Filter pandas DataFrame by substring criteria

python

pandas

dataframe

string-filtering

byNikita Barsukov·Jan 7, 2025

The go-to pattern to filter rows in pandas by a substring is df.loc[df['col'].str.contains('substring')]. This nifty snippet selects the rows where the column 'col' contains the 'substring'. Here is how you employ it:

import pandas as pd

# Assuming 'data' is your DataFrame and 'target' is the substring.
filtered_df = data.loc[data['col'].str.contains('target', na=False)]

In the code above, data signifies your DataFrame, 'col' represents the column you want to search, and 'target' is the sought-after substring. na=False ensures that NaNs don't crash the party.

Leveraging pandas for powerful string filtering

Regex is your best friend when you need pattern matching. In str.contains(), regex is off by default. For standard substring searches keep it that way. If your task requires pattern matching, set regex=True. Just like an overeager beaver, regex treats special characters as wildcards. Be sure to escape them when they are a part of your search string.

Managing case-sensitivity

Overlooked case sensitivity can play hide and seek with your matches. Enable case=False to get a case-insensitive search:

filtered_df = data[data['col'].str.contains('target', case=False)]
# Now, even 'TARGET' can't hide.

Seeking out whole words

To capture the whole words in your net and let the partial matches swim by, rely on \b, the gatekeeper of your regex kingdom:

filtered_df = data[data['col'].str.contains(r'\btarget\b', regex=True)]
# Word boundaries: because 'ton' should not match 'stone'

Hiring lambda functions for complex filtering

Lambda functions are your mercenaries when the filtering conditions get too intricate for str.contains to solve alone:

filtered_df = data.apply(lambda row: 'substring' in row['col'], axis=1)
# Lambda: the swiss army knife of Python

This method scores high on flexibility but could be slower as it trades speed for complex logic abilities.

Boosting efficiency in string filtering

Time is money, but efficiency is the bank where you save it. To speed up your substring filtering:

Prioritize methods smartly

str.contains is your trusty old horse for its balance of ease and efficiency.
List comprehensions can outrun others for purely string data.
np.vectorize is a dark horse that might win in certain cases.

Harness the power of `query`

query emerges as the victor when conditions get convoluted:

filtered_df = data.query("col.str.contains('target')", engine='python')
# So readable, it's almost English.

It’s simultaneously powerful and readable, but beware of the overhead it incurs.

Scatter filtering across multiple columns

For substring hunting in multiple columns, use an apply method and a lambda function combo:

filtered_df = data[data.apply(lambda row: row.str.contains('substring').any(), axis=1)]
# Why search one, when you can search all!

Unleashing the full potential of pandas

Routing for rows via substrings in indices

To check the DataFrame index for your substring, first change it to a series:

filtered_df = data[data.index.to_series().str.contains('substring')]
# Even the index can't escape the search!

Dodging performance potholes

Steer clear of complex regex patterns unless crucial. They may render your code sluggish. Go for plain yesteryear text searches wherever possible:

fast_filtered_df = data[data['col'].str.contains('simple_text', regex=False)]
# Keep it simple, silly!

Dealing with NaNs safely

Stand guard against issues with NA/NaN values by employing == True with string methods:

guarded_df = data[data['col'].str.contains('substring') == True]
# NaN ain't got nothing on us!

Discovering numpy alternatives

np.char.find() offers an alternate path to non-regex substring searching:

import numpy as np

# Applying find to each element in 'col'.
matches = np.char.find(np.array(data['col'].astype(str)), 'substring') != -1
filtered_df = data[matches]
# Diversification is key, in finance and in code!