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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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