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 providing 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, moving window linear regressions, date shifting and lagging, etc.

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.

Note

Windows binaries built against NumPy 1.8.1

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

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

This version

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 Distributions

pandas-0.15.2.zip (5.0 MB view details)

Uploaded Source

pandas-0.15.2.tar.gz (4.6 MB view details)

Uploaded Source

Built Distributions

pandas-0.15.2.win-amd64-py3.4.exe (3.7 MB view details)

Uploaded Source

pandas-0.15.2.win-amd64-py3.3.exe (3.7 MB view details)

Uploaded Source

pandas-0.15.2.win-amd64-py3.2.exe (3.8 MB view details)

Uploaded Source

pandas-0.15.2.win-amd64-py2.7.exe (3.8 MB view details)

Uploaded Source

pandas-0.15.2.win-amd64-py2.6.exe (3.8 MB view details)

Uploaded Source

pandas-0.15.2.win32-py3.4.exe (3.5 MB view details)

Uploaded Source

pandas-0.15.2.win32-py3.3.exe (3.5 MB view details)

Uploaded Source

pandas-0.15.2.win32-py3.2.exe (3.5 MB view details)

Uploaded Source

pandas-0.15.2.win32-py2.7.exe (3.5 MB view details)

Uploaded Source

pandas-0.15.2.win32-py2.6.exe (3.5 MB view details)

Uploaded Source

pandas-0.15.2-cp34-none-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.4 Windows x86-64

pandas-0.15.2-cp34-none-win32.whl (3.3 MB view details)

Uploaded CPython 3.4 Windows x86

pandas-0.15.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.4m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

pandas-0.15.2-cp33-none-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.3 Windows x86-64

pandas-0.15.2-cp33-none-win32.whl (3.3 MB view details)

Uploaded CPython 3.3 Windows x86

pandas-0.15.2-cp33-cp33m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (6.7 MB view details)

Uploaded CPython 3.3m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

pandas-0.15.2-cp32-none-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.2 Windows x86-64

pandas-0.15.2-cp32-none-win32.whl (3.3 MB view details)

Uploaded CPython 3.2 Windows x86

pandas-0.15.2-cp32-cp32m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (6.6 MB view details)

Uploaded CPython 3.2m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

pandas-0.15.2-cp27-none-win_amd64.whl (3.6 MB view details)

Uploaded CPython 2.7 Windows x86-64

pandas-0.15.2-cp27-none-win32.whl (3.4 MB view details)

Uploaded CPython 2.7 Windows x86

pandas-0.15.2-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (6.7 MB view details)

Uploaded CPython 2.7 macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

pandas-0.15.2-cp26-none-win_amd64.whl (3.6 MB view details)

Uploaded CPython 2.6 Windows x86-64

pandas-0.15.2-cp26-none-win32.whl (3.4 MB view details)

Uploaded CPython 2.6 Windows x86

File details

Details for the file pandas-0.15.2.zip.

File metadata

  • Download URL: pandas-0.15.2.zip
  • Upload date:
  • Size: 5.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pandas-0.15.2.zip
Algorithm Hash digest
SHA256 1314489b992f368b59846ce773efb6beac3ada8a04dc67d8f11832bb027c5ff4
MD5 2ed06ac39337bd75511e74aa2131f6e9
BLAKE2b-256 ee6fd278b6c28e154c6dc6a9e0c5bde8521a8e2c06dd1b435ca9566e646a61b7

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.tar.gz.

File metadata

  • Download URL: pandas-0.15.2.tar.gz
  • Upload date:
  • Size: 4.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pandas-0.15.2.tar.gz
Algorithm Hash digest
SHA256 4a1ded49c77e109e2d5d078248f1f809962d81b508541c702d25a369af27ba17
MD5 d74481b57fda726a9ed60b223f0ad4b7
BLAKE2b-256 2d8677de5086cce674e4fe6597bec1d5daba618db4a29097758379f72ff5de5b

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win-amd64-py3.4.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win-amd64-py3.4.exe
Algorithm Hash digest
SHA256 f914b5d5af37a381adf5ecba5c582c0a169261f03157f8fce3721ee252199d09
MD5 0c0ed402ec8d65c0aa680c0286143bc9
BLAKE2b-256 ecb88c660f67cf1d227b5a825e9df0ac68f33d1801643cc74a80f6752cb55eff

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win-amd64-py3.3.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win-amd64-py3.3.exe
Algorithm Hash digest
SHA256 53774b7c7e8ce1710b9a61a20861702cf4e6f6ea30765c22f514c0b13371291b
MD5 be9862df3ad1276cbccce80906e98175
BLAKE2b-256 e4234cbd7b292b679a0b217c11b58ee903db20f70b4a8aa7cab4c44ff87fb6d1

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win-amd64-py3.2.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win-amd64-py3.2.exe
Algorithm Hash digest
SHA256 cedc7281cdfe8de6af707a295e1a15939e9ad0cacf18f2ea247a532977a79226
MD5 1d75fa6bb7415860cfde1e3ba016c94c
BLAKE2b-256 3529e754827acee314c2c822870285047b67c16c4492f68cf0770b6217f0d377

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win-amd64-py2.7.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win-amd64-py2.7.exe
Algorithm Hash digest
SHA256 24df9ff71b1b6e9a1b34442ebe87a744bb723f90c98d70b72faa83db4ab36da0
MD5 bcf35f634bbf24c3c922c87c36fa4861
BLAKE2b-256 ff9fa1190db7da169298c74bcda825baa23ac0e6402b9f909123965003789bcd

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win-amd64-py2.6.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win-amd64-py2.6.exe
Algorithm Hash digest
SHA256 a105496b620a06fadf5a5fa7e332bdf210ae8e2fbdfe1c40c46a7caa5405c5d3
MD5 dc80336f850adefc0b673e6a9b46b9cf
BLAKE2b-256 04ddd0d0beff525a0c6be1804e8b2d49a22e69c33bb8aa0e6d17324fd2e56a16

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win32-py3.4.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win32-py3.4.exe
Algorithm Hash digest
SHA256 049786b00d2b522da8178cfcf2e708aa09898464714a5e681ca76a8ea77eb267
MD5 329a0cf0178610127a3ee02b922c793d
BLAKE2b-256 ea06d0cdf16e388daa6c7c12f4512ed0f1c15b8e8bd1bce02377a74f2652a9a9

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win32-py3.3.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win32-py3.3.exe
Algorithm Hash digest
SHA256 505341f398a8d7f7b5600df52accad019bd62458dbcc67b592e73374fc47ecaa
MD5 b6854d489a8e3af5ed145e0756e7bf8f
BLAKE2b-256 5f2fbe29fa5da7d523ecfcd4f49be8b0905ed8ecc9c32f50549986c58d0a42e4

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win32-py3.2.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win32-py3.2.exe
Algorithm Hash digest
SHA256 d2ca89fcf6cd475be902c3a11c7283833fe442f9e49cf424e4e1136423c6b9d8
MD5 8a6705d95b2cfc237ac9ad52b6bd1f0a
BLAKE2b-256 ab8a7b3cf41823e1ee5c8f1d367cdce0dd6646986ce3c50a3cb784e1b01e3a4a

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win32-py2.7.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win32-py2.7.exe
Algorithm Hash digest
SHA256 49b9e2b19f3e7f2d6c7428f33c8922a3fa7add54302261c66d7ff1e2775dcc20
MD5 0673d14f771c4710bf9cc5392a6e93d1
BLAKE2b-256 cfe006bbbff1f9472b17aa3693d4cefc213bbf07246ed0f7df8dcb591f13b60d

See more details on using hashes here.

File details

Details for the file pandas-0.15.2.win32-py2.6.exe.

File metadata

File hashes

Hashes for pandas-0.15.2.win32-py2.6.exe
Algorithm Hash digest
SHA256 dcc523361f11e0d587593f3711d6aee2dc8fa70664dde39713ffe6269298e7dc
MD5 7fb54d21e3a02f2c5d55103c3ae8b9f5
BLAKE2b-256 41dcabbe8d70be778c2c2c9e8a26a3e9b9fe68022819bb02d24148d43ad74d35

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp34-none-win_amd64.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp34-none-win_amd64.whl
Algorithm Hash digest
SHA256 c397856e20c6b8453f5a2b2a44178269740819406bad7f231052a4adc53d627f
MD5 46a643edb600b3c6acd4eafc6723d1f5
BLAKE2b-256 4b8c67cf8a8d06d53970d01a0435c0663620eea1fc20b6b391c40c4a06521893

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp34-none-win32.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp34-none-win32.whl
Algorithm Hash digest
SHA256 dfc9ddab1b8fc88f8707b2d90d03408816ac3ff38cf31aa43c581d8fa6598115
MD5 eb3944144fdd4d20f1937f2ec9d42c4f
BLAKE2b-256 e09bcc36c5ac1899bedc978d7d1ebb91eccfa2e9a0d20aa8c339b02e37c2e374

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 60a30f190cfef22ac9e0bcc9cd2100bc1c7d58e54f99becb2d9aa077bc1175d8
MD5 9cc4c190efa8fc4f70ebb2944e4c200c
BLAKE2b-256 d4b590ef9237a9680612493ee1223ecc5b134fefa24966e62764e5f5828f1db5

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp33-none-win_amd64.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp33-none-win_amd64.whl
Algorithm Hash digest
SHA256 591ec4de5c999871969ef20d38047f8c5e966526546798861607cd285f7eaf19
MD5 29bada2730f6e65eeb8012e465dc0554
BLAKE2b-256 07b002309b2d17ddab7704c682561eb346daf6485923c56b456f931ec75dad89

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp33-none-win32.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp33-none-win32.whl
Algorithm Hash digest
SHA256 8b2c4530983420085938fa54acc708341a7a4c408ceb0d98ac8f57468fe64352
MD5 ec190123254f3cdeddfaa7a176b6e8ec
BLAKE2b-256 a8139d5a21f0030ec34748803bcba33f9480408abaf78cbb00238be0c1479db4

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp33-cp33m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp33-cp33m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 92f1bdf901e5bbef606c99a2dd7850c5b9bf67ead71eb7c791a28314a5ce8883
MD5 05f8dffce7e9e63da5e8ad30927804d7
BLAKE2b-256 34cf992e13d02d7742899f5477ee5725d36af508a47a0a26c15b7fd863cb3a65

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp32-none-win_amd64.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp32-none-win_amd64.whl
Algorithm Hash digest
SHA256 8ff2643305dd883681dc204f3db606c332fe67bbe2090faa7367b35c6b9ca89a
MD5 670a6a84c4397c6bc2d3dd1e16d45f46
BLAKE2b-256 2762ac8a3de62c6d4258c069ddd1133d51e2704e28cc613cfd7c48eb9828ec46

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp32-none-win32.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp32-none-win32.whl
Algorithm Hash digest
SHA256 ae466bda00f926ecc5820045392f908c1e2bc0beb112c2cecfaf3f8b3a86471a
MD5 75ab2c511cda416cfb2618bee0b44b23
BLAKE2b-256 f5fec815e66cb86f8d6dceb538750f949f358bf486bf3fc84187b822004745d7

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp32-cp32m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp32-cp32m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 6c0c5196be690fb62091e2c97328821e27aa11bb1b20b654f4666585abde69d8
MD5 26d2178ae05a166eb89eecd6b079e4e9
BLAKE2b-256 e5e7b13e317f6b8803a3375f5618166c00b3526fcc37af3601727a2f9307c1cc

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 ad6a840f3311107efc72393039b5f7195ccf7557c2aad41f53b6ab015d759c1a
MD5 42b0423399ba3890f2d39985b2bf36d5
BLAKE2b-256 d71b75db5fdee77073c684c67c1c0092fd75f11d54d018ca53c732afa3200d7a

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp27-none-win32.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp27-none-win32.whl
Algorithm Hash digest
SHA256 855dc6ded055c2a8c1f39081d6a1b081ff891f088aa501b0290b7a01d2c0edf6
MD5 ec85a4035106e1e97af4d0bca9a42ae2
BLAKE2b-256 fc1f68c30255659c2ec406f3322be43ac71cbeff2675df32d6a7b0a14072f2b1

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 c7498c0de40fcdcb2ee4a6b1b8d846c0e2a93f31be25255600fcdf117680fdf2
MD5 d9ee1d5d60f6fede2beeb44d4e41ee1e
BLAKE2b-256 997777503a334da9f2d78ff9873d72a6ca3e1fe1bdc5d9295b572c369b32c927

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp26-none-win_amd64.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp26-none-win_amd64.whl
Algorithm Hash digest
SHA256 677840abca9533602f20a38c2b490a2d0b9b0821bb09fd3966fcd2af08083609
MD5 076cfdca0df8d986072804c1eefdcf00
BLAKE2b-256 161654642665d631973c3aac1035a98996317f1a30d0ea5dfdf3cfbd574af12e

See more details on using hashes here.

File details

Details for the file pandas-0.15.2-cp26-none-win32.whl.

File metadata

File hashes

Hashes for pandas-0.15.2-cp26-none-win32.whl
Algorithm Hash digest
SHA256 2287b052457ea9ad9ce0f39c7cae90c8d8f765123f8002f615283ae830023b73
MD5 7b00ffb2ec0fff59d95086b816d97f1d
BLAKE2b-256 b481ab132e71fcef69787addd6745da81f5a6090f2f50d9017dcf6ac872874d5

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