pandas.DataFrame.values#

propertyDataFrame.values[source] #

Return a Numpy representation of the DataFrame.

Warning

We recommend using DataFrame.to_numpy() instead.

Only the values in the DataFrame will be returned, the axes labels will be removed.

Returns:

numpy.ndarray: The values of the DataFrame.

See also

DataFrame.to_numpy: Recommended alternative to this method.
DataFrame.index: Retrieve the index labels.
DataFrame.columns: Retrieving the column names.

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 upcast to int32. By numpy.find_common_type() convention, mixing int64 and uint64 will result in a float64 dtype.

Examples

A DataFrame where all columns are the same type (e.g., int64) results in an array of the same type.

>>> 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.dtypes
age int64
height int64
weight int64
dtype: object
>>> df.values
array([[ 3, 94, 31],
 [ 29, 170, 115]])

A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray of the broadest type that accommodates these mixed types (e.g., object).

>>> 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)