How to efficiently copy DataFrame whole columns of different structures in Pandas?

Pandas efficient DataFrame column replication skills

In data processing, it is often necessary to copy a column of a DataFrame to another DataFrame with a different structure. This article introduces an efficient Pandas whole column copy method to avoid inefficiency in cell-by-cell copying.

Suppose there are two DataFrames with different structures, df1 and df2 , the goal is to copy a column of df2 to the corresponding column of df1 .

The following code example demonstrates how to copy column data from df2 to df1 :

 import pandas as pd

# Example DataFrame df1
df1 = pd.DataFrame({
    'A': range(4),
    'B': range(4),
    'C': range(4),
    'D': range(4)
})

# Example DataFrame df2
df2 = pd.DataFrame({
    'D': [11, 22, 33],
    'E': ['aa', 'bb', 'cc']
})

# Method 1: Use `loc` for efficient assignment (recommended)
df1['A'] = df2['D'].reset_index(drop=True)[:df1.shape[0]]
df1['B'] = df2['E'].reset_index(drop=True)[:df1.shape[0]]

# Method 2: Use `concat` and `reindex` (original method improvement)
new_A = pd.concat([df1['A'], df2['D']], ignore_index=True)
df1 = df1.reindex(range(len(new_A)))
df1['A'] = new_A[:df1.shape[0]]

# Print result print(df1)

Method 1: Use loc for efficient assignment

This method uses loc directly for assignment, which is more efficient and simpler and easier to understand. reset_index(drop=True) resets the index, [:df1.shape[0]] ensures that the length of the copy matches df1 and avoids index misalignment.

Method 2: Improved concat and reindex methods

This method improves the original code to make it clearer, easier to understand and maintain. It avoids unnecessary row count expansion and only performs reindex operations when needed.

Both methods can efficiently copy the columns of df2 to df1 , which method to choose depends on personal preference and code style. But the loc method is usually considered to be more concise and efficient. Remember that efficient column replication methods are crucial when working with large datasets.

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