Sometimes we have more than one Series and want to combine or compare them.
Suppose we have:

marks = pd.Series([80, 90, 70, 85])

grace_marks = pd.Series([5, 0, 10, 5])

• combine(): The combine() method combines two Series by applying a function to corresponding values.
  For example:

marks.combine(grace_marks, max)

output:

0    80

1    90

2    70

3    85

dtype: int64

Here, Pandas compares the corresponding values from both Series and applies the max function.
For example:
max(80, 5) → 80
max(90, 0) → 90
max(70, 10) → 70
max(85, 5) → 85

• combine_first(): The combine_first() method uses values from another Series to fill missing values in the first Series.
  For example:

marks = pd.Series([80, None, 70])

other_marks = pd.Series([85, 90, 75])

marks.combine_first(other_marks)

output:

0    80.0

1    90.0

2    70.0

dtype: float64

Here, the missing value at index 1 is filled using the value from other_marks at the same index.

• compare(): The compare() method compares two Series and shows the values that are different.
  For example:

marks = pd.Series([80, 90, 70])

other_marks = pd.Series([80, 95, 70])

marks.compare(other_marks)

output:

   self  other

1  90.0   95.0

Here, only the value at index 1 is different, so Pandas shows that difference.
This is useful when we want to compare two versions of the same data.

• equals(): The equals() method checks whether two Series contain the same values and have the same index.
  For example:

marks = pd.Series([80, 90, 70])

other_marks = pd.Series([80, 90, 70])

marks.equals(other_marks)

output:

True

If the values or indexes are different, it returns False.