In Databricks, you can replace values in strings and DataFrames using a couple of methods:

1. SQL Functions:

• replace(str, search, replace): This function replaces all occurrences of a specific substring (search) within a string (str) with another substring (replace).
• SQL
• SELECT replace(‘Hello world’, ‘world’, ‘Databricks’); — Output: ‘Hello Databricks’
• regexp_replace(str, regexp, rep): This function replaces parts of a string (str) that match a regular expression (regexp) with another string (rep).
• SQL
• SELECT regexp_replace(‘Hello 123 world’, ‘[0-9]+’, ‘456’); — Output: ‘Hello 456 world’

2. DataFrame API:

• withColumn() and replace(): You can use the DataFrame API to create or replace columns. In combination with the replace function, you can also replace values within a specific column.
• Python
• from pyspark.sql.functions import col, replace
•  
• df = df.withColumn(“new_column”, replace(col(“old_column”), “old_value”, “new_value”))

Example: Replace Values in a DataFrame Column

Python

from pyspark.sql.functions import col, regexp_replace

df = spark.createDataFrame([(“This is a test string.”,), (“Another string 123.”,)], [“text”])

# Replace specific substrings

df = df.withColumn(“replaced_text”, replace(col(“text”), “string”, “replaced_string”))

# Replace values using regular expressions

df = df.withColumn(“replaced_text_regex”, regexp_replace(col(“text”), “[0-9]+”, “456”))

df.show(truncate=False)

Output:

+———————–+——————————+—————————–+

|text |replaced_text |replaced_text_regex |

+———————–+——————————+—————————–+

|This is a test string. |This is a test replaced_string.|This is a test string. |

|Another string 123. |Another replaced_string 123. |Another replaced_string 456. |

+———————–+——————————+—————————–+

Replacing Data in Delta Tables

For a full replacement of data in a Delta table that might be used in concurrent operations, use the following pattern:

SQL

CREATE OR REPLACE TABLE table_name AS SELECT * FROM parquet. `/path/to/files`;