CODEWITHSUNDEEP
pythonpandasboxplotcategorical-data
smci
smci

Reputation: 33950

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

EDIT: this question arose back in 2013 with pandas ~0.13 and was obsoleted by direct support for boxplot somewhere between version 0.15-0.18 (as per @Cireo's late answer; also pandas greatly improved support for categorical since this was asked.)

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

train.boxplot(column='Salary', 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 (really, should be a categorical, but this was back in 0.13, where categorical was a third-class citizen) 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:

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.

Upvotes: 18

Views: 26151

Answers (8)

Tomas Giro
Tomas Giro

Reputation: 4267

Use the new positions= attribute:

df.boxplot(column=['Data'], by=['PlotBy'], positions=df.index.values)

Upvotes: 1

Cireo
Cireo

Reputation: 4427

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)

Upvotes: 7

VH2020
VH2020

Reputation: 29

This can be resolved by applying a categorical order. You can decide on the ranking yourself. I'll give an example with days of week.

  • Provide categorical order to weekday

    #List categorical variables in correct order
weekday = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
#Assign the above list to category ranking
wDays = pd.api.types.CategoricalDtype(ordered= True, categories=Weekday)
#Apply this to the specific column in DataFrame
df['Weekday'] = df['Weekday'].astype(wDays)
# Then generate your plot
plt.figure(figsize = [15, 10])
sns.boxplot(data = flights_samp, x = 'Weekday', y = 'Y Axis Variable', color = colour)

Upvotes: 0

eric R
eric R

Reputation: 321

If you're not happy with the default column order in your boxplot, you can change it to a specific order by setting the column parameter in the boxplot function.

check the two examples below:

np.random.seed(0)
df = pd.DataFrame(np.random.rand(37,4), columns=list('ABCD'))

##
plt.figure()
df.boxplot()
plt.title("default column order")

##
plt.figure()
df.boxplot(column=['C','A', 'D', 'B'])
plt.title("Specified column order")

enter image description here

Upvotes: 1

Paul H
Paul H

Reputation: 68156

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()

enter image description here

Upvotes: 12

Fernanda
Fernanda

Reputation: 21

It might sound kind of silly, but many of the plot allow you to determine the order. For example:

Library & dataset

import seaborn as sns
df = sns.load_dataset('iris')

Specific order

p1=sns.boxplot(x='species', y='sepal_length', data=df, order=["virginica", "versicolor", "setosa"])
sns.plt.show()

Upvotes: 2

Zhenyu
Zhenyu

Reputation: 487

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'))

Upvotes: 3

Cireo
Cireo

Reputation: 4427

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.

Upvotes: 2

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