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DOC: Improved the docstring of pandas.DataFrame.values #20065
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Original file line number | Diff line number | Diff line change |
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@@ -4232,7 +4232,50 @@ def as_matrix(self, columns=None): | |
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@property | ||
def values(self): | ||
"""Numpy representation of NDFrame | ||
""" | ||
Return a Numpy representation of the DataFrame. | ||
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Only the values in the DataFrame will be returned, the axes labels | ||
will be removed. | ||
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Returns | ||
------- | ||
numpy.ndarray | ||
The values of the DataFrame | ||
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Examples | ||
-------- | ||
A DataFrame where all columns are the same type (e.g., int64) results | ||
in an ndarray of the same type. | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I would use here 'array' instead of 'ndarray' (that looks a bit more user friendly, the full name is already in the Returns section |
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>>> df = pd.DataFrame({'age': [ 3, 29], | ||
... 'height': [94, 170], | ||
... 'weight': [31, 115]}) | ||
>>> df | ||
age height weight | ||
0 3 94 31 | ||
1 29 170 115 | ||
>>> df.values | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Can you show here |
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array([[ 3, 94, 31], | ||
[ 29, 170, 115]], dtype=int64) | ||
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A DataFrame with mixed type columns(e.g., str/object, int64, float32) | ||
results in an ndarray of the broadest type encompasing these mixed | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. The note belows uses "accommodates" instead of "encompasing". Best to use the same terminolgy in both cases |
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types (e.g., object). | ||
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>>> df2 = pd.DataFrame([('parrot', 24.0, 'second'), | ||
... ('lion', 80.5, 1), | ||
... ('monkey', np.nan, None)], | ||
... columns=('name', 'max_speed', 'rank')) | ||
>>> df2.dtypes | ||
name object | ||
max_speed float64 | ||
rank object | ||
dtype: object | ||
>>> df2.values | ||
array([['parrot', 24.0, 'second'], | ||
['lion', 80.5, 1], | ||
['monkey', nan, None]], dtype=object) | ||
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Notes | ||
----- | ||
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@@ -4243,8 +4286,14 @@ def values(self): | |
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e.g. If the dtypes are float16 and float32, dtype will be upcast to | ||
float32. If dtypes are int32 and uint8, dtype will be upcast to | ||
int32. By numpy.find_common_type convention, mixing int64 and uint64 | ||
will result in a flot64 dtype. | ||
int32. By :func:`numpy.find_common_type` convention, mixing int64 | ||
and uint64 will result in a float64 dtype. | ||
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See Also | ||
-------- | ||
pandas.DataFrame.from_records : Creating a DataFrame from an ndarray | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. In this case There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I included this as "inverse operation": DF <-> array |
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pandas.DataFrame.keys : Retrieving the 'info axis' (column names) | ||
pandas.DataFrame.columns : Retrieving the column names | ||
""" | ||
self._consolidate_inplace() | ||
return self._data.as_array(transpose=self._AXIS_REVERSED) | ||
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I think
from_array
could be a good option for aSee Also
section. If I'm not wrong it's kind of the inverse method.There was a problem hiding this comment.
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I went with
from_records
, thank you for suggesting to include the inverse in that section.