Class SeriesGroupBy (0.22.0)

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.

aggregate

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

API documentation for aggregate method.

all

all() -> bigframes.series.Series

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

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

any

any() -> bigframes.series.Series

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

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

count

count() -> bigframes.series.Series

Compute count of group, excluding missing values.

Returns
Type	Description
`Series or DataFrame`	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.

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

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

cummax

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

Cumulative max for each group.

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

cummin

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

Cumulative min for each group.

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

cumprod

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

Cumulative product for each group.

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

cumsum

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

Cumulative sum for each group.

Returns
Type	Description
`Series or DataFrame`	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).

Returns
Type	Description
`Series or DataFrame`	First differences.

expanding

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

Provides expanding functionality.

Returns
Type	Description
`Series or DataFrame`	A expanding grouper, providing expanding functionality per group.

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.

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

kurtosis

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

API documentation for kurtosis method.

max

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

Compute max of group values.

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` non-NA values are present the result will be NA.

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

mean

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

Compute mean of groups, excluding missing values.

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

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

median

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

Compute median of groups, excluding missing values.

Parameters
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.
`exact`	`bool, default False` Calculate the exact median instead of an approximation. Note: `exact=True` not yet supported.

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

min

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

Compute min of group values.

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` non-NA values are present the result will be NA.

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

nunique

nunique() -> bigframes.series.Series

Return number of unique elements in the group.

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

prod

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

Compute prod of group values.

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` non-NA values are present the result will be NA.

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

rolling

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

Returns a rolling grouper, providing rolling functionality per group.

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
`Series or DataFrame`	Return a new grouper with our rolling appended.

shift

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

Shift index by desired number of periods.

skew

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

Return unbiased skew within groups.

Normalized by N-1.

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

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.

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

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

sum

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

Compute sum of group values.

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` non-NA values are present the result will be NA.

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

var

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

Compute variance of groups, excluding missing values.