Data visualization is one of the first things you have to perform before you analyze data. The moment you have a glance at some data, your mind creates a rough idea of the way you want it to look when you map it on a graph.
Matplotlib might seem rather complex at first, but with basic coding knowledge, it should be easier for you. We will highlight some of the important concepts that will guide your work going forward.
Plotting data for visualization will need you to work with different data ranges. You might need to work with general or specific data ranges. The whole point behind Matplotlib is to help you work with data with as minimal challenges as possible. As a data analyst, you are in full control over the data you use, hence you must also understand the necessary commands to alter the same.
Remember that the machine learning environment in Matplotlib is almost similar to MATLAB. Therefore, if you have some experience with MATLAB, you should find things easier here. All the work you do in Matplotlib is built in a hierarchical manner. At the highest point, you have a state-machine environment, while at the lowest level you have the object-oriented interfaces where pyplot only performs a limited number of functions. At this level, it is up to you to build figures, and from them you can create axes. The axes will help in all, if not most of your plotting needs.
To install Matplotlib on your machine, run the following Python command:
python -m pip install -U pip
python -m pip install -U matplotlib
To set you off, install Matplotlib on your device using the following commands:
pip install matplotlib
xcode-select -install (if you are working on a Mac)
There are several dependencies that you might need to install with Matplotlib, including NumPy and Python if it is not already installed on your device. To further enhance your interface output, you might also need to install other packages like Tornado and pycairo.
If you are going to work on animations from time to time, you might need to install ImageMagick or any other packages that could assist you like LaTeX.
Fundamentals of Matplotlib
Below are some of the important concepts that you shall come across and use in Matplotlib, and their meanings or roles:
● Axis – This represents a number line, and is used to determine the graph limits.
● Axes – These represent what we construe as plots. A single figure can hold as many axes as possible. In the event of a 3D object, you can have two or three objects. Take note that for all axes, you must have an x and y label.
● Artist – Refers to everything that you can see on your figure, for example collection objects, Line2D objects and Text objects. You will notice that most of the Artists are on the Axes.
● Figure – Refers to the entire figure you are working on. It might include more than one plots or axes.
Pyplot is a Matplotlib module that allows you to work with simple functions, in the process adding elements like text, images, and lines within the figure you are working on. A simple plot can be created in the following manner:
import matplotlib.pyplot as plt
import numpy as np
Matplotlib might seem rather complex at first, but with basic coding knowledge, it should be easier for you. We will highlight some of the important concepts that will guide your work going forward.
Plotting data for visualization will need you to work with different data ranges. You might need to work with general or specific data ranges. The whole point behind Matplotlib is to help you work with data with as minimal challenges as possible. As a data analyst, you are in full control over the data you use, hence you must also understand the necessary commands to alter the same.
Remember that the machine learning environment in Matplotlib is almost similar to MATLAB. Therefore, if you have some experience with MATLAB, you should find things easier here. All the work you do in Matplotlib is built in a hierarchical manner. At the highest point, you have a state-machine environment, while at the lowest level you have the object-oriented interfaces where pyplot only performs a limited number of functions. At this level, it is up to you to build figures, and from them you can create axes. The axes will help in all, if not most of your plotting needs.
To install Matplotlib on your machine, run the following Python command:
python -m pip install -U pip
python -m pip install -U matplotlib
To set you off, install Matplotlib on your device using the following commands:
pip install matplotlib
xcode-select -install (if you are working on a Mac)
There are several dependencies that you might need to install with Matplotlib, including NumPy and Python if it is not already installed on your device. To further enhance your interface output, you might also need to install other packages like Tornado and pycairo.
If you are going to work on animations from time to time, you might need to install ImageMagick or any other packages that could assist you like LaTeX.
Fundamentals of Matplotlib
Below are some of the important concepts that you shall come across and use in Matplotlib, and their meanings or roles:
● Axis – This represents a number line, and is used to determine the graph limits.
● Axes – These represent what we construe as plots. A single figure can hold as many axes as possible. In the event of a 3D object, you can have two or three objects. Take note that for all axes, you must have an x and y label.
● Artist – Refers to everything that you can see on your figure, for example collection objects, Line2D objects and Text objects. You will notice that most of the Artists are on the Axes.
● Figure – Refers to the entire figure you are working on. It might include more than one plots or axes.
Pyplot is a Matplotlib module that allows you to work with simple functions, in the process adding elements like text, images, and lines within the figure you are working on. A simple plot can be created in the following manner:
import matplotlib.pyplot as plt
import numpy as np
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