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import matplotlib

%matplotlib inline


    

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"""
VARIABLE DESCRIPTIONS:
survival        Survival
                (0 = No; 1 = Yes)
pclass          Passenger Class
                (1 = 1st; 2 = 2nd; 3 = 3rd)
name            Name
sex             Sex
age             Age
sibsp           Number of Siblings/Spouses Aboard
parch           Number of Parents/Children Aboard
ticket          Ticket Number
fare            Passenger Fare
cabin           Cabin
embarked        Port of Embarkation
                (C = Cherbourg; Q = Queenstown; S = Southampton)

SPECIAL NOTES:
Pclass is a proxy for socio-economic status (SES)
 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower

Age is in Years; Fractional if Age less than One (1)
 If the Age is Estimated, it is in the form xx.5

With respect to the family relation variables (i.e. sibsp and parch)
some relations were ignored.  The following are the definitions used
for sibsp and parch.

Sibling:  Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic
Spouse:   Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)
Parent:   Mother or Father of Passenger Aboard Titanic
Child:    Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic

Other family relatives excluded from this study include cousins,
nephews/nieces, aunts/uncles, and in-laws.  Some children travelled
only with a nanny, therefore parch=0 for them.  As well, some
travelled with very close friends or neighbors in a village, however,
the definitions do not support such relations.
"""
True


    

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# The first thing to do is to import the relevant packages
# that I will need for my script, 
# these include the Numpy (for maths and arrays)
# and csv for reading and writing csv files
# If i want to use something from this I need to call 
# csv.[function] or np.[function] first

import csv as csv 
import numpy as np

# Open up the csv file in to a Python object
data_all = []
with open('train.csv') as train_file:
    csv_reader = csv.reader(train_file, delimiter=',', quotechar='"')
    for row in csv_reader:
        data_all.append(row)
data_all = np.array(data_all)
data = data_all[1::]

test_all = []
with open('test.csv') as test_file:
    csv_reader = csv.reader(test_file, delimiter=',', quotechar='"')
    for row in csv_reader:
        test_all.append(row)
test_all = np.array(test_all)
test = test_all[1::]


    

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# The size() function counts how many elements are in
# in the array and sum() (as you would expects) sums up
# the elements in the array.

number_passengers = np.size(data[0::,1].astype(np.float))
number_survived = np.sum(data[0::,1].astype(np.float))
proportion_survivors = number_survived / number_passengers

women_only_stats = data[0::,4] == "female" # This finds where all 
                                           # the elements in the gender
                                           # column that equals “female”
men_only_stats = data[0::,4] != "female"   # This finds where all the 
                                           # elements do not equal 
                                           # female (i.e. male)

# Using the index from above we select the females and males separately
women_onboard = data[women_only_stats,1].astype(np.float)     
men_onboard = data[men_only_stats,1].astype(np.float)

# Then we finds the proportions of them that survived
proportion_women_survived = \
                       np.sum(women_onboard) / np.size(women_onboard)  
proportion_men_survived = \
                       np.sum(men_onboard) / np.size(men_onboard) 

# and then print it out
print('Proportion of women who survived is {p:.2f}'.format(
        p=proportion_women_survived))
print('Proportion of men who survived is {p:.2f}'.format(
        p=proportion_men_survived))


    

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data[0::,5].astype(np.float)


    

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import pandas as pd

# For .read_csv, always use header=0 when you know row 0 is the header row
df = pd.read_csv('train.csv', header=0)


    

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for i in range(1,4):
    print(i, len(df[ (df['Sex'] == 'male') & (df['Pclass'] == i) ]))


    

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import pylab as P
df['Age'].hist()
P.show()

df['Age'].dropna().hist(bins=16, range=(0,80), alpha = .5)
P.show()


    

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# Ajouter une colonne :
df['Gender'] = 4

# Peut-être avec des valeurs plus intéressantes :
df['Gender'] = df['Sex'].map( lambda x: x[0].upper() )

# Ou binaire :
df['Gender'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)


    

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median_ages = np.zeros((2,3))
for i in range(0, 2):
    for j in range(0, 3):
        median_ages[i,j] = df[(df['Gender'] == i) & \
                              (df['Pclass'] == j+1)]['Age'].dropna().median()
median_ages


    

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# On commence avec une copie :
df['AgeFill'] = df['Age']

df[ df['Age'].isnull() ][['Gender','Pclass','Age','AgeFill']].head(10)


    

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# Et puis on le rempli :
for i in range(0, 2):
    for j in range(0, 3):
        df.loc[ (df.Age.isnull()) & (df.Gender == i) & 
                (df.Pclass == j+1),\
                'AgeFill'] = median_ages[i,j]
df[ df['Age'].isnull() ][['Gender','Pclass','Age','AgeFill']].head(10)


    

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df['AgeIsNull'] = pd.isnull(df.Age).astype(int)


    

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# Feature engineering


    

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# parch is number of parents or children on board.
df['FamilySize'] = df['SibSp'] + df['Parch']

# Class affected survival.  Maybe age will, too.
# Who knows, maybe the product will be predictive, too.  Let's set it up.
df['Age*Class'] = df.AgeFill * df.Pclass


    

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# We can find the columns with strings.
df.dtypes[df.dtypes.map(lambda x: x=='object')]

# We can drop some columns we think won't be interesting.
# Most of these are string columns (see above).  We made a
# copy of age.
df_clean = df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked', 'Age'],
                   axis=1)

# Numpy arrays are more convenient for doing maths.
train_data = df_clean.values

# Compare to the original data array.