Teradata Package for Python Function Reference on VantageCloud Lake - ge - Teradata Package for Python - Look here for syntax, methods and examples for the functions included in the Teradata Package for Python.
Teradata® Package for Python Function Reference on VantageCloud Lake
- Deployment
- VantageCloud
- Edition
- Lake
- Product
- Teradata Package for Python
- Release Number
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Lake_2000
- Product Category
- Teradata Vantage
- teradataml.dataframe.sql.DataFrameColumn.ge = ge(self, other)
- Compare the ColumnExpressions to check if one ColumnExpression
has values greater than or equal to the other or not.
PARAMETERS:
other :
Required Argument.
Specifies Python literal or another ColumnExpression.
Types: ColumnExpression, Python literal
RETURNS:
ColumnExpression
EXAMPLES:
>>> load_example_data("dataframe", "admissions_train")
>>> df = DataFrame("admissions_train")
>>> df
masters gpa stats programming admitted
id
13 no 4.00 Advanced Novice 1
36 no 3.00 Advanced Novice 0
15 yes 4.00 Advanced Advanced 1
40 yes 3.95 Novice Beginner 0
22 yes 3.46 Novice Beginner 0
38 yes 2.65 Advanced Beginner 1
26 yes 3.57 Advanced Advanced 1
5 no 3.44 Novice Novice 0
7 yes 2.33 Novice Novice 1
19 yes 1.98 Advanced Advanced 0
# Example 1: Get all the students with gpa greater than
# or equal to 3.
>>> df[df.gpa.ge(3)]
masters gpa stats programming admitted
id
3 no 3.70 Novice Beginner 1
40 yes 3.95 Novice Beginner 0
22 yes 3.46 Novice Beginner 0
39 yes 3.75 Advanced Beginner 0
36 no 3.00 Advanced Novice 0
15 yes 4.00 Advanced Advanced 1
30 yes 3.79 Advanced Novice 0
14 yes 3.45 Advanced Advanced 0
37 no 3.52 Novice Novice 1
1 yes 3.95 Beginner Beginner 0
>>> load_example_data("burst", "finance_data")
>>> df = DataFrame("finance_data")
>>> df
start_time_column end_time_column expenditure income investment
id
1 67/06/30 07/07/10 415.0 451.0 180.0
4 67/06/30 07/07/10 448.0 493.0 192.0
2 67/06/30 07/07/10 421.0 465.0 179.0
3 67/06/30 07/07/10 434.0 485.0 185.0
5 67/06/30 07/07/10 459.0 509.0 211.0
# Example 2: Get all rows with expenditure greater than or equal to 450 and
# investment is greater than or equal to 200.
>>> df[(df.expenditure.ge(450)) & (df.investment.ge(200))]
start_time_column end_time_column expenditure income investment
id
5 67/06/30 07/07/10 459.0 509.0 211.0