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math-and-datajorisvandenbossche
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DOC: Improved the docstring of pandas.DataFrame.values (#20065)
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pandas/core/generic.py

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@@ -4232,7 +4232,55 @@ def as_matrix(self, columns=None):
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@property
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def values(self):
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"""Numpy representation of NDFrame
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"""
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Return a Numpy representation of the DataFrame.
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Only the values in the DataFrame will be returned, the axes labels
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will be removed.
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Returns
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-------
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numpy.ndarray
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The values of the DataFrame.
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Examples
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--------
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A DataFrame where all columns are the same type (e.g., int64) results
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in an array of the same type.
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>>> df = pd.DataFrame({'age': [ 3, 29],
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... 'height': [94, 170],
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... 'weight': [31, 115]})
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>>> df
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age height weight
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0 3 94 31
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1 29 170 115
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>>> df.dtypes
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age int64
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height int64
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weight int64
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dtype: object
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>>> df.values
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array([[ 3, 94, 31],
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[ 29, 170, 115]], dtype=int64)
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A DataFrame with mixed type columns(e.g., str/object, int64, float32)
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results in an ndarray of the broadest type that accommodates these
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mixed types (e.g., object).
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>>> df2 = pd.DataFrame([('parrot', 24.0, 'second'),
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... ('lion', 80.5, 1),
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... ('monkey', np.nan, None)],
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... columns=('name', 'max_speed', 'rank'))
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>>> df2.dtypes
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name object
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max_speed float64
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rank object
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dtype: object
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>>> df2.values
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array([['parrot', 24.0, 'second'],
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['lion', 80.5, 1],
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['monkey', nan, None]], dtype=object)
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Notes
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-----
@@ -4243,8 +4291,13 @@ def values(self):
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e.g. If the dtypes are float16 and float32, dtype will be upcast to
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float32. If dtypes are int32 and uint8, dtype will be upcast to
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int32. By numpy.find_common_type convention, mixing int64 and uint64
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will result in a flot64 dtype.
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int32. By :func:`numpy.find_common_type` convention, mixing int64
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and uint64 will result in a float64 dtype.
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See Also
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--------
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pandas.DataFrame.index : Retrievie the index labels
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pandas.DataFrame.columns : Retrieving the column names
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"""
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self._consolidate_inplace()
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return self._data.as_array(transpose=self._AXIS_REVERSED)

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