pandas

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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.

Release files for pandas 0.23.4

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

File added late

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Source distribution (sdist)

Source distribution for pandas 0.23.4
File	Size	Uploaded
pandas-0.23.4.tar.gz	10.5 MB	Aug 4, 2018	Details

Built distributions (wheels)

Table of built distributions (wheels) for pandas 0.23.4
File	Interpreter	ABI	Platform	Reset
pandas-0.23.4-cp37-cp37m-win_amd64.whl 7.9 MB Aug 4, 2018	CPython 3.7	CPython 3.7 pymalloc	Windows x86-64	Details
pandas-0.23.4-cp37-cp37m-win32.whl 6.8 MB Aug 4, 2018	CPython 3.7	CPython 3.7 pymalloc	Windows x86-32	Details
pandas-0.23.4-cp37-cp37m-manylinux1_x86_64.whl 8.8 MB Aug 4, 2018	CPython 3.7	CPython 3.7 pymalloc	Linux glibc 2.5+ x86-64	Details
pandas-0.23.4-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 14.4 MB Aug 4, 2018	CPython 3.7	CPython 3.7 pymalloc	macOS 10.10+ x86-64, macOS 10.9+ Intel (x86-64, i386), macOS 10.10+ Intel (x86-64, i386), macOS 10.6+ Intel (x86-64, i386), macOS 10.9+ x86-64	Details
pandas-0.23.4-cp36-cp36m-win_amd64.whl 7.7 MB Aug 4, 2018	CPython 3.6	CPython 3.6 pymalloc	Windows x86-64	Details
pandas-0.23.4-cp36-cp36m-win32.whl 6.6 MB Aug 4, 2018	CPython 3.6	CPython 3.6 pymalloc	Windows x86-32	Details
pandas-0.23.4-cp36-cp36m-manylinux1_x86_64.whl 8.9 MB Aug 4, 2018	CPython 3.6	CPython 3.6 pymalloc	Linux glibc 2.5+ x86-64	Details
pandas-0.23.4-cp36-cp36m-manylinux1_i686.whl 7.8 MB Aug 4, 2018	CPython 3.6	CPython 3.6 pymalloc	Linux glibc 2.5+ x86-32	Details
pandas-0.23.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 14.6 MB Aug 4, 2018	CPython 3.6	CPython 3.6 pymalloc	macOS 10.10+ Intel (x86-64, i386), macOS 10.6+ Intel (x86-64, i386), macOS 10.9+ x86-64, macOS 10.10+ x86-64, macOS 10.9+ Intel (x86-64, i386)	Details
pandas-0.23.4-cp35-cp35m-win_amd64.whl 7.6 MB Aug 4, 2018	CPython 3.5	CPython 3.5 pymalloc	Windows x86-64	Details
pandas-0.23.4-cp35-cp35m-win32.whl 6.6 MB Aug 4, 2018	CPython 3.5	CPython 3.5 pymalloc	Windows x86-32	Details
pandas-0.23.4-cp35-cp35m-manylinux1_x86_64.whl 8.7 MB Aug 4, 2018	CPython 3.5	CPython 3.5 pymalloc	Linux glibc 2.5+ x86-64	Details
pandas-0.23.4-cp35-cp35m-manylinux1_i686.whl 7.7 MB Sep 5, 2018	CPython 3.5	CPython 3.5 pymalloc	Linux glibc 2.5+ x86-32	Details
pandas-0.23.4-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 14.4 MB Aug 4, 2018	CPython 3.5	CPython 3.5 pymalloc	macOS 10.9+ Intel (x86-64, i386), macOS 10.10+ Intel (x86-64, i386), macOS 10.10+ x86-64, macOS 10.6+ Intel (x86-64, i386), macOS 10.9+ x86-64	Details
pandas-0.23.4-cp27-cp27mu-manylinux1_x86_64.whl 8.9 MB Aug 4, 2018	CPython 2.7	CPython 2.7 pymalloc wide-unicode	Linux glibc 2.5+ x86-64	Details
pandas-0.23.4-cp27-cp27mu-manylinux1_i686.whl 7.8 MB Aug 4, 2018	CPython 2.7	CPython 2.7 pymalloc wide-unicode	Linux glibc 2.5+ x86-32	Details
pandas-0.23.4-cp27-cp27m-win_amd64.whl 7.3 MB Aug 4, 2018	CPython 2.7	CPython 2.7 pymalloc	Windows x86-64	Details
pandas-0.23.4-cp27-cp27m-win32.whl 6.5 MB Aug 4, 2018	CPython 2.7	CPython 2.7 pymalloc	Windows x86-32	Details
pandas-0.23.4-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 15.0 MB Aug 4, 2018	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+ x86-64, macOS 10.9+ Intel (x86-64, i386)	Details

Total release size: 184.5 MB

Release files / pandas-0.23.4.tar.gz

Download URL	pandas-0.23.4.tar.gz
Size	10.5 MB
Tags	Source
SHA-256 checksum How to use checksums	`5b24ca47acf69222e82530e89111dd9d14f9b970ab2cd3a1c2c78f0c4fbba4f4`
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Upload date	Aug 4, 2018
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Uploaded via	`twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.14.0 CPython/3.6.5`

Release files / pandas-0.23.4-cp37-cp37m-win_amd64.whl

Download URL	pandas-0.23.4-cp37-cp37m-win_amd64.whl
Size	7.9 MB
Tags	CPython 3.7 CPython 3.7 pymalloc Windows x86-64
SHA-256 checksum How to use checksums	`1c87fcb201e1e06f66e23a61a5fea9eeebfe7204a66d99df24600e3f05168051`
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Release files / pandas-0.23.4-cp37-cp37m-win32.whl

Download URL	pandas-0.23.4-cp37-cp37m-win32.whl
Size	6.8 MB
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SHA-256 checksum How to use checksums	`31b7a48b344c14691a8e92765d4023f88902ba3e96e2e4d0364d3453cdfd50db`
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Release files / pandas-0.23.4-cp37-cp37m-manylinux1_x86_64.whl

Download URL	pandas-0.23.4-cp37-cp37m-manylinux1_x86_64.whl
Size	8.8 MB
Tags	CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`647b3b916cc8f6aeba240c8171be3ab799c3c1b2ea179a3be0bd2712c4237553`
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Release files / pandas-0.23.4-cp37-cp37m-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-0.23.4-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Size	14.4 MB
Tags	CPython 3.7 CPython 3.7 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
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Release files / pandas-0.23.4-cp36-cp36m-win_amd64.whl

Download URL	pandas-0.23.4-cp36-cp36m-win_amd64.whl
Size	7.7 MB
Tags	CPython 3.6 CPython 3.6 pymalloc Windows x86-64
SHA-256 checksum How to use checksums	`fb944c8f0b0ab5c1f7846c686bc4cdf8cde7224655c12edcd59d5212cd57bec0`
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Release files / pandas-0.23.4-cp36-cp36m-win32.whl

Download URL	pandas-0.23.4-cp36-cp36m-win32.whl
Size	6.6 MB
Tags	CPython 3.6 CPython 3.6 pymalloc Windows x86-32
SHA-256 checksum How to use checksums	`11975fad9edbdb55f1a560d96f91830e83e29bed6ad5ebf506abda09818eaf60`
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Release files / pandas-0.23.4-cp36-cp36m-manylinux1_x86_64.whl

Download URL	pandas-0.23.4-cp36-cp36m-manylinux1_x86_64.whl
Size	8.9 MB
Tags	CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`6efa9fa6e1434141df8872d0fa4226fc301b17aacf37429193f9d70b426ea28f`
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Release files / pandas-0.23.4-cp36-cp36m-manylinux1_i686.whl

Download URL	pandas-0.23.4-cp36-cp36m-manylinux1_i686.whl
Size	7.8 MB
Tags	CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-32
SHA-256 checksum How to use checksums	`2e1e88f9d3e5f107b65b59cd29f141995597b035d17cc5537e58142038942e1a`
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Release files / pandas-0.23.4-cp36-cp36m-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-0.23.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Size	14.6 MB
Tags	CPython 3.6 CPython 3.6 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
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Release files / pandas-0.23.4-cp35-cp35m-win_amd64.whl

Download URL	pandas-0.23.4-cp35-cp35m-win_amd64.whl
Size	7.6 MB
Tags	CPython 3.5 CPython 3.5 pymalloc Windows x86-64
SHA-256 checksum How to use checksums	`26c903d0ae1542890cb9abadb4adcb18f356b14c2df46e4ff657ae640e3ac9e7`
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Release files / pandas-0.23.4-cp35-cp35m-win32.whl

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Size	6.6 MB
Tags	CPython 3.5 CPython 3.5 pymalloc Windows x86-32
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Release files / pandas-0.23.4-cp35-cp35m-manylinux1_x86_64.whl

Download URL	pandas-0.23.4-cp35-cp35m-manylinux1_x86_64.whl
Size	8.7 MB
Tags	CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`de9559287c4fe8da56e8c3878d2374abc19d1ba2b807bfa7553e912a8e5ba87c`
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Release files / pandas-0.23.4-cp35-cp35m-manylinux1_i686.whl

File added late

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Download URL	pandas-0.23.4-cp35-cp35m-manylinux1_i686.whl
Size	7.7 MB
Tags	CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-32
SHA-256 checksum How to use checksums	`d318d77ab96f66a59e792a481e2701fba879e1a453aefeebdb17444fe204d1ed`
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Release files / pandas-0.23.4-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-0.23.4-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	14.4 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
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Release files / pandas-0.23.4-cp27-cp27mu-manylinux1_x86_64.whl

Download URL	pandas-0.23.4-cp27-cp27mu-manylinux1_x86_64.whl
Size	8.9 MB
Tags	CPython 2.7 CPython 2.7 pymalloc wide-unicode Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`be4715c9d8367e51dbe6bc6d05e205b1ae234f0dc5465931014aa1c4af44c1ba`
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Release files / pandas-0.23.4-cp27-cp27mu-manylinux1_i686.whl

Download URL	pandas-0.23.4-cp27-cp27mu-manylinux1_i686.whl
Size	7.8 MB
Tags	CPython 2.7 CPython 2.7 pymalloc wide-unicode Linux glibc 2.5+ x86-32
SHA-256 checksum How to use checksums	`d785fc08d6f4207437e900ffead930a61e634c5e4f980ba6d3dc03c9581748c7`
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Release files / pandas-0.23.4-cp27-cp27m-win_amd64.whl

Download URL	pandas-0.23.4-cp27-cp27m-win_amd64.whl
Size	7.3 MB
Tags	CPython 2.7 CPython 2.7 pymalloc Windows x86-64
SHA-256 checksum How to use checksums	`12e13d127ca1b585dd6f6840d3fe3fa6e46c36a6afe2dbc5cb0b57032c902e31`
BLAKE2b-256 checksum How to use checksums	`a53aa590fc19fd61e67893432e46ff4c5cd765b659d350b760f284f56a0a2641`
Upload date	Aug 4, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.14.0 CPython/3.6.5`

Release files / pandas-0.23.4-cp27-cp27m-win32.whl

Download URL	pandas-0.23.4-cp27-cp27m-win32.whl
Size	6.5 MB
Tags	CPython 2.7 CPython 2.7 pymalloc Windows x86-32
SHA-256 checksum How to use checksums	`66b060946046ca27c0e03e9bec9bba3e0b918bafff84c425ca2cc2e157ce121e`
BLAKE2b-256 checksum How to use checksums	`f41904e8f29157136e85c977517e71cea1187b971efc80d772c0d8c3967018da`
Upload date	Aug 4, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.14.0 CPython/3.6.5`

Release files / pandas-0.23.4-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-0.23.4-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	15.0 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	`242e9900de758e137304ad4b5663c2eff0d798c2c3b891250bd0bd97144579da`
BLAKE2b-256 checksum How to use checksums	`86ad89670f4017b2459dfb5577775efbc4c6c20eb46728ac6e5b721602493724`
Upload date	Aug 4, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.14.0 CPython/3.6.5`

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pandas 0.23.4

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Release files for pandas 0.23.4

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Release files / pandas-0.23.4.tar.gz

Release files / pandas-0.23.4-cp37-cp37m-win_amd64.whl

Release files / pandas-0.23.4-cp37-cp37m-win32.whl

Release files / pandas-0.23.4-cp37-cp37m-manylinux1_x86_64.whl

Release files / pandas-0.23.4-cp37-cp37m-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-0.23.4-cp36-cp36m-win_amd64.whl

Release files / pandas-0.23.4-cp36-cp36m-win32.whl

Release files / pandas-0.23.4-cp36-cp36m-manylinux1_x86_64.whl

Release files / pandas-0.23.4-cp36-cp36m-manylinux1_i686.whl

Release files / pandas-0.23.4-cp36-cp36m-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-0.23.4-cp35-cp35m-win_amd64.whl

Release files / pandas-0.23.4-cp35-cp35m-win32.whl

Release files / pandas-0.23.4-cp35-cp35m-manylinux1_x86_64.whl

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Release files / pandas-0.23.4-cp27-cp27mu-manylinux1_x86_64.whl

Release files / pandas-0.23.4-cp27-cp27mu-manylinux1_i686.whl

Release files / pandas-0.23.4-cp27-cp27m-win_amd64.whl

Release files / pandas-0.23.4-cp27-cp27m-win32.whl

Release files / pandas-0.23.4-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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