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)

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])
value_counts([subset, normalize, sort, ...])	Return a Series or DataFrame containing counts of unique rows.
var(*[, numeric_only])