Introduction to Data Cleaning in Data Science in Data Science with Python
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
