Use the rollup() function to create a multidimensional rollup for a DataFrame using specified columns, and there by running aggregates on it produce the aggregations on different dimensions.
Required Parameter
- columns
- Specifies the names of input teradataml DataFrame columns.
Optional Parameter
- include_grouping_columns
- Specifies whether to include aggregations on the grouping columns or not. When set to True, the resultant DataFrame will have the aggregations on the columns mentioned in "columns_expr". Otherwise, resultant DataFrame will not have aggregations on the columns mentioned in "columns_expr".
Default value: False
Example setup
In this example, "admission_train" dataset is used.
>>> from teradataml import *
>>> load_example_data("dataframe", "admissions_train")
>>> df = DataFrame("admissions_train")
Example 1: Analyzes the data by grouping into masters and stats dimensions
>>> df1 = df.rollup(["masters", "stats"]).sum()
>>> df1 masters stats sum_id sum_gpa sum_admitted 0 no None 343 63.96 16 1 yes None 477 77.71 10 2 None None 820 141.67 26 3 no Novice 146 25.41 6 4 no Beginner 8 3.60 1 5 yes Novice 98 13.74 1 6 yes Beginner 13 14.71 2 7 yes Advanced 366 49.26 7 8 no Advanced 189 34.95 9
Example 2: Find the average of all valid columns by grouping the DataFrame with columns 'masters' and 'admitted'
Include grouping columns in aggregate function 'avg'.
>>> df1 = df.rollup(["masters", "admitted"], include_grouping_columns=True).avg()
>>> df1
masters admitted avg_id avg_gpa avg_admitted
0 no NaN 19.055556 3.553333 0.888889
1 yes NaN 21.681818 3.532273 0.454545
2 None NaN 20.500000 3.541750 0.650000
3 yes 0.0 24.083333 3.613333 0.000000
4 no 1.0 18.875000 3.595000 1.000000
5 yes 1.0 18.800000 3.435000 1.000000
6 no 0.0 20.500000 3.220000 0.000000
>>>
Example 3: Find the average of all valid columns by grouping the DataFrame with columns 'masters' and 'admitted'
Do not include grouping columns in aggregate function 'avg'.
>>> df1 = df.rollup(["masters", "admitted"], include_grouping_columns=False).avg()
>>> df1
masters admitted avg_id avg_gpa
0 no NaN 19.055556 3.553333
1 yes NaN 21.681818 3.532273
2 None NaN 20.500000 3.541750
3 yes 0.0 24.083333 3.613333
4 no 1.0 18.875000 3.595000
5 yes 1.0 18.800000 3.435000
6 no 0.0 20.500000 3.220000
>>>