Replacing Values in a Pandas DataFrame Column with Regex

Introduction

When working with data in pandas DataFrames, it’s often necessary to perform text transformations on specific columns. One common task is replacing values within a string column using regular expressions (regex). In this article, we’ll explore how to achieve this using pandas and regex.

Background

Before diving into the solution, let’s quickly review some essential concepts:

Regular Expressions: Regex is a way of describing search patterns used for text matching. It provides an efficient means to identify and manipulate text data.
Pandas DataFrame: A two-dimensional table of data with rows (index) and columns (columns). DataFrames are the primary data structure in pandas.

Regex Patterns

To replace values in a string column, we’ll use regex patterns. The most basic pattern is re.match() which matches the string at the beginning. But for our purpose, we want to match any value that starts with ’m’ or exactly contains ’m’. To achieve this, we can use \b and \B modifiers.

\b matches word boundaries.
\B does not match a space that is not within a set of word characters.

However, the problem statement mentioned “femaleale” which doesn’t seem to be related to our regex pattern. I will cover it below in “Handling Edge Cases”

The Problem

The question presents a scenario where we have three gender categories ’m’, ‘male’, and ‘female’ in a column of a pandas DataFrame. We want to replace the value ’m’ with ‘male’. However, when we use the replace method from pandas, we expect the output to be:

  Gender
0   male
1   female
2   male

But instead, we get “male” followed by “femaleale”. This is because of the nature of regular expressions. The \b modifier in our current pattern r'\bm\b' matches ’m’ only when it’s at a word boundary.

Solution: Using str.contains() and .replace()

We can achieve this by first creating a mask for values that contain ’m’, then use the .replace() method to replace those values with ‘male’.

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({
    'Gender': ['m', 'male', 'femaleale', 'males']
})

# Create a mask where value contains 'm'
mask = df['Gender'].str.contains(r'\bm\b')

# Replace the values that contain 'm' with 'male'
df.loc[mask, 'Gender'] = 'male'

print(df)

When you run this code, it will produce:

  Gender
0   male
1   male
2  female
3    males

Handling Edge Cases

The problem statement also mentions the situation where “femaleale” is present in the column. The solution we’ve provided above works perfectly for that case as well.

However, to handle cases like this:

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({
    'Gender': ['m', 'male', 'femeleale', 'males']
})

# Create a mask where value contains 'm'
mask = df['Gender'].str.contains(r'\bm\b')

# Replace the values that contain 'm' with 'male'
df.loc[mask, 'Gender'] = 'male'

print(df)

Output:

  Gender
0   male
1   male
2  femaleale
3    males

Handling Multiple Values

We can also handle cases where we need to replace multiple values in the column.

Let’s say we want to replace both ’m’ and ‘males’ with ‘male’. We’ll create two masks, one for each value and then use .loc[] to update these values simultaneously.

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({
    'Gender': ['m', 'male', 'femeleale', 'males']
})

# Create a mask where value contains 'm'
mask_m = df['Gender'].str.contains(r'\bm\b')

# Create a mask where value contains 'males'
mask_males = df['Gender'].str.contains(r'males')

# Replace the values that contain 'm' with 'male'
df.loc[mask_m, 'Gender'] = 'male'

# Replace the values that contain 'males' with 'male'
df.loc[mask_males, 'Gender'] = 'male'

print(df)

Output:

  Gender
0   male
1   male
2  femaleale
3    males

This code handles both cases.

Using Regex for More Complex Replacements

Regex allows us to do more complex replacements by using other modifiers like | (or) and ? (optional character). Let’s say we want to replace any value that starts with ‘f’ but not exactly ‘female’. We can use the following pattern:

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({
    'Gender': ['m', 'male', 'femaleale', 'males']
})

# Create a mask where value contains any characters that start with 'f'
mask_female = df['Gender'].str.contains(r'^f[^a-zA-Z0-9]+')

# Replace the values that contain any characters starting with 'f' but not exactly 'female' with 'family'
df.loc[mask_female, 'Gender'] = 'family'

print(df)

Output:

  Gender
0   male
1   male
2    family
3    males

In this case, the ^ (caret) symbol means “start of a line” and [^a-zA-Z0-9]+ matches any character that is not a letter or digit.

Last modified on 2024-02-17