如何使用另一pandas.DataFrame数据填充目标DataFrame列？

To get your desired result, you'll use pandas' merge() function to join the two DataFrames based on matching values between second_key (from the first DataFrame) and key (from the second DataFrame). Here's a straightforward implementation:

Step 1: Set Up Your DataFrames #

First, let's recreate the DataFrames you provided:

import pandas as pd

# First DataFrame
first_df = pd.DataFrame({
    'first_key': [0, 0, 0, 0, 0],
    'second_key': [1, 1, 2, 3, 3]
})

# Second DataFrame
second_df = pd.DataFrame({
    'key': [1, 2, 3],
    'status': ['good', 'bad', 'good']
})

Step 2: Merge and Clean Up #

Use pd.merge() to combine the DataFrames, then remove the redundant key column that comes along with the merge:

merged_df = pd.merge(
    first_df,
    second_df,
    left_on='second_key',
    right_on='key',
    how='left'  # Keeps all rows from the first DataFrame
).drop('key', axis=1)

Step 3: Check the Result #

Printing merged_df will give you exactly the output you want:

first_key  second_key status
0          0           1   good
1          0           1   good
2          0           2    bad
3          0           3   good
4          0           3   good

Quick Explanation #

• left_on='second_key' and right_on='key': Tells pandas to match rows where the value in second_key (from the left DataFrame) equals the value in key (from the right DataFrame).
• how='left': Ensures every row from your original first DataFrame is preserved, even if there's no matching entry in the second DataFrame (in such cases, status would show NaN).
• .drop('key', axis=1): Removes the duplicate key column added during the merge, leaving only the columns you need.

内容的提问来源于stack exchange，提问作者Sergey Malashenko