Introduction to Data Cleaning in Data Science
What is Data Cleaning?
Data cleaning is the process of detecting and correcting errors, missing values, and inconsistencies in datasets. In real-world projects, raw data is rarely perfect and often contains duplicate records, missing values, and incorrect formats.
Why Data Cleaning is Important
- Improves data quality
- Ensures accurate analysis
- Prepares datasets for machine learning
Python Example
python
import pandas as pd
df = pd.read_csv("data.csv")
print(df.info())
Next Tutorial: Handling Missing Data
