Data Cleaning with Pandas: A Practical Workflow

The 80/20 Rule of Data Science

Data scientists spend 80% of their time cleaning data. Here's a systematic approach.

Cleaning Checklist

1. Inspect data types and convert as needed
2. Handle missing values strategically
3. Detect and treat outliers
4. Standardize text fields
5. Validate against business rules

import pandas as pd

df = pd.read_csv('data.csv')
df.info()
df.describe()
df.isnull().sum()