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Updated on 2025-03-02

Python Dataframe string merging operation method

Python: An efficient method of merging Dataframe strings (detailed explanation in one article)

1. Summary

The string merging of Dataframe includes 2 scenarios, 1. Merge several columns of strings in df; 2. Merge the string in df with external strings.
This article mainly introduces the method of string merging operation on Dataframe in Python. First, the main methods to implement the above functions are summarized:1. Use the combination of Lambda function and apply function(Recommended method 1: concise, flexible and clear logic).2. Use the apply function3. Use the applymap function4. Directly string splicing of columns;Next, each method is explained in sequence with specific examples, including input, processing, and output, just feel the effect. .

2. Explain the solutions of 2 scenarios

2.1 For scenarios where scenarios 1: merge several columns of strings in df, there are two solutions below.

"""df sample:"""
import pandas as pd
df = ({
    'id': [1, 2, 3, 4],
    'name': ['Alice', 'Bob', 'Charlie', 'David']})
"""Method 1: Use a combination of Lambda and apply functions"""
df['combined1'] = (lambda row: f"Serial number:{row['id']},Name: {row['name']}", axis=1)
"""Method 4: Directly string splicing of columns"""
df['combined2'] = "Serial number:" + df['id'].astype(str) + ",Name:" + df['name'].astype(str)
#Output:0   Serial number:1,Name: Alice
1   Serial number:2,Name: Bob
2   Serial number:3,Name: Charlie
3   Serial number:4,Name: David

2.2 For scenario 2: Merge strings in df with external strings, there are 4 solutions:

"""df sample:"""
import pandas as pd
df = ({
    'id': [1, 2, 3, 4],
    'name': ['Alice', 'Bob', 'Charlie', 'David']})
"""Method 1: Use a combination of Lambda and apply functions"""
df['combined1'] = (lambda row: f"Hello,{row['name']},Have you eaten today", axis=1)
"""Method 2: Use the apply function"""
df['combined2']  = df['name'].apply(lambda x: "Hello," + str(x) + ", did you eat today?")
"""Method 3: Use the applymap function"""
df['new_id'] = (lambda x: "aaaa" + str(x) + "bbb")['id']
"""Method 4: Directly string splicing of columns"""
def merge_strings(name):
    return "Hello," + str(name) + ", did you eat today?"
df['new_id'] = df['name'].apply(merge_strings)
#Output:0    Hello,Alice,Have you eaten today
1    Hello,Bob,Have you eaten today
2    Hello,Charlie,Have you eaten today
3    Hello,David,Have you eaten today

3. Summary method

In general, there are 4 ways to merge strings in Python.

Method 1: Use the combination of Lambda function and apply function: highly recommended, the code is concise and the logic is clear

Lambda functions are a concise way of defining functions. Combined with the apply function, we can define string operations more concisely.

"""df sample:"""
import pandas as pd
df = ({
    'id': [1, 2, 3, 4],
    'name': ['Alice', 'Bob', 'Charlie', 'David']})
"""Scene of combining several columns of strings in df"""
df['combined1'] = (lambda row: f"Serial number:{row['id']},Name: {row['name']}", axis=1)
#Output:0   Serial number:1,Name: Alice
1   Serial number:2,Name: Bob
2   Serial number:3,Name: Charlie
3   Serial number:4,Name: David
"""Merge strings in df with external strings"""
df['combined1'] = (lambda row: f"Hello,{row['name']},Have you eaten today", axis=1)
#Output:0    Hello,Alice,Have you eaten today
1    Hello,Bob,Have you eaten today
2    Hello,Charlie,Have you eaten today
3    Hello,David,Have you eaten today

Method 2: Use the apply function

"""df sample:"""
import pandas as pd
df = ({
    'id': [1, 2, 3, 4],
    'name': ['Alice', 'Bob', 'Charlie', 'David']})
"""Merge strings in df with external strings"""
def merge_strings(name):
    return "Hello," + str(name) + ", did you eat today?"
df['new_id'] = df['name'].apply(merge_strings)
#Output:0    Hello,Alice,Have you eaten today
1    Hello,Bob,Have you eaten today
2    Hello,Charlie,Have you eaten today
3    Hello,David,Have you eaten today
#Output:0    Hello,Alice,Have you eaten today
1    Hello,Bob,Have you eaten today
2    Hello,Charlie,Have you eaten today
3    Hello,David,Have you eaten today

applyFunctions can apply a function to each row in a DataFrame. In this method, you need to first define a function that acts on df.

Method 3: Use the applymap function

The applymap function can apply a custom function to each element in the DataFrame. By using the applymap function, we can implement the merge of the ID column and the name column.

"""Merge strings in df with external strings"""
df['new_id'] = (lambda x: "aaaa" + str(x) + "bbb")['id']
#Output:0    Hello,Alice,Have you eaten today
1    Hello,Bob,Have you eaten today
2    Hello,Charlie,Have you eaten today
3    Hello,David,Have you eaten today

Method 4: Directly string splicing of columns

"""Scene of combining several columns of strings in df"""
df['combined2'] = "Serial number:" + df['id'].astype(str) + ",Name:" + df['name'].astype(str)
#Output:0   Serial number:1,Name: Alice
1   Serial number:2,Name: Bob
2   Serial number:3,Name: Charlie
3   Serial number:4,Name: David

The above are several methods for string merging of id columns in DataFrame in Pandas. Each method has its applicable scenarios, and you can choose the most suitable method according to your specific needs.

This is the end of this article about the efficient method of python Dataframe string merging. For more related python Dataframe string merging content, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!