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Programmatically creating a highlight

You are continuing your work for the city of Houston. Now you want to look at the behavior of both NO2 and SO2 when the un-plotted ozone (O3) value was at its highest.

To do this, replace the logic in the current list comprehension with one that compares a row's O3 value with the highest observed O3 in the dataset. Note: use sns.scatterplot() instead of sns.regplot(). This is because sns.scatterplot() can take a non-color vector as its hue argument and colors the points automatically while providing a helpful legend.

Este exercício faz parte do curso

Improving Your Data Visualizations in Python

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Instruções do exercício

  • Find the value corresponding to the highest observed O3 value in the houston_pollution DataFrame. Make sure to type the letter O and not the number zero!
  • Append the column 'point_type' to the houston_pollution DataFrame to mark if the row contains the highest observed O3.
  • Pass this newly created column to the hue argument of sns.scatterplot() to color the points.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

houston_pollution = pollution[pollution.city  ==  'Houston'].copy()

# Find the highest observed O3 value
max_O3 = houston_pollution.O3.____

# Make a column that denotes which day had highest O3
houston_pollution['____'] = ['Highest O3 Day' if ____  ==  ____ else 'Others' for O3 in houston_pollution.O3]

# Encode the hue of the points with the O3 generated column
sns.scatterplot(x = 'NO2',
                y = 'SO2',
                hue = '____',
                data = houston_pollution)
plt.show()
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