Install Python & Anaconda

Python is an extremely popular programming language that has quickly become the language of choice for programmers, data scientists, and computational social scientists alike due to its rich ecosystem of statistical and data management libraries, a vibrant open source community, and its clean and easy to read syntax.

While Python can be installed by itself from the official website, for most social science researchers, it makes much more sense to install Python as part of the Anaconda distribution. Anaconda is data science platform that supports all platforms—Windows, macOS, and Linux—and makes installing and managing Python and its scientific computing libraries a breeze.

What You’ll Learn

How to install Python as part of the Anaconda distribution.
How to install packages with conda and pip.
How to create and manage programming environments with conda.

What You’ll Need

A computer running a recent version of Windows, macOS, or Linux.WWWW

Let’s get started!

Note: All tutorial examples will be illustrated using a Windows PC.

Download the Installer

Click here to go to the Windows Anaconda download page (Mac users click here).
On the site, you will see the following download landing page:

Click the green Download button.
Save the exe file to a folder of your choice.
When the download completes, continue to the next section of this tutorial.

Installation

Navigate to where you downloaded Anaconda.

Note: On PC, this will be a .exe file. On macOS, this will be a .dmg file.

Double click the file to launch the installer.
Click Next.
Read and accept the licensing the agreement.
Choose if you want to install Anaconda for only you (“Just Me”) or every account on your computer.
- As a rule of thumb, it is probably best to choose “Just Me."
Choose where you’d like to install Anaconda and click Next.

Note: It is generally safest and recommended to install Anaconda in the default directory.

Important: Make sure both Add Anaconda3 to my PATH environment variable and Register Anaconda3 as my default Python 3.x are selected!

Click the Install button (this can take several minutes).
Anaconda will offer you the option to install optional software packages. Choose if you’d like to try these software. Do note that they are completely optional: Anaconda will function perfectly fine without them.
When the installer completes, click Finish to exit the installer.

Note: We will be illustrating how to use Anaconda from your computer’s terminal (e.g., command line on Windows, Terminal on macOS and Linux). This is the recommended usage of Anaconda. If you would like to use the Anaconda GUI, consult the documentation on the Anaconda Navigator application.

The following steps should work on all operating systems.

Check for Anaconda:

Launch your computer’s terminal app.
- Windows: click Start and type cmd; right-click Command Prompt and click Run as Administrator.
- macOS: open your Terminal app; if Terminal is not in your dock, you can find it in the Launchpad.
In your computer’s terminal window, type conda just as below and hit enter.


$ > conda

Your terminal should output a message similar to the one below:

usage: conda-script.py [-h] [-V] command ...

conda is a tool for managing and deploying applications, environments and packages.

Options:

positional arguments:
  command
    clean        Remove unused packages and caches.
    compare      Compare packages between conda environments.
    config       Modify configuration values in .condarc. This is modeled after the git config command. Writes to the user
                 .condarc file (C:\Users\[USERNAME]\.condarc) by default.
    create       Create a new conda environment from a list of specified packages.
    help         Displays a list of available conda commands and their help strings.
    info         Display information about current conda install.
    init         Initialize conda for shell interaction. [Experimental]
    install      Installs a list of packages into a specified conda environment.
    list         List linked packages in a conda environment.
    package      Low-level conda package utility. (EXPERIMENTAL)
    remove       Remove a list of packages from a specified conda environment.
    uninstall    Alias for conda remove.
    run          Run an executable in a conda environment. [Experimental]
    search       Search for packages and display associated information. The input is a MatchSpec, a query language for conda
                 packages. See examples below.
    update       Updates conda packages to the latest compatible version.
    upgrade      Alias for conda update.

optional arguments:
  -h, --help     Show this help message and exit.
  -V, --version  Show the conda version number and exit.

conda commands available from other packages:
  env

If you see this output, Anaconda has been successfully installed.
- You should also have two new applications installed on your computer: Anaconda Navigator and Anaconda Prompt.
conda is shorthand for Anaconda, and its the command we will use to call Anaconda from your computer’s terminal to install and manage all of your Python packages.

Check for Python:

In your terminal window, type python as below:

$ > python

Your terminal should now launch a Python prompt, as indicated by the following output:

Python 3.8.10 (default, May 19 2021, 13:12:57) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>>

If you see this output, Python has been successfully installed.

Now, let’s learn about creating and managing environments with conda.

What are environments?

In Python, a programming environment (also known as a “virtual environment” or just an “environment”) is a self-contained ecosystem containing:

Your Python interpreter.
The Python libraries you have installed with conda or pip.
The collection of relevant scripts related to your libraries and Python interpreter.

Python environments are isolated from one another, meaning the packages and scripts installed in one environment do not interfere or interact with the packages and scripts in another environment.

Why use environments?

You should always use a fresh and unique environment for every project you work on. There are four main reasons for this:

Only installing the libraries necessary for a project to avoid dependency conflicts.
Avoiding library version conflicts (e.g., some packages require different versions of the same library).
Installing and using different versions Python.
Allowing for easy reproducibility of your research and programming environment by providing a list of packages that other scholars can quickly install.

Creating Environments with `conda`

We can create, activate, and manage environments using conda in your computer’s terminal.

Launch your computer’s terminal.
We’re now going to create an environment. I am just going to call it test for now.
The syntax for creating a conda environment follows this order:
1. call conda
2. call the create command
3. provide a name for your environment
Here’s the code:

$ > conda create --name test

After running the script, you should have an output that looks like this:

Collecting package metadata (current_repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: **YOUR_PATH_HERE**\test



Proceed ([y]/n)?

Type y and hit enter to continue.
You should see the following output:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate test
#
# To deactivate an active environment, use
#
#     $ conda deactivate

Using the Environment

In order to use your conda environment, you need to call the activate command in your terminal. Since we named our environment test, we will tell conda to activate the test environment:

$ > conda activate test

Your terminal should now look something like this (note: this will vary slightly by operating system):

$ (test) >

The parentheses around test tells you your current active conda environment.

Closing the Environment

When you are done using your environment, make sure you deactivate it. This is accomplished easily with the following command:

$ (test) > conda deactivate

Installing Packages

There are two primary ways to install packages in Python: (1.) using conda and (2.) using pip. It is recommended to use conda when you can, but in some cases, some libraries are not available in conda’s repository. In that case, use pip. It is also easiest to install packages loaded from GitHub with pip as well.

Installing Packages with Conda

Note: Before installing packages, make sure you activate your conda environment.

Usually, you will want to use conda to install Python packages. This can be accomplished easily from the terminal using the install argument:

$ > conda install PACKAGE_NAME_HERE

An important caveat is that conda has differnt channels for installing packages. If you do not specify the channel when you install the package, the installation could take a very long time. Anaconda provides code snippets for all packages in its package repository. Click here to search the repository.

Here’s an example of choosing a channel when installing the numpy package:

$ > conda install -c conda-forge numpy

By calling -c and conda-forge, conda knows to install numpy from the conda-forge channel. Often, specifying -c conda-forge will work, but consult the Anaconda documentation for your particular package.

Installing Packages with Pip

pip is Python’s default package manager. While it comes installed with Python, it is a good idea to install pip with conda within each of your programming environments. This prevents pip from installig packages globally and creating conflicts:

$ > conda install pip

Sometimes, conda will not have a package that you want, and you will need to use pip. In this case, pip is very similar to conda. Let’s install the Tabulate package with pip as an example:

$ > pip install tabulate

Note: Unlike conda, pip does not need to choose a channel to optimize downloads and installs.

pip is also great when you want to install a pacakge from GitHub, like so:

$ > pip install git+https://github.com/tabulate/tabulate.git

Uninstalling Packages

Uninstalling packages follows the same basic syntax:

$ > conda uninstall PACKAGE_NAME_HERE
$ > pip uninstall PACKAGE_NAME_HERE

Due to the potential for dependency conflicts, however, you are unlikely to uninstall packages much, if at all.

Note: Regardless of what compiler or IDE you are using, you must execute your Python scripts from within the environment you have installed your libraries in to use your libraries.

Using Packages

To use the packages you install, you have to import them into your Python program, whether that be a standard Python script, an IDE, or a Jupyter Notebook (this assumes you have activated the appropriate programming environment).

# Import the default datetime library
import datetime

# Now you can use the methods from the tabulate package:
date = datetime.datetime(2021, 1, 1)

# Print year:
print(date.year)
>>> 2021

Alternatively, you can import submodules of a library to call them directly:

from datetime import datetime

date = datetime(2021, 1, 1)

print(date.year)
>>> 2021

Aliases

Many Python packages are imported as aliases, which are usually shorthand representations of the full library name. For example, two extremely popular libraries that you will use on a regular basis, NumPy and Pandas, are aliased as np and pd, respectively:

import numpy as np
import pandas as pd

# Now you can call methods from these libraries with less text:
array_one = np.array([1,2,3,4])
array_one.reshape(2,2)
>>> array([[1, 2]
          [3, 4]])

# Load a file:
df = pd.read_csv("your_datac.csv")
df.head()
>>> "HEAD_OF_YOUR_DATA_HERE"

Summary

Figuring out the submodules and aliases of the Python packages you use is something that comes from experience. Consult the API documentation of the packages you use to see specific examples about usage, imports, submodles, and aliases.

Conclusion

Congratulations! You have completed this tutorial!

You learned:

How to install Python with the Anaconda distribution.
How to create programming environments for your projects.
How to use conda and pip to install Python libraries.
How to import libraries into your Python programs.

The ins-and-outs of knowing which methods to use from the libraries you install, the specific syntax of libraries and submodules, and the common aliases used by different Python libraries is something that only comes with experience.

So get out there, code, and have some fun!

Important Resources

Here are some useful resources related to this tutorial: