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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

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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["____"])
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