Teradata Package for Python Function Reference | 20.00 - to_interval - 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.to_interval = to_interval(self, value=None, type_=<class 'teradatasqlalchemy.types.INTERVAL_DAY_TO_SECOND'>)
- DESCRIPTION:
Converts a numeric value or string value into an INTERVAL_DAY_TO_SECOND or INTERVAL_YEAR_TO_MONTH value.
PARAMETERS:
value:
Optional, when column type is VARCHAR or CHAR, otherwise required.
Specifies the unit of value for numeric value.
when type_ is INTERVAL_DAY_TO_SECOND permitted values:
* DAY, HOUR, MINUTE, SECOND
when type_ is INTERVAL_YEAR_TO_MONTH permitted values:
* YEAR, MONTH
Note:
* Permitted Values are case insensitive.
Type: str or ColumnExpression
type_:
Optional Argument.
Specifies a teradatasqlalchemy type or an object of a teradatasqlalchemy type
that the column needs to be cast to.
Default value: TIMESTAMP
Permitted Values: INTERVAL_DAY_TO_SECOND or INTERVAL_YEAR_TO_MONTH type.
Types: teradatasqlalchemy type or object of teradatasqlalchemy type
Returns:
ColumnExpression
EXAMPLES:
# Load the data to run the example.
>>> load_example_data("teradataml", "interval_data")
# Create a DataFrame on 'interval_data' table.
>>> df = DataFrame("interval_data")
>>> df
id int_col value_col value_col1 str_col1 str_col2
2 657 MINUTE MONTH PT73H -P14M
3 1234 SECOND MONTH 100 04:23:59 06-10
1 240 HOUR YEAR P100DT4H23M59S P100Y4M
0 20 DAY YEAR 100 04:23:59 04-10
>>> df.tdtypes
id INTEGER()
int_col BIGINT()
value_col VARCHAR(length=30, charset='LATIN')
value_col1 VARCHAR(length=30, charset='LATIN')
str_col1 VARCHAR(length=30, charset='LATIN')
str_col2 VARCHAR(length=30, charset='LATIN')
# Example 1: Convert "int_col" column to INTERVAL_DAY_TO_SECOND with value
# provided in "value_col".
>>> df.assign(col = df.int_col.to_interval(df.value_col))
id int_col value_col value_col1 str_col1 str_col2 col
2 657 MINUTE MONTH PT73H -P14M 0 10:57:00.000000
3 1234 SECOND MONTH 100 04:23:59 06-10 0 00:20:34.000000
1 240 HOUR YEAR P100DT4H23M59S P100Y4M 10 00:00:00.000000
0 20 DAY YEAR 100 04:23:59 04-10 20 00:00:00.000000
# Example 2: Convert int_col to INTERVAL_YEAR_TO_MONTH when value = 'MONTH'.
>>> df.assign(col = df.int_col.to_interval('MONTH', INTERVAL_YEAR_TO_MONTH))
id int_col value_col value_col1 str_col1 str_col2 col
2 657 MINUTE MONTH PT73H -P14M 54-09
3 1234 SECOND MONTH 100 04:23:59 06-10 102-10
1 240 HOUR YEAR P100DT4H23M59S P100Y4M 20-00
0 20 DAY YEAR 100 04:23:59 04-10 1-08
# Example 3: Convert string column "str_col1" to INTERVAL_DAY_TO_SECOND.
>>> df.assign(col = df.str_col1.to_interval())
id int_col value_col value_col1 str_col1 str_col2 col
2 657 MINUTE MONTH PT73H -P14M 3 01:00:00.000000
3 1234 SECOND MONTH 100 04:23:59 06-10 100 04:23:59.000000
1 240 HOUR YEAR P100DT4H23M59S P100Y4M 100 04:23:59.000000
0 20 DAY YEAR 100 04:23:59 04-10 100 04:23:59.000000
# Example 4: Convert string column "str_col2" to INTERVAL_DAY_TO_MONTH.
>>> df.assign(col = df.str_col2.to_interval(type_=INTERVAL_YEAR_TO_MONTH))
id int_col value_col value_col1 str_col1 str_col2 col
2 657 MINUTE MONTH PT73H -P14M -1-02
3 1234 SECOND MONTH 100 04:23:59 06-10 6-10
1 240 HOUR YEAR P100DT4H23M59S P100Y4M 100-04
0 20 DAY YEAR 100 04:23:59 04-10 4-10