Every Series contains an index for its values. These methods help us work with that index.

• reset_index(): The reset_index() method resets the existing index and creates a new default integer index.
  For example:

• marks = pd.Series([85, 90, 75], index=["A", "B", "C"])

• marks.reset_index()

output:

  index   0

0     A  85

1     B  90

2     C  75

By default, the old index is retained as a column in the resulting DataFrame.
If we do not want to keep the old index, we can use:

marks.reset_index(drop=True)

output:

0    85

1    90

2    75

dtype: int64

• set_axis(): The set_axis() method changes the labels of the Series index.
  For example:

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

marks.set_axis(["A", "B", "C"])

output:

A    85

B    90

C    75

dtype: int64

Here, the existing index labels are replaced with the given labels.

• reindex(): The reindex() method changes the index according to the labels we provide.
  For example:

marks = pd.Series([85, 90, 75], index=["A", "B", "C"])

marks.reindex(["A", "C", "D"])

output:

A    85.0

C    75.0

D     NaN

dtype: float64

Here, the requested index D does not exist in the original Series, so Pandas places a missing value (NaN) for that index.

• take(): The take() method returns elements based on their positional indexes.
  For example:

marks = pd.Series([85, 90, 75, 95])

marks.take([0, 2])

output:

0    85

2    75

dtype: int64

Here, Pandas returns the values at positions 0 and 2.