Learn PANDAS with Real Code Examples

Install Python 3.8+

Install Pandas via pip: pip install pandas

Optionally install Anaconda for pre-bundled packages

Verify installation: import pandas as pd; pd.__version__

Set up IDE or Jupyter Notebook for experimentation

Install Python 3.8+

Install Pandas via pip or conda

Set up Jupyter Notebook or IDE

Install visualization/ML libraries (Matplotlib, scikit-learn)

Verify by loading a sample DataFrame

main.py / notebook.ipynb

data/ - raw and cleaned datasets

utils/ - helper data manipulation functions

plots/ - saved visualizations

models/ - preprocessed features for ML

python main.py - run script

pip install pandas - install library

python -m pip show pandas - verify installation

jupyter notebook - interactive analysis

pytest - run data-related unit tests

Supports Unicode in strings

Handles international date/time formats

Works with global datasets in CSV/Excel/SQL

Supports various currency, locale, and numeric formats

Compatible with global ML and visualization libraries

Cross-platform: Windows, macOS, Linux

Accessible via Python ecosystem

Open-source and free

Documentation and community support widely available

Beginner-friendly tutorials and examples

Jupyter Notebook displays DataFrames with rich formatting

Use pandas styling API for conditional formatting

Highlight missing data or outliers

Integrate with Plotly, Matplotlib, or Seaborn for plots

Render interactive dashboards with Streamlit/Dash

Track DataFrame transformations

Store intermediate cleaned datasets

Manage categorical and numeric types consistently

Log preprocessing steps for reproducibility

Maintain backup copies of raw data

Organize datasets in structured directories

Clean, validate, and annotate data

Handle missing and duplicate entries

Merge, join, and reshape datasets efficiently

Export to desired formats for downstream tasks