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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; otherwise False. |
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; otherwise False. |
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 | An 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 |
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 |
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, exact: bool = True, **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 True
Calculate the exact median instead of an approximation. |
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 |
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 |
Returns | |
---|---|
Type | Description |
Series or DataFrame | 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 |
Returns | |
---|---|
Type | Description |
Series or DataFrame | 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.
Parameter | |
---|---|
Name | Description |
min_periods |
int, default None
Minimum number of observations in window required to have a value; otherwise, result is |
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 |
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 |
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 |
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.
For multiple groupings, the result index will be a MultiIndex.
Parameter | |
---|---|
Name | Description |
numeric_only |
bool, default False
Include only |