Python Tutorial: Dealing with categorical features

Want to learn more? Take the full course at https://learn.datacamp.com/courses/feature-engineering-for-machine-learning-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Categorical variables are used to represent groups that are qualitative in nature. Some examples are colors, such as blue, red, black etc. or country of birth, such as Ireland, England or USA. While these can easily be understood by a human, you will need to encode categorical features as numeric values to use them in your machine learning models. As an example, here is a table which consists of the country of residence of different respondents in the Stackoverflow survey. To get from qualitative inputs to quantitative features, one may naively think that assigning every category in a column a number would suffice, for example India could be 1, USA 2 etc. But these categories are unordered, so assigning this order may greatly penalize the effectiveness of your model. Thus, you cannot allocate arbitrary numbers to each category as that would imply some form of ordering in the categories. Instead, values can be encoded by creating additional binary features corresponding to whether each value was picked or not as shown in the table on the right. In doing so your model can leverage the information of what country is given, without inferring any order between the different options. There are two main approaches when representing categorical columns in this way, one hot encoding and dummy encoding. These are very similar and often confused. In fact, by default, pandas performs one-hot encoding when you use the get_dummies() function. One-hot encoding converts n categories into n features as shown here. You can use the get_dummies() function to one-hot encode columns. The function takes a DataFrame and a list of categorical columns you want converted into one hot encoded columns, and returns an updated DataFrame with these col