Like scatter plots, line plots show the relationship between two variables. However, in a line plot we are exploring the continuous pattern of one variable over increments of another. Frequently, the incremental variable is a measurement of time.

We can use sns.lineplot() with the standard parameters data, x, and y to create a line plot with seaborn. Because we want to read the pattern continuously from left to right, we set x to the incremental variable and y to the variable of interest. For example, we could use the following code to plot total_sales over each month of a year from dataset df.

sns.lineplot(data=df, x='month', y='sales_totals')

This code will work whether you have one value or multiple values of the continuous variable for each increment.

  • When there is only one value per increment, seaborn will plot the line at that value.
  • When there are multiple values per increment, seaborn will plot the line at the mean value for that increment.

When plotting means, seaborn will also plot a shaded area around the line that shows the 95% confidence interval at each increment. We can hide these shaded areas by setting the ci parameter to None.

Note: For seaborn version >= 0.12.0, the ci parameter is deprecated in favor of the errorbar parameter. Set errorbar=None for later versions of seaborn.



After running the first two code cells, display the first 10 rows of the plant1data dataset. This dataset contains measurements over time for only one plant.


Create a line plot of Lateral_spread over Time using the plant1data dataset. This plot shows Lateral_spread over time for Plant 1 only.


Now let’s view the full dataset for five plants. Show the first 10 rows of the plants dataset.


Make a line plot of Lateral_spread over Time using the plants dataset. This plot shows the average Lateral_spread of all five plants over Time with a 95% confidence interval given as a shaded region around the line.


Make the same plot of average Lateral_spread but remove the confidence interval shading.

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