windows user. Now type `ipython` into the console and hit ``. You should see the following display on your screen:
diff --git a/Python/Module1_GettingStartedWithPython/Installing_Python.md b/Python/Module1_GettingStartedWithPython/Installing_Python.md
index c415deee..67a21a98 100644
--- a/Python/Module1_GettingStartedWithPython/Installing_Python.md
+++ b/Python/Module1_GettingStartedWithPython/Installing_Python.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module1_GettingStartedWithPython/Jupyter_Notebooks.md b/Python/Module1_GettingStartedWithPython/Jupyter_Notebooks.md
index 0bb42a42..cf817578 100644
--- a/Python/Module1_GettingStartedWithPython/Jupyter_Notebooks.md
+++ b/Python/Module1_GettingStartedWithPython/Jupyter_Notebooks.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -19,19 +19,57 @@ jupyter:
# Jupyter Notebooks
-In recent years, the Jupyter Notebook has become a massively popular tool for doing research-oriented work in Python and other languages alike. Its emergence marked a paradigm shift in the way data science is conducted.
-A Jupyter notebook is similar to the IPython console, but, instead of only being able to work with a single line of code at a time, you can easily edit and re-execute *any* code that had been written in a notebook. Furthermore, you can save a notebook, and thus return to it later. Additionally, a notebook provides many terrific features. For instance, you can embed visualizations of data within a notebook, and write blocks of nicely-formatted text (using the [Markdown syntax](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)), for presenting and explaining the contents of the notebook.
+
+
+**Before You Start This Section**:
+
+In the following section we will be using IPython or a Jupyter notebook to run our code.
+Presently, there is an incompatibility with these programs and a Python package called `jedi`, which typically is responsible for performing auto-completions in our code (when prompted by hitting ``, which we will be doing below).
+It is really useful!
+
+First, let's check to see if we have an incompatible version of `jedi` installed.
+In your terminal (before starting a Python/IPython/Jupyter session), run
+
+```
+conda list
+```
+
+And look for the line that starts with `jedi`
+
+```
+jedi 0.18.0
+```
+
+If you see that you have version `0.18.0` installed (as above), then you will want to downgrade it.
+In the same terminal, run the following command
+
+```
+conda install jedi=0.17.2
+```
+You should be all set once you have followed the prompts and the installation has completed!
+
+Note that you will need to repeat this process if you [create a new conda environment](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Installing_Python.html#A-Brief-Introduction-to-Conda-Environments) with IPython/Jupter installed in it.
+
+
+In recent years, the Jupyter Notebook has become a massively popular tool for doing research-oriented work in Python and other languages alike.
+Its emergence marked a paradigm shift in the way data science is conducted.
+
+A Jupyter notebook is similar to the IPython console, but, instead of only being able to work with a single line of code at a time, you can easily edit and re-execute *any* code that had been written in a notebook. Furthermore, you can save a notebook, and thus return to it later.
+Additionally, a notebook provides many terrific features. For instance, you can embed visualizations of data within a notebook, and write blocks of nicely-formatted text (using the [Markdown syntax](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)), for presenting and explaining the contents of the notebook.
In this way, the Jupyter Notebook stands out as an excellent tool for many practical applications. You could work on a notebook while you are working through sections of this website, for instance, testing out snippets of code, and answering reading-comprehension questions as you proceed through the text, and using markdown-headers to visually separate different portions of the notebook. When I do research, I am always creating Jupyter notebooks in which I write code that analyzes data, I plot various results, presented in different ways, and I write detailed markdown-text blocks to document my work. The end result is something that I can share with my labmates, and easily revisit months later without having to struggle to recall what I had done.
## Jupyter Lab
-[Jupyter lab](https://jupyterlab.readthedocs.io/) is a new web interface from Project Jupyter that provides a rich web-based interface for managing and running Jupyter notebooks, console terminals, and text editors, all within your browser. Among its useful features and polished user interface - compared to that a Jupyter notebook server - Jupyter lab provides moveable panes for viewing data, images, and code output apart from the rest of the notebook. This is facilitates effective data science work flows.
+[Jupyter lab](https://jupyterlab.readthedocs.io/) is a new web interface from Project Jupyter that provides a rich web-based interface for managing and running Jupyter notebooks, console terminals, and text editors, all within your browser.
+Among its useful features and polished user interface, Jupyter lab provides moveable panes for viewing data, images, and code output apart from the rest of the notebook.
+This is facilitates effective data science work flows.
It is recommended that you peruse the [Jupyter lab documentation](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html) to get a feel for all of its added capabilities.
-The following instructions are laid out for running a Jupyter notebook server. That being said, the process for running a Jupyter lab server and working with notebooks therein is nearly identical. Both Jupyter notebook and Jupyter lab should already be [installed via Anaconda](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Installing_Python.html).
+The following instructions are laid out for running a Jupyter notebook server. That being said, the process for running a Jupyter lab server and working with notebooks therein is nearly identical.
+Both Jupyter notebook and Jupyter lab should already be [installed via Anaconda](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Installing_Python.html).
## Running a Notebook Server & Creating a Notebook
@@ -100,7 +138,7 @@ import numpy as np
import matplotlib.pyplot as plt
# this tells Jupyter to embed matplotlib plots in the notebook
-%matplotlib notebook
+%matplotlib inline
```
```python
@@ -186,4 +224,4 @@ The Jupyter Notebook does not work exclusively with Python. A "kernel" can be de
The ever-growing list of available kernels for use with Jupyter can be found [here](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels). It should be noted that these efforts are not all equally mature. For instance, whereas the Python and Julia kernels are robust, the Haskell kernel cannot run natively on Windows machines, and the C++ kernel is still in early development, as of writing this.
### Jupyter Notebook Support in Visual Studio Code
-Native Jupyter notebook support was [recently added to Visual Studio Code](https://devblogs.microsoft.com/python/announcing-support-for-native-editing-of-jupyter-notebooks-in-vs-code/). This means that you can now edit Jupyter notebooks within the [Visual Studio Code IDE](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.html), and that you will benefit from added features like code-completion, debugging, and variable inspection.
\ No newline at end of file
+Native Jupyter notebook support was [recently added to Visual Studio Code](https://devblogs.microsoft.com/python/announcing-support-for-native-editing-of-jupyter-notebooks-in-vs-code/). This means that you can now edit Jupyter notebooks within the [Visual Studio Code IDE](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.html), and that you will benefit from added features like code-completion, debugging, and variable inspection.
diff --git a/Python/Module1_GettingStartedWithPython/Numerical_Work_In_Python.md b/Python/Module1_GettingStartedWithPython/Numerical_Work_In_Python.md
index f68dab65..fab7fe1c 100644
--- a/Python/Module1_GettingStartedWithPython/Numerical_Work_In_Python.md
+++ b/Python/Module1_GettingStartedWithPython/Numerical_Work_In_Python.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module1_GettingStartedWithPython/SiteFormatting.md b/Python/Module1_GettingStartedWithPython/SiteFormatting.md
index 0f38f692..482651ca 100644
--- a/Python/Module1_GettingStartedWithPython/SiteFormatting.md
+++ b/Python/Module1_GettingStartedWithPython/SiteFormatting.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -65,8 +65,36 @@ We can spend some time talking about `my_func` and then see it in action:
```
Lastly, the input and output of an iPython console and a Jupyter notebook alike is displayed as follows:
-
```python
2 + 3
```
+
+## Running Code Snippets from this Site
+
+In PLYMI, we typically precede every code snippet with one or more commented lines.
+This is useful because it makes a page more "skimmable", since the code snippets essentially come with
+descriptive, self-explanatory captions.
+That being said, there is a downside to this.
+
+Python terminals don't like having multiple comment lines precede an input-prompt.
+E.g. if you paste and run the following code into a terminal
+
+```python
+# demonstrating the distinction of
+# input and output via >>>
+
+>>> x = 1
+```
+
+you will get a syntax error.
+To fix this issue, simply exclude the comments when you copy this block to your clipboard.
+Running
+
+```python
+>>> x = 1
+```
+
+will work without any issue.
+Keep this in mind if you ever find yourself having trouble running code that you copied from this site.
+
diff --git a/Python/Module2_EssentialsOfPython/Basic_Objects.md b/Python/Module2_EssentialsOfPython/Basic_Objects.md
index 5a6dd54c..fcb69895 100644
--- a/Python/Module2_EssentialsOfPython/Basic_Objects.md
+++ b/Python/Module2_EssentialsOfPython/Basic_Objects.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/ConditionalStatements.md b/Python/Module2_EssentialsOfPython/ConditionalStatements.md
index b6192abb..9abff3a2 100644
--- a/Python/Module2_EssentialsOfPython/ConditionalStatements.md
+++ b/Python/Module2_EssentialsOfPython/ConditionalStatements.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -227,11 +227,11 @@ if 3 < len(x):
# bool(3 < 2) returns False, this code
# block is skipped
print("`x` has more than three items in it")
-elif len(x) == 2
+elif len(x) == 2:
# bool(len(x) == 2) returns True
# this code block is executed
print("`x` has two items in it")
-elif len(x) == 1
+elif len(x) == 1:
# this statement is never reached
print("`x` has one items in it")
else:
@@ -332,6 +332,8 @@ That is:
Python supports a syntax for writing a restricted version of if-else statements in a single line. The following code:
```python
+num = 2
+
if num >= 0:
sign = "positive"
else:
@@ -353,7 +355,7 @@ This is suggestive of the general underlying syntax for inline if-else statement
The expression `A if else B` returns `A` if `bool()` evaluates to `True`, otherwise this expression will return `B`.
-This syntax is highly restricted compared to the full "if-elif-else" expressions - no "elif" statement is permitted by this inline syntax, nor are muli-line code blocks within the if/else clauses.
+This syntax is highly restricted compared to the full "if-elif-else" expressions - no "elif" statement is permitted by this inline syntax, nor are multi-line code blocks within the if/else clauses.
Inline if-else statements can be used anywhere, not just on the right side of an assignment statement, and can be quite convenient:
```python
diff --git a/Python/Module2_EssentialsOfPython/DataStructures.md b/Python/Module2_EssentialsOfPython/DataStructures.md
index 81631b47..6fc26338 100644
--- a/Python/Module2_EssentialsOfPython/DataStructures.md
+++ b/Python/Module2_EssentialsOfPython/DataStructures.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/DataStructures_III_Sets_and_More.md b/Python/Module2_EssentialsOfPython/DataStructures_III_Sets_and_More.md
index c399d068..7290ca04 100644
--- a/Python/Module2_EssentialsOfPython/DataStructures_III_Sets_and_More.md
+++ b/Python/Module2_EssentialsOfPython/DataStructures_III_Sets_and_More.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/DataStructures_II_Dictionaries.md b/Python/Module2_EssentialsOfPython/DataStructures_II_Dictionaries.md
index 1b7d4d7a..a01d8831 100644
--- a/Python/Module2_EssentialsOfPython/DataStructures_II_Dictionaries.md
+++ b/Python/Module2_EssentialsOfPython/DataStructures_II_Dictionaries.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/ForLoops.md b/Python/Module2_EssentialsOfPython/ForLoops.md
index 821c4d40..b447734f 100644
--- a/Python/Module2_EssentialsOfPython/ForLoops.md
+++ b/Python/Module2_EssentialsOfPython/ForLoops.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -14,7 +14,7 @@ jupyter:
.. meta::
- :description: Topic: Contorl flow with for-loops and while-loops, Difficulty: Easy, Category: Section
+ :description: Topic: Control flow with for-loops and while-loops, Difficulty: Easy, Category: Section
:keywords: for-loop, while-loop, break, control flow, basic programming
diff --git a/Python/Module2_EssentialsOfPython/Functions.md b/Python/Module2_EssentialsOfPython/Functions.md
index c57eb4c2..4b7d1cc9 100644
--- a/Python/Module2_EssentialsOfPython/Functions.md
+++ b/Python/Module2_EssentialsOfPython/Functions.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -137,7 +137,7 @@ Write a function named `count_even`. It should accept one input argument, named
## The `return` Statement
-In general, any Python object can follow a function's `return` statement. Furthermore, an **empty** `return` statement can be specified, or the **return** statement of a function can be omitted altogether. In both of these cases, *the function will return the `None` object*.
+In general, any Python object can follow a function's `return` statement. Furthermore, an **empty** `return` statement can be specified, or the **return** statement of a function can be omitted altogether. In both of these cases, *the function will return the* `None` *object*.
```python
# this function returns `None`
@@ -244,7 +244,7 @@ This should be used sparingly, for exceedingly simple functions that can be easi
## Arguments
-A sequence of comma-separated variable names can specified in the function signature to indicated *positional* arguments for the function. For example, the following specifies `x`, `lower`, and `upper` as input arguments to a function, `is_bounded`:
+A sequence of comma-separated variable names can be specified in the function signature to indicated *positional* arguments for the function. For example, the following specifies `x`, `lower`, and `upper` as input arguments to a function, `is_bounded`:
```python
def is_bounded(x, lower, upper):
diff --git a/Python/Module2_EssentialsOfPython/Generators_and_Comprehensions.md b/Python/Module2_EssentialsOfPython/Generators_and_Comprehensions.md
index 3f6b9a2d..d18a4dfa 100644
--- a/Python/Module2_EssentialsOfPython/Generators_and_Comprehensions.md
+++ b/Python/Module2_EssentialsOfPython/Generators_and_Comprehensions.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -544,12 +544,12 @@ Use a tuple-comprehension to extract comma-separated numbers from a string, conv
Replicate the functionality of the the following code by writing a list comprehension.
```python
# skip all non-lowercased letters (including punctuation)
-# append 1 if lowercase letter is "o"
+# append 1 if lowercase letter is equal to "o"
# append 0 if lowercase letter is not "o"
out = []
for i in "Hello. How Are You?":
if i.islower():
- out.append(1 if i is "o" else 0)
+ out.append(1 if i == "o" else 0)
```
@@ -653,7 +653,7 @@ or
**Translating a For-Loop: Solution**
```python
->>> out = [(1 if i is "o" else 0) for i in "Hello. How Are You?" if i.islower()]
+>>> out = [(1 if i == "o" else 0) for i in "Hello. How Are You?" if i.islower()]
>>> out
[0, 0, 0, 1, 1, 0, 0, 0, 1, 0]
```
diff --git a/Python/Module2_EssentialsOfPython/Introduction.md b/Python/Module2_EssentialsOfPython/Introduction.md
index 7f48f672..98e40d26 100644
--- a/Python/Module2_EssentialsOfPython/Introduction.md
+++ b/Python/Module2_EssentialsOfPython/Introduction.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/Iterables.md b/Python/Module2_EssentialsOfPython/Iterables.md
index 2a53dcd7..ce3e4fdc 100644
--- a/Python/Module2_EssentialsOfPython/Iterables.md
+++ b/Python/Module2_EssentialsOfPython/Iterables.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/Itertools.md b/Python/Module2_EssentialsOfPython/Itertools.md
index c4efa8f4..05febea0 100644
--- a/Python/Module2_EssentialsOfPython/Itertools.md
+++ b/Python/Module2_EssentialsOfPython/Itertools.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/Problems/DifferenceFanout.md b/Python/Module2_EssentialsOfPython/Problems/DifferenceFanout.md
index 6bebd45f..a362d832 100644
--- a/Python/Module2_EssentialsOfPython/Problems/DifferenceFanout.md
+++ b/Python/Module2_EssentialsOfPython/Problems/DifferenceFanout.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/Problems/EncodeAsString.md b/Python/Module2_EssentialsOfPython/Problems/EncodeAsString.md
index 1b90cf5a..a7b63234 100644
--- a/Python/Module2_EssentialsOfPython/Problems/EncodeAsString.md
+++ b/Python/Module2_EssentialsOfPython/Problems/EncodeAsString.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/Problems/MarginPercentage.md b/Python/Module2_EssentialsOfPython/Problems/MarginPercentage.md
index ddce8aeb..1d7f7f91 100644
--- a/Python/Module2_EssentialsOfPython/Problems/MarginPercentage.md
+++ b/Python/Module2_EssentialsOfPython/Problems/MarginPercentage.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/Problems/MergeMaxDicts.md b/Python/Module2_EssentialsOfPython/Problems/MergeMaxDicts.md
index 95f9be13..096d983c 100644
--- a/Python/Module2_EssentialsOfPython/Problems/MergeMaxDicts.md
+++ b/Python/Module2_EssentialsOfPython/Problems/MergeMaxDicts.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/Problems/Palindrome.md b/Python/Module2_EssentialsOfPython/Problems/Palindrome.md
index 098fa12b..3f9bd182 100644
--- a/Python/Module2_EssentialsOfPython/Problems/Palindrome.md
+++ b/Python/Module2_EssentialsOfPython/Problems/Palindrome.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/Scope.md b/Python/Module2_EssentialsOfPython/Scope.md
index 15b6b594..afc6f8d8 100644
--- a/Python/Module2_EssentialsOfPython/Scope.md
+++ b/Python/Module2_EssentialsOfPython/Scope.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module2_EssentialsOfPython/SequenceTypes.md b/Python/Module2_EssentialsOfPython/SequenceTypes.md
index 2ada1cdd..1e8a1149 100644
--- a/Python/Module2_EssentialsOfPython/SequenceTypes.md
+++ b/Python/Module2_EssentialsOfPython/SequenceTypes.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -320,7 +320,7 @@ The basic syntax for slicing is: `seq[start:stop:step]`, using colons to separat
>>> seq[1:4:1]
'bcd'
-# start:1, stop:5, step:2
+# start:0, stop:5, step:2
>>> seq[0:5:2] # get every other entry within [start, stop)
'ace'
diff --git a/Python/Module2_EssentialsOfPython/Variables_and_Assignment.md b/Python/Module2_EssentialsOfPython/Variables_and_Assignment.md
index 0869b676..0e9c5fc3 100644
--- a/Python/Module2_EssentialsOfPython/Variables_and_Assignment.md
+++ b/Python/Module2_EssentialsOfPython/Variables_and_Assignment.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module3_IntroducingNumpy/AccessingDataAlongMultipleDimensions.md b/Python/Module3_IntroducingNumpy/AccessingDataAlongMultipleDimensions.md
index af775e97..79c5e8f3 100644
--- a/Python/Module3_IntroducingNumpy/AccessingDataAlongMultipleDimensions.md
+++ b/Python/Module3_IntroducingNumpy/AccessingDataAlongMultipleDimensions.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module3_IntroducingNumpy/AdvancedIndexing.md b/Python/Module3_IntroducingNumpy/AdvancedIndexing.md
index 64df9ccc..2dcb7a2f 100644
--- a/Python/Module3_IntroducingNumpy/AdvancedIndexing.md
+++ b/Python/Module3_IntroducingNumpy/AdvancedIndexing.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module3_IntroducingNumpy/ArrayTraversal.md b/Python/Module3_IntroducingNumpy/ArrayTraversal.md
index 98c255a4..d7979257 100644
--- a/Python/Module3_IntroducingNumpy/ArrayTraversal.md
+++ b/Python/Module3_IntroducingNumpy/ArrayTraversal.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module3_IntroducingNumpy/BasicArrayAttributes.md b/Python/Module3_IntroducingNumpy/BasicArrayAttributes.md
index 8cd241a3..30f1130b 100644
--- a/Python/Module3_IntroducingNumpy/BasicArrayAttributes.md
+++ b/Python/Module3_IntroducingNumpy/BasicArrayAttributes.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module3_IntroducingNumpy/BasicIndexing.md b/Python/Module3_IntroducingNumpy/BasicIndexing.md
index 74a6fde4..12fe7a0d 100644
--- a/Python/Module3_IntroducingNumpy/BasicIndexing.md
+++ b/Python/Module3_IntroducingNumpy/BasicIndexing.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -202,18 +202,16 @@ Basic indexing is triggered whenever a tuple of: integer, `slice`, `numpy.newaxi
-
-
+
**Reading Comprehension: Ellipsis**
Given a $N$-dimensional array, `x`, index into `x` such that you access entry-0 of axis-0, the last entry of axis-$N-1$, slicing along all intermediate dimensions. $N$ is at least $2$.
-
-
-
+
+
**Reading Comprehension: Basic Indexing**
Given a shape-(4, 3) array,
@@ -291,9 +289,9 @@ False
Thus updating a variable `subarray` via `subarray = subarray + 2` does *not* overwrite the original data referenced by `subarray`. Rather, `subarray + 2` assigns that new array to the variable `subarray`. NumPy does provide mechanisms for performing mathematical operations to directly update the underlying data of an array without having to create a distinct array. We will discuss these mechanisms in the next subsection.
-
-
+
+
**Reading Comprehension: Views**
Given,
diff --git a/Python/Module3_IntroducingNumpy/Broadcasting.md b/Python/Module3_IntroducingNumpy/Broadcasting.md
index 0f8c47d1..c11e0503 100644
--- a/Python/Module3_IntroducingNumpy/Broadcasting.md
+++ b/Python/Module3_IntroducingNumpy/Broadcasting.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -486,16 +486,16 @@ Performing this computation using for-loops proceeds as follows:
def pairwise_dists_looped(x, y):
""" Computing pairwise distances using for-loops
- Parameters
- ----------
- x : numpy.ndarray, shape=(M, D)
- y : numpy.ndarray, shape=(N, D)
+ Parameters
+ ----------
+ x : numpy.ndarray, shape=(M, D)
+ y : numpy.ndarray, shape=(N, D)
- Returns
- -------
- numpy.ndarray, shape=(M, N)
- The Euclidean distance between each pair of
- rows between `x` and `y`."""
+ Returns
+ -------
+ numpy.ndarray, shape=(M, N)
+ The Euclidean distance between each pair of
+ rows between `x` and `y`."""
# `dists[i, j]` will store the Euclidean
# distance between `x[i]` and `y[j]`
dists = np.empty((5, 6))
@@ -514,6 +514,16 @@ def pairwise_dists_looped(x, y):
```
Be sure to step through this code and see that `dists` stores each pair of Euclidean distances between the rows of `x` and `y`.
+
+```python
+# produces a shape-(5, 6) result
+>>> pairwise_dists_looped(x, y)
+array([[ 3.678 , 8.4524, 10.3057, 7.3711, 6.2152, 5.5548],
+ [10.1457, 5.8793, 2.9274, 4.1114, 3.9098, 5.2259],
+ [ 7.3219, 0.8439, 6.8734, 4.5687, 7.3283, 4.8216],
+ [10.339 , 7.032 , 7.4745, 7.0633, 3.5999, 4.0107],
+ [ 8.2878, 3.5468, 6.336 , 4.9014, 4.1858, 2.0257]])
+```
@@ -545,26 +555,34 @@ Voilà! We have produced the distances in a vectorized way. Let's write this out
def pairwise_dists_crude(x, y):
""" Computing pairwise distances using vectorization.
- This method uses memory-inefficient broadcasting.
-
- Parameters
- ----------
- x : numpy.ndarray, shape=(M, D)
- y : numpy.ndarray, shape=(N, D)
-
- Returns
- -------
- numpy.ndarray, shape=(M, N)
- The Euclidean distance between each pair of
- rows between `x` and `y`."""
+ This method uses memory-inefficient broadcasting.
+
+ Parameters
+ ----------
+ x : numpy.ndarray, shape=(M, D)
+ y : numpy.ndarray, shape=(N, D)
+
+ Returns
+ -------
+ numpy.ndarray, shape=(M, N)
+ The Euclidean distance between each pair of
+ rows between `x` and `y`."""
# The use of `np.newaxis` here is equivalent to our
# use of the `reshape` function
return np.sqrt(np.sum((x[:, np.newaxis] - y[np.newaxis])**2, axis=2))
```
-
+```python
+# produces a shape-(5, 6) result
+>>> pairwise_dists_crude(x, y)
+array([[ 3.678 , 8.4524, 10.3057, 7.3711, 6.2152, 5.5548],
+ [10.1457, 5.8793, 2.9274, 4.1114, 3.9098, 5.2259],
+ [ 7.3219, 0.8439, 6.8734, 4.5687, 7.3283, 4.8216],
+ [10.339 , 7.032 , 7.4745, 7.0633, 3.5999, 4.0107],
+ [ 8.2878, 3.5468, 6.336 , 4.9014, 4.1858, 2.0257]])
+```
Regrettably, there is a glaring issue with the vectorized computation that we just performed. Consider the largest sized array that is created in the for-loop computation, compared to that of this vectorized computation. The for-loop version need only create a shape-$(M, N)$ array, whereas the vectorized computation creates an intermediate array (i.e. `diffs`) of shape-$(M, N, D)$. This intermediate array is even created in the one-line version of the code. This will create a massive array if $D$ is a large number!
-Suppose, for instance, that you are finding the Euclidean between pairs of RGB images that each have a resolution of $32 \times 32$ (in order to see if the images resemble one another). Thus in this scenario, each image is comprised of $D = 32 \times 32 \times 3 = 3072$ numbers ($32^2$ pixels, and each pixel has 3 values: a red, blue, and green-color value). Computing all the distances between a stack of 5000 images with a stack of 100 images would form an intermediate array of shape-$(5000, 100, 3072)$. Even though this large array only exists temporarily, it would have to consume over 6GB of RAM! The for-loop version requires $\frac{1}{3027}$ as much memory (about 2MB).
+Suppose, for instance, that you are finding the Euclidean between pairs of RGB images that each have a resolution of $32 \times 32$ (in order to see if the images resemble one another). Thus in this scenario, each image is comprised of $D = 32 \times 32 \times 3 = 3072$ numbers ($32^2$ pixels, and each pixel has 3 values: a red, blue, and green-color value). Computing all the distances between a stack of 5000 images with a stack of 100 images would form an intermediate array of shape-$(5000, 100, 3072)$. Even though this large array only exists temporarily, it would have to consume over 6GB of RAM! The for-loop version requires $\frac{1}{3072}$ as much memory (about 2MB).
Is our goose cooked? Are we doomed to pick between either slow for-loops, or a memory-inefficient use of vectorization? No! We can refactor the mathematical form of the Euclidean distance in order to avoid the creation of that bloated intermediate array.
@@ -611,7 +629,7 @@ Thus the third term in our equation, $-2\sum_{i=0}^{D-1}{x_{i} y_{i}}$, for all
```python
# computing the third term in the distance
# equation, for all pairs of rows
->>> x_y_prod = -2 * np.matmul(x, y.T) # `np.dot` can also be used to the same effect
+>>> x_y_prod = 2 * np.matmul(x, y.T) # `np.dot` can also be used to the same effect
>>> x_y_prod.shape
(5, 6)
```
@@ -620,34 +638,106 @@ Having accounted for all three terms, we can finally compute the Euclidean dista
```python
# computing all the distances
->>> dists = np.sqrt(x_y_sqrd + x_y_prod)
+>>> dists = np.sqrt(x_y_sqrd - x_y_prod)
>>> dists.shape
(5, 6)
```
-In total, we have successfully used vectorization to compute the all pairs of distances, while only requiring an array of shape-$(M, N)$ to do so! This is the memory-efficient, vectorized form - the stuff that dreams are made of. Let's write the function that performs this computation in full.
+
+#### A Subtle Issue with Floating-point Precision
+
+There is one more important and very subtle detail that we have to deal with.
+In terms of pure mathematics, `x_y_sqrd - x_y_prod` must be a strictly non-negative value (i.e. its smallest possible value is $0$), since it is equivalent to
+
+\begin{equation}
+\sum_{i=0}^{D-1}{(x_{i} - y_{i})^2}
+\end{equation}
+
+That being said, are working with floating-point numbers, which do not always behave exactly like rational numbers when we do arithmetic with them.
+Indeed, we saw earlier that the [quirks of floating-point arithmetic](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Understanding-Numerical-Precision) can lead to surprising results.
+Here, the strange behavior is that `x_y_sqrd - x_y_prod` **can produce negative numbers**!
+
+Let's see this in action.
+We'll take the following shape-(2, 3) array
```python
-def pairwise_dists(x, y):
- """ Computing pairwise distances using memory-efficient
- vectorization.
+# A carefully-selected input that will trigger surprising
+# floating-point precision issues in our distance calculation
+>>> x = np.array([[4.700867387959219, 4.700867387959219, 4.700867387959219],
+... [4.700867387959219, 4.700867387959219, 4.700867387959219]])
+```
- Parameters
- ----------
- x : numpy.ndarray, shape=(M, D)
- y : numpy.ndarray, shape=(N, D)
+And compute the square distance between all combinations of rows `x`; the result should simply be a shape-(2, 2) array of zeros.
- Returns
- -------
- numpy.ndarray, shape=(M, N)
- The Euclidean distance between each pair of
- rows between `x` and `y`."""
- dists = -2 * np.matmul(x, y.T)
- dists += np.sum(x**2, axis=1)[:, np.newaxis]
- dists += np.sum(y**2, axis=1)
- return np.sqrt(dists)
+```python
+# The square-distances should be exactly zero, but they
+# will instead be (*very* small) negative numbers!
+>>> sqr_dists = -2 * np.matmul(x, x.T) # `x_x_prod`
+>>> sqr_dists += np.sum(x**2, axis=1)[:, np.newaxis]
+>>> sqr_dists += np.sum(x**2, axis=1)
+>>> sqr_dists
+array([[-2.842170943040401e-14, -2.842170943040401e-14],
+ [-2.842170943040401e-14, -2.842170943040401e-14]])
```
+These values are *very* close to being zero, so it is not as if the magnitude of our result is wildly off, but the critical issue here is that the quirks of floating-point arithmetic produced (very small) negative numbers.
+We are going to be in for a rude awakening when we take the square-root of these values to get our final distances:
+```python
+# Taking the square-root of negative floats will produce NaNs
+>>> np.sqrt(sqr_dists)
+array([[nan, nan],
+ [nan, nan]])
+```
+
+`nan` stands for "Not a Number", which is to say that the square-root of a negative number cannot produce a real-valued float (the result will be an [imaginary number](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Complex-Numbers), but NumPy will not swap out real numbers for imaginary ones unless we explicitly permit it to).
+
+The solution to this problem comes from the realization that this occurrence is truly an edge-case, and that it will only manifest when the result *ought* to have been zero and indeed is very close to zero.
+Thus we can [clip](https://numpy.org/doc/stable/reference/generated/numpy.clip.html) `sqr_dists` such that any value that falls below zero gets set to zero and all other values will be left alone:
+
+```python
+# "Clipping" all negative entries so that they are set to 0
+>>> np.sqrt(np.clip(sqr_dists, a_min=0, a_max=None))
+array([[0., 0.],
+ [0., 0.]])
+```
+
+
+
+#### The Final Answer, At Last!
+
+In total, we have successfully used vectorization to compute all the pairs of distances, while only requiring an array of shape-$(M, N)$ to do so! This is the memory-efficient, vectorized form – the stuff that dreams are made of.
+Let's write the function that performs this computation in full.
+
+```python
+def pairwise_dists(x, y):
+ """ Computing pairwise distances using memory-efficient
+ vectorization.
+
+ Parameters
+ ----------
+ x : numpy.ndarray, shape=(M, D)
+ y : numpy.ndarray, shape=(N, D)
+
+ Returns
+ -------
+ numpy.ndarray, shape=(M, N)
+ The Euclidean distance between each pair of
+ rows between `x` and `y`."""
+ sqr_dists = -2 * np.matmul(x, y.T)
+ sqr_dists += np.sum(x**2, axis=1)[:, np.newaxis]
+ sqr_dists += np.sum(y**2, axis=1)
+ return np.sqrt(np.clip(sqr_dists, a_min=0, a_max=None))
+```
+
+```python
+# produces a shape-(5, 6) result
+>>> pairwise_dists(x, y)
+array([[ 3.678 , 8.4524, 10.3057, 7.3711, 6.2152, 5.5548],
+ [10.1457, 5.8793, 2.9274, 4.1114, 3.9098, 5.2259],
+ [ 7.3219, 0.8439, 6.8734, 4.5687, 7.3283, 4.8216],
+ [10.339 , 7.032 , 7.4745, 7.0633, 3.5999, 4.0107],
+ [ 8.2878, 3.5468, 6.336 , 4.9014, 4.1858, 2.0257]])
+```
@@ -676,7 +766,7 @@ Use the function [numpy.allclose](https://numpy.org/doc/stable/reference/generat
## Reading Comprehension Solutions
-
+
**Broadcast Compatibility: Solution**
1\. Incompatible
@@ -688,7 +778,6 @@ Use the function [numpy.allclose](https://numpy.org/doc/stable/reference/generat
4\. `9 x 2 x 5`
5\. Incompatible
-
**Basic Broadcasting: Solution**
diff --git a/Python/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.md b/Python/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.md
index 84669d4d..2ef6f05d 100644
--- a/Python/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.md
+++ b/Python/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module3_IntroducingNumpy/IntroducingTheNDarray.md b/Python/Module3_IntroducingNumpy/IntroducingTheNDarray.md
index 17a3d192..b91339cb 100644
--- a/Python/Module3_IntroducingNumpy/IntroducingTheNDarray.md
+++ b/Python/Module3_IntroducingNumpy/IntroducingTheNDarray.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module3_IntroducingNumpy/Problems/ComputeAccuracy.md b/Python/Module3_IntroducingNumpy/Problems/ComputeAccuracy.md
index 17833303..451d91c4 100644
--- a/Python/Module3_IntroducingNumpy/Problems/ComputeAccuracy.md
+++ b/Python/Module3_IntroducingNumpy/Problems/ComputeAccuracy.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module3_IntroducingNumpy/VectorizedOperations.md b/Python/Module3_IntroducingNumpy/VectorizedOperations.md
index 8edd973e..f6edddb7 100644
--- a/Python/Module3_IntroducingNumpy/VectorizedOperations.md
+++ b/Python/Module3_IntroducingNumpy/VectorizedOperations.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module4_OOP/Applications_of_OOP.md b/Python/Module4_OOP/Applications_of_OOP.md
index fe93d859..00399112 100644
--- a/Python/Module4_OOP/Applications_of_OOP.md
+++ b/Python/Module4_OOP/Applications_of_OOP.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -73,7 +73,7 @@ class ShoppingList:
items = [items]
# only mark items as purchased that are on our list to begin with
self._purchased.update(set(items) & self._needed)
- # remove all purchased items from out unpurchased set
+ # remove all purchased items from our unpurchased set
self._needed.difference_update(self._purchased)
def list_purchased_items(self):
diff --git a/Python/Module4_OOP/Brief_Review.md b/Python/Module4_OOP/Brief_Review.md
index 29d35c97..96fd1a64 100644
--- a/Python/Module4_OOP/Brief_Review.md
+++ b/Python/Module4_OOP/Brief_Review.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module4_OOP/ClassDefinition.md b/Python/Module4_OOP/ClassDefinition.md
index 6419727b..ffcd8683 100644
--- a/Python/Module4_OOP/ClassDefinition.md
+++ b/Python/Module4_OOP/ClassDefinition.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module4_OOP/ClassInstances.md b/Python/Module4_OOP/ClassInstances.md
index 7d5cc65c..30da5961 100644
--- a/Python/Module4_OOP/ClassInstances.md
+++ b/Python/Module4_OOP/ClassInstances.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module4_OOP/Inheritance.md b/Python/Module4_OOP/Inheritance.md
index 95114973..1e47a4e1 100644
--- a/Python/Module4_OOP/Inheritance.md
+++ b/Python/Module4_OOP/Inheritance.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module4_OOP/Introduction_to_OOP.md b/Python/Module4_OOP/Introduction_to_OOP.md
index e061c9c9..25c0ebe9 100644
--- a/Python/Module4_OOP/Introduction_to_OOP.md
+++ b/Python/Module4_OOP/Introduction_to_OOP.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module4_OOP/Methods.md b/Python/Module4_OOP/Methods.md
index 6bf8e4fc..7bb440e8 100644
--- a/Python/Module4_OOP/Methods.md
+++ b/Python/Module4_OOP/Methods.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module4_OOP/ObjectOrientedProgramming.md b/Python/Module4_OOP/ObjectOrientedProgramming.md
index 8c5802a6..f4de8d27 100644
--- a/Python/Module4_OOP/ObjectOrientedProgramming.md
+++ b/Python/Module4_OOP/ObjectOrientedProgramming.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module4_OOP/Special_Methods.md b/Python/Module4_OOP/Special_Methods.md
index 45a91811..a9477dcf 100644
--- a/Python/Module4_OOP/Special_Methods.md
+++ b/Python/Module4_OOP/Special_Methods.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module5_OddsAndEnds/Modules_and_Packages.md b/Python/Module5_OddsAndEnds/Modules_and_Packages.md
index 3324669e..30b52e27 100644
--- a/Python/Module5_OddsAndEnds/Modules_and_Packages.md
+++ b/Python/Module5_OddsAndEnds/Modules_and_Packages.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/Python/Module5_OddsAndEnds/WorkingWithFiles.md b/Python/Module5_OddsAndEnds/WorkingWithFiles.md
index d68d8f27..f33c737e 100644
--- a/Python/Module5_OddsAndEnds/WorkingWithFiles.md
+++ b/Python/Module5_OddsAndEnds/WorkingWithFiles.md
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -254,7 +254,7 @@ with open("a_poem.txt", mode="r") as my_open_file:
There are many cases in which we may want to construct a list of files to iterate over. For example, if we have several data files, it would be useful to create a file list which we can iterate through and process in sequence. One way to do this would be to manually construct such a list of files:
-``` python
+```python
my_files = ['data/file1.txt', 'data/file2.txt', 'data/file3.txt', 'data/file4.txt']
```
diff --git a/Python/Module5_OddsAndEnds/Writing_Good_Code.md b/Python/Module5_OddsAndEnds/Writing_Good_Code.md
index 1fb91532..3cebc424 100644
--- a/Python/Module5_OddsAndEnds/Writing_Good_Code.md
+++ b/Python/Module5_OddsAndEnds/Writing_Good_Code.md
@@ -5,14 +5,13 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
name: python3
---
-
.. meta::
:description: Topic: Writing good code, Difficulty: Easy, Category: Section
@@ -440,7 +439,36 @@ This saves us the trouble of having to run our code, hit an error, read through
It does not take long to experience the benefits of type-hinting through your IDE. This both accelerates your coding by informing you of the object types that you are working with on the fly, and helps to expose oversights in your code as soon as they are made.
-Finally, it is also worthwhile to highlight two projects, [mypy](http://mypy-lang.org/) and [pyright](https://github.com/microsoft/pyright), which are used to perform static type-checking on your code based on your type-hints. That is, mypy and pyright will both automatically traverse your code and find potential bugs by identifying type conflicts in your code (e.g. trying to capitalize an integer) by checking their annotated and inferred types. These tools are especially useful for large-scale code bases. Companies like Dropbox and Microsoft make keen use of static type-checking to identify inconsistencies in their code without having to hit runtime errors. Keep mypy, pyright, and other type-checking utilities in mind as you mature as a Python developer and find yourself working on projects of growing complexity. If you are using [VSCode as your IDE](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.html), you can install the [pyright vscode extension](https://marketplace.visualstudio.com/items?itemName=ms-pyright.pyright) to leverage type checking within your IDE.
+Finally, it is also worthwhile to highlight two projects, [mypy](http://mypy-lang.org/) and [pyright](https://github.com/microsoft/pyright), which are used to perform static type-checking on your code based on your type-hints.
+That is, mypy and pyright will both automatically traverse your code and find potential bugs by identifying type conflicts in your code (e.g. trying to capitalize an integer) by checking their annotated and inferred types.
+These tools are especially useful for large-scale code bases. Companies like Dropbox and Microsoft make keen use of static type-checking to identify inconsistencies in their code without having to hit runtime errors. Keep mypy, pyright, and other type-checking utilities in mind as you mature as a Python developer and find yourself working on projects of growing complexity.
+If you are using [VSCode as your IDE](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.html), you can install the [PyLance VSCode extension](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance) to leverage pyright's type checking within your IDE.
+
+
+
+
+
+**Reading Comprehension: Type-Assisted Code Completion**
+
+The `jedi` package, which is installed by default alongside Jupyter and IPython, enables type-informed code completion.
+This means that we can benefit from type-hints even when we are working in a Jupyter notebook!
+
+In a Jupyter notebook, write the following type-annotated signature for a function: `def f(x: str):`.
+Now in the body of the function (i.e. on the next line, and indented inwards), try typing `x.` and then hit ``.
+
+```python
+
+def f(x: str):
+ x.#hit here
+
+```
+
+You should see a list of methods appear for `x`.
+What are the first three methods that appear?
+Do these make sense based on the annotation associated with `x`?
+Now change the annotation for `x` to be `list` and trigger the auto-completion in the editor again; do you see an updated list of methods?
+
+
@@ -959,8 +987,17 @@ Take some time to review the NumPy and Google docstring specifications, pick one
## Reading Comprehension Solutions
+
+**Type-Assisted Code Completion: Solution**
+
+The tab-completion reveals methods like "capitalize", "casefold", and "center" – all of which are string methods.
+These are made available despite the fact that `x` did not yet refer to a specific string.
+Instead, the type-hint was able to tell the editor that `x` _will_ refer to a string, and this was enough to inform these type completions.
+
+Revising the type-annotation for `x` to be `list` affects the auto-completion options accordingly: the editor will now suggest list methods like "append", "clear", and "copy".
+
-**Type Hinting: Solutions**
+**Type Hinting: Solution**
The following is a well-annotated version of `get_first_and_last`:
diff --git a/Python/changes.rst b/Python/changes.rst
index 24dd88f0..a5bf2207 100644
--- a/Python/changes.rst
+++ b/Python/changes.rst
@@ -3,8 +3,69 @@ Changelog
=========
This is a record of all past PLYMI releases and what went into them,
-in reverse chronological order. All previous releases should still be available
-on pip.
+in reverse chronological order.
+
+----------
+2021-02-28
+----------
+
+Fixes a syntax error (missing colons) in a code snippet in `a subsection about conditional expressions `_.
+
+
+----------
+2021-01-31
+----------
+
+Upgrades the tools used to build PLYMI:
+
+- sphinx 3.4.3
+- nbsphinx 0.8.1
+- jupytext 1.9.1
+- sphinx-rtd-theme 0.5.1
+
+Adds a reading comprehension problem in `the section on type-hinting `_
+to show that ``jedi`` provides annotation-informed autocompletion abilities in notebooks.
+
+
+----------
+2021-01-30
+----------
+
+Updated the discussion of `computing pairwise differences `_
+to account for potential floating-point edge cases that can produce "NaNs" as a result.
+
+There is currently an incompatibility between ``jedi 0.18.0`` and IPython, which breaks autocompletion. See `here `_ for more details.
+Added temporary callout boxes to the `informal introduction to Python `_ and to
+the `introduction to Jupyter notebooks `_, which instruct readers to remedy this by downgrading jedi.
+
+Fixed a missing plot in the `introduction to Jupyter `_ section.
+
+Reformatted the `section on IDEs `_ and added a description of PyLance.
+
+Add link to `PLYMI's discussion board `_.
+
+----------
+2021-01-24
+----------
+
+Added a brief `discussion of Python versions `_. Thanks `@samaocarpenter `_!
+
+Fixed typos `#160 `_ `#158 `_
+`#155 `_
+
+
+----------
+2020-06-17
+----------
+
+Various typo fixes. Thanks to Darshan and David!
+
+
+----------
+2020-05-10
+----------
+
+Various typo fixes. Thanks to Patrick O'Shea and David Mascharka!
----------
diff --git a/Python/conf.py b/Python/conf.py
index 34bd820e..8cdd5180 100644
--- a/Python/conf.py
+++ b/Python/conf.py
@@ -59,7 +59,7 @@
# General information about the project.
project = "Python Like You Mean It"
-copyright = "2019, Ryan Soklaski"
+copyright = "2021, Ryan Soklaski"
author = "Ryan Soklaski"
html_title = "Python Like You Mean It"
diff --git a/Python/index.rst b/Python/index.rst
index 1cb6cfab..d2e400d2 100644
--- a/Python/index.rst
+++ b/Python/index.rst
@@ -1,3 +1,7 @@
+.. meta::
+ :description: Topic: Top page of Python Like You Mean It, Difficulty: Easy, Category: Introduction
+ :keywords: PLYMI, Python Like You Mean It, tutorial, python for machine learning, python for deep learning, python for data science, numpy, python from scratch, basic python tutorial, teaching python
+
=======================
Python Like You Mean It
=======================
@@ -12,7 +16,8 @@ What this is
------------
Python Like You Mean It (PLYMI) is a free resource for learning the basics of Python & NumPy, and moreover, becoming a competent Python user. The features of the Python language that are emphasized here were chosen to help those who are particularly interested in STEM applications (data analysis, machine learning, numerical work, etc.).
-I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.8, as of writing this).
+I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.9, as of writing this).
+This material has proven to be fruitful for high school and college teachers alike to teach Python as part of their STEM curriculum.
What this isn't
@@ -32,10 +37,18 @@ Python shouldn't be *too* easy
Python is a relatively easy language to pick up, and it doesn't require much rigor to make code work. Unfortunately, this means that there are many Python users out there who know enough to just get by, but lack a sound understanding of the language. You don't want to get caught in the "know enough Python to be dangerous" zone; therein lies complacency, stagnation, and the genesis of a lot of bad code. You've got to Python like you mean it!
+Join Our Discussion Board
+-------------------------
+`Join the PLYMI community `_ to ask questions, recommend new content, or to just say hello!
+
+(A note to `BWSI students `_: please stick to your class' piazza board for posting questions)
+
+
PLYMI is on GitHub
------------------
If you have questions about the reading, think that you have spotted some mistakes, or would like to contribute to PLYMI, please visit `our GitHub page `_. You will need to create a GitHub account in order to post an issue; this process is free and easy. We would love for you to join us to discuss the website!
+
Contributors
------------
The following people made significant contributions to PLYMI, adding problems with worked solutions and valuable feedback on the text:
@@ -44,13 +57,13 @@ The following people made significant contributions to PLYMI, adding problems wi
- AJ Federici (GitHub:`@Federici `_)
- Annabelle Lew (GitHub:`@AnnabelleLew `_)
- Petar Griggs (GitHub:`@petarmhg `_)
-- Sam Carpenter (GitHub:`@HardBoiled800 `_)
+- Sam Carpenter (GitHub:`@samaocarpenter `_)
- Patrick O'Shea (GitHub:`@ArtichokeSap `_)
About Me
--------
-I started learning to use Python in graduate school for my physics research, and I was *so* bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I'd like to think that this resource would have been a huge help to me back then). I've been coding in Python for at least six years now, and I've taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
+I started learning to use Python in graduate school for my physics research, and I was *so* bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I'd like to think that this resource would have been a huge help to me back then). I've been coding in Python for at least eight years now, and I've taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
.. toctree::
diff --git a/Python/intro.rst b/Python/intro.rst
index 97d3801e..1d822383 100644
--- a/Python/intro.rst
+++ b/Python/intro.rst
@@ -1,3 +1,7 @@
+.. meta::
+ :description: Topic: Top page of Python Like You Mean It, Difficulty: Easy, Category: Introduction
+ :keywords: PLYMI, Python Like You Mean It, tutorial, python for machine learning, python for deep learning, python for data science, numpy, python from scratch, basic python tutorial, teaching python
+
=======================
Python Like You Mean It
=======================
@@ -12,7 +16,8 @@ What this is
------------
Python Like You Mean It (PLYMI) is a free resource for learning the basics of Python & NumPy, and moreover, becoming a competent Python user. The features of the Python language that are emphasized here were chosen to help those who are particularly interested in STEM applications (data analysis, machine learning, numerical work, etc.).
-I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.8, as of writing this).
+I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.9, as of writing this).
+This material has proven to be fruitful for high school and college teachers alike to teach Python as part of their STEM curriculum.
What this isn't
@@ -31,6 +36,12 @@ Python shouldn't be *too* easy
------------------------------
Python is a relatively easy language to pick up, and it doesn't require much rigor to make code work. Unfortunately, this means that there are many Python users out there who know enough to just get by, but lack a sound understanding of the language. You don't want to get caught in the "know enough Python to be dangerous" zone; therein lies complacency, stagnation, and the genesis of a lot of bad code. You've got to Python like you mean it!
+Join Our Discussion Board
+-------------------------
+`Join the PLYMI community `_ to ask questions, recommend new content, or to just say hello!
+
+(A note to `BWSI students `_: please stick to your class' piazza board for posting questions)
+
PLYMI is on GitHub
------------------
@@ -44,10 +55,10 @@ The following people made significant contributions to PLYMI, adding problems wi
- AJ Federici (GitHub:`@Federici `_)
- Annabelle Lew (GitHub:`@AnnabelleLew `_)
- Petar Griggs (GitHub:`@petarmhg `_)
-- Sam Carpenter (GitHub:`@HardBoiled800 `_)
+- Sam Carpenter (GitHub:`@samaocarpenter `_)
- Patrick O'Shea (GitHub:`@ArtichokeSap `_)
About Me
--------
-I started learning to use Python in graduate school for my physics research, and I was *so* bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I'd like to think that this resource would have been a huge help to me back then). I've been coding in Python for at least six years now, and I've taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
+I started learning to use Python in graduate school for my physics research, and I was *so* bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I'd like to think that this resource would have been a huge help to me back then). I've been coding in Python for at least eight years now, and I've taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
diff --git a/Python/module_4.rst b/Python/module_4.rst
index 021a2c97..0251813f 100644
--- a/Python/module_4.rst
+++ b/Python/module_4.rst
@@ -2,7 +2,7 @@ Module 4: Object Oriented Programming
=====================================
In this module, we will come to see that Python is an object-oriented language. That is, the language is all about defining different types of objects that encapsulate data, and that possess functions that permit users to access and manipulate this data. Our tour through `the essentials of Python `_ and `the essentials of NumPy `_ brought us into contact with various types of objects, such as the :code:`int`, :code:`str`, :code:`list`, :code:`tuple`, :code:`dict`, and the :code:`numpy.ndarray`, to name a few. We have seen that these different types of objects have starkly different functionality from one another. We will study the syntax and constructs for creating and interacting with objects in Python. Ultimately, will find our skill sets as Python-users much fuller and more sophisticated having understood object-oriented programming in Python.
-We being this module by establishing some key terminology for discussing object-oriented programming, drawing attention to the important fact that the terms 'class' and 'type' mean the same thing in modern Python. Next, we study the syntax for defining a class, and take time to distinguish between the resulting class object and the subsequent class instances that can then be created. Having defined our first custom class of object, we then study the syntax for defining class-methods, which permits us add customized functionality to our class. Further, we will introduce ourselves to Python's reserved special methods, which empower us to fully interface our class with the Python's protocols for behaving like a sequence, an iterable, a function, etc. We conclude this module with a brief discussion of the concept of inheritance, which is a mechanism by which a new class can inherit attributes from an existing class.
+We begin this module by establishing some key terminology for discussing object-oriented programming, drawing attention to the important fact that the terms 'class' and 'type' mean the same thing in modern Python. Next, we study the syntax for defining a class, and take time to distinguish between the resulting class object and the subsequent class instances that can then be created. Having defined our first custom class of object, we then study the syntax for defining class-methods, which permits us to add customized functionality to our class. Further, we will introduce ourselves to Python's reserved special methods, which empower us to fully interface our class with Python's protocols for behaving like a sequence, an iterable, a function, etc. We conclude this module with a brief discussion of the concept of inheritance, which is a mechanism by which a new class can inherit attributes from an existing class.
.. toctree::
:maxdepth: 2
diff --git a/README.md b/README.md
index d1929075..bd686732 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ Contributions to this project are very welcome! I will be sure to credit any/al
- create lengthier standalone problems to serve as holistic examples of how to apply the concepts presented in the reading
- recommend new sections to be added to PLYMI
-You can either open an issue to provide feedback or point out errors, or you can create a [pull request](https://help.github.com/articles/creating-a-pull-request/)) if you want to submit new/modified materials. The other maintainers of PLYMI and I are happy to help you refine your issues and PRs, so please do not be discouraged if you are new to Git/GitHub.
+You can either open an issue to provide feedback or point out errors, or you can create a [pull request](https://help.github.com/articles/creating-a-pull-request/) if you want to submit new/modified materials. The other maintainers of PLYMI and I are happy to help you refine your issues and PRs, so please do not be discouraged if you are new to Git/GitHub.
I have posted a number of "To-Do" tasks in this project's [issues](https://github.com/rsokl/Learning_Python/issues) page, and I will be adding to these as this project progresses. Included are requests for proofreading and exercises. Please post within an issue if you are working on a to-do item, so multiple people don't end up working on the same task. General feedback on content is also hugely valuable, so feel free to open a new issue to provide your feedback on any of the material.
@@ -22,28 +22,45 @@ I have posted a number of "To-Do" tasks in this project's [issues](https://githu
### Making a Pull Request
If you want to submit a change to some of the content (e.g. correcting typos), do the following:
-1. Clone this repository
+1. [Fork](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) this repository
2. Create a new branch, appropriately named for whatever task you are performing: `git checkout -b your_branch_name`
3. In your new branch, make the relevant changes and commit them.
-4. Push your branch: `git push origin your_branch_name` (you should have permission to do this, if you are added as a "contributor" to this project)
-5. Create a [Pull Request](https://help.github.com/articles/creating-a-pull-request/) from your branch into the master branch.
+4. Push your branch to your fork: `git push origin your_branch_name`
+5. Create a [Pull Request](https://help.github.com/articles/creating-a-pull-request/) from your fork branch into the master branch of this repo.
# Building the Site
**Important Note: it is strongly preferred that pull requests *do not* contain changes to the HTML of this site. Rather, it is better if PRs simply contain changes to text files (.rst or .md). A site administrator (@rsokl, @davidmascharka) will be responsible for publishing the actual site-HTML**. Thus the following instructions are useful for you to view your changes as they will appear in the site, but you likely need not go through the process of committing the changes to the HTML.
-First, clone this repository.
+## Creating a Conda Environment From Scratch
-You will need to have [anaconda installed](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Installing_Python.html) on your computer. Then create a new conda environment using the yaml file, `plymi.yml`, that is located at the top-level of this repository. To create the `plymi` conda environment, run:
+First, create a miniconda environment. We'll call it `plymi` and will use Python 3.8
```shell
-conda env create -f plymi.yml
+conda create -n plymi python=3.8
```
-Once this environment is created activate it. You may need to manually install a couple of dependencies (check if these are installed in your environment first):
+It is important that we activate the environment before proceeding
```shell
-pip install sphinx-rtd-theme==0.4.3
-pip install jupytext==1.4.2
+conda activate plymi
+```
+
+Next, we will install ipython, Jupyter, numpy, and matplotlib
+
+```shell
+conda install ipython jupyter notebook numpy matplotlib
+```
+
+Next, we'll use the `conda-forge` package channel to install some critical packages for building the HTML
+
+```shell
+conda install -c conda-forge sphinx==3.4.3 nbsphinx==0.8.1 pandoc==2.1.3 jupytext=1.9.1 nbformat=5.0.8
+```
+
+Finally, we will use PyPi to install jupytext and our website's stylistic theme
+
+```shell
+pip install sphinx-rtd-theme==0.5.1
```
and install the `plymi` code base from this repo. Clone the present repository and run:
diff --git a/docs/.buildinfo b/docs/.buildinfo
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+++ b/docs/.buildinfo
@@ -1,4 +1,4 @@
# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 3c60093d6906b2b9dda08d0aee018696
+config: 30bed98bcfc3f0fafb4c6590d4dc316d
tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/GettingStartedWithPython.ipynb b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/GettingStartedWithPython.ipynb
index fc0f34e8..60a8cebb 100644
--- a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/GettingStartedWithPython.ipynb
+++ b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/GettingStartedWithPython.ipynb
@@ -8,7 +8,7 @@
"source": [
".. meta::\n",
" :description: Topic: Basic description of the Python programming language, Difficulty: Easy, Category: Background\n",
- " :keywords: python, install, basics, scripts, interpreter, foundations"
+ " :keywords: python, install, basics, scripts, interpreter, foundations, versions"
]
},
{
@@ -153,6 +153,64 @@
"We will be relying heavily on Python and a library for doing optimized numerical computations, called NumPy, throughout this course."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Understanding Different Versions of Python\n",
+ "\n",
+ "New versions of Python come out periodically, bringing new features and fixes. \n",
+ "You can keep track of what's new in Python by [bookmarking and checking this page every few months](https://docs.python.org/3/whatsnew/index.html).\n",
+ "As we take on coding projects in Python, whether it be for work, school, or a personal hobby, it is important that we remain aware of\n",
+ "the ways in which the language is changing, and that we take care to write code that will remain functional in the future.\n",
+ "\n",
+ "All Python version numbers use the `A.B.C` format, in accordance with [semantic versioning](https://semver.org/). \n",
+ "The three numbers denote major releases, minor releases, and patches.\n",
+ "For example, as of writing this, the current release of Python is version `3.9.1`.\n",
+ "\n",
+ "The first number denotes major releases to the language. \n",
+ "When a major release comes out, it means that older code will not necessarily work with the new release, and vice versa. \n",
+ "For example, the following code worked in Python 2 but no longer works because the `xrange` function was removed from the language\n",
+ "upon the release of Python 3.\n",
+ "\n",
+ "```python\n",
+ "# `xrange` was a frequently-used function in Python 2, but\n",
+ "# was removed from the language in Python 3 in favor of `range`. \n",
+ "# Thus the following code does not work in any version of Python 3.\n",
+ "count = 0\n",
+ "for i in xrange(10):\n",
+ " count = count + 1\n",
+ "```\n",
+ "\n",
+ "The most current major release is Python 3; Python 2 is no longer supported by any bug or security fixes and should not be used.\n",
+ "All releases in the near future will be improvements to Python 3, and thus they will come in the form of minor releases and patches.\n",
+ "\n",
+ "The second number denotes a minor release. \n",
+ "A minor release will be compatible with code from the preceding release, but it might add new features to the language that are not backwards-compatible.\n",
+ "For example, Python 3.6 introduced [formatted string literals](https://docs.python.org/3/whatsnew/3.6.html#pep-498-formatted-string-literals), which are \n",
+ "commonly referred to as \"f-strings\".\n",
+ "Thus as of Python 3.6 you could write code like\n",
+ "\n",
+ "```python\n",
+ "# Python 3.6 introduced the \"f-string\" feature, which is not\n",
+ "# backwards compatible with Python 3.5\n",
+ ">>> f\"one plus two is {1 + 2}\"\n",
+ "'one plus two is 3'\n",
+ "```\n",
+ "\n",
+ "but this code would not run in Python 3.5.X.\n",
+ "\n",
+ "The third and final number denotes a patch, which generally means bug fixes and performance improvements. \n",
+ "All code within the same minor release will run on all other patches within that minor release\n",
+ "For example, all Python 3.7.8 code is compatible with a Python 3.7.1 interpreter, and vice versa. \n",
+ "Patches are released fairly often, and their changes only occur 'under the hood'.\n",
+ "[Here is a list of changes](https://docs.python.org/3/whatsnew/changelog.html#python-3-9-1-final) were introduced by the patch level increment from Python 3.9.0 to Python 3.9.1;\n",
+ "few of these would affect our day-to-day experience with using Python (which isn't to say that they aren't important!).\n",
+ "\n",
+ "In simpler terms, major releases are neither backward nor forward compatible.\n",
+ "Minor releases are forward compatible but not necessarily fully backward compatible, and patches are both forward and backward compatible.\n"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -175,7 +233,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.ipynb b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.ipynb
index 4ad5c023..4d3fd6b4 100644
--- a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.ipynb
+++ b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.ipynb
@@ -40,39 +40,49 @@
"\n",
"An IDE also often provides debugging tools so that you can test your code; it will also typically interface with version-control software, like Git, so that you can keep track of versions of your code as you modify it. We will not discuss these useful, but more advanced features here.\n",
"\n",
- "### Recommended IDEs\n",
+ "## Recommended IDEs\n",
"There are many excellent IDEs that can be configured to work well with Python. Two IDEs that we endorse are:\n",
" \n",
- "[PyCharm](https://www.jetbrains.com/pycharm/download): A powerful IDE dedicated to Python.\n",
+ "### PyCharm\n",
+ "\n",
+ "[PyCharm](https://www.jetbrains.com/pycharm/download) is a powerful and highly-polished IDE dedicated to developing Python code.\n",
"\n",
"**Pros**\n",
"\n",
- "- works well out-of-the-box\n",
- "- long-supported by professionals and thus is very reliable\n",
- "- highly configurable\n",
- "- fully-featured, with an excellent debugger, context-dependent \"intellisense\", type-inference, and more\n",
- "- the free \"community version\" is extremely robust and feature-rich. \n",
+ "- Works well out-of-the-box.\n",
+ "- Long-supported by professionals and thus is very reliable.\n",
+ "- Highly configurable.\n",
+ "- Fully-featured, with an excellent debugger, context-dependent \"intellisense\", type-inference, and more.\n",
+ "- The free \"community version\" is extremely robust and feature-rich.\n",
+ "- Generally provides an extremely high-quality and responsive experience for doing Python development.\n",
"\n",
"**Cons**\n",
"\n",
- " - can be resource-heavy, especially for a laptop\n",
- " - may be overwhelming to new users (but has good documentation and tutorials)\n",
- " - Jupyter notebook support requires the premium version of PyCharm, making it inaccessible to newcomers\n",
+ " - Can be resource-heavy, especially for a laptop.\n",
+ " - May be overwhelming to new users (but has good documentation and tutorials).\n",
+ " - Jupyter notebook support requires the premium version of PyCharm, making it inaccessible to newcomers.\n",
" \n",
- "[Visual Studio Code](https://code.visualstudio.com/) with the [Python extension](https://code.visualstudio.com/docs/languages/python): A lightweight, highly customizable IDE.\n",
+ "### Visual Studio Code\n",
+ "\n",
+ "[Visual Studio Code](https://code.visualstudio.com/) (with the [Python extension](https://code.visualstudio.com/docs/languages/python)) is a lightweight, highly customizable IDE that works with many different languages.\n",
+ "\n",
+ "Note: if you decide to use VSCode to do Python development, it is highly recommended that you install Microsoft's [PyLance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)\n",
+ "extension.\n",
+ "This adds many useful features to the IDE that will make writing Python code a more delightful experience. \n",
"\n",
"**Pros**\n",
"\n",
- "- lightweight and elegant\n",
- "- completely free\n",
- "- works with many different languages, so you only need to familiarize yourself with one IDE if you are a polyglot programmer\n",
- "- a huge number of extensions can be downloaded to add functionality to the editor; these are created by a large community of open-source developers.\n",
- "- [has native support for Jupyter notebooks](https://code.visualstudio.com/docs/python/jupyter-support), meaning that you get VSCode's intellisense, debugger, and ability to inspect variables, all in a notebook.\n",
+ "- Lightweight and elegant.\n",
+ "- Completely free.\n",
+ "- Works with many different languages, so you only need to familiarize yourself with one IDE if you are a polyglot programmer.\n",
+ "- Offers a huge number of extensions that can be downloaded to add functionality to the editor; these are created by a large community of open-source developers.\n",
+ "- [Has native support for Jupyter notebooks](https://code.visualstudio.com/docs/python/jupyter-support), meaning that you get VSCode's intellisense, debugger, and ability to inspect variables, all in a notebook.\n",
+ "- Provides incredibly robust [remote coding](https://code.visualstudio.com/docs/remote/remote-overview) and [collaborative coding](https://visualstudio.microsoft.com/services/live-share/) capabilities.\n",
"\n",
"**Cons**\n",
"\n",
- "- configuring VSCode for python development can have a moderate learning curve for newcomers\n",
- "- many features, like context-aware intellisense and type-inference, are simply more polished and powerful in PyCharm"
+ "- Configuring VSCode for Python development can have a moderate learning curve for newcomers.\n",
+ "- Some features, like context-aware intellisense and type-inference, are simply more polished and powerful in PyCharm."
]
},
{
@@ -94,7 +104,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Informal_Intro_Python.ipynb b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Informal_Intro_Python.ipynb
index 0c7f332e..b514c4ed 100644
--- a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Informal_Intro_Python.ipynb
+++ b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Informal_Intro_Python.ipynb
@@ -16,6 +16,39 @@
"metadata": {},
"source": [
"# An Informal Introduction to Python\n",
+ "\n",
+ "
\n",
+ "\n",
+ "**Before You Start This Section**: \n",
+ "\n",
+ "In the following section we will be using IPython or a Jupyter notebook to run our code.\n",
+ "Presently, there is an incompatibility with these programs and a Python package called `jedi`, which typically is responsible for performing auto-completions in our code (when prompted by hitting ``, which we will be doing below).\n",
+ "It is really useful!\n",
+ "\n",
+ "First, let's check to see if we have an incompatible version of `jedi` installed.\n",
+ "In your terminal (before starting a Python/IPython/Jupyter session), run\n",
+ " \n",
+ "```\n",
+ "conda list\n",
+ "```\n",
+ "\n",
+ "And look for the line that starts with `jedi`\n",
+ " \n",
+ "```\n",
+ "jedi 0.18.0\n",
+ "```\n",
+ "\n",
+ "If you see that you have version `0.18.0` installed (as above), then you will want to downgrade it.\n",
+ "In the same terminal, run the following command\n",
+ "\n",
+ "```\n",
+ "conda install jedi=0.17.2\n",
+ "```\n",
+ "You should be all set once you have followed the prompts and the installation has completed!\n",
+ " \n",
+ "Note that you will need to repeat this process if you [create a new conda environment](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Installing_Python.html#A-Brief-Introduction-to-Conda-Environments) with IPython/Jupter installed in it.\n",
+ "
\n",
+ "\n",
+ "**Before You Start This Section**: \n",
+ "\n",
+ "In the following section we will be using IPython or a Jupyter notebook to run our code.\n",
+ "Presently, there is an incompatibility with these programs and a Python package called `jedi`, which typically is responsible for performing auto-completions in our code (when prompted by hitting ``, which we will be doing below).\n",
+ "It is really useful!\n",
+ "\n",
+ "First, let's check to see if we have an incompatible version of `jedi` installed.\n",
+ "In your terminal (before starting a Python/IPython/Jupyter session), run\n",
+ " \n",
+ "```\n",
+ "conda list\n",
+ "```\n",
+ "\n",
+ "And look for the line that starts with `jedi`\n",
+ " \n",
+ "```\n",
+ "jedi 0.18.0\n",
+ "```\n",
+ "\n",
+ "If you see that you have version `0.18.0` installed (as above), then you will want to downgrade it.\n",
+ "In the same terminal, run the following command\n",
+ "\n",
+ "```\n",
+ "conda install jedi=0.17.2\n",
+ "```\n",
+ "You should be all set once you have followed the prompts and the installation has completed!\n",
+ " \n",
+ "Note that you will need to repeat this process if you [create a new conda environment](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Installing_Python.html#A-Brief-Introduction-to-Conda-Environments) with IPython/Jupter installed in it.\n",
+ "
\n",
+ "\n",
+ "In recent years, the Jupyter Notebook has become a massively popular tool for doing research-oriented work in Python and other languages alike.\n",
+ "Its emergence marked a paradigm shift in the way data science is conducted. \n",
+ "\n",
+ "A Jupyter notebook is similar to the IPython console, but, instead of only being able to work with a single line of code at a time, you can easily edit and re-execute *any* code that had been written in a notebook. Furthermore, you can save a notebook, and thus return to it later.\n",
+ "Additionally, a notebook provides many terrific features. For instance, you can embed visualizations of data within a notebook, and write blocks of nicely-formatted text (using the [Markdown syntax](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)), for presenting and explaining the contents of the notebook. \n",
"\n",
"In this way, the Jupyter Notebook stands out as an excellent tool for many practical applications. You could work on a notebook while you are working through sections of this website, for instance, testing out snippets of code, and answering reading-comprehension questions as you proceed through the text, and using markdown-headers to visually separate different portions of the notebook. When I do research, I am always creating Jupyter notebooks in which I write code that analyzes data, I plot various results, presented in different ways, and I write detailed markdown-text blocks to document my work. The end result is something that I can share with my labmates, and easily revisit months later without having to struggle to recall what I had done.\n",
"\n",
"## Jupyter Lab\n",
- "[Jupyter lab](https://jupyterlab.readthedocs.io/) is a new web interface from Project Jupyter that provides a rich web-based interface for managing and running Jupyter notebooks, console terminals, and text editors, all within your browser. Among its useful features and polished user interface - compared to that a Jupyter notebook server - Jupyter lab provides moveable panes for viewing data, images, and code output apart from the rest of the notebook. This is facilitates effective data science work flows.\n",
+ "[Jupyter lab](https://jupyterlab.readthedocs.io/) is a new web interface from Project Jupyter that provides a rich web-based interface for managing and running Jupyter notebooks, console terminals, and text editors, all within your browser.\n",
+ "Among its useful features and polished user interface, Jupyter lab provides moveable panes for viewing data, images, and code output apart from the rest of the notebook.\n",
+ "This is facilitates effective data science work flows.\n",
"\n",
"It is recommended that you peruse the [Jupyter lab documentation](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html) to get a feel for all of its added capabilities."
]
@@ -32,7 +69,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The following instructions are laid out for running a Jupyter notebook server. That being said, the process for running a Jupyter lab server and working with notebooks therein is nearly identical. Both Jupyter notebook and Jupyter lab should already be [installed via Anaconda](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Installing_Python.html)."
+ "The following instructions are laid out for running a Jupyter notebook server. That being said, the process for running a Jupyter lab server and working with notebooks therein is nearly identical.\n",
+ "Both Jupyter notebook and Jupyter lab should already be [installed via Anaconda](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Installing_Python.html)."
]
},
{
@@ -115,20 +153,34 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-31T21:47:30.495595Z",
+ "iopub.status.busy": "2021-01-31T21:47:30.494594Z",
+ "iopub.status.idle": "2021-01-31T21:47:31.168637Z",
+ "shell.execute_reply": "2021-01-31T21:47:31.167635Z"
+ }
+ },
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# this tells Jupyter to embed matplotlib plots in the notebook\n",
- "%matplotlib notebook "
+ "%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-31T21:47:31.173637Z",
+ "iopub.status.busy": "2021-01-31T21:47:31.172634Z",
+ "iopub.status.idle": "2021-01-31T21:47:31.183635Z",
+ "shell.execute_reply": "2021-01-31T21:47:31.183635Z"
+ }
+ },
"outputs": [],
"source": [
"def sinc(x):\n",
@@ -142,7 +194,14 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-31T21:47:31.187635Z",
+ "iopub.status.busy": "2021-01-31T21:47:31.186634Z",
+ "iopub.status.idle": "2021-01-31T21:47:31.198636Z",
+ "shell.execute_reply": "2021-01-31T21:47:31.198636Z"
+ }
+ },
"outputs": [],
"source": [
"# evaluate functions at 1000 points evenly spaced in [-15, 15]\n",
@@ -154,798 +213,35 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-31T21:47:31.233635Z",
+ "iopub.status.busy": "2021-01-31T21:47:31.232637Z",
+ "iopub.status.idle": "2021-01-31T21:47:31.719634Z",
+ "shell.execute_reply": "2021-01-31T21:47:31.720634Z"
+ }
+ },
"outputs": [
{
"data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " if (mpl.ratio != 1) {\n",
- " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
- " }\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " fig.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '');\n",
- " var titletext = $(\n",
- " '');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var backingStore = this.context.backingStorePixelRatio ||\n",
- "\tthis.context.webkitBackingStorePixelRatio ||\n",
- "\tthis.context.mozBackingStorePixelRatio ||\n",
- "\tthis.context.msBackingStorePixelRatio ||\n",
- "\tthis.context.oBackingStorePixelRatio ||\n",
- "\tthis.context.backingStorePixelRatio || 1;\n",
- "\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- "\n",
- " var rubberband = $('');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('');\n",
- "\n",
- " var fmt_picker = $('');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('');\n",
- " var button = $('');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
"text/plain": [
- ""
+ ""
]
},
+ "execution_count": 1,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
},
{
"data": {
- "text/html": [
- ""
- ],
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\n",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
@@ -1038,7 +334,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
@@ -1056,7 +352,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.6"
+ "version": "3.8.5"
}
},
"nbformat": 4,
diff --git a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Numerical_Work_In_Python.ipynb b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Numerical_Work_In_Python.ipynb
index 043e09e5..36fd6d83 100644
--- a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Numerical_Work_In_Python.ipynb
+++ b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/Numerical_Work_In_Python.ipynb
@@ -65,7 +65,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/SiteFormatting.ipynb b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/SiteFormatting.ipynb
index a2f740c2..45b4ffdd 100644
--- a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/SiteFormatting.ipynb
+++ b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython/SiteFormatting.ipynb
@@ -60,27 +60,39 @@
"100.\n",
"```\n",
"\n",
- "Lastly, the input and output of an iPython console and a Jupyter notebook alike is displayed as follows:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "5"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "2 + 3"
+ "Lastly, the input and output of an iPython console and a Jupyter notebook alike is displayed as follows:\n",
+ "\n",
+ "```python\n",
+ "2 + 3\n",
+ "```\n",
+ "\n",
+ "## Running Code Snippets from this Site\n",
+ "\n",
+ "In PLYMI, we typically precede every code snippet with one or more commented lines.\n",
+ "This is useful because it makes a page more \"skimmable\", since the code snippets essentially come with\n",
+ "descriptive, self-explanatory captions.\n",
+ "That being said, there is a downside to this. \n",
+ "\n",
+ "Python terminals don't like having multiple comment lines precede an input-prompt.\n",
+ "E.g. if you paste and run the following code into a terminal\n",
+ "\n",
+ "```python\n",
+ "# demonstrating the distinction of\n",
+ "# input and output via >>>\n",
+ "\n",
+ ">>> x = 1\n",
+ "```\n",
+ "\n",
+ "you will get a syntax error.\n",
+ "To fix this issue, simply exclude the comments when you copy this block to your clipboard.\n",
+ "Running\n",
+ "\n",
+ "```python\n",
+ ">>> x = 1\n",
+ "```\n",
+ "\n",
+ "will work without any issue.\n",
+ "Keep this in mind if you ever find yourself having trouble running code that you copied from this site."
]
}
],
@@ -90,25 +102,13 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython_Jupyter_Notebooks_10_1.png b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython_Jupyter_Notebooks_10_1.png
new file mode 100644
index 00000000..d8ad74ac
Binary files /dev/null and b/docs/.doctrees/nbsphinx/Module1_GettingStartedWithPython_Jupyter_Notebooks_10_1.png differ
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Basic_Objects.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Basic_Objects.ipynb
index 56354f32..a4d4ef23 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Basic_Objects.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Basic_Objects.ipynb
@@ -1136,7 +1136,7 @@
"source": [
"**Understanding the modulo operator: Solution**\n",
"\n",
- "If `n` is an integer, then 2 will divide into it evenly, and thus there is no remainder. If `n` is odd, then `n / 2` must have a remainder of 1. Thus:\n",
+ "If `n` is an even integer, then 2 will divide into it evenly, and thus there is no remainder. If `n` is odd, then `n / 2` must have a remainder of 1. Thus:\n",
"\n",
"- `n % 2 ` = 0 if `n` is an even number\n",
"- `n % 2 ` = 1 if `n` is an odd number"
@@ -1306,7 +1306,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/ConditionalStatements.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/ConditionalStatements.ipynb
index ed4b2dc2..286c0572 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/ConditionalStatements.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/ConditionalStatements.ipynb
@@ -237,11 +237,11 @@
" # bool(3 < 2) returns False, this code \n",
" # block is skipped\n",
" print(\"`x` has more than three items in it\")\n",
- "elif len(x) == 2\n",
+ "elif len(x) == 2:\n",
" # bool(len(x) == 2) returns True\n",
" # this code block is executed\n",
" print(\"`x` has two items in it\")\n",
- "elif len(x) == 1\n",
+ "elif len(x) == 1:\n",
" # this statement is never reached\n",
" print(\"`x` has one items in it\")\n",
"else:\n",
@@ -348,6 +348,8 @@
"Python supports a syntax for writing a restricted version of if-else statements in a single line. The following code:\n",
"\n",
"```python\n",
+ "num = 2\n",
+ "\n",
"if num >= 0:\n",
" sign = \"positive\"\n",
"else:\n",
@@ -369,7 +371,7 @@
"The expression `A if else B` returns `A` if `bool()` evaluates to `True`, otherwise this expression will return `B`.\n",
"
\n",
"\n",
- "This syntax is highly restricted compared to the full \"if-elif-else\" expressions - no \"elif\" statement is permitted by this inline syntax, nor are muli-line code blocks within the if/else clauses.\n",
+ "This syntax is highly restricted compared to the full \"if-elif-else\" expressions - no \"elif\" statement is permitted by this inline syntax, nor are multi-line code blocks within the if/else clauses.\n",
"\n",
"Inline if-else statements can be used anywhere, not just on the right side of an assignment statement, and can be quite convenient:\n",
"```python\n",
@@ -533,7 +535,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures.ipynb
index 005092b7..d7c792f9 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures.ipynb
@@ -166,7 +166,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures_III_Sets_and_More.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures_III_Sets_and_More.ipynb
index 2a714978..f019bc77 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures_III_Sets_and_More.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures_III_Sets_and_More.ipynb
@@ -421,7 +421,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures_II_Dictionaries.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures_II_Dictionaries.ipynb
index edfcb34e..dd5b25d3 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures_II_Dictionaries.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/DataStructures_II_Dictionaries.ipynb
@@ -567,7 +567,7 @@
" max_val = max(dictionary.values())\n",
" return tuple(k for k,v in dictionary.items() if v == max_val)\n",
"\n",
- ">>> get_maxes(dict(a=1, b=2,c= 2, d=1))\n",
+ ">>> get_maxes(dict(a=1, b=2, c=2, d=1))\n",
"('b', 'c')\n",
"```\n",
"\n"
@@ -580,7 +580,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/ForLoops.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/ForLoops.ipynb
index d3800b84..f5daf218 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/ForLoops.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/ForLoops.ipynb
@@ -7,7 +7,7 @@
},
"source": [
".. meta::\n",
- " :description: Topic: Contorl flow with for-loops and while-loops, Difficulty: Easy, Category: Section\n",
+ " :description: Topic: Control flow with for-loops and while-loops, Difficulty: Easy, Category: Section\n",
" :keywords: for-loop, while-loop, break, control flow, basic programming"
]
},
@@ -329,7 +329,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Functions.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Functions.ipynb
index b970961c..d95ef949 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Functions.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Functions.ipynb
@@ -153,7 +153,7 @@
"metadata": {},
"source": [
"## The `return` Statement\n",
- "In general, any Python object can follow a function's `return` statement. Furthermore, an **empty** `return` statement can be specified, or the **return** statement of a function can be omitted altogether. In both of these cases, *the function will return the `None` object*.\n",
+ "In general, any Python object can follow a function's `return` statement. Furthermore, an **empty** `return` statement can be specified, or the **return** statement of a function can be omitted altogether. In both of these cases, *the function will return the* `None` *object*.\n",
"\n",
"```python\n",
"# this function returns `None`\n",
@@ -830,7 +830,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Generators_and_Comprehensions.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Generators_and_Comprehensions.ipynb
index bbd00a35..cb762bf2 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Generators_and_Comprehensions.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Generators_and_Comprehensions.ipynb
@@ -605,12 +605,12 @@
"Replicate the functionality of the the following code by writing a list comprehension.\n",
"```python\n",
"# skip all non-lowercased letters (including punctuation)\n",
- "# append 1 if lowercase letter is \"o\"\n",
+ "# append 1 if lowercase letter is equal to \"o\"\n",
"# append 0 if lowercase letter is not \"o\"\n",
"out = []\n",
"for i in \"Hello. How Are You?\":\n",
" if i.islower():\n",
- " out.append(1 if i is \"o\" else 0)\n",
+ " out.append(1 if i == \"o\" else 0)\n",
"```\n",
"\n",
"
"
@@ -747,7 +747,7 @@
"source": [
"**Translating a For-Loop: Solution**\n",
"```python\n",
- ">>> out = [(1 if i is \"o\" else 0) for i in \"Hello. How Are You?\" if i.islower()]\n",
+ ">>> out = [(1 if i == \"o\" else 0) for i in \"Hello. How Are You?\" if i.islower()]\n",
">>> out\n",
"[0, 0, 0, 1, 1, 0, 0, 0, 1, 0]\n",
"```"
@@ -769,7 +769,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Introduction.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Introduction.ipynb
index 37fa681b..c4a22d60 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Introduction.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Introduction.ipynb
@@ -156,7 +156,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Iterables.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Iterables.ipynb
index 993cefa2..293ce0da 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Iterables.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Iterables.ipynb
@@ -300,7 +300,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Itertools.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Itertools.ipynb
index 38e4d347..82977d29 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Itertools.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Itertools.ipynb
@@ -178,7 +178,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/DifferenceFanout.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/DifferenceFanout.ipynb
index c4221ed1..fd5a3fd8 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/DifferenceFanout.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/DifferenceFanout.ipynb
@@ -147,7 +147,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/EncodeAsString.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/EncodeAsString.ipynb
index 93fcc8e3..87cc0de4 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/EncodeAsString.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/EncodeAsString.ipynb
@@ -176,7 +176,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/MarginPercentage.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/MarginPercentage.ipynb
index e8de22e6..50512b62 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/MarginPercentage.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/MarginPercentage.ipynb
@@ -116,7 +116,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/MergeMaxDicts.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/MergeMaxDicts.ipynb
index b2ac7ea4..7ea86925 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/MergeMaxDicts.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/MergeMaxDicts.ipynb
@@ -333,7 +333,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/Palindrome.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/Palindrome.ipynb
index 377d18bb..e6a56688 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/Palindrome.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Problems/Palindrome.ipynb
@@ -103,7 +103,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Scope.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Scope.ipynb
index 96d4f836..1753b3e9 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Scope.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Scope.ipynb
@@ -172,7 +172,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/SequenceTypes.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/SequenceTypes.ipynb
index abb89d82..4d4e0054 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/SequenceTypes.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/SequenceTypes.ipynb
@@ -336,7 +336,7 @@
">>> seq[1:4:1]\n",
"'bcd'\n",
"\n",
- "# start:1, stop:5, step:2\n",
+ "# start:0, stop:5, step:2\n",
">>> seq[0:5:2] # get every other entry within [start, stop)\n",
"'ace'\n",
"\n",
@@ -583,7 +583,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Variables_and_Assignment.ipynb b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Variables_and_Assignment.ipynb
index 862e29ce..d635f7bd 100644
--- a/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Variables_and_Assignment.ipynb
+++ b/docs/.doctrees/nbsphinx/Module2_EssentialsOfPython/Variables_and_Assignment.ipynb
@@ -264,7 +264,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/AccessingDataAlongMultipleDimensions.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/AccessingDataAlongMultipleDimensions.ipynb
index be7ecb87..292fc0b0 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/AccessingDataAlongMultipleDimensions.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/AccessingDataAlongMultipleDimensions.ipynb
@@ -239,7 +239,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Thus far, we have discussed some rules for accessing data in arrays, all of which fall into the category that is designated [\"basic indexing\"](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#basic-slicing-and-indexing) by the NumPy documentation. We will discuss the details of basic indexing and of [\"advanced indexing\"](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing), in full, in a later section. Note, however, that all of the indexing/slicing reviewed here produces a \"view\" of the original array. That is, *no data is copied* when you index into an array using integer indices and/or slices. Recall that slicing lists and tuples *do* produce copies of the data.\n"
+ "Thus far, we have discussed some rules for accessing data in arrays, all of which fall into the category that is designated [\"basic indexing\"](https://numpy.org/doc/stable/reference/arrays.indexing.html#basic-slicing-and-indexing) by the NumPy documentation. We will discuss the details of basic indexing and of [\"advanced indexing\"](https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing), in full, in a later section. Note, however, that all of the indexing/slicing reviewed here produces a \"view\" of the original array. That is, *no data is copied* when you index into an array using integer indices and/or slices. Recall that slicing lists and tuples *do* produce copies of the data.\n"
]
},
{
@@ -448,7 +448,7 @@
" [ 3, 7, 11],\n",
" [ 4, 8, 12]])\n",
"```\n",
- "A complete listing of the available array-manipulation functions can be found in the [official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html). Among these functions, the reshape function is especially useful. \n",
+ "A complete listing of the available array-manipulation functions can be found in the [official NumPy documentation](https://numpy.org/doc/stable/reference/routines.array-manipulation.html). Among these functions, the reshape function is especially useful. \n",
"\n",
"### Introducing the `reshape` Function\n",
"The `reshape` function allows you to change the dimensionality and axis-layout of a given array. This adjusts the indexing interface used to access the array's underlying data, as was discussed in earlier in this module. Let's take a shape-(6,) array, and reshape it to a shape-(2, 3) array:\n",
@@ -532,10 +532,10 @@
"source": [
"## Links to Official Documentation\n",
"\n",
- "- [The N-dimensional array](https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html)\n",
- "- [Array indexing](https://docs.scipy.org/doc/numpy/user/basics.indexing.html#indexing)\n",
- "- [Indexing routines](https://docs.scipy.org/doc/numpy/reference/routines.indexing.html#indexing-routines)\n",
- "- [Array manipulation routines](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html)"
+ "- [The N-dimensional array](https://numpy.org/doc/stable/reference/arrays.ndarray.html)\n",
+ "- [Array indexing](https://numpy.org/doc/stable/user/basics.indexing.html#indexing)\n",
+ "- [Indexing routines](https://numpy.org/doc/stable/reference/routines.indexing.html#indexing-routines)\n",
+ "- [Array manipulation routines](https://numpy.org/doc/stable/reference/routines.array-manipulation.html)"
]
},
{
@@ -596,7 +596,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/AdvancedIndexing.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/AdvancedIndexing.ipynb
index 79a8d5ad..1928e828 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/AdvancedIndexing.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/AdvancedIndexing.ipynb
@@ -66,7 +66,7 @@
">>> np.shares_memory(x, x[bool_index])\n",
"False\n",
"```\n",
- "The flexibility permitted by advanced indexing makes it a difficult topic to treat exhaustively without delving into somewhat terse and abstract notation. It is best to refer to [the official documentation](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing) for such a treatment of the topic. Here, we will discuss the essential aspects of advanced indexing, with the aim of the discussion being both thorough and accessible."
+ "The flexibility permitted by advanced indexing makes it a difficult topic to treat exhaustively without delving into somewhat terse and abstract notation. It is best to refer to [the official documentation](https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing) for such a treatment of the topic. Here, we will discuss the essential aspects of advanced indexing, with the aim of the discussion being both thorough and accessible."
]
},
{
@@ -451,7 +451,7 @@
"... [-0.29, 0.13, -0.26, 0.33]])\n",
"```\n",
"\n",
- "Use boolean array-indexing and NumPy's [logical functions](https://docs.scipy.org/doc/numpy/reference/routines.logic.html) to select the contents of `h` that satisfy the following conditions. Because you are dealing with floating-point numbers, [you should not require that two values are exactly equal](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Understanding-Numerical-Precision); rather, make use of the function [numpy.isclose](https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.isclose.html).\n",
+ "Use boolean array-indexing and NumPy's [logical functions](https://numpy.org/doc/stable/reference/routines.logic.html) to select the contents of `h` that satisfy the following conditions. Because you are dealing with floating-point numbers, [you should not require that two values are exactly equal](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Understanding-Numerical-Precision); rather, make use of the function [numpy.isclose](https://numpy.org/doc/stable/reference/generated/numpy.isclose.html#numpy.isclose).\n",
"\n",
"1. All negative entries in `h`\n",
"2. All entries in `h` \"equal\" to `0.01` or `0.33`\n",
@@ -465,7 +465,7 @@
"metadata": {},
"source": [
"### Converting a Boolean Index-Array to Integer Index-Arrays: numpy.where\n",
- "The function [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) can be used to take a boolean-valued array, and produce the *tuple* of index-arrays that access the `True` entries of that array, via integer array indexing (discussed at the beginning of this section).\n",
+ "The function [numpy.where](https://numpy.org/doc/stable/reference/generated/numpy.where.html) can be used to take a boolean-valued array, and produce the *tuple* of index-arrays that access the `True` entries of that array, via integer array indexing (discussed at the beginning of this section).\n",
"\n",
"```python\n",
"# demonstrating `np.where`\n",
@@ -515,7 +515,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Armed with NumPy's suite of [logical functions](https://docs.scipy.org/doc/numpy/reference/routines.logic.html), boolean-array indexing provides a sleek interface for accessing the particular contents of an array, irrespective of the array's shape and the layout of its contents. This method of indexing is especially powerful in the context of performing augmented updates to an array, which is the subject of the following subsection."
+ "Armed with NumPy's suite of [logical functions](https://numpy.org/doc/stable/reference/routines.logic.html), boolean-array indexing provides a sleek interface for accessing the particular contents of an array, irrespective of the array's shape and the layout of its contents. This method of indexing is especially powerful in the context of performing augmented updates to an array, which is the subject of the following subsection."
]
},
{
@@ -616,7 +616,7 @@
"... [ 0.84, 0.76, 0.25, 0.07]])\n",
"```\n",
"\n",
- "Replace the diagonal elements of `x` with `(-1, -2, -3, -4)`, and add `1` to all values in `x` that are greater than `0.8`.\n",
+ "Replace the diagonal elements of `x` with `(0, 1, 2, 3)`, and add `1` to all values in `x` that are greater than `0.8`.\n",
"
"
]
},
@@ -649,7 +649,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The rules for resolving the various possible combinations of basic and advanced indexing are nontrivial. Refer to the [official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#combining-advanced-and-basic-indexing) for a detailed description for these rules. In practice, basic and advanced indexing can typically be used independently from one another."
+ "The rules for resolving the various possible combinations of basic and advanced indexing are nontrivial. Refer to the [official NumPy documentation](https://numpy.org/doc/stable/reference/arrays.indexing.html#combining-advanced-and-basic-indexing) for a detailed description for these rules. In practice, basic and advanced indexing can typically be used independently from one another."
]
},
{
@@ -658,10 +658,10 @@
"source": [
"## Links to Official Documentation\n",
"\n",
- "- [Advanced Indexing](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing)\n",
- "- [Logical Functions](https://docs.scipy.org/doc/numpy/reference/routines.logic.html)\n",
- "- [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html)\n",
- "- [Combining Basics and Advanced Indexing](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#combining-advanced-and-basic-indexing)"
+ "- [Advanced Indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing)\n",
+ "- [Logical Functions](https://numpy.org/doc/stable/reference/routines.logic.html)\n",
+ "- [numpy.where](https://numpy.org/doc/stable/reference/generated/numpy.where.html)\n",
+ "- [Combining Basics and Advanced Indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#combining-advanced-and-basic-indexing)"
]
},
{
@@ -805,6 +805,13 @@
"... [ 0.84, 0.76, 0.25, 0.07]])\n",
"\n",
">>> x[np.arange(4), np.arange(4)] = range(4)\n",
+ "# equivalent (works for the general case of a square matrix of N-dims)\n",
+ "# x[tuple(np.arange(x) for x in x.shape)] = range(x.shape[0])\n",
+ "\n",
+ "# equivalent (using numpy built-in functions):\n",
+ "# x[np.diag_indices_from(x)] = np.arange(4)\n",
+ "# np.fill_diagonal(x, np.arange(4))\n",
+ "\n",
">>> x[0.8 < x] += 1\n",
">>> x\n",
"array([[ 0. , 0.05, 1.84, 0.21],\n",
@@ -821,7 +828,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/ArrayTraversal.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/ArrayTraversal.ipynb
index f1b9c2ae..7e45594c 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/ArrayTraversal.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/ArrayTraversal.ipynb
@@ -35,7 +35,7 @@
"[(0, 'a'), (1, 'b'), (2, 'c'), (3, 'd'), (4, 'e'), (5, 'f')]\n",
"```\n",
"\n",
- "Similarly, NumPy provides the [ndenumerate](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndenumerate.html#numpy.ndenumerate) function, which enumerates each element in an N-dimensional array, specifying the N-dimensional index for each element.\n",
+ "Similarly, NumPy provides the [ndenumerate](https://numpy.org/doc/stable/reference/generated/numpy.ndenumerate.html#numpy.ndenumerate) function, which enumerates each element in an N-dimensional array, specifying the N-dimensional index for each element.\n",
"```python\n",
">>> import numpy as np\n",
"\n",
@@ -78,7 +78,7 @@
"9\n",
"```\n",
"\n",
- "See [the official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/routines.indexing.html#iterating-over-arrays) for a complete listing of functions that facilitate iterating over arrays. The official documentation also provides [a detailed treatment of array iteration](https://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#iterating-over-arrays), which is far more detailed than is warranted here. Next, we must discuss the default ordering that NumPy uses when traversing a N-dimensional array.\n",
+ "See [the official NumPy documentation](https://numpy.org/doc/stable/reference/routines.indexing.html#iterating-over-arrays) for a complete listing of functions that facilitate iterating over arrays. The official documentation also provides [a detailed treatment of array iteration](https://numpy.org/doc/stable/reference/arrays.nditer.html#iterating-over-arrays), which is far more detailed than is warranted here. Next, we must discuss the default ordering that NumPy uses when traversing a N-dimensional array.\n",
"\n"
]
},
@@ -218,10 +218,10 @@
"source": [
"## Links to Official Documentation\n",
"\n",
- "- [Definition of row major ordering](https://docs.scipy.org/doc/numpy/glossary.html#term-row-major)\n",
- "- [Definition of column major ordering](https://docs.scipy.org/doc/numpy/glossary.html#term-column-major)\n",
- "- [Routines for iterating over arrays](https://docs.scipy.org/doc/numpy/reference/routines.indexing.html#iterating-over-arrays)\n",
- "- [Detailed description of array iteration](https://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#iterating-over-arrays)"
+ "- [Definition of row major ordering](https://numpy.org/doc/stable/glossary.html#term-row-major)\n",
+ "- [Definition of column major ordering](https://numpy.org/doc/stable/glossary.html#term-column-major)\n",
+ "- [Routines for iterating over arrays](https://numpy.org/doc/stable/reference/routines.indexing.html#iterating-over-arrays)\n",
+ "- [Detailed description of array iteration](https://numpy.org/doc/stable/reference/arrays.nditer.html#iterating-over-arrays)"
]
}
],
@@ -231,7 +231,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/BasicArrayAttributes.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/BasicArrayAttributes.ipynb
index c9cf7157..c4830fe5 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/BasicArrayAttributes.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/BasicArrayAttributes.ipynb
@@ -97,7 +97,7 @@
"source": [
"## Links to Official Documentation\n",
"\n",
- "- [Array attributes](https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html#array-attributes)"
+ "- [Array attributes](https://numpy.org/doc/stable/reference/arrays.ndarray.html#array-attributes)"
]
}
],
@@ -107,7 +107,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/BasicIndexing.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/BasicIndexing.ipynb
index b839d8cb..8160ac87 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/BasicIndexing.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/BasicIndexing.ipynb
@@ -223,16 +223,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "
\n",
+ " \n",
"**Reading Comprehension: Ellipsis**\n",
"\n",
- "Given a $N$-dimensional array, `x`, index into `x` such that you axis entry-0 of axis-0, the last entry of axis-$N-1$, slicing along all intermediate dimensions. $N$ is at least $2$.\n",
+ "Given a $N$-dimensional array, `x`, index into `x` such that you access entry-0 of axis-0, the last entry of axis-$N-1$, slicing along all intermediate dimensions. $N$ is at least $2$.\n",
"\n",
"
"
]
},
@@ -656,8 +645,8 @@
"source": [
"## Links to Official Documentation\n",
"\n",
- "- [Basic indexing](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#indexing)\n",
- "- [Definition of 'view'](https://docs.scipy.org/doc/numpy/glossary.html#term-view)"
+ "- [Basic indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#indexing)\n",
+ "- [Definition of 'view'](https://numpy.org/doc/stable/glossary.html#term-view)"
]
},
{
@@ -802,7 +791,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Broadcasting.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Broadcasting.ipynb
index f2b52b44..de10f270 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Broadcasting.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Broadcasting.ipynb
@@ -197,7 +197,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "NumPy provides the function [broadcast_to](https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_to.html#numpy.broadcast_to), which can be used to broadcast an array to a specified shape. This can help us build our intuition for broadcasting. Let's broadcast a shape-(3,4) array to a shape-(2,3,4) array:\n",
+ "NumPy provides the function [broadcast_to](https://numpy.org/doc/stable/reference/generated/numpy.broadcast_to.html#numpy.broadcast_to), which can be used to broadcast an array to a specified shape. This can help us build our intuition for broadcasting. Let's broadcast a shape-(3,4) array to a shape-(2,3,4) array:\n",
"\n",
"```python\n",
"# Demonstrating `np.broadcast_to`\n",
@@ -479,7 +479,7 @@
"\n",
"A digital image is simply an array of numbers, which instructs a grid of pixels on a monitor to shine light of specific colors, according to the numerical values in that array. \n",
"\n",
- "An RGB-image can thus be stored as a 3D NumPy array of shape-$(V, H, 3)$. $V$ is the number of pixels along the vertical direction, $H$ is the number of pixels along the horizontal, and the size-3 dimension stores the red, blue, and green color values for a given pixel. Thus a $(32, 32, 3)$ array would be a 32x32 RBG image.\n",
+ "An RGB-image can thus be stored as a 3D NumPy array of shape-$(V, H, 3)$. $V$ is the number of pixels along the vertical direction, $H$ is the number of pixels along the horizontal, and the size-3 dimension stores the red, blue, and green color values for a given pixel. Thus a $(32, 32, 3)$ array would be a 32x32 RGB image.\n",
"\n",
"You often work with a collection of images. Suppose we want to store N images in a single array; thus we now consider a 4D shape-(N, V, H, 3) array. For the sake of convenience, let's simply generate a 4D-array of random numbers as a placeholder for real image data. We will generate 500, 48x48 RGB images:\n",
"\n",
@@ -545,16 +545,16 @@
"def pairwise_dists_looped(x, y):\n",
" \"\"\" Computing pairwise distances using for-loops\n",
"\n",
- " Parameters\n",
- " ----------\n",
- " x : numpy.ndarray, shape=(M, D)\n",
- " y : numpy.ndarray, shape=(N, D)\n",
+ " Parameters\n",
+ " ----------\n",
+ " x : numpy.ndarray, shape=(M, D)\n",
+ " y : numpy.ndarray, shape=(N, D)\n",
"\n",
- " Returns\n",
- " -------\n",
- " numpy.ndarray, shape=(M, N)\n",
- " The Euclidean distance between each pair of\n",
- " rows between `x` and `y`.\"\"\"\n",
+ " Returns\n",
+ " -------\n",
+ " numpy.ndarray, shape=(M, N)\n",
+ " The Euclidean distance between each pair of\n",
+ " rows between `x` and `y`.\"\"\"\n",
" # `dists[i, j]` will store the Euclidean \n",
" # distance between `x[i]` and `y[j]`\n",
" dists = np.empty((5, 6))\n",
@@ -572,7 +572,17 @@
" return np.sqrt(dists)\n",
"```\n",
"\n",
- "Be sure to step through this code and see that `dists` stores each pair of Euclidean distances between the rows of `x` and `y`."
+ "Be sure to step through this code and see that `dists` stores each pair of Euclidean distances between the rows of `x` and `y`.\n",
+ "\n",
+ "```python\n",
+ "# produces a shape-(5, 6) result\n",
+ ">>> pairwise_dists_looped(x, y)\n",
+ "array([[ 3.678 , 8.4524, 10.3057, 7.3711, 6.2152, 5.5548],\n",
+ " [10.1457, 5.8793, 2.9274, 4.1114, 3.9098, 5.2259],\n",
+ " [ 7.3219, 0.8439, 6.8734, 4.5687, 7.3283, 4.8216],\n",
+ " [10.339 , 7.032 , 7.4745, 7.0633, 3.5999, 4.0107],\n",
+ " [ 8.2878, 3.5468, 6.336 , 4.9014, 4.1858, 2.0257]])\n",
+ "```"
]
},
{
@@ -607,26 +617,34 @@
"def pairwise_dists_crude(x, y):\n",
" \"\"\" Computing pairwise distances using vectorization.\n",
" \n",
- " This method uses memory-inefficient broadcasting.\n",
- " \n",
- " Parameters\n",
- " ----------\n",
- " x : numpy.ndarray, shape=(M, D)\n",
- " y : numpy.ndarray, shape=(N, D)\n",
- " \n",
- " Returns\n",
- " -------\n",
- " numpy.ndarray, shape=(M, N)\n",
- " The Euclidean distance between each pair of\n",
- " rows between `x` and `y`.\"\"\"\n",
+ " This method uses memory-inefficient broadcasting.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " x : numpy.ndarray, shape=(M, D)\n",
+ " y : numpy.ndarray, shape=(N, D)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " numpy.ndarray, shape=(M, N)\n",
+ " The Euclidean distance between each pair of\n",
+ " rows between `x` and `y`.\"\"\"\n",
" # The use of `np.newaxis` here is equivalent to our \n",
" # use of the `reshape` function\n",
" return np.sqrt(np.sum((x[:, np.newaxis] - y[np.newaxis])**2, axis=2))\n",
"```\n",
- "\n",
+ "```python\n",
+ "# produces a shape-(5, 6) result\n",
+ ">>> pairwise_dists_crude(x, y)\n",
+ "array([[ 3.678 , 8.4524, 10.3057, 7.3711, 6.2152, 5.5548],\n",
+ " [10.1457, 5.8793, 2.9274, 4.1114, 3.9098, 5.2259],\n",
+ " [ 7.3219, 0.8439, 6.8734, 4.5687, 7.3283, 4.8216],\n",
+ " [10.339 , 7.032 , 7.4745, 7.0633, 3.5999, 4.0107],\n",
+ " [ 8.2878, 3.5468, 6.336 , 4.9014, 4.1858, 2.0257]])\n",
+ "```\n",
"Regrettably, there is a glaring issue with the vectorized computation that we just performed. Consider the largest sized array that is created in the for-loop computation, compared to that of this vectorized computation. The for-loop version need only create a shape-$(M, N)$ array, whereas the vectorized computation creates an intermediate array (i.e. `diffs`) of shape-$(M, N, D)$. This intermediate array is even created in the one-line version of the code. This will create a massive array if $D$ is a large number!\n",
"\n",
- "Suppose, for instance, that you are finding the Euclidean between pairs of RGB images that each have a resolution of $32 \\times 32$ (in order to see if the images resemble one another). Thus in this scenario, each image is comprised of $D = 32 \\times 32 \\times 3 = 3072$ numbers ($32^2$ pixels, and each pixel has 3 values: a red, blue, and green-color value). Computing all the distances between a stack of 5000 images with a stack of 100 images would form an intermediate array of shape-$(5000, 100, 3072)$. Even though this large array only exists temporarily, it would have to consume over 6GB of RAM! The for-loop version requires $\\frac{1}{3027}$ as much memory (about 2MB).\n",
+ "Suppose, for instance, that you are finding the Euclidean between pairs of RGB images that each have a resolution of $32 \\times 32$ (in order to see if the images resemble one another). Thus in this scenario, each image is comprised of $D = 32 \\times 32 \\times 3 = 3072$ numbers ($32^2$ pixels, and each pixel has 3 values: a red, blue, and green-color value). Computing all the distances between a stack of 5000 images with a stack of 100 images would form an intermediate array of shape-$(5000, 100, 3072)$. Even though this large array only exists temporarily, it would have to consume over 6GB of RAM! The for-loop version requires $\\frac{1}{3072}$ as much memory (about 2MB).\n",
"\n",
"Is our goose cooked? Are we doomed to pick between either slow for-loops, or a memory-inefficient use of vectorization? No! We can refactor the mathematical form of the Euclidean distance in order to avoid the creation of that bloated intermediate array. "
]
@@ -681,7 +699,7 @@
"```python\n",
"# computing the third term in the distance\n",
"# equation, for all pairs of rows\n",
- ">>> x_y_prod = -2 * np.matmul(x, y.T) # `np.dot` can also be used to the same effect\n",
+ ">>> x_y_prod = 2 * np.matmul(x, y.T) # `np.dot` can also be used to the same effect\n",
">>> x_y_prod.shape\n",
"(5, 6)\n",
"```\n",
@@ -690,33 +708,109 @@
"\n",
"```python\n",
"# computing all the distances\n",
- ">>> dists = np.sqrt(x_y_sqrd + x_y_prod)\n",
+ ">>> dists = np.sqrt(x_y_sqrd - x_y_prod)\n",
">>> dists.shape\n",
"(5, 6)\n",
"```\n",
- "In total, we have successfully used vectorization to compute the all pairs of distances, while only requiring an array of shape-$(M, N)$ to do so! This is the memory-efficient, vectorized form - the stuff that dreams are made of. Let's write the function that performs this computation in full. \n",
+ "\n",
+ "#### A Subtle Issue with Floating-point Precision\n",
+ "\n",
+ "There is one more important and very subtle detail that we have to deal with.\n",
+ "In terms of pure mathematics, `x_y_sqrd - x_y_prod` must be a strictly non-negative value (i.e. its smallest possible value is $0$), since it is equivalent to\n",
+ "\n",
+ "\\begin{equation}\n",
+ "\\sum_{i=0}^{D-1}{(x_{i} - y_{i})^2}\n",
+ "\\end{equation}\n",
+ "\n",
+ "That being said, are working with floating-point numbers, which do not always behave exactly like rational numbers when we do arithmetic with them.\n",
+ "Indeed, we saw earlier that the [quirks of floating-point arithmetic](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Understanding-Numerical-Precision) can lead to surprising results. \n",
+ "Here, the strange behavior is that `x_y_sqrd - x_y_prod` **can produce negative numbers**!\n",
+ "\n",
+ "Let's see this in action.\n",
+ "We'll take the following shape-(2, 3) array\n",
+ "\n",
+ "```python\n",
+ "# A carefully-selected input that will trigger surprising\n",
+ "# floating-point precision issues in our distance calculation\n",
+ ">>> x = np.array([[4.700867387959219, 4.700867387959219, 4.700867387959219],\n",
+ "... [4.700867387959219, 4.700867387959219, 4.700867387959219]])\n",
+ "```\n",
+ "\n",
+ "And compute the square distance between all combinations of rows `x`; the result should simply be a shape-(2, 2) array of zeros.\n",
+ "\n",
+ "```python\n",
+ "# The square-distances should be exactly zero, but they\n",
+ "# will instead be (*very* small) negative numbers!\n",
+ ">>> sqr_dists = -2 * np.matmul(x, x.T) # `x_x_prod`\n",
+ ">>> sqr_dists += np.sum(x**2, axis=1)[:, np.newaxis]\n",
+ ">>> sqr_dists += np.sum(x**2, axis=1)\n",
+ ">>> sqr_dists\n",
+ "array([[-2.842170943040401e-14, -2.842170943040401e-14],\n",
+ " [-2.842170943040401e-14, -2.842170943040401e-14]])\n",
+ "```\n",
+ "\n",
+ "These values are *very* close to being zero, so it is not as if the magnitude of our result is wildly off, but the critical issue here is that the quirks of floating-point arithmetic produced (very small) negative numbers.\n",
+ "We are going to be in for a rude awakening when we take the square-root of these values to get our final distances:\n",
+ "\n",
+ "```python\n",
+ "# Taking the square-root of negative floats will produce NaNs\n",
+ ">>> np.sqrt(sqr_dists)\n",
+ "array([[nan, nan],\n",
+ " [nan, nan]])\n",
+ "```\n",
+ "\n",
+ "`nan` stands for \"Not a Number\", which is to say that the square-root of a negative number cannot produce a real-valued float (the result will be an [imaginary number](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Complex-Numbers), but NumPy will not swap out real numbers for imaginary ones unless we explicitly permit it to).\n",
+ "\n",
+ "The solution to this problem comes from the realization that this occurrence is truly an edge-case, and that it will only manifest when the result *ought* to have been zero and indeed is very close to zero.\n",
+ "Thus we can [clip](https://numpy.org/doc/stable/reference/generated/numpy.clip.html) `sqr_dists` such that any value that falls below zero gets set to zero and all other values will be left alone:\n",
+ "\n",
+ "```python\n",
+ "# \"Clipping\" all negative entries so that they are set to 0 \n",
+ ">>> np.sqrt(np.clip(sqr_dists, a_min=0, a_max=None))\n",
+ "array([[0., 0.],\n",
+ " [0., 0.]])\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### The Final Answer, At Last!\n",
+ "\n",
+ "In total, we have successfully used vectorization to compute all the pairs of distances, while only requiring an array of shape-$(M, N)$ to do so! This is the memory-efficient, vectorized form – the stuff that dreams are made of.\n",
+ "Let's write the function that performs this computation in full. \n",
"\n",
"```python\n",
"def pairwise_dists(x, y):\n",
" \"\"\" Computing pairwise distances using memory-efficient \n",
- " vectorization.\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " x : numpy.ndarray, shape=(M, D)\n",
- " y : numpy.ndarray, shape=(N, D)\n",
- "\n",
- " Returns\n",
- " -------\n",
- " numpy.ndarray, shape=(M, N)\n",
- " The Euclidean distance between each pair of\n",
- " rows between `x` and `y`.\"\"\"\n",
- " dists = -2 * np.matmul(x, y.T)\n",
- " dists += np.sum(x**2, axis=1)[:, np.newaxis]\n",
- " dists += np.sum(y**2, axis=1)\n",
- " return np.sqrt(dists)\n",
+ " vectorization.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " x : numpy.ndarray, shape=(M, D)\n",
+ " y : numpy.ndarray, shape=(N, D)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " numpy.ndarray, shape=(M, N)\n",
+ " The Euclidean distance between each pair of\n",
+ " rows between `x` and `y`.\"\"\"\n",
+ " sqr_dists = -2 * np.matmul(x, y.T)\n",
+ " sqr_dists += np.sum(x**2, axis=1)[:, np.newaxis]\n",
+ " sqr_dists += np.sum(y**2, axis=1)\n",
+ " return np.sqrt(np.clip(sqr_dists, a_min=0, a_max=None))\n",
"```\n",
- "\n"
+ "\n",
+ "```python\n",
+ "# produces a shape-(5, 6) result\n",
+ ">>> pairwise_dists(x, y)\n",
+ "array([[ 3.678 , 8.4524, 10.3057, 7.3711, 6.2152, 5.5548],\n",
+ " [10.1457, 5.8793, 2.9274, 4.1114, 3.9098, 5.2259],\n",
+ " [ 7.3219, 0.8439, 6.8734, 4.5687, 7.3283, 4.8216],\n",
+ " [10.339 , 7.032 , 7.4745, 7.0633, 3.5999, 4.0107],\n",
+ " [ 8.2878, 3.5468, 6.336 , 4.9014, 4.1858, 2.0257]])\n",
+ "```"
]
},
{
@@ -740,7 +834,7 @@
"\n",
"**Reading Comprehension: Checking the equivalence of the three pairwise distance functions**\n",
"\n",
- "Use the function [numpy.allclose](https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html) to verify that the three methods for computing the pairwise distances produce the same numerical results.\n",
+ "Use the function [numpy.allclose](https://numpy.org/doc/stable/reference/generated/numpy.allclose.html) to verify that the three methods for computing the pairwise distances produce the same numerical results.\n",
"\n",
"
"
]
@@ -751,8 +845,8 @@
"source": [
"## Links to Official Documentation\n",
"\n",
- "- [Basics of broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html#broadcasting)\n",
- "- [Broadcasting routines](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html#changing-number-of-dimensions)"
+ "- [Basics of broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.html#broadcasting)\n",
+ "- [Broadcasting routines](https://numpy.org/doc/stable/reference/routines.array-manipulation.html#changing-number-of-dimensions)"
]
},
{
@@ -762,6 +856,23 @@
"## Reading Comprehension Solutions"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Broadcast Compatibility: Solution**\n",
+ "\n",
+ "1\\. Incompatible\n",
+ "\n",
+ "2\\. `3 x 4`\n",
+ "\n",
+ "3\\. `8 x 3 x 1`\n",
+ "\n",
+ "4\\. `9 x 2 x 5`\n",
+ "\n",
+ "5\\. Incompatible"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -785,23 +896,6 @@
"```"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Broadcast Compatibility: Solution**\n",
- "\n",
- "1\\. Incompatible\n",
- "\n",
- "2\\. `3 x 4`\n",
- "\n",
- "3\\. `8 x 3 x 1`\n",
- "\n",
- "4\\. `9 x 2 x 5`\n",
- "\n",
- "5\\. Incompatible"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -913,7 +1007,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.ipynb
index 07bfdc4c..012497e2 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.ipynb
@@ -112,7 +112,7 @@
"array([ 1., 1., 1., 1.])\n",
"```\n",
"\n",
- "NumPy provides additional functions for creating constant-valued arrays. Please refer to [the official documentation](https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#ones-and-zeros) for a complete listing. "
+ "NumPy provides additional functions for creating constant-valued arrays. Please refer to [the official documentation](https://numpy.org/doc/stable/reference/routines.array-creation.html#ones-and-zeros) for a complete listing. "
]
},
{
@@ -120,7 +120,7 @@
"metadata": {},
"source": [
"## Creating Sequential Arrays: `arange` and `linspace`\n",
- "The [arange](https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html#numpy.arange) function allows you to initialize a sequence of integers based on a starting point (inclusive), stopping point (exclusive), and step size. This is very similar to the `range` function; however `arange` immediately creates this sequence as an array, whereas `range` produces a generator.\n",
+ "The [arange](https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange) function allows you to initialize a sequence of integers based on a starting point (inclusive), stopping point (exclusive), and step size. This is very similar to the `range` function; however `arange` immediately creates this sequence as an array, whereas `range` produces a generator.\n",
"```python\n",
">>> np.arange(0, 10, 1) # start (included): 0, stop (excluded): 10, step:1 \n",
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
@@ -134,7 +134,7 @@
"array([-5, -3, -1, 1, 3, 5])\n",
"```\n",
"\n",
- "The [linspace](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) function allows you to generate $N$ *evenly-spaced* points within a user-specified interval $[i, j]$ ($i$ and $j$ are included in the interval). This is often used to generate a domain of values on which to evaluate a mathematical function (e.g. if you want to the sine function from $-\\pi$ to $\\pi$ on a finely-divided grid).\n",
+ "The [linspace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html) function allows you to generate $N$ *evenly-spaced* points within a user-specified interval $[i, j]$ ($i$ and $j$ are included in the interval). This is often used to generate a domain of values on which to evaluate a mathematical function (e.g. if you want to the sine function from $-\\pi$ to $\\pi$ on a finely-divided grid).\n",
"\n",
"```python\n",
"# generate five evenly-spaced points on the interval [-1, 1]\n",
@@ -150,7 +150,7 @@
"array([-3.14159265, ..., 3.14159265])\n",
"```\n",
"\n",
- "Numpy has other functions for creating sequential arrays, such as producing an array spaced evenly on a log-scaled interval. See the [official documentation](https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#numerical-ranges) for a complete listing."
+ "Numpy has other functions for creating sequential arrays, such as producing an array spaced evenly on a log-scaled interval. See the [official documentation](https://numpy.org/doc/stable/reference/routines.array-creation.html#numerical-ranges) for a complete listing."
]
},
{
@@ -172,7 +172,7 @@
">>> np.random.randn(5)\n",
"array([-1.11262121, -0.35392007, 0.4245215 , -0.81995588, 0.65412323])\n",
"```\n",
- "There are [many more functions](https://docs.scipy.org/doc/numpy/reference/routines.random.html#distributions) to read about that allow you to draw from a wide variety of statistical distributions. This only scratches the surface of random number generation in NumPy.\n"
+ "There are [many more functions](https://numpy.org/doc/stable/reference/routines.random.html#distributions) to read about that allow you to draw from a wide variety of statistical distributions. This only scratches the surface of random number generation in NumPy.\n"
]
},
{
@@ -203,7 +203,7 @@
" [ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]], dtype=complex64)\n",
"```\n",
"\n",
- "Refer to [the official NumPy documentation](https://docs.scipy.org/doc/numpy/user/basics.types.html#array-types-and-conversions-between-types) for the complete list of available array datatypes."
+ "Refer to [the official NumPy documentation](https://numpy.org/doc/stable/user/basics.types.html#array-types-and-conversions-between-types) for the complete list of available array datatypes."
]
},
{
@@ -227,7 +227,7 @@
"array([ 1, 2, 3, -1, -2, -3])\n",
"```\n",
"\n",
- "A complete listing of functions for joining arrays can be [found in the official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html#joining-arrays). There are also corresponding functions for splitting an array into independent arrays."
+ "A complete listing of functions for joining arrays can be [found in the official NumPy documentation](https://numpy.org/doc/stable/reference/routines.array-manipulation.html#joining-arrays). There are also corresponding functions for splitting an array into independent arrays."
]
},
{
@@ -236,12 +236,12 @@
"source": [
"## Links to Official Documentation\n",
"\n",
- "- [Constant arrays](https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#ones-and-zeros)\n",
- "- [numpy.array](https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html#numpy-array)\n",
- "- [Sequential arrays](https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#numerical-ranges)\n",
- "- [Random distributions](https://docs.scipy.org/doc/numpy/reference/routines.random.html#distributions)\n",
- "- [Array types](https://docs.scipy.org/doc/numpy/user/basics.types.html#array-types-and-conversions-between-types)\n",
- "- [Joining arrays](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html#joining-arrays)"
+ "- [Constant arrays](https://numpy.org/doc/stable/reference/routines.array-creation.html#ones-and-zeros)\n",
+ "- [numpy.array](https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy-array)\n",
+ "- [Sequential arrays](https://numpy.org/doc/stable/reference/routines.array-creation.html#numerical-ranges)\n",
+ "- [Random distributions](https://numpy.org/doc/stable/reference/routines.random.html#distributions)\n",
+ "- [Array types](https://numpy.org/doc/stable/user/basics.types.html#array-types-and-conversions-between-types)\n",
+ "- [Joining arrays](https://numpy.org/doc/stable/reference/routines.array-manipulation.html#joining-arrays)"
]
}
],
@@ -251,7 +251,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/IntroducingTheNDarray.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/IntroducingTheNDarray.ipynb
index fd6b8b0e..a3731239 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/IntroducingTheNDarray.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/IntroducingTheNDarray.ipynb
@@ -109,9 +109,9 @@
"source": [
"## Links to Official Documentation\n",
"\n",
- "- [The N-dimensional array](https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html)\n",
- "- [NumPy Basics](https://docs.scipy.org/doc/numpy/user/basics.html#numpy-basics)\n",
- "- [NumPy reference](https://docs.scipy.org/doc/numpy/reference/index.html)"
+ "- [The N-dimensional array](https://numpy.org/doc/stable/reference/arrays.ndarray.html)\n",
+ "- [NumPy Basics](https://numpy.org/doc/stable/user/basics.html#numpy-basics)\n",
+ "- [NumPy reference](https://numpy.org/doc/stable/reference/index.html)"
]
}
],
@@ -121,7 +121,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Problems/Approximating_pi.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Problems/Approximating_pi.ipynb
index ad788160..c93cbea6 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Problems/Approximating_pi.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Problems/Approximating_pi.ipynb
@@ -30,7 +30,7 @@
"\n",
"## Problem 1\n",
"\n",
- "Write code that simulates the dart throwing and tallying process, and keep a running estimate of $\\pi$ as \"darts are being thrown\". For simplicity, you can assume that the board is centered at $(0, 0)$, and that $r = 1$ (the radius of the circle). Use `numpy.random.rand` ([link to docs](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.rand.html)) to randomly generate the positions on the board where the darts land. Do this for $N = 10,000$ darts in total. For each dart thrown determine whether or not it landed within the circle, and update your estimate of $\\pi$ according to the formula: $N_{circle} / N_{total} \\approx \\pi / 4$\n",
+ "Write code that simulates the dart throwing and tallying process, and keep a running estimate of $\\pi$ as \"darts are being thrown\". For simplicity, you can assume that the board is centered at $(0, 0)$, and that $r = 1$ (the radius of the circle). Use `numpy.random.rand` ([link to docs](https://numpy.org/doc/stable/reference/generated/numpy.random.rand.html)) to randomly generate the positions on the board where the darts land. Do this for $N = 10,000$ darts in total. For each dart thrown determine whether or not it landed within the circle, and update your estimate of $\\pi$ according to the formula: $N_{circle} / N_{total} \\approx \\pi / 4$\n",
"\n",
"Keep in mind that each dart can land in $(x \\in [-1, 1], y \\in [-1, 1])$ and that a dart that lands at $(x, y)$ falls within the circle if \n",
"\n",
@@ -58,7 +58,7 @@
"source": [
"## Tips\n",
"\n",
- "It is helpful to know about NumPy's [cumulative-sum function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.cumsum.html), `numpy.cumsum`. This is useful for keeping a running tally - i.e. keeping the history of the number of darts that have fallen within the circle, and not just the current count."
+ "It is helpful to know about NumPy's [cumulative-sum function](https://numpy.org/doc/stable/reference/generated/numpy.cumsum.html), `numpy.cumsum`. This is useful for keeping a running tally - i.e. keeping the history of the number of darts that have fallen within the circle, and not just the current count."
]
},
{
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Problems/ComputeAccuracy.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Problems/ComputeAccuracy.ipynb
index ca2e8bf7..30beec35 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Problems/ComputeAccuracy.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/Problems/ComputeAccuracy.ipynb
@@ -56,7 +56,7 @@
"\n",
"To generalize this problem, assume that your classifier is dealing with $K$ classes (instead of $4$). Complete the following function. \n",
"\n",
- "**Tip:** You will find it useful to leverage [numpy's argmax function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html#numpy.argmax)`f\n",
+ "**Tip:** You will find it useful to leverage [numpy's argmax function](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html#numpy.argmax)`f\n",
"\n",
"```python\n",
"def classification_accuracy(classification_scores, true_labels):\n",
@@ -229,7 +229,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/VectorizedOperations.ipynb b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/VectorizedOperations.ipynb
index 4f929753..cd32ee1b 100644
--- a/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/VectorizedOperations.ipynb
+++ b/docs/.doctrees/nbsphinx/Module3_IntroducingNumpy/VectorizedOperations.ipynb
@@ -194,7 +194,7 @@
"- binary functions: $f(x,y)$\n",
"- functions that operate on sequences of numbers: $f(\\{x_i\\}_{i=0}^{n-1})$ \n",
"\n",
- "These represent a substantial portion of the essential mathematical tools in the NumPy library. An exhaustive list of NumPy's mathematical functions is available in the [official documentation](https://docs.scipy.org/doc/numpy/reference/ufuncs.html#math-operations).\n",
+ "These represent a substantial portion of the essential mathematical tools in the NumPy library. An exhaustive list of NumPy's mathematical functions is available in the [official documentation](https://numpy.org/doc/stable/reference/ufuncs.html#math-operations).\n",
"\n",
"### Unary Functions\n",
"A unary function is a mathematical function that only accepts one operand (i.e. argument): $f(x)$. NumPy supplies many familiar unary functions:\n",
@@ -675,7 +675,7 @@
"metadata": {},
"source": [
"## Logical Operations\n",
- "NumPy provides [a suite of logical operations](https://docs.scipy.org/doc/numpy/reference/routines.logic.html) that can operate on arrays. Many of these map logical operations over array entries in the same fashion as NumPy's mathematical functions. These functions return either a single boolean object, or a boolean-type array.\n",
+ "NumPy provides [a suite of logical operations](https://numpy.org/doc/stable/reference/routines.logic.html) that can operate on arrays. Many of these map logical operations over array entries in the same fashion as NumPy's mathematical functions. These functions return either a single boolean object, or a boolean-type array.\n",
"\n",
"```python\n",
"# check which entries of `x` are less than 6\n",
@@ -718,7 +718,7 @@
"metadata": {},
"source": [
"## Linear Algebra\n",
- "Lastly, we note that NumPy provides a suite of functions that can perform optimized computations and routines relevant to linear algebra. Included here are functions for performing matrix products and tensor products, solving eigenvalue problems, inverting matrices, and computing vector normalizations. Please refer to the [official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/routines.linalg.html) for a full listing of these functions.\n",
+ "Lastly, we note that NumPy provides a suite of functions that can perform optimized computations and routines relevant to linear algebra. Included here are functions for performing matrix products and tensor products, solving eigenvalue problems, inverting matrices, and computing vector normalizations. Please refer to the [official NumPy documentation](https://numpy.org/doc/stable/reference/routines.linalg.html) for a full listing of these functions.\n",
"\n"
]
},
@@ -736,9 +736,9 @@
"source": [
"## Links to Official Documentation\n",
"\n",
- "- [Math functions](https://docs.scipy.org/doc/numpy/reference/ufuncs.html#math-operations)\n",
- "- [Logic functions](https://docs.scipy.org/doc/numpy/reference/routines.logic.html)\n",
- "- [Linear algebra functions](https://docs.scipy.org/doc/numpy/reference/routines.linalg.html)"
+ "- [Math functions](https://numpy.org/doc/stable/reference/ufuncs.html#math-operations)\n",
+ "- [Logic functions](https://numpy.org/doc/stable/reference/routines.logic.html)\n",
+ "- [Linear algebra functions](https://numpy.org/doc/stable/reference/routines.linalg.html)"
]
},
{
@@ -781,7 +781,7 @@
"... [ 8, 9, 10, 11],\n",
"... [12, 13, 14, 15]])\n",
"\n",
- " # top-left top-right bottom-left bottom-right\n",
+ "# top-left top-right bottom-left bottom-right\n",
">>> x[:2, :2] + x[:2, -2:] + x[-2:, :2] + x[-2:, -2:]\n",
"array([[20, 24],\n",
" [36, 40]])\n",
@@ -831,7 +831,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module4_OOP/Applications_of_OOP.ipynb b/docs/.doctrees/nbsphinx/Module4_OOP/Applications_of_OOP.ipynb
index 4c5b518c..9bccf9a3 100644
--- a/docs/.doctrees/nbsphinx/Module4_OOP/Applications_of_OOP.ipynb
+++ b/docs/.doctrees/nbsphinx/Module4_OOP/Applications_of_OOP.ipynb
@@ -79,7 +79,7 @@
" items = [items]\n",
" # only mark items as purchased that are on our list to begin with\n",
" self._purchased.update(set(items) & self._needed)\n",
- " # remove all purchased items from out unpurchased set\n",
+ " # remove all purchased items from our unpurchased set\n",
" self._needed.difference_update(self._purchased)\n",
" \n",
" def list_purchased_items(self):\n",
@@ -145,7 +145,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module4_OOP/Brief_Review.ipynb b/docs/.doctrees/nbsphinx/Module4_OOP/Brief_Review.ipynb
index 166c442c..350e8b52 100644
--- a/docs/.doctrees/nbsphinx/Module4_OOP/Brief_Review.ipynb
+++ b/docs/.doctrees/nbsphinx/Module4_OOP/Brief_Review.ipynb
@@ -65,7 +65,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module4_OOP/ClassDefinition.ipynb b/docs/.doctrees/nbsphinx/Module4_OOP/ClassDefinition.ipynb
index 072f1a98..1d94fc76 100644
--- a/docs/.doctrees/nbsphinx/Module4_OOP/ClassDefinition.ipynb
+++ b/docs/.doctrees/nbsphinx/Module4_OOP/ClassDefinition.ipynb
@@ -270,7 +270,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module4_OOP/ClassInstances.ipynb b/docs/.doctrees/nbsphinx/Module4_OOP/ClassInstances.ipynb
index 925cf355..746deeb3 100644
--- a/docs/.doctrees/nbsphinx/Module4_OOP/ClassInstances.ipynb
+++ b/docs/.doctrees/nbsphinx/Module4_OOP/ClassInstances.ipynb
@@ -507,7 +507,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module4_OOP/Inheritance.ipynb b/docs/.doctrees/nbsphinx/Module4_OOP/Inheritance.ipynb
index 7f876d62..ed3850e0 100644
--- a/docs/.doctrees/nbsphinx/Module4_OOP/Inheritance.ipynb
+++ b/docs/.doctrees/nbsphinx/Module4_OOP/Inheritance.ipynb
@@ -169,7 +169,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module4_OOP/Introduction_to_OOP.ipynb b/docs/.doctrees/nbsphinx/Module4_OOP/Introduction_to_OOP.ipynb
index 3934ddc3..4a454768 100644
--- a/docs/.doctrees/nbsphinx/Module4_OOP/Introduction_to_OOP.ipynb
+++ b/docs/.doctrees/nbsphinx/Module4_OOP/Introduction_to_OOP.ipynb
@@ -185,7 +185,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module4_OOP/Methods.ipynb b/docs/.doctrees/nbsphinx/Module4_OOP/Methods.ipynb
index 9f74df97..9690edff 100644
--- a/docs/.doctrees/nbsphinx/Module4_OOP/Methods.ipynb
+++ b/docs/.doctrees/nbsphinx/Module4_OOP/Methods.ipynb
@@ -216,7 +216,7 @@
"`dict.fromkeys` is an example of a class method that takes in an iterable, and returns a dictionary whose keys are the elements of that iterable, and whose values all default to `None`.\n",
"\n",
"```python\n",
- ">>> dict.fromkeys(\"abcd\")\n",
+ ">>> dict.fromkeys(\"abcd\", 2.3)\n",
"{'a': 2.3, 'b': 2.3, 'c': 2.3, 'd': 2.3}\n",
"```\n",
"\n",
@@ -327,7 +327,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module4_OOP/ObjectOrientedProgramming.ipynb b/docs/.doctrees/nbsphinx/Module4_OOP/ObjectOrientedProgramming.ipynb
index 5122a67c..ae8e1fb4 100644
--- a/docs/.doctrees/nbsphinx/Module4_OOP/ObjectOrientedProgramming.ipynb
+++ b/docs/.doctrees/nbsphinx/Module4_OOP/ObjectOrientedProgramming.ipynb
@@ -453,7 +453,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module4_OOP/Special_Methods.ipynb b/docs/.doctrees/nbsphinx/Module4_OOP/Special_Methods.ipynb
index 676f2d31..2537065a 100644
--- a/docs/.doctrees/nbsphinx/Module4_OOP/Special_Methods.ipynb
+++ b/docs/.doctrees/nbsphinx/Module4_OOP/Special_Methods.ipynb
@@ -331,7 +331,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/Modules_and_Packages.ipynb b/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/Modules_and_Packages.ipynb
index cd3be9d1..966207bd 100644
--- a/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/Modules_and_Packages.ipynb
+++ b/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/Modules_and_Packages.ipynb
@@ -638,7 +638,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/WorkingWithFiles.ipynb b/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/WorkingWithFiles.ipynb
index b7daab71..578265c8 100644
--- a/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/WorkingWithFiles.ipynb
+++ b/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/WorkingWithFiles.ipynb
@@ -280,7 +280,14 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-31T21:48:04.883894Z",
+ "iopub.status.busy": "2021-01-31T21:48:04.883894Z",
+ "iopub.status.idle": "2021-01-31T21:48:04.887896Z",
+ "shell.execute_reply": "2021-01-31T21:48:04.886895Z"
+ }
+ },
"outputs": [],
"source": [
"my_files = ['data/file1.txt', 'data/file2.txt', 'data/file3.txt', 'data/file4.txt']"
@@ -526,7 +533,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
@@ -544,7 +551,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.6"
+ "version": "3.8.5"
}
},
"nbformat": 4,
diff --git a/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/Writing_Good_Code.ipynb b/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/Writing_Good_Code.ipynb
index 30ed6d3a..af81edfa 100644
--- a/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/Writing_Good_Code.ipynb
+++ b/docs/.doctrees/nbsphinx/Module5_OddsAndEnds/Writing_Good_Code.ipynb
@@ -1,12 +1,5 @@
{
"cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "lines_to_next_cell": 0
- },
- "source": []
- },
{
"cell_type": "raw",
"metadata": {
@@ -474,7 +467,39 @@
"\n",
"It does not take long to experience the benefits of type-hinting through your IDE. This both accelerates your coding by informing you of the object types that you are working with on the fly, and helps to expose oversights in your code as soon as they are made. \n",
"\n",
- "Finally, it is also worthwhile to highlight two projects, [mypy](http://mypy-lang.org/) and [pyright](https://github.com/microsoft/pyright), which are used to perform static type-checking on your code based on your type-hints. That is, mypy and pyright will both automatically traverse your code and find potential bugs by identifying type conflicts in your code (e.g. trying to capitalize an integer) by checking their annotated and inferred types. These tools are especially useful for large-scale code bases. Companies like Dropbox and Microsoft make keen use of static type-checking to identify inconsistencies in their code without having to hit runtime errors. Keep mypy, pyright, and other type-checking utilities in mind as you mature as a Python developer and find yourself working on projects of growing complexity. If you are using [VSCode as your IDE](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.html), you can install the [pyright vscode extension](https://marketplace.visualstudio.com/items?itemName=ms-pyright.pyright) to leverage type checking within your IDE."
+ "Finally, it is also worthwhile to highlight two projects, [mypy](http://mypy-lang.org/) and [pyright](https://github.com/microsoft/pyright), which are used to perform static type-checking on your code based on your type-hints.\n",
+ "That is, mypy and pyright will both automatically traverse your code and find potential bugs by identifying type conflicts in your code (e.g. trying to capitalize an integer) by checking their annotated and inferred types.\n",
+ "These tools are especially useful for large-scale code bases. Companies like Dropbox and Microsoft make keen use of static type-checking to identify inconsistencies in their code without having to hit runtime errors. Keep mypy, pyright, and other type-checking utilities in mind as you mature as a Python developer and find yourself working on projects of growing complexity.\n",
+ "If you are using [VSCode as your IDE](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.html), you can install the [PyLance VSCode extension](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance) to leverage pyright's type checking within your IDE."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "\n",
+ "**Reading Comprehension: Type-Assisted Code Completion**\n",
+ "\n",
+ "The `jedi` package, which is installed by default alongside Jupyter and IPython, enables type-informed code completion.\n",
+ "This means that we can benefit from type-hints even when we are working in a Jupyter notebook!\n",
+ " \n",
+ "In a Jupyter notebook, write the following type-annotated signature for a function: `def f(x: str):`.\n",
+ "Now in the body of the function (i.e. on the next line, and indented inwards), try typing `x.` and then hit ``.\n",
+ " \n",
+ "```python\n",
+ "\n",
+ "def f(x: str):\n",
+ " x.#hit here\n",
+ " \n",
+ "```\n",
+ "\n",
+ "You should see a list of methods appear for `x`.\n",
+ "What are the first three methods that appear?\n",
+ "Do these make sense based on the annotation associated with `x`?\n",
+ "Now change the annotation for `x` to be `list` and trigger the auto-completion in the editor again; do you see an updated list of methods?\n",
+ " \n",
+ "
"
]
},
{
@@ -1086,7 +1111,20 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "**Type Hinting: Solutions**\n",
+ "**Type-Assisted Code Completion: Solution**\n",
+ "\n",
+ "The tab-completion reveals methods like \"capitalize\", \"casefold\", and \"center\" – all of which are string methods.\n",
+ "These are made available despite the fact that `x` did not yet refer to a specific string.\n",
+ "Instead, the type-hint was able to tell the editor that `x` _will_ refer to a string, and this was enough to inform these type completions.\n",
+ "\n",
+ "Revising the type-annotation for `x` to be `list` affects the auto-completion options accordingly: the editor will now suggest list methods like \"append\", \"clear\", and \"copy\"."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Type Hinting: Solution**\n",
"\n",
"The following is a well-annotated version of `get_first_and_last`:\n",
"\n",
@@ -1110,7 +1148,7 @@
"extension": ".md",
"format_name": "markdown",
"format_version": "1.2",
- "jupytext_version": "1.3.0rc1"
+ "jupytext_version": "1.9.1"
}
},
"kernelspec": {
diff --git a/docs/Module1_GettingStartedWithPython/Exercises/Informal_Intro_Python.html b/docs/Module1_GettingStartedWithPython/Exercises/Informal_Intro_Python.html
index 390d750d..97746d58 100644
--- a/docs/Module1_GettingStartedWithPython/Exercises/Informal_Intro_Python.html
+++ b/docs/Module1_GettingStartedWithPython/Exercises/Informal_Intro_Python.html
@@ -1,44 +1,47 @@
-
-
+
-
+
-
+
Exercises — Python Like You Mean It
+
+
+
+
+
+
+
+
-
+
-
-
+
-
-
-
-
-
@@ -81,6 +84,7 @@
New versions of Python come out periodically, bringing new features and fixes. You can keep track of what’s new in Python by bookmarking and checking this page every few months. As we take on coding projects in Python, whether it be for work, school, or a personal hobby, it is important that we remain aware of the ways in which the language is changing, and that we take care to write code that will remain functional in the future.
+
All Python version numbers use the A.B.C format, in accordance with semantic versioning. The three numbers denote major releases, minor releases, and patches. For example, as of writing this, the current release of Python is version 3.9.1.
+
The first number denotes major releases to the language. When a major release comes out, it means that older code will not necessarily work with the new release, and vice versa. For example, the following code worked in Python 2 but no longer works because the xrange function was removed from the language upon the release of Python 3.
+
# `xrange` was a frequently-used function in Python 2, but
+# was removed from the language in Python 3 in favor of `range`.
+# Thus the following code does not work in any version of Python 3.
+count=0
+foriinxrange(10):
+ count=count+1
+
+
+
The most current major release is Python 3; Python 2 is no longer supported by any bug or security fixes and should not be used. All releases in the near future will be improvements to Python 3, and thus they will come in the form of minor releases and patches.
+
The second number denotes a minor release. A minor release will be compatible with code from the preceding release, but it might add new features to the language that are not backwards-compatible. For example, Python 3.6 introduced formatted string literals, which are commonly referred to as “f-strings”. Thus as of Python 3.6 you could write code like
+
# Python 3.6 introduced the "f-string" feature, which is not
+# backwards compatible with Python 3.5
+>>>f"one plus two is {1+2}"
+'one plus two is 3'
+
+
+
but this code would not run in Python 3.5.X.
+
The third and final number denotes a patch, which generally means bug fixes and performance improvements. All code within the same minor release will run on all other patches within that minor release For example, all Python 3.7.8 code is compatible with a Python 3.7.1 interpreter, and vice versa. Patches are released fairly often, and their changes only occur ‘under the hood’. Here is a list of changes were introduced by
+the patch level increment from Python 3.9.0 to Python 3.9.1; few of these would affect our day-to-day experience with using Python (which isn’t to say that they aren’t important!).
+
In simpler terms, major releases are neither backward nor forward compatible. Minor releases are forward compatible but not necessarily fully backward compatible, and patches are both forward and backward compatible.
Note: if you decide to use VSCode to do Python development, it is highly recommended that you install Microsoft’s PyLance extension. This adds many useful features to the IDE that will make writing Python code a more delightful experience.
Pros
-
lightweight and elegant
-
completely free
-
works with many different languages, so you only need to familiarize yourself with one IDE if you are a polyglot programmer
-
a huge number of extensions can be downloaded to add functionality to the editor; these are created by a large community of open-source developers.
Works with many different languages, so you only need to familiarize yourself with one IDE if you are a polyglot programmer.
+
Offers a huge number of extensions that can be downloaded to add functionality to the editor; these are created by a large community of open-source developers.
In the following section we will be using IPython or a Jupyter notebook to run our code. Presently, there is an incompatibility with these programs and a Python package called jedi, which typically is responsible for performing auto-completions in our code (when prompted by hitting <TAB>, which we will be doing below). It is really useful!
+
First, let’s check to see if we have an incompatible version of jedi installed. In your terminal (before starting a Python/IPython/Jupyter session), run
+
condalist
+
+
+
And look for the line that starts with jedi
+
jedi0.18.0
+
+
+
If you see that you have version 0.18.0 installed (as above), then you will want to downgrade it. In the same terminal, run the following command
+
condainstalljedi=0.17.2
+
+
+
You should be all set once you have followed the prompts and the installation has completed!
+
Note that you will need to repeat this process if you create a new conda environment with IPython/Jupter installed in it.
+
Now that you have the Anaconda distribution of Python installed on your machine, let’s write some simple Python code! We are going to forego writing a full Python script for now, and instead make use of a convenient tool for doing quick code scratchwork. The IPython console was installed as a part of Anaconda; it will allow us to build incrementally off of snippets of code, instead of having to execute an entire script all at once.
Let’s open an IPython console. Open your terminal if you are a Mac/Linux user, or start cmd.exe if you are a Windows user. Now type ipython into the console and hit <Enter>. You should see the following display on your screen:
In the following section we will be using IPython or a Jupyter notebook to run our code. Presently, there is an incompatibility with these programs and a Python package called jedi, which typically is responsible for performing auto-completions in our code (when prompted by hitting <TAB>, which we will be doing below). It is really useful!
+
First, let’s check to see if we have an incompatible version of jedi installed. In your terminal (before starting a Python/IPython/Jupyter session), run
+
condalist
+
+
+
And look for the line that starts with jedi
+
jedi0.18.0
+
+
+
If you see that you have version 0.18.0 installed (as above), then you will want to downgrade it. In the same terminal, run the following command
+
condainstalljedi=0.17.2
+
+
+
You should be all set once you have followed the prompts and the installation has completed!
+
Note that you will need to repeat this process if you create a new conda environment with IPython/Jupter installed in it.
+
In recent years, the Jupyter Notebook has become a massively popular tool for doing research-oriented work in Python and other languages alike. Its emergence marked a paradigm shift in the way data science is conducted.
A Jupyter notebook is similar to the IPython console, but, instead of only being able to work with a single line of code at a time, you can easily edit and re-execute any code that had been written in a notebook. Furthermore, you can save a notebook, and thus return to it later. Additionally, a notebook provides many terrific features. For instance, you can embed visualizations of data within a notebook, and write blocks of nicely-formatted text (using the Markdown
syntax), for presenting and explaining the contents of the notebook.
Jupyter lab is a new web interface from Project Jupyter that provides a rich web-based interface for managing and running Jupyter notebooks, console terminals, and text editors, all within your browser. Among its useful features and polished user interface - compared to that a Jupyter notebook server - Jupyter lab provides moveable panes for viewing data, images, and code output apart from the rest of the notebook. This is facilitates effective data
-science work flows.
+
Jupyter lab is a new web interface from Project Jupyter that provides a rich web-based interface for managing and running Jupyter notebooks, console terminals, and text editors, all within your browser. Among its useful features and polished user interface, Jupyter lab provides moveable panes for viewing data, images, and code output apart from the rest of the notebook. This is facilitates effective data science work flows.
It is recommended that you peruse the Jupyter lab documentation to get a feel for all of its added capabilities.
The following instructions are laid out for running a Jupyter notebook server. That being said, the process for running a Jupyter lab server and working with notebooks therein is nearly identical. Both Jupyter notebook and Jupyter lab should already be installed via Anaconda.
This is a record of all past PLYMI releases and what went into them,
-in reverse chronological order. All previous releases should still be available
-on pip.
Adds a reading comprehension problem in the section on type-hinting
+to show that jedi provides annotation-informed autocompletion abilities in notebooks.
Updated the discussion of computing pairwise differences
+to account for potential floating-point edge cases that can produce “NaNs” as a result.
+
There is currently an incompatibility between jedi0.18.0 and IPython, which breaks autocompletion. See here for more details.
+Added temporary callout boxes to the informal introduction to Python and to
+the introduction to Jupyter notebooks, which instruct readers to remedy this by downgrading jedi.
Updated some of the “backend” technologies behind PLYMI: upgraded sphinx and nbsphinx.
Fixed a broken subsection header: “Converting a Boolean Index-Array to Integer Index-Arrays: numpy.where” will now appear in the navigation bar under Module 3, Advanced Indexing.
We’re finally keeping a formal changelog! This update includes our first discussion of features that were introduced in Python 3.8. Also includes various typo/grammar fixes.
Python Like You Mean It (PLYMI) is a free resource for learning the basics of Python & NumPy, and moreover, becoming a competent Python user. The features of the Python language that are emphasized here were chosen to help those who are particularly interested in STEM applications (data analysis, machine learning, numerical work, etc.).
-
I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.8, as of writing this).
+
I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.9, as of writing this).
+This material has proven to be fruitful for high school and college teachers alike to teach Python as part of their STEM curriculum.
Python is a relatively easy language to pick up, and it doesn’t require much rigor to make code work. Unfortunately, this means that there are many Python users out there who know enough to just get by, but lack a sound understanding of the language. You don’t want to get caught in the “know enough Python to be dangerous” zone; therein lies complacency, stagnation, and the genesis of a lot of bad code. You’ve got to Python like you mean it!
If you have questions about the reading, think that you have spotted some mistakes, or would like to contribute to PLYMI, please visit our GitHub page. You will need to create a GitHub account in order to post an issue; this process is free and easy. We would love for you to join us to discuss the website!
I started learning to use Python in graduate school for my physics research, and I was so bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I’d like to think that this resource would have been a huge help to me back then). I’ve been coding in Python for at least six years now, and I’ve taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
+
I started learning to use Python in graduate school for my physics research, and I was so bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I’d like to think that this resource would have been a huge help to me back then). I’ve been coding in Python for at least eight years now, and I’ve taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
Python Like You Mean It (PLYMI) is a free resource for learning the basics of Python & NumPy, and moreover, becoming a competent Python user. The features of the Python language that are emphasized here were chosen to help those who are particularly interested in STEM applications (data analysis, machine learning, numerical work, etc.).
-
I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.8, as of writing this).
+
I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.9, as of writing this).
+This material has proven to be fruitful for high school and college teachers alike to teach Python as part of their STEM curriculum.
Python is a relatively easy language to pick up, and it doesn’t require much rigor to make code work. Unfortunately, this means that there are many Python users out there who know enough to just get by, but lack a sound understanding of the language. You don’t want to get caught in the “know enough Python to be dangerous” zone; therein lies complacency, stagnation, and the genesis of a lot of bad code. You’ve got to Python like you mean it!
If you have questions about the reading, think that you have spotted some mistakes, or would like to contribute to PLYMI, please visit our GitHub page. You will need to create a GitHub account in order to post an issue; this process is free and easy. We would love for you to join us to discuss the website!
I started learning to use Python in graduate school for my physics research, and I was so bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I’d like to think that this resource would have been a huge help to me back then). I’ve been coding in Python for at least six years now, and I’ve taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
+
I started learning to use Python in graduate school for my physics research, and I was so bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I’d like to think that this resource would have been a huge help to me back then). I’ve been coding in Python for at least eight years now, and I’ve taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
In this module, we will come to see that Python is an object-oriented language. That is, the language is all about defining different types of objects that encapsulate data, and that possess functions that permit users to access and manipulate this data. Our tour through the essentials of Python and the essentials of NumPy brought us into contact with various types of objects, such as the int, str, list, tuple, dict, and the numpy.ndarray, to name a few. We have seen that these different types of objects have starkly different functionality from one another. We will study the syntax and constructs for creating and interacting with objects in Python. Ultimately, will find our skill sets as Python-users much fuller and more sophisticated having understood object-oriented programming in Python.
-
We being this module by establishing some key terminology for discussing object-oriented programming, drawing attention to the important fact that the terms ‘class’ and ‘type’ mean the same thing in modern Python. Next, we study the syntax for defining a class, and take time to distinguish between the resulting class object and the subsequent class instances that can then be created. Having defined our first custom class of object, we then study the syntax for defining class-methods, which permits us add customized functionality to our class. Further, we will introduce ourselves to Python’s reserved special methods, which empower us to fully interface our class with the Python’s protocols for behaving like a sequence, an iterable, a function, etc. We conclude this module with a brief discussion of the concept of inheritance, which is a mechanism by which a new class can inherit attributes from an existing class.
+
We begin this module by establishing some key terminology for discussing object-oriented programming, drawing attention to the important fact that the terms ‘class’ and ‘type’ mean the same thing in modern Python. Next, we study the syntax for defining a class, and take time to distinguish between the resulting class object and the subsequent class instances that can then be created. Having defined our first custom class of object, we then study the syntax for defining class-methods, which permits us to add customized functionality to our class. Further, we will introduce ourselves to Python’s reserved special methods, which empower us to fully interface our class with Python’s protocols for behaving like a sequence, an iterable, a function, etc. We conclude this module with a brief discussion of the concept of inheritance, which is a mechanism by which a new class can inherit attributes from an existing class.
Create an empty list, x=[]. Using - a while loop - the list-method append - the built-in function len append numbers to x until it contains 0-9: [0,1,2,3,4,5,6,7,8,9].
-
WARNING: if you give the while-loop a condition that never evaluates to False then your code will run forever. If this happens, at the top of your Jupyter notebook go to Kernel and then click Interrupt. If you are in the IPython shell, hit CTRL-C or close the terminal.
+
WARNING: if you give the while-loop a condition that never evaluates to False then your code will run forever. If this happens, at the top of your Jupyter notebook go to Kernel and then click Interrupt. If you are in the IPython shell, hit CTRL-C or close the terminal.
+
Bonus: accomplish this without using any variables, other than x.
-
Note that there are much simpler ways to construct a list of numbers than by appending to one in a while loop. For instance, one can use list comprehensions. We will learn about these later.
+
Note that there are much simpler ways to construct a list of numbers than by appending to one in a while loop. For instance, one can use list comprehensions. We will learn about these later.
Python is a programming language. That is, it provides us with a strict set of grammatical rules that correspond to well-defined instructions that a computer will obey. The tremendous value of this is that we can write text documents that are relatively simple and intuitive for humans to read, and yet can inform the computer to perform precise operations. Python code is simply text that conforms to the Python language.
For example, the following text obeys the rules of the Python language:
You can save the the valid code from the preceding section in a text file using a simple text editor, and voilà you have written a Python script: a text file containing Python code. It is standard to save this text file using the suffix .py (e.g. my_code.py), rather than the familiar .txt (e.g. my_text.txt). There is nothing special about the .py suffix; it simply helps differentiate files that contain Python code from run-of-the-mill text files, which contain plain
English.
-
Although you can use simple text editors to write Python scripts (e.g. notepad (Win), TextEdit (Mac), nano (Linux)), there are much more sophisticated editors that provide an “integrated development environment” (IDE) for writing code. An IDE, configured to support Python, will warn you if you have written code that violates Python’s grammar rules, similar to the way word processing software warns you if you have made a spelling mistake. More on IDEs in a later section.
+
Although you can use simple text editors to write Python scripts (e.g. notepad (Win), TextEdit (Mac), nano (Linux)), there are much more sophisticated editors that provide an “integrated development environment” (IDE) for writing code. An IDE, configured to support Python, will warn you if you have written code that violates Python’s grammar rules, similar to the way word processing software warns you if you have made a spelling mistake. More on IDEs in a later section.
WARNING:
Do not use word processing programs, like Microsoft Word, to write code. They will “silently” change the characters written in your file, such as the type of quotation marks being used. This will cause errors in your code.
A Python interpreter is any computer program that is capable of doing the following:
-
Read in a text file (e.g. my_code.py).
-
Parse the text and determine if it obeys the rules of the Python language (the interpreter will raise an error if the rules are not obeyed).
-
Translate the parsed text into instructions, according to the Python language’s specifications.
-
Instruct the computer to carry out the tasks.
-
As a whole, a Python script is executed from top-to-bottom by the interpreter.
+
Read in a text file (e.g. my_code.py).
+
Parse the text and determine if it obeys the rules of the Python language (the interpreter will raise an error if the rules are not obeyed).
+
Translate the parsed text into instructions, according to the Python language’s specifications.
+
Instruct the computer to carry out the tasks.
+
As a whole, a Python script is executed from top-to-bottom by the interpreter.
The first Python interpeter was written in the programming language C, and is known as CPython. Accordingly, the CPython interpreter is the official interpreter of the Python language. Any new rules/features that are introduced to the Python language are guaranteed be implemented in the CPython code base.
@@ -285,7 +294,7 @@
What is a Python Interpreter and What Does it Mean to “Install Python”?<
Python has become a hugely popular programming language. In fact, it is likely the most popular introductory language at universities. First and foremost, its syntax is designed to be intuitive and readable. For example, the following Python code sums the numbers 0-9, and prints the result:
New versions of Python come out periodically, bringing new features and fixes. You can keep track of what’s new in Python by bookmarking and checking this page every few months. As we take on coding projects in Python, whether it be for work, school, or a personal hobby, it is important that we remain aware of the ways in which the language is changing, and that we take care to write code that will remain functional in the future.
+
All Python version numbers use the A.B.C format, in accordance with semantic versioning. The three numbers denote major releases, minor releases, and patches. For example, as of writing this, the current release of Python is version 3.9.1.
+
The first number denotes major releases to the language. When a major release comes out, it means that older code will not necessarily work with the new release, and vice versa. For example, the following code worked in Python 2 but no longer works because the xrange function was removed from the language upon the release of Python 3.
+
# `xrange` was a frequently-used function in Python 2, but
+# was removed from the language in Python 3 in favor of `range`.
+# Thus the following code does not work in any version of Python 3.
+count=0
+foriinxrange(10):
+ count=count+1
+
+
+
The most current major release is Python 3; Python 2 is no longer supported by any bug or security fixes and should not be used. All releases in the near future will be improvements to Python 3, and thus they will come in the form of minor releases and patches.
+
The second number denotes a minor release. A minor release will be compatible with code from the preceding release, but it might add new features to the language that are not backwards-compatible. For example, Python 3.6 introduced formatted string literals, which are commonly referred to as “f-strings”. Thus as of Python 3.6 you could write code like
+
# Python 3.6 introduced the "f-string" feature, which is not
+# backwards compatible with Python 3.5
+>>>f"one plus two is {1+2}"
+'one plus two is 3'
+
+
+
but this code would not run in Python 3.5.X.
+
The third and final number denotes a patch, which generally means bug fixes and performance improvements. All code within the same minor release will run on all other patches within that minor release For example, all Python 3.7.8 code is compatible with a Python 3.7.1 interpreter, and vice versa. Patches are released fairly often, and their changes only occur ‘under the hood’. Here is a list of changes were introduced by
+the patch level increment from Python 3.9.0 to Python 3.9.1; few of these would affect our day-to-day experience with using Python (which isn’t to say that they aren’t important!).
+
In simpler terms, major releases are neither backward nor forward compatible. Minor releases are forward compatible but not necessarily fully backward compatible, and patches are both forward and backward compatible.
Python is a programming language - it provides us with a simple set of grammatical rules that allow us to write human-readable text, which can be translated unambiguously to instruct a computer to perform tasks.
-
Python code is text that conforms to the Python language.
-
A Python script is a text file containing Python code. Traditionally such a file will be saved using the suffix .py.
-
A Python interpreter is a program that is capable of reading and parsing a text file, and translating the code into machine instructions, as specified by the Python language.
-
To “install Python” onto you machine boils down to saving a functioning Python interpreter onto your machine.
-
The official Python interpreter is a program written in the language C, and is known as the CPython interpreter.
-
The “standard library” is a large collection of tools and functions that comes packaged with the CPython interpreter.
+
Python is a programming language - it provides us with a simple set of grammatical rules that allow us to write human-readable text, which can be translated unambiguously to instruct a computer to perform tasks.
+
Python code is text that conforms to the Python language.
+
A Python script is a text file containing Python code. Traditionally such a file will be saved using the suffix .py.
+
A Python interpreter is a program that is capable of reading and parsing a text file, and translating the code into machine instructions, as specified by the Python language.
+
To “install Python” onto you machine boils down to saving a functioning Python interpreter onto your machine.
+
The official Python interpreter is a program written in the language C, and is known as the CPython interpreter.
+
The “standard library” is a large collection of tools and functions that comes packaged with the CPython interpreter.
An IDE is a sophisticated text editor that allows you edit, run, and debug code.
-
The Python shell is an interface for typing Python code and executing it directly in your computer’s terminal.
-
The IPython shell is a much nicer version of the Python shell - it provides syntax highlighting, autocompletion, and other features.
-
The Jupyter Notebook is a powerful tool for prototyping and experimenting with code, as well as visualizing data and writing nicely-formatted text. We will be using this throughout the course.
+
An IDE is a sophisticated text editor that allows you edit, run, and debug code.
+
The Python shell is an interface for typing Python code and executing it directly in your computer’s terminal.
+
The IPython shell is a much nicer version of the Python shell - it provides syntax highlighting, autocompletion, and other features.
+
The Jupyter Notebook is a powerful tool for prototyping and experimenting with code, as well as visualizing data and writing nicely-formatted text. We will be using this throughout the course.
Note: if you decide to use VSCode to do Python development, it is highly recommended that you install Microsoft’s PyLance extension. This adds many useful features to the IDE that will make writing Python code a more delightful experience.
Pros
-
lightweight and elegant
-
completely free
-
works with many different languages, so you only need to familiarize yourself with one IDE if you are a polyglot programmer
-
a huge number of extensions can be downloaded to add functionality to the editor; these are created by a large community of open-source developers.
Works with many different languages, so you only need to familiarize yourself with one IDE if you are a polyglot programmer.
+
Offers a huge number of extensions that can be downloaded to add functionality to the editor; these are created by a large community of open-source developers.
In the following section we will be using IPython or a Jupyter notebook to run our code. Presently, there is an incompatibility with these programs and a Python package called jedi, which typically is responsible for performing auto-completions in our code (when prompted by hitting <TAB>, which we will be doing below). It is really useful!
+
First, let’s check to see if we have an incompatible version of jedi installed. In your terminal (before starting a Python/IPython/Jupyter session), run
+
condalist
+
+
+
And look for the line that starts with jedi
+
jedi0.18.0
+
+
+
If you see that you have version 0.18.0 installed (as above), then you will want to downgrade it. In the same terminal, run the following command
+
condainstalljedi=0.17.2
+
+
+
You should be all set once you have followed the prompts and the installation has completed!
+
Note that you will need to repeat this process if you create a new conda environment with IPython/Jupter installed in it.
+
Now that you have the Anaconda distribution of Python installed on your machine, let’s write some simple Python code! We are going to forego writing a full Python script for now, and instead make use of a convenient tool for doing quick code scratchwork. The IPython console was installed as a part of Anaconda; it will allow us to build incrementally off of snippets of code, instead of having to execute an entire script all at once.
Let’s open an IPython console. Open your terminal if you are a Mac/Linux user, or start cmd.exe if you are a Windows user. Now type ipython into the console and hit <Enter>. You should see the following display on your screen:
This console session is persistent, meaning that we can define a variable and then reference it in our code later on within this console session. Let’s define the variable x and assign it to the integer 10 (please follow along in the IPython console)
Python’s “standard library”, the various tools and functions that come packaged as part of the core language, includes plenty of familiar mathematical functions. As a matter of organization, Python stores these mathematical functions away in a module named “math”. To make use of this math module, we must import it into our code.
You might wonder why the result displayed as 10.0 and not simply as 10; in Module 2 we will see that these are two different types of numbers in the Python language. The former is called a floating-point number, indicating the presence of its decimal point, whereas the latter is an integer. The math.sqrt function is defined such that it always returns its results as floating-point numbers.
In the case that we want to make frequent use of a certain function from the math module, it’d be nice to avoid having to type out the math module prefix, math., repeatedly. We can accomplish this by importing an individual function from the math module. Let’s import the factorial function from the math module.
-'He picked up the phone, "Hello? What do you want?" Bob was a rather impolite dude.'
-
-
+
+
+'He picked up the phone, "Hello? What do you want?" Bob was a rather impolite dude.'
+
There are designated special characters that allow us to affect the way a string is formatted when it is printed to the string. For example, if \n ever occurs in a string, it is treated as a single character that indicates a line-break. This will only manifest if such a string is fed to the built-in print function, which informs the computer to print text to a user’s screen.
Let’s create a string that will display as three separate lines, when printed to the screen.
As a quick way to check out the built-in functions that strings have available to them, we can make use of IPython’s autocompletion feature once again. Type sentence. (including the trailing period) and then hit <TAB>. A menu of functions should appear as so:
Let’s see what the replace function does. IPython provides a great utility for looking up the documentation for a function by simply putting two question marks after the function name. For example:
Like a string, a list is sequential in nature and we can access the items in a list by specifying its position in the sequence. This is known as “indexing” the list; the index of the first item in a sequence is always 0.
Without further ado, we now provide instructions for installing Python and other useful Python libraries on your machine via the Anaconda platform. Installing the Anaconda platform will install the following:
-
Python; specifically the CPython interpreter that we discussed in the previous section.
-
A number of useful Python packages, like matplotlib, NumPy, and SciPy.
-
Jupyter, which provides an interactive “notebook” environment for prototyping code.
-
conda: a package manager that will allow you to install and update Python and additional Python packages, while handling all compatibility issues for you.
+
Python; specifically the CPython interpreter that we discussed in the previous section.
+
A number of useful Python packages, like matplotlib, NumPy, and SciPy.
+
Jupyter, which provides an interactive “notebook” environment for prototyping code.
+
conda: a package manager that will allow you to install and update Python and additional Python packages, while handling all compatibility issues for you.
Note that installing Python via Anaconda will not break any other installations of Python that already exist on your computer. See What did this just do to my computer? for more details.
Some of the packages provided by Anaconda, like NumPy, have been optimized and will run significantly faster than if you installed them manually.
Navigate to this page, and click the “Download” button for Python 3.
-
After the download is complete, begin the installation process. There will be an installation option: AddAnacondatothesystemPATHenvironmentvariable; we advise you to enable this installation option (advanced users: see below for caveats).
-
Complete the 30 minute “Getting Started” tutorial to familiarize yourself with conda. This is very important!
+
Navigate to this page, and click the “Download” button for Python 3.
+
After the download is complete, begin the installation process. There will be an installation option: AddAnacondatothesystemPATHenvironmentvariable; we advise you to enable this installation option (advanced users: see below for caveats).
+
Complete the 30 minute “Getting Started” tutorial to familiarize yourself with conda. This is very important!
You will need to know how to open a terminal (cmd.exe for Windows users) on your computer, and how to navigate between directories in the terminal. If you do not know how to do this, read a ‘how-to’ for whatever operating system you are using.
This created a directory called Anaconda3 (or some variant of this) on your computer, which contains all of the files associated with the CPython interpreter, all of the modules in Python’s standard library, the aforementioned 3rd party packages that come as part of the Anaconda distribution (e.g. NumPy, SciPy, Jupyter, iPython), and the conda package manager. It also contains the executable files for all of these applications. The default install location for Anaconda is:
+
This created a directory called Anaconda3 (or some variant of this) on your computer, which contains all of the files associated with the CPython interpreter, all of the modules in Python’s standard library, the aforementioned 3rd party packages that come as part of the Anaconda distribution (e.g. NumPy, SciPy, Jupyter, iPython), and the conda package manager. It also contains the executable files for all of these applications. The default install location for Anaconda is:
-
(Linux): /home/<your_username>/Anaconda3
-
(Windows): C:\Users\<your_username>\Anaconda3
-
(Mac): /Users/<your_username>/Anaconda3
+
(Linux): /home/<your_username>/Anaconda3
+
(Windows): C:\Users\<your_username>\Anaconda3
+
(Mac): /Users/<your_username>/Anaconda3
If you followed the install instructions as specified above, then the Anaconda-installer also placed this directory in your system’s “path”. Let’s briefly discuss what this means. Your system’s path is simply a list of directories. Whenever you execute any command in your computer’s terminal, the computer will quickly search through the directories that are specified in the path for an executable with that name; it will execute the first such executable that it finds. Thus, by placing the
Anaconda3 directory at the beginning of your path, the Anaconda-installer has ensured that your computer will prioritize Anaconda’s python executable over any other installations of Python on your computer, because it will find that executable first.
conda is not only a package manager, but also a powerful environment manager. Let’s take a moment to motivate a common use case for environment management before we dive into what it actually is:
-
It is expected and encouraged that you work through Python Like You Mean It using the latest version of Python (Python 3.X). That being said, it is not uncommon to encounter courses and projects that require you to use Python 2.7, which is a dead version of the language. Is there a simple and sane way to switch back and forth between working in Python 3 and Python 2.7 environments? Yes! Utilizing conda environments is a perfect way to solve this problem.
-
Assuming that your initial installation of Anaconda contained Python 3, then your root (i.e. default) conda environment is that Python 3 environment. You can now create a conda environment that instead includes Python 2.7 and all of the 3rd party packages that come with Anaconda. Execute the following command in your terminal:
+
It is expected and encouraged that you work through Python Like You Mean It using the latest version of Python (Python 3.X). That being said, it is not uncommon to encounter courses and projects that require you to use Python 2.7, which is a dead version of the language. Is there a simple and sane way to switch back and forth between working in Python 3 and Python 2.7 environments? Yes! Utilizing conda environments is a perfect way to solve this problem.
+
+
Assuming that your initial installation of Anaconda contained Python 3, then your root (i.e. default) conda environment is that Python 3 environment. You can now create a conda environment that instead includes Python 2.7 and all of the 3rd party packages that come with Anaconda. Execute the following command in your terminal:
In the following section we will be using IPython or a Jupyter notebook to run our code. Presently, there is an incompatibility with these programs and a Python package called jedi, which typically is responsible for performing auto-completions in our code (when prompted by hitting <TAB>, which we will be doing below). It is really useful!
+
First, let’s check to see if we have an incompatible version of jedi installed. In your terminal (before starting a Python/IPython/Jupyter session), run
+
condalist
+
+
+
And look for the line that starts with jedi
+
jedi0.18.0
+
+
+
If you see that you have version 0.18.0 installed (as above), then you will want to downgrade it. In the same terminal, run the following command
+
condainstalljedi=0.17.2
+
+
+
You should be all set once you have followed the prompts and the installation has completed!
+
Note that you will need to repeat this process if you create a new conda environment with IPython/Jupter installed in it.
+
In recent years, the Jupyter Notebook has become a massively popular tool for doing research-oriented work in Python and other languages alike. Its emergence marked a paradigm shift in the way data science is conducted.
A Jupyter notebook is similar to the IPython console, but, instead of only being able to work with a single line of code at a time, you can easily edit and re-execute any code that had been written in a notebook. Furthermore, you can save a notebook, and thus return to it later. Additionally, a notebook provides many terrific features. For instance, you can embed visualizations of data within a notebook, and write blocks of nicely-formatted text (using the Markdown
syntax), for presenting and explaining the contents of the notebook.
Jupyter lab is a new web interface from Project Jupyter that provides a rich web-based interface for managing and running Jupyter notebooks, console terminals, and text editors, all within your browser. Among its useful features and polished user interface - compared to that a Jupyter notebook server - Jupyter lab provides moveable panes for viewing data, images, and code output apart from the rest of the notebook. This is facilitates effective data
-science work flows.
+
Jupyter lab is a new web interface from Project Jupyter that provides a rich web-based interface for managing and running Jupyter notebooks, console terminals, and text editors, all within your browser. Among its useful features and polished user interface, Jupyter lab provides moveable panes for viewing data, images, and code output apart from the rest of the notebook. This is facilitates effective data science work flows.
It is recommended that you peruse the Jupyter lab documentation to get a feel for all of its added capabilities.
The following instructions are laid out for running a Jupyter notebook server. That being said, the process for running a Jupyter lab server and working with notebooks therein is nearly identical. Both Jupyter notebook and Jupyter lab should already be installed via Anaconda.
-import numpy as np
-import matplotlib.pyplot as plt
+
+importnumpyasnp
+importmatplotlib.pyplotasplt
-# this tells Jupyter to embed matplotlib plots in the notebook
-%matplotlib notebook
+# this tells Jupyter to embed matplotlib plots in the notebook
+%matplotlib inline
-# plot the sinc-function and its derivative
-fig, ax = plt.subplots()
-ax.plot(x, f, color="red", label=r"$sinc(x)$")
-ax.plot(x, df, color="blue", ls="--", label=r"$\frac{d(sinc(x))}{dx}$")
+
+# plot the sinc-function and its derivative
+fig,ax=plt.subplots()
+ax.plot(x,f,color="red",label=r"$sinc(x)$")
+ax.plot(x,df,color="blue",ls="--",label=r"$\frac{d(sinc(x))}{dx}$")
-ax.set_title("Example Notebook Plot")
-ax.set_xlabel(r"$x$ [radians]")
+ax.set_title("Example Notebook Plot")
+ax.set_xlabel(r"$x$ [radians]")
-ax.grid(True)
-ax.legend();
+ax.grid(True)
+ax.legend();
+
+
[4]:
+
+
+
+
+<matplotlib.legend.Legend at 0x2a496ed7f10>
+
+
-
+
Notice that this notebook interface is great for making adjustments to this plot. You can easily change the color or line-style of the plot and redraw it without having to recompute the functions. You simply re-execute the cell containing the plot code. This is especially nice when the numerical computations required to generate the curves are costly.
The Jupyter Notebook does not work exclusively with Python. A “kernel” can be developed in order to provide support for a given programming language in Jupyter. Indeed, kernels for several prominent programming languages are being developed for use with Jupyter:
The ever-growing list of available kernels for use with Jupyter can be found here. It should be noted that these efforts are not all equally mature. For instance, whereas the Python and Julia kernels are robust, the Haskell kernel cannot run natively on Windows machines, and the C++ kernel is still in early development, as of writing this.
The fundamental package for scientific computing with Python. NumPy provides an N-dimensional array that can be used to represent, say, a large matrix of numbers. It also provides a huge number of mathematical functions that can operate on these arrays. It is crucial to note that these functions will actually be executed in C, so they are incredibly fast compared to the same functions written in vanilla Python. NumPy will be used throughout the machine learning component of the course.
Here is example code for summing the numbers 1-100 in NumPy:
-
importnumpyasnp
+
importnumpyasnpnumbers=np.arange(1,101)# an array storing the numbers 1-100numpy_result=numbers.sum()
Tools like NumPy and Numba allow you do fast numerical computations in Python by “secretly” doing the numerical work behind the scenes in a fast language, like C.
-
We will be working with NumPy (but Numba is fantastic and very worthwhile to learn if you do any serious numerical work in Python).
-
Both NumPy and Numba came with your installation of Anaconda
+
Python is slow for large numerical computations.
+
Tools like NumPy and Numba allow you do fast numerical computations in Python by “secretly” doing the numerical work behind the scenes in a fast language, like C.
+
We will be working with NumPy (but Numba is fantastic and very worthwhile to learn if you do any serious numerical work in Python).
+
Both NumPy and Numba came with your installation of Anaconda
Armed with our understanding of multidimensional NumPy arrays, we now look at methods for programmatically inspecting an array’s attributes (e.g. its dimensionality). It is especially important to understand what an array’s “shape” is.
+
Armed with our understanding of multidimensional NumPy arrays, we now look at methods for programmatically inspecting an array’s attributes (e.g. its dimensionality). It is especially important to understand what an array’s “shape” is.
We will use the following array to provide context for our discussion:
Accessing the contents of an array via basic indexing does not create a copy of those contents. Rather, a “view” of the same underlying data is produced.
Utilizing an array in a mathematical expression involving the arithmetic operators (+,-,*,/,//,**) returns an entirely distinct array, that does not share memory with the original array.
# mathematical expressions like `subarray + 2`# produce distinct arrays, not views>>>np.shares_memory(subarray+2,subarray)
-False
+False
Thus updating a variable subarray via subarray=subarray+2 does not overwrite the original data referenced by subarray. Rather, subarray+2 assigns that new array to the variable subarray. NumPy does provide mechanisms for performing mathematical operations to directly update the underlying data of an array without having to create a distinct array. We will discuss these mechanisms in the next subsection.
Because basic indexing produces a view of an array’s underlying data, we must take time to understand the ways in which we can augment that underlying data, versus performing operations that produce an array with distinct data. Here we will see that:
NumPy provides the function broadcast_to, which can be used to broadcast an array to a specified shape. This can help us build our intuition for broadcasting. Let’s broadcast a shape-(3,4) array to a shape-(2,3,4) array:
+
NumPy provides the function broadcast_to, which can be used to broadcast an array to a specified shape. This can help us build our intuition for broadcasting. Let’s broadcast a shape-(3,4) array to a shape-(2,3,4) array:
As conveyed by the broadcasting rules, dimensions of size-1 are special in that they can be broadcasted to any size. Here we will learn about introducing size-1 dimensions into an array, for the purpose of tailoring its shape for broadcasting.
-
You can introduce size-1 dimensions to an array without changing the overall size (i.e. total number of entries in an array. Thus we are free to add size-1 dimensions to an array via the reshape function. Let’s reshape a shape-(3,) array into a shape-(1, 3, 1, 1) array:
-
>>> importnumpyasnp
+
You can introduce size-1 dimensions to an array without changing the overall size (i.e. total number of entries in an array. Thus we are free to add size-1 dimensions to an array via the reshape function. Let’s reshape a shape-(3,) array into a shape-(1, 3, 1, 1) array:
+
>>> importnumpyasnp# Reshaping an array to introduce size-1 dimensions.# The size of the array is 3, regardless of introducing
@@ -557,7 +565,7 @@
# produces a shape-(5, 6) result
+>>>pairwise_dists_crude(x,y)
+array([[3.678,8.4524,10.3057,7.3711,6.2152,5.5548],
+ [10.1457,5.8793,2.9274,4.1114,3.9098,5.2259],
+ [7.3219,0.8439,6.8734,4.5687,7.3283,4.8216],
+ [10.339,7.032,7.4745,7.0633,3.5999,4.0107],
+ [8.2878,3.5468,6.336,4.9014,4.1858,2.0257]])
+
+
Regrettably, there is a glaring issue with the vectorized computation that we just performed. Consider the largest sized array that is created in the for-loop computation, compared to that of this vectorized computation. The for-loop version need only create a shape-\((M, N)\) array, whereas the vectorized computation creates an intermediate array (i.e. diffs) of shape-\((M, N, D)\). This intermediate array is even created in the one-line version of the code. This will create a
massive array if \(D\) is a large number!
Suppose, for instance, that you are finding the Euclidean between pairs of RGB images that each have a resolution of \(32 \times 32\) (in order to see if the images resemble one another). Thus in this scenario, each image is comprised of \(D = 32 \times 32 \times 3 = 3072\) numbers (\(32^2\) pixels, and each pixel has 3 values: a red, blue, and green-color value). Computing all the distances between a stack of 5000 images with a stack of 100 images would form an intermediate array of
-shape-\((5000, 100, 3072)\). Even though this large array only exists temporarily, it would have to consume over 6GB of RAM! The for-loop version requires \(\frac{1}{3027}\) as much memory (about 2MB).
+shape-\((5000, 100, 3072)\). Even though this large array only exists temporarily, it would have to consume over 6GB of RAM! The for-loop version requires \(\frac{1}{3072}\) as much memory (about 2MB).
Is our goose cooked? Are we doomed to pick between either slow for-loops, or a memory-inefficient use of vectorization? No! We can refactor the mathematical form of the Euclidean distance in order to avoid the creation of that bloated intermediate array.
There is one more important and very subtle detail that we have to deal with. In terms of pure mathematics, x_y_sqrd-x_y_prod must be a strictly non-negative value (i.e. its smallest possible value is \(0\)), since it is equivalent to
That being said, are working with floating-point numbers, which do not always behave exactly like rational numbers when we do arithmetic with them. Indeed, we saw earlier that the quirks of floating-point arithmetic can lead to surprising results. Here, the strange behavior is that x_y_sqrd-x_y_prodcan produce negative numbers!
+
Let’s see this in action. We’ll take the following shape-(2, 3) array
+
# A carefully-selected input that will trigger surprising
+# floating-point precision issues in our distance calculation
+>>>x=np.array([[4.700867387959219,4.700867387959219,4.700867387959219],
+...[4.700867387959219,4.700867387959219,4.700867387959219]])
+
+
+
And compute the square distance between all combinations of rows x; the result should simply be a shape-(2, 2) array of zeros.
+
# The square-distances should be exactly zero, but they
+# will instead be (*very* small) negative numbers!
+>>>sqr_dists=-2*np.matmul(x,x.T)# `x_x_prod`
+>>>sqr_dists+=np.sum(x**2,axis=1)[:,np.newaxis]
+>>>sqr_dists+=np.sum(x**2,axis=1)
+>>>sqr_dists
+array([[-2.842170943040401e-14,-2.842170943040401e-14],
+ [-2.842170943040401e-14,-2.842170943040401e-14]])
+
+
+
These values are very close to being zero, so it is not as if the magnitude of our result is wildly off, but the critical issue here is that the quirks of floating-point arithmetic produced (very small) negative numbers. We are going to be in for a rude awakening when we take the square-root of these values to get our final distances:
+
# Taking the square-root of negative floats will produce NaNs
+>>>np.sqrt(sqr_dists)
+array([[nan,nan],
+ [nan,nan]])
+
+
+
nan stands for “Not a Number”, which is to say that the square-root of a negative number cannot produce a real-valued float (the result will be an imaginary number, but NumPy will not swap out real numbers for imaginary ones unless we explicitly permit it to).
+
The solution to this problem comes from the realization that this occurrence is truly an edge-case, and that it will only manifest when the result ought to have been zero and indeed is very close to zero. Thus we can clipsqr_dists such that any value that falls below zero gets set to zero and all other values will be left alone:
+
# "Clipping" all negative entries so that they are set to 0
+>>>np.sqrt(np.clip(sqr_dists,a_min=0,a_max=None))
+array([[0.,0.],
+ [0.,0.]])
+
In total, we have successfully used vectorization to compute all the pairs of distances, while only requiring an array of shape-\((M, N)\) to do so! This is the memory-efficient, vectorized form – the stuff that dreams are made of. Let’s write the function that performs this computation in full.
defpairwise_dists(x,y):""" Computing pairwise distances using memory-efficient
- vectorization.
-
- Parameters
- ----------
- x : numpy.ndarray, shape=(M, D)
- y : numpy.ndarray, shape=(N, D)
-
- Returns
- -------
- numpy.ndarray, shape=(M, N)
- The Euclidean distance between each pair of
- rows between `x` and `y`."""
- dists=-2*np.matmul(x,y.T)
- dists+=np.sum(x**2,axis=1)[:,np.newaxis]
- dists+=np.sum(y**2,axis=1)
- returnnp.sqrt(dists)
+ vectorization.
+
+ Parameters
+ ----------
+ x : numpy.ndarray, shape=(M, D)
+ y : numpy.ndarray, shape=(N, D)
+
+ Returns
+ -------
+ numpy.ndarray, shape=(M, N)
+ The Euclidean distance between each pair of
+ rows between `x` and `y`."""
+ sqr_dists=-2*np.matmul(x,y.T)
+ sqr_dists+=np.sum(x**2,axis=1)[:,np.newaxis]
+ sqr_dists+=np.sum(y**2,axis=1)
+ returnnp.sqrt(np.clip(sqr_dists,a_min=0,a_max=None))
+
+
+
# produces a shape-(5, 6) result
+>>>pairwise_dists(x,y)
+array([[3.678,8.4524,10.3057,7.3711,6.2152,5.5548],
+ [10.1457,5.8793,2.9274,4.1114,3.9098,5.2259],
+ [7.3219,0.8439,6.8734,4.5687,7.3283,4.8216],
+ [10.339,7.032,7.4745,7.0633,3.5999,4.0107],
+ [8.2878,3.5468,6.336,4.9014,4.1858,2.0257]])
Normalize x such that each of its rows, within each sheet, will sum to a value of 1. Make use of the sequential function np.sum, which should be called only once, and broadcast-division.
This section presents standard methods for creating NumPy arrays of varying shapes and contents. NumPy provides a laundry list of functions for creating arrays:
-
>>> importnumpyasnp
+
>>> importnumpyasnp# creating an array from a Python sequence>>> np.array([i**2foriinrange(5)])
@@ -289,11 +297,11 @@
Creating Constant Arrays: array([1.,1.,1.,1.])
-
NumPy provides additional functions for creating constant-valued arrays. Please refer to the official documentation for a complete listing.
+
NumPy provides additional functions for creating constant-valued arrays. Please refer to the official documentation for a complete listing.
The arange function allows you to initialize a sequence of integers based on a starting point (inclusive), stopping point (exclusive), and step size. This is very similar to the range function; however arange immediately creates this sequence as an array, whereas range produces a generator.
+
The arange function allows you to initialize a sequence of integers based on a starting point (inclusive), stopping point (exclusive), and step size. This is very similar to the range function; however arange immediately creates this sequence as an array, whereas range produces a generator.
The linspace function allows you to generate \(N\)evenly-spaced points within a user-specified interval \([i, j]\) (\(i\) and \(j\) are included in the interval). This is often used to generate a domain of values on which to evaluate a mathematical function (e.g. if you want to the sine function from \(-\pi\) to \(\pi\) on a finely-divided grid).
+
The linspace function allows you to generate \(N\)evenly-spaced points within a user-specified interval \([i, j]\) (\(i\) and \(j\) are included in the interval). This is often used to generate a domain of values on which to evaluate a mathematical function (e.g. if you want to the sine function from \(-\pi\) to \(\pi\) on a finely-divided grid).
# generate five evenly-spaced points on the interval [-1, 1]>>>np.linspace(-1,1,5)array([-1.,-0.5,0.,0.5,1.])
@@ -320,7 +328,7 @@
Numpy has other functions for creating sequential arrays, such as producing an array spaced evenly on a log-scaled interval. See the official documentation for a complete listing.
+
Numpy has other functions for creating sequential arrays, such as producing an array spaced evenly on a log-scaled interval. See the official documentation for a complete listing.
There are many more functions to read about that allow you to draw from a wide variety of statistical distributions. This only scratches the surface of random number generation in NumPy.
+
There are many more functions to read about that allow you to draw from a wide variety of statistical distributions. This only scratches the surface of random number generation in NumPy.
A complete listing of functions for joining arrays can be found in the official NumPy documentation. There are also corresponding functions for splitting an array into independent arrays.
+
A complete listing of functions for joining arrays can be found in the official NumPy documentation. There are also corresponding functions for splitting an array into independent arrays.
It is time to start familiarizing ourselves with NumPy, the premiere library for doing numerical work in Python. To use this package, we need to be sure to “import” the NumPy module into our code:
-
importnumpyasnp
+
importnumpyasnp
You could have run importnumpy instead, but the prescribed method allows us to use the abbreviation ‘np’ throughout our code, instead of having to write ‘numpy’. This is a very common abbreviation to use.
The ND-array (N-dimensional array) is the star of the show for NumPy. This array simply stores a sequence of numbers. Like a Python list, you can access individual entries in this array by “indexing” into the array, and you can access a sub-sequence of the array by “slicing” it. So what distinguishes NumPy’s ND-array from a Python list, and why is there a whole numerical library that revolves around this array? There are two major features that makes the ND-array special. It can:
-
Provide an interface for its underlying data to be accessed along multiple dimensions.
-
Rapidly perform mathematical operations over all of its elements, or over patterned subsequences of its elements, using compiled C code instead of Python; this is a process called vectorization.
+
Provide an interface for its underlying data to be accessed along multiple dimensions.
+
Rapidly perform mathematical operations over all of its elements, or over patterned subsequences of its elements, using compiled C code instead of Python; this is a process called vectorization.
Let’s take a sneak peek to see what this module has in store. The following code creates an ND-array containing the numbers 0-8:
Write code that simulates the dart throwing and tallying process, and keep a running estimate of \(\pi\) as “darts are being thrown”. For simplicity, you can assume that the board is centered at \((0, 0)\), and that \(r = 1\) (the radius of the circle). Use numpy.random.rand (link to docs) to randomly generate the positions on the board where the darts land. Do this for \(N = 10,000\) darts in
+
Write code that simulates the dart throwing and tallying process, and keep a running estimate of \(\pi\) as “darts are being thrown”. For simplicity, you can assume that the board is centered at \((0, 0)\), and that \(r = 1\) (the radius of the circle). Use numpy.random.rand (link to docs) to randomly generate the positions on the board where the darts land. Do this for \(N = 10,000\) darts in
total. For each dart thrown determine whether or not it landed within the circle, and update your estimate of \(\pi\) according to the formula: \(N_{circle} / N_{total} \approx \pi / 4\)
Keep in mind that each dart can land in \((x \in [-1, 1], y \in [-1, 1])\) and that a dart that lands at \((x, y)\) falls within the circle if
You can start this problem by writing a solution that uses explicit for-loops to help make clear the solution. That being said, you should strive to write a fully-vectorized solution (i.e. compute the running estimation of \(\pi\) during \(10,000\) dart throws without any explicit for-loops).
+\end{equation}
You can start this problem by writing a solution that uses explicit for-loops to help make clear the solution. That being said, you should strive to write a fully-vectorized solution (i.e. compute the running estimation of \(\pi\) during \(10,000\) dart throws without any explicit for-loops).
It is helpful to know about NumPy’s cumulative-sum function, numpy.cumsum. This is useful for keeping a running tally - i.e. keeping the history of the number of darts that have fallen within the circle, and not just the current count.
+
It is helpful to know about NumPy’s cumulative-sum function, numpy.cumsum. This is useful for keeping a running tally - i.e. keeping the history of the number of darts that have fallen within the circle, and not just the current count.
defclassification_accuracy(classification_scores,true_labels):""" Returns the fractional classification accuracy for a batch of N predictions.
@@ -260,7 +268,7 @@
A simple approach to this problem is to first loop over the rows of our classification scores. We know that each such row stores the scores for each class for a particular data point, and that the index of the highest score in that row gives us the predicted label for that data point (e.g. image in our hypothetical use-case). We can then directly compare these predicted labels with the true labels to compute the accuracy.
+
A simple approach to this problem is to first loop over the rows of our classification scores. We know that each such row stores the scores for each class for a particular data point, and that the index of the highest score in that row gives us the predicted label for that data point (e.g. image in our hypothetical use-case). We can then directly compare these predicted labels with the true labels to compute the accuracy.
We can use the function numpy.argmax to get the index of the highest score, and thus the predicted class-ID, for each data point. Recall that NumPy arrays use row-major traversal ordering, so performing a for-loop over classification_scores will yield one row of the array at a time.
pred_labels=[]# Will store the N predicted class-IDsforrowinclassification_scores:
@@ -380,29 +388,29 @@
The ND-array can be utilized in mathematical expressions to perform mathematical computations using an array’s entries. In general, NumPy implements mathematical functions such that, when a function acts on an array, the mathematical operation is applied to each entry in the array.
# Demonstrating the application of common# mathematical operations to a NumPy array
->>>importnumpyasnp
+>>>importnumpyasnp>>>x=np.array([[0.,1.,2.],...[3.,4.,5.],
@@ -309,10 +317,10 @@
We will now take some time to survey the various types of vectorized mathematical functions that NumPy supplies, and how these mathematical operations, which traditionally are defined on individual numbers, are applied to arrays of numbers. We will look at
-
unary functions: \(f(x)\)
-
binary functions: \(f(x,y)\)
-
functions that operate on sequences of numbers: \(f(\{x_i\}_{i=0}^{n-1})\)
+
unary functions: \(f(x)\)
+
binary functions: \(f(x,y)\)
+
functions that operate on sequences of numbers: \(f(\{x_i\}_{i=0}^{n-1})\)
-
These represent a substantial portion of the essential mathematical tools in the NumPy library. An exhaustive list of NumPy’s mathematical functions is available in the official documentation.
+
These represent a substantial portion of the essential mathematical tools in the NumPy library. An exhaustive list of NumPy’s mathematical functions is available in the official documentation.
A unary function is a mathematical function that only accepts one operand (i.e. argument): \(f(x)\). NumPy supplies many familiar unary functions:
-
+
A unary function is a mathematical function that only accepts one operand (i.e. argument): \(f(x)\). NumPy supplies many familiar unary functions:
+
-
-
+
+
-
-
Unary Function: \(f(x)\)
-
NumPy Function
+
+
Unary Function: \(f(x)\)
+
NumPy Function
-
-
\(\vert x \vert\)
-
np.absolute
+
+
\(\vert x \vert\)
+
np.absolute
-
\(\sqrt{x}\)
-
np.sqrt
+
\(\sqrt{x}\)
+
np.sqrt
-
Trigonometric Functions
-
+
Trigonometric Functions
+
-
\(\sin{x}\)
-
np.sin
+
\(\sin{x}\)
+
np.sin
-
\(\cos{x}\)
-
np.cos
+
\(\cos{x}\)
+
np.cos
-
\(\tan{x}\)
-
np.tan
+
\(\tan{x}\)
+
np.tan
-
Logarithmic Functions
-
+
Logarithmic Functions
+
-
\(\ln{x}\)
-
np.log
+
\(\ln{x}\)
+
np.log
-
\(\log_{10}{x}\)
-
np.log10
+
\(\log_{10}{x}\)
+
np.log10
-
\(\log_{2}{x}\)
-
np.log2
+
\(\log_{2}{x}\)
+
np.log2
-
Exponential Functions
-
+
Exponential Functions
+
-
\(e^{x}\)
-
np.exp
+
\(e^{x}\)
+
np.exp
This is by no means an exhaustive list of the available unary functions, for example the hyperbolic and inverse trigonometric functions are available too. These familiar functions are defined to work on individual numbers (i.e. “scalars”), not sequences of numbers. How, then, does NumPy implement these functions so that they behave in a coherent way when operating on arrays? The answer is that it maps the function over the array - applying \(f(x)\) to each element within the array, and
-producing a new array as a result (i.e. the input array is not overwritten).
-
importnumpyasnp
+producing a new array as a result (i.e. the input array is not overwritten).
+
There are two cases that we must consider when working with binary functions, in the context of NumPy arrays:
-
When both operands of the function are arrays (of the same shape).
-
When one operand of the function is a scalar (i.e. a single number) and the other is an array.
+
When both operands of the function are arrays (of the same shape).
+
When one operand of the function is a scalar (i.e. a single number) and the other is an array.
Similar to the behavior of unary functions applied to an array, a binary function will operate on two same-shape arrays by applying the function to their pairwise elements.
By now, you may be able to guess NumPy’s behavior when you perform feed a binary function a scalar (i.e. a single number) and an array: the function is applied elementwise on the array, with each application filling one of the function’s arguments, and the single scalar provided everywhere as the other operand. This matches exactly the behavior seen in traditional linear algebra.
+
By now, you may be able to guess NumPy’s behavior when you perform feed a binary function a scalar (i.e. a single number) and an array: the function is applied elementwise on the array, with each application filling one of the function’s arguments, and the single scalar provided everywhere as the other operand. This matches exactly the behavior seen in traditional linear algebra.
A sequential function expects a variable-length sequence of numbers as an input, and produces a single number as an output: \(f(\{x_i\}_{i=0}^{n-1})\). The following are some sequential NumPy functions:
-
+
-
-
+
+
-
-
Sequential Function: \(f(\{x_i\}_{i=0}^{n-1})\)
-
NumPy Function
+
+
Sequential Function: \(f(\{x_i\}_{i=0}^{n-1})\)
+
NumPy Function
-
-
Mean of \(\{x_i\}_{i=0}^{n-1}\)
-
np.mean
+
+
Mean of \(\{x_i\}_{i=0}^{n-1}\)
+
np.mean
-
Median of \(\{x_i\}_{i=0}^{n-1}\)
-
np.median
+
Median of \(\{x_i\}_{i=0}^{n-1}\)
+
np.median
-
Variance of \(\{x_i\}_{i=0}^{n-1}\)
-
np.var
+
Variance of \(\{x_i\}_{i=0}^{n-1}\)
+
np.var
-
Standard Deviation of \(\{x_i\}_{i=0}^{n-1}\)
-
np.std
+
Standard Deviation of \(\{x_i\}_{i=0}^{n-1}\)
+
np.std
-
Maximum Value of \(\{x_i\}_{i=0}^{n-1}\)
-
np.max
+
Maximum Value of \(\{x_i\}_{i=0}^{n-1}\)
+
np.max
-
Minimum Value of \(\{x_i\}_{i=0}^{n-1}\)
-
np.min
+
Minimum Value of \(\{x_i\}_{i=0}^{n-1}\)
+
np.min
-
Index of the Maximum Value of \(\{x_i\}_{i=0}^{n-1}\)
-
np.argmax
+
Index of the Maximum Value of \(\{x_i\}_{i=0}^{n-1}\)
+
np.argmax
-
Index of the Minimum Value of \(\{x_i\}_{i=0}^{n-1}\)
-
np.argmin
+
Index of the Minimum Value of \(\{x_i\}_{i=0}^{n-1}\)
+
np.argmin
-
Sum of \(\{x_i\}_{i=0}^{n-1}\)
-
np.sum
+
Sum of \(\{x_i\}_{i=0}^{n-1}\)
+
np.sum
@@ -777,11 +785,11 @@
Understanding the
-
The average 32x32 RGB image.
-
The total sum of all the values in the array.
-
The minimum blue value, respective to each image.
-
The standard deviation among all the RGB values in all the images, respective to each pixel position (thus you should produce a shape-(32, 32) array of values).
-
The maximum red-value in the top-left quadrant, respective to each image.
+
The average 32x32 RGB image.
+
The total sum of all the values in the array.
+
The minimum blue value, respective to each image.
+
The standard deviation among all the RGB values in all the images, respective to each pixel position (thus you should produce a shape-(32, 32) array of values).
+
The maximum red-value in the top-left quadrant, respective to each image.
NumPy provides a suite of logical operations that can operate on arrays. Many of these map logical operations over array entries in the same fashion as NumPy’s mathematical functions. These functions return either a single boolean object, or a boolean-type array.
+
NumPy provides a suite of logical operations that can operate on arrays. Many of these map logical operations over array entries in the same fashion as NumPy’s mathematical functions. These functions return either a single boolean object, or a boolean-type array.
# check which entries of `x` are less than 6>>>x=np.array([[0,1,2,3],...[4,5,6,7],
@@ -798,14 +806,14 @@
Recall from the Essentials of Python module that, due to effect of floating point numbers having limited numerical precision, that you should never rely on two floating point numbers being exactly equal. Rather, you should require that they are sufficiently “close” in value. In this same vein, you ought not check that the entries of two float-type arrays are precisely equal. Towards this end, the function allclose can be used to verify that all corresponding pairs of entries between two
@@ -815,13 +823,13 @@
Lastly, we note that NumPy provides a suite of functions that can perform optimized computations and routines relevant to linear algebra. Included here are functions for performing matrix products and tensor products, solving eigenvalue problems, inverting matrices, and computing vector normalizations. Please refer to the official NumPy documentation for a full listing of these functions.
+
Lastly, we note that NumPy provides a suite of functions that can perform optimized computations and routines relevant to linear algebra. Included here are functions for performing matrix products and tensor products, solving eigenvalue problems, inverting matrices, and computing vector normalizations. Please refer to the official NumPy documentation for a full listing of these functions.
Let’s create a shopping list class, where an instance of this class stores a list of item-names (strings) to be purchased and a list of names that have already been purchased. We will write an __init__ function that accepts one or more strings, which will be added to the shopping list. Then, we will create methods that will allow us to:
-
add new items to the shopping list
-
mark items on the list as “purchased”
-
remove items, purchased or not, from the list
-
list the name of the items to-be purchased (in alphabetical order)
-
list the name of the items have been purchased (in alphabetical order)
+
add new items to the shopping list
+
mark items on the list as “purchased”
+
remove items, purchased or not, from the list
+
list the name of the items to-be purchased (in alphabetical order)
+
list the name of the items have been purchased (in alphabetical order)
We do not want redundant items to be included on our shopping list - if someone enters “apples” twice, we should only list it once. Thus we will make use of sets, which store unique elements, to store the items on our list.
Lastly, we will want our methods to accept a variable that is either a single item name (a string) or multiple item names (a list/tuple/iterable of strings). To accommodate this, if we receive a sole string as an input, we will place it in a list before carrying on. This will ensure that we are always working with an iterable containing a string.
Recall that the is operator checks to see if two items reference the exact same object in your computer’s memory. Also recall that the built-in isinstance function checks to see if an object is an instance of a class/type. These will help us understand the relationship between class objects, their instances, and references to objects.
Python’s rules for referencing objects with variables still apply here: assigning an object to a variable, be it a class object or an instance, does not create a distinct copy of that object. The variable merely references that object, serving only as an alias for it.
Next, identify each of the following objects as a class object or an instance (and if instance, an instance of what)
-
"helloworld"
-
True
-
int
-
{"a":22}
-
tuple
+
"helloworld"
+
True
+
int
+
{"a":22}
+
tuple
Now that we know the basics of how to create an instance of a class, understand the relationship between class objects and instances, and understand the distinction between instances that are independent from one another, we can move on to learning about creating instance-level attributes for our class.
@@ -406,18 +414,18 @@
Defining Instance-Level Attributes: the Person (this is very important):
-
Invoking Person("Kamasi") first creates an instance of Person as if there was no __init__ method specified. The resulting object does not yet have a name attribute. It only has the class-level attribute x.
-
Next, that instance of Person is passed to __init__ as the argument self, and "Kamasi", which we provided explicitly, is passed as the argument name.
-
With these arguments, Person.__init__(self,"Kamasi") executes its body of instructions. Specifically, self.name=name sets the attribute name on self, using the value "Kamasi".
-
Having finished executing the __init__ method, Person("Kamasi") resolves by returning the instance-object that was created.
+
Invoking Person("Kamasi") first creates an instance of Person as if there was no __init__ method specified. The resulting object does not yet have a name attribute. It only has the class-level attribute x.
+
Next, that instance of Person is passed to __init__ as the argument self, and "Kamasi", which we provided explicitly, is passed as the argument name.
+
With these arguments, Person.__init__(self,"Kamasi") executes its body of instructions. Specifically, self.name=name sets the attribute name on self, using the value "Kamasi".
+
Having finished executing the __init__ method, Person("Kamasi") resolves by returning the instance-object that was created.
We now have the ability to define and set attributes on an instance-level! Understanding this process is critical to mastering object oriented programming in Python. Let’s create several Person-instances, all stored in a list:
# creating several instances of `Person`>>>list_of_people=[Person(n)fornin("Fermi","Noether","Euler")]>>>forpersoninlist_of_people:
-...print(person.name)
-...print(person.x)
+...print(person.name)
+...print(person.x)Fermi1Noether
@@ -432,8 +440,8 @@
Defining Instance-Level Attributes: the # this affects all instances of `Person`
>>>forpersoninlist_of_people:
-...print(person.name)
-...print(person.x)
+...print(person.name)
+...print(person.x)Fermi22Noether
@@ -538,11 +546,11 @@
and y1 share?: They reference the same instance of Cat.
Identify each of the following objects as a class object or an instance (and if so, an instance of what)
-
"helloworld": An instance of the str type (a.k.a class)
-
True: an instance of the bool type
-
int: a class object describing integers
-
{"a":22}: an instance of the dict type
-
tuple: a class object describing tuples
+
"helloworld": An instance of the str type (a.k.a class)
+
True: an instance of the bool type
+
int: a class object describing integers
+
{"a":22}: an instance of the dict type
+
tuple: a class object describing tuples
Reading Comprehension: Instance Attributes
Define a class, Tmp, that has three instance attributes: x, y, and z. x and y should be numbers that are set according to values passed to the instance creation, and z should be the product of these two values.
The built-in issubclass function allows us to verify the relationship between Square and Rectangle.
# `Square` and `Rectangle` are distinct classes>>>SquareisnotRectangle
-True
+True# `Square` is a subclass of `Rectangle`>>>issubclass(Square,Rectangle)
-True
+True# `my_square is an both an instance of `Square` and `Rectangle`>>>isinstance(my_square,Square)
-True
+True>>>isinstance(my_square,Rectangle)
-True
+True
whose differences are relatively minor but are important to understand. The functions “append” and “capitalize” are both examples of instance methods, specifically, as they are designed to be invoked by a particular list instance and string instance, respectively.
We have already worked with the instance method __init__, which is special in that it is reserved by Python to be executed whenever class-initialization is invoked. Similarly, the special instance method __add__ informs how an object interacts with the + operator. For example, float.__add__ specifies that + will sum the values of float instances, whereas list.__add__ specifies that + will concatenate list instances together. We will conclude our discussion of
@@ -283,7 +291,7 @@
Note that this “under the hood” behavior only occurs when the method is being called from an instance; this is why we didn’t face this issue when invoking func from Dummy - Dummy is a class object, not an instance. Thus, inst.func() is equivalent to Dummy.func(inst):
def__add__(self,other):''' Overload the `+` operator for `self` on the left '''num=self.num*other.denom+self.denom*other.numdenom=self.denom*other.denomreturnRational(num,denom)
-def__radd__(self,other):
+def__radd__(self,other):''' Overload the `+` operator for `self` on the right '''returnself.__add__(other)
-def__sub__(self,other):
+def__sub__(self,other):''' Overload the `-` operator '''num=self.num*other.denom-self.denom*other.numdenom=self.denom*other.denomreturnRational(num,denom)
-def__rsub__(self,other):
+def__rsub__(self,other):''' Overload the `-` operator when `self` is on the right'''returnRational(-self.num,self.denom)+other
-def__lt__(self,other):
+def__lt__(self,other):''' Overload the `<` operator '''returnself.num*other.denom<self.denom*other.num
-def__eq__(self,other):
+def__eq__(self,other):''' Overload the `==` operator '''returnself.num==other.numandself.denom==other.denom
-def__str__(self):
+def__str__(self):''' Overload the `str()` operator; useful for printing '''return'{} / {}'.format(self.num,self.denom)
-def__repr__(self):
+def__repr__(self):''' Overload the repr, which is used in the console: >>> rat = Rational(1, 2) >>> rat
@@ -363,10 +371,10 @@
The following methods determines how an object should be represented as a string in various contexts. For example, this text consistently utilizes the fact that passing an object to the Python console will prompt the console to print out a representation of that object as a string. That is,
-
>>> x=list(("a",1,True))
+
>>> x=list(("a",1,True))>>> x['a', 1, True]
Under the hood, the special method x.__repr__ is being called to obtain this string representation whenever an object is displayed in a console/notebook like this. The method returns the string "['a',1,True]", which is then printed out to the console. This is an extremely useful for creating classes whose objects can be inspected conveniently in a Python console or in a Jupyter notebook. Similarly __str__ returns the string that will be produced when str is called on the
object.
-
+
-
-
-
+
+
+
-
-
Method
-
Signature
-
Explanation
+
+
Method
+
Signature
+
Explanation
-
-
Returns string for a printable representation of object
-
__repr__(self)
-
repr(x) invokes x.__repr__(), this is also invoked when an object is returned by a console
+
+
Returns string for a printable representation of object
+
__repr__(self)
+
repr(x) invokes x.__repr__(), this is also invoked when an object is returned by a console
The following special methods control how an object interacts with +, *, **, and other mathematical operators. A full listing of all the special methods used to emulate numeric types can be found here
The following special methods allow us to give our class a container interface, like that of a dictionary, set, or list. An exhaustive listing and discussion of these methods can be found here
Matplotlib is included in the Anaconda distribution of Python packages. The pyplot submodule of Matplotlib contains all of the essential plotting functionality, thus we will always need to import pyplot. It is standard to utilize the abbreviation “plt” when importing the pyplot submodule from Matplotlib:
There are many tutorials and examples available online, which demonstrate Matplotlib’s wide range of capabilities for visualizing data. Here, we will only distill the essentials needed for understanding how to use Matplotlib effectively for typical data science applications.
There are many different wasy to create blank figure and axes objects in Matplotlib. Here, we will introduce the function pyplot.subplots, which creates a figure that contains axes on which data is plotted. We will see that the keyword argument figsize can be used to control the size of the figure, and that the keyword arguments ncols and nrows can be used to specify the grid on which the axes will be laid out within the figure.
In Matplotlib, the Figure object is used to contain one or more sets of Axes objects. Data is plotted within a given set of axes. The subplots function can be used to create a figure along with a specified layout of axes. By default, subplots will create a figure with a single set of axes; calling it will return the figure object and its axes object in a tuple:
-
importmatplotlib.pyplotasplt
+
importmatplotlib.pyplotasplt%matplotlibnotebook# Creating a figure that contains a single
@@ -502,7 +558,7 @@
Despite our regular use of the import statement, we have thus far swept its details under the rug. Here, we will finally pay our due diligence and discuss Python’s import system, which entails understanding the way that code can be organized into modules and packages. Modules are individual .py files from which we can import functions and objects, and packages are collections of such modules. Detailing this packaging system will not only provide us with insight into the organization of
@@ -250,9 +258,9 @@
Python provides a flexible framework for importing modules and specific members of a module. We have already seen in our work with NumPy that we can specify an alias in our import statement; this can be especially convenient for import modules with long names:
# the module object for numpy is returned with the variable name `np`
->>>importnumpyasnp
+>>>importnumpyasnp>>>np.array([2.,3.])array([2.,3.])
It is not unusual, when working on larger scale projects, to want to organize one’s code into several modules. For example, suppose that we are writing software for performing facial recognition. We might want to have a camera module for taking pictures, a face-detection module for storing a model-class capable of detecting faces, and a database module that is used to store and update “seen” faces. These modules can be stored in a collective package.
-
A Python package is a directory containing a file with the name __init__.py, along with other Python modules and subpackages (i.e. subdirectories, each with its own __init__.py file and associated modules). The __init__.py file is of special importance - it serves as the indicator that its housing directory is to be treated as a package. For example, let’s build a bare-bones package with the following directory structure:
+
A Python package is a directory containing a file with the name __init__.py, along with other Python modules and subpackages (i.e. subdirectories, each with its own __init__.py file and associated modules). The __init__.py file is of special importance - it serves as the indicator that its housing directory is to be treated as a package. For example, let’s build a bare-bones package with the following directory structure:
Note that your interactive Python session (e.g. Jupyter notebook) should be in active in the same directory as a_dir/. The name of the directory containing the __init__.py file is the name of the package, thus the name of this package is a_dir. Suppose the contents of __init__.py is as follows:
+
Note that your interactive Python session (e.g. Jupyter notebook) should be in active in the same directory as a_dir/. The name of the directory containing the __init__.py file is the name of the package, thus the name of this package is a_dir. Suppose the contents of __init__.py is as follows:
This section will discuss the best practices for writing Python code that involves reading from and writing to files. We will learn about the built-in pathlib.Path object, which will help to ensure that the code that we write is portable across operating systems (OS) (e.g. Windows, MacOS, Linux). We will also be introduced to a context manager, open, which will permit us to read-from and write-to a file safely; by “safely” we mean that we will be assured that any file that we open will
+
This section will discuss the best practices for writing Python code that involves reading from and writing to files. We will learn about the built-in pathlib.Path object, which will help to ensure that the code that we write is portable across operating systems (OS) (e.g. Windows, MacOS, Linux). We will also be introduced to a context manager, open, which will permit us to read-from and write-to a file safely; by “safely” we mean that we will be assured that any file that we open will
eventually be closed properly, so that it will not be corrupted even in the event that our code hits an error. Next, we will learn how to “glob” for files, meaning that we will learn to search for and list files whose names match specific patterns. Lastly, we will briefly encounter the pickle module which allows us to save (or “pickle”) and load Python objects to and from your computer’s file system.
Specifying mode='w' indicates that we will be writing to the file anew - if the file already has any content, that content will be erased before being written to. The following are the available “modes” for opening a file:
-
+
-
-
+
+
-
-
Mode
-
Explanation
+
+
Mode
+
Explanation
-
-
r
-
Open the file for reading text
+
+
r
+
Open the file for reading text
-
w
-
Open the file, clearing its contents, for writing text anew
+
w
+
Open the file, clearing its contents, for writing text anew
-
a
-
Open the file to write text to end of any existing content, thus “appending” to the file
+
a
+
Open the file to write text to end of any existing content, thus “appending” to the file
-
x
-
Open the file for writing text, failing if the file already exists
+
x
+
Open the file for writing text, failing if the file already exists
There are many cases in which we may want to construct a list of files to iterate over. For example, if we have several data files, it would be useful to create a file list which we can iterate through and process in sequence. One way to do this would be to manually construct such a list of files:
However, this is extraordinarily tedious and prone to error, either by mis-typing a file name or forgetting a file. A much more powerful way to construct such a list of files is by file globbing. A glob is a set of file names matching some pattern. To glob files, we use special wildcard characters that will match all the files with a certain part of a file name. In our case, * will be the wildcard character we use the most - it matches any character. This is much better motivated with an
example. Below, we see some globs and the types of patterns they will match:
# matches anything that starts with `file` and ends with `.txt` like
@@ -473,8 +733,8 @@
The * wildcard is not the only pattern available to us. Sometimes it can be useful to match certain subsets of characters. For example, we may only want to match file names that start with a number. With the * wildcard alone, that’s not possible. Luckily for us, these common use-cases are also taken care of.
NumPy provides its own functions for saving and loading arrays. Although these arrays can be pickled, it is strongly advised to leverage NumPy’s file-IO functions. NumPy’s standard binary file type used to store array data is known as an ‘.npy’ file. The NumPy binary archive format, which stores multiple arrays in one file, is known as the ‘.npz’ format.
Let’s save the array x=np.array([1,2,3]) to the binary file (not a text file) “my_array.npz”. numpy.save and numpy.load will save and load arrays, handling all of the file opening and closing for you. Thus there is no need to use a context manager when using these functions.
-
>>> importnumpyasnp
+
>>> importnumpyasnp>>> x=np.array([1,2,3])# save a numpy array to disk
@@ -586,12 +846,13 @@
Throughout PLYMI we have been concerned with learning the rules for writing valid Python code. That is, we have taken care to ensure that our computers can understand the instructions that we have written for them. Here, we will discuss methods for making our code easier for humans to understand. Specifically, we will study:
-
“PEP8”: the official style guide for Python code.
-
Python’s system for adding so-called “type hints” to functions.
-
Formal documentation specifications such as NumPy docs and Napolean docs.
+
“PEP8”: the official style guide for Python code.
+
Python’s system for adding so-called “type hints” to functions.
+
Formal documentation specifications such as NumPy docs and Napolean docs.
The immediate purpose of discussing these items is that they will help us write code that is easy to understand and maintain. In the long term, they will serve to make our projects long-lived and useful to many more people. It can be surprising to see how quickly a code base can atrophy, becoming opaque and unusable even to its creator, when it wasn’t designed with a sufficient attention to detail and documentation.
It is difficult to overstate the importance of this material.
Do not use parentheses to create unnecessary closures. Parentheses should be used for specifying function signatures, calling objects (e.g. calling a function), associating mathematical operations, defining generator-expressions, and grouping long multi-line expressions.
+
Do not use parentheses to create unnecessary closures. Parentheses should be used for specifying function signatures, calling objects (e.g. calling a function), associating mathematical operations, defining generator-expressions, and grouping long multi-line expressions.
flake8: Analyzes your code to enforce the PEP8 standards and to catch bad code patterns, such as unused variables.
-
IDEs like Visual Studio Code and PyCharm will automatically configure themselves to run flake8 or comparable linters on your code. They will add visual marks to your code to highlight problematic sections of code. This tool can also be run from the command line, or be configured to run automatically with other IDEs and text editors.
+
flake8: Analyzes your code to enforce the PEP8 standards and to catch bad code patterns, such as unused variables.
+
+
IDEs like Visual Studio Code and PyCharm will automatically configure themselves to run flake8 or comparable linters on your code. They will add visual marks to your code to highlight problematic sections of code. This tool can also be run from the command line, or be configured to run automatically with other IDEs and text editors.
-
isort: “I sort your Python imports so you don’t have to”
-
As promised, this tool manages the unruly stack of import statements that quickly accrue at the top of our code. It will sort import statements alphabetically, and will group them in accordance with PEP8.
+
isort: “I sort your Python imports so you don’t have to”
+
+
As promised, this tool manages the unruly stack of import statements that quickly accrue at the top of our code. It will sort import statements alphabetically, and will group them in accordance with PEP8.
-
black: “Any code style you like, as long as it is black”
-
Black is an uncompromising code formatter. You need not spend time formatting code correctly if you run black - it will format your code the same way every time. While this will take care of managing things like line-breaks, indentation, spacing, and brackets for you, it is still your responsibility to write code that is Pythonic and to use good naming
-conventions.
+
black: “Any code style you like, as long as it is black”
+
+
Black is an uncompromising code formatter. You need not spend time formatting code correctly if you run black - it will format your code the same way every time. While this will take care of managing things like line-breaks, indentation, spacing, and brackets for you, it is still your responsibility to write code that is Pythonic and to use good naming
+conventions.
This saves us the trouble of having to run our code, hit an error, read through the stack trace, and debug our mistake. Instead, we immediately see our inconsistency get flagged. In fact, we probably never would have even made the mistake at all, as our IDE would have shown a list of integer-methods for us to choose from, and not string-methods. Type-hints would have likely alerted us to our misapprehension.
It does not take long to experience the benefits of type-hinting through your IDE. This both accelerates your coding by informing you of the object types that you are working with on the fly, and helps to expose oversights in your code as soon as they are made.
-
Finally, it is also worthwhile to highlight two projects, mypy and pyright, which are used to perform static type-checking on your code based on your type-hints. That is, mypy and pyright will both automatically traverse your code and find potential bugs by identifying type conflicts in your code (e.g. trying to capitalize an integer) by checking their annotated and inferred types. These tools are especially useful for
+
Finally, it is also worthwhile to highlight two projects, mypy and pyright, which are used to perform static type-checking on your code based on your type-hints. That is, mypy and pyright will both automatically traverse your code and find potential bugs by identifying type conflicts in your code (e.g. trying to capitalize an integer) by checking their annotated and inferred types. These tools are especially useful for
large-scale code bases. Companies like Dropbox and Microsoft make keen use of static type-checking to identify inconsistencies in their code without having to hit runtime errors. Keep mypy, pyright, and other type-checking utilities in mind as you mature as a Python developer and find yourself working on projects of growing complexity. If you are using VSCode as your IDE, you
-can install the pyright vscode extension to leverage type checking within your IDE.
+can install the PyLance VSCode extension to leverage pyright’s type checking within your IDE.
+
The jedi package, which is installed by default alongside Jupyter and IPython, enables type-informed code completion. This means that we can benefit from type-hints even when we are working in a Jupyter notebook!
+
In a Jupyter notebook, write the following type-annotated signature for a function: deff(x:str):. Now in the body of the function (i.e. on the next line, and indented inwards), try typing x. and then hit <TAB>.
+
deff(x:str):
+ x.#hit <TAB> here
+
+
+
You should see a list of methods appear for x. What are the first three methods that appear? Do these make sense based on the annotation associated with x? Now change the annotation for x to be list and trigger the auto-completion in the editor again; do you see an updated list of methods?
+
Takeaway:
There is a simple syntax for annotating functions, class-methods, and variables with type-hints; this is a formal mechanism for documenting the types of objects that are expected as inputs to the function, as well as the return type of the function. It is critical to note that that type-hints are never enforced by Python - they only serve as a form of documentation.
What it hints: A tuple containing a specific number of objects of the specified types. ... can be used to indicate an arbitrary number of objects in the tuple
-
Examples:
-
Hint a tuple containing one string and one boolean: Tuple[str,bool]
-
Hint a tuple containing an arbitrary number of integers: Tuple[int,...]
+
What it hints: A tuple containing a specific number of objects of the specified types. ... can be used to indicate an arbitrary number of objects in the tuple
+
Examples:
+
+
Hint a tuple containing one string and one boolean: Tuple[str,bool]
+
Hint a tuple containing an arbitrary number of integers: Tuple[int,...]
Literal[<val
List[Tuple[str, Any]] The name-stats tuple pair for each specified student. """
- ifstudent_listisNone:# default to all-students
+ ifstudent_listisNone:# default to all-studentsstudent_list=sorted(grade_book)# iterates over the dictionary's keysreturn[(name,stat_function(grade_book[name]))fornameinstudent_list]
@@ -744,9 +771,9 @@
Literal[<val
...
-
Object types defined in 3rd-party libraries, e.g. NumPy’s ndarray behave no differently from our own custom custom classes; simply provide the class object in the type-hint annotation:
+
Object types defined in 3rd-party libraries, e.g. NumPy’s ndarray behave no differently from our own custom custom classes; simply provide the class object in the type-hint annotation:
# type-hinting an nd-array from numpy
-importnumpyasnp
+importnumpyasnpdefcustom_dot_product(x:np.ndarray,y:np.ndarray)->float:returnfloat(np.sum(x*y))
@@ -764,7 +791,7 @@
You should strive to write type-hints that pass the duck test: if your function is expecting a duck then hint for something that walks like a duck, quacks like a duck, etc. This will help you avoid writing type-hints that are overly narrow, and which are ultimately non-Pythonic in their strictness.
To be more concrete, let’s revisit our count_vowels function:
defcount_vowels(x:str,include_y:bool=False)->int:"""Returns the number of vowels contained in `in_string`"""vowels=set("aeiouAEIOU")ifinclude_y:
@@ -791,7 +818,7 @@
Writing Good Type-Hints (quack quack)typing module provides a so-called abstract base class, Iterable, which is a generic type for any class that supports iteration. Thus we can improve our type-hint by making it more general. This encapsulates all of the above use-cases that we demonstrated.
fromtypingimportIterable
-defcount_vowels(x:Iterable[str],include_y:bool=False)->int:
+defcount_vowels(x:Iterable[str],include_y:bool=False)->int:"""Returns the number of vowels contained in `in_string`"""vowels=set("aeiouAEIOU")ifinclude_y:
@@ -819,7 +846,7 @@
Davis King, a prolific and talented open source developer, is the creator of the dlib C++ library. Among the library’s major features, like machine learning algorithms, Davis lists its documentation as the first and foremost feature. In fact, he says about dlib:
-
I consider the documentation to be the most important part of the library. So if you find anything that isn’t documented, isn’t clear, or has out of date documentation, tell me and I will fix it.
+
I consider the documentation to be the most important part of the library. So if you find anything that isn’t documented, isn’t clear, or has out of date documentation, tell me and I will fix it.
+
There is great wisdom in placing such a high value on documentation. In this section, we will learn about two popular docstring-style specifications for Python: the NumPy specification and the Google specification. Both of these are rich extensions of the rudimentary docstring conventions that are proposed in PEP 257, and, critically, they both place an emphasis on documenting the variable types.
PLYMI uses Numpy-style docstrings throughout most of the text (except for when we are trying to keep the functions brief). This is ultimately just a choice of style/aesthetics. Ultimately, the critical takeaway here is to pick a documentation style, learn it, and stick to it faithfully. Once again, it is hard to overstate how important it is to anchor your code with clear and consistent documentation. It will aid you in your code-writing process, it will enable users to adopt and perhaps
contribute to your code, and it will ensure longevity for your hard work.
The Google documentation style is specified in full here; it is part of the complete Google Python style guide. The docstring style specified here is quite succinct in comparison to the NumPy spec. It consists of the following sections:
-
A succinct, single-line description of the function.
-
An extended summary of the function, which provides a more verbose description of things.
-
An Args section, which details the types of the input parameters along with descriptions of them. (This section is not necessary if your function is parameterless).
-
A Returns section (or Yields for a generator), which details the object that is returned by the function. (This is not necessary if your function always returns None).
+
A succinct, single-line description of the function.
+
An extended summary of the function, which provides a more verbose description of things.
+
An Args section, which details the types of the input parameters along with descriptions of them. (This section is not necessary if your function is parameterless).
+
A Returns section (or Yields for a generator), which details the object that is returned by the function. (This is not necessary if your function always returns None).
There is also a Raises section in the case that your function raises exceptions under known conditions.
Let’s reproduce the docstrings for pairwise_dists and compute_student_stats using the Google style.
The tab-completion reveals methods like “capitalize”, “casefold”, and “center” – all of which are string methods. These are made available despite the fact that x did not yet refer to a specific string. Instead, the type-hint was able to tell the editor that xwill refer to a string, and this was enough to inform these type completions.
+
Revising the type-annotation for x to be list affects the auto-completion options accordingly: the editor will now suggest list methods like “append”, “clear”, and “copy”.
+
Type Hinting: Solution
The following is a well-annotated version of get_first_and_last:
-Thus far, we have discussed some rules for accessing data in arrays, all of which fall into the category that is designated ["basic indexing"](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#basic-slicing-and-indexing) by the NumPy documentation. We will discuss the details of basic indexing and of ["advanced indexing"](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing), in full, in a later section. Note, however, that all of the indexing/slicing reviewed here produces a "view" of the original array. That is, *no data is copied* when you index into an array using integer indices and/or slices. Recall that slicing lists and tuples *do* produce copies of the data.
+Thus far, we have discussed some rules for accessing data in arrays, all of which fall into the category that is designated ["basic indexing"](https://numpy.org/doc/stable/reference/arrays.indexing.html#basic-slicing-and-indexing) by the NumPy documentation. We will discuss the details of basic indexing and of ["advanced indexing"](https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing), in full, in a later section. Note, however, that all of the indexing/slicing reviewed here produces a "view" of the original array. That is, *no data is copied* when you index into an array using integer indices and/or slices. Recall that slicing lists and tuples *do* produce copies of the data.
@@ -417,7 +417,7 @@ array([[ 1, 5, 9],
[ 3, 7, 11],
[ 4, 8, 12]])
```
-A complete listing of the available array-manipulation functions can be found in the [official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html). Among these functions, the reshape function is especially useful.
+A complete listing of the available array-manipulation functions can be found in the [official NumPy documentation](https://numpy.org/doc/stable/reference/routines.array-manipulation.html). Among these functions, the reshape function is especially useful.
### Introducing the `reshape` Function
The `reshape` function allows you to change the dimensionality and axis-layout of a given array. This adjusts the indexing interface used to access the array's underlying data, as was discussed in earlier in this module. Let's take a shape-(6,) array, and reshape it to a shape-(2, 3) array:
@@ -497,10 +497,10 @@ For all straightforward applications of reshape, NumPy does not actually create
## Links to Official Documentation
-- [The N-dimensional array](https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html)
-- [Array indexing](https://docs.scipy.org/doc/numpy/user/basics.indexing.html#indexing)
-- [Indexing routines](https://docs.scipy.org/doc/numpy/reference/routines.indexing.html#indexing-routines)
-- [Array manipulation routines](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html)
+- [The N-dimensional array](https://numpy.org/doc/stable/reference/arrays.ndarray.html)
+- [Array indexing](https://numpy.org/doc/stable/user/basics.indexing.html#indexing)
+- [Indexing routines](https://numpy.org/doc/stable/reference/routines.indexing.html#indexing-routines)
+- [Array manipulation routines](https://numpy.org/doc/stable/reference/routines.array-manipulation.html)
## Reading Comprehension Solutions
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/AdvancedIndexing.md.txt b/docs_backup/_sources/Module3_IntroducingNumpy/AdvancedIndexing.md.txt
index 101ed3d2..2dcb7a2f 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/AdvancedIndexing.md.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/AdvancedIndexing.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -70,7 +70,7 @@ False
>>> np.shares_memory(x, x[bool_index])
False
```
-The flexibility permitted by advanced indexing makes it a difficult topic to treat exhaustively without delving into somewhat terse and abstract notation. It is best to refer to [the official documentation](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing) for such a treatment of the topic. Here, we will discuss the essential aspects of advanced indexing, with the aim of the discussion being both thorough and accessible.
+The flexibility permitted by advanced indexing makes it a difficult topic to treat exhaustively without delving into somewhat terse and abstract notation. It is best to refer to [the official documentation](https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing) for such a treatment of the topic. Here, we will discuss the essential aspects of advanced indexing, with the aim of the discussion being both thorough and accessible.
@@ -392,7 +392,7 @@ Given the following array:
... [-0.29, 0.13, -0.26, 0.33]])
```
-Use boolean array-indexing and NumPy's [logical functions](https://docs.scipy.org/doc/numpy/reference/routines.logic.html) to select the contents of `h` that satisfy the following conditions. Because you are dealing with floating-point numbers, [you should not require that two values are exactly equal](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Understanding-Numerical-Precision); rather, make use of the function [numpy.isclose](https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.isclose.html).
+Use boolean array-indexing and NumPy's [logical functions](https://numpy.org/doc/stable/reference/routines.logic.html) to select the contents of `h` that satisfy the following conditions. Because you are dealing with floating-point numbers, [you should not require that two values are exactly equal](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Understanding-Numerical-Precision); rather, make use of the function [numpy.isclose](https://numpy.org/doc/stable/reference/generated/numpy.isclose.html#numpy.isclose).
1. All negative entries in `h`
2. All entries in `h` "equal" to `0.01` or `0.33`
@@ -402,8 +402,8 @@ Use boolean array-indexing and NumPy's [logical functions](https://docs.scipy.or
-#### Converting a Boolean Index-Array to Integer index-arrays: numpy.where
-The function [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) can be used to take a boolean-valued array, and produce the *tuple* of index-arrays that access the `True` entries of that array, via integer array indexing (discussed at the beginning of this section).
+### Converting a Boolean Index-Array to Integer Index-Arrays: numpy.where
+The function [numpy.where](https://numpy.org/doc/stable/reference/generated/numpy.where.html) can be used to take a boolean-valued array, and produce the *tuple* of index-arrays that access the `True` entries of that array, via integer array indexing (discussed at the beginning of this section).
```python
# demonstrating `np.where`
@@ -449,7 +449,7 @@ array([0], dtype=int64)
```
-Armed with NumPy's suite of [logical functions](https://docs.scipy.org/doc/numpy/reference/routines.logic.html), boolean-array indexing provides a sleek interface for accessing the particular contents of an array, irrespective of the array's shape and the layout of its contents. This method of indexing is especially powerful in the context of performing augmented updates to an array, which is the subject of the following subsection.
+Armed with NumPy's suite of [logical functions](https://numpy.org/doc/stable/reference/routines.logic.html), boolean-array indexing provides a sleek interface for accessing the particular contents of an array, irrespective of the array's shape and the layout of its contents. This method of indexing is especially powerful in the context of performing augmented updates to an array, which is the subject of the following subsection.
@@ -537,7 +537,7 @@ Given the array
... [ 0.84, 0.76, 0.25, 0.07]])
```
-Replace the diagonal elements of `x` with `(-1, -2, -3, -4)`, and add `1` to all values in `x` that are greater than `0.8`.
+Replace the diagonal elements of `x` with `(0, 1, 2, 3)`, and add `1` to all values in `x` that are greater than `0.8`.
@@ -563,15 +563,15 @@ array([[ 3, 7, 11]])
```
-The rules for resolving the various possible combinations of basic and advanced indexing are nontrivial. Refer to the [official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#combining-advanced-and-basic-indexing) for a detailed description for these rules. In practice, basic and advanced indexing can typically be used independently from one another.
+The rules for resolving the various possible combinations of basic and advanced indexing are nontrivial. Refer to the [official NumPy documentation](https://numpy.org/doc/stable/reference/arrays.indexing.html#combining-advanced-and-basic-indexing) for a detailed description for these rules. In practice, basic and advanced indexing can typically be used independently from one another.
## Links to Official Documentation
-- [Advanced Indexing](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing)
-- [Logical Functions](https://docs.scipy.org/doc/numpy/reference/routines.logic.html)
-- [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html)
-- [Combining Basics and Advanced Indexing](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#combining-advanced-and-basic-indexing)
+- [Advanced Indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing)
+- [Logical Functions](https://numpy.org/doc/stable/reference/routines.logic.html)
+- [numpy.where](https://numpy.org/doc/stable/reference/generated/numpy.where.html)
+- [Combining Basics and Advanced Indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#combining-advanced-and-basic-indexing)
## Reading Comprehension Solutions
@@ -695,6 +695,13 @@ This will return a tuple of two integer-valued index-arrays. These contain the i
... [ 0.84, 0.76, 0.25, 0.07]])
>>> x[np.arange(4), np.arange(4)] = range(4)
+# equivalent (works for the general case of a square matrix of N-dims)
+# x[tuple(np.arange(x) for x in x.shape)] = range(x.shape[0])
+
+# equivalent (using numpy built-in functions):
+# x[np.diag_indices_from(x)] = np.arange(4)
+# np.fill_diagonal(x, np.arange(4))
+
>>> x[0.8 < x] += 1
>>> x
array([[ 0. , 0.05, 1.84, 0.21],
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/ArrayTraversal.md.txt b/docs_backup/_sources/Module3_IntroducingNumpy/ArrayTraversal.md.txt
index da718d02..d7979257 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/ArrayTraversal.md.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/ArrayTraversal.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -34,7 +34,7 @@ NumPy provides valuable tools for iterating over any array, such that each eleme
[(0, 'a'), (1, 'b'), (2, 'c'), (3, 'd'), (4, 'e'), (5, 'f')]
```
-Similarly, NumPy provides the [ndenumerate](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndenumerate.html#numpy.ndenumerate) function, which enumerates each element in an N-dimensional array, specifying the N-dimensional index for each element.
+Similarly, NumPy provides the [ndenumerate](https://numpy.org/doc/stable/reference/generated/numpy.ndenumerate.html#numpy.ndenumerate) function, which enumerates each element in an N-dimensional array, specifying the N-dimensional index for each element.
```python
>>> import numpy as np
@@ -77,7 +77,7 @@ See that each triplet of integers specifies the index for the corresponding arra
9
```
-See [the official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/routines.indexing.html#iterating-over-arrays) for a complete listing of functions that facilitate iterating over arrays. The official documentation also provides [a detailed treatment of array iteration](https://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#iterating-over-arrays), which is far more detailed than is warranted here. Next, we must discuss the default ordering that NumPy uses when traversing a N-dimensional array.
+See [the official NumPy documentation](https://numpy.org/doc/stable/reference/routines.indexing.html#iterating-over-arrays) for a complete listing of functions that facilitate iterating over arrays. The official documentation also provides [a detailed treatment of array iteration](https://numpy.org/doc/stable/reference/arrays.nditer.html#iterating-over-arrays), which is far more detailed than is warranted here. Next, we must discuss the default ordering that NumPy uses when traversing a N-dimensional array.
@@ -203,7 +203,7 @@ According to this discussion, `reshape` can effectively "undo" itself: `np.arang
## Links to Official Documentation
-- [Definition of row major ordering](https://docs.scipy.org/doc/numpy/glossary.html#term-row-major)
-- [Definition of column major ordering](https://docs.scipy.org/doc/numpy/glossary.html#term-column-major)
-- [Routines for iterating over arrays](https://docs.scipy.org/doc/numpy/reference/routines.indexing.html#iterating-over-arrays)
-- [Detailed description of array iteration](https://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#iterating-over-arrays)
+- [Definition of row major ordering](https://numpy.org/doc/stable/glossary.html#term-row-major)
+- [Definition of column major ordering](https://numpy.org/doc/stable/glossary.html#term-column-major)
+- [Routines for iterating over arrays](https://numpy.org/doc/stable/reference/routines.indexing.html#iterating-over-arrays)
+- [Detailed description of array iteration](https://numpy.org/doc/stable/reference/arrays.nditer.html#iterating-over-arrays)
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/BasicArrayAttributes.md.txt b/docs_backup/_sources/Module3_IntroducingNumpy/BasicArrayAttributes.md.txt
index 1cee79bf..30f1130b 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/BasicArrayAttributes.md.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/BasicArrayAttributes.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -94,4 +94,4 @@ The size, in bytes (8 bits is 1 byte), of each element of the array. For example
## Links to Official Documentation
-- [Array attributes](https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html#array-attributes)
+- [Array attributes](https://numpy.org/doc/stable/reference/arrays.ndarray.html#array-attributes)
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/BasicIndexing.md.txt b/docs_backup/_sources/Module3_IntroducingNumpy/BasicIndexing.md.txt
index fae04f4b..12fe7a0d 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/BasicIndexing.md.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/BasicIndexing.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -202,18 +202,16 @@ Basic indexing is triggered whenever a tuple of: integer, `slice`, `numpy.newaxi
-
-
+
**Reading Comprehension: Ellipsis**
-Given a $N$-dimensional array, `x`, index into `x` such that you axis entry-0 of axis-0, the last entry of axis-$N-1$, slicing along all intermediate dimensions. $N$ is at least $2$.
+Given a $N$-dimensional array, `x`, index into `x` such that you access entry-0 of axis-0, the last entry of axis-$N-1$, slicing along all intermediate dimensions. $N$ is at least $2$.
-
-
-
+
+
**Reading Comprehension: Basic Indexing**
Given a shape-(4, 3) array,
@@ -291,9 +289,9 @@ False
Thus updating a variable `subarray` via `subarray = subarray + 2` does *not* overwrite the original data referenced by `subarray`. Rather, `subarray + 2` assigns that new array to the variable `subarray`. NumPy does provide mechanisms for performing mathematical operations to directly update the underlying data of an array without having to create a distinct array. We will discuss these mechanisms in the next subsection.
-
-
+
+
**Reading Comprehension: Views**
Given,
@@ -546,7 +544,6 @@ Which of the following expressions updates the data originally referenced by `x`
```python
# 5.
>>> np.log(x[1:3], out=x[1:3])
->>> y += 3
```
```python
@@ -556,7 +553,7 @@ Which of the following expressions updates the data originally referenced by `x`
```python
# 7.
->>> y = y + 2
+>>> x = np.square(x)
```
```python
@@ -570,6 +567,11 @@ Which of the following expressions updates the data originally referenced by `x`
>>> f(y)
```
+```python
+# 10.
+>>> np.square(y, out=y)
+```
+
@@ -584,8 +586,8 @@ Assignments to views of an array, augmented assignments, and NumPy functions tha
## Links to Official Documentation
-- [Basic indexing](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#indexing)
-- [Definition of 'view'](https://docs.scipy.org/doc/numpy/glossary.html#term-view)
+- [Basic indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#indexing)
+- [Definition of 'view'](https://numpy.org/doc/stable/glossary.html#term-view)
## Reading Comprehension Solutions
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/Broadcasting.md.txt b/docs_backup/_sources/Module3_IntroducingNumpy/Broadcasting.md.txt
index 5c8a96fc..c11e0503 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/Broadcasting.md.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/Broadcasting.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -182,7 +182,7 @@ result-shape: 2 x 1
-NumPy provides the function [broadcast_to](https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_to.html#numpy.broadcast_to), which can be used to broadcast an array to a specified shape. This can help us build our intuition for broadcasting. Let's broadcast a shape-(3,4) array to a shape-(2,3,4) array:
+NumPy provides the function [broadcast_to](https://numpy.org/doc/stable/reference/generated/numpy.broadcast_to.html#numpy.broadcast_to), which can be used to broadcast an array to a specified shape. This can help us build our intuition for broadcasting. Let's broadcast a shape-(3,4) array to a shape-(2,3,4) array:
```python
# Demonstrating `np.broadcast_to`
@@ -426,7 +426,7 @@ Normalize `x` such that *each of its rows, within each sheet, will sum to a valu
A digital image is simply an array of numbers, which instructs a grid of pixels on a monitor to shine light of specific colors, according to the numerical values in that array.
-An RGB-image can thus be stored as a 3D NumPy array of shape-$(V, H, 3)$. $V$ is the number of pixels along the vertical direction, $H$ is the number of pixels along the horizontal, and the size-3 dimension stores the red, blue, and green color values for a given pixel. Thus a $(32, 32, 3)$ array would be a 32x32 RBG image.
+An RGB-image can thus be stored as a 3D NumPy array of shape-$(V, H, 3)$. $V$ is the number of pixels along the vertical direction, $H$ is the number of pixels along the horizontal, and the size-3 dimension stores the red, blue, and green color values for a given pixel. Thus a $(32, 32, 3)$ array would be a 32x32 RGB image.
You often work with a collection of images. Suppose we want to store N images in a single array; thus we now consider a 4D shape-(N, V, H, 3) array. For the sake of convenience, let's simply generate a 4D-array of random numbers as a placeholder for real image data. We will generate 500, 48x48 RGB images:
@@ -486,16 +486,16 @@ Performing this computation using for-loops proceeds as follows:
def pairwise_dists_looped(x, y):
""" Computing pairwise distances using for-loops
- Parameters
- ----------
- x : numpy.ndarray, shape=(M, D)
- y : numpy.ndarray, shape=(N, D)
+ Parameters
+ ----------
+ x : numpy.ndarray, shape=(M, D)
+ y : numpy.ndarray, shape=(N, D)
- Returns
- -------
- numpy.ndarray, shape=(M, N)
- The Euclidean distance between each pair of
- rows between `x` and `y`."""
+ Returns
+ -------
+ numpy.ndarray, shape=(M, N)
+ The Euclidean distance between each pair of
+ rows between `x` and `y`."""
# `dists[i, j]` will store the Euclidean
# distance between `x[i]` and `y[j]`
dists = np.empty((5, 6))
@@ -514,6 +514,16 @@ def pairwise_dists_looped(x, y):
```
Be sure to step through this code and see that `dists` stores each pair of Euclidean distances between the rows of `x` and `y`.
+
+```python
+# produces a shape-(5, 6) result
+>>> pairwise_dists_looped(x, y)
+array([[ 3.678 , 8.4524, 10.3057, 7.3711, 6.2152, 5.5548],
+ [10.1457, 5.8793, 2.9274, 4.1114, 3.9098, 5.2259],
+ [ 7.3219, 0.8439, 6.8734, 4.5687, 7.3283, 4.8216],
+ [10.339 , 7.032 , 7.4745, 7.0633, 3.5999, 4.0107],
+ [ 8.2878, 3.5468, 6.336 , 4.9014, 4.1858, 2.0257]])
+```
@@ -545,26 +555,34 @@ Voilà! We have produced the distances in a vectorized way. Let's write this out
def pairwise_dists_crude(x, y):
""" Computing pairwise distances using vectorization.
- This method uses memory-inefficient broadcasting.
-
- Parameters
- ----------
- x : numpy.ndarray, shape=(M, D)
- y : numpy.ndarray, shape=(N, D)
-
- Returns
- -------
- numpy.ndarray, shape=(M, N)
- The Euclidean distance between each pair of
- rows between `x` and `y`."""
+ This method uses memory-inefficient broadcasting.
+
+ Parameters
+ ----------
+ x : numpy.ndarray, shape=(M, D)
+ y : numpy.ndarray, shape=(N, D)
+
+ Returns
+ -------
+ numpy.ndarray, shape=(M, N)
+ The Euclidean distance between each pair of
+ rows between `x` and `y`."""
# The use of `np.newaxis` here is equivalent to our
# use of the `reshape` function
return np.sqrt(np.sum((x[:, np.newaxis] - y[np.newaxis])**2, axis=2))
```
-
+```python
+# produces a shape-(5, 6) result
+>>> pairwise_dists_crude(x, y)
+array([[ 3.678 , 8.4524, 10.3057, 7.3711, 6.2152, 5.5548],
+ [10.1457, 5.8793, 2.9274, 4.1114, 3.9098, 5.2259],
+ [ 7.3219, 0.8439, 6.8734, 4.5687, 7.3283, 4.8216],
+ [10.339 , 7.032 , 7.4745, 7.0633, 3.5999, 4.0107],
+ [ 8.2878, 3.5468, 6.336 , 4.9014, 4.1858, 2.0257]])
+```
Regrettably, there is a glaring issue with the vectorized computation that we just performed. Consider the largest sized array that is created in the for-loop computation, compared to that of this vectorized computation. The for-loop version need only create a shape-$(M, N)$ array, whereas the vectorized computation creates an intermediate array (i.e. `diffs`) of shape-$(M, N, D)$. This intermediate array is even created in the one-line version of the code. This will create a massive array if $D$ is a large number!
-Suppose, for instance, that you are finding the Euclidean between pairs of RGB images that each have a resolution of $32 \times 32$ (in order to see if the images resemble one another). Thus in this scenario, each image is comprised of $D = 32 \times 32 \times 3 = 3072$ numbers ($32^2$ pixels, and each pixel has 3 values: a red, blue, and green-color value). Computing all the distances between a stack of 5000 images with a stack of 100 images would form an intermediate array of shape-$(5000, 100, 3072)$. Even though this large array only exists temporarily, it would have to consume over 6GB of RAM! The for-loop version requires $\frac{1}{3027}$ as much memory (about 2MB).
+Suppose, for instance, that you are finding the Euclidean between pairs of RGB images that each have a resolution of $32 \times 32$ (in order to see if the images resemble one another). Thus in this scenario, each image is comprised of $D = 32 \times 32 \times 3 = 3072$ numbers ($32^2$ pixels, and each pixel has 3 values: a red, blue, and green-color value). Computing all the distances between a stack of 5000 images with a stack of 100 images would form an intermediate array of shape-$(5000, 100, 3072)$. Even though this large array only exists temporarily, it would have to consume over 6GB of RAM! The for-loop version requires $\frac{1}{3072}$ as much memory (about 2MB).
Is our goose cooked? Are we doomed to pick between either slow for-loops, or a memory-inefficient use of vectorization? No! We can refactor the mathematical form of the Euclidean distance in order to avoid the creation of that bloated intermediate array.
@@ -611,7 +629,7 @@ Thus the third term in our equation, $-2\sum_{i=0}^{D-1}{x_{i} y_{i}}$, for all
```python
# computing the third term in the distance
# equation, for all pairs of rows
->>> x_y_prod = -2 * np.matmul(x, y.T) # `np.dot` can also be used to the same effect
+>>> x_y_prod = 2 * np.matmul(x, y.T) # `np.dot` can also be used to the same effect
>>> x_y_prod.shape
(5, 6)
```
@@ -620,34 +638,106 @@ Having accounted for all three terms, we can finally compute the Euclidean dista
```python
# computing all the distances
->>> dists = np.sqrt(x_y_sqrd + x_y_prod)
+>>> dists = np.sqrt(x_y_sqrd - x_y_prod)
>>> dists.shape
(5, 6)
```
-In total, we have successfully used vectorization to compute the all pairs of distances, while only requiring an array of shape-$(M, N)$ to do so! This is the memory-efficient, vectorized form - the stuff that dreams are made of. Let's write the function that performs this computation in full.
+
+#### A Subtle Issue with Floating-point Precision
+
+There is one more important and very subtle detail that we have to deal with.
+In terms of pure mathematics, `x_y_sqrd - x_y_prod` must be a strictly non-negative value (i.e. its smallest possible value is $0$), since it is equivalent to
+
+\begin{equation}
+\sum_{i=0}^{D-1}{(x_{i} - y_{i})^2}
+\end{equation}
+
+That being said, are working with floating-point numbers, which do not always behave exactly like rational numbers when we do arithmetic with them.
+Indeed, we saw earlier that the [quirks of floating-point arithmetic](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Understanding-Numerical-Precision) can lead to surprising results.
+Here, the strange behavior is that `x_y_sqrd - x_y_prod` **can produce negative numbers**!
+
+Let's see this in action.
+We'll take the following shape-(2, 3) array
```python
-def pairwise_dists(x, y):
- """ Computing pairwise distances using memory-efficient
- vectorization.
+# A carefully-selected input that will trigger surprising
+# floating-point precision issues in our distance calculation
+>>> x = np.array([[4.700867387959219, 4.700867387959219, 4.700867387959219],
+... [4.700867387959219, 4.700867387959219, 4.700867387959219]])
+```
+
+And compute the square distance between all combinations of rows `x`; the result should simply be a shape-(2, 2) array of zeros.
+
+```python
+# The square-distances should be exactly zero, but they
+# will instead be (*very* small) negative numbers!
+>>> sqr_dists = -2 * np.matmul(x, x.T) # `x_x_prod`
+>>> sqr_dists += np.sum(x**2, axis=1)[:, np.newaxis]
+>>> sqr_dists += np.sum(x**2, axis=1)
+>>> sqr_dists
+array([[-2.842170943040401e-14, -2.842170943040401e-14],
+ [-2.842170943040401e-14, -2.842170943040401e-14]])
+```
- Parameters
- ----------
- x : numpy.ndarray, shape=(M, D)
- y : numpy.ndarray, shape=(N, D)
+These values are *very* close to being zero, so it is not as if the magnitude of our result is wildly off, but the critical issue here is that the quirks of floating-point arithmetic produced (very small) negative numbers.
+We are going to be in for a rude awakening when we take the square-root of these values to get our final distances:
- Returns
- -------
- numpy.ndarray, shape=(M, N)
- The Euclidean distance between each pair of
- rows between `x` and `y`."""
- dists = -2 * np.matmul(x, y.T)
- dists += np.sum(x**2, axis=1)[:, np.newaxis]
- dists += np.sum(y**2, axis=1)
- return np.sqrt(dists)
+```python
+# Taking the square-root of negative floats will produce NaNs
+>>> np.sqrt(sqr_dists)
+array([[nan, nan],
+ [nan, nan]])
```
+`nan` stands for "Not a Number", which is to say that the square-root of a negative number cannot produce a real-valued float (the result will be an [imaginary number](https://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Basic_Objects.html#Complex-Numbers), but NumPy will not swap out real numbers for imaginary ones unless we explicitly permit it to).
+
+The solution to this problem comes from the realization that this occurrence is truly an edge-case, and that it will only manifest when the result *ought* to have been zero and indeed is very close to zero.
+Thus we can [clip](https://numpy.org/doc/stable/reference/generated/numpy.clip.html) `sqr_dists` such that any value that falls below zero gets set to zero and all other values will be left alone:
+```python
+# "Clipping" all negative entries so that they are set to 0
+>>> np.sqrt(np.clip(sqr_dists, a_min=0, a_max=None))
+array([[0., 0.],
+ [0., 0.]])
+```
+
+
+
+#### The Final Answer, At Last!
+
+In total, we have successfully used vectorization to compute all the pairs of distances, while only requiring an array of shape-$(M, N)$ to do so! This is the memory-efficient, vectorized form – the stuff that dreams are made of.
+Let's write the function that performs this computation in full.
+
+```python
+def pairwise_dists(x, y):
+ """ Computing pairwise distances using memory-efficient
+ vectorization.
+
+ Parameters
+ ----------
+ x : numpy.ndarray, shape=(M, D)
+ y : numpy.ndarray, shape=(N, D)
+
+ Returns
+ -------
+ numpy.ndarray, shape=(M, N)
+ The Euclidean distance between each pair of
+ rows between `x` and `y`."""
+ sqr_dists = -2 * np.matmul(x, y.T)
+ sqr_dists += np.sum(x**2, axis=1)[:, np.newaxis]
+ sqr_dists += np.sum(y**2, axis=1)
+ return np.sqrt(np.clip(sqr_dists, a_min=0, a_max=None))
+```
+
+```python
+# produces a shape-(5, 6) result
+>>> pairwise_dists(x, y)
+array([[ 3.678 , 8.4524, 10.3057, 7.3711, 6.2152, 5.5548],
+ [10.1457, 5.8793, 2.9274, 4.1114, 3.9098, 5.2259],
+ [ 7.3219, 0.8439, 6.8734, 4.5687, 7.3283, 4.8216],
+ [10.339 , 7.032 , 7.4745, 7.0633, 3.5999, 4.0107],
+ [ 8.2878, 3.5468, 6.336 , 4.9014, 4.1858, 2.0257]])
+```
@@ -663,19 +753,32 @@ The specific form of an equation can have a major impact on the memory-footprint
**Reading Comprehension: Checking the equivalence of the three pairwise distance functions**
-Use the function [numpy.allclose](https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html) to verify that the three methods for computing the pairwise distances produce the same numerical results.
+Use the function [numpy.allclose](https://numpy.org/doc/stable/reference/generated/numpy.allclose.html) to verify that the three methods for computing the pairwise distances produce the same numerical results.
## Links to Official Documentation
-- [Basics of broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html#broadcasting)
-- [Broadcasting routines](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html#changing-number-of-dimensions)
+- [Basics of broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.html#broadcasting)
+- [Broadcasting routines](https://numpy.org/doc/stable/reference/routines.array-manipulation.html#changing-number-of-dimensions)
## Reading Comprehension Solutions
+
+**Broadcast Compatibility: Solution**
+
+1\. Incompatible
+
+2\. `3 x 4`
+
+3\. `8 x 3 x 1`
+
+4\. `9 x 2 x 5`
+
+5\. Incompatible
+
**Basic Broadcasting: Solution**
@@ -696,18 +799,6 @@ Generating the random array of 10,000, 2D points, and their "center-of-mass".
```
-**Broadcast Compatibility: Solution**
-
-1\. Incompatible
-
-2\. `3 x 4`
-
-3\. `8 x 3 x 1`
-
-4\. `9 x 2 x 5`
-
-5\. Incompatible
-
**Basic Broadcasting II: Solution**
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.md.txt b/docs_backup/_sources/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.md.txt
index 2fac459f..2ef6f05d 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.md.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -108,12 +108,12 @@ array([[ 0., 0., 0., 0.],
array([ 1., 1., 1., 1.])
```
-NumPy provides additional functions for creating constant-valued arrays. Please refer to [the official documentation](https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#ones-and-zeros) for a complete listing.
+NumPy provides additional functions for creating constant-valued arrays. Please refer to [the official documentation](https://numpy.org/doc/stable/reference/routines.array-creation.html#ones-and-zeros) for a complete listing.
## Creating Sequential Arrays: `arange` and `linspace`
-The [arange](https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html#numpy.arange) function allows you to initialize a sequence of integers based on a starting point (inclusive), stopping point (exclusive), and step size. This is very similar to the `range` function; however `arange` immediately creates this sequence as an array, whereas `range` produces a generator.
+The [arange](https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange) function allows you to initialize a sequence of integers based on a starting point (inclusive), stopping point (exclusive), and step size. This is very similar to the `range` function; however `arange` immediately creates this sequence as an array, whereas `range` produces a generator.
```python
>>> np.arange(0, 10, 1) # start (included): 0, stop (excluded): 10, step:1
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
@@ -127,7 +127,7 @@ array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
array([-5, -3, -1, 1, 3, 5])
```
-The [linspace](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) function allows you to generate $N$ *evenly-spaced* points within a user-specified interval $[i, j]$ ($i$ and $j$ are included in the interval). This is often used to generate a domain of values on which to evaluate a mathematical function (e.g. if you want to the sine function from $-\pi$ to $\pi$ on a finely-divided grid).
+The [linspace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html) function allows you to generate $N$ *evenly-spaced* points within a user-specified interval $[i, j]$ ($i$ and $j$ are included in the interval). This is often used to generate a domain of values on which to evaluate a mathematical function (e.g. if you want to the sine function from $-\pi$ to $\pi$ on a finely-divided grid).
```python
# generate five evenly-spaced points on the interval [-1, 1]
@@ -143,7 +143,7 @@ array([ 3., 4.])
array([-3.14159265, ..., 3.14159265])
```
-Numpy has other functions for creating sequential arrays, such as producing an array spaced evenly on a log-scaled interval. See the [official documentation](https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#numerical-ranges) for a complete listing.
+Numpy has other functions for creating sequential arrays, such as producing an array spaced evenly on a log-scaled interval. See the [official documentation](https://numpy.org/doc/stable/reference/routines.array-creation.html#numerical-ranges) for a complete listing.
@@ -162,7 +162,7 @@ array([[ 0.09542611, 0.13183498, 0.39836068],
>>> np.random.randn(5)
array([-1.11262121, -0.35392007, 0.4245215 , -0.81995588, 0.65412323])
```
-There are [many more functions](https://docs.scipy.org/doc/numpy/reference/routines.random.html#distributions) to read about that allow you to draw from a wide variety of statistical distributions. This only scratches the surface of random number generation in NumPy.
+There are [many more functions](https://numpy.org/doc/stable/reference/routines.random.html#distributions) to read about that allow you to draw from a wide variety of statistical distributions. This only scratches the surface of random number generation in NumPy.
@@ -191,7 +191,7 @@ array([[ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
[ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]], dtype=complex64)
```
-Refer to [the official NumPy documentation](https://docs.scipy.org/doc/numpy/user/basics.types.html#array-types-and-conversions-between-types) for the complete list of available array datatypes.
+Refer to [the official NumPy documentation](https://numpy.org/doc/stable/user/basics.types.html#array-types-and-conversions-between-types) for the complete list of available array datatypes.
@@ -212,14 +212,14 @@ array([[ 1, 2, 3],
array([ 1, 2, 3, -1, -2, -3])
```
-A complete listing of functions for joining arrays can be [found in the official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html#joining-arrays). There are also corresponding functions for splitting an array into independent arrays.
+A complete listing of functions for joining arrays can be [found in the official NumPy documentation](https://numpy.org/doc/stable/reference/routines.array-manipulation.html#joining-arrays). There are also corresponding functions for splitting an array into independent arrays.
## Links to Official Documentation
-- [Constant arrays](https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#ones-and-zeros)
-- [numpy.array](https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html#numpy-array)
-- [Sequential arrays](https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#numerical-ranges)
-- [Random distributions](https://docs.scipy.org/doc/numpy/reference/routines.random.html#distributions)
-- [Array types](https://docs.scipy.org/doc/numpy/user/basics.types.html#array-types-and-conversions-between-types)
-- [Joining arrays](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html#joining-arrays)
+- [Constant arrays](https://numpy.org/doc/stable/reference/routines.array-creation.html#ones-and-zeros)
+- [numpy.array](https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy-array)
+- [Sequential arrays](https://numpy.org/doc/stable/reference/routines.array-creation.html#numerical-ranges)
+- [Random distributions](https://numpy.org/doc/stable/reference/routines.random.html#distributions)
+- [Array types](https://numpy.org/doc/stable/user/basics.types.html#array-types-and-conversions-between-types)
+- [Joining arrays](https://numpy.org/doc/stable/reference/routines.array-manipulation.html#joining-arrays)
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/IntroducingTheNDarray.md.txt b/docs_backup/_sources/Module3_IntroducingNumpy/IntroducingTheNDarray.md.txt
index 89840a5d..b91339cb 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/IntroducingTheNDarray.md.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/IntroducingTheNDarray.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -106,6 +106,6 @@ By the end of this module, these code snippets should make good sense, and NumPy
## Links to Official Documentation
-- [The N-dimensional array](https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html)
-- [NumPy Basics](https://docs.scipy.org/doc/numpy/user/basics.html#numpy-basics)
-- [NumPy reference](https://docs.scipy.org/doc/numpy/reference/index.html)
+- [The N-dimensional array](https://numpy.org/doc/stable/reference/arrays.ndarray.html)
+- [NumPy Basics](https://numpy.org/doc/stable/user/basics.html#numpy-basics)
+- [NumPy reference](https://numpy.org/doc/stable/reference/index.html)
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/Problems/Approximating_pi.ipynb.txt b/docs_backup/_sources/Module3_IntroducingNumpy/Problems/Approximating_pi.ipynb.txt
index ad788160..c93cbea6 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/Problems/Approximating_pi.ipynb.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/Problems/Approximating_pi.ipynb.txt
@@ -30,7 +30,7 @@
"\n",
"## Problem 1\n",
"\n",
- "Write code that simulates the dart throwing and tallying process, and keep a running estimate of $\\pi$ as \"darts are being thrown\". For simplicity, you can assume that the board is centered at $(0, 0)$, and that $r = 1$ (the radius of the circle). Use `numpy.random.rand` ([link to docs](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.rand.html)) to randomly generate the positions on the board where the darts land. Do this for $N = 10,000$ darts in total. For each dart thrown determine whether or not it landed within the circle, and update your estimate of $\\pi$ according to the formula: $N_{circle} / N_{total} \\approx \\pi / 4$\n",
+ "Write code that simulates the dart throwing and tallying process, and keep a running estimate of $\\pi$ as \"darts are being thrown\". For simplicity, you can assume that the board is centered at $(0, 0)$, and that $r = 1$ (the radius of the circle). Use `numpy.random.rand` ([link to docs](https://numpy.org/doc/stable/reference/generated/numpy.random.rand.html)) to randomly generate the positions on the board where the darts land. Do this for $N = 10,000$ darts in total. For each dart thrown determine whether or not it landed within the circle, and update your estimate of $\\pi$ according to the formula: $N_{circle} / N_{total} \\approx \\pi / 4$\n",
"\n",
"Keep in mind that each dart can land in $(x \\in [-1, 1], y \\in [-1, 1])$ and that a dart that lands at $(x, y)$ falls within the circle if \n",
"\n",
@@ -58,7 +58,7 @@
"source": [
"## Tips\n",
"\n",
- "It is helpful to know about NumPy's [cumulative-sum function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.cumsum.html), `numpy.cumsum`. This is useful for keeping a running tally - i.e. keeping the history of the number of darts that have fallen within the circle, and not just the current count."
+ "It is helpful to know about NumPy's [cumulative-sum function](https://numpy.org/doc/stable/reference/generated/numpy.cumsum.html), `numpy.cumsum`. This is useful for keeping a running tally - i.e. keeping the history of the number of darts that have fallen within the circle, and not just the current count."
]
},
{
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/Problems/ComputeAccuracy.md.txt b/docs_backup/_sources/Module3_IntroducingNumpy/Problems/ComputeAccuracy.md.txt
index b2b59efa..451d91c4 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/Problems/ComputeAccuracy.md.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/Problems/ComputeAccuracy.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -60,7 +60,7 @@ Our model classified three out of five images correctly; thus, our accuracy fun
To generalize this problem, assume that your classifier is dealing with $K$ classes (instead of $4$). Complete the following function.
-**Tip:** You will find it useful to leverage [numpy's argmax function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html#numpy.argmax)`f
+**Tip:** You will find it useful to leverage [numpy's argmax function](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html#numpy.argmax)`f
```python
def classification_accuracy(classification_scores, true_labels):
diff --git a/docs_backup/_sources/Module3_IntroducingNumpy/VectorizedOperations.md.txt b/docs_backup/_sources/Module3_IntroducingNumpy/VectorizedOperations.md.txt
index 1c893ee6..f6edddb7 100644
--- a/docs_backup/_sources/Module3_IntroducingNumpy/VectorizedOperations.md.txt
+++ b/docs_backup/_sources/Module3_IntroducingNumpy/VectorizedOperations.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -182,7 +182,7 @@ We will now take some time to survey the various types of vectorized mathematica
- binary functions: $f(x,y)$
- functions that operate on sequences of numbers: $f(\{x_i\}_{i=0}^{n-1})$
-These represent a substantial portion of the essential mathematical tools in the NumPy library. An exhaustive list of NumPy's mathematical functions is available in the [official documentation](https://docs.scipy.org/doc/numpy/reference/ufuncs.html#math-operations).
+These represent a substantial portion of the essential mathematical tools in the NumPy library. An exhaustive list of NumPy's mathematical functions is available in the [official documentation](https://numpy.org/doc/stable/reference/ufuncs.html#math-operations).
### Unary Functions
A unary function is a mathematical function that only accepts one operand (i.e. argument): $f(x)$. NumPy supplies many familiar unary functions:
@@ -605,7 +605,7 @@ Now, compute the following:
## Logical Operations
-NumPy provides [a suite of logical operations](https://docs.scipy.org/doc/numpy/reference/routines.logic.html) that can operate on arrays. Many of these map logical operations over array entries in the same fashion as NumPy's mathematical functions. These functions return either a single boolean object, or a boolean-type array.
+NumPy provides [a suite of logical operations](https://numpy.org/doc/stable/reference/routines.logic.html) that can operate on arrays. Many of these map logical operations over array entries in the same fashion as NumPy's mathematical functions. These functions return either a single boolean object, or a boolean-type array.
```python
# check which entries of `x` are less than 6
@@ -641,7 +641,7 @@ True
## Linear Algebra
-Lastly, we note that NumPy provides a suite of functions that can perform optimized computations and routines relevant to linear algebra. Included here are functions for performing matrix products and tensor products, solving eigenvalue problems, inverting matrices, and computing vector normalizations. Please refer to the [official NumPy documentation](https://docs.scipy.org/doc/numpy/reference/routines.linalg.html) for a full listing of these functions.
+Lastly, we note that NumPy provides a suite of functions that can perform optimized computations and routines relevant to linear algebra. Included here are functions for performing matrix products and tensor products, solving eigenvalue problems, inverting matrices, and computing vector normalizations. Please refer to the [official NumPy documentation](https://numpy.org/doc/stable/reference/routines.linalg.html) for a full listing of these functions.
@@ -652,9 +652,9 @@ NumPy provides users with a wide variety of functions capable of performing oper
## Links to Official Documentation
-- [Math functions](https://docs.scipy.org/doc/numpy/reference/ufuncs.html#math-operations)
-- [Logic functions](https://docs.scipy.org/doc/numpy/reference/routines.logic.html)
-- [Linear algebra functions](https://docs.scipy.org/doc/numpy/reference/routines.linalg.html)
+- [Math functions](https://numpy.org/doc/stable/reference/ufuncs.html#math-operations)
+- [Logic functions](https://numpy.org/doc/stable/reference/routines.logic.html)
+- [Linear algebra functions](https://numpy.org/doc/stable/reference/routines.linalg.html)
## Reading Comprehension Solutions
@@ -686,7 +686,7 @@ Add the four quadrants of `x`, producing a shape-(2, 2) output.
... [ 8, 9, 10, 11],
... [12, 13, 14, 15]])
- # top-left top-right bottom-left bottom-right
+# top-left top-right bottom-left bottom-right
>>> x[:2, :2] + x[:2, -2:] + x[-2:, :2] + x[-2:, -2:]
array([[20, 24],
[36, 40]])
diff --git a/docs_backup/_sources/Module4_OOP/Applications_of_OOP.md.txt b/docs_backup/_sources/Module4_OOP/Applications_of_OOP.md.txt
index fe93d859..00399112 100644
--- a/docs_backup/_sources/Module4_OOP/Applications_of_OOP.md.txt
+++ b/docs_backup/_sources/Module4_OOP/Applications_of_OOP.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -73,7 +73,7 @@ class ShoppingList:
items = [items]
# only mark items as purchased that are on our list to begin with
self._purchased.update(set(items) & self._needed)
- # remove all purchased items from out unpurchased set
+ # remove all purchased items from our unpurchased set
self._needed.difference_update(self._purchased)
def list_purchased_items(self):
diff --git a/docs_backup/_sources/Module4_OOP/Brief_Review.md.txt b/docs_backup/_sources/Module4_OOP/Brief_Review.md.txt
index 29d35c97..96fd1a64 100644
--- a/docs_backup/_sources/Module4_OOP/Brief_Review.md.txt
+++ b/docs_backup/_sources/Module4_OOP/Brief_Review.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/docs_backup/_sources/Module4_OOP/ClassDefinition.md.txt b/docs_backup/_sources/Module4_OOP/ClassDefinition.md.txt
index 6419727b..ffcd8683 100644
--- a/docs_backup/_sources/Module4_OOP/ClassDefinition.md.txt
+++ b/docs_backup/_sources/Module4_OOP/ClassDefinition.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/docs_backup/_sources/Module4_OOP/ClassInstances.md.txt b/docs_backup/_sources/Module4_OOP/ClassInstances.md.txt
index 7d5cc65c..30da5961 100644
--- a/docs_backup/_sources/Module4_OOP/ClassInstances.md.txt
+++ b/docs_backup/_sources/Module4_OOP/ClassInstances.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/docs_backup/_sources/Module4_OOP/Inheritance.md.txt b/docs_backup/_sources/Module4_OOP/Inheritance.md.txt
index 95114973..1e47a4e1 100644
--- a/docs_backup/_sources/Module4_OOP/Inheritance.md.txt
+++ b/docs_backup/_sources/Module4_OOP/Inheritance.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/docs_backup/_sources/Module4_OOP/Introduction_to_OOP.md.txt b/docs_backup/_sources/Module4_OOP/Introduction_to_OOP.md.txt
index e061c9c9..25c0ebe9 100644
--- a/docs_backup/_sources/Module4_OOP/Introduction_to_OOP.md.txt
+++ b/docs_backup/_sources/Module4_OOP/Introduction_to_OOP.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/docs_backup/_sources/Module4_OOP/Methods.md.txt b/docs_backup/_sources/Module4_OOP/Methods.md.txt
index 92d9723a..7bb440e8 100644
--- a/docs_backup/_sources/Module4_OOP/Methods.md.txt
+++ b/docs_backup/_sources/Module4_OOP/Methods.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -200,7 +200,7 @@ __main__.Dummy
`dict.fromkeys` is an example of a class method that takes in an iterable, and returns a dictionary whose keys are the elements of that iterable, and whose values all default to `None`.
```python
->>> dict.fromkeys("abcd")
+>>> dict.fromkeys("abcd", 2.3)
{'a': 2.3, 'b': 2.3, 'c': 2.3, 'd': 2.3}
```
diff --git a/docs_backup/_sources/Module4_OOP/ObjectOrientedProgramming.md.txt b/docs_backup/_sources/Module4_OOP/ObjectOrientedProgramming.md.txt
index 8c5802a6..f4de8d27 100644
--- a/docs_backup/_sources/Module4_OOP/ObjectOrientedProgramming.md.txt
+++ b/docs_backup/_sources/Module4_OOP/ObjectOrientedProgramming.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/docs_backup/_sources/Module4_OOP/Special_Methods.md.txt b/docs_backup/_sources/Module4_OOP/Special_Methods.md.txt
index 45a91811..a9477dcf 100644
--- a/docs_backup/_sources/Module4_OOP/Special_Methods.md.txt
+++ b/docs_backup/_sources/Module4_OOP/Special_Methods.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/docs_backup/_sources/Module5_OddsAndEnds/Modules_and_Packages.md.txt b/docs_backup/_sources/Module5_OddsAndEnds/Modules_and_Packages.md.txt
index 3324669e..30b52e27 100644
--- a/docs_backup/_sources/Module5_OddsAndEnds/Modules_and_Packages.md.txt
+++ b/docs_backup/_sources/Module5_OddsAndEnds/Modules_and_Packages.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
diff --git a/docs_backup/_sources/Module5_OddsAndEnds/WorkingWithFiles.md.txt b/docs_backup/_sources/Module5_OddsAndEnds/WorkingWithFiles.md.txt
index 1c76cbfc..f33c737e 100644
--- a/docs_backup/_sources/Module5_OddsAndEnds/WorkingWithFiles.md.txt
+++ b/docs_backup/_sources/Module5_OddsAndEnds/WorkingWithFiles.md.txt
@@ -5,7 +5,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
@@ -14,7 +14,7 @@ jupyter:
.. meta::
- :description: Topic: Working with aths and files, Difficulty: Medium, Category: Section
+ :description: Topic: Working with paths and files, Difficulty: Medium, Category: Section
:keywords: open file, read file, pathlib, join directory, context manager, close file, rb, binary file, utf-8, encoding, pickle, numpy, load, archive, npy, npz, pkl, glob, read lines, write, save
@@ -254,7 +254,7 @@ with open("a_poem.txt", mode="r") as my_open_file:
There are many cases in which we may want to construct a list of files to iterate over. For example, if we have several data files, it would be useful to create a file list which we can iterate through and process in sequence. One way to do this would be to manually construct such a list of files:
-``` python
+```python
my_files = ['data/file1.txt', 'data/file2.txt', 'data/file3.txt', 'data/file4.txt']
```
@@ -283,6 +283,8 @@ data/*.txt
The `pathlib` module provides convenient functionality for globbing files. Once we have a `Path` object, we can simply call `glob()` on it and pass in a glob string. This will return a [generator](http://www.pythonlikeyoumeanit.com/Module2_EssentialsOfPython/Generators_and_Comprehensions.html#Introducing-Generators) that will yield each of the globbed files.
+
+
``` python
# glob all of the text files in the present directory
# that start with 'test' and end with '.txt'
@@ -303,6 +305,8 @@ The `pathlib` module provides convenient functionality for globbing files. Once
... pass
```
+
+
For more details on globbing, see [the documentation](https://docs.python.org/3/library/pathlib.html#pathlib.Path.glob).
diff --git a/docs_backup/_sources/Module5_OddsAndEnds/Writing_Good_Code.md.txt b/docs_backup/_sources/Module5_OddsAndEnds/Writing_Good_Code.md.txt
index 1fb91532..3cebc424 100644
--- a/docs_backup/_sources/Module5_OddsAndEnds/Writing_Good_Code.md.txt
+++ b/docs_backup/_sources/Module5_OddsAndEnds/Writing_Good_Code.md.txt
@@ -5,14 +5,13 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
- jupytext_version: 1.3.0rc1
+ jupytext_version: 1.9.1
kernelspec:
display_name: Python 3
language: python
name: python3
---
-
.. meta::
:description: Topic: Writing good code, Difficulty: Easy, Category: Section
@@ -440,7 +439,36 @@ This saves us the trouble of having to run our code, hit an error, read through
It does not take long to experience the benefits of type-hinting through your IDE. This both accelerates your coding by informing you of the object types that you are working with on the fly, and helps to expose oversights in your code as soon as they are made.
-Finally, it is also worthwhile to highlight two projects, [mypy](http://mypy-lang.org/) and [pyright](https://github.com/microsoft/pyright), which are used to perform static type-checking on your code based on your type-hints. That is, mypy and pyright will both automatically traverse your code and find potential bugs by identifying type conflicts in your code (e.g. trying to capitalize an integer) by checking their annotated and inferred types. These tools are especially useful for large-scale code bases. Companies like Dropbox and Microsoft make keen use of static type-checking to identify inconsistencies in their code without having to hit runtime errors. Keep mypy, pyright, and other type-checking utilities in mind as you mature as a Python developer and find yourself working on projects of growing complexity. If you are using [VSCode as your IDE](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.html), you can install the [pyright vscode extension](https://marketplace.visualstudio.com/items?itemName=ms-pyright.pyright) to leverage type checking within your IDE.
+Finally, it is also worthwhile to highlight two projects, [mypy](http://mypy-lang.org/) and [pyright](https://github.com/microsoft/pyright), which are used to perform static type-checking on your code based on your type-hints.
+That is, mypy and pyright will both automatically traverse your code and find potential bugs by identifying type conflicts in your code (e.g. trying to capitalize an integer) by checking their annotated and inferred types.
+These tools are especially useful for large-scale code bases. Companies like Dropbox and Microsoft make keen use of static type-checking to identify inconsistencies in their code without having to hit runtime errors. Keep mypy, pyright, and other type-checking utilities in mind as you mature as a Python developer and find yourself working on projects of growing complexity.
+If you are using [VSCode as your IDE](https://www.pythonlikeyoumeanit.com/Module1_GettingStartedWithPython/Getting_Started_With_IDEs_and_Notebooks.html), you can install the [PyLance VSCode extension](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance) to leverage pyright's type checking within your IDE.
+
+
+
+
+
+**Reading Comprehension: Type-Assisted Code Completion**
+
+The `jedi` package, which is installed by default alongside Jupyter and IPython, enables type-informed code completion.
+This means that we can benefit from type-hints even when we are working in a Jupyter notebook!
+
+In a Jupyter notebook, write the following type-annotated signature for a function: `def f(x: str):`.
+Now in the body of the function (i.e. on the next line, and indented inwards), try typing `x.` and then hit ``.
+
+```python
+
+def f(x: str):
+ x.#hit here
+
+```
+
+You should see a list of methods appear for `x`.
+What are the first three methods that appear?
+Do these make sense based on the annotation associated with `x`?
+Now change the annotation for `x` to be `list` and trigger the auto-completion in the editor again; do you see an updated list of methods?
+
+
@@ -959,8 +987,17 @@ Take some time to review the NumPy and Google docstring specifications, pick one
## Reading Comprehension Solutions
+
+**Type-Assisted Code Completion: Solution**
+
+The tab-completion reveals methods like "capitalize", "casefold", and "center" – all of which are string methods.
+These are made available despite the fact that `x` did not yet refer to a specific string.
+Instead, the type-hint was able to tell the editor that `x` _will_ refer to a string, and this was enough to inform these type completions.
+
+Revising the type-annotation for `x` to be `list` affects the auto-completion options accordingly: the editor will now suggest list methods like "append", "clear", and "copy".
+
-**Type Hinting: Solutions**
+**Type Hinting: Solution**
The following is a well-annotated version of `get_first_and_last`:
diff --git a/docs_backup/_sources/changes.rst.txt b/docs_backup/_sources/changes.rst.txt
index c9d05a55..3c3b291a 100644
--- a/docs_backup/_sources/changes.rst.txt
+++ b/docs_backup/_sources/changes.rst.txt
@@ -3,8 +3,71 @@ Changelog
=========
This is a record of all past PLYMI releases and what went into them,
-in reverse chronological order. All previous releases should still be available
-on pip.
+in reverse chronological order.
+
+----------
+2021-01-31
+----------
+
+Upgrades the tools used to build PLYMI:
+
+- sphinx 3.4.3
+- nbsphinx 0.8.1
+- jupytext 1.9.1
+- sphinx-rtd-theme 0.5.1
+
+Adds a reading comprehension problem in `the section on type-hinting `_
+to show that ``jedi`` provides annotation-informed autocompletion abilities in notebooks.
+
+
+----------
+2021-01-30
+----------
+
+Updated the discussion of `computing pairwise differences `_
+to account for potential floating-point edge cases that can produce "NaNs" as a result.
+
+There is currently an incompatibility between ``jedi 0.18.0`` and IPython, which breaks autocompletion. See `here `_ for more details.
+Added temporary callout boxes to the `informal introduction to Python `_ and to
+the `introduction to Jupyter notebooks `_, which instruct readers to remedy this by downgrading jedi.
+
+Fixed a missing plot in the `introduction to Jupyter `_ section.
+
+Reformatted the `section on IDEs `_ and added a description of PyLance.
+
+Add link to `PLYMI's discussion board `_.
+
+----------
+2021-01-24
+----------
+
+Added a brief `discussion of Python versions `_. Thanks `@samaocarpenter `_!
+
+Fixed typos `#160 `_ `#158 `_
+`#155 `_
+
+
+----------
+2020-06-17
+----------
+
+Various typo fixes. Thanks to Darshan and David!
+
+
+----------
+2020-05-10
+----------
+
+Various typo fixes. Thanks to Patrick O'Shea and David Mascharka!
+
+
+----------
+2020-04-11
+----------
+
+Updated some of the "backend" technologies behind PLYMI: upgraded sphinx and nbsphinx.
+
+Fixed a broken subsection header: "Converting a Boolean Index-Array to Integer Index-Arrays: numpy.where" will now appear in the navigation bar under Module 3, Advanced Indexing.
----------
diff --git a/docs_backup/_sources/index.rst.txt b/docs_backup/_sources/index.rst.txt
index c37f76bf..d2e400d2 100644
--- a/docs_backup/_sources/index.rst.txt
+++ b/docs_backup/_sources/index.rst.txt
@@ -1,3 +1,7 @@
+.. meta::
+ :description: Topic: Top page of Python Like You Mean It, Difficulty: Easy, Category: Introduction
+ :keywords: PLYMI, Python Like You Mean It, tutorial, python for machine learning, python for deep learning, python for data science, numpy, python from scratch, basic python tutorial, teaching python
+
=======================
Python Like You Mean It
=======================
@@ -12,7 +16,8 @@ What this is
------------
Python Like You Mean It (PLYMI) is a free resource for learning the basics of Python & NumPy, and moreover, becoming a competent Python user. The features of the Python language that are emphasized here were chosen to help those who are particularly interested in STEM applications (data analysis, machine learning, numerical work, etc.).
-I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.8, as of writing this).
+I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.9, as of writing this).
+This material has proven to be fruitful for high school and college teachers alike to teach Python as part of their STEM curriculum.
What this isn't
@@ -32,10 +37,18 @@ Python shouldn't be *too* easy
Python is a relatively easy language to pick up, and it doesn't require much rigor to make code work. Unfortunately, this means that there are many Python users out there who know enough to just get by, but lack a sound understanding of the language. You don't want to get caught in the "know enough Python to be dangerous" zone; therein lies complacency, stagnation, and the genesis of a lot of bad code. You've got to Python like you mean it!
+Join Our Discussion Board
+-------------------------
+`Join the PLYMI community `_ to ask questions, recommend new content, or to just say hello!
+
+(A note to `BWSI students `_: please stick to your class' piazza board for posting questions)
+
+
PLYMI is on GitHub
------------------
If you have questions about the reading, think that you have spotted some mistakes, or would like to contribute to PLYMI, please visit `our GitHub page `_. You will need to create a GitHub account in order to post an issue; this process is free and easy. We would love for you to join us to discuss the website!
+
Contributors
------------
The following people made significant contributions to PLYMI, adding problems with worked solutions and valuable feedback on the text:
@@ -44,12 +57,13 @@ The following people made significant contributions to PLYMI, adding problems wi
- AJ Federici (GitHub:`@Federici `_)
- Annabelle Lew (GitHub:`@AnnabelleLew `_)
- Petar Griggs (GitHub:`@petarmhg `_)
-- Sam Carpenter (GitHub:`@HardBoiled800 `_)
+- Sam Carpenter (GitHub:`@samaocarpenter `_)
+- Patrick O'Shea (GitHub:`@ArtichokeSap `_)
About Me
--------
-I started learning to use Python in graduate school for my physics research, and I was *so* bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I'd like to think that this resource would have been a huge help to me back then). I've been coding in Python for at least six years now, and I've taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
+I started learning to use Python in graduate school for my physics research, and I was *so* bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I'd like to think that this resource would have been a huge help to me back then). I've been coding in Python for at least eight years now, and I've taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
.. toctree::
diff --git a/docs_backup/_sources/intro.rst.txt b/docs_backup/_sources/intro.rst.txt
index a417399c..1d822383 100644
--- a/docs_backup/_sources/intro.rst.txt
+++ b/docs_backup/_sources/intro.rst.txt
@@ -1,3 +1,7 @@
+.. meta::
+ :description: Topic: Top page of Python Like You Mean It, Difficulty: Easy, Category: Introduction
+ :keywords: PLYMI, Python Like You Mean It, tutorial, python for machine learning, python for deep learning, python for data science, numpy, python from scratch, basic python tutorial, teaching python
+
=======================
Python Like You Mean It
=======================
@@ -12,7 +16,8 @@ What this is
------------
Python Like You Mean It (PLYMI) is a free resource for learning the basics of Python & NumPy, and moreover, becoming a competent Python user. The features of the Python language that are emphasized here were chosen to help those who are particularly interested in STEM applications (data analysis, machine learning, numerical work, etc.).
-I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.8, as of writing this).
+I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.9, as of writing this).
+This material has proven to be fruitful for high school and college teachers alike to teach Python as part of their STEM curriculum.
What this isn't
@@ -31,6 +36,12 @@ Python shouldn't be *too* easy
------------------------------
Python is a relatively easy language to pick up, and it doesn't require much rigor to make code work. Unfortunately, this means that there are many Python users out there who know enough to just get by, but lack a sound understanding of the language. You don't want to get caught in the "know enough Python to be dangerous" zone; therein lies complacency, stagnation, and the genesis of a lot of bad code. You've got to Python like you mean it!
+Join Our Discussion Board
+-------------------------
+`Join the PLYMI community `_ to ask questions, recommend new content, or to just say hello!
+
+(A note to `BWSI students `_: please stick to your class' piazza board for posting questions)
+
PLYMI is on GitHub
------------------
@@ -44,9 +55,10 @@ The following people made significant contributions to PLYMI, adding problems wi
- AJ Federici (GitHub:`@Federici `_)
- Annabelle Lew (GitHub:`@AnnabelleLew `_)
- Petar Griggs (GitHub:`@petarmhg `_)
-- Sam Carpenter (GitHub:`@HardBoiled800 `_)
+- Sam Carpenter (GitHub:`@samaocarpenter `_)
+- Patrick O'Shea (GitHub:`@ArtichokeSap `_)
About Me
--------
-I started learning to use Python in graduate school for my physics research, and I was *so* bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I'd like to think that this resource would have been a huge help to me back then). I've been coding in Python for at least six years now, and I've taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
+I started learning to use Python in graduate school for my physics research, and I was *so* bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I'd like to think that this resource would have been a huge help to me back then). I've been coding in Python for at least eight years now, and I've taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
diff --git a/docs_backup/_sources/module_4.rst.txt b/docs_backup/_sources/module_4.rst.txt
index 021a2c97..0251813f 100644
--- a/docs_backup/_sources/module_4.rst.txt
+++ b/docs_backup/_sources/module_4.rst.txt
@@ -2,7 +2,7 @@ Module 4: Object Oriented Programming
=====================================
In this module, we will come to see that Python is an object-oriented language. That is, the language is all about defining different types of objects that encapsulate data, and that possess functions that permit users to access and manipulate this data. Our tour through `the essentials of Python `_ and `the essentials of NumPy `_ brought us into contact with various types of objects, such as the :code:`int`, :code:`str`, :code:`list`, :code:`tuple`, :code:`dict`, and the :code:`numpy.ndarray`, to name a few. We have seen that these different types of objects have starkly different functionality from one another. We will study the syntax and constructs for creating and interacting with objects in Python. Ultimately, will find our skill sets as Python-users much fuller and more sophisticated having understood object-oriented programming in Python.
-We being this module by establishing some key terminology for discussing object-oriented programming, drawing attention to the important fact that the terms 'class' and 'type' mean the same thing in modern Python. Next, we study the syntax for defining a class, and take time to distinguish between the resulting class object and the subsequent class instances that can then be created. Having defined our first custom class of object, we then study the syntax for defining class-methods, which permits us add customized functionality to our class. Further, we will introduce ourselves to Python's reserved special methods, which empower us to fully interface our class with the Python's protocols for behaving like a sequence, an iterable, a function, etc. We conclude this module with a brief discussion of the concept of inheritance, which is a mechanism by which a new class can inherit attributes from an existing class.
+We begin this module by establishing some key terminology for discussing object-oriented programming, drawing attention to the important fact that the terms 'class' and 'type' mean the same thing in modern Python. Next, we study the syntax for defining a class, and take time to distinguish between the resulting class object and the subsequent class instances that can then be created. Having defined our first custom class of object, we then study the syntax for defining class-methods, which permits us to add customized functionality to our class. Further, we will introduce ourselves to Python's reserved special methods, which empower us to fully interface our class with Python's protocols for behaving like a sequence, an iterable, a function, etc. We conclude this module with a brief discussion of the concept of inheritance, which is a mechanism by which a new class can inherit attributes from an existing class.
.. toctree::
:maxdepth: 2
diff --git a/docs_backup/_static/ajax-loader.gif b/docs_backup/_static/ajax-loader.gif
deleted file mode 100644
index 61faf8ca..00000000
Binary files a/docs_backup/_static/ajax-loader.gif and /dev/null differ
diff --git a/docs_backup/_static/basic.css b/docs_backup/_static/basic.css
index 0807176e..be19270e 100644
--- a/docs_backup/_static/basic.css
+++ b/docs_backup/_static/basic.css
@@ -4,7 +4,7 @@
*
* Sphinx stylesheet -- basic theme.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -15,6 +15,12 @@ div.clearer {
clear: both;
}
+div.section::after {
+ display: block;
+ content: '';
+ clear: left;
+}
+
/* -- relbar ---------------------------------------------------------------- */
div.related {
@@ -231,6 +237,16 @@ a.headerlink {
visibility: hidden;
}
+a.brackets:before,
+span.brackets > a:before{
+ content: "[";
+}
+
+a.brackets:after,
+span.brackets > a:after {
+ content: "]";
+}
+
h1:hover > a.headerlink,
h2:hover > a.headerlink,
h3:hover > a.headerlink,
@@ -279,6 +295,12 @@ img.align-center, .figure.align-center, object.align-center {
margin-right: auto;
}
+img.align-default, .figure.align-default {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+
.align-left {
text-align: left;
}
@@ -287,6 +309,10 @@ img.align-center, .figure.align-center, object.align-center {
text-align: center;
}
+.align-default {
+ text-align: center;
+}
+
.align-right {
text-align: right;
}
@@ -296,21 +322,27 @@ img.align-center, .figure.align-center, object.align-center {
div.sidebar {
margin: 0 0 0.5em 1em;
border: 1px solid #ddb;
- padding: 7px 7px 0 7px;
+ padding: 7px;
background-color: #ffe;
width: 40%;
float: right;
+ clear: right;
+ overflow-x: auto;
}
p.sidebar-title {
font-weight: bold;
}
+div.admonition, div.topic, blockquote {
+ clear: left;
+}
+
/* -- topics ---------------------------------------------------------------- */
div.topic {
border: 1px solid #ccc;
- padding: 7px 7px 0 7px;
+ padding: 7px;
margin: 10px 0 10px 0;
}
@@ -332,10 +364,6 @@ div.admonition dt {
font-weight: bold;
}
-div.admonition dl {
- margin-bottom: 0;
-}
-
p.admonition-title {
margin: 0px 10px 5px 0px;
font-weight: bold;
@@ -346,9 +374,28 @@ div.body p.centered {
margin-top: 25px;
}
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+ margin-bottom: 0;
+}
+
+div.sidebar::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+ display: block;
+ content: '';
+ clear: both;
+}
+
/* -- tables ---------------------------------------------------------------- */
table.docutils {
+ margin-top: 10px;
+ margin-bottom: 10px;
border: 0;
border-collapse: collapse;
}
@@ -358,6 +405,11 @@ table.align-center {
margin-right: auto;
}
+table.align-default {
+ margin-left: auto;
+ margin-right: auto;
+}
+
table caption span.caption-number {
font-style: italic;
}
@@ -391,6 +443,16 @@ table.citation td {
border-bottom: none;
}
+th > :first-child,
+td > :first-child {
+ margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+ margin-bottom: 0px;
+}
+
/* -- figures --------------------------------------------------------------- */
div.figure {
@@ -433,6 +495,10 @@ table.field-list td, table.field-list th {
/* -- hlist styles ---------------------------------------------------------- */
+table.hlist {
+ margin: 1em 0;
+}
+
table.hlist td {
vertical-align: top;
}
@@ -460,11 +526,78 @@ ol.upperroman {
list-style: upper-roman;
}
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+ margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+ margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+ margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+ margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+ margin-bottom: 0;
+}
+
+dl.footnote > dt,
+dl.citation > dt {
+ float: left;
+ margin-right: 0.5em;
+}
+
+dl.footnote > dd,
+dl.citation > dd {
+ margin-bottom: 0em;
+}
+
+dl.footnote > dd:after,
+dl.citation > dd:after {
+ content: "";
+ clear: both;
+}
+
+dl.field-list {
+ display: grid;
+ grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+ font-weight: bold;
+ word-break: break-word;
+ padding-left: 0.5em;
+ padding-right: 5px;
+}
+
+dl.field-list > dt:after {
+ content: ":";
+}
+
+dl.field-list > dd {
+ padding-left: 0.5em;
+ margin-top: 0em;
+ margin-left: 0em;
+ margin-bottom: 0em;
+}
+
dl {
margin-bottom: 15px;
}
-dd p {
+dd > :first-child {
margin-top: 0px;
}
@@ -478,6 +611,11 @@ dd {
margin-left: 30px;
}
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+ margin-bottom: 0;
+}
+
dt:target, span.highlighted {
background-color: #fbe54e;
}
@@ -537,6 +675,12 @@ dl.glossary dt {
font-style: oblique;
}
+.classifier:before {
+ font-style: normal;
+ margin: 0.5em;
+ content: ":";
+}
+
abbr, acronym {
border-bottom: dotted 1px;
cursor: help;
@@ -549,6 +693,10 @@ pre {
overflow-y: hidden; /* fixes display issues on Chrome browsers */
}
+pre, div[class*="highlight-"] {
+ clear: both;
+}
+
span.pre {
-moz-hyphens: none;
-ms-hyphens: none;
@@ -556,22 +704,57 @@ span.pre {
hyphens: none;
}
+div[class*="highlight-"] {
+ margin: 1em 0;
+}
+
td.linenos pre {
- padding: 5px 0px;
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
- margin-left: 0.5em;
+ display: block;
+}
+
+table.highlighttable tbody {
+ display: block;
+}
+
+table.highlighttable tr {
+ display: flex;
}
table.highlighttable td {
- padding: 0 0.5em 0 0.5em;
+ margin: 0;
+ padding: 0;
+}
+
+table.highlighttable td.linenos {
+ padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+ flex: 1;
+ overflow: hidden;
+}
+
+.highlight .hll {
+ display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+ margin: 0;
+}
+
+div.code-block-caption + div {
+ margin-top: 0;
}
div.code-block-caption {
+ margin-top: 1em;
padding: 2px 5px;
font-size: small;
}
@@ -580,8 +763,10 @@ div.code-block-caption code {
background-color: transparent;
}
-div.code-block-caption + div > div.highlight > pre {
- margin-top: 0;
+table.highlighttable td.linenos,
+span.linenos,
+div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
+ user-select: none;
}
div.code-block-caption span.caption-number {
@@ -593,11 +778,7 @@ div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
- padding: 1em 1em 0;
-}
-
-div.literal-block-wrapper div.highlight {
- margin: 0;
+ margin: 1em 0;
}
code.descname {
@@ -648,8 +829,7 @@ span.eqno {
}
span.eqno a.headerlink {
- position: relative;
- left: 0px;
+ position: absolute;
z-index: 1;
}
diff --git a/docs_backup/_static/comment-bright.png b/docs_backup/_static/comment-bright.png
deleted file mode 100644
index 15e27edb..00000000
Binary files a/docs_backup/_static/comment-bright.png and /dev/null differ
diff --git a/docs_backup/_static/comment-close.png b/docs_backup/_static/comment-close.png
deleted file mode 100644
index 4d91bcf5..00000000
Binary files a/docs_backup/_static/comment-close.png and /dev/null differ
diff --git a/docs_backup/_static/comment.png b/docs_backup/_static/comment.png
deleted file mode 100644
index dfbc0cbd..00000000
Binary files a/docs_backup/_static/comment.png and /dev/null differ
diff --git a/docs_backup/_static/css/badge_only.css b/docs_backup/_static/css/badge_only.css
index 3c33cef5..e380325b 100644
--- a/docs_backup/_static/css/badge_only.css
+++ b/docs_backup/_static/css/badge_only.css
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.woff b/docs_backup/_static/css/fonts/Roboto-Slab-Bold.woff
similarity index 100%
rename from docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.woff
rename to docs_backup/_static/css/fonts/Roboto-Slab-Bold.woff
diff --git a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.woff2 b/docs_backup/_static/css/fonts/Roboto-Slab-Bold.woff2
similarity index 100%
rename from docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.woff2
rename to docs_backup/_static/css/fonts/Roboto-Slab-Bold.woff2
diff --git a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff b/docs_backup/_static/css/fonts/Roboto-Slab-Regular.woff
similarity index 100%
rename from docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff
rename to docs_backup/_static/css/fonts/Roboto-Slab-Regular.woff
diff --git a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff2 b/docs_backup/_static/css/fonts/Roboto-Slab-Regular.woff2
similarity index 100%
rename from docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff2
rename to docs_backup/_static/css/fonts/Roboto-Slab-Regular.woff2
diff --git a/docs_backup/_static/fonts/fontawesome-webfont.eot b/docs_backup/_static/css/fonts/fontawesome-webfont.eot
similarity index 100%
rename from docs_backup/_static/fonts/fontawesome-webfont.eot
rename to docs_backup/_static/css/fonts/fontawesome-webfont.eot
diff --git a/docs_backup/_static/fonts/fontawesome-webfont.svg b/docs_backup/_static/css/fonts/fontawesome-webfont.svg
similarity index 100%
rename from docs_backup/_static/fonts/fontawesome-webfont.svg
rename to docs_backup/_static/css/fonts/fontawesome-webfont.svg
diff --git a/docs_backup/_static/fonts/fontawesome-webfont.ttf b/docs_backup/_static/css/fonts/fontawesome-webfont.ttf
similarity index 100%
rename from docs_backup/_static/fonts/fontawesome-webfont.ttf
rename to docs_backup/_static/css/fonts/fontawesome-webfont.ttf
diff --git a/docs_backup/_static/fonts/fontawesome-webfont.woff b/docs_backup/_static/css/fonts/fontawesome-webfont.woff
similarity index 100%
rename from docs_backup/_static/fonts/fontawesome-webfont.woff
rename to docs_backup/_static/css/fonts/fontawesome-webfont.woff
diff --git a/docs_backup/_static/fonts/fontawesome-webfont.woff2 b/docs_backup/_static/css/fonts/fontawesome-webfont.woff2
similarity index 100%
rename from docs_backup/_static/fonts/fontawesome-webfont.woff2
rename to docs_backup/_static/css/fonts/fontawesome-webfont.woff2
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similarity index 100%
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format("woff");font-weight:400;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold.woff2?cccb897485813c7c256901dbca54ecf2) format("woff2"),url(fonts/lato-bold.woff?d878b6c29b10beca227e9eef4246111b) format("woff");font-weight:700;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold-italic.woff2?0b6bb6725576b072c5d0b02ecdd1900d) format("woff2"),url(fonts/lato-bold-italic.woff?9c7e4e9eb485b4a121c760e61bc3707c) format("woff");font-weight:700;font-style:italic;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-normal-italic.woff2?4eb103b4d12be57cb1d040ed5e162e9d) format("woff2"),url(fonts/lato-normal-italic.woff?f28f2d6482446544ef1ea1ccc6dd5892) format("woff");font-weight:400;font-style:italic;font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:400;src:url(fonts/Roboto-Slab-Regular.woff2?7abf5b8d04d26a2cafea937019bca958) format("woff2"),url(fonts/Roboto-Slab-Regular.woff?c1be9284088d487c5e3ff0a10a92e58c) format("woff");font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:700;src:url(fonts/Roboto-Slab-Bold.woff2?9984f4a9bda09be08e83f2506954adbe) format("woff2"),url(fonts/Roboto-Slab-Bold.woff?bed5564a116b05148e3b3bea6fb1162a) format("woff");font-display:block}
\ No newline at end of file
diff --git a/docs_backup/_static/doctools.js b/docs_backup/_static/doctools.js
index 344db17d..144884ea 100644
--- a/docs_backup/_static/doctools.js
+++ b/docs_backup/_static/doctools.js
@@ -4,7 +4,7 @@
*
* Sphinx JavaScript utilities for all documentation.
*
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
@@ -87,14 +87,13 @@ jQuery.fn.highlightText = function(text, className) {
node.nextSibling));
node.nodeValue = val.substr(0, pos);
if (isInSVG) {
- var bbox = span.getBBox();
var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
- rect.x.baseVal.value = bbox.x;
+ var bbox = node.parentElement.getBBox();
+ rect.x.baseVal.value = bbox.x;
rect.y.baseVal.value = bbox.y;
rect.width.baseVal.value = bbox.width;
rect.height.baseVal.value = bbox.height;
rect.setAttribute('class', className);
- var parentOfText = node.parentNode.parentNode;
addItems.push({
"parent": node.parentNode,
"target": rect});
@@ -284,10 +283,12 @@ var Documentation = {
},
initOnKeyListeners: function() {
- $(document).keyup(function(event) {
+ $(document).keydown(function(event) {
var activeElementType = document.activeElement.tagName;
- // don't navigate when in search box or textarea
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
+ // don't navigate when in search box, textarea, dropdown or button
+ if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+ && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
+ && !event.shiftKey) {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
diff --git a/docs_backup/_static/documentation_options.js b/docs_backup/_static/documentation_options.js
index dede2f07..9c356c51 100644
--- a/docs_backup/_static/documentation_options.js
+++ b/docs_backup/_static/documentation_options.js
@@ -3,8 +3,10 @@ var DOCUMENTATION_OPTIONS = {
VERSION: '1.4.1',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
+ BUILDER: 'html',
FILE_SUFFIX: '.html',
+ LINK_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt',
- NAVIGATION_WITH_KEYS: false,
+ NAVIGATION_WITH_KEYS: false
};
\ No newline at end of file
diff --git a/docs_backup/_static/down-pressed.png b/docs_backup/_static/down-pressed.png
deleted file mode 100644
index 5756c8ca..00000000
Binary files a/docs_backup/_static/down-pressed.png and /dev/null differ
diff --git a/docs_backup/_static/down.png b/docs_backup/_static/down.png
deleted file mode 100644
index 1b3bdad2..00000000
Binary files a/docs_backup/_static/down.png and /dev/null differ
diff --git a/docs_backup/_static/fonts/Inconsolata-Bold.ttf b/docs_backup/_static/fonts/Inconsolata-Bold.ttf
deleted file mode 100644
index 809c1f58..00000000
Binary files a/docs_backup/_static/fonts/Inconsolata-Bold.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/Inconsolata-Regular.ttf b/docs_backup/_static/fonts/Inconsolata-Regular.ttf
deleted file mode 100644
index fc981ce7..00000000
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diff --git a/docs_backup/_static/fonts/Inconsolata.ttf b/docs_backup/_static/fonts/Inconsolata.ttf
deleted file mode 100644
index 4b8a36d2..00000000
Binary files a/docs_backup/_static/fonts/Inconsolata.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/Lato-Bold.ttf b/docs_backup/_static/fonts/Lato-Bold.ttf
deleted file mode 100644
index 1d23c706..00000000
Binary files a/docs_backup/_static/fonts/Lato-Bold.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/Lato-Regular.ttf b/docs_backup/_static/fonts/Lato-Regular.ttf
deleted file mode 100644
index 0f3d0f83..00000000
Binary files a/docs_backup/_static/fonts/Lato-Regular.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/Lato/lato-bold.eot b/docs_backup/_static/fonts/Lato/lato-bold.eot
deleted file mode 100644
index 3361183a..00000000
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diff --git a/docs_backup/_static/fonts/Lato/lato-bold.ttf b/docs_backup/_static/fonts/Lato/lato-bold.ttf
deleted file mode 100644
index 29f691d5..00000000
Binary files a/docs_backup/_static/fonts/Lato/lato-bold.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/Lato/lato-bolditalic.eot b/docs_backup/_static/fonts/Lato/lato-bolditalic.eot
deleted file mode 100644
index 3d415493..00000000
Binary files a/docs_backup/_static/fonts/Lato/lato-bolditalic.eot and /dev/null differ
diff --git a/docs_backup/_static/fonts/Lato/lato-bolditalic.ttf b/docs_backup/_static/fonts/Lato/lato-bolditalic.ttf
deleted file mode 100644
index f402040b..00000000
Binary files a/docs_backup/_static/fonts/Lato/lato-bolditalic.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/Lato/lato-italic.eot b/docs_backup/_static/fonts/Lato/lato-italic.eot
deleted file mode 100644
index 3f826421..00000000
Binary files a/docs_backup/_static/fonts/Lato/lato-italic.eot and /dev/null differ
diff --git a/docs_backup/_static/fonts/Lato/lato-italic.ttf b/docs_backup/_static/fonts/Lato/lato-italic.ttf
deleted file mode 100644
index b4bfc9b2..00000000
Binary files a/docs_backup/_static/fonts/Lato/lato-italic.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/Lato/lato-regular.eot b/docs_backup/_static/fonts/Lato/lato-regular.eot
deleted file mode 100644
index 11e3f2a5..00000000
Binary files a/docs_backup/_static/fonts/Lato/lato-regular.eot and /dev/null differ
diff --git a/docs_backup/_static/fonts/Lato/lato-regular.ttf b/docs_backup/_static/fonts/Lato/lato-regular.ttf
deleted file mode 100644
index 74decd9e..00000000
Binary files a/docs_backup/_static/fonts/Lato/lato-regular.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/RobotoSlab-Bold.ttf b/docs_backup/_static/fonts/RobotoSlab-Bold.ttf
deleted file mode 100644
index df5d1df2..00000000
Binary files a/docs_backup/_static/fonts/RobotoSlab-Bold.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/RobotoSlab-Regular.ttf b/docs_backup/_static/fonts/RobotoSlab-Regular.ttf
deleted file mode 100644
index eb52a790..00000000
Binary files a/docs_backup/_static/fonts/RobotoSlab-Regular.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.eot b/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.eot
deleted file mode 100644
index 79dc8efe..00000000
Binary files a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.eot and /dev/null differ
diff --git a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.ttf b/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.ttf
deleted file mode 100644
index df5d1df2..00000000
Binary files a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-bold.ttf and /dev/null differ
diff --git a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.eot b/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.eot
deleted file mode 100644
index 2f7ca78a..00000000
Binary files a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.eot and /dev/null differ
diff --git a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.ttf b/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.ttf
deleted file mode 100644
index eb52a790..00000000
Binary files a/docs_backup/_static/fonts/RobotoSlab/roboto-slab-v7-regular.ttf and /dev/null differ
diff --git a/docs_backup/_static/jquery-3.2.1.js b/docs_backup/_static/jquery-3.5.1.js
similarity index 81%
rename from docs_backup/_static/jquery-3.2.1.js
rename to docs_backup/_static/jquery-3.5.1.js
index d2d8ca47..50937333 100644
--- a/docs_backup/_static/jquery-3.2.1.js
+++ b/docs_backup/_static/jquery-3.5.1.js
@@ -1,5 +1,5 @@
/*!
- * jQuery JavaScript Library v3.2.1
+ * jQuery JavaScript Library v3.5.1
* https://jquery.com/
*
* Includes Sizzle.js
@@ -9,7 +9,7 @@
* Released under the MIT license
* https://jquery.org/license
*
- * Date: 2017-03-20T18:59Z
+ * Date: 2020-05-04T22:49Z
*/
( function( global, factory ) {
@@ -47,13 +47,16 @@
var arr = [];
-var document = window.document;
-
var getProto = Object.getPrototypeOf;
var slice = arr.slice;
-var concat = arr.concat;
+var flat = arr.flat ? function( array ) {
+ return arr.flat.call( array );
+} : function( array ) {
+ return arr.concat.apply( [], array );
+};
+
var push = arr.push;
@@ -71,16 +74,72 @@ var ObjectFunctionString = fnToString.call( Object );
var support = {};
+var isFunction = function isFunction( obj ) {
+
+ // Support: Chrome <=57, Firefox <=52
+ // In some browsers, typeof returns "function" for HTML
This is a record of all past PLYMI releases and what went into them,
-in reverse chronological order. All previous releases should still be available
-on pip.
Adds a reading comprehension problem in the section on type-hinting
+to show that jedi provides annotation-informed autocompletion abilities in notebooks.
Updated the discussion of computing pairwise differences
+to account for potential floating-point edge cases that can produce “NaNs” as a result.
+
There is currently an incompatibility between jedi0.18.0 and IPython, which breaks autocompletion. See here for more details.
+Added temporary callout boxes to the informal introduction to Python and to
+the introduction to Jupyter notebooks, which instruct readers to remedy this by downgrading jedi.
Updated some of the “backend” technologies behind PLYMI: upgraded sphinx and nbsphinx.
+
Fixed a broken subsection header: “Converting a Boolean Index-Array to Integer Index-Arrays: numpy.where” will now appear in the navigation bar under Module 3, Advanced Indexing.
We’re finally keeping a formal changelog! This update includes our first discussion of features that were introduced in Python 3.8. Also includes various typo/grammar fixes.
Python Like You Mean It (PLYMI) is a free resource for learning the basics of Python & NumPy, and moreover, becoming a competent Python user. The features of the Python language that are emphasized here were chosen to help those who are particularly interested in STEM applications (data analysis, machine learning, numerical work, etc.).
-
I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.8, as of writing this).
+
I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.9, as of writing this).
+This material has proven to be fruitful for high school and college teachers alike to teach Python as part of their STEM curriculum.
Python is a relatively easy language to pick up, and it doesn’t require much rigor to make code work. Unfortunately, this means that there are many Python users out there who know enough to just get by, but lack a sound understanding of the language. You don’t want to get caught in the “know enough Python to be dangerous” zone; therein lies complacency, stagnation, and the genesis of a lot of bad code. You’ve got to Python like you mean it!
If you have questions about the reading, think that you have spotted some mistakes, or would like to contribute to PLYMI, please visit our GitHub page. You will need to create a GitHub account in order to post an issue; this process is free and easy. We would love for you to join us to discuss the website!
I started learning to use Python in graduate school for my physics research, and I was so bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I’d like to think that this resource would have been a huge help to me back then). I’ve been coding in Python for at least six years now, and I’ve taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
+
I started learning to use Python in graduate school for my physics research, and I was so bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I’d like to think that this resource would have been a huge help to me back then). I’ve been coding in Python for at least eight years now, and I’ve taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
Python Like You Mean It (PLYMI) is a free resource for learning the basics of Python & NumPy, and moreover, becoming a competent Python user. The features of the Python language that are emphasized here were chosen to help those who are particularly interested in STEM applications (data analysis, machine learning, numerical work, etc.).
-
I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.8, as of writing this).
+
I want this to be a lean, one-stop resource for learning the essentials of Python from scratch. The reader will begin by learning about what Python is and what installing Python even means, and will hopefully walk away with a solid understanding of a substantial core of the language and its premiere numerical library, NumPy. I am also placing an emphasis on best practices throughout this site and am teaching to the latest version of Python (version 3.9, as of writing this).
+This material has proven to be fruitful for high school and college teachers alike to teach Python as part of their STEM curriculum.
Python is a relatively easy language to pick up, and it doesn’t require much rigor to make code work. Unfortunately, this means that there are many Python users out there who know enough to just get by, but lack a sound understanding of the language. You don’t want to get caught in the “know enough Python to be dangerous” zone; therein lies complacency, stagnation, and the genesis of a lot of bad code. You’ve got to Python like you mean it!
If you have questions about the reading, think that you have spotted some mistakes, or would like to contribute to PLYMI, please visit our GitHub page. You will need to create a GitHub account in order to post an issue; this process is free and easy. We would love for you to join us to discuss the website!
I started learning to use Python in graduate school for my physics research, and I was so bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I’d like to think that this resource would have been a huge help to me back then). I’ve been coding in Python for at least six years now, and I’ve taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
+
I started learning to use Python in graduate school for my physics research, and I was so bad at using it. Fortunately, one of my labmates was more experienced and had the good sense to tell me that my code and work flow was terrible. He pointed me to tooling, style guides, and documentation pages so that I could improve (I’d like to think that this resource would have been a huge help to me back then). I’ve been coding in Python for at least eight years now, and I’ve taken to following the language, along with its new features and changes, quite closely. Currently I do machine learning research, and have served as a core developer for a machine learning library. I also love to teach, so this has been a fun project for me to take on!
In this module, we will come to see that Python is an object-oriented language. That is, the language is all about defining different types of objects that encapsulate data, and that possess functions that permit users to access and manipulate this data. Our tour through the essentials of Python and the essentials of NumPy brought us into contact with various types of objects, such as the int, str, list, tuple, dict, and the numpy.ndarray, to name a few. We have seen that these different types of objects have starkly different functionality from one another. We will study the syntax and constructs for creating and interacting with objects in Python. Ultimately, will find our skill sets as Python-users much fuller and more sophisticated having understood object-oriented programming in Python.
-
We being this module by establishing some key terminology for discussing object-oriented programming, drawing attention to the important fact that the terms ‘class’ and ‘type’ mean the same thing in modern Python. Next, we study the syntax for defining a class, and take time to distinguish between the resulting class object and the subsequent class instances that can then be created. Having defined our first custom class of object, we then study the syntax for defining class-methods, which permits us add customized functionality to our class. Further, we will introduce ourselves to Python’s reserved special methods, which empower us to fully interface our class with the Python’s protocols for behaving like a sequence, an iterable, a function, etc. We conclude this module with a brief discussion of the concept of inheritance, which is a mechanism by which a new class can inherit attributes from an existing class.
+
We begin this module by establishing some key terminology for discussing object-oriented programming, drawing attention to the important fact that the terms ‘class’ and ‘type’ mean the same thing in modern Python. Next, we study the syntax for defining a class, and take time to distinguish between the resulting class object and the subsequent class instances that can then be created. Having defined our first custom class of object, we then study the syntax for defining class-methods, which permits us to add customized functionality to our class. Further, we will introduce ourselves to Python’s reserved special methods, which empower us to fully interface our class with Python’s protocols for behaving like a sequence, an iterable, a function, etc. We conclude this module with a brief discussion of the concept of inheritance, which is a mechanism by which a new class can inherit attributes from an existing class.
![](\"')
');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '
+
+