ITEC696: Python Programming for Business Analytics
Welcome to the course home page!
The steps below describe how to install Python on your machine, including an IDE (Spyder) and the course repo.
- Option 1: If you are viewing this on Github, you should be able to start viewing the book by clicking on
- Option 2: Once you download the course repo, you can read these notes as a browser-based book by opening
contents.html. In that event you can navigate forward and back using your browser.
- Option 3: If you also have Jupyter Notebook, you can read the
.ipynbfiles and try running their content.
Installing Python and Spyder
Install Miniconda (preferred) or Anaconda. Anaconda comes with many packages already installed, while Miniconda is a more lightweight version allowing you to install only the packages you want. Here we prefer Miniconda since we'll soon install our own packages.
To install packages we'll use the Python package manager pip (short for "pip installs packages"), which runs on the command line. We will use both Anaconda and pip via the command prompt. While the integrated development environment (IDE) Spyder offers a way to install packages, using the command line will allow us more flexibility to manage virtual environments, self-contained versions of Python. It also practices skills used in remote computing, i.e. doing computations on machines that are far away (often more powerful machines like desktops or supercomputing clusters).
Install Spyder by opening a command prompt (Windows: Anaconda command prompt; Mac/Linux: Terminal) and running
conda install -c anaconda spyder
Spyder is an Integrated Development Environment (IDE) named so because it brings together several software tools that are useful when programming in Python. The two main tools are
- Text Editor (default left pane)
- Python / IPython Console (default bottom right pane)
There are other helpful tools, too, like a Code Profiler, a Help Viewer, and a Plot Viewer. You can find them under
View -> Panes.
One of the key benefits to Anaconda is that it manages virtual environments for us. A virtual environment is a self-contained copy of Python with its own unique packages installed. Virtual environments are useful for managing multiple projects and making it much easier to reproduce code across different machines.
To make a new virtual environment using Anaconda, open a terminal and run the following:
conda create -n itec696 python=3.6
This will make a new directory called
itec696 (or whatever name you use) and copy the base Python 3.6 installation within Anaconda there.
Now we'll run a quick experiment to see where the virtual environment lives and test whether it works. Try running the lines below in the terminal.
source activate itec696
You should see your command prompt change
has now become
(itec696) user@machine-name$ |
(itec696) signifies the virtual environment is active and commands like
pip refer to the copy located in the virtual environment.
If you have Windows and your command prompt is slightly different, don't worry. We'll standardize our terminal next. This will give us practice working with a Unix-style command prompt, common to virtually all computers.
Cygwin Terminal (Windows users)Download
Run the setup file and choose a mirror. When you get to the package selection prompt, use the search bar at the top to type in the following package names. For each package, search for the matching name, description, and category, then click the package to select it.
git: Distributed version control system Category: Devel. For file version control.
openssh: The OpenSSH server and client programs. Category: Net. For remote computing. (Optional)
vim: Vi IMproved Text Editor. Category: Editors (Optional)
We'll work in the terminal (aka command line) frequently through this course. While many new programmers find the terminal daunting, it's extremely useful, and the simple operations will be familiar to anyone who has used a graphical file explorer.
Like the file explorer, the terminal has a current directory where it's operating. From the current directory, one can navigate to other directories, make new folders, copy or move files, delete items, and so on.
To start, try typing these lines, which use some of the common commands. A description appears next to each command as a comment, a segment of code that is ignored. In both terminal and in Python, comments begin with a hashtag
# and can be an entire line or a partial line.
pwd # show the Present Working Directory (PWD) ls # LiSt the contents of pwd echo "This is a test" # print text cd ~/Code # Change Directory to ~/Code mkdir test # MaKe DIRectory cd test ls cd .. # Change Directory to go up one level rmdir test # ReMove DIRectory
Some commands above overlap with the usual Windows command prompt, while others have synonymous functions (e.g.
ls on Unix is
dir on Windows).
Where do these commands live? Use the
which command to probe each one. Underneath your input, the terminal will print the directory (i.e. folder) where the command lives.
which ls which echo which cd which rm which cp which mv which mkdir which rmdir which git # should work if git is installed which ssh # optional, if you installed openssh which vim # optional, if you installed vim
What do the paths have in common?
Finally, let's make a couple symbolic links (i.e. shortcuts) with command
ln. These will save us from typing out long Windows paths like
/cygdrive/c/Users/$USER/ all the time.
ln -s /cygdrive/c/Users/$USER/Code ~/Code
You can consider adding other folders like
In addition to
python, Anaconda also comes with a file called
pip. Pip - short for "Pip installs packages" - is a package manager that handles downloading and setting up packages hosted on the Python Package Index (PyPi).
Pip can handle installing packages one-by-one (e.g.
pip install numpy) or from a text list (e.g.
pip install -r requirements.txt). Typically we'll use the latter option when setting up a virtual environment. To install the requirements included in the course repo, try running the sequence below.
pip install -r ~/Code/ITEC696/requirements.txt
You should begin to see lots of output printing to the terminal as Pip downloads each package then installs it. This may take several minutes to finish, depending on your internet connection and processor speed.
pip freeze will print the list of currently installed packages. So, if you happen to install a new package and want to generate a revised list of requirements, you can export the output of
pip freeze to a file like so:
pip freeze > requirements_v2.txt
Let's now ensure our libraries load correctly. You can run the test script in Python from the command line:
It should generate some output:
Starting imports... Done! < DATE AND TIME >
Setting PYTHONPATH (Optional)
If you skip this step altogether, you can still write Python code. You'll just need to comment out any of the Table of Contents cells when running
We can always add more folders to the
PYTHONPATH later by appending them to the variable we just assigned, so long as we separate the folders with a colon
:, similar to the system
Option 1: Open Spyder. From the user menu, navigate
Tools -> Current user environment variables. Add key
Option 2: Press the Windows key and search "environment variables". Click
Set user environment variables. Then add key
Additionally, we'll need to add this folder to
PYTHONPATH through the Spyder GUI under
On Unix systems you can run the following, substituting
... for the written out path on your system. Note the usual home folder shortcut
~ may not work here.
echo 'export PATH="/.../Code/ITEC696/unit5:$PATH"' >> ~/.bashrc echo 'export PYTHONPATH="/.../Code/ITEC696"' >> ~/.bashrc source ~/.bashrc
Testing Jupyter Notebook
Now that our Python environment setup is complete, we will test one other tool we'll use in this course: Jupyter Notebook. Jupyter Notebook is an interactive, browser-based Python interpreter and is extremely useful for exporting documents.
In your terminal run
source activate itec696 # may be optional, depending on system cd ~/Code/ITEC696 jupyter notebook
And watch as a browser window opens, beginning in the same folder where you launched. You can now both
- Start a new notebook
New -> Python 3from the dropdown menu in the upper right corner.
- Open any of the course notebooks, e.g.