How to Handle Missing Values in a Pandas DataFrame?

[soni21]

I'm currently working on a data science project using Python's Pandas library, and I've encountered an issue with missing values in my DataFrame. My dataset contains various columns, and some of them have missing values represented as NaN.

Here's a snippet of my DataFrame:

import pandas as pd

# Sample DataFrame with missing values
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 28, None, 32, 22],
    'Score': [85, None, 78, 92, None],
    'Salary': [50000, 60000, 55000, None, 48000]
}

df = pd.DataFrame(data)

I want to handle these missing values effectively before proceeding with my analysis. I'm considering a few options like removing rows with NaN, imputing the missing values with the mean, or using interpolation.
I've been seeking assistance from the scalers data science project website, but I've been unable to find the answer. I would appreciate some advice on how to handle missing values in my DataFrame. I would also welcome some code samples that show how the selected method is implemented. I appreciate your assistance in advance!

 

[mintjulep]

Using the mean or interpolation is "making up data". Probably not the best approach.

Deleting records with missing data is perhaps ok, if you have sufficient other records for the same demographic.