Use the cube() function to create a multidimensional cube for a teradataml DataFrame using specified columns, and there by running aggregates on it produce the aggregations on different dimensions.
Required Argument:
- columns: Specifies the names of input teradataml DataFrame columns.
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.cube(["masters", "stats"]).sum()
>>> df1 masters stats sum_id sum_gpa sum_admitted 0 no Beginner 8 3.60 1 1 None Advanced 555 84.21 16 2 None Beginner 21 18.31 3 3 yes Beginner 13 14.71 2 4 None None 820 141.67 26 5 yes Advanced 366 49.26 7 6 no None 343 63.96 16 7 None Novice 244 39.15 7 8 no Advanced 189 34.95 9 9 yes Novice 98 13.74 1