8th day of python challenges 111-117

venv/lib/python3.6/site-packages/pandas-0.25.0.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: pandas
+Version: 0.25.0
+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
+Project-URL: Bug Tracker, https://github.com/pandas-dev/pandas/issues
+Project-URL: Documentation, http://pandas.pydata.org/pandas-docs/stable/
+Project-URL: Source Code, https://github.com/pandas-dev/pandas
+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 :: 3
+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: >=3.5.3
+Provides-Extra: test
+Requires-Dist: python-dateutil (>=2.6.1)
+Requires-Dist: pytz (>=2017.2)
+Requires-Dist: numpy (>=1.13.3)
+Provides-Extra: test
+Requires-Dist: pytest (>=4.0.2); extra == 'test'
+Requires-Dist: pytest-xdist; extra == 'test'
+Requires-Dist: hypothesis (>=3.58); extra == 'test'
+
+
+**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.
+
+