Teradata Package for Python Function Reference | 20.00 - __gt__ - 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 - 20.00
- Deployment
- VantageCloud
- VantageCore
- Edition
- Enterprise
- IntelliFlex
- VMware
- 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_Enterprise_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
RAISES:
Exception
A TeradataMlException gets thrown if SQLAlchemy
throws an exception when evaluating the expression.
EXAMPLES:
>>> load_example_data("dataframe", "admissions_train")
>>> df = DataFrame("admissions_train")
>>> df
masters gpa stats programming admitted
id
15 yes 4.00 Advanced Advanced 1
40 yes 3.95 Novice Beginner 0
7 yes 2.33 Novice Novice 1
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
24 no 1.87 Advanced Novice 1
39 yes 3.75 Advanced Beginner 0
30 yes 3.79 Advanced Novice 0
# Example 1: Get all students with gpa greater than
# or equal to 3.
>>> df[df.gpa >= 3]
masters gpa stats programming admitted
id
30 yes 3.79 Advanced Novice 0
40 yes 3.95 Novice Beginner 0
22 yes 3.46 Novice Beginner 0
39 yes 3.75 Advanced Beginner 0
26 yes 3.57 Advanced Advanced 1
5 no 3.44 Novice Novice 0
3 no 3.70 Novice Beginner 1
1 yes 3.95 Beginner Beginner 0
37 no 3.52 Novice Novice 1
14 yes 3.45 Advanced Advanced 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 >= 450) & (df.investment >= 200)]
start_time_column end_time_column expenditure income investment
id
5 67/06/30 07/07/10 459.0 509.0 211.0