Matplotlib Tutorials
Matplotlib offers a structured learning path for Python data visualization. Our tutorials cover various plot types, customization options, and best practices, suitable for both beginner and intermediate data scientists. With free labs and practical examples, you'll gain hands - on experience in creating informative and attractive visualizations. Our data visualization playground allows you to experiment with Matplotlib functions and see immediate graphical results.
Customizing Matplotlib Visualizations
This lab will guide you through the process of customizing Matplotlib using style sheets and rcParams. Matplotlib is a powerful library for creating visualizations in Python. By customizing the properties and default styles of Matplotlib, you can create unique and visually appealing plots.
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Matplotlib Pyplot Interface Tutorial
This tutorial provides a step-by-step guide to using the pyplot interface in Matplotlib. The pyplot module is a collection of functions that make Matplotlib work like MATLAB, allowing you to easily create and customize plots. This tutorial assumes you have a basic understanding of Matplotlib and its concepts.
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Simple Axis Pad
This lab will teach you how to use the add_floating_axis function in Matplotlib to add floating axes to a plot, which can be used to display additional information about the plot. Specifically, you will learn how to adjust the padding of tick labels and axis labels, as well as how to adjust the position of ticks on the floating axes.
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The Lifecycle of a Plot
In this lab, we will explore the lifecycle of a plot using Matplotlib. We will start with raw data and end by saving a customized visualization. We will learn how to create a plot, control its style, customize its appearance, combine multiple visualizations, and save the plot to disk.
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Image Plotting with Matplotlib
In this lab, you will learn how to plot and manipulate images using the Matplotlib library in Python. You will learn how to import image data into NumPy arrays, plot numpy arrays as images, apply pseudocolor schemes, add color scale references, examine specific data ranges, and explore different interpolation schemes.
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Create a Line Plot with Matplotlib
In this lab, we will learn how to create a line plot using Matplotlib. Line plots are a basic visualization that can be used to represent data points connected by straight line segments. We will use the Matplotlib library in Python to create a line plot.
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Creating Text and Mathtext Using Pyplot
Matplotlib is a powerful data visualization library in Python. It provides a wide range of tools to create graphs and plots in Python. In this tutorial, we will learn how to create text and mathtext using pyplot.
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Advanced Quiver and Quiverkey Functions
Matplotlib is a data visualization library in Python that allows users to create a wide range of 2D and 3D plots. One of the many useful features of Matplotlib is the ability to create quiver plots, which display vector fields.
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Create Radian Plots with Matplotlib
This tutorial will show you how to create a plot with radians using the Python Matplotlib package. You will learn how to use the unit class to determine the tick locating, formatting, and axis labeling.
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Animated 3D Random Walk in Matplotlib
In this lab, we will learn how to create an animated 3D random walk plot using Matplotlib library in Python. We will create a 3D plot and simulate a random walk with 40 particles that move randomly in 3D space.
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Matplotlib Radio Button Sine Wave Visualization
Radio buttons are a type of input element that allows users to select one option from a group of predefined options. In this lab, we will use the matplotlib library to create a visualization with radio buttons that let the user choose between different sine waves to be shown in the plot.
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Plotting Multiple Datasets
Matplotlib is a popular data visualization library in Python. In this lab, you will learn how to plot multiple datasets using a single call to the plot function in Matplotlib.
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Matplotlib Quiver Plot
This lab will guide you through how to create a quiver plot using Matplotlib in Python. A quiver plot displays vector fields as arrows. It is useful in visualizing fluid flows, electric and magnetic fields, and other types of vector fields.
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Create Radar Charts with Python Matplotlib
In this lab, you will learn how to create a radar chart using Python's Matplotlib library. A radar chart, also known as a spider or star chart, is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. It is often used to compare different products or solutions based on several factors.
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Creating Matplotlib Subplots in Python
Matplotlib is a Python library used for creating visualizations such as line plots, scatter plots, bar plots, and subplots, among others. In this lab, you will learn how to create a figure with two subplots using .pyplot.subplot.
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Matplotlib Text Object Concatenation
In this lab, you will learn how to concatenate text objects with different properties using Matplotlib. Concatenation is the process of combining multiple text objects into a single string. This can be useful when creating annotations or labels for visualizations.
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Thresholding an Image with RangeSlider
This lab will demonstrate how to use the RangeSlider widget in Matplotlib to control the thresholding of an image. The purpose of thresholding is to convert a grayscale image into a binary image, where the pixels are either black or white. This is useful for image segmentation, where we want to extract certain features from the image.
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Matplotlib QuadMesh Plotting Tutorial
This tutorial will guide you through the usage of the Matplotlib library to create a QuadMesh plot. QuadMesh is a faster generalization of the pcolor function, but with some restrictions. The demo in this tutorial will illustrate a bug in QuadMesh with masked data.
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