Prerequisites: Matplotlib
In this article, we will create a scatter plot with error bars using Matplotlib. Error bar charts are a great way to represent the variability in your data. It can be applied to graphs to provide an additional layer of detailed information on the presented data.
Approach
- Import required python library.
- Create data.
- Pass required values to errorbar() function
- Plot graph.
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)
Parameters: This method accept the following parameters that are described below:
- x, y: These parameters are the horizontal and vertical coordinates of the data points.
- fmt: This parameter is an optional parameter and it contains the string value.
- capsize: This parameter is also an optional parameter. And it is the length of the error bar caps in points with default value NONE.
Implementation of the concept discussed above is given below:
Example 1: Adding Some error in a ‘y’ value.
Python3
import matplotlib.pyplot as plta = [1, 3, 5, 7]b = [11, -2, 4, 19]plt.scatter(a, b)c = [1, 3, 2, 1]plt.errorbar(a, b, yerr=c, fmt="o")plt.show() |
Output:
Example 2: Adding Some errors in the ‘x’ value.
Python3
import matplotlib.pyplot as plta = [1, 3, 5, 7]b = [11, -2, 4, 19]plt.scatter(a, b)c = [1, 3, 2, 1]plt.errorbar(a, b, xerr=c, fmt="o")plt.show() |
Output:
Example 3: Adding error in x & y
Python3
import matplotlib.pyplot as plta = [1, 3, 5, 7]b = [11, -2, 4, 19]plt.scatter(a, b)c = [1, 3, 2, 1]d = [1, 3, 2, 1]# you can use color ="r" for red or skip to default as blueplt.errorbar(a, b, xerr=c, yerr=d, fmt="o", color="r")plt.show() |
Output:
Example 4: Adding variable error in x and y.
Python3
# importing matplotlibimport matplotlib.pyplot as plt# making a simple plotx = [1, 2, 3, 4, 5]y = [1, 2, 1, 2, 1]# creating errory_errormin = [0.1, 0.5, 0.9, 0.1, 0.9]y_errormax = [0.2, 0.4, 0.6, 0.4, 0.2]x_error = 0.5y_error = [y_errormin, y_errormax]# plotting graph# plt.plot(x, y)plt.errorbar(x, y, yerr=y_error, xerr=x_error, fmt='o') plt.show() |
Output:
