pandas

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6 files were added to this release more than 14 days after its initial publication. Inspect the release files before installing.

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

Metadata

Release files for pandas 0.18.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Files added late

6 files were uploaded more than 14 days after the first file in this release.

While project maintainers occasionally add legitimate files to an existing release, late additions can also indicate a security compromise.

We recommend inspecting the release files before installing.

Source distributions (sdists)

Source distribution for pandas 0.18.1
File	Size	Uploaded
pandas-0.18.1.zip	8.5 MB	May 5, 2016	Details
pandas-0.18.1.tar.gz	7.3 MB	May 5, 2016	Details

Built distributions (wheels)

Table of built distributions (wheels) for pandas 0.18.1
File	Interpreter	ABI	Platform	Reset
pandas-0.18.1-cp35-cp35m-win_amd64.whl 6.0 MB May 5, 2016	CPython 3.5	CPython 3.5 pymalloc	Windows x86-64	Details
pandas-0.18.1-cp35-cp35m-win32.whl 5.8 MB May 5, 2016	CPython 3.5	CPython 3.5 pymalloc	Windows x86-32	Details
pandas-0.18.1-cp35-cp35m-manylinux1_x86_64.whl 15.1 MB Jul 19, 2016	CPython 3.5	CPython 3.5 pymalloc	Linux glibc 2.5+ x86-64	Details
pandas-0.18.1-cp35-cp35m-manylinux1_i686.whl 14.3 MB Jul 19, 2016	CPython 3.5	CPython 3.5 pymalloc	Linux glibc 2.5+ x86-32	Details
pandas-0.18.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 9.9 MB May 5, 2016	CPython 3.5	CPython 3.5 pymalloc	macOS 10.9+ x86-64, macOS 10.10+ x86-64, macOS 10.9+ Intel (x86-64, i386), macOS 10.6+ Intel (x86-64, i386), macOS 10.10+ Intel (x86-64, i386)	Details
pandas-0.18.1-cp34-cp34m-win_amd64.whl 6.0 MB May 5, 2016	CPython 3.4	CPython 3.4 pymalloc	Windows x86-64	Details
pandas-0.18.1-cp34-cp34m-win32.whl 5.8 MB May 5, 2016	CPython 3.4	CPython 3.4 pymalloc	Windows x86-32	Details
pandas-0.18.1-cp34-cp34m-manylinux1_x86_64.whl 15.4 MB Jul 19, 2016	CPython 3.4	CPython 3.4 pymalloc	Linux glibc 2.5+ x86-64	Details
pandas-0.18.1-cp34-cp34m-manylinux1_i686.whl 14.4 MB Jul 19, 2016	CPython 3.4	CPython 3.4 pymalloc	Linux glibc 2.5+ x86-32	Details
pandas-0.18.1-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 9.9 MB May 5, 2016	CPython 3.4	CPython 3.4 pymalloc	macOS 10.9+ Intel (x86-64, i386), macOS 10.6+ Intel (x86-64, i386), macOS 10.10+ Intel (x86-64, i386), macOS 10.9+ x86-64, macOS 10.10+ x86-64	Details
pandas-0.18.1-cp27-cp27mu-manylinux1_x86_64.whl 14.2 MB Jul 19, 2016	CPython 2.7	CPython 2.7 pymalloc wide-unicode	Linux glibc 2.5+ x86-64	Details
pandas-0.18.1-cp27-cp27m-win_amd64.whl 6.2 MB May 5, 2016	CPython 2.7	CPython 2.7 pymalloc	Windows x86-64	Details
pandas-0.18.1-cp27-cp27m-win32.whl 5.9 MB May 5, 2016	CPython 2.7	CPython 2.7 pymalloc	Windows x86-32	Details
pandas-0.18.1-cp27-cp27m-manylinux1_x86_64.whl 14.2 MB Jul 19, 2016	CPython 2.7	CPython 2.7 pymalloc	Linux glibc 2.5+ x86-64	Details
pandas-0.18.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 10.0 MB May 5, 2016	CPython 2.7	CPython 2.7 pymalloc	macOS 10.10+ Intel (x86-64, i386), macOS 10.6+ Intel (x86-64, i386), macOS 10.10+ x86-64, macOS 10.9+ Intel (x86-64, i386), macOS 10.9+ x86-64	Details

Total release size: 169.0 MB

Release files / pandas-0.18.1.zip

Download URL	pandas-0.18.1.zip
Size	8.5 MB
Tags	Source
SHA-256 checksum How to use checksums	`de8661d3a71bac8b5100c2a85fdb1b55c9b41534aba7a9671d1130d43ab2de59`
BLAKE2b-256 checksum How to use checksums	`a6897a0fe355598c647b54d1a626b709f4b21708be3c6b5ad645dcb988be3791`
Upload date	May 5, 2016
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Release files / pandas-0.18.1.tar.gz

Download URL	pandas-0.18.1.tar.gz
Size	7.3 MB
Tags	Source
SHA-256 checksum How to use checksums	`d2e483692c7915916dffd1b83256ea9761b4224c8d45646ceddf48b977ee77b2`
BLAKE2b-256 checksum How to use checksums	`1109e66eb844daba8680ddff26335d5b4fead77f60f957678243549a8dd4830d`
Upload date	May 5, 2016
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Release files / pandas-0.18.1-cp35-cp35m-win_amd64.whl

Download URL	pandas-0.18.1-cp35-cp35m-win_amd64.whl
Size	6.0 MB
Tags	CPython 3.5 CPython 3.5 pymalloc Windows x86-64
SHA-256 checksum How to use checksums	`c850d8c41b5417ba361967d3e2b6119c681b9f0bd5eb77f4c013c46dbf0ebe95`
BLAKE2b-256 checksum How to use checksums	`bdd06cc3b06f2b3809cd033140c2725646b4f159836acee4383c8a888d5290a0`
Upload date	May 5, 2016
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Release files / pandas-0.18.1-cp35-cp35m-win32.whl

Download URL	pandas-0.18.1-cp35-cp35m-win32.whl
Size	5.8 MB
Tags	CPython 3.5 CPython 3.5 pymalloc Windows x86-32
SHA-256 checksum How to use checksums	`563720b6302a4e2b513471c16bd7e89db2ae44d3f6b0745896b9c289f3c6b2fb`
BLAKE2b-256 checksum How to use checksums	`08188f2b90b048a878909fd24a91115db1ccb84dedb5974e5b361e72bef01bcb`
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Release files / pandas-0.18.1-cp35-cp35m-manylinux1_x86_64.whl

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Download URL	pandas-0.18.1-cp35-cp35m-manylinux1_x86_64.whl
Size	15.1 MB
Tags	CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`931d25b391eb01c52239a41e2b1c29c8337a6789852ecc0d4ce39ce2491424e6`
BLAKE2b-256 checksum How to use checksums	`05cee3534d549710084d2c1bac7e708c671c7f23d7ddae88824f4001ecf64608`
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Release files / pandas-0.18.1-cp35-cp35m-manylinux1_i686.whl

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Download URL	pandas-0.18.1-cp35-cp35m-manylinux1_i686.whl
Size	14.3 MB
Tags	CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-32
SHA-256 checksum How to use checksums	`80bf0d32432fe588a0e94ff6b216aa5c61ddba2348ca904bda240218f9cbe122`
BLAKE2b-256 checksum How to use checksums	`68c297c03dc981f6bd2576b2c5e8b4da260e93b460c8ded4441616b72fa5af27`
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Release files / pandas-0.18.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl

Download URL	pandas-0.18.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Size	9.9 MB
Tags	CPython 3.5 CPython 3.5 pymalloc macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64
SHA-256 checksum How to use checksums	`fccbc771a23d51b366182c136cd735cf1642744270fee964f5b1fe9103d66239`
BLAKE2b-256 checksum How to use checksums	`581261cbe10e056a57f6da74947599325be680631d469741b48455e7eaa21d44`
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Release files / pandas-0.18.1-cp34-cp34m-win_amd64.whl

Download URL	pandas-0.18.1-cp34-cp34m-win_amd64.whl
Size	6.0 MB
Tags	CPython 3.4 CPython 3.4 pymalloc Windows x86-64
SHA-256 checksum How to use checksums	`c39dbc38bc031f099bcfa408a93c801f0141ee49a7d4e0df09cdf9dcf01f27e6`
BLAKE2b-256 checksum How to use checksums	`2aada42570426adc2a19b0c0e3138d26ea4e00135d53bf2b9f858758f25678dc`
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Release files / pandas-0.18.1-cp34-cp34m-win32.whl

Download URL	pandas-0.18.1-cp34-cp34m-win32.whl
Size	5.8 MB
Tags	CPython 3.4 CPython 3.4 pymalloc Windows x86-32
SHA-256 checksum How to use checksums	`6621db235422aa48d7513a7f332a7bfc6e9a54b0283bac145cccec7c4c0ccd7d`
BLAKE2b-256 checksum How to use checksums	`9f216b62e0a57a45aaa01175751764ff3a94e578f22131a2604ca8c090e83678`
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Release files / pandas-0.18.1-cp34-cp34m-manylinux1_x86_64.whl

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Download URL	pandas-0.18.1-cp34-cp34m-manylinux1_x86_64.whl
Size	15.4 MB
Tags	CPython 3.4 CPython 3.4 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`9b1a7834e10c5a2afacaae8ba10054dc2ee5ae81eeaecf44d9eaa4d726962817`
BLAKE2b-256 checksum How to use checksums	`183c90cb8afd7a0be85b8b4ae5616b8f1078611a64ead46adb1dd670a81432d7`
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Release files / pandas-0.18.1-cp34-cp34m-manylinux1_i686.whl

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Download URL	pandas-0.18.1-cp34-cp34m-manylinux1_i686.whl
Size	14.4 MB
Tags	CPython 3.4 CPython 3.4 pymalloc Linux glibc 2.5+ x86-32
SHA-256 checksum How to use checksums	`a0af231d6bf20d3f94a4d694bb3cd26c1b330aa4ed124ea99eff49a583ed10ff`
BLAKE2b-256 checksum How to use checksums	`642e6aa0a2e6c5ae2bfb2bade79658a5bd10045211fdf320a0a175f200b38ba1`
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Release files / pandas-0.18.1-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

Download URL	pandas-0.18.1-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
Size	9.9 MB
Tags	CPython 3.4 CPython 3.4 pymalloc macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64
SHA-256 checksum How to use checksums	`9984b284ab6d7672c720ea960f4d19b9dd0bea061c2ccd641b0c20d34ce03f7a`
BLAKE2b-256 checksum How to use checksums	`6764c516cd1386a3816df69403861409a97b715369d51230f69d5bb7956e83af`
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Release files / pandas-0.18.1-cp27-cp27mu-manylinux1_x86_64.whl

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Download URL	pandas-0.18.1-cp27-cp27mu-manylinux1_x86_64.whl
Size	14.2 MB
Tags	CPython 2.7 CPython 2.7 pymalloc wide-unicode Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`b7a6ce196452bf9a020074b68c184b174c12a22c285603ceebb09c645cf001d1`
BLAKE2b-256 checksum How to use checksums	`a6d3e1a8048402fc0381f2c3094d02bf33fbaf6c778878700bc03eb2f5454785`
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Release files / pandas-0.18.1-cp27-cp27m-win_amd64.whl

Download URL	pandas-0.18.1-cp27-cp27m-win_amd64.whl
Size	6.2 MB
Tags	CPython 2.7 CPython 2.7 pymalloc Windows x86-64
SHA-256 checksum How to use checksums	`aa50475fafbc689dead2e9a4e98b96fc43f1190f6661d1daf560f8c05ac26496`
BLAKE2b-256 checksum How to use checksums	`b9f5ff7a22e253c0587bba641f38bae3d6669b6b5ab8ed7519ca9155db773901`
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Release files / pandas-0.18.1-cp27-cp27m-win32.whl

Download URL	pandas-0.18.1-cp27-cp27m-win32.whl
Size	5.9 MB
Tags	CPython 2.7 CPython 2.7 pymalloc Windows x86-32
SHA-256 checksum How to use checksums	`42c933501341263194926d00c1039d314039f6fbe763e13d983918d273a0ad68`
BLAKE2b-256 checksum How to use checksums	`525c9f6a2cbf86abc467d1617ddbac30ced1640955dac9a56612aefb619bd53d`
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Release files / pandas-0.18.1-cp27-cp27m-manylinux1_x86_64.whl

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Download URL	pandas-0.18.1-cp27-cp27m-manylinux1_x86_64.whl
Size	14.2 MB
Tags	CPython 2.7 CPython 2.7 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`6f31b4510da92f8beec17fe9ecb3f386984a4b35e1d1dee062b3463f63e70bbc`
BLAKE2b-256 checksum How to use checksums	`88dbc0f365d4305f1f0cade150f4ffc3ec92fbdfca8f480ce22ffddeb784f26c`
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Release files / pandas-0.18.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl

Download URL	pandas-0.18.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Size	10.0 MB
Tags	CPython 2.7 CPython 2.7 pymalloc macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64
SHA-256 checksum How to use checksums	`2aeebd55027eb1fcb5020ec141696be47fff65fb86c276e46bae42f04b3bfeaf`
BLAKE2b-256 checksum How to use checksums	`e1a02ebfdd5ca50733997d1e16ad104324fbf544709c24e08cabe1a4a16d9867`
Upload date	May 5, 2016
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0.25.3

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0.24.2

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0.20.3

Jul 7, 2017 24 release files

0.20.2

Jun 5, 2017 23 release files

0.20.1

May 5, 2017 23 release files

0.20.0

May 5, 2017 9 release files

0.19.2

Dec 24, 2016 18 release files

0.19.1

Nov 3, 2016 19 release files

0.19.0

Oct 2, 2016 15 release files

This release

0.18.1 This release

May 5, 2016 17 release files

0.18.0

Mar 12, 2016 10 release files

0.17.1

Nov 20, 2015 14 release files

0.17.0

Oct 9, 2015 14 release files

0.16.2

Jun 13, 2015 14 release files

0.16.1

May 11, 2015 14 release files

0.16.0

Mar 22, 2015 14 release files

0.15.2

Dec 11, 2014 26 release files

0.15.1

Nov 8, 2014 16 release files

0.15.0

Oct 19, 2014 16 release files

0.14.1

Jul 10, 2014 16 release files

0.14.0

May 30, 2014 15 release files

0.13.1

Feb 9, 2014 10 release files

0.13.0

Jan 14, 2014 9 release files

0.12.0

Jul 24, 2013 10 release files

0.11.0

Apr 23, 2013 10 release files

0.10.1

Jan 22, 2013 10 release files

0.10.0

Dec 17, 2012 12 release files

0.9.1

Nov 15, 2012 11 release files

0.9.0

Oct 8, 2012 11 release files

0.8.1

Jul 22, 2012 11 release files

0.8.0

Jun 29, 2012 11 release files

0.7.3

Apr 12, 2012 11 release files

0.7.2

Mar 16, 2012 11 release files

0.7.1

Feb 29, 2012 11 release files

0.7.0

Feb 9, 2012 11 release files

0.6.1

Dec 13, 2011 8 release files

0.6.0

Nov 26, 2011 11 release files

0.5.0

Oct 25, 2011 10 release files

0.4.3

Oct 9, 2011 8 release files

0.4.2

Oct 3, 2011 6 release files

0.4.1

Sep 26, 2011 6 release files

0.4.0

Sep 12, 2011 6 release files

0.3.0

Feb 20, 2011 6 release files

0.2

May 18, 2010 1 release file

0.1

Dec 25, 2009 2 release files

pandas 0.18.1

Note

Project links

Key dates

3 maintainers

Credits

License

Classifiers

Metadata

Project links

Key dates

3 maintainers

Credits

License

Classifiers

Release files for pandas 0.18.1

Files added late

Source distributions (sdists)

Built distributions (wheels)

Release files / pandas-0.18.1.zip

Release files / pandas-0.18.1.tar.gz

Release files / pandas-0.18.1-cp35-cp35m-win_amd64.whl

Release files / pandas-0.18.1-cp35-cp35m-win32.whl

Release files / pandas-0.18.1-cp35-cp35m-manylinux1_x86_64.whl

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Release files / pandas-0.18.1-cp35-cp35m-manylinux1_i686.whl

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Release files / pandas-0.18.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl

Release files / pandas-0.18.1-cp34-cp34m-win_amd64.whl

Release files / pandas-0.18.1-cp34-cp34m-win32.whl

Release files / pandas-0.18.1-cp34-cp34m-manylinux1_x86_64.whl

File added late

Release files / pandas-0.18.1-cp34-cp34m-manylinux1_i686.whl

File added late

Release files / pandas-0.18.1-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

Release files / pandas-0.18.1-cp27-cp27mu-manylinux1_x86_64.whl

File added late

Release files / pandas-0.18.1-cp27-cp27m-win_amd64.whl

Release files / pandas-0.18.1-cp27-cp27m-win32.whl

Release files / pandas-0.18.1-cp27-cp27m-manylinux1_x86_64.whl

File added late

Release files / pandas-0.18.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl

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