<commit_before><div align="center">
  <img src="https://dev.pandas.io/static/img/pandas.svg"><br>
</div>

-----------------

# pandas: powerful Python data analysis toolkit

<table>
<tr>
  <td>Latest Release</td>
  <td>
    <a href="https://pypi.org/project/pandas/">
    <img src="https://img.shields.io/pypi/v/pandas.svg" alt="latest release" />
    </a>
  </td>
</tr>
  <td></td>
  <td>
    <a href="https://anaconda.org/anaconda/pandas/">
    <img src="https://anaconda.org/conda-forge/pandas/badges/version.svg" alt="latest release" />
    </a>
</td>
</tr>
<tr>
  <td>Package Status</td>
  <td>
		<a href="https://pypi.org/project/pandas/">
		<img src="https://img.shields.io/pypi/status/pandas.svg" alt="status" />
		</a>
  </td>
</tr>
<tr>
  <td>License</td>
  <td>
    <a href="https://github.com/pandas-dev/pandas/blob/master/LICENSE">
    <img src="https://img.shields.io/pypi/l/pandas.svg" alt="license" />
    </a>
</td>
</tr>
<tr>
  <td>Build Status</td>
  <td>
    <a href="https://travis-ci.org/pandas-dev/pandas">
    <img src="https://travis-ci.org/pandas-dev/pandas.svg?branch=master" alt="travis build status" />
    </a>
  </td>
</tr>
<tr>
  <td></td>
  <td>
    <a href="https://dev.azure.com/pandas-dev/pandas/_build/latest?definitionId=1&branch=master">
      <img src="https://dev.azure.com/pandas-dev/pandas/_apis/build/status/pandas-dev.pandas?branch=master" alt="Azure Pipelines build status" />
    </a>
  </td>
</tr>
<tr>
  <td>Coverage</td>
  <td>
    <a href="https://codecov.io/gh/pandas-dev/pandas">
    <img src="https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=master" alt="coverage" />
    </a>
  </td>
</tr>
<tr>
  <td>Downloads</td>
  <td>
    <a href="https://pandas.pydata.org">
    <img src="https://anaconda.org/conda-forge/pandas/badges/downloads.svg" alt="conda-forge downloads" />
    </a>
  </td>
</tr>
<tr>
	<td>Gitter</td>
	<td>
		<a href="https://gitter.im/pydata/pandas">
		<img src="https://badges.gitter.im/Join%20Chat.svg" />
		</a>
	</td>
</tr>
</table>



## What is it?

**pandas** is a Python package providing 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**][missing-data] (represented as
    `NaN`) in floating point as well as non-floating point data
  - Size mutability: columns can be [**inserted and
    deleted**][insertion-deletion] from DataFrame and higher dimensional
    objects
  - Automatic and explicit [**data alignment**][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**][groupby] functionality to perform
    split-apply-combine operations on data sets, for both aggregating
    and transforming data
  - Make it [**easy to convert**][conversion] ragged,
    differently-indexed data in other Python and NumPy data structures
    into DataFrame objects
  - Intelligent label-based [**slicing**][slicing], [**fancy
    indexing**][fancy-indexing], and [**subsetting**][subsetting] of
    large data sets
  - Intuitive [**merging**][merging] and [**joining**][joining] data
    sets
  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
    data sets
  - [**Hierarchical**][mi] labeling of axes (possible to have multiple
    labels per tick)
  - Robust IO tools for loading data from [**flat files**][flat-files]
    (CSV and delimited), [**Excel files**][excel], [**databases**][db],
    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
  - [**Time series**][timeseries]-specific functionality: date range
    generation and frequency conversion, moving window statistics,
    moving window linear regressions, date shifting and lagging, etc.


   [missing-data]: https://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data
   [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion
   [alignment]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures
   [groupby]: https://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine
   [conversion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe
   [slicing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges
   [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix
   [subsetting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing
   [merging]: https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging
   [joining]: https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index
   [reshape]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables
   [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations
   [mi]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex
   [flat-files]: https://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files
   [excel]: https://pandas.pydata.org/pandas-docs/stable/io.html#excel-files
   [db]: https://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries
   [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables
   [timeseries]: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality

## 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](https://pypi.org/project/pandas) and on conda.

```sh
# conda
conda install pandas
```

```sh
# or PyPI
pip install pandas
```

## Dependencies
- [NumPy](https://www.numpy.org): 1.13.3 or higher
- [python-dateutil](https://labix.org/python-dateutil): 2.5.0 or higher
- [pytz](https://pythonhosted.org/pytz): 2015.4 or higher

See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies)
for 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:

```sh<commit_msg>DOC: README.md wrong minimum versions (#29844)<commit_after><div align="center">
  <img src="https://dev.pandas.io/static/img/pandas.svg"><br>
</div>

-----------------

# pandas: powerful Python data analysis toolkit

<table>
<tr>
  <td>Latest Release</td>
  <td>
    <a href="https://pypi.org/project/pandas/">
    <img src="https://img.shields.io/pypi/v/pandas.svg" alt="latest release" />
    </a>
  </td>
</tr>
  <td></td>
  <td>
    <a href="https://anaconda.org/anaconda/pandas/">
    <img src="https://anaconda.org/conda-forge/pandas/badges/version.svg" alt="latest release" />
    </a>
</td>
</tr>
<tr>
  <td>Package Status</td>
  <td>
		<a href="https://pypi.org/project/pandas/">
		<img src="https://img.shields.io/pypi/status/pandas.svg" alt="status" />
		</a>
  </td>
</tr>
<tr>
  <td>License</td>
  <td>
    <a href="https://github.com/pandas-dev/pandas/blob/master/LICENSE">
    <img src="https://img.shields.io/pypi/l/pandas.svg" alt="license" />
    </a>
</td>
</tr>
<tr>
  <td>Build Status</td>
  <td>
    <a href="https://travis-ci.org/pandas-dev/pandas">
    <img src="https://travis-ci.org/pandas-dev/pandas.svg?branch=master" alt="travis build status" />
    </a>
  </td>
</tr>
<tr>
  <td></td>
  <td>
    <a href="https://dev.azure.com/pandas-dev/pandas/_build/latest?definitionId=1&branch=master">
      <img src="https://dev.azure.com/pandas-dev/pandas/_apis/build/status/pandas-dev.pandas?branch=master" alt="Azure Pipelines build status" />
    </a>
  </td>
</tr>
<tr>
  <td>Coverage</td>
  <td>
    <a href="https://codecov.io/gh/pandas-dev/pandas">
    <img src="https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=master" alt="coverage" />
    </a>
  </td>
</tr>
<tr>
  <td>Downloads</td>
  <td>
    <a href="https://pandas.pydata.org">
    <img src="https://anaconda.org/conda-forge/pandas/badges/downloads.svg" alt="conda-forge downloads" />
    </a>
  </td>
</tr>
<tr>
	<td>Gitter</td>
	<td>
		<a href="https://gitter.im/pydata/pandas">
		<img src="https://badges.gitter.im/Join%20Chat.svg" />
		</a>
	</td>
</tr>
</table>



## What is it?

**pandas** is a Python package providing 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**][missing-data] (represented as
    `NaN`) in floating point as well as non-floating point data
  - Size mutability: columns can be [**inserted and
    deleted**][insertion-deletion] from DataFrame and higher dimensional
    objects
  - Automatic and explicit [**data alignment**][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**][groupby] functionality to perform
    split-apply-combine operations on data sets, for both aggregating
    and transforming data
  - Make it [**easy to convert**][conversion] ragged,
    differently-indexed data in other Python and NumPy data structures
    into DataFrame objects
  - Intelligent label-based [**slicing**][slicing], [**fancy
    indexing**][fancy-indexing], and [**subsetting**][subsetting] of
    large data sets
  - Intuitive [**merging**][merging] and [**joining**][joining] data
    sets
  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
    data sets
  - [**Hierarchical**][mi] labeling of axes (possible to have multiple
    labels per tick)
  - Robust IO tools for loading data from [**flat files**][flat-files]
    (CSV and delimited), [**Excel files**][excel], [**databases**][db],
    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
  - [**Time series**][timeseries]-specific functionality: date range
    generation and frequency conversion, moving window statistics,
    moving window linear regressions, date shifting and lagging, etc.


   [missing-data]: https://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data
   [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion
   [alignment]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures
   [groupby]: https://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine
   [conversion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe
   [slicing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges
   [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix
   [subsetting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing
   [merging]: https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging
   [joining]: https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index
   [reshape]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables
   [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations
   [mi]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex
   [flat-files]: https://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files
   [excel]: https://pandas.pydata.org/pandas-docs/stable/io.html#excel-files
   [db]: https://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries
   [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables
   [timeseries]: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality

## 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](https://pypi.org/project/pandas) and on conda.

```sh
# conda
conda install pandas
```

```sh
# or PyPI
pip install pandas
```

## Dependencies
- [NumPy](https://www.numpy.org)
- [python-dateutil](https://labix.org/python-dateutil)
- [pytz](https://pythonhosted.org/pytz)

See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) 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:

```sh
{'fraction_non_alphanumeric': 0.16327414465136422, 'fraction_numerical': 0.0019488956258120397, 'mean_word_length': 4.067666422823701, 'pattern_counts': {'":': 0, '<': 175, '<?xml version=': 0, '>': 175, 'https://': 86, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 6, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '4450264', 'n_tokens_mistral': 5021, 'n_tokens_neox': 4813, 'n_words': 1044}