 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]] '''
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