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Python Libraries for Data Scientists: NumPy, pandas, Polars & More

The core Python data science stack: arrays, dataframes, statistics, machine learning, visualization and scaling.

26 topics•5 categories•Updated October 2026

Arrays, Dataframes & Statistics

6 entries

The foundation of Python data work.

01NumPyFundamental array computing library. 02pandasDataframes for data analysis. 03PolarsFast dataframe library written in Rust. 04Apache Arrow (PyArrow)Columnar in-memory format used by many data tools. 05SciPyScientific computing: optimization, integration, statistics. 06statsmodelsStatistical models, tests and data exploration.

Machine Learning

6 entries

Libraries for modeling.

07scikit-learnClassical machine learning. 08XGBoostGradient boosting library. 09TensorFlowDeep learning platform. 10KerasHigh-level deep learning API. 11PyTorchDeep learning framework. 12PyTensorTensor expression library, the successor to Theano and Aesara.

Visualization & Apps

6 entries

Charts and interactive apps.

13MatplotlibThe foundational plotting library. 14SeabornStatistical visualization built on Matplotlib. 15Plotly and DashInteractive charts and analytical web apps. 16AltairDeclarative statistical visualization. 17GradioQuick web UIs for models. 18Project JupyterNotebooks for exploratory work.

Scaling & SQL

2 entries

Larger-than-memory and in-process analytics.

19DaskParallel computing for pandas and NumPy workloads. 20DuckDBIn-process analytical SQL database.

Related Pages

6 entries

Neighbouring pages on this site.

21PythonPython hub. 22Python for Data ScienceTutorials and resources. 23Python Libraries for Data ScientistsCore data science libraries. 24Machine Learning FrameworksML frameworks. 25Deep Learning FrameworksDeep learning frameworks. 26Data Visualization ToolsVisualization tools.

Frequently Asked Questions

Which Python libraries do data scientists use most?

NumPy and pandas for data handling, scikit-learn for machine learning, Matplotlib and Seaborn for plots, and Jupyter notebooks as the working environment.

Should I use pandas or Polars?

pandas has the largest ecosystem, while Polars is faster on many workloads and uses lazy evaluation; many teams use both and exchange data through Arrow.

How do I handle data that does not fit in memory?

Use Dask for parallel dataframes, DuckDB for SQL on large files, or Polars lazy mode.

How current is this page?

Links were checked in October 2026. Sites that block automated checks are kept and may need a manual click. Last-commit dates are from GitHub in October 2026 and show activity only; check each project's release notes before adopting it.