How to Set up your Python Environment for Data Analysis

Posted on Posted in Data Science with Python

Python is a programming language, and for Learning Data Science with Python, you’ll be writing your Python code in the programming environment called Spyder.

The Anaconda distribution simplifies the installation process by including Python, Spyder, and other packages and tools in one installation file. It contains the core Python language, as well as all of the essential libraries including NumPy, Pandas, SciPy, Matplotlib, and IPython. By using the graphical installer, downloading Python is as easy as downloading any computer program

Installing Anaconda

Step 1:

Go to http://continuum.io/downloads

Step 2:

Scroll down to find your operating system and click on Python 3.6 to download the graphical installer.

Step 3:

For windows, see http://windows.microsoft.com/en-us/windows/32-bitand-64-bit-windows to find out whether your computer has a 32-bit or 64-bit version of windows. Click on the FAQ “How can I tell if my computer is running a 32-bit or 64-bit version of windows?”, and follow the instructions to find out. If your computer is running a 32-bit version of Windows, click on “Windows 32-bit Python 3.4 Graphical Installer” under OTHER INSTALLERS, instead of the Windows 64-bit installer, and follow the same directions to install
it.

Step 4:

Save the file to your computer.

Step 5:

Double click on the downloaded file to open it

Step 6:

Follow the on-screen installation instructions, leaving options as they are currently set. This finishes the
installation process.

Step 7:

Next, check for any updates using Conda. Conda is one of the extras that is installed through the distribution Anaconda. It handles things like updates, set-up, and package installation through a command line interface. If there are many updates this can take 10 minutes or more. It is rare that you will have to use it.

WINDOWS:

a.  Open Anaconda Command Prompt. Start typing Anaconda Command Prompt into the search box in the start menu, and it will show up.

b.   Type conda update conda at the command prompt, typing y for Yes and then pressing enter when it asks if you want to proceed. Your installation may identify different packages that need updated

c.    After that completes, type conda update anaconda at the command prompt. If it prompts you to proceed with installation or updating, type y for Yes and press enter

d.    After that, type conda install seaborn at the command prompt, then type y after the Proceed ([y]/n)? line

e.    After that completes, you can then close the command prompt window.

MAC:

a.    Open Terminal, which you can find by using Spotlight.

b.    Type conda update conda at the command prompt in the terminal, typing y for Yes and
then pressing enter when it asks if you want to proceed. Your installation may identify different
packages that need updated (see Windows image above for the text shown in the terminal
window).

c. After that completes, type conda update anaconda at the command prompt. If it prompts you to proceed with installation or updating, type y for Yes and press enter

d.    After that, type conda install seaborn at the command prompt, then type y after the Proceed ([y]/n)? line

e.   After that completes, you can then close the command prompt window.

Getting to Know Spyder

You will write your programs and run them inside the Spyder IDE.

There are various ways to find and open this program – we only describe one way below.

On Windows, in the search box associated with the start menu, type Spyder. Click on the program with its logo. Right clicking on it will give you the option to pin it to your start menu or taskbar for easier access in the future.

On a Mac, use Spotlight to find the program Launcher. It has a green tear-drop shaped icon associated with it.

That opens the following window, in which you will click Launch, on the last line for spyder-app

FYI: The icon for Python that goes in the OS X dock, once Spyder has started, is generic, and keeping it on the dock will not work.

 

After Spyder has launched, you should see an interface that is organized into multiple windows (marked with red
rectangles below) each of which has or could have multiple tabs (marked with green rectangles below).

The default starting screen has three windows visible: Editor, Object Inspector, and Console. If you accidentally close a window or change the layout, you can revert to the default by clicking “View” on the menu bar and then clicking “Reset Window Layout”.
Editor – where you can write and save sequences of commands – essentially, where you will write your full program

In the sample script that shows up in the editor, anything after # is a comment, meaning that Python will ignore that text. Three double quotation marks are a special type of comment that spans multiple lines. Here, you would actually begin writing your syntax on line 8 after the comments that say what your file does.

In the sample script that shows up in the editor, anything after # is a comment, meaning that Python  will ignore that text. Three double quotation marks are a special type of comment that spans multiple lines. Here, you would actually begin writing your syntax on line 8 after the comments that say what your file does.

Console – where Python runs your code.

Spyder offers two choices for a console – a basic one and an IPython (Interactive Python) console. It is the IPython console we want to use. We will refer to it, however, as just the console. The prompt (where you can type in a command) for an IPython console is

Caution: If your console looks like the one shown to the right, where the prompt is >>> then you are in the wrong type of

Object Inspector – On starting Spyder, the object inspector (as shown above) just gives you a link to the tutorial (this
can be useful if you want to know more details about using Spyder). The rest of the time, this window will continue to
give you information.

The Object Inspector gives you more information about a function – it gives you the documentation from the help files and tells you what parameters (or input) the function takes.

The Variable Explorer shows you the value and type of any variables you have created. In the image to the right, we created three variables in the console, and those three variables showed up in the Variable Explorer. This is useful
when you are trying to debug your code.

Getting to Know Jupyter Notebook

After installing, you will get a launcher containing a number of programs. The most important one is the iPython notebook, which is also called Jupyter notebook. Once  you launch the notebook, the terminal is opened and a notebook is opened in your browser. Don’t get confused here! You don’t need internet connection to create or use the notebooks. Simply, the browser is used instead of a separate program and serves as your environment,where you can code.

 

However, you are not limited to using the browser based Jupyter notebooks. If you prefer an IDE, a great option for data analysis is Rodeo from Yhat. If you are familiar with RStudio for R, Rodeo is something very similar for Python. Be sure to try out both alternatives, as, ultimately, the Python environment you use will depend on your personal preference.