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DOC: Polishing typos out of doc/source/user_guide/indexing.rst (#25528)
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Diff for: doc/source/user_guide/indexing.rst

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@@ -435,7 +435,7 @@ Selection By Position
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This is sometimes called ``chained assignment`` and should be avoided.
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See :ref:`Returning a View versus Copy <indexing.view_versus_copy>`.
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Pandas provides a suite of methods in order to get **purely integer based indexing**. The semantics follow closely Python and NumPy slicing. These are ``0-based`` indexing. When slicing, the start bounds is *included*, while the upper bound is *excluded*. Trying to use a non-integer, even a **valid** label will raise an ``IndexError``.
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Pandas provides a suite of methods in order to get **purely integer based indexing**. The semantics follow closely Python and NumPy slicing. These are ``0-based`` indexing. When slicing, the start bound is *included*, while the upper bound is *excluded*. Trying to use a non-integer, even a **valid** label will raise an ``IndexError``.
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The ``.iloc`` attribute is the primary access method. The following are valid inputs:
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.. versionadded:: 0.18.1
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``.loc``, ``.iloc``, and also ``[]`` indexing can accept a ``callable`` as indexer.
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The ``callable`` must be a function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing.
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The ``callable`` must be a function with one argument (the calling Series, DataFrame or Panel) that returns valid output for indexing.
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.. ipython:: python
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@@ -569,7 +569,7 @@ You can use callable indexing in ``Series``.
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df1.A.loc[lambda s: s > 0]
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Using these methods / indexers, you can chain data selection operations
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without using temporary variable.
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without using a temporary variable.
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.. ipython:: python
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@@ -907,7 +907,7 @@ of the DataFrame):
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df[df['A'] > 0]
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List comprehensions and ``map`` method of Series can also be used to produce
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List comprehensions and the ``map`` method of Series can also be used to produce
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more complex criteria:
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.. ipython:: python
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ind
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``set_names``, ``set_levels``, and ``set_codes`` also take an optional
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`level`` argument
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``level`` argument
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.. ipython:: python
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