Numpy

• 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]]
  '''