Home Values in California

California's major cities have been in the news for skyrocketing housing prices. How has the median home value changed over the last several years? In this exercise you will use a loop to request variable B25077_001E from seven ACS years and plot the value over time.

The predicates dictionary has been created, and is printed to the console. Notice that state:06 sets the GEOID for California. pandas and seaborn have been imported using the usual aliases. HOST and dataset have been defined, and dfs is an empty list that has been initialized as a collector for the requested DataFrames.

This exercise is part of the course

Analyzing US Census Data in Python

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Exercise instructions

Construct a range object with integers from 2011 to 2017
Create a column named "year", with its value set to the current value of the year variable
Set the median_home_value column data type to int
Create a lineplot of home values. Set the first parameter (x) to "year", set second parameter (y) to "median_home_value"

Hands-on interactive exercise

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

# Loop over years 2011 to 2017
for year in ____:
    base_url = "/".join([HOST, str(year), dataset])
    r = requests.get(base_url, params=predicates)
    df = pd.DataFrame(columns=col_names, data=r.json()[1:])    
    # Add column to df to hold year value, append df to collector dfs
    ____
    dfs.append(df)

# Concatenate all DataFrames, fix column type
states = pd.concat(dfs, ignore_index=True)
states["median_home_value"] = ____

sns.lineplot(____, ____, data = states)
plt.show()

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