5 Best Ways to Merge Two DataFrames Based on Matching Data in a Column

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πŸ’‘ Problem Formulation: When working with data in Python, it’s common to need to combine two datasets based on a related column. For this article, imagine you have one DataFrame containing user IDs and names, and another DataFrame with user IDs and email addresses. The goal is to merge these DataFrames to match each user ID with its corresponding name and email address.

Method 1: Using pandas.merge()

Merging two DataFrames using the pandas.merge() function is the most direct way to combine datasets based on common columns. This method supports different types of joins like inner, outer, left, and right joins. The function signature for an inner join, which returns only the rows with matching keys in both DataFrames, is quite simple: pandas.merge(left, right, on='key').

Here’s an example:

import pandas as pd

df1 = pd.DataFrame({'user_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie']})
df2 = pd.DataFrame({'user_id': [2, 3, 4], 'email': ['bob@example.com', 'charlie@example.com', 'dave@example.com']})

merged_df = pd.merge(df1, df2, on='user_id')
print(merged_df)

Output:

   user_id     name              email
0        2      Bob     bob@example.com
1        3  Charlie  charlie@example.com

This snippet combines df1 and df2 along the column ‘user_id’, using an inner join by default. The result is a new DataFrame containing rows with matching ‘user_id’s and includes the ‘name’ and ’email’ columns.

Method 2: Using DataFrame.join()

The DataFrame.join() method is used for combining columns from two potentially differently-indexed DataFrames into a single DataFrame. It provides a convenient way to combine DataFrames on their indices, but can also be used to join on a column by setting one DataFrame’s index to the join key beforehand.

Here’s an example:

df1.set_index('user_id', inplace=True)
df2.set_index('user_id', inplace=True)

joined_df = df1.join(df2, how='inner')
print(joined_df)

Output:

             name              email
user_id                                 
2              Bob     bob@example.com
3          Charlie  charlie@example.com

Before joining, we set the ‘user_id’ column as the index for both DataFrames. The join() method is then used to combine the DataFrames based on this index. By specifying how='inner', it performs an inner join, keeping only the entries that match in both DataFrames.

Method 3: Using DataFrame.merge()

Similar to pandas.merge(), the merge method available on DataFrame instances can be used to combine DataFrames based on a key column. The signatures are similar, and it allows specifying types of merges and suffixes for overlapping column names.

Here’s an example:

merged_df = df1.merge(df2, on='user_id')
print(merged_df)

Output:

   user_id     name              email
0        2      Bob     bob@example.com
1        3  Charlie  charlie@example.com

This code snippet performs the same operation as pandas.merge(), merging df1 with df2 based on the ‘user_id’ column that is present in both DataFrames, by default performing an inner join.

Method 4: Using DataFrame.concat()

The pandas.concat() method is useful for appending rows or columns from one DataFrame to another. To achieve a merge-like function, we first need to align the DataFrames on their indexes and then concatenate them along the columns.

Here’s an example:

df1.set_index('user_id', inplace=True)
df2.set_index('user_id', inplace=True)

concatenated_df = pd.concat([df1, df2], axis=1, join='inner')
print(concatenated_df)

Output:

             name              email
user_id                                 
2              Bob     bob@example.com
3          Charlie  charlie@example.com

After setting ‘user_id’ as the index for both DataFrames, pandas.concat() combines them horizontally (since axis=1). By specifying join='inner', we ensure that only the keys present in both DataFrames are included, similar to an inner join.

Bonus One-Liner Method 5: Using a dictionary comprehension

A one-liner that is more Pythonic than Pandas-specific can be used to merge two DataFrames. This involves creating a dictionary where the keys are the common columns and the values are the joined rows, but this method is less common and not as readable or efficient for large datasets.

Here’s an example:

merged_dict = {uid: (df1.loc[uid, 'name'], df2.loc[uid, 'email']) 
                for uid in set(df1['user_id']).intersection(set(df2['user_id']))}
print(merged_dict)

Output:

{2: ('Bob', 'bob@example.com'), 3: ('Charlie', 'charlie@example.com')}

Using a dictionary comprehension, this code identifies user IDs present in both DataFrames and then retrieves the ‘name’ and ’email’ for those IDs. This method results in a dictionary rather than a DataFrame, and its practicality is limited to simple cases and smaller DataFrames.

Summary/Discussion

  • Method 1: pandas.merge(). Most straightforward method with various join options. Ideal for merging on columns. Less intuitive if merging on indexes.
  • Method 2: DataFrame.join(). Good for index-based joining but can be used on columns with some pre-processing. Potentially confusing when not joining on indexes directly.
  • Method 3: DataFrame.merge(). Instance-based alternative to pandas.merge(), with identical functionality. Personal preference whether to use module function or instance method.
  • Method 4: DataFrame.concat(). Great for combining DataFrames along an axis but requires proper indexing. Not as specifically tailored to column-based merges as merge().
  • Method 5: One-Liner. More of a Pythonic approach than Pandas. Better suited for smaller datasets or when a DataFrame output is not required.