Teradata Package for Python Function Reference | 20.00 - lt - 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
- lifecycle
- latest
- Product Category
- Teradata Vantage
- teradataml.dataframe.sql.DataFrameColumn.lt = lt(self, other)
- Compare the ColumnExpressions to check if one ColumnExpression
has values less than 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 less than 4.
>>> df[df.gpa.lt(4)]
masters gpa stats programming admitted
id
5 no 3.44 Novice Novice 0
34 yes 3.85 Advanced Beginner 0
32 yes 3.46 Advanced Beginner 0
40 yes 3.95 Novice Beginner 0
22 yes 3.46 Novice Beginner 0
19 yes 1.98 Advanced Advanced 0
36 no 3.00 Advanced Novice 0
30 yes 3.79 Advanced Novice 0
7 yes 2.33 Novice Novice 1
17 no 3.83 Advanced Advanced 1
>>> 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 less than 440 and
# income greater than 180.
>>> df[(df.expenditure.lt(440)) & (df.income.lt(180))]
start_time_column end_time_column expenditure income investment
id
3 67/06/30 07/07/10 434.0 485.0 185.0