# New Technology

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• Numpy ndarray
``````
# Import numpy
import numpy as np

data = [[2, 6, 1, 3, 7],
[5, 10, 4, 9, 8]]

data = np.array(data)

print data
'''
Output:

[[2, 6, 1, 3, 7], [5, 10, 4, 9, 8]]
'''

print data.shape
'''
Output:

(2, 5)
'''

# produce an array of all 0's
print np.zeros((2,3))
'''
Output:

[[ 0.  0.  0.]
[ 0.  0.  0.]]
'''

# produce an array of all 1's
print np.ones((2,3))
'''
Output:

[[ 1.  1.  1.]
[ 1.  1.  1.]]
'''

array = np.arange(10)
print array
'''
Output:

[0 1 2 3 4 5 6 7 8 9]
'''

print array[2:5]
'''
Output:

[2 3 4]
'''

array[5:8] = 0
print array
'''
Output:

[0 1 2 3 4 0 0 0 8 9]
'''
``````

## Array operations

``````
# 1. Arithmetic operations
data = np.array([[5,6,7],
[8,9,10]])

print data + 5
'''
Output:

[[10 11 12]
[13 14 15]]
'''

print data - 3
'''
Output:

[[2 3 4]
[5 6 7]]
'''

print data * data
'''
Output:

[[ 25  36  49]
[ 64  81 100]]
'''

# 2. Transposing  and swapping axis

data = np.arange(16).reshape((4,4))
print data
'''
Output:

[[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]
[12 13 14 15]]
'''

# Transpose of array
print data.T
'''
Output:

[[ 0  4  8 12]
[ 1  5  9 13]
[ 2  6 10 14]
[ 3  7 11 15]]
'''

x = np.random.randn(2,3)
print x
'''
Output:

[[ 0.37993325  0.60038379  0.18884729]
[ 1.23487005  0.07912221 -1.03242702]]
'''
``````

## Statistical Methods

``````
# Random data
data = np.random.randn(3,2)

print data.mean()
'''
Output: 0.2428949
'''

print data.min()
'''
Output: -0.6057506
'''

print data.max()
'''
Output: 2.2677427
'''
``````
Matrix Class
``````
X = np.random.randn(3,2)
X = np.matrix(X)

Y = np.random.randn(2,2)
Y = np.matrix(Y)

# Multiply the matrix
print X * Y
'''
Output:

[[-0.70717074  2.33580395]
[ 0.36777429 -1.75207365]
[ 0.06491743 -0.87368241]]
'''``````