Comparing
Fundamental package for array computing in Python
Powerful data structures for data analysis, time series, and statistics
| Metric | numpy | pandas |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Beginner | Beginner |
| Weekly downloads | 0 | 0↑ |
| Latest version | v2.4.4 | v3.0.2 |
| Recipes | 1 | 1 |
numpy only
Shared
pandas only
Use NumPy any time you're doing numerical computation: matrix math, statistics, signal processing, image data as pixel arrays, or feeding data into ML libraries. It's the foundation most scientific Python libraries (pandas, scipy, PyTorch) are built on.
Use pandas for data cleaning, exploration, and transformation: reading CSV/Excel/JSON files, computing statistics, reshaping data before feeding it to a model, or generating reports. It's the standard tool for the 'data wrangling' phase of any data science or analytics project.
NumPy is not for general-purpose data manipulation with mixed types or strings — use pandas DataFrames there. For very large datasets that don't fit in RAM, look at Dask or CuPy (GPU arrays).
Pandas struggles with datasets larger than a few GB in RAM. For big data, use Polars (faster, lower memory) or Dask (distributed pandas). For pure numerical math without labels, NumPy is more appropriate.
numpy
pandas