Teradata Package for Python Function Reference on VantageCloud Lake - array_update - 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

20.00.00.11

Publication Date

August 2026

Content Type

Programming Reference

Publication ID

B700-4500-200K

ft:locale

en-US

ft:lastEdition

2026-08-13

ft:sourceId

FTML

dita:id

TeradataPython_FxRef_Lake_2000

Product Category

Teradata Vantage

teradataml.dataframe.sql.DataFrameColumn.array_update = array_update(self, updated_value, index=None, lower_bound=None, upper_bound=None, stride=None): DESCRIPTION: Update the value at a specific index or subset of the elements in the array with a new value. PARAMETERS: updated_value: Required Argument. Specifies the value to be updated in the array. Types: int OR float OR str index: Optional Argument. Specifies the index of the array to be updated. Types: int lower_bound: Optional Argument. Specifies the starting index for the update operation (inclusive). Note: * If specified, both "lower_bound" and "upper_bound" arguments must be provided. Types: int upper_bound: Optional Argument. Specifies the ending index for the update operation (inclusive). Note: * If specified, both "lower_bound" and "upper_bound" arguments must be provided. Types: int stride: Optional Argument. Specifies the number of elements that should be skipped after each update. Note: * If "index" value is specified, then "stride" is ignored. Types: int RETURNS: ColumnExpression EXAMPLES: >>> from teradataml import * >>> load_example_data("array", "array_table") # Create a DataFrame on 'array_table'. >>> df = DataFrame("array_table") >>> df arr1 arr2 arr3 id 1 (10,20,30,40,50) (23,200,215,40,21) ('ab','bc','cd','ab','ef') 4 (180,28,38,48,58) (30,NULL,NULL,250,27) ('mn','no',NULL,'op','pq') 2 (150,25,35,45,55) (28,50,95,90,26) ('xy','yz','za','ab','xy') 6 (16,261,36,46,56) (29,170,160,46,25) ('uv','xy','ab','xy','yz') 3 (12,22,320,42,52) (25,22,140,200,23) ('pq','ab','ab','st','tu') 5 (14,24,34,44,54) (26,NULL,NULL,180,24) ('ij','jk',NULL,'kl','lm') # Example 1: Update element at index 3 in array column 'arr1' with value 999. >>> res = df.assign(updated_arr = df.arr1.array_update(999, index=3)) >>> res arr1 arr2 arr3 updated_arr id 1 (10,20,30,40,50) (23,200,215,40,21) ('ab','bc','cd','ab','ef') (10,20,999,40,50) 4 (180,28,38,48,58) (30,NULL,NULL,250,27) ('mn','no',NULL,'op','pq') (180,28,999,48,58) 2 (150,25,35,45,55) (28,50,95,90,26) ('xy','yz','za','ab','xy') (150,25,999,45,55) 6 (16,261,36,46,56) (29,170,160,46,25) ('uv','xy','ab','xy','yz') (16,261,999,46,56) 3 (12,22,320,42,52) (25,22,140,200,23) ('pq','ab','ab','st','tu') (12,22,999,42,52) 5 (14,24,34,44,54) (26,NULL,NULL,180,24) ('ij','jk',NULL,'kl','lm') (14,24,999,44,54) # Example 2: Update all elements in array column 'arr1' with value 0. >>> res = df.assign(updated_arr = df.arr1.array_update(0)) >>> res arr1 arr2 arr3 updated_arr id 1 (10,20,30,40,50) (23,200,215,40,21) ('ab','bc','cd','ab','ef') (0,0,0,0,0) 4 (180,28,38,48,58) (30,NULL,NULL,250,27) ('mn','no',NULL,'op','pq') (0,0,0,0,0) 2 (150,25,35,45,55) (28,50,95,90,26) ('xy','yz','za','ab','xy') (0,0,0,0,0) 6 (16,261,36,46,56) (29,170,160,46,25) ('uv','xy','ab','xy','yz') (0,0,0,0,0) 3 (12,22,320,42,52) (25,22,140,200,23) ('pq','ab','ab','st','tu') (0,0,0,0,0) 5 (14,24,34,44,54) (26,NULL,NULL,180,24) ('ij','jk',NULL,'kl','lm') (0,0,0,0,0) # Example 3: Update elements from position 2 to 4 in array column 'arr1' with value 100. >>> res = df.assign(updated_arr = df.arr1.array_update(100, lower_bound=2, upper_bound=4)) >>> res arr1 arr2 arr3 updated_arr id 1 (10,20,30,40,50) (23,200,215,40,21) ('ab','bc','cd','ab','ef') (10,100,100,100,50) 4 (180,28,38,48,58) (30,NULL,NULL,250,27) ('mn','no',NULL,'op','pq') (180,100,100,100,58) 2 (150,25,35,45,55) (28,50,95,90,26) ('xy','yz','za','ab','xy') (150,100,100,100,55) 6 (16,261,36,46,56) (29,170,160,46,25) ('uv','xy','ab','xy','yz') (16,100,100,100,56) 3 (12,22,320,42,52) (25,22,140,200,23) ('pq','ab','ab','st','tu') (12,100,100,100,52) 5 (14,24,34,44,54) (26,NULL,NULL,180,24) ('ij','jk',NULL,'kl','lm') (14,100,100,100,54) # Example 4: Update elements in the array column 'arr1'with value 50 using stride of 2. >>> res = df.assign(updated_arr = df.arr1.array_update(50, stride=2)) >>> res arr1 arr2 arr3 updated_arr id 1 (10,20,30,40,50) (23,200,215,40,21) ('ab','bc','cd','ab','ef') (50,20,30,50,50,NULL,50,NULL,NULL,50) 4 (180,28,38,48,58) (30,NULL,NULL,250,27) ('mn','no',NULL,'op','pq') (50,28,38,50,58,NULL,50,NULL,NULL,50) 2 (150,25,35,45,55) (28,50,95,90,26) ('xy','yz','za','ab','xy') (50,25,35,50,55,NULL,50,NULL,NULL,50) 6 (16,261,36,46,56) (29,170,160,46,25) ('uv','xy','ab','xy','yz') (50,261,36,50,56,NULL,50,NULL,NULL,50) 3 (12,22,320,42,52) (25,22,140,200,23) ('pq','ab','ab','st','tu') (50,22,320,50,52,NULL,50,NULL,NULL,50) 5 (14,24,34,44,54) (26,NULL,NULL,180,24) ('ij','jk',NULL,'kl','lm') (50,24,34,50,54,NULL,50,NULL,NULL,50) # Example 5: Update elements in the array column 'arr1' from second position to fifth position # using stride of 1. >>> res = df.assign(updated_arr = df.arr1.array_update(0, lower_bound=2, upper_bound=5, stride=1)) >>> res arr1 arr2 arr3 updated_arr id 1 (10,20,30,40,50) (23,200,215,40,21) ('ab','bc','cd','ab','ef') (10,0,30,0,50) 4 (180,28,38,48,58) (30,NULL,NULL,250,27) ('mn','no',NULL,'op','pq') (180,0,38,0,58) 2 (150,25,35,45,55) (28,50,95,90,26) ('xy','yz','za','ab','xy') (150,0,35,0,55) 6 (16,261,36,46,56) (29,170,160,46,25) ('uv','xy','ab','xy','yz') (16,0,36,0,56) 3 (12,22,320,42,52) (25,22,140,200,23) ('pq','ab','ab','st','tu') (12,0,320,0,52) 5 (14,24,34,44,54) (26,NULL,NULL,180,24) ('ij','jk',NULL,'kl','lm') (14,0,34,0,54)