HANDLING MISSING VALUES [ A Comprehensive Guide With Pandas]

In data analysis, handling missing values is crucial for accurate and reliable results. Here are three key methods to address missing values:

Drop Missing Values: Remove rows or columns with missing values using the dropna() method. This is useful when the amount of missing data is minimal and its removal won’t significantly impact the dataset.

Fill Missing Values: Use the fillna() method to replace missing values with specific values:

Mean: Replace missing values with the average of the column (suitable for numerical data).
Median: Replace missing values with the median of the column (robust to outliers).
Mode: Replace missing values with the most frequent value (useful for categorical data).
Impute Missing Values: Use custom logic to replace missing values

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