How to build beautiful graphs in Python with Seaborn

pipenv install seaborn notebook

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib

flights_data = sns.load_dataset("flights")
flights_data.head()

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

All the magic happens when the function is called load_datasetwhich expects the name of the data to load and returns dataframe… All these datasets are available in repositories on Github…

Scatter Plot – Scatter Plot

A scatter plot is a chart that displays points based on two dimensions of a dataset. It is very easy to create a scatterplot in seaborn, as it only requires one line of code.

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

Easy, isn’t it? Function scatterplot takes in the dataset to be rendered and the columns that will act as the x and y axes.

Line Plot – Line Plot

This graph draws a line that represents the development of continuous or categorical data. This kind of graph is popular and famous and easy to create. As before, we will use the function lineplot with a dataset and columns representing the x and y axes. Will do the rest for us seaborn…

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

Bar Chart – Bar Plot

This is probably the most well-known type of chart, and you guessed it we can build this type of chart using seaborn, just as we did it for line and scatterplots using the function barplot…

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

She’s very colorful, I know. Later we will learn how to customize it.

Extending functionality with matplotlib

Seaborn builds on matplotlib, extending its functionality and abstracting complexity. At the same time, seaborn does not lose its power. Any seaborn chart can be customized using functions from the matplotlib library. This mechanic can come in handy in certain cases and allows seaborn to take advantage of matplotlib’s capabilities without having to rewrite all of its functions.

Let’s say you want to plot several charts at the same time using seaborn, in this case you can use the function subplot 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)

Using the function subplot several diagrams can be built on one graph. The function takes three parameters: the first is the number of rows, the second is the number of columns, and the third is the number of charts.

We render one seaborn plot on each plot part, mixing matplotlib and seaborn functionality.

Seaborn and Pandas

We have already said that seaborn loves pandas to the point that all of its functions are built on a structure dataframe… Up to this point we have been using seaborn with preloaded data, but what if we want to render data that has already been loaded with pandas?

drinks_df = pd.read_csv("data/drinks.csv")
sns.barplot(x="country", y="beer_servings", data=drinks_df)

Create beautiful graphs with styles

Seaborn gives you the ability to change the interface of your charts. To do this, we have five styles at our disposal out of the box: darkgrid, whitegrid, dark, white and ticks…

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

Here’s another example.

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

Cool use cases

We’ve learned the basics of seaborn, and now let’s put this knowledge into practice and build several charts with the same dataset. In our case, you are using the data set “tips»Which can be downloaded directly from seaborn.

First, let’s load the dataset.

tips_df = sns.load_dataset('tips')
tips_df.head()