ydb/contrib/python/pandas/py2/.dist-info/METADATA

Metadata-Version: 2.1
Name: pandas
Version: 0.24.2
Summary: Powerful data structures for data analysis, time series, and statistics
Home-page: http://pandas.pydata.org
Maintainer: The PyData Development Team
Maintainer-email: pydata@googlegroups.com
License: BSD
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Cython
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*
Requires-Dist: python-dateutil (>=2.5.0)
Requires-Dist: pytz (>=2011k)
Requires-Dist: numpy (>=1.12.0)

**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.