import itertools
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings # current version of seaborn generates a bunch of warnings that we'll ignore
warnings.filterwarnings("ignore")
sns.set(style="white", color_codes=True)

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))

datass = pd.read_csv('../input/winequality-red.csv')

#data.plot(kind = 'hist',bins = 100,figsize = (15,15))
datass['quality'].value_counts()

datass.groupby(['quality']).count().plot(kind='bar',legend=False,color='blue')

datass.plot.scatter(x='residual sugar', y='alcohol', s=1, c='red')

#datass.hist(column='quality', bins = 6, figsize=(15,12))

#'''print ("Skew is:", datass.quality.skew())
#plt.hist(datass.quality, color='blue')
#plt.show()'''

sns.FacetGrid(datass, hue = 'quality', size=5).map(plt.scatter, "residual sugar", "alcohol", s=10).add_legend()

#data['quality'].value_counts().plot(kind = 'hist',bins = 6,figsize = (15,15))