如何用Python基于关键词为表格列文本分类？

Here's a straightforward, pandas-based approach to replicate that Excel keyword categorization logic in Python—perfect for processing your table data:

Step 1: Define Your Keyword-Category Mapping #

First, set up a dictionary that links each category to its associated keywords. You can easily expand this as your rules grow:

category_keyword_map = {
    "AAAA": ["keyword1", "keyword2", "keyword3"],
    "BBBB": ["keyword4"]
}

Step 2: Create a Categorization Helper Function #

This function checks a single description against your keyword rules and returns the matching category. It handles case insensitivity by default (adjustable if needed):

def get_category(description, keyword_map):
    # Normalize text to lowercase for consistent matching
    desc_lower = description.lower()
    
    # Check each category in order (priority goes to the first matching category)
    for category, keywords in keyword_map.items():
        if any(keyword.lower() in desc_lower for keyword in keywords):
            return category
    
    # Fallback if no keywords are found
    return "Uncategorized"

Step 3: Apply to Your DataFrame #

Use pandas to load your data (from Excel, CSV, etc.) and apply the function to generate the category column:

import pandas as pd

# Sample input data (replace with your actual data source)
data = {
    "Description": [
        "this free text keyword1 is done",
        "free sample2 keyword4 keyword3",
        "random text with no matching keywords"
    ]
}

df = pd.DataFrame(data)

# Add the category column
df["category"] = df["Description"].apply(lambda x: get_category(x, category_keyword_map))

# View the result
print(df)

Output: #

Description     category
0       this free text keyword1 is done        AAAA
1        free sample2 keyword4 keyword3        BBBB
2  random text with no matching keywords  Uncategorized

Customization Tips: #

• Exact Word Matching: If you want to match whole words only (not substrings), modify the check to use regex word boundaries:

  import re
  if any(re.search(rf"\b{re.escape(keyword.lower())}\b", desc_lower) for keyword in keywords):
  

• Adjust Priority: The function returns the first category that has a matching keyword. Reorder the dictionary entries to change which category takes precedence when multiple keywords overlap.
• Load/Save Excel Files: Use pd.read_excel("your_file.xlsx") to load your data and df.to_excel("categorized_file.xlsx", index=False) to save the result back to Excel.

内容的提问来源于stack exchange，提问作者Mouad