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Copy file name to clipboardExpand all lines: doc/source/user_guide/advanced.rst
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Int64Index and RangeIndex
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~~~~~~~~~~~~~~~~~~~~~~~~~
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.. warning::
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Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see :ref:`here <whatsnew_0180.float_indexers>`.
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:class:`Int64Index` is a fundamental basic index in pandas.
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This is an immutable array implementing an ordered, sliceable set.
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Prior to 0.18.0, the ``Int64Index`` would provide the default index for all ``NDFrame`` objects.
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:class:`Int64Index` is a fundamental basic index in pandas. This is an immutable array
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implementing an ordered, sliceable set.
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:class:`RangeIndex` is a sub-class of ``Int64Index`` added in version 0.18.0, now providing the default index for all ``NDFrame`` objects.
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:class:`RangeIndex` is a sub-class of ``Int64Index`` that provides the default index for all ``NDFrame`` objects.
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``RangeIndex`` is an optimized version of ``Int64Index`` that can represent a monotonic ordered set. These are analogous to Python `range types <https://docs.python.org/3/library/stdtypes.html#typesseq-range>`__.
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.. _indexing.float64index:
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In [1]: pd.Series(range(5))[3.5:4.5]
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TypeError: the slice start [3.5] is not a proper indexer for this index type (Int64Index)
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.. warning::
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Using a scalar float indexer for ``.iloc`` has been removed in 0.18.0, so the following will raise a ``TypeError``:
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.. code-block:: ipython
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In [3]: pd.Series(range(5)).iloc[3.0]
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TypeError: cannot do positional indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'>
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Here is a typical use-case for using this type of indexing. Imagine that you have a somewhat
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irregular timedelta-like indexing scheme, but the data is recorded as floats. This could, for
Copy file name to clipboardExpand all lines: doc/source/user_guide/indexing.rst
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should be avoided. See :ref:`Returning a View versus Copy
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<indexing.view_versus_copy>`.
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.. warning::
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Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see :ref:`here <whatsnew_0180.float_indexers>`.
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See the :ref:`MultiIndex / Advanced Indexing <advanced>` for ``MultiIndex`` and more advanced indexing documentation.
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See the :ref:`cookbook<cookbook.selection>` for some advanced strategies.
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* A ``callable`` function with one argument (the calling Series or DataFrame) and
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that returns valid output for indexing (one of the above).
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.. versionadded:: 0.18.1
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See more at :ref:`Selection by Position <indexing.integer>`,
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:ref:`Advanced Indexing <advanced>` and :ref:`Advanced
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Hierarchical <advanced.advanced_hierarchical>`.
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df2 = df.copy()
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df.apply(lambdax, y: x.where(x >0, y), y=df['A'])
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.. versionadded:: 0.18.1
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Where can accept a callable as condition and ``other`` arguments. The function must
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``where`` can accept a callable as condition and ``other`` arguments. The function must
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be with one argument (the calling Series or DataFrame) and that returns valid output
Copy file name to clipboardExpand all lines: doc/source/user_guide/style.ipynb
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"source": [
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"# Styling\n",
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"\n",
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"*New in version 0.17.1*\n",
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"\n",
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"<span style=\"color: red\">*Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your feedback.*</span>\n",
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"\n",
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"This document is written as a Jupyter Notebook, and can be viewed or downloaded [here](http://nbviewer.ipython.org/github/pandas-dev/pandas/blob/master/doc/source/style.ipynb).\n",
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"\n",
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"You can apply **conditional formatting**, the visual styling of a DataFrame\n",
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