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Revert changes on some terms. Add two lists of words - one for names one for terms
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doc/source/advanced.rst

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@@ -611,7 +611,7 @@ index positions. ``take`` will also accept negative integers as relative positio
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ser.iloc[positions]
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ser.take(positions)
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For DataFrames, the given indexes should be a 1d list or ndarray that specifies
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For DataFrames, the given indices should be a 1d list or ndarray that specifies
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row or column positions.
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.. ipython:: python
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frm.take([0, 2], axis=1)
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It is important to note that the ``take`` method on pandas objects are not
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intended to work on boolean indexes and may return unexpected results.
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intended to work on boolean indices and may return unexpected results.
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.. ipython:: python
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doc/source/categorical.rst

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@@ -370,7 +370,7 @@ Renaming categories is done by assigning new values to the
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.. note::
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Be aware that assigning new categories is an in place operation, while most other operations
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Be aware that assigning new categories is an inplace operation, while most other operations
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under ``Series.cat`` per default return a new ``Series`` of dtype `category`.
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Categories must be unique or a `ValueError` is raised:

doc/source/comparison_with_sql.rst

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@@ -228,9 +228,9 @@ Grouping by more than one column is done by passing a list of columns to the
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JOIN
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----
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JOINs can be performed with :meth:`~pandas.DataFrame.join` or :meth:`~pandas.merge`. By default,
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:meth:`~pandas.DataFrame.join` will join the DataFrames on their indexes. Each method has
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:meth:`~pandas.DataFrame.join` will join the DataFrames on their indices. Each method has
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parameters allowing you to specify the type of join to perform (LEFT, RIGHT, INNER, FULL) or the
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columns to join on (column names or indexes).
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columns to join on (column names or indices).
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.. ipython:: python
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doc/source/conf.py

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exclude_patterns = ['**.ipynb_checkpoints']
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spelling_word_list_filename = 'spelling_wordlist.txt'
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spelling_word_list_filename = ['spelling_wordlist.txt', 'names_wordlist.txt']
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spelling_ignore_pypi_package_names = True
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with open("index.rst") as f:

doc/source/contributing_docstring.rst

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@@ -513,7 +513,7 @@ instead of at the beginning, it is good to let the users know about it.
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To give an intuition on what can be considered related, here there are some
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examples:
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* ``loc`` and ``iloc``, as they do the same, but in one case providing indexes
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* ``loc`` and ``iloc``, as they do the same, but in one case providing indices
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and in the other positions
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* ``max`` and ``min``, as they do the opposite
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* ``iterrows``, ``itertuples`` and ``iteritems``, as it is easy that a user
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matplotlib).
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When illustrating examples with a single ``Series`` use the name ``s``, and if
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illustrating with a single ``DataFrame`` use the name ``df``. For indexes,
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illustrating with a single ``DataFrame`` use the name ``df``. For indices,
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``idx`` is the preferred name. If a set of homogeneous ``Series`` or
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``DataFrame`` is used, name them ``s1``, ``s2``, ``s3``... or ``df1``,
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``df2``, ``df3``... If the data is not homogeneous, and more than one structure

doc/source/dsintro.rst

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@@ -882,7 +882,7 @@ dictionary of DataFrames as above, and the following named parameters:
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:header: "Parameter", "Default", "Description"
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:widths: 10, 10, 40
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intersect, ``False``, drops elements whose indexes do not align
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intersect, ``False``, drops elements whose indices do not align
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orient, ``items``, use ``minor`` to use DataFrames' columns as panel items
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For example, compare to the construction above:

doc/source/enhancingperf.rst

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@@ -601,7 +601,7 @@ on the original ``DataFrame`` or return a copy with the new column.
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For backwards compatibility, ``inplace`` defaults to ``True`` if not
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specified. This will change in a future version of pandas - if your
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code depends on an in place assignment you should update to explicitly
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code depends on an inplace assignment you should update to explicitly
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set ``inplace=True``.
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.. ipython:: python

doc/source/groupby.rst

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@@ -942,7 +942,7 @@ that is itself a series, and possibly upcast the result to a DataFrame:
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``apply`` can act as a reducer, transformer, *or* filter function, depending on exactly what is passed to it.
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So depending on the path taken, and exactly what you are grouping. Thus the grouped columns(s) may be included in
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the output as well as set the indexes.
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the output as well as set the indices.
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.. warning::
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Resampling produces new hypothetical samples (resamples) from already existing observed data or from a model that generates data. These new samples are similar to the pre-existing samples.
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In order to resample to work on indexes that are non-datetimelike, the following procedure can be utilized.
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In order to resample to work on indices that are non-datetimelike, the following procedure can be utilized.
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In the following examples, **df.index // 5** returns a binary array which is used to determine what gets selected for the groupby operation.
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doc/source/io.rst

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@@ -2756,7 +2756,7 @@ Specifying Sheets
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- The arguments ``sheet_name`` allows specifying the sheet or sheets to read.
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- The default value for ``sheet_name`` is 0, indicating to read the first sheet
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- Pass a string to refer to the name of a particular sheet in the workbook.
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- Pass an integer to refer to the index of a sheet. Indexes follow Python
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- Pass an integer to refer to the index of a sheet. Indices follow Python
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convention, beginning at 0.
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- Pass a list of either strings or integers, to return a dictionary of specified sheets.
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- Pass a ``None`` to return a dictionary of all available sheets.

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