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DataFrameGroupBy(
block: bigframes.core.blocks.Block,
by_col_ids: typing.Sequence[str],
*,
selected_cols: typing.Optional[typing.Sequence[str]] = None,
dropna: bool = True,
as_index: bool = True
)
Class for grouping and aggregating relational data.
Methods
agg
agg(func=None, **kwargs) -> bigframes.dataframe.DataFrame
Aggregate using one or more operations.
Parameter | |
---|---|
Name | Description |
func |
function, str, list, dict or None
Function to use for aggregating the data. Accepted combinations are: - string function name - list of function names, e.g. |
aggregate
aggregate(func=None, **kwargs) -> bigframes.dataframe.DataFrame
API documentation for aggregate
method.
all
all() -> bigframes.dataframe.DataFrame
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.dataframe.DataFrame
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.dataframe.DataFrame
Compute count of group, excluding missing values.
Returns | |
---|---|
Type | Description |
Series or DataFrame |
Count of values within each group. |
cumcount
cumcount(ascending: bool = True)
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, numeric_only: bool = False, **kwargs
) -> bigframes.dataframe.DataFrame
Cumulative max for each group.
Returns | |
---|---|
Type | Description |
Series or DataFrame |
Cumulative max for each group. |
cummin
cummin(
*args, numeric_only: bool = False, **kwargs
) -> bigframes.dataframe.DataFrame
Cumulative min for each group.
Returns | |
---|---|
Type | Description |
Series or DataFrame |
Cumulative min for each group. |
cumprod
cumprod(*args, **kwargs) -> bigframes.dataframe.DataFrame
Cumulative product for each group.
Returns | |
---|---|
Type | Description |
Series or DataFrame |
Cumulative product for each group. |
cumsum
cumsum(
*args, numeric_only: bool = False, **kwargs
) -> bigframes.dataframe.DataFrame
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(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame
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(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame
API documentation for kurtosis
method.
max
max(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrame
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(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrame
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(
numeric_only: bool = False, *, exact: bool = True
) -> bigframes.dataframe.DataFrame
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(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrame
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.dataframe.DataFrame
Return DataFrame with counts of unique elements in each position.
prod
prod(numeric_only: bool = False, min_count: int = 0)
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.dataframe.DataFrame
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 each group by periods observations.
Parameter | |
---|---|
Name | Description |
periods |
int, default 1
Number of periods to shift. |
Returns | |
---|---|
Type | Description |
Series or DataFrame |
Object shifted within each group. |
skew
skew(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame
Return unbiased skew within groups.
Normalized by N-1.
Parameter | |
---|---|
Name | Description |
numeric_only |
bool, default False
Include only |
std
std(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame
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(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrame
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(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame
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 |