A repository dedicated to visualizing and preprocessing agricultural data at the district level (ICRISAT dataset) using Python and data analysis techniques. The primary tool is a Jupyter Notebook that walks through data cleaning, transformation, and visualization steps.
ICRISAT-District Level Data.csv: Raw dataset—likely includes district-level agricultural metrics from ICRISAT.crops.ipynb: Jupyter Notebook that explores and visualizes the data. It likely covers data loading, cleaning, transformation, and generating insightful visualizations.
Ensure you have the following installed:
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Python 3.7+
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Jupyter Notebook or JupyterLab
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Common data science libraries, e.g.:
pip install pandas numpy matplotlib seaborn
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Clone the repository:
git clone https://github.com/aastha280/Data-Visualization-and-Pre-processing.git cd Data-Visualization-and-Pre-processing -
Open and run the notebook:
jupyter notebook crops.ipynb
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Walk through the notebook to see:
- Loading and exploring the dataset
- Data cleaning and preprocessing steps
- Visualizations like charts, plots, and maps to reveal trends or insights
The Jupyter Notebook typically includes:
- Data Loading – Reading the CSV into a Pandas DataFrame.
- Exploratory Data Analysis (EDA) – Generating summary stats and checking for missing values or anomalies.
- Data Cleaning – Handling missing or erroneous entries, type conversions, etc.
- Preprocessing – Scaling, normalizing, or encoding where necessary.
- Visualizations – Charts and plots (bar charts, line plots, scatter plots, etc.) to understand trends across districts, crops, seasons, etc.
- Conclusions & Insights – Key findings from the data shown through visuals and analysis.
- Add More Visualizations – Use interactive plotting libraries like Plotly or Folium for maps.
- Compute Derived Metrics – Per-district averages, seasonal comparisons, yield forecasts, etc.
- Modularize Code – Refactor logic into standalone
.pyscripts for clean reuse. - Documentation – Expand explanations in markdown cells, add a badges section, or link to key findings.
Feel free to open issues or propose improvements via pull requests. Additional data sources, enhanced visualizations, or structure enhancements are welcome!
| Aspect | Details |
|---|---|
| Objective | Data cleaning and visualization of ICRISAT district-level data |
| Primary File | crops.ipynb (Jupyter Notebook) |
| Data Source | ICRISAT-District Level Data.csv |
| Key Workflows | Data loading, preprocessing, visualizing |