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There are several ways to solve the issue of not being able to transform a string into an integer or numeric type in Databricks:

1. Use the CAST function to explicitly convert the string into the desired data type:

df['col_name'] = df['col_name'].cast("integer")

1. If the string contains non-numeric characters, such as commas or dollar signs, remove them before converting the string:

df['col_name'] = df['col_name'].replace(",", "").replace("$", "").cast("integer")

1. Use regular expressions to remove any non-numeric characters:

import re
df['col_name'] = df['col_name'].apply(lambda x: re.sub('\D', '', x)).cast("integer")

1. If the dataset is large and the issue cannot be resolved with the above methods, consider using the spark-csv package to handle the data conversion automatically:

!pip install spark-csv

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("MyApp").getOrCreate()

df = spark.read.csv("path/to/csv/file", header=True, inferSchema=True)

By setting inferSchema=True, Databricks will attempt to automatically detect the data types of each column in the CSV file. This can save a lot of time and effort when working with large datasets.