基于Pandas DataFrame匹配关键词并提取其前至首个逗号的文本

Got it, let's walk through how to solve this problem. You've got a Pandas DataFrame with id, product_details, and taxo columns—where product_details holds comma-separated key-value pairs wrapped in square brackets. Your goal is to find the segment containing a specific keyword, then pull the text from the start of that segment up to the first comma (or the whole segment if there's no comma in it).

Step 1: Set Up Sample Data (to replicate your scenario) #

First, let's create a sample DataFrame matching your example to test our solution:

import pandas as pd

# Recreate your sample DataFrame
data = {
    'id': [100, 200],
    'product_details': [
        "[Sales Package=6 Pair slipper, Strap Material=Rubber, qty=1, categoryPath=Footwear>Men>Slippers & Flip Flops, codAvailable=true, detailedSpecs=Multicolor Color, None Character Type Slippers For Men Sole Material Rubber]",
        "[Brand Fit=Regular, Fabric=Cotton Polyester Blend, Fabric Care=Hand wash, Fit=Regular, Ideal For=Mens, Neck Type=Round Neck, Pack of=1, Pattern=Graphic Print, Reversible=Yes]"
    ],
    'taxo': ['', '']
}

df = pd.DataFrame(data)

Step 2: Define the Extraction Function #

We'll create a reusable function that handles parsing the product_details column, finding the keyword, and extracting the desired text:

def extract_target_text(df, target_keyword):
    # Clean up the product_details: remove brackets and split into individual key-value segments
    df['temp_segments'] = df['product_details'].str.strip('[]').str.split(', ')
    
    # Helper function to process each row's segments
    def find_and_extract(segments):
        for segment in segments:
            if target_keyword in segment:
                # Grab everything up to the first comma (handles edge cases where segments might have commas)
                return segment.split(',')[0]
        return None  # Return None if keyword isn't found in the row
    
    # Apply the helper to each row and create a new column with results
    df['extracted_text'] = df['temp_segments'].apply(find_and_extract)
    
    # Optional: Remove the temporary segments column to clean up the DataFrame
    df = df.drop(columns='temp_segments')
    
    return df

Step 3: Use the Function #

Let's test this with a keyword like slipper:

# Extract text for the keyword "slipper"
result_df = extract_target_text(df, "slipper")

# Print the relevant columns to verify
print(result_df[['id', 'extracted_text']])

Output:

id       extracted_text
0  100  Sales Package=6 Pair slipper
1  200                     None

Key Notes & Adjustments #

• Multiple Matches: If a row has multiple segments containing the keyword, this function returns the first match. To get all matches, modify the helper function to return a list of extracted texts instead of a single value.
• Robust Formatting: If your product_details has inconsistent formatting (like extra spaces around brackets or commas), replace the strip('[]') with str.replace(r'^\s*\[\s*|\s*\]\s*$', '') to handle whitespace better.
• Case Sensitivity: The current check is case-sensitive. To make it case-insensitive, change if target_keyword in segment: to if target_keyword.lower() in segment.lower():.

内容的提问来源于stack exchange，提问作者outlier