Seaborn

Data visualization is a technique that allows data scientists to convert raw data into charts and plots that generate valuable insights. Charts reduce the complexity of the data and make it easier to understand for any user

There are many tools to perform data visualization, such as Tableau, Power BI, ChartBlocks, and more, which are no-code tools. They are very powerful tools, and they have their audience. However, when working with raw data that requires transformation and a good playground for data, Python is an excellent choice.

Though more complicated as it requires programming knowledge, Python allows you to perform any manipulation, transformation, and visualization of your data. It is ideal for data scientists.

There are many reasons why Python is the best choice for data science, but one of the most important ones is its ecosystem of libraries. Many great libraries are available for Python to work with data like?numpy,?pandas,?matplotlib,?tensorflow.

Matplotlib?is probably the most recognized plotting library out there, available for Python and other programming languages like?R. It is its level of customization and operability that set it in the first place. However, some actions or customizations can be hard to deal with when using it.

Developers created a new library based on matplotlib called?seaborn.?Seaborn?is as powerful as?matplotlib?while also providing an abstraction to simplify plots and bring some unique features.

In this article, we will focus on how to work with Seaborn to create best-in-class plots. If you want to follow along you can create your own project or simply check out my?seaborn guide project?on GitHub.

What is Seaborn??

Seaborn?is a library for making statistical graphics in Python. It builds on top of?matplotlib?and integrates closely with?pandas data structures.

Seaborn design allows you to explore and understand your data quickly. Seaborn works by capturing entire dataframes or arrays containing all your data and performing all the internal functions necessary for semantic mapping and statistical aggregation to convert data into informative plots.

It abstracts complexity while allowing you to design your plots to your requirements.

Installing Seaborn?

Installing?seaborn?is as easy as installing one library using your favorite Python package manager. When installing?seaborn, the library will install its dependencies, including?matplotlib,?pandas,?numpy, and?scipy.

Let’s then install seaborn, and of course, also the package?notebook?to get access to our data playground.

pipenv install seaborn notebook


Additionally, we are going to import a few modules before we get started.

import seaborn as sns

import pandas as pd

import numpy as np

import matplotlib

Building your first plots?

Before we can start plotting anything, we need data. The beauty of?seaborn?is that it works directly with?pandas?dataframes, making it super convenient. Even more so, the library comes with some built-in datasets that you can now load from code, no need to manually downloading files.

Let’s see how that works by loading a dataset that contains information about flights.

flights_data = sns.load_dataset("flights")

flights_data.head()

	year	month	passengers
0	1949	Jan	112
1	1949	Feb	118
2	1949	Mar	132
3	1949	Apr	129
4	1949	May	121

All the magic happens when calling the function?load_dataset, which expects the name of the data to be loaded and returns a dataframe. All these datasets are available on a?GitHub repository.

Scatter Plot?

A scatter plot is a diagram that displays points based on two dimensions of the dataset. Creating a scatter plot in the seaborn library is so simple and with just one line of code.

sns.scatterplot(data=flights_data, x="year", y="passengers")


Sample scatter plot

Very easy, right? The function?scatterplot?expects the dataset we want to plot and the columns representing the?x?and?y?axis.

Line Plot?

This plot draws a line that represents the revolution of continuous or categorical data. It is a popular and known type of chart, and it’s super easy to produce. Similarly to before, we use the function?lineplot?with the dataset and the columns representing the?x?and?y?axis.?Seaborn?will do the rest.

sns.lineplot(data=flights_data, x="year", y="passengers")


Sample line plot

Bar Plot?

It is probably the best-known type of chart, and as you may have predicted, we can plot this type of plot with?seaborn?in the same way we do for lines and scatter plots by using the function?barplot.

sns.barplot(data=flights_data, x="year", y="passengers")


Sample bar plot

It’s very colorful, I know, we will learn how to customize it later on in the guide.

Extending with matplotlib?

Seaborn builds on top of?matplotlib, extending its functionality and abstracting complexity. With that said, it does not limit its capabilities. Any?seaborn?chart can be customized using functions from the?matplotlib?library. It can come in handy for specific operations and allows seaborn to leverage the power of?matplotlib?without having to rewrite all its functions.

Let’s say that you, for example, want to plot multiple graphs simultaneously using?seaborn; then you could use the?subplot?function from?matplotlib.

diamonds_data = sns.load_dataset('diamonds')

plt.subplot(1, 2, 1)

sns.countplot(x='carat', data=diamonds_data)

plt.subplot(1, 2, 2)

sns.countplot(x='depth', data=diamonds_data)


Sample plot with sub-plots

Using the?subplot?function, we can draw more than one chart on a single plot. The function takes three parameters, the first is the number of rows, the second is the number of columns, and the last one is the plot number.

We are rendering a?seaborn?chart in each subplot, mixing?matplotlib?with?seaborn?functions.

Seaborn loves Pandas?

We already talked about this, but?seaborn?loves?pandas?to such an extent that all its functions build on top of the?pandas?dataframe. So far, we saw examples of using?seaborn?with pre-loaded data, but what if we want to draw a plot from data we already have loaded using?pandas?

drinks_df = pd.read_csv("data/drinks.csv")

sns.barplot(x="country", y="beer_servings", data=drinks_df)


Sample plot with pandas

Making beautiful plots with styles?

Seaborn gives you the ability to change your graphs’ interface, and it provides five different styles out of the box:?darkgrid,?whitegrid,?dark,?white, and?ticks.

sns.set_style("darkgrid")

sns.lineplot(data = data, x = "year", y = "passengers")


Sample plot with darkgrid style

Here is another example

sns.set_style("whitegrid")

sns.lineplot(data=flights_data, x="year", y="passengers")


Sample plot with whitegrid style

Cool use cases?

We know the basics of?seaborn, now let’s get them into practice by building multiple charts over the same dataset. In our case, we will use the dataset “tips” that you can download directly using?seaborn.

First, load the dataset.

tips_df = sns.load_dataset('tips')

tips_df.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

I like to print the first few rows of the data set to get a feeling of the columns and the data itself. Usually, I use some?pandas?functions to fix some data issues like?null?values and add information to the data set that may be helpful. You can read more about this on the?guide to working with pandas.

Let’s create an additional column to the data set with the percentage that represents the tip amount over the total of the bill.

tips_df["tip_percentage"] = tips_df["tip"] / tips_df["total_bill"]

tips_df.head()


Now our data frame looks like the following:

	total_bill	tip	sex	smoker	day	time	size	tip_percentage
0	16.99	1.01	Female	No	Sun	Dinner	2	0.059447
1	10.34	1.66	Male	No	Sun	Dinner	3	0.160542
2	21.01	3.50	Male	No	Sun	Dinner	3	0.166587
3	23.68	3.31	Male	No	Sun	Dinner	2	0.139780
4	24.59	3.61	Female	No	Sun	Dinner	4	0.146808

Next, we can start plotting some charts.

Understanding tip percentages?

Let’s try first to understand the tip percentage distribution. For that, we can use?histplot?that will generate a histogram chart.

sns.histplot(tips_df["tip_percentage"], binwidth=0.05)


Understanding tip percentages plot

That’s good, we had to customize the?binwidth?property to make it more readable, but now we can quickly appreciate our understanding of the data. Most customers would tip between 15 to 20%, and we have some edge cases where the tip is over 70%. Those values are anomalies, and they are always worth exploring to determine if the values are errors or not.

It would also be interesting to know if the tip percentage changes depending on the moment of the day,

sns.histplot(data=tips_df, x="tip_percentage", binwidth=0.05, hue="time")


Understanding tip percentages by time plot

This time we loaded the chart with the full dataset instead of just one column, and then we set the property?hue?to the column?time. This will force the chart to use different colors for each value of?time?and add a legend to it.

Total of tips per day of the week?

Another interesting metric is to know how much money in tips can the personnel expect depending on the day of the week.

sns.barplot(data=tips_df, x="day", y="tip", estimator=np.sum)


Understanding tip percentages per day plot

It looks like Friday is a good day to stay home.

Impact of table size and day on the tip?

Sometimes we want to understand how to variables play together to determine output. For example, how do the day of the week and the table size impact the tip percentage?

To draw the next chart we will combine the?pivot?function of pandas to pre-process the information and then draw a heatmap chart.

pivot = tips_df.pivot_table(

    index=["day"],

    columns=["size"],

    values="tip_percentage",

    aggfunc=np.average)

sns.heatmap(pivot)