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 is a Python package that provides fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series 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 toward this goal.

pandas is well suited for many different kinds of data:

  • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet

  • Ordered and unordered (not necessarily fixed-frequency) time series data.

  • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels

  • Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure

The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame provides everything that R’s data.frame provides and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries.

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

  • Easy handling of missing data (represented as NaN) 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.

Many of these principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis into a form suitable for plotting or tabular display. pandas is the ideal tool for all of these tasks.

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

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

This version

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-1.2.4.tar.gz (5.5 MB view details)

Uploaded Source

Built Distributions

pandas-1.2.4-cp39-cp39-win_amd64.whl (9.3 MB view details)

Uploaded CPython 3.9 Windows x86-64

pandas-1.2.4-cp39-cp39-win32.whl (8.2 MB view details)

Uploaded CPython 3.9 Windows x86

pandas-1.2.4-cp39-cp39-manylinux1_x86_64.whl (9.7 MB view details)

Uploaded CPython 3.9

pandas-1.2.4-cp39-cp39-manylinux1_i686.whl (9.4 MB view details)

Uploaded CPython 3.9

pandas-1.2.4-cp39-cp39-macosx_10_9_x86_64.whl (10.7 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

pandas-1.2.4-cp38-cp38-win_amd64.whl (9.3 MB view details)

Uploaded CPython 3.8 Windows x86-64

pandas-1.2.4-cp38-cp38-win32.whl (8.2 MB view details)

Uploaded CPython 3.8 Windows x86

pandas-1.2.4-cp38-cp38-manylinux1_x86_64.whl (9.7 MB view details)

Uploaded CPython 3.8

pandas-1.2.4-cp38-cp38-manylinux1_i686.whl (9.4 MB view details)

Uploaded CPython 3.8

pandas-1.2.4-cp38-cp38-macosx_10_9_x86_64.whl (10.5 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

pandas-1.2.4-cp37-cp37m-win_amd64.whl (9.1 MB view details)

Uploaded CPython 3.7m Windows x86-64

pandas-1.2.4-cp37-cp37m-win32.whl (8.1 MB view details)

Uploaded CPython 3.7m Windows x86

pandas-1.2.4-cp37-cp37m-manylinux1_x86_64.whl (9.9 MB view details)

Uploaded CPython 3.7m

pandas-1.2.4-cp37-cp37m-manylinux1_i686.whl (9.5 MB view details)

Uploaded CPython 3.7m

pandas-1.2.4-cp37-cp37m-macosx_10_9_x86_64.whl (10.4 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file pandas-1.2.4.tar.gz.

File metadata

  • Download URL: pandas-1.2.4.tar.gz
  • Upload date:
  • Size: 5.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4.tar.gz
Algorithm Hash digest
SHA256 649ecab692fade3cbfcf967ff936496b0cfba0af00a55dfaacd82bdda5cb2279
MD5 39b84ed4694056be20e8ce4955316a6f
BLAKE2b-256 e881f7be049fe887865200a0450b137f2c574647b9154503865502cfd720ab5d

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pandas-1.2.4-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 9.3 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 2b063d41803b6a19703b845609c0b700913593de067b552a8b24dd8eeb8c9895
MD5 9aa57a4b9723dc32a4f311c28e97f160
BLAKE2b-256 f858027a1564dbdea488f135d8a8555531cc92b654640455d440a1cf483d1ac0

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp39-cp39-win32.whl.

File metadata

  • Download URL: pandas-1.2.4-cp39-cp39-win32.whl
  • Upload date:
  • Size: 8.2 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 d0877407359811f7b853b548a614aacd7dea83b0c0c84620a9a643f180060950
MD5 ca41b1bd96f2986205102383ef2c12a4
BLAKE2b-256 0c95310673f34fd3c29104b9f93cc197ff5461b85b77d9b9e7ef47869fd1dafd

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp39-cp39-manylinux1_x86_64.whl.

File metadata

  • Download URL: pandas-1.2.4-cp39-cp39-manylinux1_x86_64.whl
  • Upload date:
  • Size: 9.7 MB
  • Tags: CPython 3.9
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp39-cp39-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 52d2472acbb8a56819a87aafdb8b5b6d2b3386e15c95bde56b281882529a7ded
MD5 b470a644f2c73bb188d167986035c8dd
BLAKE2b-256 ada8cdc88844ee0935ad8ecf6fa2f2d445fdb9ed947ff75c9dbb7fc1e7effca1

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp39-cp39-manylinux1_i686.whl.

File metadata

  • Download URL: pandas-1.2.4-cp39-cp39-manylinux1_i686.whl
  • Upload date:
  • Size: 9.4 MB
  • Tags: CPython 3.9
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp39-cp39-manylinux1_i686.whl
Algorithm Hash digest
SHA256 298f0553fd3ba8e002c4070a723a59cdb28eda579f3e243bc2ee397773f5398b
MD5 9c2000a85e6f6072b7db2bffc05667fc
BLAKE2b-256 6cc3a170729a0e5dc0adce6af8c54029987076c4f20a1593ed2c3709ff78045c

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pandas-1.2.4-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 10.7 MB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9db70ffa8b280bb4de83f9739d514cd0735825e79eef3a61d312420b9f16b758
MD5 ec005d55873ccf04ae6eb873e8797aee
BLAKE2b-256 43a3ef218577452349cc1b1063e0f1a48fa99d49a82981aa8efe6f7f8f2b3c80

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pandas-1.2.4-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 9.3 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 bd659c11a4578af740782288cac141a322057a2e36920016e0fc7b25c5a4b686
MD5 1d9c7b1e3e7c7e0f3d38fd2065f7c7e4
BLAKE2b-256 b9b96a13093ca4e4ea11af84fd40076601397f725944add620937f27319a940b

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp38-cp38-win32.whl.

File metadata

  • Download URL: pandas-1.2.4-cp38-cp38-win32.whl
  • Upload date:
  • Size: 8.2 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 68d7baa80c74aaacbed597265ca2308f017859123231542ff8a5266d489e1858
MD5 16b4371b0149aff93546e1aef01c718f
BLAKE2b-256 a3dc82eefd2b6eaea91c0a04b574441925b3e2549e729600c03d8b83ac56d399

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp38-cp38-manylinux1_x86_64.whl.

File metadata

  • Download URL: pandas-1.2.4-cp38-cp38-manylinux1_x86_64.whl
  • Upload date:
  • Size: 9.7 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 971e2a414fce20cc5331fe791153513d076814d30a60cd7348466943e6e909e4
MD5 cfaef022d49983a4540f0ac739cc9adf
BLAKE2b-256 c83b50dd12d901d5ec760d5ee169dfcb4917f73caa200e19c5da9623b62f2cea

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp38-cp38-manylinux1_i686.whl.

File metadata

  • Download URL: pandas-1.2.4-cp38-cp38-manylinux1_i686.whl
  • Upload date:
  • Size: 9.4 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp38-cp38-manylinux1_i686.whl
Algorithm Hash digest
SHA256 612add929bf3ba9d27b436cc8853f5acc337242d6b584203f207e364bb46cb12
MD5 c7ec3318ea2381eee1b23e2cfa4b64b1
BLAKE2b-256 132c5a8afc50cd2508905c23322c1d80dceb640717ce93f47f52aa4a4742553c

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pandas-1.2.4-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 10.5 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 167693a80abc8eb28051fbd184c1b7afd13ce2c727a5af47b048f1ea3afefff4
MD5 5511359325684619c1961cd498674d45
BLAKE2b-256 d66635235bc1d1ebc34d807c100c4b54a13660695f18c2fb29d2192ebbb5a613

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: pandas-1.2.4-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 9.1 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 2111c25e69fa9365ba80bbf4f959400054b2771ac5d041ed19415a8b488dc70a
MD5 e3b65a0e51ac0732353673df75d1d988
BLAKE2b-256 748c9cf2e5304f4466dbc759a799b97bfd75cd3dc93b00d49558ca93bfc29173

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp37-cp37m-win32.whl.

File metadata

  • Download URL: pandas-1.2.4-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 8.1 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 2cb7e8f4f152f27dc93f30b5c7a98f6c748601ea65da359af734dd0cf3fa733f
MD5 d8ad3bfadfd055e2ae1b6e714812b5db
BLAKE2b-256 8632fccb5c4d31b66585d84fb3a4831b5e9982b13a5b00c88c397b78ff89ec5f

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: pandas-1.2.4-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 9.9 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b730add5267f873b3383c18cac4df2527ac4f0f0eed1c6cf37fcb437e25cf558
MD5 397a21df367940f327cd1a10c0c41e9a
BLAKE2b-256 515148f3fc47c4e2144da2806dfb6629c4dd1fa3d5a143f9652b141e979a8ca9

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp37-cp37m-manylinux1_i686.whl.

File metadata

  • Download URL: pandas-1.2.4-cp37-cp37m-manylinux1_i686.whl
  • Upload date:
  • Size: 9.5 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp37-cp37m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 8d4c74177c26aadcfb4fd1de6c1c43c2bf822b3e0fc7a9b409eeaf84b3e92aaa
MD5 1840c1ca3074e57a8c99630a63c43cd1
BLAKE2b-256 5e7d7aaa6a20a7d3b6481b60b73de49dd6dc4c6780d24a6bdcff7a662e7a5b4e

See more details on using hashes here.

File details

Details for the file pandas-1.2.4-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pandas-1.2.4-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 10.4 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2

File hashes

Hashes for pandas-1.2.4-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c601c6fdebc729df4438ec1f62275d6136a0dd14d332fc0e8ce3f7d2aadb4dd6
MD5 66792aa223a39986e5a8f2024b469868
BLAKE2b-256 e201d6ab319ffec641987d574ad2d1a9adee281389d5e24955f140d5e7c20283

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