如何在DataFrame列中筛选保留6位数字，其余标记为‘无效’

算力阿峰

2026-5-15

Clean Pandas DataFrame Post Code to Keep Only 6-Digit Values

Hey there! Let's sort out this post code cleaning task for your DataFrame. The goal is to retain only entries that are exactly 6 digits, and mark everything else as Nil (swap to 'Not Valid' if you prefer—just adjust the string in the code). Here are two straightforward ways to get your desired output:

Method 1: Using `numpy.where` with Regex Matching

This approach checks each value against a strict regex pattern and replaces non-matching entries in one clean line:

import pandas as pd
import numpy as np

# Create the cleaned post code column
data['Cleaned_Post_Code'] = np.where(
    # Convert all values to strings first, then check for exactly 6 digits
    data['Post_Code'].astype(str).str.match(r'^\d{6}$'),
    # Keep the original value if it matches the pattern
    data['Post_Code'],
    # Mark non-matching values as 'Nil'
    'Nil'
)

Breakdown of how this works:

astype(str) ensures numeric values (like 8 or 4) are treated as strings, so length checks work correctly.
The regex r'^\d{6}$' enforces strict rules:
- ^ = start of the string, $ = end of the string
- \d{6} = exactly 6 numeric digits
  This rejects any value with extra characters, wrong length, or non-numeric content.
np.where acts as a conditional switch: keep the original value if it matches, else use Nil.

Method 2: Pandas-Native `str.extract` with `fillna`

If you prefer avoiding numpy, this pandas-only approach works equally well:

import pandas as pd

# Extract valid 6-digit values, fill invalid entries with 'Nil'
data['Cleaned_Post_Code'] = (
    data['Post_Code']
    .astype(str)
    .str.extract(r'^(\d{6})$', expand=False)
    .fillna('Nil')
)

Breakdown of how this works:

str.extract(r'^(\d{6})$') pulls out the 6-digit pattern if it exists; non-matching values return NaN.
fillna('Nil') replaces all NaN entries with your desired invalid marker.