dask.dataframe.DataFrame.var

dask.dataframe.DataFrame.var

DataFrame.var(axis=None, skipna=True, ddof=1, split_every=False, dtype=None, out=None, numeric_only=None)

Return unbiased variance over requested axis.

This docstring was copied from pandas.core.frame.DataFrame.var.

Some inconsistencies with the Dask version may exist.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters
axis{index (0), columns (1)}

For Series this parameter is unused and defaults to 0.

skipnabool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

levelint or level name, default None (Not supported in Dask)

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.

ddofint, default 1

Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

numeric_onlybool, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.

Returns
Series or DataFrame (if level specified)

Examples

>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],  
...                   'age': [21, 25, 62, 43],
...                   'height': [1.61, 1.87, 1.49, 2.01]}
...                  ).set_index('person_id')
>>> df  
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01
>>> df.var()  
age       352.916667
height      0.056367

Alternatively, ddof=0 can be set to normalize by N instead of N-1:

>>> df.var(ddof=0)  
age       264.687500
height      0.042275