Learn PANDAS with Real Code Examples
Updated Nov 24, 2025
Practical Examples
Read CSV: pd.read_csv('data.csv')
Filter rows: df[df['column'] > 10]
Compute mean: df['column'].mean()
Merge datasets: pd.merge(df1, df2, on='key')
Resample time-series: df.resample('M').sum()
Troubleshooting
Check for correct file paths and formats
Handle missing data before aggregation
Ensure consistent data types across columns
Avoid SettingWithCopyWarning by using .loc
Optimize memory usage for large datasets
Testing Guide
Verify data loads correctly
Check for missing or duplicate values
Validate transformations and aggregations
Compare sample outputs against expected results
Profile memory and runtime for large datasets
Deployment Options
Scripts for local analysis
Jupyter notebooks for exploration
ETL pipelines in production
Integration with web dashboards (Dash, Streamlit)
Cloud-based data processing (AWS, GCP, Azure)
Tools Ecosystem
NumPy for numerical operations
Matplotlib/Seaborn for visualization
SciPy for advanced statistical analysis
Scikit-learn for ML preprocessing
SQLAlchemy for database integration
Integrations
CSV, Excel, SQL, HDF5, JSON I/O
Matplotlib/Seaborn for plotting
NumPy for fast numeric operations
Scikit-learn for ML pipelines
Dask or PySpark for large-scale datasets
Productivity Tips
Use vectorized operations for speed
Leverage built-in aggregation and transform methods
Avoid loops over DataFrame rows
Document and version datasets
Use notebooks for exploratory analysis
Challenges
Efficiently clean and transform messy datasets
Handle missing and inconsistent data
Perform complex aggregations and joins
Optimize memory usage for large tables
Design reproducible data analysis pipelines