Plot mean and standard deviation

Question

I have several values of a function at different x points. I want to plot the mean and std in python, like the answer of this SO question. I know this must be easy using matplotlib, but I have no idea of the function's name that can do that. Does anyone know it?

Answer 1

plt.errorbar can be used to plot x, y, error data (as opposed to the usual plt.plot)

import matplotlib.pyplot as plt
import numpy as np

x = np.array([1, 2, 3, 4, 5])
y = np.power(x, 2) # Effectively y = x**2
e = np.array([1.5, 2.6, 3.7, 4.6, 5.5])

plt.errorbar(x, y, e, linestyle='None', marker='^')

plt.show()

plt.errorbar accepts the same arguments as plt.plot with additional yerr and xerr which default to None (i.e. if you leave them blank it will act as plt.plot).

Answer 2

You may find an answer with this example : errorbar_demo_features.py

"""
Demo of errorbar function with different ways of specifying error bars.

Errors can be specified as a constant value (as shown in `errorbar_demo.py`),
or as demonstrated in this example, they can be specified by an N x 1 or 2 x N,
where N is the number of data points.

N x 1:
    Error varies for each point, but the error values are symmetric (i.e. the
    lower and upper values are equal).

2 x N:
    Error varies for each point, and the lower and upper limits (in that order)
    are different (asymmetric case)

In addition, this example demonstrates how to use log scale with errorbar.
"""
import numpy as np
import matplotlib.pyplot as plt

# example data
x = np.arange(0.1, 4, 0.5)
y = np.exp(-x)
# example error bar values that vary with x-position
error = 0.1 + 0.2 * x
# error bar values w/ different -/+ errors
lower_error = 0.4 * error
upper_error = error
asymmetric_error = [lower_error, upper_error]

fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)
ax0.errorbar(x, y, yerr=error, fmt='-o')
ax0.set_title('variable, symmetric error')

ax1.errorbar(x, y, xerr=asymmetric_error, fmt='o')
ax1.set_title('variable, asymmetric error')
ax1.set_yscale('log')
plt.show()

Which plots this: