Seaborn is one of the most widely used data visualization libraries in Python, as an extension to Matplotlib. It offers a simple, intuitive, yet highly customizable API for data visualization.
In this tutorial, we'll take a look at how to plot a Line Plot in Seaborn - one of the most basic types of plots.
Line Plots display numerical values on one axis, and categorical values on the other.
They can typically be used in much the same way Bar Plots can be used, though, they're more commonly used to keep track of changes over time.
Plot a Line Plot with Seaborn
Let's start out with the most basic form of populating data for a Line Plot, by providing a couple of lists for the X-axis and Y-axis to the
import matplotlib.pyplot as plt import seaborn as sns sns.set_theme(style="darkgrid") x = [1, 2, 3, 4, 5] y = [1, 5, 4, 7, 4] sns.lineplot(x, y) plt.show()
Here, we have two lists of values,
x list acts as our categorical variable list, while the
y list acts as the numerical variable list.
This code results in:
To that end, we can use other data types, such as strings for the categorical axis:
import matplotlib.pyplot as plt import seaborn as sns sns.set_theme(style="darkgrid") x = ['day 1', 'day 2', 'day 3'] y = [1, 5, 4] sns.lineplot(x, y) plt.show()
And this would result in:
Note: If you're using integers as your categorical list, such as
[1, 2, 3, 4, 5], but then proceed to go to
100, all values between
5..100 will be null:
import seaborn as sns sns.set_theme(style="darkgrid") x = [1, 2, 3, 4, 5, 10, 100] y = [1, 5, 4, 7, 4, 5, 6] sns.lineplot(x, y) plt.show()
This is because a dataset might simply be missing numerical values on the X-axis. In that case, Seaborn simply lets us assume that those values are missing and plots away. However, when you work with strings, this won't be the case:
import matplotlib.pyplot as plt import seaborn as sns sns.set_theme(style="darkgrid") x = ['day 1', 'day 2', 'day 3', 'day 100'] y = [1, 5, 4, 5] sns.lineplot(x, y) plt.show()
However, more typically, we don't work with simple, hand-made lists like this. We work with data imported from larger datasets or pulled directly from databases. Let's import a dataset and work with it instead.
Let's use the Hotel Bookings dataset and use the data from there:
import pandas as pd df = pd.read_csv('hotel_bookings.csv') print(df.head())
Let's take a look at the columns of this dataset:
hotel is_canceled reservation_status ... arrival_date_month stays_in_week_nights 0 Resort Hotel 0 Check-Out ... July 0 1 Resort Hotel 0 Check-Out ... July 0 2 Resort Hotel 0 Check-Out ... July 1 3 Resort Hotel 0 Check-Out ... July 1 4 Resort Hotel 0 Check-Out ... July 2
This is a truncated view, since there are a lot of columns in this dataset. For example, let's explore this dataset, by using the
arrival_date_month as our categorical X-axis, while we use the
stays_in_week_nights as our numerical Y-axis:
import matplotlib.pyplot as plt import seaborn as sns import pandas as pd sns.set_theme(style="darkgrid") df = pd.read_csv('hotel_bookings.csv') sns.lineplot(x = "arrival_date_month", y = "stays_in_week_nights", data = df) plt.show()
We've used Pandas to read in the CSV data and pack it into a
DataFrame. Then, we can assign the
y arguments of the
lineplot() function as the names of the columns in that dataframe. Of course, we'll have to specify which dataset we're working with by assigning the dataframe to the
Now, this results in:
We can clearly see that weeknight stays tend to be longer during the months of June, July and August (summer vacation), while they're the lowest in January and February, right after the chain of holidays leading up to New Year.
Additionally, you can see the confidence interval as the area around the line itself, which is the estimated central tendency of our data. Since we have multiple
y values for each
x value (many people stayed in each month), Seaborn calculates the central tendency of these records and plots that line, as well as a confidence interval for that tendency.
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In general, people stay ~2.8 days on weeknights, in July, but the confidence interval spans from 2.78-2.84.
Plotting Wide-Form Data
Now, let's take a look at how we can plot wide-form data, rather than tidy-form as we've been doing so far. We'll want to visualize the
stays_in_week_nights variable over the months, but we'll also want to take the year of that arrival into consideration. This will result in a Line Plot for each year, over the months, on a single figure.
Since the dataset isn't well-suited for this by default, we'll have to do some data pre-processing on it.
import matplotlib.pyplot as plt import seaborn as sns import pandas as pd df = pd.read_csv('hotel_bookings.csv') # Truncate df = df[['arrival_date_year', 'arrival_date_month', 'stays_in_week_nights']] # Save the order of the arrival months order = df['arrival_date_month'] # Pivot the table to turn it into wide-form df_wide = df.pivot_table(index='arrival_date_month', columns='arrival_date_year', values='stays_in_week_nights') # Reindex the DataFrame with the `order` variable to keep the same order of months as before df_wide = df_wide.reindex(order, axis=0) print(df_wide)
Here, we've firstly truncated the dataset to a few relevant columns. Then, we've saved the order of arrival date months so we can preserve it for later. You can put in any order here, though.
Then, to turn the narrow-form data into a wide-form, we've pivoted the table around the
arrival_date_month feature, turning
arrival_date_year into columns, and
stays_in_week_nights into values. Finally, we've used
reindex() to enforce the same order of arrival months as we had before.
Let's take a look at how our dataset looks like now:
arrival_date_year 2015 2016 2017 arrival_date_month July 2.789625 2.836177 2.787502 July 2.789625 2.836177 2.787502 July 2.789625 2.836177 2.787502 July 2.789625 2.836177 2.787502 July 2.789625 2.836177 2.787502 ... ... ... ... August 2.654153 2.859964 2.956142 August 2.654153 2.859964 2.956142 August 2.654153 2.859964 2.956142 August 2.654153 2.859964 2.956142 August 2.654153 2.859964 2.956142
Great! Our dataset is now correctly formatted for wide-form visualization, with the central tendency of the
stays_in_week_nights calculated. Now that we're working with a wide-form dataset, all we have to do to plot it is:
lineplot() function can natively recognize wide-form datasets and plots them accordingly. This results in:
Customizing Line Plots with Seaborn
Now that we've explored how to plot manually inserted data, how to plot simple dataset features, as well as manipulated a dataset to conform to a different type of visualization - let's take a look at how we can customize our line plots to provide more easy-to-digest information.
Plotting Line Plot with Hues
Hues can be used to segregate a dataset into multiple individual line plots, based on a feature you'd like them to be grouped (hued) by. For example, we can visualize the central tendency of the
stays_in_week_nights feature, over the months, but take the
arrival_date_year into consideration as well and group individual line plots based on that feature.
This is exactly what we've done in the previous example - manually. We've converted the dataset into a wide-form dataframe and plotted it. However, we could've grouped the years into hues as well, which would net us the exact same result:
import matplotlib.pyplot as plt import seaborn as sns import pandas as pd df = pd.read_csv('hotel_bookings.csv') sns.lineplot(x = "arrival_date_month", y = "stays_in_week_nights", hue='arrival_date_year', data = df) plt.show()
By setting the
arrival_date_year feature as the
hue argument, we've told Seaborn to segregate each X-Y mapping by the
arrival_date_year feature, so we'll end up with three different line plots:
This time around, we've also got confidence intervals marked around our central tendencies.
Customize Line Plot Confidence Interval with Seaborn
You can fiddle around, enable/disable and change the type of confidence intervals easily using a couple of arguments. The
ci argument can be used to specify the size of the interval, and can be set to an integer,
'sd' (standard deviation) or
None if you want to turn it off.
err_style can be used to specify the style of the confidence intervals -
bars. We've seen how bands work so far, so let's try out a confidence interval that uses
import matplotlib.pyplot as plt import seaborn as sns import pandas as pd df = pd.read_csv('hotel_bookings.csv') sns.lineplot(x = "arrival_date_month", y = "stays_in_week_nights", err_style='bars', data = df) plt.show()
This results in:
And let's change the confidence interval, which is by default set to
95, to display standard deviation instead:
import matplotlib.pyplot as plt import seaborn as sns import pandas as pd df = pd.read_csv('hotel_bookings.csv') sns.lineplot(x = "arrival_date_month", y = "stays_in_week_nights", err_style='bars', ci='sd', data = df) plt.show()
In this tutorial, we've gone over several ways to plot a Line Plot in Seaborn. We've taken a look at how to plot simple plots, with numerical and categorical X-axes, after which we've imported a dataset and visualized it.
We've explored how to manipulate datasets and change their form to visualize multiple features, as well as how to customize Line Plots.
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