pandas
Powerful data structures for data analysis, time series, and statistics
Decision gist · record as of 2026-08-14
Yes. pandas is a foundational tool for any Python data workflow. It is actively maintained, widely compatible (Python 3.11+), permissively licensed, has no known vulnerabilities, and ships with pre-built wheels for all major platforms. Medium install friction is typical for compiled libraries and not a barrier to adoption. Install it unless you work exclusively with unstructured data or have no need for tabular data manipulation.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy and python-dateutil as runtime dependencies; tzdata required on Windows/Emscripten only.
- Medium install friction due to compiled dependencies (numpy, Cython).
- Actively maintained with a release 23 days ago; supports Python 3.11–3.14 and ships pre-built wheels for all major platforms (macOS, Linux, Windows, including ARM).
License · maintenance · safety
permissive license (permissive) — BSD 3-Clause license is permissive and allows commercial use, modification, and redistribution with minimal restrictions. Includes bundled code under compatible licenses (Apache 2.0, MIT); no copyleft obligations.
last release 2026-07-22 (23 days) · last repo commit 2026-08-14 · 49,521 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 769,110,377 downloads/mo, #38 on PyPI
Alternatives
Verify before relying
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
print(df.groupby('A').sum())- Whether medium install friction reflects typical user experience or only applies to source builds
- Performance characteristics for datasets larger than memory
- Specific version constraints for optional dependencies (e.g., openpyxl for Excel I/O)
What it is and what it does
pandas is a foundational Python library for data manipulation and analysis, built on top of numpy. It introduces two core data structures—Series (1-D labeled arrays) and DataFrame (2-D labeled tables)—that make it natural to work with relational or time-indexed data. The library handles missing values (NaN, NA, NaT), aligns data automatically across operations, and provides a large API for reshaping, merging, grouping, and I/O tasks (CSV, Excel, HDF5, databases). It has been actively developed since 2008 and is widely used in data science, finance, and scientific computing workflows.
The package depends on numpy for array operations, python-dateutil for calendar logic, and tzdata for timezone handling (Windows/Emscripten only). Installation is straightforward via pip or conda, with pre-built wheels available for modern Python versions and common architectures. The BSD 3-Clause license permits commercial and derivative use without copyleft obligations.
Use it for
- Load and clean CSV or Excel files, handle missing values, and prepare data for analysis or machine learning.
- Perform time-series operations: resample data by frequency, compute rolling statistics, shift or lag values.
- Group data by one or more columns and apply aggregations (sum, mean, count) or custom transformations.
- Merge or join multiple datasets on common keys or indices, and reshape data via pivot tables or stacking.
- Explore data interactively: inspect dtypes, compute descriptive statistics, and filter rows/columns by label or condition.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
pandas is a foundational tool for any Python data workflow. It is actively maintained, widely compatible (Python 3.11+), permissively licensed, has no known vulnerabilities, and ships with pre-built wheels for all major platforms. Medium install friction is typical for compiled libraries and not a barrier to adoption. Install it unless you work exclusively with unstructured data or have no need for tabular data manipulation.
Install
pandas on PyPI
Before you install
Medium install friction due to compiled dependencies (numpy, Cython). Actively maintained with a release 23 days ago; supports Python 3.11–3.14 and ships pre-built wheels for all major platforms (macOS, Linux, Windows, including ARM).
Requires numpy and python-dateutil as runtime dependencies; tzdata required on Windows/Emscripten only.
License in practice
BSD 3-Clause license is permissive and allows commercial use, modification, and redistribution with minimal restrictions. Includes bundled code under compatible licenses (Apache 2.0, MIT); no copyleft obligations.
Quickstart
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
print(df.groupby('A').sum())Verify before relying
- Whether medium install friction reflects typical user experience or only applies to source builds
- Performance characteristics for datasets larger than memory
- Specific version constraints for optional dependencies (e.g., openpyxl for Excel I/O)
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesnumpypython-dateutiltzdata |
| Maintenance | Actively maintained 23 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 769,110,377 / month, #38 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: CythonProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: pandas-3.0.5-cp311-cp311-macosx_10_9_x86_64.whl; pandas-3.0.5-cp311-cp311-macosx_11_0_arm64.whl; pandas-3.0.5-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; pandas-3.0.5-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; pandas-3.0.5-cp311-cp311-musllinux_1_2_aarch64.whl; pandas-3.0.5-cp311-cp311-musllinux_1_2_x86_64.whl; pandas-3.0.5-cp311-cp311-win_amd64.whl; pandas-3.0.5-cp311-cp311-win_arm64.whl; pandas-3.0.5-cp312-cp312-macosx_10_13_x86_64.whl; pandas-3.0.5-cp312-cp312-macosx_11_0_arm64.whl; pandas-3.0.5-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; pandas-3.0.5-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; pandas-3.0.5-cp312-cp312-musllinux_1_2_aarch64.whl; pandas-3.0.5-cp312-cp312-musllinux_1_2_x86_64.whl; pandas-3.0.5-cp312-cp312-pyemscripten_2024_0_wasm32.whl; pandas-3.0.5-cp312-cp312-win_amd64.whl; pandas-3.0.5-cp312-cp312-win_arm64.whl; pandas-3.0.5-cp313-cp313-macosx_10_13_x86_64.whl; pandas-3.0.5-cp313-cp313-macosx_11_0_arm64.whl; pandas-3.0.5-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl
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