# data analysis and wrangling
import pandas as pd
import numpy as np
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())

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

print(train[['Survived','Pclass']].groupby(['Pclass'], as_index = False).mean().sort_values(by = 'Survived', ascending = False))