Real-world datasets are rarely perfect. Common issues include:

• Missing values
• Duplicate records
• Inconsistent formats
• Incorrect entries

Pandas provides tools such as:

df.isnull()
df.dropna()
df.fillna()

Data cleaning is often the most time-consuming stage of a project. Clean data leads to more accurate analysis and reliable conclusions.