Here is the syntax to sort Pandas Series:

(1) Sort Pandas Series in an **ascending order**:

ser.sort_values(ascending=True, inplace=True)

(2) Sort Pandas Series in a **descending order**. In this case, simply set *ascending=False*:

ser.sort_values(ascending=False, inplace=True)

In this guide, you’ll see how to sort Pandas Series that contains:

- String/text values
- Numeric values
- NaN values

## Sort Pandas Series that Contains String/Text Values

To start with a simple example, create Pandas Series that contains string/text values:

import pandas as pd ser = pd.Series(['Emma', 'Maria', 'Bill', 'William', 'Jill', 'Jack']) print(ser)

Run the code in Python, and you’ll get the following *unsorted* Series:

```
0 Emma
1 Maria
2 Bill
3 William
4 Jill
5 Jack
```

Let’s say that you’d like to sort the Series in an *ascending* order.

In that case, you’ll need to add the following syntax to the code:

ser.sort_values(ascending=True, inplace=True)

So the complete code to sort the Series in an **ascending order** is as follows:

import pandas as pd ser = pd.Series(['Emma', 'Maria', 'Bill', 'William', 'Jill', 'Jack']) ser.sort_values(ascending=True, inplace=True) print(ser)

As you can see, the Series is now sorted in an ascending order, where ‘**Bill**‘ is the first name while ‘**William**‘ is the last name:

```
2 Bill
0 Emma
5 Jack
4 Jill
1 Maria
3 William
```

What if you’d like to sort the Series in a *descending* order?

In that case, simply set **ascending=False** as captured below:

import pandas as pd ser = pd.Series(['Emma', 'Maria', 'Bill', 'William', 'Jill', 'Jack']) ser.sort_values(ascending=False, inplace=True) print(ser)

The Series is now sorted in a descending order, where ‘**William**‘ is the first name, and ‘**Bill**‘ is the last name:

```
3 William
1 Maria
4 Jill
5 Jack
0 Emma
2 Bill
```

## Sort Pandas Series that Contains Numeric Values

Let’s now see how to sort a Series that includes *numeric values*.

To start, create the following Pandas Series that contains only numeric values:

import pandas as pd ser = pd.Series([45, 99, 23, 15, 117, 72]) print(ser)

Run the code, and you’ll get an *unsorted* Series with the following numeric data:

```
0 45
1 99
2 23
3 15
4 117
5 72
```

You can then sort the Series in an *ascending *order:

import pandas as pd ser = pd.Series([45, 99, 23, 15, 117, 72]) ser.sort_values(ascending=True, inplace=True) print(ser)

You’ll notice that the Series is now sorted in an ascending order, where 15 is the first number while 117 is the last number:

```
3 15
2 23
0 45
5 72
1 99
4 117
```

Alternatively, you can sort the Series in a *descending* order:

import pandas as pd ser = pd.Series([45, 99, 23, 15, 117, 72]) ser.sort_values(ascending=False, inplace=True) print(ser)

Here is the result, where 117 appears first, and 15 appears last:

```
4 117
1 99
5 72
0 45
2 23
3 15
```

## Sort Pandas Series that Contains NaN Values

For the final scenario, create a Series with NaN values using the Numpy library:

import pandas as pd import numpy as np ser = pd.Series([45, np.nan, np.nan, 99, 23, 15, 117, np.nan, 72]) print(ser)

As you may observe, the unsorted Series now includes 3 NaNs as highlighted in yellow:

```
0 45.0
1 NaN
2 NaN
3 99.0
4 23.0
5 15.0
6 117.0
7 NaN
8 72.0
```

You can then place the NaN values at the top (while the other values will be sorted in an ascending order).

To accomplish this goal, you’ll need to set **na_position=’first’** as follows:

import pandas as pd import numpy as np ser = pd.Series([45, np.nan, np.nan, 99, 23, 15, 117, np.nan, 72]) ser.sort_values(ascending=True, na_position='first', inplace=True) print(ser)

The NaN values will now appear at the top, while the remaining values will be sorted in an ascending order:

```
1 NaN
2 NaN
7 NaN
5 15.0
4 23.0
0 45.0
8 72.0
3 99.0
6 117.0
```

Alternatively, you can place the NaN values at the bottom (while the other values will be sorted in an ascending order):

import pandas as pd import numpy as np ser = pd.Series([45, np.nan, np.nan, 99, 23, 15, 117, np.nan, 72]) ser.sort_values(ascending=True, inplace=True) print(ser)

You’ll now notice that the NaN values appear at the bottom, while the other values are sorted in an ascending order:

```
5 15.0
4 23.0
0 45.0
8 72.0
3 99.0
6 117.0
1 NaN
2 NaN
7 NaN
```

You can read more about sorting a Series by visiting the Pandas Documentation.