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DOC: Use a standard header for all rst files (#24086)
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‎doc/source/10min.rst

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.. _10min:
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.. currentmodule:: pandas
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.. ipython:: python
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:suppress:
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import os
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import numpy as np
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import pandas as pd
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np.random.seed(123456)
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np.set_printoptions(precision=4, suppress=True)
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pd.options.display.max_rows = 15
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# portions of this were borrowed from the
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# Pandas cheatsheet
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# created during the PyData Workshop-Sprint 2012
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# Hannah Chen, Henry Chow, Eric Cox, Robert Mauriello
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{{ header }}
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********************
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10 Minutes to pandas
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.. ipython:: python
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:suppress:
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import os
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os.remove('foo.csv')
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HDF5

‎doc/source/advanced.rst

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.. _advanced:
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.. currentmodule:: pandas
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.. ipython:: python
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:suppress:
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import numpy as np
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import pandas as pd
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np.random.seed(123456)
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np.set_printoptions(precision=4, suppress=True)
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pd.options.display.max_rows = 15
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{{ header }}
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******************************
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MultiIndex / Advanced Indexing

‎doc/source/basics.rst

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.. currentmodule:: pandas
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.. ipython:: python
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:suppress:
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import numpy as np
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import pandas as pd
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np.set_printoptions(precision=4, suppress=True)
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pd.options.display.max_rows = 15
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.. _basics:
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{{ header }}
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==============================
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Essential Basic Functionality
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==============================

‎doc/source/categorical.rst

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.. _categorical:
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.. currentmodule:: pandas
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.. ipython:: python
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:suppress:
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import numpy as np
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import pandas as pd
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np.random.seed(123456)
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np.set_printoptions(precision=4, suppress=True)
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pd.options.display.max_rows = 15
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{{ header }}
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****************
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Categorical Data

‎doc/source/comparison_with_r.rst

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.. currentmodule:: pandas
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.. ipython:: python
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:suppress:
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import pandas as pd
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import numpy as np
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pd.options.display.max_rows = 15
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{{ header }}
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Comparison with R / R libraries
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*******************************

‎doc/source/comparison_with_sas.rst

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.. currentmodule:: pandas
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{{ header }}
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Comparison with SAS
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********************
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For potential users coming from `SAS <https://en.wikipedia.org/wiki/SAS_(software)>`__

‎doc/source/comparison_with_sql.rst

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.. currentmodule:: pandas
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{{ header }}
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Comparison with SQL
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********************
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Since many potential pandas users have some familiarity with

‎doc/source/comparison_with_stata.rst

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.. currentmodule:: pandas
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{{ header }}
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Comparison with Stata
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*********************
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For potential users coming from `Stata <https://en.wikipedia.org/wiki/Stata>`__
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`dask.dataframe <http://dask.pydata.org/en/latest/dataframe.html>`_
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library, which provides a subset of pandas functionality for an
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on-disk ``DataFrame``.
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‎doc/source/computation.rst

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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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np.random.seed(123456)
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np.set_printoptions(precision=4, suppress=True)
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pd.options.display.max_rows = 15
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plt.close('all')
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{{ header }}
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Computational tools
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===================
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‎doc/source/conf.py

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}
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common_imports = """\
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header = """\
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.. currentmodule:: pandas
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import numpy as np
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from pandas import *
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import pandas as pd
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randn = np.random.randn
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np.random.seed(123456)
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np.set_printoptions(precision=4, suppress=True)
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options.display.max_rows = 15
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from pandas.compat import StringIO
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pd.options.display.max_rows = 15
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"""
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html_context = {
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'redirects': {old: new for old, new in moved_api_pages},
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'common_imports': common_imports,
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'header': header
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}
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# If false, no module index is generated.

‎doc/source/contributing.rst

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.. _contributing:
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{{ header }}
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**********************
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Contributing to pandas
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**********************
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def old_func():
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"""Summary of the function.
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new_func()
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def new_func():
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pass
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@pytest.mark.parametrize('dtype', ['int8', 'int16', 'int32', 'int64'])
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import pandas.util.testing as tm
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‎doc/source/cookbook.rst

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.. _cookbook:
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Cookbook
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‎doc/source/dsintro.rst

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************************
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Intro to Data Structures
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‎doc/source/enhancingperf.rst

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Enhancing Performance

‎doc/source/gotchas.rst

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********************************
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Frequently Asked Questions (FAQ)
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********************************
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DataFrame memory usage

‎doc/source/groupby.rst

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Group By: split-apply-combine

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