pandas-0.24.2.tar.gz

<div align="center"> <img src="https://github.com/pandas-dev/pandas/blob/master/doc/logo/pandas_logo.png"><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" /></td> </a> </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): 1.12.0 or higher - [python-dateutil](https://labix.org/python-dateutil): 2.5.0 or higher - [pytz](https://pythonhosted.org/pytz): 2011k 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 pip install cython ``` In the `pandas` directory (same one where you found this file after cloning the git repo), execute: ```sh python setup.py install ``` or for installing in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs): ```sh python setup.py develop ``` Alternatively, you can use `pip` if you want all the dependencies pulled in automatically (the `-e` option is for installing it in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs)): ```sh pip install -e . ``` See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/install.html#installing-from-source). ## License [BSD 3](LICENSE) ## Documentation The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable ## Background Work on ``pandas`` started at AQR (a quantitative hedge fund) in 2008 and has been under active development since then. ## Getting Help For usage questions, the best place to go to is [StackOverflow](https://stackoverf