Sometimes we do not just want to view the data. We want to change or transform it.

• apply(): The apply() method applies a function to each value of the Series.
  For example, suppose we want to add 5 marks to every student's marks:

• marks = pd.Series([70, 80, 90])

• marks.apply(lambda x: x + 5)

output:

0    75

1    85

2    95

dtype: int64

Here, the function is applied to each value one by one.

• map(): The map() method is used to map or transform values using a function, dictionary, or another Series.
  For example:

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

marks.map({

    90: "A",

    80: "B",

    70: "C"

})

output:

0    A

1    B

2    C

dtype: object

Here, each mark is mapped to a grade according to the given dictionary.

• replace(): The replace() method replaces specific values with other values.
  For example:

marks = pd.Series([80, 0, 90, 0])

marks.replace(0, 50)

output:

0    80

1    50

2    90

3    50

dtype: int64

Here, every 0 in the Series is replaced with 50.

• astype(): The astype() method converts the Series into another data type.
  For example:

marks = pd.Series([85, 72, 90])

marks.astype(float)

output:

0    85.0

1    72.0

2    90.0

dtype: float64

Here, the values are converted into the float data type.

• round(): The round() method rounds numerical values to the specified number of decimal places.
  For example:

marks = pd.Series([85.678, 72.456, 90.123])

marks.round(2)

output:

0    85.68

1    72.46

2    90.12

dtype: float64

Here, the values are rounded to 2 decimal places.

• clip(): The clip() method limits values within a specified minimum and maximum range.
  For example:

marks = pd.Series([30, 50, 75, 95])

marks.clip(40, 90)

output:

0    40

1    50

2    75

3    90

dtype: int64

Here:

• Values below 40 become 40

• Values above 90 become 90

• Values between 40 and 90 remain unchanged