Learn PANDAS with Real Code Examples

Series: one-dimensional array with labels

DataFrame: two-dimensional labeled data table

Index: metadata for row/column labeling

IO tools: CSV, Excel, SQL, HDF5, JSON

Extension and categorical types for advanced use cases

Data represented as Series or DataFrame

Operations applied row-wise, column-wise, or element-wise

Vectorized operations for speed

GroupBy-split-apply-combine paradigm

Time-series handled with built-in resampling and rolling windows

DataFrame-centric architecture

Integration with NumPy for efficient computation

I/O abstraction for multiple file types

Extension types for categorical, datetime, and nullable data

Chaining operations for workflow clarity

Financial analysis and stock data processing

Data cleaning and ETL pipelines

Scientific data processing (climate, genomics, etc.)

Preprocessing for machine learning pipelines

Business analytics dashboards and reporting

High-performance and expressive API

Flexible data structures for structured data

Integration with Python data science ecosystem

Ease of use and intuitive syntax

Robust handling of missing data

Use Dask or PySpark for out-of-memory datasets

Chunk reading/writing large files

Optimize memory with category and nullable types

Vectorize operations instead of loops

Profile and monitor large dataset workflows

Upgrade via pip or conda

Check for deprecated APIs

Test existing scripts for compatibility

Update I/O and type handling if necessary

Review new performance features in latest versions