Python3 文件内容拆分与个人信息分类及性别判定需求

Got it, let's break down how to tackle this task step by step. I’ve worked on similar structured data extraction projects before, so here’s a practical, hands-on approach you can follow:

First, you’ll need to load the file content and split each entry into its individual components. Assuming your TXT file uses a consistent delimiter (like commas, tabs, or pipes) to separate fields, Python makes this straightforward.

# Open and read the text file
with open('character_info.txt', 'r', encoding='utf-8') as file:
    # Skip the header line if your file has one (adjust as needed)
    header = file.readline()
    # Store all lines as a list of entries
    entries = [line.strip() for line in file if line.strip()]

Since you need to preserve the original order, create dedicated lists for each data category. Append each field to its corresponding list as you iterate through the entries.

# Initialize ordered lists for each data type
last_names = []
first_names = []
ssns = []
addresses = []

# Process each entry to populate the lists
for entry in entries:
    # Split the entry using your file's delimiter (replace ',' with your actual delimiter)
    ln, fn, ssn, addr = entry.split(',')
    last_names.append(ln)
    first_names.append(fn)
    ssns.append(ssn)
    addresses.append(addr)

To verify the data is preserved correctly, loop through the lists using their shared index (since all lists will have the same length) and print each character’s full set of info.

# Print classified info in the original order
for idx in range(len(first_names)):
    print(f"Character {idx + 1}:")
    print(f"  Full Name: {first_names[idx]} {last_names[idx]}")
    print(f"  SSN: {ssns[idx]}")
    print(f"  Address: {addresses[idx]}")
    print("-" * 30)

For gender classification, the most reliable method for fictional characters is to use first name patterns. You can either use a pre-built library or maintain a custom dictionary of name-gender mappings (great for unique fictional names).

Option 1: Use a Name-Guessing Library #

The gender-guesser library works well for common names. Install it first with pip install gender-guesser, then use this code:

import gender_guesser.detector as gender

# Initialize the gender detector
gd = gender.Detector()

# Guess gender for each character
for idx in range(len(first_names)):
    first_name = first_names[idx]
    gender_result = gd.get_gender(first_name)
    # Format the result for readability
    gender_label = {
        'male': 'Male',
        'female': 'Female',
        'andy': 'Ambiguous',
        'unknown': 'Unknown'
    }.get(gender_result, 'Unknown')
    print(f"{first_names[idx]} {last_names[idx]} | Gender: {gender_label}")

Option 2: Custom Name Dictionary (For Unique Fictional Names) #

If your characters have made-up names, create a custom dictionary to map names directly to genders:

# Custom gender mapping for fictional names
fictional_gender_map = {
    "Frodo": "Male",
    "Arwen": "Female",
    "Gimli": "Male"
}

# Assign gender using the custom map
for idx in range(len(first_names)):
    first_name = first_names[idx]
    gender = fictional_gender_map.get(first_name, "Unknown")
    print(f"{first_names[idx]} {last_names[idx]} | Gender: {gender}")

• If your TXT file has inconsistent formatting (e.g., line breaks between fields), add error handling with try-except blocks to avoid crashes.
• For large datasets, consider using pandas DataFrames instead of lists—they’re easier to manage for sorting, filtering, and exporting later.

内容的提问来源于stack exchange，提问作者Matt