Plotting Margins of Error over Time

In this exercise you will inspect changing home prices in Philadelphia, PA, using a line plot with error bars. The data come from ACS 1-year sample Table B25077. The estimates and margin of error for each year from 2011 to 2017 have been downloaded and concatenated into a pandas DataFrame named philly. ACS table variables for the estimate and margin of error have been renamed to median_home_value and median_home_value_moe, respectively. (See the DataFrame in the console.)

pandas has been imported as pd.

This exercise is part of the course

Analyzing US Census Data in Python

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Exercise instructions

Import matplotlib.pyplot using the alias plt
Create column rmoe (to hold the median home value Relative MOE) as 100 times the margin of error column divided by the estimate column
print the DataFrame to inspect the Relative MOE
Create an error bar plot: set the first argument to "year"; set the second argument to the name of the median home value column; set parameter yerr to the median home value MOE column; finally, set the data argument to the philly DataFrame

Hands-on interactive exercise

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

# Import graphics packages
import seaborn as sns
sns.set()
____

# Calculate and inspect Relative Margin of Error
philly["rmoe"] = ____
____

# Create line plot with error bars of 90% MOE
plt.errorbar(____, ____, yerr = ____, data = ____)
plt.show()

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