We use pandas series objects for various data processing tasks in python. In this article, we will discuss how to rename the index in a pandas series.

## Rename Index in a Pandas Series Using the index Attribute

When a series is created, the name of the index is empty. To rename the index of the series, you can use the `name`

attribute of the series index object. You can assign the new index name to the `name`

attribute of the index object to rename the series index as shown below.

```
import pandas as pd
import numpy as np
letters=["a","b","c","ab","abc","abcd","bc","d"]
numbers=[3,23,11,14,16,2,45,65]
series=pd.Series(letters,index=numbers)
series.index.name="Numbers"
print("The series is:")
print(series)
```

Output:

```
The series is:
Numbers
3 a
23 b
11 c
14 ab
16 abc
2 abcd
45 bc
65 d
dtype: object
```

In the above example, We have first created a pandas series using the `Series()`

constructor. Then, we assigned the string `"Numbers"`

to the` index.name `

attribute of the pandas series. Hence, the series index is renamed to `"Numbers"`

.

To rename the index of a series, you can also use the `rename_axis() `

method.

## Rename the Index of a Series Using the rename_axis() Method

The `rename_axis()`

method has the following syntax.

`Series.rename_axis(mapper=_NoDefault.no_default, *, inplace=False, **kwargs)`

Here,

- The
`mapper`

parameter takes the new name of the index as its input argument. - By default, the
`rename_axis()`

method returns a new series object. To modify the original series on which the`rename_axis()`

method is invoked, you can set the`inplace`

parameter to`True`

.

After execution, the `rename_axis()`

method returns a new series with renamed index as shown below.

```
import pandas as pd
import numpy as np
letters=["a","b","c","ab","abc","abcd","bc","d"]
numbers=[3,23,11,14,16,2,45,65]
series=pd.Series(letters,index=numbers)
series=series.rename_axis("Numbers")
print("The series is:")
print(series)
```

Output:

```
The series is:
Numbers
3 a
23 b
11 c
14 ab
16 abc
2 abcd
45 bc
65 d
dtype: object
```

In the above example, we first created a series. Then, we used the `rename_axis()`

method to rename the index column of the series. Here, the `rename_axis() `

method returns a new series instead of modifying the original series.

Suggested Reading: If you are into machine learning, you can read this MLFlow tutorial with code examples. You might also like this article on clustering mixed data types in Python.

## Rename Index in a Series Inplace in Python

You can also modify the original series instead of creating a new series object after renaming the index. For this, you can set the `inplace`

parameter to True in the `rename_axis()`

method as shown below.

```
import pandas as pd
import numpy as np
letters=["a","b","c","ab","abc","abcd","bc","d"]
numbers=[3,23,11,14,16,2,45,65]
series=pd.Series(letters,index=numbers)
series.rename_axis("Numbers",inplace=True)
print("The series is:")
print(series)
```

Output:

```
The series is:
Numbers
3 a
23 b
11 c
14 ab
16 abc
2 abcd
45 bc
65 d
dtype: object
```

In this example, we have set the `inplace`

parameter to True in the `rename_axis()`

parameter. Hence, the index of the original series has been renamed instead of creating a new series.

## Conclusion

In this article, we have discussed how to rename the index in a pandas series using the index attribute and the renam_axis() method. To know more about the pandas module, you can read this article on how to sort a pandas dataframe. You might also like this article on how to drop columns from a pandas dataframe.

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