Logistic regression is used in cases where there is a binary success or failure condition on the response variable. This will typically be expressed as 0 for fail and 1 for success, and can therefore not be normally distributed.
Logistic Regression in Python 3
In the above example we train the model with the training data, X_train and y_train respectively. Afterwards, we use the model to predict the test values to see how accurate it is.
Logistic Regression in R
In the above example the variable we have assigned our data to is called dataVar, but you could explicitly call that with each of the variables by stating dataVar$indVar1.