Create confidence interval plot in python

Question

I want to create a graph with different variables on the x axis, and plot two confidence intervals for each variable. I have used plt.errorbar to achieve this. My problem is that I can't find a way to specify labels for the x-axis variables. Currently, the x-axis is just integers. If I change it to strings, then I am unable to plot the two confidence intervals per variable. I use "x = ... +0.2" to get them on the same plot.

plt.errorbar(
    x=range(10,
    y= data1,
    yerr = data1_err,
)

plt.errorbar(
    x=range(10),
    y= data2,
    yerr=data2_err,
)
plt.show()

Could anyone offer some hints or if its not possible with this function, to perhaps recommend another one? I need to plot vertical lines indicating confidence intervals, and they will be over both positive and negative values of y.

Could you clarify what exactly you want to do? Are you able to provide an example image with the expected output? — Gabriel Jablonski
– Gabriel Jablonski, Commented Jun 16, 2018 at 20:57
@Gabriel Something like this: researchgate.net/figure/… I want to have two confidence intervals per variable, but I also want to have words as my x axis variables, rather than numbers — mrnovice
– mrnovice, Commented Jun 16, 2018 at 21:15

Gabriel Jablonski · Accepted Answer · 2018-06-16 22:16:02Z

The following code makes use of matplotlib's plot markers and ticks customization:

import matplotlib.pyplot as plt
import numpy as np

x_ticks = ("Thing 1", "Thing 2", "Other thing", "Yet another thing")

x_1 = np.arange(1, 5)
x_2 = x_1 + 0.1

y_1 = np.random.choice(np.arange(1, 7, 0.1), 4)
y_2 = np.random.choice(np.arange(1, 7, 0.1), 4)

err_1 = np.random.choice(np.arange(0.5, 3, 0.1), 4)
err_2 = np.random.choice(np.arange(0.5, 3, 0.1), 4)

plt.errorbar(x=x_1, y=y_1, yerr=err_1, color="black", capsize=3,
             linestyle="None",
             marker="s", markersize=7, mfc="black", mec="black")

plt.errorbar(x=x_2, y=y_2, yerr=err_2, color="gray", capsize=3,
             linestyle="None",
             marker="s", markersize=7, mfc="gray", mec="gray")

plt.xticks(x_1, x_ticks, rotation=90)

plt.tight_layout()
plt.show()

The output image will look something like this:

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