Summary and Schedule

This is a new lesson built with The Carpentries Workbench.

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

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 Installing extra packages (rarely needed, but if something is missing or outdated):Python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