bigframes.pandas.DataFrame.std#

DataFrame.std(axis: str | int = 0, *, numeric_only: bool = False) → Series[source]#

Return sample standard deviation over columns.

Normalized by N-1 by default.

Examples:

>>> df = bpd.DataFrame({"A": [1, 2, 3, 4, 5],
...                     "B": [3, 4, 3, 2, 1],
...                     "C": [2, 2, 3, 2, 2]})
>>> df
    A       B       C
0   1       3       2
1   2       4       2
2   3       3       3
3   4       2       2
4   5       1       2

[5 rows x 3 columns]

Calculating the standard deviation of each column:

>>> df.std()
A    1.581139
B    1.140175
C    0.447214
dtype: Float64

Parameters:

numeric_only (bool. default False) Default False. Include only float, int, boolean columns.

Returns:

Series with sample standard deviation.

Return type:

bigframes.pandas.Series