Grouping all predictor insight graph tables
In the previous exercise, you constructed a function that calculates the predictor insight graph table for a given variable as follows:
pig_table = create_pig_table(basetable, "target","variable")
If you want to calculate the predictor insight graph table for many variables at once, it is a good idea to store them in a dictionary. You can create a new dictionary using dictionary = {}, add elements with a key using dictionary["key"] = value and retrieve elements using the key print(dictionary["key"]).
This exercise is part of the course
Introduction to Predictive Analytics in Python
Exercise instructions
- Create an empty dictionary
pig_tables. - For each variable, create a predictor insight graph table.
- For each variable, add this predictor insight graph table to the dictionary, with as key the name of the variable.
- Print the predictor insight graph table of
disc_time_since_last_gift.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create the list of variables for our predictor insight graph tables
variables = ["income","gender","disc_mean_gift","disc_time_since_last_gift"]
# Create an empty dictionary
pig_tables = ____
# Loop through the variables
for variable in variables:
# Create a predictor insight graph table
pig_table = ____(basetable, ____, ____)
# Add the table to the dictionary
pig_tables[____] = ____
# Print the predictor insight graph table of the variable "disc_time_since_last_gift"
print(pig_tables["____"])