convertingNonNumData.py

#titanic data set link below
#https://pythonprogramming.net/static/downloads/machine-learning-data/titanic.xls
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
import numpy as np
from sklearn.cluster import KMeans
from sklearn import preprocessing, cross_validation
import pandas as pd

'''
1st class was the best and 3rd class the worst
Pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)
survival Survival (0 = No; 1 = Yes)
name Name
sex Sex
age Age
sibsp Number of Siblings/Spouses Aboard
parch Number of Parents/Children Aboard
ticket Ticket Number
fare Passenger Fare (British pound)
cabin Cabin
embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)
boat Lifeboat
body Body Identification Number
home.dest Home/Destination
'''

df = pd.read_excel('titanic.xls')
print(df.head())
#dropping values we dont need
df.drop(['body','name'], 1, inplace=True)
#converts all of the columns in the dataframe to numeric
df.convert_objects(convert_numeric=True)
df.fillna(0, inplace=True)
#print(df.head())

def handle_non_numerical_data(df):
    #every column
    columns = df.columns.values
    for column in columns:
        text_digit_vals = {}
        def convert_to_int(val):
            #ex {'Male':0, 'Female':1}
            return text_digit_vals[val]

        if df[column].dtype != np.int64 and df[column].dtype != np.float64:
            column_contents = df[column].values.tolist()
            #get the unique non repetitive string values
            unique_elements = set(column_contents)
            x = 0
            for unique in unique_elements:
                if unique not in text_digit_vals:
                    text_digit_vals[unique] = x
                    x+=1

            df[column] = list(map(convert_to_int, df[column]))

    return df

df = handle_non_numerical_data(df)
print(df.head())