Now suppose our marks Series contains missing values.

marks = pd.Series([85, 72, 90, None, 88])

Pandas provides several methods to handle these missing values.

• dropna(): The dropna() method removes missing values from the Series.
  For example:

marks.dropna()

output:

0    85.0

1    72.0

2    90.0

4    88.0

dtype: float64

The missing value is removed, while the remaining values are returned.

• fillna(): The fillna() method is used to replace missing values with a specified value.
  For example:

marks.fillna(0)

output:

0    85.0

1    72.0

2    90.0

3     0.0

4    88.0

dtype: float64

Here, the missing value is replaced with 0.
We can also replace the missing value with something more meaningful.
For example:

marks.fillna(marks.mean())

Here, the missing mark is replaced with the average of the available marks.

• interpolate(): The interpolate() method fills missing values by estimating a value based on surrounding data.
  For example:

marks = pd.Series([80, 85, None, 95])

marks.interpolate()

output:

0    80.0

1    85.0

2    90.0

3    95.0

dtype: float64

Here, Pandas estimates the missing value between 85 and 95 as 90.
This method is especially useful when the data follows a sequence or trend.