snowflake.snowpark.RelationalGroupedDataFrame¶

class snowflake.snowpark.RelationalGroupedDataFrame(df: DataFrame, grouping_exprs: List[Expression], group_type: _GroupType)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.16.0/src/snowflake/snowpark/relational_grouped_dataframe.py#L120-L528)¶

Bases: object

Represents an underlying DataFrame with rows that are grouped by common values. Can be used to define aggregations on these grouped DataFrames.

See also

snowflake.snowpark.DataFrame.agg().

Methods

agg(*exprs)	Returns a DataFrame with computed aggregates.
applyInPandas(func, output_schema, **kwargs)	Maps each grouped dataframe in to a pandas.DataFrame, applies the given function on data of each grouped dataframe, and returns a pandas.DataFrame.
apply_in_pandas(func, output_schema, **kwargs)	Maps each grouped dataframe in to a pandas.DataFrame, applies the given function on data of each grouped dataframe, and returns a pandas.DataFrame.
avg(*cols)	Return the average for the specified numeric columns.
builtin(agg_name)	Computes the builtin aggregate agg_name over the specified columns.
count()	Return the number of rows for each group.
function(agg_name)	Computes the builtin aggregate agg_name over the specified columns.
max(*cols)	Return the max for the specified numeric columns.
mean(*cols)	Return the average for the specified numeric columns.
median(*cols)	Return the median for the specified numeric columns.
min(*cols)	Return the min for the specified numeric columns.
pivot(pivot_col[, values, default_on_null])	Rotates this DataFrame by turning unique values from one column in the input expression into multiple columns and aggregating results where required on any remaining column values.
sum(*cols)	Return the sum for the specified numeric columns.