Oxana said @ 2025-12-05 08:10:54:

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 snow rider 3d 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

Missing values ​​show up in almost every dataset, so you're definitely on the right track by deciding how to handle them before you move forward. The “best” approach depends on what the data represents and how much information you can afford to lose. Here are a few simple options you can try, along with example code:

1. Remove rows that contain NaN
This works if the dataset is large and the missing values ​​are rare.

df_clean = df.dropna()

2. Fill missing values ​​with the mean, median, or a fixed number
This is common for numeric columns. Mean works when your data has a roughly normal distribution. Median works better when the column has outliers.

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