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DOC: update df.as_matrix and df.values. Closes gh7413 #7417

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Jun 14, 2014
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56 changes: 41 additions & 15 deletions pandas/core/generic.py
Original file line number Diff line number Diff line change
Expand Up @@ -1929,25 +1929,39 @@ def _get_bool_data(self):

def as_matrix(self, columns=None):
"""
Convert the frame to its Numpy-array matrix representation. Columns
are presented in sorted order unless a specific list of columns is
provided.

NOTE: the dtype will be a lower-common-denominator dtype (implicit
upcasting) that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen use this
with care if you are not dealing with the blocks

e.g. if the dtypes are float16,float32 -> float32
float16,float32,float64 -> float64
int32,uint8 -> int32

Convert the frame to its Numpy-array representation.

Parameters
----------
columns: list, optional, default:None
If None, return all columns, otherwise, returns specified columns.

Returns
-------
values : ndarray
If the caller is heterogeneous and contains booleans or objects,
the result will be of dtype=object
the result will be of dtype=object. See Notes.


Notes
-----
Return is NOT a Numpy-matrix, rather, a Numpy-array.

The dtype will be a lower-common-denominator dtype (implicit
upcasting); that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen. Use this
with care if you are not dealing with the blocks.

e.g. If the dtypes are float16 and float32, dtype will be upcast to
float32. If dtypes are int32 and uint8, dtype will be upcase to
int32.

This method is provided for backwards compatibility. Generally,
it is recommended to use '.values'.

See Also
--------
pandas.DataFrame.values
"""
self._consolidate_inplace()
if self._AXIS_REVERSED:
Expand All @@ -1956,7 +1970,19 @@ def as_matrix(self, columns=None):

@property
def values(self):
"Numpy representation of NDFrame"
"""Numpy representation of NDFrame

Notes
-----
The dtype will be a lower-common-denominator dtype (implicit
upcasting); that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen. Use this
with care if you are not dealing with the blocks.

e.g. If the dtypes are float16 and float32, dtype will be upcast to
float32. If dtypes are int32 and uint8, dtype will be upcase to
int32.
"""
return self.as_matrix()

@property
Expand Down