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

# visualization
import seaborn as sns
import matplotlib.pyplot as plt


# machine learning
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


#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )

#Print to standard output, and see the results in the "log" section below after running your script
#print("\n\nTop of the training data:")
#print(train.head())

print("\n\nSummary statistics of training data")
#print(train.describe(include=['O']))

print(train[['Pclass','Survived']].groupby(['Pclass'], as_index = False).mean().sort_values(by='Survived',ascending=False)) 
print("\n")
print(train[['Sex','Survived']].groupby(['Sex'], as_index = False).mean().sort_values(by='Survived',ascending=False)) 
print("\n")
print(train[['SibSp','Survived']].groupby(['SibSp'], as_index = False).mean().sort_values(by='Survived',ascending=False)) 
print("\n")
print(train[['Parch','Survived']].groupby(['Parch'], as_index = False).mean().sort_values(by='Survived',ascending=False)) 
print("\n")

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

#Any files you save will be available in the output tab below
#train.to_csv('copy_of_the_training_data.csv', index=False)