How to Efficiently Clean DataFrame Column Strings with Python's Lambda Function?

Cleaning DataFrame Column Strings Efficiently

Removing undesirable portions from strings in a DataFrame column is a common task in data cleaning. This can require the removal of specific characters, prefixes, or suffixes.

Consider a DataFrame with the following data structure:

Time	Result
09:00	52A
10:00	62B
11:00	44a
12:00	30b
13:00	-110a

Our goal is to extract the numerical portion from each 'Result' string, removing the ' ' and '-' signs and the trailing characters. The desired output should look like this:

Time	Result
09:00	52
10:00	62
11:00	44
12:00	30
13:00	110

To achieve this, we can utilize Python's lambda function. The following code effectively cleans the 'Result' column data:

data['Result'] = data['Result'].map(lambda x: x.lstrip('+-').rstrip('aAbBcC'))

This lambda function iterates through each element in the 'Result' column:

• x.lstrip(' -'): Removes any leading ' ' or '-' signs.
• x.rstrip('aAbBcC'): Removes any trailing characters from the specified character list ('aAbBcC').

By applying these operations, we achieve the desired result, with unwanted parts removed from the strings in the 'Result' column.

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