92 lines
4.4 KiB
Text
92 lines
4.4 KiB
Text
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Metadata-Version: 2.1
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Name: pandas
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Version: 0.23.4
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Summary: Powerful data structures for data analysis, time series, and statistics
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Home-page: http://pandas.pydata.org
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Maintainer: The PyData Development Team
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Maintainer-email: pydata@googlegroups.com
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License: BSD
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Platform: any
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Environment :: Console
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Classifier: Operating System :: OS Independent
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Classifier: Intended Audience :: Science/Research
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 2
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 2.7
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Classifier: Programming Language :: Python :: 3.5
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Classifier: Programming Language :: Python :: 3.6
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Classifier: Programming Language :: Python :: 3.7
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Classifier: Programming Language :: Cython
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Classifier: Topic :: Scientific/Engineering
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Requires-Python: >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*
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Requires-Dist: python-dateutil (>=2.5.0)
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Requires-Dist: pytz (>=2011k)
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Requires-Dist: numpy (>=1.9.0)
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**pandas** is a Python package providing fast, flexible, and expressive data
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structures designed to make working with structured (tabular, multidimensional,
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potentially heterogeneous) and time series data both easy and intuitive. It
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aims to be the fundamental high-level building block for doing practical,
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**real world** data analysis in Python. Additionally, it has the broader goal
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of becoming **the most powerful and flexible open source data analysis /
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manipulation tool available in any language**. It is already well on its way
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toward this goal.
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pandas is well suited for many different kinds of data:
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- Tabular data with heterogeneously-typed columns, as in an SQL table or
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Excel spreadsheet
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- Ordered and unordered (not necessarily fixed-frequency) time series data.
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- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and
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column labels
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- Any other form of observational / statistical data sets. The data actually
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need not be labeled at all to be placed into a pandas data structure
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The two primary data structures of pandas, Series (1-dimensional) and DataFrame
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(2-dimensional), handle the vast majority of typical use cases in finance,
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statistics, social science, and many areas of engineering. For R users,
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DataFrame provides everything that R's ``data.frame`` provides and much
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more. pandas is built on top of `NumPy <http://www.numpy.org>`__ and is
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intended to integrate well within a scientific computing environment with many
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other 3rd party libraries.
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Here are just a few of the things that pandas does well:
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- Easy handling of **missing data** (represented as NaN) in floating point as
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well as non-floating point data
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- Size mutability: columns can be **inserted and deleted** from DataFrame and
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higher dimensional objects
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- Automatic and explicit **data alignment**: objects can be explicitly
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aligned to a set of labels, or the user can simply ignore the labels and
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let `Series`, `DataFrame`, etc. automatically align the data for you in
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computations
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- Powerful, flexible **group by** functionality to perform
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split-apply-combine operations on data sets, for both aggregating and
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transforming data
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- Make it **easy to convert** ragged, differently-indexed data in other
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Python and NumPy data structures into DataFrame objects
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- Intelligent label-based **slicing**, **fancy indexing**, and **subsetting**
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of large data sets
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- Intuitive **merging** and **joining** data sets
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- Flexible **reshaping** and pivoting of data sets
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- **Hierarchical** labeling of axes (possible to have multiple labels per
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tick)
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- Robust IO tools for loading data from **flat files** (CSV and delimited),
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Excel files, databases, and saving / loading data from the ultrafast **HDF5
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format**
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- **Time series**-specific functionality: date range generation and frequency
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conversion, moving window statistics, moving window linear regressions,
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date shifting and lagging, etc.
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Many of these principles are here to address the shortcomings frequently
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experienced using other languages / scientific research environments. For data
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scientists, working with data is typically divided into multiple stages:
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munging and cleaning data, analyzing / modeling it, then organizing the results
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of the analysis into a form suitable for plotting or tabular display. pandas is
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the ideal tool for all of these tasks.
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