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Pandas – Get Max Value in Ordered Categorical Column

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?

max value in ordered categorical column in pandas

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.

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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Author

  • Piyush

    Piyush is a data scientist passionate about using data to understand things better and make informed decisions. In the past, he's worked as a Data Scientist for ZS and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.