import numpy as np
import pandas as pd
import random as rnd

import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier

train_df=pd.read_csv('../input/train.csv')
test_df=pd.read_csv('../input/test.csv')
combine=[train_df,test_df]

print(train_df.columns.values)
print(train_df.head())

train_df.info()

train_df.describe()

print(train_df[['Pclass','Survived']].groupby(['Pclass']).mean())

g=sns.FacetGrid(train_df,col='Survived')
g.map(plt.hist,'Age',bins=20)