Powerful data structures for data analysis, time series, and statistics

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for datapythonista from gravatar.com datapythonista Avatar for jbrockmendel from gravatar.com jbrockmendel Avatar for jorisvandenbossche from gravatar.com jorisvandenbossche Avatar for jreback from gravatar.com jreback Avatar for jreback1 from gravatar.com jreback1 Avatar for lithomas1 from gravatar.com lithomas1 Avatar for MarcoGorelli from gravatar.com MarcoGorelli Avatar for simonjayhawkins from gravatar.com simonjayhawkins Avatar for tomaugspurger from gravatar.com tomaugspurger Avatar for wesm from gravatar.com wesm Avatar for willayd from gravatar.com willayd

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Project description


pandas: powerful Python data analysis toolkit

PyPI Latest Release Conda Latest Release DOI Package Status License Coverage Downloads Gitter Powered by NumFOCUS Code style: black Imports: isort

What is it?

pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal.

Main Features

Here are just a few of the things that pandas does well:

  • Easy handling of missing data (represented as NaN, NA, or NaT) in floating point as well as non-floating point data
  • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
  • Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
  • Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
  • Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
  • Intuitive merging and joining data sets
  • Flexible reshaping and pivoting of data sets
  • Hierarchical labeling of axes (possible to have multiple labels per tick)
  • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format
  • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging

Where to get it

The source code is currently hosted on GitHub at: https://github.com/pandas-dev/pandas

Binary installers for the latest released version are available at the Python Package Index (PyPI) and on Conda.

# conda
conda install pandas

# or PyPI
pip install pandas

Dependencies

See the full installation instructions for minimum supported versions of required, recommended and optional dependencies.

Installation from sources

To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from PyPI:

pip install cython

In the pandas directory (same one where you found this file after cloning the git repo), execute:

python setup.py install

or for installing in development mode:

python -m pip install -e . --no-build-isolation --no-use-pep517

If you have make, you can also use make develop to run the same command.

or alternatively

python setup.py develop

See the full instructions for installing from source.

License

BSD 3

Documentation

The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable

Background

Work on pandas started at AQR (a quantitative hedge fund) in 2008 and has been under active development since then.

Getting Help

For usage questions, the best place to go to is StackOverflow. Further, general questions and discussions can also take place on the pydata mailing list.

Discussion and Development

Most development discussions take place on GitHub in this repo. Further, the pandas-dev mailing list can also be used for specialized discussions or design issues, and a Gitter channel is available for quick development related questions.

Contributing to pandas Open Source Helpers

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

A detailed overview on how to contribute can be found in the contributing guide.

If you are simply looking to start working with the pandas codebase, navigate to the GitHub "issues" tab and start looking through interesting issues. There are a number of issues listed under Docs and good first issue where you could start out.

You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to subscribe to pandas on CodeTriage.

Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!

Feel free to ask questions on the mailing list or on Gitter.

As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: Contributor Code of Conduct

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for datapythonista from gravatar.com datapythonista Avatar for jbrockmendel from gravatar.com jbrockmendel Avatar for jorisvandenbossche from gravatar.com jorisvandenbossche Avatar for jreback from gravatar.com jreback Avatar for jreback1 from gravatar.com jreback1 Avatar for lithomas1 from gravatar.com lithomas1 Avatar for MarcoGorelli from gravatar.com MarcoGorelli Avatar for simonjayhawkins from gravatar.com simonjayhawkins Avatar for tomaugspurger from gravatar.com tomaugspurger Avatar for wesm from gravatar.com wesm Avatar for willayd from gravatar.com willayd

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

2.2.3

2.2.2

2.2.1

2.2.0

2.2.0rc0 pre-release

2.1.4

2.1.3

2.1.2

2.1.1

2.1.0

2.1.0rc0 pre-release

2.0.3

2.0.2

2.0.1

2.0.0

2.0.0rc1 pre-release

2.0.0rc0 pre-release

1.5.3

1.5.2

1.5.1

This version

1.5.0

1.5.0rc0 pre-release

1.4.4

1.4.3

1.4.2

1.4.1

1.4.0

1.4.0rc0 pre-release

1.3.5

1.3.4

1.3.3

1.3.2

1.3.1

1.3.0

1.2.5

1.2.4

1.2.3

1.2.2

1.2.1

1.2.0

1.1.5

1.1.4

1.1.3

1.1.2

1.1.1

1.1.0

1.0.5

1.0.4

1.0.3

1.0.2

1.0.1

1.0.0

0.25.3

0.25.2

0.25.1

0.25.0

0.24.2

0.24.1

0.24.0

0.23.4

0.23.3

0.23.2

0.23.1

0.23.0

0.22.0

0.21.1

0.21.0

0.20.3

0.20.2

0.20.1

0.20.0

0.19.2

0.19.1

0.19.0

0.18.1

0.18.0

0.17.1

0.17.0

0.16.2

0.16.1

0.16.0

0.15.2

0.15.1

0.15.0

0.14.1

0.14.0

0.13.1

0.13.0

0.12.0

0.11.0

0.10.1

0.10.0

0.9.1

0.9.0

0.8.1

0.8.0

0.7.3

0.7.2

0.7.1

0.7.0

0.6.1

0.6.0

0.5.0

0.4.3

0.4.2

0.4.1

0.4.0

0.3.0

0.2

0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pandas-1.5.0.tar.gz (5.2 MB view details)

Uploaded Source

Built Distributions

pandas-1.5.0-cp311-cp311-win_amd64.whl (10.3 MB view details)

Uploaded CPython 3.11 Windows x86-64

pandas-1.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.0 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

pandas-1.5.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (11.4 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ ARM64

pandas-1.5.0-cp311-cp311-macosx_11_0_arm64.whl (10.7 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pandas-1.5.0-cp311-cp311-macosx_10_9_x86_64.whl (11.9 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

pandas-1.5.0-cp311-cp311-macosx_10_9_universal2.whl (18.3 MB view details)

Uploaded CPython 3.11 macOS 10.9+ universal2 (ARM64, x86-64)

pandas-1.5.0-cp310-cp310-win_amd64.whl (10.4 MB view details)

Uploaded CPython 3.10 Windows x86-64

pandas-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.1 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pandas-1.5.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (11.4 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

pandas-1.5.0-cp310-cp310-macosx_11_0_arm64.whl (10.8 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pandas-1.5.0-cp310-cp310-macosx_10_9_x86_64.whl (12.0 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

pandas-1.5.0-cp310-cp310-macosx_10_9_universal2.whl (18.5 MB view details)

Uploaded CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64)

pandas-1.5.0-cp39-cp39-win_amd64.whl (10.9 MB view details)

Uploaded CPython 3.9 Windows x86-64

pandas-1.5.0-cp39-cp39-win32.whl (9.7 MB view details)

Uploaded CPython 3.9 Windows x86

pandas-1.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pandas-1.5.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (11.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

pandas-1.5.0-cp39-cp39-macosx_11_0_arm64.whl (10.9 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pandas-1.5.0-cp39-cp39-macosx_10_9_x86_64.whl (12.1 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

pandas-1.5.0-cp39-cp39-macosx_10_9_universal2.whl (18.7 MB view details)

Uploaded CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64)

pandas-1.5.0-cp38-cp38-win_amd64.whl (11.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

pandas-1.5.0-cp38-cp38-win32.whl (9.7 MB view details)

Uploaded CPython 3.8 Windows x86

pandas-1.5.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

pandas-1.5.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (11.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

pandas-1.5.0-cp38-cp38-macosx_11_0_arm64.whl (10.7 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

pandas-1.5.0-cp38-cp38-macosx_10_9_x86_64.whl (11.9 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

pandas-1.5.0-cp38-cp38-macosx_10_9_universal2.whl (18.3 MB view details)

Uploaded CPython 3.8 macOS 10.9+ universal2 (ARM64, x86-64)

File details

Details for the file pandas-1.5.0.tar.gz.

File metadata

  • Download URL: pandas-1.5.0.tar.gz
  • Upload date:
  • Size: 5.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for pandas-1.5.0.tar.gz
Algorithm Hash digest
SHA256 3ee61b881d2f64dd90c356eb4a4a4de75376586cd3c9341c6c0fcaae18d52977
MD5 5ce1f56ed3a09af45230d0d46a068231
BLAKE2b-256 2a24f5042daa59b91e94e6ea41edbb28d2b7e3712d0cf54a76f9ffde394efbe7

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: pandas-1.5.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 10.3 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for pandas-1.5.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 de34636e2dc04e8ac2136a8d3c2051fd56ebe9fd6cd185581259330649e73ca9
MD5 12936805db179b7bb51d93b400ff63ed
BLAKE2b-256 91e261f674f92e4a13a9c4b24260d2ca8c964e4affccfa3cb9fc2284996618a3

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e252a9e49b233ff96e2815c67c29702ac3a062098d80a170c506dff3470fd060
MD5 821822e95acaf15ed310edfb0fbd737d
BLAKE2b-256 fafec81ad3991f2c6aeacf01973f1d37b1dc76c0682f312f104741602a9557f1

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 207d63ac851e60ec57458814613ef4b3b6a5e9f0b33c57623ba2bf8126c311f8
MD5 0e92453730251c112d73784a8cee6030
BLAKE2b-256 0fdc9e6655b5403b2d29e96e950e6fcecb4dc57826325ef5fc9fd777abe666c1

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 86d87279ebc5bc20848b4ceb619073490037323f80f515e0ec891c80abad958a
MD5 717125ae1713a8eef04d81d8319341fb
BLAKE2b-256 1d4c59163e34135e72eda5f083aa89183f582d7a24415132f4bfc8ecc486125a

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a68a9b9754efff364b0c5ee5b0f18e15ca640c01afe605d12ba8b239ca304d6b
MD5 e9233171ffb99731014714456bd51a03
BLAKE2b-256 86561166f3d36fda4e16f4d7296207fae1fa5c01352b32e9c480e1ed360b180e

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp311-cp311-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp311-cp311-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 171cef540bfcec52257077816a4dbbac152acdb8236ba11d3196ae02bf0959d8
MD5 40314eeca5063b23ea996b7faf567770
BLAKE2b-256 10ee11b87cb8c1f7528f49c3eae401461759f7481eaf6b3f76f690d400a89410

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pandas-1.5.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 10.4 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for pandas-1.5.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 1642fc6138b4e45d57a12c1b464a01a6d868c0148996af23f72dde8d12486bbc
MD5 c4130496326c681f1a599b0d4f0504df
BLAKE2b-256 2892d8ede5d604e7970172e23e3bed9e9a19b841baed95e2fd6d87ac90f87026

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c76f1d104844c5360c21d2ef0e1a8b2ccf8b8ebb40788475e255b9462e32b2be
MD5 6fb3f1e0663a6f778a75e123e635e3c0
BLAKE2b-256 a2bb33c637f9d284a8b314aee0a6fc2c2ef243d9145e9480f910f4eca54e6887

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 41aec9f87455306496d4486df07c1b98c15569c714be2dd552a6124cd9fda88f
MD5 62d26e4eb8b079f9011c65f2e13fdd53
BLAKE2b-256 fa9dc9820dbe371c1d6297f59ef76a17e032df2659addadfc76bb87106ca35db

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8e8e5edf97d8793f51d258c07c629bd49d271d536ce15d66ac00ceda5c150eb3
MD5 9a5b855b445fddf22b6bf574ba064b31
BLAKE2b-256 8c00e247f93bd07133c47b1b6e7df5a5d5d1a3525d9ba790d38057e1e8ca66a0

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 5cc47f2ebaa20ef96ae72ee082f9e101b3dfbf74f0e62c7a12c0b075a683f03c
MD5 6b75f5a9bf90cee94b4e61d8c1a5cea4
BLAKE2b-256 f65d4020469c1f7df76644b8575b160e9ccafdf1eeb5af7d0b5627e0f565fd18

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 0d8d7433d19bfa33f11c92ad9997f15a902bda4f5ad3a4814a21d2e910894484
MD5 7d1c08adb08e9ba73bd36fefcbb20231
BLAKE2b-256 826cc35ea6ed019829714a8432da14690f442f96ffeb050343278fe0733fb768

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pandas-1.5.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 10.9 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for pandas-1.5.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 8a4fc04838615bf0a8d3a03ed68197f358054f0df61f390bcc64fbe39e3d71ec
MD5 f3180bc42266bb227a623dff7e731d67
BLAKE2b-256 2ba9f5e78b7dea7ab35e713425eb571d72002cfdac2e5c9113ce40176c8dcc9e

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp39-cp39-win32.whl.

File metadata

  • Download URL: pandas-1.5.0-cp39-cp39-win32.whl
  • Upload date:
  • Size: 9.7 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for pandas-1.5.0-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 2504c032f221ef9e4a289f5e46a42b76f5e087ecb67d62e342ccbba95a32a488
MD5 777d62e719f8eb0d8f31ae2a760f667e
BLAKE2b-256 8079f59ad6186a5271c7b1ddeb261738f667a997e782ecd9a5ae55c6b4c15d73

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c7f38d91f21937fe2bec9449570d7bf36ad7136227ef43b321194ec249e2149d
MD5 8da84b78afd88ab0e42050ef773a2a10
BLAKE2b-256 d083944ebe877e40b1ac9c47b4ea827f9358f10640ec0523e0b54cebf3d39d84

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 947ed9f896ee61adbe61829a7ae1ade493c5a28c66366ec1de85c0642009faac
MD5 fd78e4c6e7df69f2ff0b86ca5c112bbb
BLAKE2b-256 ba515d6a6e360a69ce88b1170db0897d7c497f02fdbbf77281a9d01aa8eaa064

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 85a516a7f6723ca1528f03f7851fa8d0360d1d6121cf15128b290cf79b8a7f6a
MD5 f77fcb46b40c049c87c48ef7ea884949
BLAKE2b-256 1394ffc33b085128c3c67f29b5bee296a7761aa53147439710a01affe0695066

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e9c5049333c5bebf993033f4bf807d163e30e8fada06e1da7fa9db86e2392009
MD5 39617c95ccb078e5993e5ce27f01fbfc
BLAKE2b-256 4aad50a7329fcd1d23cca74dcd1eae4dfb429d5fb28963f5822649fda8320bf2

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 73844e247a7b7dac2daa9df7339ecf1fcf1dfb8cbfd11e3ffe9819ae6c31c515
MD5 8416590b591bc84e8a7103d4f392f592
BLAKE2b-256 85741aafd4d480ee3c22c6b30c2449939e129f3a75bbb042ff6cc3bdcc99295c

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pandas-1.5.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 11.0 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for pandas-1.5.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 33a9d9e21ab2d91e2ab6e83598419ea6a664efd4c639606b299aae8097c1c94f
MD5 c8d2f906b85f20cb7407035f5568ae9d
BLAKE2b-256 fad2554c10c71f983040d513eca07c2661c6f4fff386652943561a948d55e13a

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp38-cp38-win32.whl.

File metadata

  • Download URL: pandas-1.5.0-cp38-cp38-win32.whl
  • Upload date:
  • Size: 9.7 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for pandas-1.5.0-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 e178ce2d7e3b934cf8d01dc2d48d04d67cb0abfaffdcc8aa6271fd5a436f39c8
MD5 ad50e35334bc1668c6f26816a00c7648
BLAKE2b-256 6091dd83dd2222b6c8f707a39b0d8da91158c42e4827d6962229553a31199548

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fc987f7717e53d372f586323fff441263204128a1ead053c1b98d7288f836ac9
MD5 677e77d06d0047b98a1b0df6aad07a4f
BLAKE2b-256 da4aa0c8d3ba8d875a25578bcb3032b6ac9b2db3d016b0a762ab41e1a13f3b52

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 62e61003411382e20d7c2aec1ee8d7c86c8b9cf46290993dd8a0a3be44daeb38
MD5 2842ec4184b73a36acf36ca75a6150da
BLAKE2b-256 4ccce4670163011b9ac92f728b871c617d8e9421461df5c30ececb65dce52ba6

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4e30a31039574d96f3d683df34ccb50bb435426ad65793e42a613786901f6761
MD5 50122152b662d25af2bc75d86337064a
BLAKE2b-256 aa420933b9430425286d2ee3059e067ece3590b63542d9d7a9bb66d3a300ca85

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1b82ccc7b093e0a93f8dffd97a542646a3e026817140e2c01266aaef5fdde11b
MD5 0e178a61e9f78708c572e2e524c875fb
BLAKE2b-256 054bcade88002bce6a808c44199617160bd9d78c23d4bb91950f4397aebf063f

See more details on using hashes here.

File details

Details for the file pandas-1.5.0-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for pandas-1.5.0-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 1d34b1f43d9e3f4aea056ba251f6e9b143055ebe101ed04c847b41bb0bb4a989
MD5 e3e9aac7dfbe3b72f074b0afe58f5bc6
BLAKE2b-256 a928b22dee58c431006f84cc6336c44be980ba3ff90ce81e95f007e14f2c484d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page