独立したデータのばらつきを可視化するBoxplotとViolinplotについて ref: http://qiita.com/TomHortons/items/5b585a6860ff5ccd5ba5

boxplot.py

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

sns.boxplot(x='types', y="A", hue='sex', data=data, palette="PRGn")
sns.despine(offset=10, trim=True)

file1.txt

          A         B  sex  types
0  2.131411 -1.754907    0      1
1 -0.046614 -1.009540    0      2
2  0.136387 -0.236662    1      1
3 -3.515190  2.117925    1      1
4 -2.099287  1.647548    1      1
5 -0.536360 -0.920529    0      0
6  0.281726 -0.572448    1      2
7  2.202351 -3.214435    0      1
8 -0.825666  0.847394    1      0
9 -1.602873  1.338847    1      2

file4.txt

   types  sex variable     value
0      1    0        A  2.131411
1      2    0        A -0.046614
2      1    1        A  0.136387
3      1    1        A -3.515190
4      1    1        A -2.099287
5      0    0        A -0.536360
6      2    1        A  0.281726
7      1    0        A  2.202351
8      0    1        A -0.825666
9      2    1        A -1.602873

make_classification.py

import numpy as np
from sklearn.datasets import make_classification
import pandas as pd

x, y = make_classification(n_samples=1000, n_features=2, n_redundant=0, n_informative=2,n_clusters_per_class=2, n_classes=2)
data = np.c_[np.c_[x, y], np.random.binomial(2, .5, len(x))]
data = pd.DataFrame(data).rename(columns={0:'A', 1:'B', 2:'sex', 3:'types'})

melt.py

data_batch = pd.melt(data, id_vars = ['types', 'sex'], value_vars = data.columns[:-2].tolist())
print data_batch[:10]

violinplot.py

data_batch_A = data_batch[data_batch.variable=='A']
sns.violinplot(x = 'types',  y = 'value', hue = 'sex', data = data_batch_A, split=True)
sns.despine(offset=10, trim=True)