Plotting Confidence Intervals Using Seaborn Library
If you’re familiar with data analysis or data visualization, you must have come across the term “confidence interval.” A confidence interval is a range of values that is likely to contain the true value of a population parameter. In other words, it gives you an idea of how certain you are about your sample estimate.
The confidence interval is affected by the sample size, standard deviation, and the level of confidence chosen. Plotting confidence intervals in a visual format is a great way to communicate the certainty of your estimates.
In this article, we will explore different ways of plotting confidence intervals using Seaborn, a popular Python library for data visualization.
1. Plotting Confidence Intervals Using lineplot()
The lineplot() function in Seaborn can be used to plot a line chart with confidence intervals. By default, the lineplot() function plots a line chart with 95% confidence intervals.
Let’s look at an example.
import seaborn as sns
tips = sns.load_dataset("tips")
sns.lineplot(x="day", y="total_bill", data=tips)
Running the above code will give you a line chart with confidence intervals. 
As you can see from the chart, the confidence intervals are wider where the data points are more spread out, indicating that we are less certain about the true value of the population parameter at those points.
Conversely, where the data points are tightly clustered, the confidence intervals are narrower, indicating that we are more certain about the true value of the population parameter. You can adjust the confidence interval by passing a different value to the ci parameter.
For example, if you want to plot a line chart with 99% confidence intervals, you can do the following.
sns.lineplot(x="day", y="total_bill", data=tips, ci=99)
Running the above code will give you a line chart with 99% confidence intervals.
2. Plotting Confidence Intervals Using regplot()
The regplot() function in Seaborn can be used to plot a scatterplot with a fitted regression line and confidence bands.
The confidence bands represent the range in which 95% of the predicted values for a new observation are expected to fall. Let’s look at an example.
import seaborn as sns
tips = sns.load_dataset("tips")
sns.regplot(x="tip", y="total_bill", data=tips)
Running the above code will give you a scatterplot with a fitted regression line and confidence bands. 
As you can see from the chart, the confidence bands are wider where the data points are more spread out, indicating that we are less certain about the predicted value for a new observation at those points.
Conversely, where the data points are tightly clustered, the confidence bands are narrower, indicating that we are more certain about the predicted value for a new observation. You can adjust the confidence interval by passing a different value to the ci parameter.
For example, if you want to plot a scatterplot with 99% confidence bands, you can do the following.
sns.regplot(x="tip", y="total_bill", data=tips, ci=99)
Running the above code will give you a scatterplot with 99% confidence bands.
In conclusion, plotting confidence intervals is an essential part of data analysis and data visualization. It helps to communicate the certainty of your estimates and gives you an idea of how accurate your predictions are.
Seaborn provides easy-to-use functions, such as lineplot() and regplot(), to plot confidence intervals in a visual format. By adjusting the confidence level, you can customize the chart to better suit your needs and communicate your results effectively.
Plotting confidence intervals using Seaborn is an essential part of data visualization that communicates the level of certainty in your estimates. Confidence intervals are affected by sample size, standard deviation, and level of confidence.
Seaborn offers easy-to-use functions like lineplot() and regplot() to visualize confidence intervals. By adjusting confidence levels, you can modify charts and create a visual representation that is easy to communicate.
The article highlights the main points to help you better understand the importance of plotting confidence intervals in data visualization while using Seaborn.