Teradata Package for Python Function Reference on VantageCloud Lake - week_start - 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
- Language
- English (United States)
- Last Update
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Lake_2000
- Product Category
- Teradata Vantage
- teradataml.dataframe.sql.DataFrameColumn.week_start = week_start()
- DESCRIPTION:
Function returns the first date or timestamp of the week that begins immediately before the specified date or
timestamp value in a column as a literal.
ALTERNATE NAME:
week_begin
RAISES:
TypeError, ValueError, TeradataMlException
RETURNS:
DataFrameColumn
EXAMPLES:
# Load the data to run the example.
>>> load_example_data("dataframe", ["sales"])
# Create a DataFrame on 'sales' table.
>>> df = DataFrame.from_table('sales')
# Preparing the data.
>>> df_sales = df.assign(dates = case([(df.accounts == 'Alpha Co', df.datetime + random.randrange(20,1000,3)),
(df.accounts == 'Blue Inc', df.datetime + random.randrange(20,1000,3)),
(df.accounts == 'Jones LLC', df.datetime + random.randrange(20,1000,3)),
(df.accounts == 'Orange Inc', df.datetime + random.randrange(20,1000,3)),
(df.accounts == 'Yellow Inc', df.datetime + random.randrange(20,1000,3)),
(df.accounts == 'Red Inc', df.datetime + random.randrange(20,1000,3))]))
# Example 1: Returns the first date or timestamp of the week that begins immediately before the specified date
# or timestamp value in a column as a literal.
>>> df = df_sales.assign(res = df_sales.dates.week_start())
>>> print(df)
Feb Jan Mar Apr datetime dates res
accounts
Alpha Co 210.0 200.0 215.0 250.0 04/01/2017 18/03/20 18/03/18
Blue Inc 90.0 50.0 95.0 101.0 04/01/2017 19/02/25 19/02/24
Jones LLC 200.0 150.0 140.0 180.0 04/01/2017 17/04/27 17/04/23
Orange Inc 210.0 NaN NaN 250.0 04/01/2017 19/05/29 19/05/26
Yellow Inc 90.0 NaN NaN NaN 04/01/2017 18/10/22 18/10/21
Red Inc 200.0 150.0 140.0 NaN 04/01/2017 17/08/07 17/08/06