Handling Missing and Duplicate Data

Real-world datasets are rarely perfect.

You'll often encounter missing values or duplicate rows that can skew your analysis.

Pandas provides powerful tools to identify and handle these issues efficiently.

Missing values are usually represented as NaN (Not a Number) in pandas.

You can handle them in several ways:

• Detect missing values using .isnull() or .notnull()
• Drop missing data with .dropna()
• Fill missing data using .fillna() (e.g., fill with a default value or forward-fill based on previous values)

Properly handling missing values is crucial before performing calculations like mean, sum, or correlation; otherwise, your results may be skewed.

Duplicate rows can occur due to data entry errors or when merging datasets.

• Use .duplicated() to flag duplicates
• Use .drop_duplicates() to remove them

Always check if duplicates make sense in the context of your data. Not all repetition is bad.