Class SeriesGroupBy (1.26.0)

SeriesGroupBy(
    block: bigframes.core.blocks.Block,
    value_column: str,
    by_col_ids: typing.Sequence[str],
    value_name: typing.Hashable = None,
    dropna=True,
)

Class for grouping and aggregating relational data.

Methods

agg

agg(
    func=None,
) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

Aggregate using one or more operations.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> s = bpd.Series([1, 2, 3, 4], index=[1, 1, 2, 2])
>>> s.groupby(level=0).agg(['min', 'max'])
   min  max
1    1    2
2    3    4
<BLANKLINE>
[2 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.Series`	A BigQuery Series.

aggregate

aggregate(
    func=None,
) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

Aggregate using one or more operations.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> s = bpd.Series([1, 2, 3, 4], index=[1, 1, 2, 2])
>>> s.groupby(level=0).aggregate(['min', 'max'])
   min  max
1    1    2
2    3    4
<BLANKLINE>
[2 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.Series`	A BigQuery Series.

all

all() -> bigframes.series.Series

Return True if all values in the group are true, else False.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([1, 2, 0], index=lst)
>>> ser.groupby(level=0).all()
a     True
b    False
dtype: boolean

For DataFrameGroupBy:

>>> data = [[1, 0, 3], [1, 5, 6], [7, 8, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["ostrich", "penguin", "parrot"])
>>> df.groupby(by=["a"]).all()
        b       c
a
1   False    True
7   True    True
<BLANKLINE>
[2 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	DataFrame or Series of boolean values, where a value is True if all elements are True within its respective group; otherwise False.

any

any() -> bigframes.series.Series

Return True if any value in the group is true, else False.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([1, 2, 0], index=lst)
>>> ser.groupby(level=0).any()
a     True
b    False
dtype: boolean

For DataFrameGroupBy:

>>> data = [[1, 0, 3], [1, 0, 6], [7, 1, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["ostrich", "penguin", "parrot"])
>>> df.groupby(by=["a"]).any()
        b       c
a
1   False    True
7   True    True
<BLANKLINE>
[2 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	DataFrame or Series of boolean values, where a value is True if any element is True within its respective group; otherwise False.

count

count() -> bigframes.series.Series

Compute count of group, excluding missing values.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([1, 2, np.nan], index=lst)
>>> ser.groupby(level=0).count()
a     2
b     0
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, np.nan, 3], [1, np.nan, 6], [7, 8, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["cow", "horse", "bull"])
>>> df.groupby(by=["a"]).count()
   b  c
a
1  0  2
7  1  1
<BLANKLINE>
[2 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Count of values within each group.

cumcount

cumcount(*args, **kwargs) -> bigframes.series.Series

Number each item in each group from 0 to the length of that group - 1. (DataFrameGroupBy functionality is not yet available.)

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b', 'b', 'c']
>>> ser = bpd.Series([5, 1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).cumcount()
a    0
a    1
b    0
b    1
c    0
dtype: Int64
>>> ser.groupby(level=0).cumcount(ascending=False)
a    0
a    1
b    0
b    1
c    0
dtype: Int64

Parameter
Name	Description
`ascending`	`bool, default True` If False, number in reverse, from length of group - 1 to 0.

Returns
Type	Description
`bigframes.pandas.Series`	Sequence number of each element within each group.

cummax

cummax(*args, **kwargs) -> bigframes.series.Series

Cumulative max for each group.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([6, 2, 0], index=lst)
>>> ser.groupby(level=0).cummax()
a    6
a    6
b    0
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["fox", "gorilla", "lion"])
>>> df.groupby("a").cummax()
         b  c
fox      8  2
gorilla  8  5
lion     6  9
<BLANKLINE>
[3 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Cumulative max for each group.

cummin

cummin(*args, **kwargs) -> bigframes.series.Series

Cumulative min for each group.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([6, 2, 0], index=lst)
>>> ser.groupby(level=0).cummin()
a    6
a    2
b    0
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["fox", "gorilla", "lion"])
>>> df.groupby("a").cummin()
         b  c
fox      8  2
gorilla  2  2
lion     6  9
<BLANKLINE>
[3 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Cumulative min for each group.

cumprod

cumprod(*args, **kwargs) -> bigframes.series.Series

Cumulative product for each group.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([6, 2, 0], index=lst)
>>> ser.groupby(level=0).cumprod()
a     6.0
a    12.0
b     0.0
dtype: Float64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["cow", "horse", "bull"])
>>> df.groupby("a").cumprod()
          b     c
cow     8.0   2.0
horse  16.0  10.0
bull    6.0   9.0
<BLANKLINE>
[3 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Cumulative product for each group.

cumsum

cumsum(*args, **kwargs) -> bigframes.series.Series

Cumulative sum for each group.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([6, 2, 0], index=lst)
>>> ser.groupby(level=0).cumsum()
a    6
a    8
b    0
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["fox", "gorilla", "lion"])
>>> df.groupby("a").cumsum()
          b  c
fox       8  2
gorilla  10  7
lion      6  9
<BLANKLINE>
[3 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Cumulative sum for each group.

diff

diff(periods=1) -> bigframes.series.Series

First discrete difference of element. Calculates the difference of each element compared with another element in the group (default is element in previous row).

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser.groupby(level=0).diff()
a    <NA>
a      -5
a       6
b    <NA>
b      -1
b       0
dtype: Int64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog',
...                   'mouse', 'mouse', 'mouse', 'mouse'])
>>> df.groupby(level=0).diff()
          a     b
dog    <NA>  <NA>
dog       2     3
dog       2     4
mouse  <NA>  <NA>
mouse     0     0
mouse     1    -2
mouse    -5    -1
<BLANKLINE>
[7 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	First differences.

expanding

expanding(min_periods: int = 1) -> bigframes.core.window.Window

Provides expanding functionality.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'c', 'c', 'e']
>>> ser = bpd.Series([1, 0, -2, -1, 2], index=lst)
>>> ser.groupby(level=0).expanding().min()
index  index
a      a         1
       a         0
c      c        -2
       c        -2
e      e         2
dtype: Int64

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	An expanding grouper, providing expanding functionality per group.

head

head(n: int = 5) -> bigframes.series.Series

API documentation for head method.

kurt

kurt(*args, **kwargs) -> bigframes.series.Series

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b']
>>> ser = bpd.Series([0, 1, 1, 0, 0, 1, 2, 4, 5], index=lst)
>>> ser.groupby(level=0).kurt()
a        -6.0
b   -1.963223
dtype: Float64

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only `float`, `int` or `boolean` data.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Variance of values within each group.

kurtosis

kurtosis(*args, **kwargs) -> bigframes.series.Series

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b']
>>> ser = bpd.Series([0, 1, 1, 0, 0, 1, 2, 4, 5], index=lst)
>>> ser.groupby(level=0).kurtosis()
a        -6.0
b   -1.963223
dtype: Float64

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only `float`, `int` or `boolean` data.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Variance of values within each group.

max

max(*args) -> bigframes.series.Series

Compute max of group values.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).max()
a     2
b     4
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["tiger", "leopard", "cheetah", "lion"])
>>> df.groupby(by=["a"]).max()
   b  c
a
1  8  5
2  6  9
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.
`min_count`	`int, default 0` The required number of valid values to perform the operation. If fewer than `min_count` and non-NA values are present, the result will be NA.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Computed max of values within each group.

mean

mean(*args) -> bigframes.series.Series

Compute mean of groups, excluding missing values.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({'A': [1, 1, 2, 1, 2],
...                    'B': [np.nan, 2, 3, 4, 5],
...                    'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])

Groupby one column and return the mean of the remaining columns in each group.

>>> df.groupby('A').mean()
    B         C
A
1  3.0  1.333333
2  4.0       1.5
<BLANKLINE>
[2 rows x 2 columns]

Groupby two columns and return the mean of the remaining column.

>>> df.groupby(['A', 'B']).mean()
         C
A B
1 2.0  2.0
  4.0  1.0
2 3.0  1.0
  5.0  2.0
<BLANKLINE>
[4 rows x 1 columns]

Groupby one column and return the mean of only particular column in the group.

>>> df.groupby('A')['B'].mean()
A
1    3.0
2    4.0
Name: B, dtype: Float64

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Mean of groups.

median

median(*args, exact: bool = True, **kwargs) -> bigframes.series.Series

Compute median of groups, excluding missing values.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser.groupby(level=0).median()
a    7.0
b    3.0
dtype: Float64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog',
...                    'mouse', 'mouse', 'mouse', 'mouse'])
>>> df.groupby(level=0).median()
        a    b
dog    3.0  4.0
mouse  7.0  3.0
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.
`exact`	`bool, default True` Calculate the exact median instead of an approximation.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Median of groups.

min

min(*args) -> bigframes.series.Series

Compute min of group values.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).min()
a     1
b     3
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["tiger", "leopard", "cheetah", "lion"])
>>> df.groupby(by=["a"]).min()
   b  c
a
1  2  2
2  5  8
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.
`min_count`	`int, default 0` The required number of valid values to perform the operation. If fewer than `min_count` and non-NA values are present, the result will be NA.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Computed min of values within each group.

nunique

nunique() -> bigframes.series.Series

Return number of unique elements in the group.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 3], index=lst)
>>> ser.groupby(level=0).nunique()
a    2
b    1
dtype: Int64

Returns
Type	Description
`bigframes.pandas.Series`	Number of unique values within each group.

prod

prod(*args) -> bigframes.series.Series

Compute prod of group values. (DataFrameGroupBy functionality is not yet available.)

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).prod()
a     2.0
b    12.0
dtype: Float64

Parameters
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.
`min_count`	`int, default 0` The required number of valid values to perform the operation. If fewer than `min_count` and non-NA values are present, the result will be NA.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Computed prod of values within each group.

quantile

quantile(
    q: typing.Union[float, typing.Sequence[float]] = 0.5, *, numeric_only: bool = False
) -> bigframes.series.Series

Return group values at the given quantile, a la numpy.percentile.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame([
...     ['a', 1], ['a', 2], ['a', 3],
...     ['b', 1], ['b', 3], ['b', 5]
... ], columns=['key', 'val'])
>>> df.groupby('key').quantile()
     val
key
a    2.0
b    3.0
<BLANKLINE>
[2 rows x 1 columns]

Parameters
Name	Description
`q`	`float or array-like, default 0.5 (50% quantile)` Value(s) between 0 and 1 providing the quantile(s) to compute.
`numeric_only`	`bool, default False` Include only `float`, `int` or `boolean` data.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Return type determined by caller of GroupBy object.

rolling

rolling(window: int, min_periods=None) -> bigframes.core.window.Window

Returns a rolling grouper, providing rolling functionality per group.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'a', 'a', 'e']
>>> ser = bpd.Series([1, 0, -2, -1, 2], index=lst)
>>> ser.groupby(level=0).rolling(2).min()
index  index
a      a        <NA>
    a           0
    a          -2
    a          -2
e      e        <NA>
dtype: Int64

Parameter
Name	Description
`min_periods`	`int, default None` Minimum number of observations in window required to have a value; otherwise, result is `np.nan`. For a window that is specified by an offset, `min_periods` will default to 1. For a window that is specified by an integer, `min_periods` will default to the size of the window.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Return a new grouper with our rolling appended.

shift

shift(periods=1) -> bigframes.series.Series

Shift index by desired number of periods.

size

size() -> bigframes.series.Series

API documentation for size method.

skew

skew(*args, **kwargs) -> bigframes.series.Series

Return unbiased skew within groups.

Normalized by N-1.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> ser = bpd.Series([390., 350., 357., np.nan, 22., 20., 30.],
...                  index=['Falcon', 'Falcon', 'Falcon', 'Falcon',
...                         'Parrot', 'Parrot', 'Parrot'],
...                  name="Max Speed")
>>> ser.groupby(level=0).skew()
Falcon    1.525174
Parrot    1.457863
Name: Max Speed, dtype: Float64

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only `float`, `int` or `boolean` data.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Variance of values within each group.

std

std(*args, **kwargs) -> bigframes.series.Series

Compute standard deviation of groups, excluding missing values.

For multiple groupings, the result index will be a MultiIndex.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser.groupby(level=0).std()
a     3.21455
b     0.57735
dtype: Float64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog',
...                    'mouse', 'mouse', 'mouse', 'mouse'])
>>> df.groupby(level=0).std()
              a         b
dog         2.0  3.511885
mouse  2.217356       1.5
<BLANKLINE>
[2 rows x 2 columns]

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only `float`, `int` or `boolean` data.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Standard deviation of values within each group.

sum

sum(*args) -> bigframes.series.Series

Compute sum of group values.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).sum()
a     3
b     7
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["tiger", "leopard", "cheetah", "lion"])
>>> df.groupby("a").sum()
    b   c
a
1  10   7
2  11  17
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.
`min_count`	`int, default 0` The required number of valid values to perform the operation. If fewer than `min_count` and non-NA values are present, the result will be NA.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Computed sum of values within each group.

var

var(*args, **kwargs) -> bigframes.series.Series

Compute variance of groups, excluding missing values.

For multiple groupings, the result index will be a MultiIndex.

Examples:

For SeriesGroupBy:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser.groupby(level=0).var()
a   10.333333
b    0.333333
dtype: Float64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog',
...                    'mouse', 'mouse', 'mouse', 'mouse'])
>>> df.groupby(level=0).var()
              a          b
dog         4.0  12.333333
mouse  4.916667       2.25
<BLANKLINE>
[2 rows x 2 columns]

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only `float`, `int` or `boolean` data.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Variance of values within each group.