Pandas CSV数据清理：修复列值错位问题

Hey, I've got you covered on fixing this column misalignment issue! Here's how you can implement the data cleaning logic you need, building on the CSV loading code you already have:

Step-by-Step Solution #

First, here's the complete code that will handle the correction:

import pandas as pd

# Your existing data loading code
df = pd.read_csv('data.csv', dtype={
    'date': str,
    'tap': str,
    'time': str,
    'count': float
})

# Create a mask to identify rows with invalid 'tap' values
invalid_tap_mask = ~df['tap'].isin(['on', 'off'])

# Correct the column values for these misaligned rows
df.loc[invalid_tap_mask, 'count'] = df.loc[invalid_tap_mask, 'time']
df.loc[invalid_tap_mask, 'time'] = df.loc[invalid_tap_mask, 'tap']
df.loc[invalid_tap_mask, 'tap'] = 'N/A'

How This Works: #

• Mask Creation: The invalid_tap_mask variable is a boolean series where True marks rows where the 'tap' value is neither 'on' nor 'off' (the ~ operator reverses the result of isin()).
• Column Correction: Using df.loc[mask, column], we target only the problematic rows and shift values appropriately:
  • Move the original 'time' value into the 'count' column
  • Move the original 'tap' value into the 'time' column
  • Set the 'tap' column to 'N/A' to indicate the missing value

Verify the Fix #

To double-check that everything worked as expected, you can print out the modified rows with:

print(df[invalid_tap_mask])

内容的提问来源于stack exchange，提问作者Ken