Matplotlib is a Python library which is widely used by data analytics. Matplotlib.pyplot.errorbar() is a pyplot module consisting of a function which provides a MATLAB like interface.
Changing Error Bar Thickness
Before changing the thickness of error bar let us see what error bar is and how we can plot and use them.
Error Bar: Error bars are bars that we incorporate within our data conveying the unpredictability in reported measurements. In layman terms it displays graphical representations of the variableness of data and used on graphs to show the error measurement.
Syntax: matplotlib.pyplot.errorbar(x, y, yerr=None, xerr=None, fmt=”, ecolor=None, elinewidth=None, capsize=None, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, errorevery=1, capthick=None, *, data=None, **kwargs)
Example 1:
Python3
# importing necessary librariesimport matplotlib.pyplot as plt# example datax = np.arange(3, 5, 0.5)y = np.arange(9, 11, 0.5)# plotting with default thickness of barplt.title("1. Without changing thickness")plt.errorbar(x, y, xerr=0.2, yerr=0.6, fmt='o')plt.show()# plotting with changed thickness of barplt.title("1. With changed thickness of bar")# change elinewidth to change the thickness of barplt.errorbar(x, y, xerr=0.2, yerr=0.6, fmt='o', elinewidth=4)plt.show() |
Output :
Figure 1.1
Figure 1.2
As we can see the difference in both the images. The elinewidth parameter takes the integer value and then increase the thickness of error bar.
Example 2:
Python3
# importing necessary librariesimport matplotlib.pyplot as plt# example datax = np.arange(1, 3, 0.5)y = np.log(x)# plotting with default thickness of barplt.title("2. Without changing thickness")plt.errorbar(x, y, xerr=0.2, yerr=0.4, fmt='o', ecolor='black', capsize=5)plt.show()# plotting with changed thickness of bar# change elinewidth to change the thickness of barplt.title("2. With changed thickness of bar")plt.errorbar(x, y, xerr=0.2, yerr=0.4, fmt='o', elinewidth=4, ecolor='black', capsize=5, capthick=3)plt.show() |
Output :
Figure 2.1
Figure 2.2
In the above example we have applied to capsize as caps containing the complete information regarding how big the uncertainty is. If the bar is smaller, lower the std and lower the spread of data which means that data is concentrated around mean and if the bar is larger, larger the std and larger the spread of data.
