How to Convert Categorical Data to Numerical Indices in Pandas?

Pandas: Convert Categories to Numerical Indices

In Pandas, you can encounter situations where you need to convert categorical data, such as countries, into numerical indices. While pd.get_dummies can convert categories into one-hot encodings, it may not always be the most efficient solution. Here's a step-by-step guide on how to convert categories to numerical indices:

Step 1: Categorize the Column

First, change the type of the column to categorical:

<code class="python">df.cc = pd.Categorical(df.cc)</code>

This converts the countries column into a categorical column, denoted by pd.Categorical(column_name).

Step 2: Create a New Column for Codes

Next, create a new column to store the numerical indices:

<code class="python">df['code'] = df.cc.codes</code>

The codes attribute of the categorical column assigns each category a unique integer index.

Example:

Consider the following DataFrame:

   cc  temp
0  US  37.0
1  CA  12.0
2  US  35.0
3  AU  20.0

After following the steps above, you will have a new DataFrame:

   cc  temp  code
0  US  37.0     2
1  CA  12.0     1
2  US  35.0     2
3  AU  20.0     0

Additional Options:

• Get Codes Without Modifying DataFrame: df.cc.astype('category').codes
• Create Categorical Column as Index: df2 = pd.DataFrame(df.temp); df2.index = pd.CategoricalIndex(df.cc)

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