Python Side by Side Charts on One Shape

Question

I am trying to create a field in Python 2.7 for each categorical value in column E from Pandas data

          A         B         C         D  E
0  0.647366  0.317832  0.875353  0.993592  1
1  0.504790  0.041806  0.113889  0.445370  2
2  0.769335  0.120647  0.749565  0.935732  3
3  0.215003  0.497402  0.795033  0.246890  1
4  0.841577  0.211128  0.248779  0.250432  1
5  0.045797  0.710889  0.257784  0.207661  4
6  0.229536  0.094308  0.464018  0.402725  3
7  0.067887  0.591637  0.949509  0.858394  2
8  0.827660  0.348025  0.507488  0.343006  3
9  0.559795  0.820231  0.461300  0.921024  1

I would like to do this using Matplotlib or any other graphics library. So far, the above code can display all categories combined on one chart. Here is the code to generate the above data and create a graph:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
fig, ax = plt.subplots()

# Data
df = pd.DataFrame(np.random.rand(10,4),columns=list('ABCD'))
df['E'] = [1,2,3,1,1,4,3,2,3,1]

# Boxplot
bp = ax.boxplot(df.iloc[:,:-1].values, widths=0.2)
plt.show()

In this example, the categories are 1,2,3,4. I would like to separate the individual boxes side by side in the same figure, only for categories 1 and 2, and show the names of the categories in the legend.

Is there any way to do this?

Additional Information:

The result should look something like the one shown in the third figure here - replace "Yes", "No" with "1", "2".

Answer 1

:

import numpy
import pandas
from matplotlib import pyplot
import seaborn
seaborn.set(style="ticks")

# Data
df = pandas.DataFrame(numpy.random.rand(10,4), columns=list('ABCD'))
df['E'] = [1, 2, 3, 1, 1, 4, 3, 2, 3, 1]

. ,

fig, axes = pyplot.subplots(ncols=4, figsize=(12, 5), sharey=True)
df.query("E in [1, 2]").boxplot(by='E', return_type='axes', ax=axes)

1 , , . .

ax = (
    df.set_index('E', append=True)  # set E as part of the index
      .stack()                      # pull A - D into rows 
      .to_frame()                   # convert to a dataframe
      .reset_index()                # make the index into reg. columns
      .rename(columns={'level_2': 'quantity', 0: 'value'})  # rename columns
      .drop('level_0', axis='columns')   # drop junk columns
      .pipe((seaborn.boxplot, 'data'), x='E', y='value', hue='quantity', order=[1, 2])  
)
seaborn.despine(trim=True)

, . hue x, :

ax = (
    df.set_index('E', append=True)  # set E as part of the index
      .stack()                      # pull A - D into rows 
      .to_frame()                   # convert to a dataframe
      .reset_index()                # make the index into reg. columns
      .rename(columns={'level_2': 'quantity', 0: 'value'})  # rename columns
      .drop('level_0', axis='columns')   # drop junk columns
      .pipe((seaborn.boxplot, 'data'), x='quantity', y='value', hue='E', hue_order=[1, 2])  
)
seaborn.despine(trim=True)

, :

    E quantity     value
0   1        A  0.935433
1   1        B  0.862290
2   1        C  0.197243
3   1        D  0.977969
4   2        A  0.675037
5   2        B  0.494440
6   2        C  0.492762
7   2        D  0.531296
8   3        A  0.119273
9   3        B  0.303639
10  3        C  0.911700
11  3        D  0.807861