Teradata Package for Python Function Reference | 20.00 - sum - 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.window.sum = sum(distinct=False)
- DESCRIPTION:
Function returns the sum of values in a 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 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 sum 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 sum() 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 sum() along with
# max() window aggregate operations.
>>> df = admissions_train.assign(sum_gpa=window.sum(), max_gpa=window.max())
>>> df
masters gpa stats programming admitted max_gpa sum_gpa
id
15 yes 4.00 Advanced Advanced 1 4.00 11.41
16 no 3.70 Advanced Advanced 1 4.00 11.66
11 no 3.13 Advanced Advanced 1 3.96 10.79
9 no 3.82 Advanced Advanced 1 3.82 10.65
19 yes 1.98 Advanced Advanced 0 3.82 9.30
27 yes 3.96 Advanced Advanced 0 3.96 9.44
1 yes 3.95 Beginner Beginner 0 3.95 3.95
34 yes 3.85 Advanced Beginner 0 3.95 7.80
32 yes 3.46 Advanced Beginner 0 3.95 11.26
40 yes 3.95 Novice Beginner 0 3.95 11.26
>>>
# Example 2: Calculate the sum of values for all the valid columns in
# teradataml DataFrame, in an Expanding window, partitioned
# over 'programming', and order by 'id'.
# Create an Expanding window on teradataml DataFrame.
>>> window = admissions_train.window(partition_columns=admissions_train.masters,
... order_columns=admissions_train.id,
... window_start_point=None,
... window_end_point=0)
>>>
# Execute sum() on the Expanding window.
>>> df = window.sum()
>>> df
masters gpa stats programming admitted admitted_sum gpa_sum id_sum
id
4 yes 3.50 Beginner Novice 1 1 11.21 7
7 yes 2.33 Novice Novice 1 3 17.04 20
14 yes 3.45 Advanced Advanced 0 3 20.49 34
15 yes 4.00 Advanced Advanced 1 4 24.49 49
19 yes 1.98 Advanced Advanced 0 5 30.28 86
20 yes 3.90 Advanced Advanced 1 6 34.18 106
3 no 3.70 Novice Beginner 1 1 3.70 3
5 no 3.44 Novice Novice 0 1 7.14 8
8 no 3.60 Beginner Advanced 1 2 10.74 16
9 no 3.82 Advanced Advanced 1 3 14.56 25
>>>
# Example 3: Calculate the sum of values for all the valid columns in
# teradataml DataFrame, which are grouped by 'masters' and
# 'gpa' in a Contracting window, partitioned over 'masters'.
# 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,
... window_start_point=-5,
... window_end_point=None)
# Execute sum() on the Contracting window.
>>> window.sum()
masters gpa gpa_sum
0 yes 3.79 29.06
1 yes 3.50 35.29
2 yes 3.96 39.10
3 yes 4.00 42.95
4 yes 3.90 48.50
5 yes 2.33 51.96
6 no 3.52 19.59
7 no 3.83 23.24
8 no 3.82 27.24
9 no 3.55 30.95
>>>