In this tutorial, we will look at how to set a category order in a Pandas category type column with the help of some examples.

## How to set category order in Pandas?

You can use the Pandas categorical `set_categories()`

function to set and order categories in a category type column. Use the `.cat`

accessor to apply this function on a Pandas column. The following is the syntax –

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# set and order categories df["Col"] = df["Col"].cat.set_categories(category_order_list, ordered=True)

Pass the categories in the order you want as a list and `ordered=True`

as arguments to make the column an ordered categorical column with the given category order.

## Examples

Let’s look at some examples of setting the category order for a category type column in Pandas. First, we will create a sample dataframe that we will be using throughout this tutorial.

import pandas as pd # create a dataframe df = pd.DataFrame({ "Name": ["Tim", "Sarah", "Hasan", "Jyoti", "Jack"], "Year": ["Junior", "Senior", "Freshman", "Junior", "Freshman"], "Shirt Size": ["S", "M", "L", "S", "L"] }) # change to category dtype df["Year"] = df["Year"].astype("category") df["Shirt Size"] = df["Shirt Size"].astype("category") # display the dataframe print(df)

Output:

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Name Year Shirt Size 0 Tim Junior S 1 Sarah Senior M 2 Hasan Freshman L 3 Jyoti Junior S 4 Jack Freshman L

We now have a dataframe containing the name, year, and the respective t-shirt size of some students in a university. Note that the “Year” and the “Shirt Size” column is of category type.

Let’s print out the “Year” column.

# display the column print(df["Year"])

Output:

0 Junior 1 Senior 2 Freshman 3 Junior 4 Freshman Name: Year, dtype: category Categories (3, object): ['Freshman', 'Junior', 'Senior']

You can see that the “Year” column is of `category`

dtype. Note that the category values in this column are not ordered. That is, for example, the information that “Senior” is greater than “Junior” is not encoded in the values.

### Set Category Order of a Category type column in Pandas

You can convert an unordered categorical type column to an ordered categorical column. Let’s convert the “Year” column to an ordered category column with category order “Freshman” < “Sophomore” < “Junior” < “Senior”. For this, we will use the Pandas categorical `set_categories()`

function.

# set and order categories df["Year"] = df["Year"].cat.set_categories(["Freshman", "Sophomore", "Junior", "Senior"], ordered=True) # display the column print(df["Year"])

Output:

0 Junior 1 Senior 2 Freshman 3 Junior 4 Freshman Name: Year, dtype: category Categories (4, object): ['Freshman' < 'Sophomore' < 'Junior' < 'Senior']

You can see that the categories are now ordered. Note that some category values are not present in the data but the order information is still encoded in the category field.

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Let’s look at another example. First, let’s print out the “Shirt Size” column.

# display the column print(df["Shirt Size"])

Output:

0 S 1 M 2 L 3 S 4 L Name: Shirt Size, dtype: category Categories (3, object): ['L', 'M', 'S']

You can see that this column is also of `catgory`

type but is currently unordered. Let’s set the category order for the “Shirt Size” column to “S” < “M” < “L”.

# set and order categories df["Shirt Size"] = df["Shirt Size"].cat.set_categories(["S", "M", "L"], ordered=True) # display the column print(df["Shirt Size"])

Output:

0 S 1 M 2 L 3 S 4 L Name: Shirt Size, dtype: category Categories (3, object): ['S' < 'M' < 'L']

The categories in the “Shirt Size” column are now ordered.

If, on the other hand, you want to change the order or categories in an ordered categorical column, use the Pandas categorical `reorder_categories()`

function.

You might also be interested in –

- Add New Categories to a Category Column in Pandas
- Pandas – Remove Categories From a Categorical Column
- Get List of Categories in Pandas Category Column

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