One hot encoding is a technique used to represent categorical variables as numerical data in a way that can be used in machine learning algorithms. It involves creating a binary column for each possible category of a categorical variable and assigning a 1 or 0 to each column depending on whether or not the observation falls into that category. In Pandas, there are several methods to create one-hot encodings (also called dummy variables) of a categorical variable. Here are a few of them:
1. Using the "get_dummies()" :
The get_dummies() function is the simplest way to create one-hot encodings in pandas. It takes a categorical variable as input and converts it into a set of binary vectors, one for each unique value in the variable. Each binary vector is a row in a new dataframe, with a 1 in the column corresponding to the value of the original categorical variable, and 0s in all other columns.
Example:
2. Using "sklearn.preprocessing.OneHotEncoder" :
The Scikit-Learn OneHotEncoder class can also be used to create one-hot encodings for categorical variables in pandas. This method provides more flexibility than the pd.get_dummies() function, as it allows for the specification of various parameters, such as the handling of unknown categories and the output format.
Example:
3. Using "patsy.dmatrix()" :
Another way to create one-hot encodings for categorical variables in pandas is to use the patsy.dmatrix() function. This function is part of the patsy package and provides a more flexible way to create one-hot encodings, as it allows for the specification of various transformations and interactions between variables.