A learn-in-public notebook that turns IPL match records into questions, grouped summaries, and visual comparisons.
- Which teams appear most often as match winners?
- How do captains choose to bat or field after winning the toss?
- How often does the toss winner also win the match?
- Which wins by runs have the largest margins?
- Which players and venues appear most frequently in the data?
IPL.csv → schema inspection → grouped counts → derived ratios → seaborn/matplotlib charts → observations
The notebook is intentionally exploratory. It uses descriptive aggregation, not causal inference or match-outcome prediction.
git clone https://github.com/ReaperXD67/data_plotting.git
cd data_plotting
python -m venv .venv
pip install -r requirements.txt
jupyter notebook Untitled.ipynb| File | Purpose |
|---|---|
IPL.csv |
Match-level source data used by the notebook |
Untitled.ipynb |
Data cleaning, aggregation, charts, and observations |
requirements.txt |
Reproducible Python environment captured for the project |
Counts are sensitive to the seasons and teams represented in IPL.csv. Toss correlations are descriptive and do not establish that the toss caused the match result.