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‎doc/source/conf.py

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‎doc/source/core.rst

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.. _core:
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***************
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Data Structures
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***************
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.. currentmodule:: pandas
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.. toctree::
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:maxdepth: 2
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indexobj
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series
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dataframe
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panel
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.. _core:
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***************
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Data Structures
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***************
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.. currentmodule:: pandas
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.. toctree::
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:maxdepth: 2
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indexobj
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series
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dataframe
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panel

‎doc/source/index.rst

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.. Pandas documentation master file, created by
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pandas: a python data analysis library
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======================================
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:mod:`pandas` is a python package providing convenient data structures
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for time series, cross-sectional, or any other form of "labeled" data,
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with tools for building statistical and econometric models.
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This library was created with the following design principles:
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- Working with time series and cross-sectional data should be easy
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- The user should not have to worry (much) about handling missing data
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- Data alignment should be automatic and transparent
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- Speed matters
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- Perhaps most importantly: *things should work just like you want them to*
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Many of these principles are here to address the shortcomings
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frequently experienced using other languages / scientific research
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environments. In MATLAB, for example, you spend a lot of time coercing
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data into matrices, cleaning and aligning it, and keeping everything
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homogeneous. You have to use lots of functions like **nanmean, nanstd,
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repmat** (for broadcasting), and other functions which help you to
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maintain reliable data. Using `NumPy <http://www.numpy.org>`__ and a
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Pythonic approach, pandas helps hide the dirty details of working with
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unclean data, allowing you to focus on the problem you're trying to
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solve rather than the implementation.
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pandas is implemented primarily using NumPy and is intended to be able
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to integrate very easily with other NumPy-based scientific libraries,
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such as :mod:`scikits.statsmodels`.
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.. note::
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This documentation assumes general familiarity with NumPy. If you
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haven't used NumPy much or at all, please check out the `NumPy
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documentation <http://docs.scipy.org>`__ first.
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See the package overview for more detail about what's in the library.
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User manual
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-----------
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`PDF Version <pandas.pdf>`__
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.. module:: pandas
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**Date**: |today|
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**Version**: |version|
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**License:** BSD
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**Requirements:** python 2.4 to 2.7, NumPy, and dateutil
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**Code Repository:** http://github.com/wesm/pandas
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Library documentation
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~~~~~~~~~~~~~~~~~~~~~
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.. toctree::
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:maxdepth: 2
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overview
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core
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groupby
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datetools
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stats
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r_interface
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io
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Other topics of interest
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~~~~~~~~~~~~~~~~~~~~~~~~
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.. toctree::
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:maxdepth: 2
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examples
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frame_vs_matrix
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r_guide
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missing_data
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related
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Indices and tables
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------------------
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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Contact
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-------
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Please feel free to send comments or questions directly to
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wesmckinn@gmail.com or the pystatsmodels mailing list.
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.. Pandas documentation master file, created by
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3+
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pandas: a python data analysis library
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======================================
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7+
:mod:`pandas` is a python package providing convenient data structures
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for time series, cross-sectional, or any other form of "labeled" data,
9+
with tools for building statistical and econometric models.
10+
11+
This library was created with the following design principles:
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13+
- Working with time series and cross-sectional data should be easy
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- The user should not have to worry (much) about handling missing data
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- Data alignment should be automatic and transparent
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- Speed matters
17+
- Perhaps most importantly: *things should work just like you want them to*
18+
19+
Many of these principles are here to address the shortcomings
20+
frequently experienced using other languages / scientific research
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environments. In MATLAB, for example, you spend a lot of time coercing
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data into matrices, cleaning and aligning it, and keeping everything
23+
homogeneous. You have to use lots of functions like **nanmean, nanstd,
24+
repmat** (for broadcasting), and other functions which help you to
25+
maintain reliable data. Using `NumPy <http://www.numpy.org>`__ and a
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Pythonic approach, pandas helps hide the dirty details of working with
27+
unclean data, allowing you to focus on the problem you're trying to
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solve rather than the implementation.
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30+
pandas is implemented primarily using NumPy and is intended to be able
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to integrate very easily with other NumPy-based scientific libraries,
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such as :mod:`scikits.statsmodels`.
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.. note::
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This documentation assumes general familiarity with NumPy. If you
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haven't used NumPy much or at all, please check out the `NumPy
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documentation <http://docs.scipy.org>`__ first.
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See the package overview for more detail about what's in the library.
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User manual
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-----------
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`PDF Version <pandas.pdf>`__
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.. module:: pandas
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**Date**: |today|
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**Version**: |version|
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**License:** BSD
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**Requirements:** python 2.4 to 2.7, NumPy, and dateutil
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**Code Repository:** http://github.com/wesm/pandas
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Library documentation
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~~~~~~~~~~~~~~~~~~~~~
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.. toctree::
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:maxdepth: 2
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65+
overview
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core
67+
groupby
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datetools
69+
stats
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r_interface
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io
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73+
Other topics of interest
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~~~~~~~~~~~~~~~~~~~~~~~~
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.. toctree::
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:maxdepth: 2
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examples
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frame_vs_matrix
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r_guide
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missing_data
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related
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Indices and tables
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------------------
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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Contact
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-------
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Please feel free to send comments or questions directly to
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wesmckinn@gmail.com or the pystatsmodels mailing list.

‎doc/source/stats.rst

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.. currentmodule:: pandas.stats.api
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.. _stats:
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**********************************
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Built-in statistical functionality
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**********************************
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.. currentmodule:: pandas
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.. toctree::
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:maxdepth: 2
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stats_moments
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stats_ols
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.. currentmodule:: pandas.stats.api
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.. _stats:
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**********************************
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Built-in statistical functionality
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**********************************
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.. currentmodule:: pandas
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.. toctree::
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:maxdepth: 2
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stats_moments
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stats_ols

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