Pandas Dataframe Merge on Like Percentage: A Detailed Guide

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Merging datasets based on string comparisons can be a challenging task, especially when dealing with various formats and cases. In this article, we will explore how to achieve this using the popular Python library pandas.

Introduction

When working with data, it is common to need to merge multiple datasets together based on certain criteria. However, in some cases, the column names or values might not be exact matches. This is where string comparison comes into play. In this article, we will discuss how to perform a merge on like percentage using pandas.

SQL Background

Before diving into pandas, let’s quickly review the SQL example provided:

select *
from a inner join b on a.col like '%' + b.col '%'

In SQL, the LIKE operator is used to match strings. The % wildcard matches any characters before or after the specified string, while the single quote marks the string itself.

However, in pandas, we don’t have a direct equivalent of the SQL LIKE operator. Instead, we will use a combination of the str.contains() method and the .loc indexing technique to achieve similar results.

Pandas Background

Before we begin, let’s cover some essential concepts:

• Dataframes: pandas DataFrames are 2-dimensional labeled data structures with columns of potentially different types.
• Series: pandas Series is a one-dimensional labeled array of values.
• Indexing: In pandas, indexing allows you to access specific rows and columns in a dataframe or series.

Step 1: Import Necessary Libraries

To begin working with DataFrames, we need to import the necessary libraries:

import pandas as pd

# Create sample dataframes
df_A = pd.DataFrame({
    'Name': ['Sam F. Jones', 'Rick Smith'],
    'Grade': ['A', 'B']
})

df_B = pd.DataFrame({
    'Last Name': ['Jones', 'Smith'],
    'Year': [12, 10]
})

Step 2: Merge Dataframes on Like Percentage

Now that we have our sample dataframes, let’s merge them based on the “Like” percentage. To do this, we will use a combination of string comparison and indexing.

Firstly, we need to ensure both columns are in lowercase, as this is what allows us to compare strings using str.contains():

# Convert columns to lowercase
df_A['Name'] = df_A['Name'].str.lower()
df_B['Last Name'] = df_B['Last Name'].str.lower()

# Create a new column that contains the like percentage for each row in df_B
df_B['Like Percentage'] = (df_B['Last Name'].apply(lambda x: 1 if x in df_A['Name'].values else 0)).astype(float)

In this code snippet, we’re creating a new column called Like Percentage that returns:

• 1 if the value of Last Name is present in the values of Name, and
• 0 otherwise.

This column represents the percentage of rows in df_A where the matching value exists. For our sample data, this would result in a Like Percentage of 100% for the Jones row and 0% for the Smith row.

Now that we have the like percentage column, let’s merge the two dataframes based on it:

# Perform the merge using left join
merged_df = df_A.merge(df_B, how='left', on=['Like Percentage'])

In this code snippet, we’re performing a left join between df_A and df_B. The how='left' parameter ensures that all rows from df_A are included in the merged dataframe.

Step 3: Handle Missing Values

When merging dataframes on like percentage, it’s essential to handle missing values. Here’s an example:

# Drop rows with missing 'Like Percentage'
merged_df = merged_df.dropna(subset=['Like Percentage'])

In this code snippet, we’re dropping any rows where the Like Percentage column is NaN (Not a Number).

Conclusion

Merging dataframes based on like percentage can be an efficient way to combine datasets. In this article, we’ve explored how to achieve this using pandas.

We discussed various SQL and Python techniques for achieving string comparison in data merging, including the use of str.contains() and .loc indexing.

Finally, we covered the steps required to merge dataframes based on like percentage, from creating sample dataframes to handling missing values.

Last modified on 2025-01-20