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AÂ **confidence intervalÂ **is a rangeÂ of values that is likely to contain a population parameter with a certain level of confidence.

This tutorial explains how to plot a confidence interval for a dataset in Python using the seaborn visualization library.

**Plotting Confidence Intervals Using lineplot()**

The first way to plot a confidence interval is by using the lineplot() function, which connects all of the data points in a dataset with a line and displays a confidence band around each point:

import numpy as np import seaborn as sns import matplotlib.pyplot as plt #create some random data np.random.seed(0) x = np.random.randint(1, 10, 30) y = x+np.random.normal(0, 1, 30) #create lineplot ax = sns.lineplot(x, y)

By default, the lineplot() function uses a 95% confidence interval but can specify the confidence level to use with theÂ **ciÂ **command.

The smaller the confidence level, the more narrow the confidence interval will be around the line. For example, hereâ€™s what an 80% confidence interval looks like for the exact same dataset:

#create lineplot ax = sns.lineplot(x, y, ci=80)

**Plotting Confidence Intervals Using regplot()**

You can also plot confidence intervals by using the regplot() function, which displays a scatterplot of a dataset with confidence bands around the estimated regression line:

import numpy as np import seaborn as sns import matplotlib.pyplot as plt #create some random data np.random.seed(0) x = np.random.randint(1, 10, 30) y = x+np.random.normal(0, 1, 30) #create regplot ax = sns.regplot(x, y)

Similar to lineplot(), the regplot() function uses a 95% confidence interval by default but can specify the confidence level to use with theÂ **ciÂ **command.

Again, the smaller the confidence level the more narrow the confidence interval will be around the regression line. For example, hereâ€™s what an 80% confidence interval looks like for the exact same dataset:

#create regplot ax = sns.regplot(x, y, ci=80)

**Additional Resources**

What are Confidence Intervals?

How to Calculate Confidence Intervals in Python