如何在Python中基于两字符串列条件创建changeInLocation新列

Got it, let's walk through how to create the required changeInLocation column based on your specified rules.

Rule Recap #

• Set to 0: When the pastLocation value exists anywhere in the comma-separated currentLocation list (including exact matches where both values are identical)
• Set to 1: When pastLocation is not present in currentLocation at all

Row-by-Row Validation #

Let's verify each entry to make sure we're applying the rule correctly:

• Row 1: delhi vs bangalore → delhi isn't in bangalore → changeInLocation = 1
• Row 2: delhi vs london,pune,delhi → delhi is explicitly in the list → changeInLocation = 0
• Row 3: mumbai vs mumbai → Exact match, so mumbai is present → changeInLocation = 0
• Row 4: pune vs pune, noida → pune is the first entry in the list → changeInLocation = 0

Final Formatted Output #

pastLocation | currentLocation | changeInLocation
delhi | bangalore | 1
delhi | london,pune,delhi | 0
mumbai | mumbai | 0
pune | pune, noida | 0

Bonus: Python (Pandas) Implementation #

If you're working with this data programmatically, here's a quick pandas snippet to generate the column automatically:

import pandas as pd

# Initialize the data
location_data = {
    "pastLocation": ["delhi", "delhi", "mumbai", "pune"],
    "currentLocation": ["bangalore", "london,pune,delhi", "mumbai", "pune, noida"]
}
df = pd.DataFrame(location_data)

# Function to calculate the change value
def get_location_change(past, current):
    # Split current locations, clean up whitespace, check for presence
    cleaned_current = [loc.strip() for loc in current.split(",")]
    return 0 if past in cleaned_current else 1

# Apply the function to create the new column
df["changeInLocation"] = df.apply(lambda row: get_location_change(row["pastLocation"], row["currentLocation"]), axis=1)

# Output in pipe-separated format
print(df.to_csv(sep="|", index=False))

内容的提问来源于stack exchange，提问作者Mighty