pandas


Namepandas JSON
Version 0.20.0 PyPI version JSON
download
home_pagehttp://pandas.pydata.org
SummaryPowerful data structures for data analysis, time series,and statistics
upload_time2017-05-05 02:46:47
maintainer
docs_urlNone
authorThe PyData Development Team
requires_python
licenseBSD
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
Coveralis test coverage No Coveralis.
            **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 <http://www.numpy.org>`__ 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
            

Raw data

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    "description": "**pandas** is a Python package providing fast, flexible, and expressive data\nstructures designed to make working with structured (tabular, multidimensional,\npotentially heterogeneous) and time series data both easy and intuitive. It\naims to be the fundamental high-level building block for doing practical,\n**real world** data analysis in Python. Additionally, it has the broader goal\nof becoming **the most powerful and flexible open source data analysis /\nmanipulation tool available in any language**. It is already well on its way\ntoward this goal.\n\npandas is well suited for many different kinds of data:\n\n  - Tabular data with heterogeneously-typed columns, as in an SQL table or\n    Excel spreadsheet\n  - Ordered and unordered (not necessarily fixed-frequency) time series data.\n  - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and\n    column labels\n  - Any other form of observational / statistical data sets. The data actually\n    need not be labeled at all to be placed into a pandas data structure\n\nThe two primary data structures of pandas, Series (1-dimensional) and DataFrame\n(2-dimensional), handle the vast majority of typical use cases in finance,\nstatistics, social science, and many areas of engineering. For R users,\nDataFrame provides everything that R's ``data.frame`` provides and much\nmore. pandas is built on top of `NumPy <http://www.numpy.org>`__ and is\nintended to integrate well within a scientific computing environment with many\nother 3rd party libraries.\n\nHere are just a few of the things that pandas does well:\n\n  - Easy handling of **missing data** (represented as NaN) in floating point as\n    well as non-floating point data\n  - Size mutability: columns can be **inserted and deleted** from DataFrame and\n    higher dimensional objects\n  - Automatic and explicit **data alignment**: objects can be explicitly\n    aligned to a set of labels, or the user can simply ignore the labels and\n    let `Series`, `DataFrame`, etc. automatically align the data for you in\n    computations\n  - Powerful, flexible **group by** functionality to perform\n    split-apply-combine operations on data sets, for both aggregating and\n    transforming data\n  - Make it **easy to convert** ragged, differently-indexed data in other\n    Python and NumPy data structures into DataFrame objects\n  - Intelligent label-based **slicing**, **fancy indexing**, and **subsetting**\n    of large data sets\n  - Intuitive **merging** and **joining** data sets\n  - Flexible **reshaping** and pivoting of data sets\n  - **Hierarchical** labeling of axes (possible to have multiple labels per\n    tick)\n  - Robust IO tools for loading data from **flat files** (CSV and delimited),\n    Excel files, databases, and saving / loading data from the ultrafast **HDF5\n    format**\n  - **Time series**-specific functionality: date range generation and frequency\n    conversion, moving window statistics, moving window linear regressions,\n    date shifting and lagging, etc.\n\nMany of these principles are here to address the shortcomings frequently\nexperienced using other languages / scientific research environments. For data\nscientists, working with data is typically divided into multiple stages:\nmunging and cleaning data, analyzing / modeling it, then organizing the results\nof the analysis into a form suitable for plotting or tabular display. pandas is\nthe ideal tool for all of these tasks.\n\nNote\n----\nWindows binaries built against NumPy 1.8.1", 
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