Access teradataml DataFrame Column | Teradata Package for Python - Access teradataml DataFrame Column - Teradata Vantage

To use teradataml DataFrame Column (also called ColumnExpression) in a filter, assign or join, you must access the Column.

Example Setup

>>> from teradataml.dataframe.sql_functions import case

>>> load_example_data("GLM", ["admissions_train"])

>>> df = DataFrame("admissions_train")

>>> print(df)

   masters   gpa     stats programming  admitted
id
5       no  3.44    Novice      Novice         0
3       no  3.70    Novice    Beginner         1
1      yes  3.95  Beginner    Beginner         0
20     yes  3.90  Advanced    Advanced         1
8       no  3.60  Beginner    Advanced         1
25      no  3.96  Advanced    Advanced         1
18     yes  3.81  Advanced    Advanced         1
24      no  1.87  Advanced      Novice         1
26     yes  3.57  Advanced    Advanced         1
38     yes  2.65  Advanced    Beginner         1

Example 1: Access ColumnExpression as attribute and use the same as predicate for filter

>>> gpa = df.gpa

>>> good_df = df[case([(gpa > 3.0, 'good'),
                       (gpa > 2.0, 'average')],
                       else_='bad') == 'good']

>>> print(good_df)

   masters   gpa     stats programming  admitted
id
13      no  4.00  Advanced      Novice         1
11      no  3.13  Advanced    Advanced         1
9       no  3.82  Advanced    Advanced         1
26     yes  3.57  Advanced    Advanced         1
3       no  3.70    Novice    Beginner         1
1      yes  3.95  Beginner    Beginner         0
20     yes  3.90  Advanced    Advanced         1
18     yes  3.81  Advanced    Advanced         1
5       no  3.44    Novice      Novice         0
32     yes  3.46  Advanced    Beginner         0

>>> print(good_df.shape)

(35, 6)

Example 2: Access ColumnExpression like dictionary and use the same to create a new DataFrame

This example accesses ColumnExpression like dictionary and uses the same to create a new DataFrame with an additional 'rating' column using assign operation, with the same case construct used in the previous example.

>>> gpa = df['gpa']

>>> whens_df = df.assign(rating = case([(gpa > 3.0, 'good'),
                                        (gpa > 2.0, 'average')],
                                        else_='bad'))

>>> print(whens_df)

   masters   gpa     stats programming  admitted   rating
id
5       no  3.44    Novice      Novice         0     good
3       no  3.70    Novice    Beginner         1     good
1      yes  3.95  Beginner    Beginner         0     good
20     yes  3.90  Advanced    Advanced         1     good
8       no  3.60  Beginner    Advanced         1     good
25      no  3.96  Advanced    Advanced         1     good
18     yes  3.81  Advanced    Advanced         1     good
24      no  1.87  Advanced      Novice         1      bad
26     yes  3.57  Advanced    Advanced         1     good
38     yes  2.65  Advanced    Beginner         1  average

>>> print(whens_df.shape)

(40, 7)