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# Converting categorical type to int in Pandas DataFrame

schedule Aug 12, 2023
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local_offer
PythonPandas
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Consider the following DataFrame:

``` df = pd.DataFrame({"group": pd.Series(["A","B","A"], dtype="category"), "name": pd.Series(["alex","bob","cathy"], dtype="string")}) group name0 A alex1 B bob2 A cathy ```

Here, column `group` is of type `category`.

# Solution

To change the column type from `category` to `int`, use the `factorize(~)` method:

``` df["group"] = pd.factorize(df["group"])[0]df group name0 0 alex1 1 bob2 0 cathy ```

# Explanation

Here, `factorize(~)` returns a tuple of size two where the first element is the converted integers:

``` pd.factorize(df["group"]) (array([0, 1, 0]), CategoricalIndex(['A', 'B'], categories=['A', 'B'], ordered=False, dtype='category')) ```

Notice how values of the same category gets mapped to the same integer.

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