Summary and Setup

This is a new lesson built with The Carpentries Workbench.

Overview

To participate in the Data Visualization module, you will need:

  • Python 3.10 or newer (3.11–3.12 recommended in local setups)
  • The following Python packages:
    • pandas (data handling)
    • matplotlib (core plotting)
    • seaborn (statistical visualizations)
    • (optional but recommended) jupyterlab or notebook (interactive work)
    • (optional) plotly (interactive charts in later episodes)
  • A code editor or notebook interface
  • Sample datasets (provided as a zip file)

We offer two main setup paths:

  1. Google Colab (recommended for beginners / no installation needed)
  2. Local installation with Anaconda Navigator (great for offline work and full control)

Google Colab is a free, cloud-based Jupyter notebook environment hosted by Google. It runs entirely in your browser, requires only a Google account, and comes with pandas, matplotlib, seaborn, and many other data science libraries pre-installed.

Steps

  1. Go to → https://colab.research.google.com

  2. Sign in with your Google account (or create one if needed).

  3. Click New notebook (or File → New notebook).

  4. (Optional) Rename it: File → Rename (e.g., “Data Viz Workshop – Aniket”).

  5. Test the libraries right away by running this in the first cell (Shift+Enter to execute):

    PYTHON 

    import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px   # optional – usually pre-installed too

print("pandas version:", pd.__version__)
print("matplotlib version:", plt.matplotlib.__version__)
print("seaborn version:", sns.__version__)
print("plotly version:", px.__version__ if 'px' in globals() else "not imported")

# Quick test plot (should show inline)
tips = sns.load_dataset("tips")  # built-in Seaborn dataset
sns.histplot(data=tips, x="total_bill", hue="time")
plt.title("Test: Restaurant Tips Distribution")
plt.show()

  6. Installing extra packages (rarely needed, but if something is missing or outdated):Python

PYTHON 

!pip install --upgrade seaborn plotly

(The ! runs shell commands in Colab/Jupyter Notebook.)

Advantages of Colab for this workshop

  • No software installation
  • Free GPU/TPU if needed later
  • Easy sharing (File → Share)
  • Autosaves to Google Drive
  • Perfect for following along with instructor demos

Tip: Upload your own data files via the left sidebar (Files tab → Upload) or mount Google Drive: Python

PYTHON 

from google.colab import drive
drive.mount('/content/drive')
# Then read files like pd.read_csv('/content/drive/MyDrive/penguins.csv')

Option 2: Local Installation (Anaconda Navigator – For Offline / Advanced Use)

Use this if you prefer working without internet or need a persistent environment.

  1. Download and install Anaconda Navigator:
  1. You should find multiple apps after installation.
  • Launch Jupyter Notebook
  • If you do not find a package simply add !pip followed by the name of the package in code cell to install it locally.

Troubleshooting

  • Colab: Plots not showing? Add %matplotlib inline at the top (usually automatic).
  • Local: package not found: Open terminal or code cell in jupyter notebook and !pip install package.
  • Need help? Raise hand during workshop or check Carpentries Python setup guide.

You’re all set! Proceed to Introduction to Data Visualization or Creating Your First Plots.

Happy visualizing!