Plot asymmetric error bars in different colors

Question

I have a data frame in pandas and I would like to plot the error bars in different colors (The colors are given in a column 'Colors').

I'm using the errorbar function in matplotlib and my code works if the error is symmetric.

Here is my code:

import pandas as pd

from matplotlib.pyplot import plot, show, subplots

import numpy as np  

# Definition of the dataframe

df = pd.DataFrame({'Colors': {0: 'Red', 1: 'Blue', 2: 'Green', 3: 'Blue'}, 'X_values': {0: 1, 1: 2, 2: 3, 3: 4}, 'Y_values': {0: 2, 1: 4, 2: 8, 3: 10}, 'MinY_values': {0: 1.5, 1: 3, 2: 7.5, 3: 8}, 'MaxY_values': {0: 2.5, 1: 5, 2: 9.5, 3: 11}})



# Definition of the different colors

color = []
 

for i in df['Colors']:
    if i == 'Red':
        color.append('red')
    if i == 'Blue':
        color.append('blue')
    if i == 'Green':
        color.append('green')   
        
# Figure

fig,axes = subplots(2,1,sharex = True)

for x_val,y_val,min_val,max_val,colors in zip(df['X_values'],df['Y_values'],df['MinY_values'],df['MaxY_values'],color):
    axes[0].errorbar(x_val,y_val,yerr = max_val,color = colors,barsabove='True',fmt = '+')
    
for x_val,y_val,min_val,max_val,colors in zip(df['X_values'],df['Y_values'],df['MinY_values'],df['MaxY_values'],color):
    axes[1].errorbar(x_val,y_val,yerr = max_val,color = colors,barsabove='True',fmt = '+')
    
show()

It returns the following plot:

Now, I have asymmetric errors, then in the errorbar function, yerr should be define as yerr = [min_val,max_val] using the same names in the precedent code. (There is an example on how to get asymmetric errors here)

When I do that, the following error appears:

ValueError: The lengths of the data (1) and the error 2 do not match

I read this topic, but there is the same number of elements in all my data frame columns (4).

What can I do to have the same plot below but with the asymmetric errors ?

Here is my complete code with issue:

import pandas as pd

from matplotlib.pyplot import plot, show, subplots

import numpy as np  

# Definition of the dataframe

df = pd.DataFrame({'Colors': {0: 'Red', 1: 'Blue', 2: 'Green', 3: 'Blue'}, 'X_values': {0: 1, 1: 2, 2: 3, 3: 4}, 'Y_values': {0: 2, 1: 4, 2: 8, 3: 10}, 'MinY_values': {0: 1.5, 1: 3, 2: 7.5, 3: 8}, 'MaxY_values': {0: 2.5, 1: 5, 2: 9.5, 3: 11}})



# Definition of the different colors

color = []
 

for i in df['Colors']:
    if i == 'Red':
        color.append('red')
    if i == 'Blue':
        color.append('blue')
    if i == 'Green':
        color.append('green')   
        
# Figure

fig,axes = subplots(2,1,sharex = True)

for x_val,y_val,min_val,max_val,colors in zip(df['X_values'],df['Y_values'],df['MinY_values'],df['MaxY_values'],color):
    axes[0].errorbar(x_val,y_val,yerr = [min_val,max_val] ,color = colors,barsabove='True',fmt = '+')
    
for x_val,y_val,min_val,max_val,colors in zip(df['X_values'],df['Y_values'],df['MinY_values'],df['MaxY_values'],color):
    axes[1].errorbar(x_val,y_val,yerr = [min_val,max_val] ,color = colors,barsabove='True',fmt = '+')
    
show()

score 1 · Accepted Answer · answered May 25 '21 at 13:46

The problem arises because you are plotting length-1 data and errors. First, let's see the doc string:

xerr, yerr float or array-like, shape(N,) or shape(2, N), optional

The errorbar sizes:

scalar: Symmetric +/- values for all data points.

shape(N,): Symmetric +/-values for each data point.

shape(2, N): Separate - and + values for each bar. First row contains the lower errors, the second row contains the upper errors.

None: No errorbar.

When you give yerr = [min_val, max_val], it is interpreted as a shape(N,) array - which as you can see, it then thinks are symmetric values for each point. But since you only have one data point (N=1), it gets confused.

Instead, you want to give your errors as a shape(2, N) list or array. For example:

yerr=[[min_val], [max_val]]

Which looks like this in one of your calls to errorbar:

for x_val, y_val, min_val, max_val, colors in zip(df['X_values'], df['Y_values'], df['MinY_values'], df['MaxY_values'], color):
    axes[0].errorbar(x_val, y_val, yerr=[[min_val], [max_val]], color=colors, barsabove='True', fmt='+')

Plot asymmetric error bars in different colors

1 Answers1