Isolating Duplicates Based on Partial Match in a Pandas DataFrame

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In this article, we will explore how to isolate duplicates based on partial match in a pandas DataFrame. We will use the duplicated() function to achieve this goal.

Introduction

When working with data frames, it’s common to encounter duplicate values. However, sometimes we want to identify these duplicates based on certain conditions, such as partial matches. In this article, we’ll discuss how to use pandas functions to accomplish this task.

The Problem Statement

We have a pandas DataFrame that contains email addresses, and we want to check if the same email address exists without certain characters. Specifically, we’re interested in emails that start with at least two ‘x’s and their corresponding presence or absence without those ‘x’s.

The original DataFrame looks like this:

We want to create a new column, exists_in_valid_form, which indicates whether the email address exists without the ‘x’s.

Solution

To achieve this, we can use the duplicated() function in combination with other pandas functions. Here’s the step-by-step solution:

1. First, we need to create a sub-DataFrame that contains all rows with emails starting with at least two ‘x’s using the following code:

df_sub = df[df[’email’].str.contains(“xx”)]

   This will give us the desired subset of rows.

2. Next, we want to check if each email address exists without the 'x's. We can use the `str.lstrip()` function to remove the 'x's from the beginning of each email address.

3. To determine if an email address is a duplicate, we can use the `duplicated()` function with the `keep=False` argument. This will return a boolean Series indicating whether each value exists in other rows.

4. Finally, we'll combine the results using the bitwise AND operator (`&`) to get the desired output.

Here's the complete code:
   ```markdown
df['exists_in_valid_form'] = (df.email.str.lstrip('x').duplicated(keep=False) & 
                              df.email.str.startswith('xx'))

This will create a new column, exists_in_valid_form, that indicates whether each email address exists without the ‘x’s.

Explanation

Let’s break down the solution step by step:

• df['email'].str.contains("xx"): This line creates a boolean mask indicating which rows contain emails starting with “xx”.
• df_sub = df[df['email'].str.contains("xx")]: By selecting only those rows that match the condition, we create a new sub-DataFrame (df_sub) containing all relevant rows.
• df.email.str.lstrip('x'): This line removes the ‘x’s from the beginning of each email address. The result is a Series of emails without the ‘x’s.
• duplicated(keep=False): This function returns a boolean Series indicating whether each value exists in other rows. By default, keep=True, which means duplicates are kept as unique values. However, by setting keep=False, we get all duplicate values.
• & df.email.str.startswith('xx'): Finally, we combine the results using the bitwise AND operator (&). This ensures that only emails starting with “xx” are considered.

Example Use Case

Here’s an example use case:

import pandas as pd

# Create a sample DataFrame
data = {
    'email': ['[email@x.com](mailto:x.com)', '[email@xx.com](mailto:xx.com)', 
              '[email@xxx.com](mailto:xxx.com)', '[email@xx.com](mailto:xx.com)', 
              '[email@xy.com](mailto:xy.com)'],
    'col2': ['John', 'John', 'John', 'John', 'Jane'],
    'col3': ['Doe', 'Doe', 'Doe', 'Doe', 'Doe']
}

df = pd.DataFrame(data)

# Create the new column
df['exists_in_valid_form'] = (df.email.str.lstrip('x').duplicated(keep=False) & 
                              df.email.str.startswith('xx'))

print(df)

Output:

In this example, we create a sample DataFrame with email addresses and their corresponding values. We then use the solution to create a new column (exists_in_valid_form) that indicates whether each email address exists without the ‘x’s.

Conclusion

Isolating duplicates based on partial matches in a pandas DataFrame can be achieved using various techniques, including the duplicated() function. By combining this function with other pandas functions, such as str.contains(), str.lstrip(), and bitwise AND operators, we can create a new column that indicates whether each value exists without certain characters.

This solution is useful when working with data frames where duplicates need to be identified based on specific conditions.

Last modified on 2024-10-30