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# Converting string categories or labels to numeric values in Pandas

schedule Aug 12, 2023
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# Example

Consider the following DataFrame:

``` import pandas as pddf = pd.DataFrame({'name':['alex','bob','cathy','doge'], 'class':['a','b','c','a']})df name class0 alex a1 bob b2 cathy c3 doge a ```

# Solution

To create a new column `class_int` that encodes the labels with numeric integers:

``` df['class_int'] = pd.Categorical(df['class']).codesdf name class class_int0 alex a 01 bob b 12 cathy c 23 doge a 0 ```

# Explanation

Here, we are first converting the class column to Categorical type:

``` pd.Categorical(df['class']) ['a', 'b', 'c', 'a']Categories (3, object): ['a', 'b', 'c'] ```

Under the hood, `pd.Categorical` assigns numeric values starting from 0 to each unique label. These encoded numeric values can be accessed using the `codes` property:

``` pd.Categorical(df['class']).codes array([0, 1, 2, 0], dtype=int8) ```

# Supplementary information

## Converting numeric value back to string label

To convert numeric values back to string label, use the `categories` property:

``` int_label = 1pd.Categorical(df['class']).categories[int_label] 'b' ```

Here, `categories` returns an Index holding unique string categories:

``` pd.Categorical(df['class']).categories Index(['a', 'b', 'c'], dtype='object') ```
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