Handling Missing Values in Python Pandas

Handling missing values in Pandas can be done using several methods:

1. Drop missing values:
• df.dropna(axis=0, how='any', inplace=True) - This will remove the rows containing any missing value.
• df.dropna(axis=1, how='any', inplace=True) - This will remove the columns containing any missing value.
2. Fill missing values with a constant value:
• df.fillna(value, inplace=True) - This will replace all missing values with the given constant value.
3. Fill missing values with the mean/median/mode of the column:
• df.fillna(df.mean(), inplace=True) - This will replace all missing values with the mean of the column.
• df.fillna(df.median(), inplace=True) - This will replace all missing values with the median of the column.
• df.fillna(df.mode().iloc[0], inplace=True) - This will replace all missing values with the mode of the column.
4. Fill missing values with the value of the previous/next row:
• df.fillna(method='bfill', inplace=True) - This will replace all missing values with the value of the next row.
• df.fillna(method='ffill', inplace=True) - This will replace all missing values with the value of the previous row.

It is important to choose the right method for handling missing values based on the context of the data.