Pandas, derived from the term “panel data” is a Python software library used for data manipulation and analysis. It was designed to solve the complexities of working with structured data, especially in tabular format like Excel, CSV, SQL and time series type data.
Pandas use cases:
| Task | Pandas Functionality |
| Load data from CSV/Excel/SQL | pd.read_csv(), pd.read_excel(), pd.read_sql() |
| Filter rows/columns | df[df[‘col’] > 10], df[[‘col1’, ‘col2’]] |
| Handle missing data | df.dropna(), df.fillna() |
| Group / aggregate | df.groupby(‘category’).mean() |
| Merge / join datasets | pd.merge(df1, df2, on=’id’) |
| Reshape data | df.pivot_table(), df.stack() |
| Time series analysis | df.resample(‘D’).mean() |
| Plotting | df.plot() (via Matplotlib integration) |