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import csv
import numpy
filename = 'pima-indians-diabetes.csv'
raw_data = open(filename, 'rb')
reader = csv.reader(raw_data, delimiter=',', quoting=csv.QUOTE_NONE)
x = list(reader)
data = numpy.array(x).astype('float')
print(data.shape)

# Load CSV using NumPy
import pandas as pd
from numpy import loadtxt
filename = 'pima-indians-diabetes.csv'
raw_data = open(filename, 'rb')
data = loadtxt(raw_data, delimiter=",")
shape=data.shape
print shape


from pandas import read_csv
filename = 'pima-indians-diabetes.csv'
data = read_csv(filename, names=names)
peek=data.head(20)
print(peek)


# Dimensions of your data
from pandas import read_csv
filename = "pima-indians-diabetes.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
shape = data.shape
print(shape)
types=data.dtypes
print(types)
description=data.describe()
print(description)
class_count=data.groupby('class').size()
print(class_count)
corelations=data.corr(method='pearson')
print(corelations)


# Skew for each attribute
from pandas import read_csv
filename = "pima-indians-diabetes.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
skew = data.skew()
print(skew)


from matplotlib import pyplot
from pandas import read_csv
filename = 'pima-indians-diabetes.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
data.hist()
pyplot.show()

from matplotlib import pyplot
from pandas import read_csv
filename = 'pima-indians-diabetes.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
data.plot(kind='density', subplots=True, layout=(3,3), sharex=False)
pyplot.show()


from matplotlib import pyplot
from pandas import read_csv
import numpy
filename = 'pima-indians-diabetes.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
correlations = data.corr()
fig = pyplot.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(correlations, vmin=-1, vmax=1)
fig.colorbar(cax)
ticks = numpy.arange(0,9,1)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(names)
ax.set_yticklabels(names)
pyplot.show()


# Correction Matrix Plot (generic)
from matplotlib import pyplot
from pandas import read_csv
import numpy
filename = 'pima-indians-diabetes.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
correlations = data.corr()
# plot correlation matrix
fig = pyplot.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(correlations, vmin=-1, vmax=1)
fig.colorbar(cax)
pyplot.show()


# Scatterplot Matrix
from matplotlib import pyplot
from pandas import read_csv
from pandas.plotting import scatter_matrix
filename = "pima-indians-diabetes.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
scatter_matrix(data)
pyplot.show()

# Rescale data (between 0 and 1)
from pandas import read_csv
from numpy import set_printoptions
from sklearn.preprocessing import MinMaxScaler
filename = 'pima-indians-diabetes.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = read_csv(filename, names=names)
array = dataframe.values
# separate array into input and output components
X = array[:,0:8]
Y = array[:,8]
scaler = MinMaxScaler(feature_range=(0, 1))
rescaledX = scaler.fit_transform(X)
# summarize transformed data
set_printoptions(precision=3)
print(rescaledX[0:5,:])

import pandas as pd
from pandas import read_csv
filename='pima-indians-diabetes.csv'
data=read_csv(filename)
shape=data.shape
print(shape)
types=data.dtypes
print(types)

# Standardize data (0 mean, 1 stdev)
from sklearn.preprocessing import StandardScaler
from pandas import read_csv
from numpy import set_printoptions
filename = 'pima-indians-diabetes.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = read_csv(filename, names=names)
array = dataframe.values
# separate array into input and output components
X = array[:,0:8]
Y = array[:,8]
scaler = StandardScaler().fit(X)
rescaledX = scaler.transform(X)
# summarize transformed data
set_printoptions(precision=3)
print(rescaledX[0:5,:])




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