# Interval size setup
sizes = ['99%', '95%', '90%']
Z_scores = [2.58, 1.96, 1.67]
colors = ['#fee0b6','#f1a340', '#b35806']
for size, z, color in zip(sizes, Z_scores, colors):
plt.hlines(y = data.y,
# Calculate lower and upper boundaries
xmin = data['est'] - z*data['std_err'],
xmax = data['est'] + z*data['std_err'],
# Color by interval size
color = color,
# Make line thicker for visibility
linewidth = 7,
# Label line so legend text is clear
label = size)
plt.plot('est', 'y', 'ko', data = data, label = 'Point Estimate')
plt.legend()
widths = [ '99%', '90%']
z_scores = [ 2.58, 1.67]
colors = ['#99d8c9', '#41ae76']
for percent, Z, color in zip(widths, z_scores, colors):
# Set color to distinquish bands
plt.fill_between(
x=data.day,
y1=data['mean'] - Z*data['std_err'],
y2=data['mean'] + Z*data['std_err']
color=color,
# Lower opacity so grid can show through
alpha=0.5,
# Give each band id for the legend
label=percent)
sizes = ['99% Confidence Interval', '95%', '90%']
# Set up different line widths for intervals
widths = [ 5, 10, 15]
Z_scores = [2.58, 1.96, 1.67]
for size, z, width in zip(sizes, Z_scores, widths):
plt.hlines(
y = data.y, label = size,
xmin = data['est'] - z*data['std_err'],
xmax = data['est'] + z*data['std_err'],
color = 'grey'
# Adjust line thickness by interval
linewidth = width)
plt.plot('est', 'y', 'wo', data = data, label = 'Point Estimate')
plt.legend()
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