Teradata Package for Python Function Reference | 20.00 - 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 - 20.00

Deployment

VantageCloud

VantageCore

Edition

VMware

Enterprise

IntelliFlex

Product

Teradata Package for Python

Release Number

20.00.00.11

Published

August 2026

ft:locale

en-US

ft:lastEdition

2026-08-13

dita:id

TeradataPython_FxRef_Enterprise_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)