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<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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<title>Jupyter Notebooks — Python Like You Mean It alpha documentation</title>
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<link rel="top" title="Python Like You Mean It alpha documentation" href="../index.html"/>
<link rel="up" title="Module 1: Getting Started with Python" href="../module_1.html"/>
<link rel="next" title="Setting Up a Development Environment" href="Getting_Started_With_IDEs_and_Notebooks.html"/>
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<a href="../index.html" class="icon icon-home"> Python Like You Mean It
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0.9
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<p class="caption"><span class="caption-text">Table of Contents:</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../intro.html">Python Like You Mean It</a></li>
<li class="toctree-l1 current"><a class="reference internal" href="../module_1.html">Module 1: Getting Started with Python</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="SiteFormatting.html">A Quick Guide to Formatting</a></li>
<li class="toctree-l2"><a class="reference internal" href="GettingStartedWithPython.html">Introducing the Python Programming Language</a></li>
<li class="toctree-l2"><a class="reference internal" href="Installing_Python.html">Installing Python</a></li>
<li class="toctree-l2"><a class="reference internal" href="Informal_Intro_Python.html">An Informal Introduction to Python</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">Jupyter Notebooks</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#Running-a-Notebook-Server-&-Creating-a-Notebook">Running a Notebook Server & Creating a Notebook</a></li>
<li class="toctree-l3"><a class="reference internal" href="#Notebook-Cells">Notebook Cells</a></li>
<li class="toctree-l3"><a class="reference internal" href="#An-Example-Notebook">An Example Notebook</a></li>
<li class="toctree-l3"><a class="reference internal" href="#Familiarizing-Yourself-with-Jupyter-Notebooks">Familiarizing Yourself with Jupyter Notebooks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#Markdown-Cells">Markdown Cells</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="Getting_Started_With_IDEs_and_Notebooks.html">Setting Up a Development Environment</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="../module_2.html">Module 2: The Essentials of Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../module_3.html">Module 3: The Essentials of NumPy</a></li>
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<div class="section" id="Jupyter-Notebooks">
<h1>Jupyter Notebooks<a class="headerlink" href="#Jupyter-Notebooks" title="Permalink to this headline">¶</a></h1>
<p>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.</p>
<p>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 <em>any</em> 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 <a class="reference external" href="https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet">Markdown
syntax</a>),
for presenting and explaining the contents of the notebook.</p>
<p>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.</p>
<p>Enough gushing about Jupyter notebooks. Let’s start using them!</p>
<div class="section" id="Running-a-Notebook-Server-&-Creating-a-Notebook">
<h2>Running a Notebook Server & Creating a Notebook<a class="headerlink" href="#Running-a-Notebook-Server-&-Creating-a-Notebook" title="Permalink to this headline">¶</a></h2>
<p>In your terminal, navigate to a directory (a.k.a folder) that you are
okay creating files in. If you don’t know how to do this, Google it!</p>
<p>Once you are in the desired directory, execute in your terminal (type
the following, and then hit <code class="docutils literal"><span class="pre"><ENTER></span></code>): <code class="docutils literal"><span class="pre">jupyter</span> <span class="pre">notebook</span></code></p>
<p>You should see some text appear in your terminal:</p>
<div class="figure" id="id1">
<img alt="Starting a jupyter notebook server on your machine" src="../_images/jupyter_login.PNG" />
<p class="caption"><span class="caption-text">Starting a jupyter notebook server on your machine</span></p>
</div>
<p>This is a “notebook server” that is running on your machine - it
basically handles all of the communication between your browser and your
machine. A new window or tab should open in your web browser, which
looks like a file explorer.</p>
<div class="figure" id="id2">
<img alt="File explorer that opens in your browser" src="../_images/jp_files.PNG" />
<p class="caption"><span class="caption-text">File explorer that opens in your browser</span></p>
</div>
<p>You can use this to enter subdirectories and to open up any Jupyter
notebooks that you have saved. <em>You will need to use this file explorer
any time that you want to open up a Jupyter notebook, old or new.</em> In
the top-right corner of this window, click on the dropdown menu labeled
“New”, and select the option <code class="docutils literal"><span class="pre">Python</span> <span class="pre">3</span></code>.</p>
<p><img alt="image0" src="../_images/jp_dropdown.PNG" /></p>
<p>A new tab will open in your browser, revealing a “Jupyter notebook”
called <code class="docutils literal"><span class="pre">Untitled.ipynb</span></code> running a Python 3 kernel. Clicking
<code class="docutils literal"><span class="pre">File</span> <span class="pre">></span> <span class="pre">Rename</span></code> in the notebook will enable you to name your notebook.
<code class="docutils literal"><span class="pre">.ipynb</span></code> is the file-type suffix used for Jupyter notebooks (<code class="docutils literal"><span class="pre">ipynb</span></code>
stands for “IPython-notebook”, which is what these notebooks were called
prior to 2014). The commands that you run in this notebook are
interpreted and executed by Python in the essentially same way that they
would be in a IPython console.</p>
<div class="admonition warning">
<p class="first fa fa-exclamation-circle"><strong>Jupyter Notebooks Do Not Use the Internet</strong>:</p>
<p class="last">Although a Jupyter notebook opens in your browser, <em>everything is
happening locally on your machine</em>. You don’t need to be connected to
the internet to work on a Jupyter notebook! The notebook server that you
ran in your terminal is simply routing communication from your browser
to your local machine. For instance, it is sending code to your CPython
interpreter to be executed.</p>
</div>
</div>
<div class="section" id="Notebook-Cells">
<h2>Notebook Cells<a class="headerlink" href="#Notebook-Cells" title="Permalink to this headline">¶</a></h2>
<p>Whereas a Python console only allows you to work on one line of code at
a time, a notebook allows you to write code within “cells” and to
execute these chunks of code cell-by-cell. In the first cell, write the
lines of code:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">y</span> <span class="o">=</span> <span class="mi">4</span>
</pre></div>
</div>
<p>then press <code class="docutils literal"><span class="pre"><SHIFT>+<ENTER></span></code>. This will execute all of the code within
the given cell (in this instance, assigning the variables <code class="docutils literal"><span class="pre">x</span></code> and
<code class="docutils literal"><span class="pre">y</span></code> with the values 3 and 4, respectively) and then creates a new cell
below. In the next cell type the code:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
</pre></div>
</div>
<p>and hit <code class="docutils literal"><span class="pre"><SHIFT>+<ENTER></span></code> to execute this code. The number 7 will
appear beneath the cell - this, of course, is the value that is returned
when <code class="docutils literal"><span class="pre">3</span> <span class="pre">+</span> <span class="pre">4</span></code> is evaluated:</p>
<div class="figure" id="id3">
<img alt="jupyter notebook example" src="../_images/jupyter_early.png" />
<p class="caption"><span class="caption-text">jupyter notebook example</span></p>
</div>
<p>Notice that the notebook “knows” about its variables across its cells.
This doesn’t just work from top to bottom - you can define <code class="docutils literal"><span class="pre">z</span> <span class="pre">=</span> <span class="pre">2</span></code> in
the third cell, and then execute code that references <code class="docutils literal"><span class="pre">z</span></code> in the first
cell. What really matters is the <em>order</em> in which the cells are
executed. Notice that <code class="docutils literal"><span class="pre">In[1]</span></code> denotes that the top cell with the first
input-cell executed in the notebook, and <code class="docutils literal"><span class="pre">In[2]</span></code> denotes the second
cell that was executed.</p>
<p>Formally, the cells within a given notebook share a common “namespace”:
any variable defined in a cell can be referenced or redefined in any
other cell within the notebook. On the other hand, separate notebooks
are completely independent from one another. You can be working on
multiple notebooks at once, and they will never “know” about one
another.</p>
<p>A major value of using a notebook is that you can rapidly edit these
cells (say, change <code class="docutils literal"><span class="pre">x</span> <span class="pre">=</span> <span class="pre">3</span></code> to <code class="docutils literal"><span class="pre">x</span> <span class="pre">=</span> <span class="pre">10</span></code>), and re-execute them to
nimbly tinker with whatever code you are developing. Although simple,
this is a hugely powerful environment for prototyping code.</p>
</div>
<div class="section" id="An-Example-Notebook">
<h2>An Example Notebook<a class="headerlink" href="#An-Example-Notebook" title="Permalink to this headline">¶</a></h2>
<p>To show off a more exciting use-case, let’s create a notebook that plots
some data for us. We’ll use matplotlib, a Python library that is used
for plotting data, and NumPy, the premiere library for doing numerical
work in Python. We will import these libraries for use in our code. Next
we’ll define some mathematical functions. And finally, we’ll plot these
functions evaluated on a large number of closely-spaced points on the
domain.</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none"><div class="highlight"><pre>
<span></span>In [1]:
</pre></div>
</div>
<div class="input_area highlight-ipython3"><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># this tells Jupyter to embed matplotlib plots in the notebook</span>
<span class="o">%</span><span class="k">matplotlib</span> notebook
</pre></div>
</div>
</div>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none"><div class="highlight"><pre>
<span></span>In [2]:
</pre></div>
</div>
<div class="input_area highlight-ipython3"><div class="highlight"><pre>
<span></span><span class="k">def</span> <span class="nf">sinc</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">x</span>
<span class="k">def</span> <span class="nf">d_sinc</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="s2">"derivative of sinc-function"</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="n">x</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span>
</pre></div>
</div>
</div>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none"><div class="highlight"><pre>
<span></span>In [3]:
</pre></div>
</div>
<div class="input_area highlight-ipython3"><div class="highlight"><pre>
<span></span><span class="c1"># evaluate functions at 1000 points evenly spaced in [-15, 15]</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">sinc</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">d_sinc</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="nbinput docutils container">
<div class="prompt highlight-none"><div class="highlight"><pre>
<span></span>In [4]:
</pre></div>
</div>
<div class="input_area highlight-ipython3"><div class="highlight"><pre>
<span></span><span class="c1"># plot the sinc-function and its derivative</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">"$sinc(x)$"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"blue"</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">"$\frac{d(sinc(x))}</span><span class="si">{dx}</span><span class="s2">$"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Example Notebook Plot"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"$x$ [radians]"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
</div>
<div class="nboutput docutils container">
<div class="prompt empty docutils container">
</div>
<div class="output_area docutils container">
<div></div>
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