How do you handle missing values or outliers in a pandas DataFrame?

Handling missing values in pandas involves methods like filling with a mean, median, or using forward/backward fill. Outliers can be detected using statistical methods such as Z-score or IQR, and either removed or replaced. These techniques help clean data for accurate analysis.

Missing values can be handled by filling or dropping them, while outliers are usually detected via IQR or Z-score methods and then capped, removed, or replaced. These preprocessing steps improve data quality before modeling.

Code

import pandas as pd
import numpy as np

# Handling missing values: fill with mean
df['col'] = df['col'].fillna(df['col'].mean())

# Detect outliers using IQR
Q1 = df['col'].quantile(0.25)
Q3 = df['col'].quantile(0.75)
IQR = Q3 - Q1

# Remove outliers
df = df[(df['col'] >= Q1 - 1.5*IQR) & (df['col'] <= Q3 + 1.5*IQR)]