How to Visualize a 2D Array?

Introduction

NumPy is a Python library using which we can generate arrays, and these arrays can be plotted using matplotlib in Python to visualize them as a graph. NumPy arrays can be visualized as line plots, scatter plots, color mesh, etc.

NumPy Array Functions

Before visualizing the NumPy array in matplotlib, we need to know all the functions used to generate the NumPy array to understand the topic better.

• numpy.array() This function generates a NumPy array to store data in numpy.array() we have to give a list as a parameter.

Example:

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• numpy.arange() The numpy.arange() function allows us to initialize a sequence of integers based on the range (starting point, stopping point, and interval).

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• numpy.ones() The numpy.ones() function allows us to generate an array of ones of user-specific size.

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• numpy.zeros() The numpy.zeros() function allows us to generate an array of zeros of user-specific size.

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• numpy.linspace() The numpy.linspace() function allows us to generate N evenly spaced points within a user-specified interval where both starting and stopping points are inclusive. This function is different from numpy.arange() because this function generates N evenly spaced number between intervals where we have to pass N as a parameter in the function but in numpy.arange() function, we pass the interval as the parameter. Example:

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Visualize Different Types of 2D Arrays

Plot NumPy Array

In matplotlib, we can plot the NumPy array on the graph. We need two NumPy 1-D arrays of equal size for the simple plot to plot data on the graph. We can visualize it on different plots such as line plots, scatter plots, bar graphs, etc.

Example: Plot Numpy array

Output:

Code explanation:

• Importing the required modules.
• Creating the figure and increasing the resolution using the parameter dpi.
• Storing the x-axis and y-axis data points in a numpy array.
• matplotlib.pyplot.plot() function to plot the data.
• Adding details to the plot by using matplotlib.pyplot.title(), matplotlib.pyplot.xlabel() and matplotlib.pyplot.ylabel() functions.
• matplotlib.pyplot.show() function to display the plot.

Plot NumPy Array as Line

For plotting a numpy array as a line plot. First, we have to generate data (2 arrays of the same size), then plot data using matplotlib.pyplot.plt() function.

Example:

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Plot NumPy Array as Scatter Plot

As we have plotted the data as a line plot in matplotlib. Similarly, we can plot the data as a scatter plot.

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Plot NumPy Array as Multiple Lines

We can plot a numpy array as multiple lines. We must generate two arrays` for each plot.

Example:

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Plot NumPy Array as a Heatmap

To generate a heat map using a numpy array, first, we must generate data for a 2-D array, then show that array as an image file. Here is the Example of the same.

Example1: Using imshow() method

Output:

Code explanation:

• Importing the required modules.
• Generating 2x2 matrix of random values using numpy.random.random() function.
• matplotlib.pyplot.imshow() function takes data of the array and shows the color according to the heatmap.
• Adding details to the plot by using matplotlib.pyplot.title(), matplotlib.pyplot.xlabel() and matplotlib.pyplot.ylabel() functions.
• Then, adding colorbar to the plot using matplotlib.pyplot.colorbar() function.
• matplotlib.pyplot.show() function to display the plot.

Example2: Using Seaborn library

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Plot NumPy Array as Image

A numpy array can be read as an image where the array index acts like a single pixel and the value at that index as color. Here is the Example of the same.

Example:

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Plot NumPy Array as a Matrix

The matplotlib.pyplot.matshow() function displays an array as a matrix in a new figure window.

Syntax

Example:

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Using pcolormesh() Function

The pcolormesh() function creates a pseudocolor plot with a non-regular rectangular grid.

Syntax

*Example :

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