Plotting error bars on grouped bars in pandas

Question

I can plot error bars on single series barplots like so:

import pandas as pd
df = pd.DataFrame([[4,6,1,3], [5,7,5,2]], columns = ['mean1', 'mean2', 'std1', 'std2'], index=['A', 'B'])
print(df)
     mean1  mean2  std1  std2
A      4      6     1     3
B      5      7     5     2

df['mean1'].plot(kind='bar', yerr=df['std1'], alpha = 0.5,error_kw=dict(ecolor='k'))

As expected, the mean of index A is paired with the standard deviation of the same index, and the error bar shows the +/- of this value.

However, when I try to plot both 'mean1' and 'mean2' in the same plot I cannot use the standard deviations in the same way:

df[['mean1', 'mean2']].plot(kind='bar', yerr=df[['std1', 'std2']], alpha = 0.5,error_kw=dict(ecolor='k'))

    Traceback (most recent call last):

  File "<ipython-input-587-23614d88a3c5>", line 1, in <module>
    df[['mean1', 'mean2']].plot(kind='bar', yerr=df[['std1', 'std2']], alpha = 0.5,error_kw=dict(ecolor='k'))

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.py", line 1705, in plot_frame
    plot_obj.generate()

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.py", line 878, in generate
    self._make_plot()

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.py", line 1534, in _make_plot
    start=start, label=label, **kwds)

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.py", line 1481, in f
    return ax.bar(x, y, w, bottom=start,log=self.log, **kwds)

  File "C:\Users\nameDropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\matplotlib\axes.py", line 5075, in bar
    fmt=None, **error_kw)

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\matplotlib\axes.py", line 5749, in errorbar
    iterable(yerr[0]) and iterable(yerr[1])):

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\core\frame.py", line 1635, in __getitem__
    return self._getitem_column(key)

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\core\frame.py", line 1642, in _getitem_column
    return self._get_item_cache(key)

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\core\generic.py", line 983, in _get_item_cache
    values = self._data.get(item)

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\core\internals.py", line 2754, in get
    _, block = self._find_block(item)

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\core\internals.py", line 3065, in _find_block
    self._check_have(item)

  File "C:\Users\name\Dropbox\Tools\WinPython-64bit-2.7.6.2\python-2.7.6.amd64\lib\site-packages\pandas\core\internals.py", line 3072, in _check_have
    raise KeyError('no item named %s' % com.pprint_thing(item))

KeyError: u'no item named 0'

The closest I have gotten to my desired output is this:

df[['mean1', 'mean2']].plot(kind='bar', yerr=df[['std1', 'std2']].values.T, alpha = 0.5,error_kw=dict(ecolor='k'))

But now the error bars are not plotted symmetrically. Instead the green and blur bars in each series use the same positive and negative error and this is where I am stuck. How can I get the error bars of my multiseries barplot to have a similar appearance as when I had only one series?

Update: Seems like this is fixed in pandas 0.14, I was reading the docs for 0.13 earlier. I don't have the possibility to upgrade my pandas right now though. Will do later and see how it turns out.

Answer 1

• yerr=df[['std1', 'std2']] in the OP doesn't work, because the column names are not the same as for df[['mean1', 'mean2']]
  • When passing values to yerr as a dataframe, the column names must be the same as the data columns (e.g. mean1 and mean2)
  • See Adding error bars to grouped bar plot in pandas
• Using df[['std1', 'std2']].to_numpy().T bypasses the issue by passing an error array without named columns
• Tested in python 3.8.11, pandas 1.3.3, matplotlib 3.4.3

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame([[4,6,1,3], [5,7,5,2]], columns = ['mean1', 'mean2', 'std1', 'std2'], index=['A', 'B'])

   mean1  mean2  std1  std2
A      4      6     1     3
B      5      7     5     2

# convert the std columns to an array
yerr = df[['std1', 'std2']].to_numpy().T

# print(yerr)
array([[1, 5],
       [3, 2]], dtype=int64)

df[['mean1', 'mean2']].plot(kind='bar', yerr=yerr, alpha=0.5, error_kw=dict(ecolor='k'))
plt.show()