In this tutorial, we will look at how to get the max value in an ordered categorical column (or series) in Pandas.

## How to get the max value in an ordered categorical column?

You can apply the Pandas series `max()`

function to get the max value in a categorical Pandas column (or a series). The following is the syntax –

# s is a categorical type ordered pandas series s.max()

It returns the maximum value in the series based on the categorical order. If the categorical data is not ordered, it will result in a `TypeError`

.

## Examples

Let’s look at some examples of using the above method to get the maximum value in a category type series in Pandas.

### Applying the `max()`

function to an unordered categorical field in Pandas

First, let’s see what happens if we apply the `max()`

function to an unordered categorical type series in Pandas.

import pandas as pd # create a dataframe df = pd.DataFrame({ "Name": ["Tim", "Sarah", "Hasan", "Jyoti", "Jack"], "Shirt Size": ["M", "S", "M", "M", "L"] }) # change to category dtype df["Shirt Size"] = df["Shirt Size"].astype("category") # get the max value in shirt size print(df["Shirt Size"].max())

Output:

--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Input In [6], in <module> 9 df["Shirt Size"] = df["Shirt Size"].astype("category") 10 # get the max value in shirt size ---> 11 print(df["Shirt Size"].max()) TypeError: Categorical is not ordered for operation max you can use .as_ordered() to change the Categorical to an ordered one

We get a `TypeError`

. Here, we first create a Pandas dataframe with names and shirt sizes of students in a university. We then convert the “Shirt Size” column to `category`

dtype. And finally, we apply the `max()`

function to the “Shirt Size” column.

All categorical fields, by default, are unordered unless specified otherwise. We get a `TypeError`

because there’s no way to compare one categorical value with another for an unordered series and thus computing the max value doesn’t make any sense.

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### Max value in an ordered category field in Pandas

Let’s now modify the “Shirt Size” column to an ordered categorical field with the order of sizes as “S” < “M” < “L”.

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

Output:

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

The “Shirt Size” column is now ordered. Let’s now get the maximum value in the column with the `max()`

function.

# get the max value in shirt size print(df["Shirt Size"].max())

Output:

L

We get “L” as the maximum value.

Let’s look at another example. What if the possible values in a categorical series are “S”, “M”, and “L” but the data contains only “S” and “M”, what do you think we’d get on applying the `max()`

function?

# create a pandas series shirt_size = pd.Series(["M", "S", "S", "M"], dtype="category") # set and order categories shirt_size = shirt_size.cat.set_categories(["S", "M", "L"], ordered=True) # get the max value in the series print(shirt_size.max())

Output:

M

We get “M” as the maximum value because it is the maximum value that occurs in our data.

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