Non overlapping error bars in line plot

Question

I am using Pandas and Matplotlib to create some plots. I want line plots with error bars on them. The code I am using currently looks like this

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(index=[10,100,1000,10000], columns=['A', 'B', 'C', 'D', 'E', 'F'], data=np.random.rand(4,6))
df_yerr = pd.DataFrame(index=[10,100,1000,10000], columns=['A', 'B', 'C', 'D', 'E', 'F'], data=np.random.rand(4,6))

fig, ax = plt.subplots()
df.plot(yerr=df_yerr, ax=ax, fmt="o-", capsize=5)
ax.set_xscale("log")
plt.show()

With this code, I get 6 lines on a single plot (which is what I want). However, the error bars completely overlap, making the plot difficult to read.

Is there a way I could slightly shift the position of each point on the x-axis so that the error bars no longer overlap?

Here is a screenshot:

Answer 1

One way to achieve what you want is to plot the error bars 'by hand', but it is neither straight forward nor much better looking than your original. Basically, what you do is make pandas produce the line plot and then iterate through the data frame columns and do a pyplot errorbar plot for each of them such, that the index is slightly shifted sideways (in your case, with the logarithmic scale on the x axis, this would be a shift by a factor). In the error bar plots, the marker size is set to zero:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

colors = ['red','blue','green','yellow','purple','black']

df = pd.DataFrame(index=[10,100,1000,10000], columns=['A', 'B', 'C', 'D', 'E', 'F'], data=np.random.rand(4,6))
df_yerr = pd.DataFrame(index=[10,100,1000,10000], columns=['A', 'B', 'C', 'D', 'E', 'F'], data=np.random.rand(4,6))

fig, ax = plt.subplots()
df.plot(ax=ax, marker="o",color=colors)

index = df.index
rows = len(index)
columns = len(df.columns)

factor = 0.95
for column,color in zip(range(columns),colors):
    y = df.values[:,column]
    yerr = df_yerr.values[:,column]
    ax.errorbar(
        df.index*factor, y, yerr=yerr, markersize=0, capsize=5,color=color,
        zorder = 10,
    )
    factor *= 1.02

ax.set_xscale("log")
plt.show()

As I said, the result is not pretty:

UPDATE

In my opinion a bar plot would be much more informative:

fig2,ax2 = plt.subplots()
df.plot(kind='bar',yerr=df_yerr, ax=ax2)
plt.show()