Calculating D in a Loop

Is Georgia's Index of Dissimilarity of of 0.544 high or low? Let's compare it to Illinois (FIPS = 17), home of Chicago.

In this exercise we will use a loop to calculate $D$ for all states, then compare Georgia and Illinois.

Remember that the formula for the Index of Dissimilarity is:

$$D = \frac{1}{2}\sum{\left\lvert \frac{a}{A} - \frac{b}{B} \right\rvert}$$

pandas has been imported using the usual alias, and the tracts DataFrame with population columns "white" and "black" has been loaded. The variables w and b have been defined with the column names "white" and "black".

This exercise is part of the course

Analyzing US Census Data in Python

View Course

Exercise instructions

Use the unique() method on the "state" column to create a list of state FIPS codes.
Use a for-loop to store each element of states (that is, each FIPS code) in a variable named state.
Filter the tracts DataFrame on each value of state, and assign to tmp.
Calculate $D$ for each state by applying the formula to tmp, and store the result in the dictionary state_D.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Get list of state FIPS Codes
states = list(tracts["state"].____)

state_D = {}  # Initialize dictionary as collector
for state in ____:
    # Filter by state
    tmp = ____
    
    # Add Index of Dissimilarity to Dictionary
    state_D[state] = 0.5 * sum(____)

# Print D for Georgia (FIPS = 13) and Illinois (FIPS = 17)    
print("Georgia D =", round(state_D["13"], 3))
print("Illinois D =", round(state_D["17"], 3))

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