Remove unwanted parts from strings in a column

Real-world mapping: Lambda meets Pandas

Intricate changes? Call map()

The mighty map() function is here. It can apply a custom transformation function, a lambda, to every element of a column.

df['col'] = df['col'].map(lambda x: x.lstrip('this').rstrip('that'))
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This slices off 'this' from the start and 'that' from the end of each string.

Supercharge it with regex pre-compiling

Compiling your regex patterns upfront can boost performance and save pandas from an energy drink:

import re
magic_wand = re.compile(r'\D')  # Wand chooses the wizard, remember?
df['col'] = df['col'].map(lambda x: magic_wand.sub('', x))
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Here, '\D' seeks and destroys all non-digits in 'col'.

NaNs and type conversions every programmer has nightmares about

For numeric operations, convert to the right type using astype(int) and don't forget to handle NaNs:

df['col'] = df['col'].map(lambda x: '' if pd.isna(x) else x).astype(int)
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This ensures that Harry NaN-ger prints no surprises during your journey.

More than meets the eye: advanced string methods

Master the art of the split and extract

Get sophisticated with str.extract, str.split, and str.get to achieve consistent results:

df['col'] = df['col'].str.split('_').str.get(1)
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The above line is splitting strings when it finds a underscore and selecting the second element.

In-place modifications? replace to the rescue

Perform a conjuring spell with replace method and inplace=True to update the DataFrame directly:

df['col'].replace(to_replace='unwanted_pattern', value='', inplace=True, regex=True)
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Careful! Casting this spell directly affects your DataFrame, no Felix Felicis can undo it!

Deep Dive into String Operations

Slicing for fun and profit

Combine slicing with map() and lambda for tailored transformations:

df['col'] = df['col'].map(lambda x: x[2:-3])
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This cuts first 2 and last 3 characters like a vegetable dicer in a cooking show.

Regex: A wizard's best friend

For complex manipulations, regex is your Patronus. Use it creatively for beneficial results:

df['col'] = df['col'].str.replace('(?<=prefix)(unwanted)(?=suffix)', '')
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This spell gets rid of 'unwanted', if it is sandwiched between 'prefix' and 'suffix'.

Keep your data cauldron clean

Consider data diversity when brewing your cleaning potions. Remember, messy transformations leading to empty strings or unforeseen data types can spoil your potion (data integrity).