Remove Quotes From All Rows in DataFrame Column

When string-based columns have quotes - we'll oftentimes want to get rid of them, in large part because 'string is technically a different string to string, which more often than not isn't a distinction we want to make.

Whether you'll be performing NLP and tokenizing words (in which case, you'll have different tokens for the same words because they're "glued" to a quote) or any other form of manipulation - removing the quotes will be of importance.

There are quite a few ways to remove quotes in a Pandas DataFrame.

DataFrame.applymap (lambda...)

To remove all quotes from all rows and columns of an entire DataFrame, you can use applymap() with a lambda function:

# applymap() works on entire DataFrame
df = df.applymap(lambda x: x.replace('"', ''))

Note: This will apply the lambda function on every row of every column, and will result in an error if not all columns are of str type.

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To remove all quotes from all rows in a single column, just apply the function to a single column:

# apply() works on column
df['ColumnName'] = df['ColumnName'].apply(lambda x: x.replace('"', ''))

These two approaches are generic and can apply any lambda function, besides one that leverages replace().

Series' str Method - str.replace()

Each Series offers the str function, which lets you use other functions such as replace() to manipulate strings within rows of a single column:

df['ColumnName'] = df['ColumnName'].str.replace(r'"', '')

str.replace() with RegEx

To use Regular Expressions with the replace() method, you pass in regex=True:

Last Updated: April 12th, 2023
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