How to apply custom column order (on Categorical) to pandas boxplot?

Hard to say how to do this without a working example. My first guess would be to just add an integer column with the orders that you want.

A simple, brute-force way would be to add each boxplot one at a time.

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

df = pd.DataFrame(np.random.rand(37,4), columns=list('ABCD'))
columns_my_order = ['C', 'A', 'D', 'B']
fig, ax = plt.subplots()
for position, column in enumerate(columns_my_order):
    ax.boxplot(df[column], positions=[position])

ax.set_xticks(range(position+1))
ax.set_xticklabels(columns_my_order)
ax.set_xlim(xmin=-0.5)
plt.show()

Actually I got stuck with the same question. And I solved it by making a map and reset the xticklabels, with code as follows:

df = pd.DataFrame({"A":["d","c","d","c",'d','c','a','c','a','c','a','c']})
df['val']=(np.random.rand(12))
df['B']=df['A'].replace({'d':'0','c':'1','a':'2'})
ax=df.boxplot(column='val',by='B')
ax.set_xticklabels(list('dca'))

EDIT: this is the right answer after direct support was added somewhere between version 0.15-0.18

---

tl;dr: for recent pandas - use positions argument to boxplot.

Adding a separate answer, which perhaps could be another question - feedback appreciated.

I wanted to add a custom column order within a groupby, which posed many problems for me. In the end, I had to avoid trying to use boxplot from a groupby object, and instead go through each subplot myself to provide explicit positions.

import matplotlib.pyplot as plt
import pandas as pd

df = pd.DataFrame()
df['GroupBy'] = ['g1', 'g2', 'g3', 'g4'] * 6
df['PlotBy'] = [chr(ord('A') + i) for i in xrange(24)]
df['SortBy'] = list(reversed(range(24)))
df['Data'] = [i * 10 for i in xrange(24)]

# Note that this has no effect on the boxplot
df = df.sort_values(['GroupBy', 'SortBy'])
for group, info in df.groupby('GroupBy'):
    print 'Group: %r\n%s\n' % (group, info)

# With the below, cannot use
#  - sort data beforehand (not preserved, can't access in groupby)
#  - categorical (not all present in every chart)
#  - positional (different lengths and sort orders per group)
# df.groupby('GroupBy').boxplot(layout=(1, 5), column=['Data'], by=['PlotBy'])

fig, axes = plt.subplots(1, df.GroupBy.nunique(), sharey=True)
for ax, (g, d) in zip(axes, df.groupby('GroupBy')):
    d.boxplot(column=['Data'], by=['PlotBy'], ax=ax, positions=d.index.values)
plt.show()

Within my final code, it was even slightly more involved to determine positions because I had multiple data points for each sortby value, and I ended up having to do the below:

to_plot = data.sort_values([sort_col]).groupby(group_col)
for ax, (group, group_data) in zip(axes, to_plot):
    # Use existing sorting
    ordering = enumerate(group_data[sort_col].unique())
    positions = [ind for val, ind in sorted((v, i) for (i, v) in ordering)]
    ax = group_data.boxplot(column=[col], by=[plot_by], ax=ax, positions=positions)