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- 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.
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