Identify Extreme Values

Now that you have created a DataFrame with the percentage of Hispanic racial self-identification by state, you will explore it further, beginning by creating a boxplot using seaborn.

You will also find the states with the largest or smallest percentage of Hispanics identifying as particular races. To do so, you will apply the squeeze() method. This method converts a single-row DataFrame to a series (with no effect on a DataFrame with more than one row).

pandas has been imported. The DataFrame states_hr is loaded, which has percentages of racial self-identification for 7 different race categories.

Questo esercizio fa parte del corso

Analyzing US Census Data in Python

Visualizza il corso

Istruzioni dell'esercizio

Create a boxplot by setting the data parameter to the name of the DataFrame. (orient = "h" will plot the boxplots horizontally.)
Using squeeze, show the state with the largest value in column hispanic_white.
Using squeeze, show the state with the smallest value in column hispanic_other.
Notice that very few Hispanics identify as Asian, but one state is a high outlier. Using squeeze, show the state with the largest value in column hispanic_asian.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import seaborn and matplotlib.plt
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()

# Create a boxplot
sns.boxplot(data = ____, orient = "h")
plt.show()

# Show states with extreme values in various columns
print(states_hr.nlargest(1, ____).squeeze())
print(states_hr.nsmallest(____).____)
print(____)

Modifica ed esegui il codice