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90 and 95% bands

You are looking at a 40-day rolling average of the NO2 pollution levels for the city of Cincinnati in 2013. To provide as detailed a picture of the uncertainty in the trend you want to look at both the 90 and 99% intervals around this rolling estimate.

To do this, set up your two interval sizes and an orange ordinal color palette. Additionally, to enable precise readings of the bands, make them semi-transparent, so the Seaborn background grids show through.

Cet exercice fait partie du cours

Improving Your Data Visualizations in Python

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Instructions

  • Set the opacity of the intervals to 40%.
  • Calculate the lower and upper confidence bounds.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

int_widths = ['90%', '99%']
z_scores = [1.67, 2.58]
colors = ['#fc8d59', '#fee08b']

for percent, Z, color in zip(int_widths, z_scores, colors):
    
    # Pass lower and upper confidence bounds and lower opacity
    plt.fill_between(
        x = cinci_13_no2.day, alpha = ____, color = color,
        y1 = cinci_13_no2['mean'] ____ ____*cinci_13_no2['std_err'],
        y2 = cinci_13_no2['mean'] ____ ____*cinci_13_no2['std_err'],
        label = percent)
    
plt.legend()
plt.show()
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