Plots Python

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes. Visit the installation page to see how you can download the package and get started with it. Python libraries to create interactive plots: mpld3; pygal; Bokeh; HoloViews; Plotly; mpld3. Custom plugin example (Jake Vanderplas) mpld3 brings together Python's core plotting library matplotlib and the popular JavaScript charting library D3 to create browser-friendly visualizations. You can make a plot in matplotlib, add interactive.

• This article is going to cover plotting basic equations in python! We are going to look at a few different examples, and then I will provide the code to do create the plots through Google Colab!
• Create custom plots in PyQt with PyQtGraph One of the major strengths of Python is in exploratory data science and visualization, using tools such as Pandas, numpy, sklearn for data analysis and matplotlib plotting.
• Click Python Notebook under Notebook in the left navigation panel. This will open a new notebook, with the results of the query loaded in as a dataframe. The first input cell is automatically populated with datasets.head (n=5). Run this code so you can see the first five rows of the dataset.

[Matplotlib](https://matplotlib.org/ is a powerful two-dimensional plotting library for the Python language. Matplotlib is capable of creating all manner of graphs, plots, charts, histograms, and much more.

In most cases, matplotlib will simply output the chart to your viewport when the .show() method is invoked, but we’ll briefly explore how to save a matplotlib creation to an actual file on disk.

Using matplotlib

While the feature-list of matplotlib is nearly limitless, we’ll quickly go over how to use the library to generate a basic chart for your own testing purposes.

Like all Python libraries, you’ll need to begin by installing matplotlib. We won’t go through the installation process here, but there’s plenty of information in the official documentation.

Once installed, import the matplotlib library. You’ll likely also want to import the pyplot sub-library, which is what you’ll generally be using to generate your charts and plots when using matplotlib.

Now to create and display a simple chart, we’ll first use the .plot() method and pass in a few arrays of numbers for our values. For this example, we’ll plot the number of books read over the span of a few months.

We can also add a few axis labels:

Finally, we can display the chart by calling .show():

The savefig Method

Interactive Plots Python

With a simple chart under our belts, now we can opt to output the chart to a file instead of displaying it (or both if desired), by using the .savefig() method.

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The .savefig() method requires a filename be specified as the first argument. This filename can be a full path and as seen above, can also include a particular file extension if desired. If no extension is provided, the configuration value of savefig.format is used instead.

Additional savefig Options

In addition to the basic functionality of saving the chart to a file, .savefig() also has a number of useful optional arguments.

• dpi can be used to set the resolution of the file to a numeric value.
• transparent can be set to True, which causes the background of the chart to be transparent.
• bbox_inches can be set to alter the size of the bounding box (whitespace) around the output image. In most cases, if no bounding box is desired, using bbox_inches='tight' is ideal.
• If bbox_inches is set to 'tight', then the pad_inches option specifies the amount of padding around the image.

There are a handful of additional options for specific occasions, but overall this should get you started with easily generating image file outputs from your matplotlib charts.

Chapter One - Introduction to Plotting with Matplotlib

In this chapter, you will learn how to create and customize plots in Python using matplotlib, including how to create different types of plots and customize plot colors and labels.

Learning Objectives

After completing this chapter, you will be able to:

• Explain the matplotlib object-oriented approach to plotting.
• Use matplotlib to create scatter, line and bar plots.
• Customize the labels, colors and look of your matplotlib plot.
• Create figures with multiple plots.
• Save figure as an image file (e.g. .png format).

What You Need

Be sure that you have followed the instructions on Setting up Git, Bash, and Conda on your computer to install the tools for your operating system (Windows, Mac, Linux).

Be sure that you have completed the chapter on Jupyter Notebook.

Overview of Plotting with Matplotlib

Matplotlib is a Python plotting package that makes it simple to create two-dimensional plots from data stored in a variety of data structures including lists, numpy arrays, and pandas dataframes.

Matplotlib uses an object oriented approach to plotting. This means that plots can be built step-by-step by adding new elements to the plot.

There are two primary objects associated with a matplotlib plot:

• figure object: the overall figure space that can contain one or more plots.
• axis objects: the individual plots that are rendered within the figure.

You can think of the figure object as your plot canvas. You can think about the axis object as an individual plot.

A figure can hold one or more axis objects. This structure allows you to create figures with one or more plots on them.

Python Plotly Tutorial

While Matplotlib contains many modules that provide different plotting functionality, the most commonly used module is pyplot.

Pyplot provides methods that can be used to add different components to figure objects, including creating the individual plots as axis objects, also known as subplots.

The pyplot module is typically imported using the alias plt as demonstrated below.

Create Plots Using Matplotlib

To create a plot using matplotlib’s object oriented approach, you first create the figure (which you can call fig) and at least one axis (which you can call ax) using the subplots() function from the pyplot module:

fig, ax = plt.subplots()

Notice that the fig and ax are created at the same time by setting them equal to the output of the pyplot.subplots() function. As no other arguments have been provided, the result is a figure with one plot that is empty but ready for data.

Data Tip: In the example above, fig and ax are variable names for the figure and axis objects. You can call these items whatever you want. For example, you might see f, ax or fig, axis1 used.

Change Figure Size

You can change the size of your figure using the argument figsize to specify a width and height for your figure:

figsize = (width, height)

Multi-plot Figures

Using matplotlib’s object-oriented approach makes it easier to include more than one plot in a figure by creating additional axis objects.

When adding more than one axis object, it is good practice to give them distinct names (such as ax1 and ax2), so you can easily work with each axis individually.

You will need to provide new arguments to plt.subplots for the layout of the figure: number of rows and columns:

plt.subplots(1, 2)

In this example, 1, 2 indicates that you want the plot layout to be 1 row across 2 columns.

Conversely, 2, 1 indicates that you want the plot layout to be 2 rows across one column.

Because you have defined figsize=(10, 6), the figure space remains the same size regardless of how many rows or columns you request.

You can play around with both the number of rows and columns as well as figsize to arrive at the look that you want.

You can continue to add as many as axis objects as you need to create the overall layout of the desired figure and continue adjusting the figsize as needed.

A key benefit of the matplotlib object oriented approach is that each axis is its own object and can be customized independently of the other plots in the figure.

You will learn how to take advantage of this capability to customize individual plots on the next page of this chapter.