@@ -583,7 +583,7 @@ def _get_index_resolvers(self) -> dict[Hashable, Series | MultiIndex]:
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@final
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def _get_cleaned_column_resolvers (self ) -> dict [Hashable , Series ]:
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"""
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- Return the special character free column resolvers of a dataframe .
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+ Return the special character free column resolvers of a DataFrame .
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Column names with special characters are 'cleaned up' so that they can
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be referred to by backtick quoting.
@@ -5077,7 +5077,8 @@ def reindex(
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Returns
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-------
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- {klass} with changed index.
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+ {klass}
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+ {klass} with changed index.
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See Also
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--------
@@ -5095,7 +5096,7 @@ def reindex(
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We *highly* recommend using keyword arguments to clarify your
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intent.
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- Create a dataframe with some fictional data.
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+ Create a DataFrame with some fictional data.
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>>> index = ["Firefox", "Chrome", "Safari", "IE10", "Konqueror"]
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>>> columns = ["http_status", "response_time"]
@@ -5112,9 +5113,9 @@ def reindex(
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IE10 404 0.08
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Konqueror 301 1.00
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- Create a new index and reindex the dataframe . By default
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+ Create a new index and reindex the DataFrame . By default
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values in the new index that do not have corresponding
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- records in the dataframe are assigned ``NaN``.
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+ records in the DataFrame are assigned ``NaN``.
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>>> new_index = ["Safari", "Iceweasel", "Comodo Dragon", "IE10", "Chrome"]
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>>> df.reindex(new_index)
@@ -5167,7 +5168,7 @@ def reindex(
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Konqueror 301 NaN
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To further illustrate the filling functionality in
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- ``reindex``, we will create a dataframe with a
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+ ``reindex``, we will create a DataFrame with a
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monotonically increasing index (for example, a sequence
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of dates).
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@@ -5184,7 +5185,7 @@ def reindex(
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2010-01-05 89.0
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2010-01-06 88.0
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- Suppose we decide to expand the dataframe to cover a wider
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+ Suppose we decide to expand the DataFrame to cover a wider
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date range.
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>>> date_index2 = pd.date_range("12/29/2009", periods=10, freq="D")
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2010-01-06 88.0
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2010-01-07 NaN
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- Please note that the ``NaN`` value present in the original dataframe
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+ Please note that the ``NaN`` value present in the original DataFrame
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(at index value 2010-01-03) will not be filled by any of the
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value propagation schemes. This is because filling while reindexing
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- does not look at dataframe values, but only compares the original and
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+ does not look at DataFrame values, but only compares the original and
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desired indexes. If you do want to fill in the ``NaN`` values present
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- in the original dataframe , use the ``fillna()`` method.
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+ in the original DataFrame , use the ``fillna()`` method.
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See the :ref:`user guide <basics.reindexing>` for more.
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"""
@@ -8373,7 +8374,7 @@ def clip(
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See Also
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--------
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Series.clip : Trim values at input threshold in series.
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- DataFrame.clip : Trim values at input threshold in dataframe .
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+ DataFrame.clip : Trim values at input threshold in DataFrame .
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numpy.clip : Clip (limit) the values in an array.
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Examples
@@ -10909,7 +10910,7 @@ def describe(
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among those with the highest count.
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For mixed data types provided via a ``DataFrame``, the default is to
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- return only an analysis of numeric columns. If the dataframe consists
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+ return only an analysis of numeric columns. If the DataFrame consists
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only of object and categorical data without any numeric columns, the
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default is to return an analysis of both the object and categorical
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columns. If ``include='all'`` is provided as an option, the result
@@ -12052,7 +12053,7 @@ def last_valid_index(self) -> Hashable:
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**DataFrames**
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- Create a dataframe from a dictionary.
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+ Create a DataFrame from a dictionary.
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>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
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>>> df
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