Teradata Package for Python Function Reference | 20.00 - extract - 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.extract = extract(self, value, timezone=None)
- DESCRIPTION:
Extracts a single specified field from any DateTime, Interval or timestamp value,
converting it to an exact numeric value.
PARAMETERS:
value:
Required Argument.
Specifies the field which needs to be extracted.
Permitted Values: YEAR, MONTH, DAY, HOUR, MINUTE, SECOND, TIMEZONE_HOUR, TIMEZONE_MINUTE
Note:
* Permitted Values are case insensitive.
Type: str
timezone:
Optional Argument.
Specifies the timezone string.
For valid timezone strings, user should check Vantage documentation.
Type: ColumnExpression or str.
RETURNS:
ColumnExpression
EXAMPLES:
# Load the data to run the example.
>>> load_example_data("uaf", "Traindata")
# Create a DataFrame on 'Traindata' table.
>>> temp_df = DataFrame("Traindata")
>>> df = temp_df.select(["seq_no", "schedule_date", "arrivalTime"])
>>> df
schedule_date arrivalTime
seq_no
26 16/03/26 2016-03-26 12:33:05
24 16/03/26 2016-03-26 12:25:06
3 16/03/26 2016-03-26 10:52:05
22 16/03/26 2016-03-26 12:18:01
20 16/03/26 2016-03-26 12:10:06
18 16/03/26 2016-03-26 12:04:01
8 16/03/26 2016-03-26 11:15:06
17 16/03/26 2016-03-26 11:56:06
15 16/03/26 2016-03-26 11:45:00
13 16/03/26 2016-03-26 11:33:00
11 16/03/26 2016-03-26 11:26:00
# Example 1: Extract year from column 'schedule_date'.
>>> df.assign(col = df.schedule_date.extract('YEAR'))
schedule_date arrivalTime col
seq_no
26 16/03/26 2016-03-26 12:33:05 2016
24 16/03/26 2016-03-26 12:25:06 2016
3 16/03/26 2016-03-26 10:52:05 2016
22 16/03/26 2016-03-26 12:18:01 2016
20 16/03/26 2016-03-26 12:10:06 2016
18 16/03/26 2016-03-26 12:04:01 2016
8 16/03/26 2016-03-26 11:15:06 2016
17 16/03/26 2016-03-26 11:56:06 2016
15 16/03/26 2016-03-26 11:45:00 2016
13 16/03/26 2016-03-26 11:33:00 2016
11 16/03/26 2016-03-26 11:26:00 2016
# Example 2: Extract hour from column 'arrivalTime'.
>>> df.assign(col = df.arrivalTime.extract('HOUR'))
schedule_date arrivalTime col
seq_no
26 16/03/26 2016-03-26 12:33:05 12
24 16/03/26 2016-03-26 12:25:06 12
3 16/03/26 2016-03-26 10:52:05 10
22 16/03/26 2016-03-26 12:18:01 12
20 16/03/26 2016-03-26 12:10:06 12
18 16/03/26 2016-03-26 12:04:01 12
8 16/03/26 2016-03-26 11:15:06 11
17 16/03/26 2016-03-26 11:56:06 11
15 16/03/26 2016-03-26 11:45:00 11
# Example 3: Extract hour from column 'arrivalTime' with offset '-11:00'.
>>> df.assign(col = df.arrivalTime.extract('HOUR', '-11:00'))
schedule_date arrivalTime col
seq_no
26 16/03/26 2016-03-26 12:33:05 1
24 16/03/26 2016-03-26 12:25:06 1
3 16/03/26 2016-03-26 10:52:05 23
22 16/03/26 2016-03-26 12:18:01 1
20 16/03/26 2016-03-26 12:10:06 1
18 16/03/26 2016-03-26 12:04:01 1
8 16/03/26 2016-03-26 11:15:06 0
17 16/03/26 2016-03-26 11:56:06 0
15 16/03/26 2016-03-26 11:45:00 0
# Example 4: Extract hour from column 'arrivalTime' with offset 10.
>>> df.assign(col = df.arrivalTime.extract('HOUR', 10))
schedule_date arrivalTime col
seq_no
26 16/03/26 2016-03-26 12:33:05 22
24 16/03/26 2016-03-26 12:25:06 22
3 16/03/26 2016-03-26 10:52:05 20
22 16/03/26 2016-03-26 12:18:01 22
20 16/03/26 2016-03-26 12:10:06 22
18 16/03/26 2016-03-26 12:04:01 22
8 16/03/26 2016-03-26 11:15:06 21
17 16/03/26 2016-03-26 11:56:06 21
15 16/03/26 2016-03-26 11:45:00 21
13 16/03/26 2016-03-26 11:33:00 21
11 16/03/26 2016-03-26 11:26:00 21