class RelationalGroupedDataFrame extends AnyRef
Represents an underlying DataFrame with rows that are grouped by common values. Can be used to define aggregations on these grouped DataFrames.
Example:
val groupedDf: RelationalGroupedDataFrame = df.groupBy("dept") val aggDf: DataFrame = groupedDf.agg(groupedDf("salary") -> "mean")
The methods DataFrame.groupBy , DataFrame.cube and DataFrame.rollup return an instance of type RelationalGroupedDataFrame
- Since
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0.1.0
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def
agg
(
exprs:
Map
[
Column
,
String
]
)
:
DataFrame
Returns a DataFrame with computed aggregates.
Returns a DataFrame with computed aggregates. The first element of the 'expr' pair is the column to aggregate and the second element is the aggregate function to compute. The following example computes the mean of the price column and the sum of the sales column. The name of the aggregate function to compute must be a valid Snowflake aggregate function "average" and "mean" can be used to specify "avg".
import com.snowflake.snowpark.functions.col df.groupBy("itemType").agg(Map( col("price") -> "mean", col("sales") -> "sum" ))
- returns
- Since
-
0.1.0
-
def
agg
(
exprs:
Array
[
Column
]
)
:
DataFrame
Returns a DataFrame with aggregated computed according to the supplied Column expressions.
Returns a DataFrame with aggregated computed according to the supplied Column expressions. com.snowflake.snowpark.functions contains some built-in aggregate functions that can be used.
- returns
- Since
-
0.9.0
-
def
agg
[
T
]
(
exprs:
Seq
[
Column
]
)
(
implicit
arg0:
ClassTag
[
T
]
)
:
DataFrame
Returns a DataFrame with aggregated computed according to the supplied Column expressions.
Returns a DataFrame with aggregated computed according to the supplied Column expressions. com.snowflake.snowpark.functions contains some built-in aggregate functions that can be used.
impoer com.snowflake.snowpark.functions._ df.groupBy("itemType").agg(Seq( mean($"price"), sum($"sales")))
- returns
- Since
-
0.2.0
-
def
agg
(
expr:
Column
,
exprs:
Column
*
)
:
DataFrame
Returns a DataFrame with aggregated computed according to the supplied Column expressions.
Returns a DataFrame with aggregated computed according to the supplied Column expressions. com.snowflake.snowpark.functions contains some built-in aggregate functions that can be used.
impoer com.snowflake.snowpark.functions._ df.groupBy("itemType").agg( mean($"price"), sum($"sales"))
- returns
- Since
-
0.1.0
-
def
agg
(
exprs:
Seq
[(
Column
,
String
)]
)
:
DataFrame
Returns a DataFrame with computed aggregates.
Returns a DataFrame with computed aggregates. The first element of the 'expr' pair is the column to aggregate and the second element is the aggregate function to compute. The following example computes the mean of the price column and the sum of the sales column. The name of the aggregate function to compute must be a valid Snowflake aggregate function "average" and "mean" can be used to specify "avg".
import com.snowflake.snowpark.functions.col df.groupBy("itemType").agg(Seq( col("price") -> "mean", col("sales") -> "sum"))
- returns
- Since
-
0.2.0
-
def
agg
(
expr: (
Column
,
String
)
,
exprs: (
Column
,
String
)*
)
:
DataFrame
Returns a DataFrame with computed aggregates.
Returns a DataFrame with computed aggregates. The first element of the 'expr' pair is the column to aggregate and the second element is the aggregate function to compute. The following example computes the mean of the price column and the sum of the sales column. The name of the aggregate function to compute must be a valid Snowflake aggregate function "average" and "mean" can be used to specify "avg".
import com.snowflake.snowpark.functions.col df.groupBy("itemType").agg( col("price") -> "mean", col("sales") -> "sum")
- returns
- Since
-
0.1.0
-
def
any_value
(
cols:
Column
*
)
:
DataFrame
Returns non-deterministic values for the specified columns.
-
final
def
asInstanceOf
[
T0
]
:
T0
- Definition Classes
- Any
-
def
avg
(
cols:
Column
*
)
:
DataFrame
Return the average for the specified numeric columns.
-
def
builtin
(
aggName:
String
)
(
cols:
Column
*
)
:
DataFrame
Computes the builtin aggregate 'aggName' over the specified columns.
Computes the builtin aggregate 'aggName' over the specified columns. Use this function to invoke any aggregates not explicitly listed in this class.
For example:
df.groupBy(col("a")).builtin("max")(col("b"))
- aggName
-
the Name of an aggregate function.
- returns
- Since
-
0.6.0
-
def
clone
()
:
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- @throws ( ... ) @native () @HotSpotIntrinsicCandidate ()
-
def
count
()
:
DataFrame
Return the number of rows for each group.
-
final
def
eq
(
arg0:
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)
:
Boolean
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def
isInstanceOf
[
T0
]
:
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-
def
max
(
cols:
Column
*
)
:
DataFrame
Return the max for the specified numeric columns.
-
def
mean
(
cols:
Column
*
)
:
DataFrame
Return the average for the specified numeric columns.
-
def
median
(
cols:
Column
*
)
:
DataFrame
Return the median for the specified numeric columns.
-
def
min
(
cols:
Column
*
)
:
DataFrame
Return the min for the specified numeric columns.
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def
sum
(
cols:
Column
*
)
:
DataFrame
Return the sum for the specified numeric columns.
-
final
def
synchronized
[
T0
]
(
arg0: ⇒
T0
)
:
T0
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()
:
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