Making Plots With Python
Python Plots With Matplotlib
Plotting Data In Python

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.

Sample plot with subplots 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. Create different types of plots (Scatter, Histograms, etc.) Change the esthetics of your figures (color, marker). Plot statistical distributions (such as Gaussian). Plot candlesticks charts. Read also: Introduction to Finance and Technical Indicators with Python. Please check the full notebook here. Happy Coding ♥ View Full Code. For more information on Matplotlib, check out Python Plotting With Matplotlib. Now that you know that the DataFrame object’s.plot method is a wrapper for Matplotlib’s pyplot.plot , let’s dive into the different kinds of plots you can create and how to make them.

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.

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

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.

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.

Making Plots With Python

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.

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.

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.

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 Daniel craig casino royale. 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?

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.

Here is another example

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.

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 fi