Teradata Package for Python Function Reference | 20.00 - first_value - 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.first_value = first_value()
- DESCRIPTION:
Function returns the first value of an ordered set of values in a teradataml
DataFrame or ColumnExpression over the specified window.
PARAMETERS:
None.
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 first value 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 first_value() 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 first_value() along with
# max() window aggregate operations.
>>> df = admissions_train.assign(first_value_gpa=window.first_value(), max_gpa=window.max())
>>> df
masters gpa stats programming admitted first_value_gpa max_gpa
id
15 yes 4.00 Advanced Advanced 1 3.60 4.00
16 no 3.70 Advanced Advanced 1 4.00 4.00
11 no 3.13 Advanced Advanced 1 3.96 3.96
9 no 3.82 Advanced Advanced 1 3.70 3.82
19 yes 1.98 Advanced Advanced 0 3.82 3.82
27 yes 3.96 Advanced Advanced 0 3.50 3.96
1 yes 3.95 Beginner Beginner 0 3.95 3.95
34 yes 3.85 Advanced Beginner 0 3.95 3.95
32 yes 3.46 Advanced Beginner 0 3.95 3.95
40 yes 3.95 Novice Beginner 0 3.85 3.95
>>>
# Example 2: Calculate the first value of all the valid columns in teradataml
# DataFrame, in an Expanding window, partitioned over 'masters',
# 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 first_value() on Expanding window.
>>> df = window.first_value()
>>> df
masters gpa stats programming admitted admitted_first_value gpa_first_value id_first_value masters_first_value programming_first_value stats_first_value
id
4 yes 3.50 Beginner Novice 1 0 3.95 1 yes Beginner Beginner
7 yes 2.33 Novice Novice 1 0 3.95 1 yes Beginner Beginner
14 yes 3.45 Advanced Advanced 0 0 3.95 1 yes Beginner Beginner
15 yes 4.00 Advanced Advanced 1 0 3.95 1 yes Beginner Beginner
19 yes 1.98 Advanced Advanced 0 0 3.95 1 yes Beginner Beginner
20 yes 3.90 Advanced Advanced 1 0 3.95 1 yes Beginner Beginner
3 no 3.70 Novice Beginner 1 1 3.70 3 no Beginner Novice
5 no 3.44 Novice Novice 0 1 3.70 3 no Beginner Novice
8 no 3.60 Beginner Advanced 1 1 3.70 3 no Beginner Novice
9 no 3.82 Advanced Advanced 1 1 3.70 3 no Beginner Novice
>>>
# Example 3: Calculate the first value of 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 first_value() on Contracting window.
>>> window.first_value()
masters gpa gpa_first_value masters_first_value
0 yes 3.76 3.50 yes
1 yes 3.81 3.50 yes
2 yes 1.98 3.50 yes
3 yes 3.85 4.00 yes
4 yes 3.75 3.90 yes
5 yes 3.95 3.81 yes
6 no 3.87 3.87 no
7 no 3.60 3.87 no
8 no 3.13 3.87 no
9 no 3.52 3.87 no
>>>