Python

Matplotlib Grid

Matplotlib plots are presentations of visual analytics. The grid would be one of those features. A grid is a set of overlapping horizontal lines that represent the partition of axes. Aside from visualization techniques like Excel Spreadsheets, figures, and Microsoft power bi, the Matplot package has several capabilities. These parameters improve and alter a data set’s visual representation (fig, graph, etc.).

Gridlines are used in the background of any graph or visual presentation of any given dataset such that we would better grasp the entire graph/plot and correlate the spots on the graph to the interval variables. The inner surface of a plot/chart is composed of parallel lines that are either linear (horizontal, vertical, and diagonal) or curled and are mostly utilized to depict data.

In this article, we are going to explore a Matplotlib program that allows us to customize the line attributes of a gridline in a graph.

Use Matplotlib grid() Function

Generally, this method is being used to make the grid. We can obtain better information about plots using Matplotlib’s grids. Finding an allusion for the data sets is simple. Consider the subsequent example. The code for this illustration is affixed below.

import matplotlib.pyplot as plt

import numpy as np

t = np.arange(0.2, 2.1 + 1.22, 2.22)

s = np.cos(3 * 3*np.pi * t)

t[21:80] = np.nan

plt.subplot(2, 1, 1)

plt.plot(t, s, '-', lw=2)

plt.xlabel('time (s)')

plt.ylabel('voltage (mV)')

plt.title('figure')

plt.grid(True)

plt.xlabel('time (s)')

plt.ylabel('more nans')

plt.grid(True)

plt.tight_layout()

plt.show()

The grid() method in the dimensions object is used to adjust the accessibility of the grid within the graphic. It could be adjusted on or off. The grid() method allows the user to change the line style and bandwidth attributes.

We can modify the grid to meet our specific needs. The grid() method in Pyplot could be used to insert parallel lines to a visualization. The output for the above described code can be seen from the cited below image.

Whatever we did in the method above would be added plt.grid (True) that displays the grids in the resulting plot.

Both the Minor and Major Grids are Displayed

The grid() function on the x and y-axis item displays the main grid by default, but it could also display the small grid or sometimes both. We can indicate to Matplotlib which grid we would want to display or layout by using which parameter, which seems to have probabilities of main, minor, or maybe both.

Minor dots/grid are not displayed by default in Matplotlib, thus we have manually enabled those with minorticks_on() function. The code for this illustration is affixed below.

from matplotlib import pyplot as plt

import numpy as np

def sinplot():

fig, ax = plt.subplots()

x = np.linspace(1, 20, 200)

for i in range(2, 9):

ax.plot(x, np.sin(x + i * .6) * (9 - i))

return ax

ax = sinplot()

ax.grid(True)

ax = sinplot()

ax.grid(which='major', color='#EEEEEE', linewidth=1.8)

ax.grid(which='minor', color='#DDDDDD', linestyle=':', linewidth=1.5)

ax.minorticks_on()

The three arguments are passed to the pyplot.grid() method in this instance. The first parameter is color, which provides the desired color. The second argument is linestyle and it is used to identify the aesthetic that we can get on the line.  It determines the size of the grid line. The entered values of this parameter are all positive numbers. The output for the above described code can be seen from the cited below image.

Visualizing Grids Between Subplots

In Python Matplotlib, we may generate many subplots and specify the axial accessibility falls on various axes to display grids between subplots. The code for this illustration is affixed below.

import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = [10.5, 6.68]

plt.rcParams["figure.autolayout"] = True

fig, (ax1, ax2) = plt.subplots(nrows=2)

ax3 = fig.add_subplot(555, zorder=-8)

for _, spine in ax3.spines.items():

spine.set_visible(False)

ax3.tick_params(labelleft=False, labelbottom=False, left=False, right=False)

ax3.get_shared_x_axes().join(ax3, ax1)

ax3.grid(axis="x")

ax1.grid()

ax2.grid()

plt.show()

We improve the spacing between and all around the subplots and the graphic size. To use the subplots() technique, we make a graph and a series of subplots. Then, we create a subplot in the original graph and hide the spine transparency. Disable the a3 identifiers. Further, we adjust the X-axis as needed. Now, set up the line segments in a1, a2, and a3. At last, we utilize the show() function to present the visual. The output for the above described code can be seen from the cited below image.

Integrating Gridlines into a Graph

The grid() method in Matplotlib’s Pyplot package inserts a grid line to a graphic. The image below illustrates how to utilize pyplot.grid() to apply a grid to a graph. The code for this illustration is affixed below.

import matplotlib.pyplot as plt

import numpy as np

x= np.array([5, 25])

y = np.array([20, 100])

plt.plot(x, y)

plt.title('figure')

plt.xlabel("x")

plt.ylabel("y"

plt.grid()

plt.show()

We are using the Matplot library to integrate the Pyplot component. The NumPy library is then included. By the use of the numpy.array() function, we construct an array having variable x. Next, the numpy.array() method is used to create a new array with variable y.

With the help of pyplot.plot() function, we draw y versus x. Then we utilize the pyplot.title() function where we provide our graph with the label ‘figure.’ The pyplot.xlabel() feature is applied and by this function, we also label the x-axis of our figure with the tag ‘x.’

Furthermore, we utilize the Upyplot.ylabel() function to tag the y-axis of our figure with the letter ‘y.’ The pyplot.grid() method is being used to insert a grid to the graph. At last, the pyplot.show() function is applied that  displays our graphic. The output for the above described code can be seen from the cited below image.

Conclusion

In this article, first we see how to insert a grid to a graph in Matplotlib. Then, we discussed the grid() function. We could effectively make grids with the grid() method, and then we may configure them with the various parameters provided. To improve the visual appeal of our plot, we should work with new grid line designs, hues, and widths. It shows the graph with grids that are set according to the dispersion of the ticks. We might adjust the grid spacing by altering the tick frequency.

About the author

Kalsoom Bibi

Hello, I am a freelance writer and usually write for Linux and other technology related content