EDA statistics
As mentioned in the slides, you'll work with New York City taxi fare prediction data. You'll start with finding some basic statistics about the data. Then you'll move forward to plot some dependencies and generate hypotheses on them.
The train and test DataFrames are already available in your workspace.
This exercise is part of the course
Winning a Kaggle Competition in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Shapes of train and test data
print('Train shape:', ____.____)
print('Test shape:', ____.____)
# Train head()
print(____.____())