如何基于其他列条件使用fillna()填充Credit_History缺失值

Got it, let's tackle this missing value filling task step by step. I’ll assume you’re working with a pandas DataFrame named df (swap this with your actual DataFrame name if needed). Here’s how to implement each of your specified scenarios using conditional logic combined with direct value assignment:

Scenario 1: Fill with 0 when Self_Employed = 'Y' and Married = 'N' #

We’ll target rows where Credit_History is NaN and the two conditions are satisfied:

# Locate matching rows and fill Credit_History with 0
df.loc[(df['Credit_History'].isna()) & (df['Self_Employed'] == 'Y') & (df['Married'] == 'N'), 'Credit_History'] = 0

Scenario 2: Fill with 1 when Self_Employed = 'N' and ApplicantIncome > 20000 #

Next, handle the second set of conditions for any remaining NaN values:

# Locate rows meeting the criteria and fill with 1
df.loc[(df['Credit_History'].isna()) & (df['Self_Employed'] == 'N') & (df['ApplicantIncome'] > 20000), 'Credit_History'] = 1

Scenario 3 (Partial): Handle Self_Employed = 'Y', Married = 'N' and ApplicantIncome > 2000 #

It looks like this scenario was cut off, but let’s use a placeholder example (replace [your_target_value] with the actual value you want to fill):

# Insert your desired fill value in place of [your_target_value]
df.loc[(df['Credit_History'].isna()) & (df['Self_Employed'] == 'Y') & (df['Married'] == 'N') & (df['ApplicantIncome'] > 2000), 'Credit_History'] = [your_target_value]

Quick Notes: #

• Run these operations sequentially if there’s any chance of overlapping conditions (though your scenarios seem mutually exclusive, this ensures no overwriting of already filled values).
• If you still have leftover NaN values after applying these rules, you can fall back to the statistical insights you already calculated—like filling with the overall median of Credit_History or using other column-specific patterns you identified.

内容的提问来源于stack exchange，提问作者uharsha33