Mobo01 said @ 2023-07-27 13:58:09:

I ran into a missing value issue in my DataFrame while working on a data science project using Python's panda package. I asked for assistance Scalers Data Science Project, but the problem has not yet been resolved. Numerous columns make up my dataset and some of them have missing values referred to as NaN.

Here’s a snippet of my DataFrame:

import pandas as pd 

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

df = pd.FrameDate(date)

I want to effectively manage these missing values before proceeding with my analysis. I'm considering some options like removing lines with NaN, imputing missing values with the mean, or using interpolation.

Could someone guide me on the best approach to handle missing values in my DataFrame? In addition, I would greatly appreciate some code examples to demonstrate the implementation of the chosen method. Thanks in advance for your help

df_clean = df.dropna()

df['Age'] = df['Age'].