Pandas：如何依据source与website的匹配修改DataFrame的type列值

Hey there! Let's walk through how to modify the 'type' column based on whether the 'source' value exists within the 'website' string. Here's a straightforward, efficient approach using pandas and numpy:

First, let's set up our original DataFrame as you provided:

import pandas as pd
import numpy as np

raw_data = {
    'website': ['bbc.com', 'cnn.com', 'google.com', 'facebook.com'],
    'type': ['image', 'audio', 'image', 'video'],
    'source': ['bbc','google','stackoverflow','facebook']
}
df = pd.DataFrame(raw_data, columns=['website', 'type', 'source'])

Next, we’ll create a row-wise condition to check if each 'source' is a substring of its corresponding 'website'. Then we’ll use this condition to append the correct suffix to the 'type' column:

# Check if source exists in website for each row
is_first_party = df.apply(lambda row: row['source'] in row['website'], axis=1)

# Update the type column with the appropriate suffix
df['type'] = np.where(is_first_party, df['type'] + '_1stParty', df['type'] + '_3rdParty')

If you print the updated DataFrame now, you’ll get exactly the result you’re looking for:

website              type        source
0        bbc.com    image_1stParty           bbc
1        cnn.com   audio_3rdParty         google
2     google.com   image_3rdParty  stackoverflow
3  facebook.com   video_1stParty       facebook

This method uses pandas' vectorized operations where possible, keeping things efficient even for larger datasets, and the lambda function handles the row-specific check cleanly.

内容的提问来源于stack exchange，提问作者Saad