# Turn categorical variables into numerics

It's essential that we work with numbers in data science regardless the * variable* content.

In case we have some data including categorical columns, it's easy to convert the corresponding categorical columns into numerical ones.

If we use * pandas* to fetch the data, that's pretty easy to overcome such case.

In the screenshot below, consider * Source Port* as the column including categorical variables. There are 3 variables in this column (80, 443 and 0).

We need to split them so that no variable outweights the others.

We'll use the one-hot method. In this way, we'll split these categorical variables into N columns each composed of 1 and 0. The following *python* line accomplishes the requirements.

```
pandas.get_dummies(data, columns=['column_name'])
```

The following screenshot prints the output data where the Source Port column is separated into 3.

If the corresponding line includes 80 as a values, then it's marked as 1 in the related column. Otherwise 0.

Using one-hot encoding method (of course applying it to all columns applicable), it becomes possible to feed the data into neural networks