How to apply custom column order to pandas boxplot

问题:

I can get a boxplot of a salary column in a pandas DataFrame...

train.boxplot(column='PredictionError',by='Category',sym='')

...however I can't figure out how to define the index-order used on column 'Category' - I want to supply my own custom order, according to another criterion:

category_order_by_mean_salary = train.groupby('Category')['Salary'].mean().order().keys()

How can I apply my custom column order to the boxplot columns? (other than ugly kludging the column names with a prefix to force ordering)

'Category' is a string column taking 27 distinct values: ['Accounting & Finance Jobs','Admin Jobs',...,'Travel Jobs']. So it can be easily factorized with pd.Categorical.from_array()

On inspection, the limitation is inside pandas.tools.plotting.py:boxplot(), which converts the column object without allowing ordering:

• pandas.core.frame.py.boxplot() is a passthrough to
• pandas.tools.plotting.py:boxplot() which instantiates ...
• matplotlib.pyplot.py:boxplot() which instantiates ...
• matplotlib.axes.py:boxplot()

I suppose I could either hack up a custom version of pandas boxplot(), or reach into the internals of the object. And also file an enhance request.

EDIT: this question arose with pandas ~0.13 and has probably been obsoleted by recent (0.19+?) versions as per @Cireo's late answer.

回答1:

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()

回答2:

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'))

回答3:

Note that pandas can now create categorical columns. If you don't mind having all the columns present in your graph, or trimming them appropriately, you can do something like the below:

http://pandas.pydata.org/pandas-docs/stable/categorical.html

df['Category'] = df['Category'].astype('category', ordered=True)

Recent pandas also appears to allow positions to pass all the way through from frame to axes.

• https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py
• https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_core.py
• https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/pyplot.py
• https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/axes/_axes.py

回答4:

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)