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Pandas – Rename Categories in Category Column

In this tutorial, we will look at how to rename the categories in a Pandas category type column (or series) with the help of examples.

How to rename categories in Pandas?

rename categories in pandas category type column

You can use the Pandas rename_categories() function to rename the categories in a category type column in Pandas. The following is the syntax –

# rename categories
df["Col"] = df["Col"].cat.rename_categories(list_of_new_categories)

Pass the new categories list as an argument to the function. You can also pass a dictionary mapping of old category names to new category names.

Examples

Let’s look at some examples of renaming categories for a category type field in Pandas. First, let’s create a Pandas dataframe with a category type column.

import pandas as pd

# create a dataframe
df = pd.DataFrame({
        "Name": ["Tim", "Sarah", "Hasan", "Jyoti", "Jack"],
        "Gender": ["M", "F", "M", "F", "M"]
})
# change to category dtype
df["Gender"] = df["Gender"].astype("category")
# display the "Gender" column
print(df["Gender"])

Output:

0    M
1    F
2    M
3    F
4    M
Name: Gender, dtype: category
Categories (2, object): ['F', 'M']

We now have a dataframe containing information on names and their respective genders. Note that the “Gender” column is of category type.

Let’s now rename the categories in the “Gender” column, “F” to “Female” and “M” to “Male” using the rename_categories() function.

# rename categories
df["Gender"] = df["Gender"].cat.rename_categories(["Female", "Male"])
# display the "Gender" column
print(df["Gender"])

Output:

0      Male
1    Female
2      Male
3    Female
4      Male
Name: Gender, dtype: category
Categories (2, object): ['Female', 'Male']

You can see that the category values in the above series have been updated. Note that it’s important to pass new category names corresponding to the order that is present in the original category field. For example, the categories in the original field are shown as ["F", "M"] and thus we pass the new category names having the same relative order ["Female", "Male"].

You can also pass a dictionary mapping of old category names to new category names to the rename_categories() function. Let’s now change the names of the categories back to “F” and “M” using this method.

# rename categories
df["Gender"] = df["Gender"].cat.rename_categories({"Female": "F", "Male": "M"})
# display the "Gender" column
print(df["Gender"])

Output:

0    M
1    F
2    M
3    F
4    M
Name: Gender, dtype: category
Categories (2, object): ['F', 'M']

You can see that we get the original category names, “F” for “Female” and “M” and “Male”.

Alternatively, you can also rename the categories by assigning new values to the categories property of the category type series.

# rename categories
df["Gender"].cat.categories = ["Female", "Male"]
# display the "Gender" column
print(df["Gender"])

Output:

0      Male
1    Female
2      Male
3    Female
4      Male
Name: Gender, dtype: category
Categories (2, object): ['Female', 'Male']

You can see that we get the same result that we got with the rename_categories() function.

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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.