Teradata Package for Python Function Reference on VantageCloud Lake - min - 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.window.min = min(distinct=False)
- DESCRIPTION:
Function returns the minimum of values in teradataml DataFrame or
ColumnExpression over the specified window.
PARAMETERS:
distinct:
Optional Argument.
Specifies a flag that decides whether to consider duplicate values in
a column or not.
Default Values: False
Types: bool
RETURNS:
* teradataml DataFrame - When aggregate is executed using window created
on teradataml DataFrame.
* ColumnExpression, also known as, teradataml DataFrameColumn - When aggregate is
executed using window created on ColumnExpression.
RAISES:
RuntimeError - If column does not support the aggregate operation.
EXAMPLES:
# Load the data to run the example.
>>> load_example_data("dataframe", "admissions_train")
>>>
# Create a teradataml DataFrame on 'admissions_train' table.
>>> admissions_train = DataFrame("admissions_train")
>>> admissions_train
masters gpa stats programming admitted
id
22 yes 3.46 Novice Beginner 0
36 no 3.00 Advanced Novice 0
15 yes 4.00 Advanced Advanced 1
38 yes 2.65 Advanced Beginner 1
5 no 3.44 Novice Novice 0
17 no 3.83 Advanced Advanced 1
34 yes 3.85 Advanced Beginner 0
13 no 4.00 Advanced Novice 1
26 yes 3.57 Advanced Advanced 1
19 yes 1.98 Advanced Advanced 0
>>>
# Example 1: Calculate the minimum of values for the 'gpa' column
# in a Rolling window, partitioned over 'programming'.
# Create a Rolling window on 'gpa'.
>>> window = admissions_train.gpa.window(partition_columns="programming",
... window_start_point=-2,
... window_end_point=0)
>>>
# Execute min() on the Rolling window and attach it to the teradataml DataFrame.
# Note: DataFrame.assign() allows combining multiple window aggregate operations
# in one single call. In this example, we are executing count() along with
# max() window aggregate operations.
>>> df = admissions_train.assign(min_gpa=window.min(), count_gpa=window.count())
>>> df
masters gpa stats programming admitted count_gpa min_gpa
id
11 no 3.13 Advanced Advanced 1 3 1.98
27 yes 3.96 Advanced Advanced 0 3 3.13
26 yes 3.57 Advanced Advanced 1 3 3.45
6 yes 3.50 Beginner Advanced 1 3 3.50
9 no 3.82 Advanced Advanced 1 3 3.50
25 no 3.96 Advanced Advanced 1 3 3.60
39 yes 3.75 Advanced Beginner 0 1 3.75
31 yes 3.50 Advanced Beginner 1 2 3.50
29 yes 4.00 Novice Beginner 0 3 3.50
21 no 3.87 Novice Beginner 1 3 3.50
>>>
# Example 2: Calculate the minimum value of all the valid columns in
# teradataml DataFrame, in an Expanding window, partitioned
# over 'programming', and order by 'id' in descending order.
# Create an Expanding window on teradataml DataFrame.
>>> window = admissions_train.window(partition_columns=admissions_train.masters,
... order_columns=admissions_train.id.desc(),
... window_start_point=None,
... window_end_point=0)
>>>
# Execute min() on the Expanding window.
>>> df = window.min()
>>> df
masters gpa stats programming admitted admitted_min gpa_min id_min masters_min programming_min stats_min
id
38 yes 2.65 Advanced Beginner 1 0 2.65 38 yes Beginner Advanced
32 yes 3.46 Advanced Beginner 0 0 2.65 32 yes Beginner Advanced
31 yes 3.50 Advanced Beginner 1 0 2.65 31 yes Beginner Advanced
30 yes 3.79 Advanced Novice 0 0 2.65 30 yes Beginner Advanced
27 yes 3.96 Advanced Advanced 0 0 2.65 27 yes Advanced Advanced
26 yes 3.57 Advanced Advanced 1 0 2.65 26 yes Advanced Advanced
37 no 3.52 Novice Novice 1 1 3.52 37 no Novice Novice
36 no 3.00 Advanced Novice 0 0 3.00 36 no Novice Advanced
35 no 3.68 Novice Beginner 1 0 3.00 35 no Beginner Advanced
33 no 3.55 Novice Novice 1 0 3.00 33 no Beginner Advanced
>>>
# Example 3: Calculate the minimum value of all the valid columns in
# teradataml DataFrame, which are grouped by 'masters' and 'gpa'
# in a Contracting window, partitioned over 'masters' and order
# by 'masters' with nulls in 'masters' listed last.
# Perform group_by() operation on teradataml DataFrame.
>>> group_by_df = admissions_train.groupby(["masters", "gpa"])
# Create a Contracting window on teradataml DataFrameGroupBy object.
>>> window = group_by_df.window(partition_columns=group_by_df.masters,
... order_columns=group_by_df.masters.nulls_last(),
... window_start_point=-5,
... window_end_point=None)
# Execute min() on the Contracting window.
>>> window.min()
masters gpa gpa_min masters_min
0 yes 3.46 1.98 yes
1 yes 2.33 1.98 yes
2 yes 3.81 1.98 yes
3 yes 1.98 1.98 yes
4 yes 3.45 1.98 yes
5 yes 3.57 1.98 yes
6 no 3.00 3.00 no
7 no 4.00 3.00 no
8 no 3.71 3.00 no
9 no 3.60 3.00 no
>>>