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

## How to convert category to int type in Pandas?

You can use the Pandas `astype()`

function to change the data type of a column. To convert a category type column to integer type, apply the `astype()`

function on the column and pass `'int'`

as the argument. The following is the syntax –

# convert pandas column to int type df["Col"] = df["Col"].astype("int")

It changes the type of the column to int. Note that if the individual values in the column cannot be converted to integers, it will result in an error. For example, `1`

and `"1"`

can be converted to integer but `"one"`

cannot be converted.

## Examples

Let’s look at some examples of converting category type column(s) to integer type in Pandas. First, we will create a Pandas dataframe that we’ll be using throughout this tutorial.

import pandas as pd # create a dataframe df = pd.DataFrame({ "Name": ["Tim", "Sarah", "Hasan", "Jyoti", "Jack"], "Class": [1, 2, 2, 3, 1] }) # change to category dtype df["Class"] = df["Class"].astype("category") # display the dataframe print(df)

Output:

Name Class 0 Tim 1 1 Sarah 2 2 Hasan 2 3 Jyoti 3 4 Jack 1

We have a dataframe containing the names and the class (or grades) of some students in a primary school.

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

Output:

0 1 1 2 2 2 3 3 4 1 Name: Class, dtype: category Categories (3, int64): [1, 2, 3]

The “Class” column in the above dataframe is of `category`

type with individual values as integers.

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Let’s convert this column from category to int type using the Pandas `astype()`

function.

# category column to integer df["Class"] = df["Class"].astype("int") # display the "Class" column print(df["Class"])

Output:

0 1 1 2 2 2 3 3 4 1 Name: Class, dtype: int64

You can see that the column is now of `int64`

type.

### Category column with non-numerical values to integer

In the above example, the individual values in the category column were numeric. What if you try to convert a category column with non-numeric values to an `int`

type column? Let’s find out.

# add a new column to store the class in words df["Class2"] = ["First", "Second", "Second", "Third", "First"] # convert column to category type df["Class2"] = df["Class2"].astype("category") # display the column print(df["Class2"])

Output:

0 First 1 Second 2 Second 3 Third 4 First Name: Class2, dtype: category Categories (3, object): ['First', 'Second', 'Third']

We added an additional column to our dataframe. The “Class2” column stores the class values in words. For example, class 1 is stored as “First”, class 2 is stored as “Second” and so on.

Let’s try to convert the “Class2” column to integer type. We’ll use the same syntax as above.

# category column to integer df["Class2"] = df["Class2"].astype("int") # display the "Class2" column print(df["Class2"])

Output:

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Input In [8], in <module> 1 # category column to integer ----> 2 df["Class2"] = df["Class2"].astype("int") 3 # display the "Class2" column 4 print(df["Class2"]) ... ValueError: Cannot cast object dtype to int64

We get a `ValueError`

because the values in the column cannot be converted to integers.

You might also be interested in –

- Pandas – Rename Categories in Category Column
- Pandas – Convert Category Type Column to String
- Count Frequency of Category Values in Pandas
- Pandas – Check If Category is Ordered

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