%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
import pandas

import seaborn
seaborn.set(style='whitegrid', gridweight='light')

data = np.random.lognormal(mean=-1, sigma=2, size=(370, 4))
df = pandas.DataFrame(data, columns=list('ABCD'))
df.min()

fig, ax = plt.subplots()
seaborn.boxplot(df, ax=ax, notch=True)
ax.set_yscale('log')       

fig, ax = plt.subplots()
seaborn.violinplot(df, ax=ax)
ax.set_yscale('log')              

fig, ax = plt.subplots()
seaborn.violinplot(np.log10(df), ax=ax)

seaborn.kdeplot(df.B, shade=True, vertical=True)

ax = seaborn.kdeplot(np.log10(df.B), shade=True, vertical=True)
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, pos: 10**x))

fig, ax = plt.subplots()
seaborn.violinplot(np.log10(df), ax=ax)
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, pos: 10**x))