bigframes.pandas.api.typing.DataFrameGroupBy#

class bigframes.pandas.api.typing.DataFrameGroupBy(block: Block, by_col_ids: Sequence[str], *, selected_cols: Sequence[str] | None = None, dropna: bool = True, as_index: bool = True, by_key_is_singular: bool = False)[source]#

Methods

__init__(block,by_col_ids,*[,...])
agg([func])	Aggregate using one or more operations.
aggregate([func])	Aggregate using one or more operations.
all()
any()
corr(*[,numeric_only])	Compute pairwise correlation of columns, excluding NA/null values.
count()
cov(*[,numeric_only])	Compute pairwise covariance of columns, excluding NA/null values.
cumcount([ascending])
cummax(*args[,numeric_only])
cummin(*args[,numeric_only])
cumprod(*args,**kwargs)
cumsum(*args[,numeric_only])
describe([include])
diff([periods])
expanding([min_periods])
first([numeric_only,min_count])
head([n])
kurt(*[,numeric_only])
kurtosis(*[,numeric_only])
last([numeric_only,min_count])
max([numeric_only])
mean([numeric_only])
median([numeric_only,exact])
min([numeric_only])
nunique()	Return DataFrame with counts of unique elements in each position.
quantile([q,numeric_only])
rank([method,ascending,na_option,pct])
rolling(window[,min_periods,on,closed])
shift([periods])
size()
skew(*[,numeric_only])
std(*[,numeric_only])
sum([numeric_only])
transform(func,*args,**kwargs)
value_counts([subset,normalize,sort,...])	Return a Series or DataFrame containing counts of unique rows.
var(*[,numeric_only])