Python

# importing libraries
import pandas
import scipy
import numpy
from sklearn.preprocessing import MinMaxScaler

# data set link
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
# data parameters
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']

# preparating of dataframe using the data at given link and defined columns list
dataframe = pandas.read_csv(url, names = names)
array = dataframe.values

# separate array into input and output components
X = array[:,0:8]
Y = array[:,8]

# initialising the MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
# learning the statistical parameters for each of the data and transforming
rescaledX = scaler.fit_transform(X)

# summarize transformed data
numpy.set_printoptions(precision=3)
print(rescaledX[0:5,:])

After rescaling see that all of the values are in the range between 0 and 1. 

Output: 

[[ 0.353  0.744  0.59   0.354  0.0    0.501  0.234  0.483]
 [ 0.059  0.427  0.541  0.293  0.0    0.396  0.117  0.167]
 [ 0.471  0.92   0.525  0.     0.0    0.347  0.254  0.183]
 [ 0.059  0.447  0.541  0.232  0.111  0.419  0.038  0.0  ]
 [ 0.0    0.688  0.328  0.354  0.199  0.642  0.944  0.2  ]]
 
2. Binarize Data (Make Binary)  

We can transform our data using a binary threshold. All values above the threshold are marked 1 and all equal to or below are marked as 0.
This is called binarizing your data or threshold your data. It can be useful when you have probabilities that you want to make crisp values. It is also useful when feature engineering and you want to add new features that indicate something meaningful.
We can create new binary attributes in Python using scikit-learn with the Binarizer class.
Code: Python code for binarization 

# import libraries
from sklearn.preprocessing import Binarizer
import pandas
import numpy

# data set link
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
# data parameters
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']

# preparating of dataframe using the data at given link and defined columns list
dataframe = pandas.read_csv(url, names = names)
array = dataframe.values

# separate array into input and output components
X = array[:, 0:8]
Y = array[:, 8]
binarizer = Binarizer(threshold = 0.0).fit(X)
binaryX = binarizer.transform(X)

# summarize transformed data
numpy.set_printoptions(precision = 3)
print(binaryX[0:5,:])

We can see that all values equal or less than 0 are marked 0 and all of those above 0 are marked 1. 

Output: 

[[ 1.  1.  1.  1.  0.  1.  1.  1.]
 [ 1.  1.  1.  1.  0.  1.  1.  1.]
 [ 1.  1.  1.  0.  0.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.]]
3. Standardize Data  

Standardization is a useful technique to transform attributes with a Gaussian distribution and differing means and standard deviations to a standard Gaussian distribution with a mean of 0 and a standard deviation of 1.
We can standardize data using scikit-learn with the StandardScaler class.
Code: Python code to Standardize data (0 mean, 1 stdev)  


# importing libraries
from sklearn.preprocessing import StandardScaler
import pandas
import numpy
 
# data set link
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
# data parameters
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
 
# preparating of dataframe using the data at given link and defined columns list
dataframe = pandas.read_csv(url, 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
numpy.set_printoptions(precision = 3)
print(rescaledX[0:5,:])
The values for each attribute now have a mean value of 0 and a standard deviation of 1. 

Output: 

[[ 0.64   0.848  0.15   0.907 -0.693  0.204  0.468  1.426]
 [-0.845 -1.123 -0.161  0.531 -0.693 -0.684 -0.365 -0.191]
 [ 1.234  1.944 -0.264 -1.288 -0.693 -1.103  0.604 -0.106]
 [-0.845 -0.998 -0.161  0.155  0.123 -0.494 -0.921 -1.042]
 [-1.142  0.504 -1.505  0.907  0.766  1.41   5.485 -0.02 ]]
 
References:  

https://www.analyticsvidhya.com/blog/2016/07/practical-guide-data-preprocessing-python-scikit-learn/
https://www.xenonstack.com/blog/data-preprocessing-data-wrangling-in-machine-learning-deep-learning
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