Setting Up Your Python Environment

Before we can start creating plots, we need to make sure your computer is ready with the necessary tools. This involves setting up a Python environment that includes the Matplotlib and Seaborn libraries, along with their common companions like NumPy and Pandas.

Think of a Python environment as a dedicated workspace for your project. It keeps the specific versions of libraries your project needs separate from other projects, preventing conflicts. For data science and visualization tasks, managing these libraries and their dependencies can sometimes be tricky, but thankfully, we have tools to simplify this process.

Using Anaconda for Environment Management

One of the most popular ways to manage Python environments and packages for data science is Anaconda (or its lighter-weight sibling, Miniconda). Anaconda is a distribution that comes bundled with Python, a powerful environment and package manager called conda, and many commonly used data science libraries pre-installed. This makes setup relatively straightforward.

Why Anaconda?

• Simplicity: Installs Python and essential data science libraries in one go.
• Dependency Handling: conda is excellent at managing complex dependencies between libraries, which is common in scientific computing.
• Environment Management: Easily create isolated environments for different projects.

Steps (General Guide):

1. Download and Install: Visit the Anaconda Distribution website and download the installer appropriate for your operating system (Windows, macOS, or Linux). Follow the installation instructions. We recommend using the latest Python 3 version offered.
2. Create a Dedicated Environment (Recommended): Open your terminal (Anaconda Prompt on Windows, Terminal on macOS/Linux) and create a new environment specifically for this course. This keeps things organized. We'll name it viz_env and include Python, Matplotlib, Seaborn, Pandas, NumPy, and JupyterLab (a useful tool for interactive plotting):

   conda create --name viz_env python=3.9 matplotlib seaborn pandas numpy jupyterlab
   

   You might be prompted to proceed (y/n); type y and press Enter. Note: You can choose a different Python version if needed, but 3.9 or higher is generally recommended.
3. Activate the Environment: Before installing packages or running code for this course, you need to activate the environment you just created:

   conda activate viz_env
   

   Your terminal prompt should now show (viz_env) at the beginning, indicating the environment is active.

Using pip and venv (Alternative)

If you prefer not to use Anaconda, you can use Python's built-in tools: venv for creating virtual environments and pip for installing packages. This approach is often favored by software developers and keeps installations minimal.

Why pip and venv?

• Built-in: venv comes standard with Python 3.3+. pip is the standard Python package installer.
• Lightweight: Creates minimal environments containing only what you explicitly install.

Steps (General Guide):

1. Ensure Python and pip are Installed: You need a Python 3 installation. pip usually comes installed with Python. You can check your Python version by typing python --version or python3 --version in your terminal.
2. Create a Virtual Environment: Navigate to your project directory in the terminal and run:

   python -m venv viz_env
   

   This creates a folder named viz_env containing a copy of the Python interpreter and a place to install libraries.
3. Activate the Environment:
   • macOS/Linux: source viz_env/bin/activate
   • Windows: viz_env\Scripts\activate Your terminal prompt should change to indicate the active environment (often showing (viz_env)).
4. Install Libraries: Use pip to install the required packages:

   pip install matplotlib seaborn pandas numpy jupyterlab
   

Recommendation for Beginners

While both methods work, we recommend using Anaconda for this course, especially if you are new to Python environments. Its ability to handle the complex interdependencies often found in data science libraries makes the setup process smoother.

Verifying Your Setup

Once you have installed the libraries using either method and activated your environment (viz_env), you can perform a quick check. Start a Python interpreter by typing python in your activated terminal, or launch JupyterLab by typing jupyter lab.

In the Python prompt or a Jupyter Notebook cell, try importing the libraries:

import matplotlib
import seaborn
import pandas
import numpy

print("Libraries imported successfully!")
print(f"Matplotlib version: {matplotlib.__version__}")
print(f"Seaborn version: {seaborn.__version__}")
print(f"Pandas version: {pandas.__version__}")
print(f"NumPy version: {numpy.__version__}")

If this code runs without any ImportError messages and prints the versions, your environment is correctly set up, and you're ready to move on to importing these libraries into your scripts and creating your first plot!