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.

### Instructions

**1.**

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.

**2.**

Create a line plot of `Lateral_spread`

over `Time`

using the `plant1data`

dataset. This plot shows `Lateral_spread`

over time for Plant 1 only.

**3.**

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

dataset.

**4.**

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.

**5.**

Make the same plot of average `Lateral_spread`

but remove the confidence interval shading.