Pandas
What it is: Python's data manipulation library. DataFrames, groupby, merge, pivot. Foundation of Python data work.
What It Does Best
Excel in code. DataFrames work like spreadsheets. Familiar operations: filter, sort, pivot, merge.
Handle messy data. Missing values, duplicates, type conversions. Built-in functions for common data problems.
Fast vectorized operations. No loops needed. Operations on entire columns at once.
Key Features
DataFrames: Two-dimensional labeled data structures (like Excel tables)
GroupBy: Split-apply-combine operations for aggregations
Merging/Joining: SQL-like joins and concatenation
Time series: Date/time indexing and resampling
I/O tools: Read/write CSV, Excel, SQL, JSON, Parquet
Pricing
Free: Open source, BSD license
When to Use It
✅ Datasets that fit in memory (< few GB)
✅ Data wrangling and cleaning
✅ Exploratory analysis in Jupyter
✅ Preparing data for ML models
✅ Converting between data formats
When NOT to Use It
❌ Very large data (use Dask, Spark, or databases)
❌ Real-time streaming (not designed for it)
❌ Complex SQL operations (just use SQL)
❌ Simple calculations (NumPy faster)
❌ Non-tabular data (lists/dicts sufficient)
Common Use Cases
Data cleaning: Handle missing values, remove duplicates, type conversions
ETL pipelines: Load, transform, and export data
Aggregations: GroupBy operations for summaries and statistics
Time series analysis: Resampling, rolling windows, date operations
Feature engineering: Create ML features from raw data
Pandas vs Alternatives
vs SQL: Pandas better for complex transformations, SQL better for large data
vs R/dplyr: Similar functionality, Pandas integrates with Python ecosystem
vs Excel: Pandas better for automation and reproducibility, Excel better for quick tasks
Unique Strengths
Method chaining: Chain operations for readable data pipelines
Flexible indexing: Label-based and integer-based indexing
Missing data handling: Sophisticated tools for NaN values
Integration: Works seamlessly with NumPy, scikit-learn, matplotlib
Bottom line: If you're doing data work in Python, you're using Pandas. Non-negotiable part of the Python data stack.