Learn PANDAS with Real Code 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()

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

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

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)

NumPy for numerical operations

Matplotlib/Seaborn for visualization

SciPy for advanced statistical analysis

Scikit-learn for ML preprocessing

SQLAlchemy for database integration

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

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

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