How to efficiently copy the entire column of one DataFrame into another DataFrame with different structures in Python?

Pandas is a powerful data analysis library in Python, but efficiency is crucial when dealing with column replication between DataFrames in different structures. This article introduces an efficient method to avoid performance bottlenecks caused by line-by-line replication.

Suppose we have two DataFrames with different structures, df1 and df2 , the goal is to copy one or more columns in df2 into df1 while maintaining the original structure of df1 .

The following code demonstrates how to do this efficiently:

 import pandas as pd

# Create a sample DataFrame
df1 = pd.DataFrame({
    'A': range(4),
    'B': range(4),
    'C': range(4),
    'D': range(4)
})
df2 = pd.DataFrame({
    'D': [11, 22, 33],
    'E': ['aa', 'bb', 'cc']
})

# Copy the 'D' column of df2 to the 'A' column of df1 (assuming that the length needs to be adjusted)
df1['A'] = df2['D'].reindex_like(df1['A']).values

# Add the 'E' column of df2 to df1 (if df1 does not have 'E' column)
df1['E'] = df2['E'].reindex_like(df1['A']).values


# Print result print(df1)

This method uses the reindex_like() function to adjust the index of df2 column to match the index of the corresponding column of df1 , and then uses the .values ​​attribute to efficiently assign data to df1 . This is more efficient than line-by-line replication, especially on large data sets. If the column length of df2 is shorter than df1 , the excess will be filled with missing values ​​(NaN). If you need to add a new column in df1 (such as the 'E' column of df2 ), you can directly assign the value. This method is simple and efficient, and is suitable for various data volumes.

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