Operations on Numpy Arrays

Reshape the array to a desired size

import numpy as np arr_3d = np.arange(24).reshape(2, 3, 4) print(arr_3d) arr_auto_reshape = np.arange(24).reshape(4, 2, -1) print(arr_auto_reshape) print(arr_auto_reshape.shape)

Note: We can use '-1' as the last dimension during reshape and numpy will figure out the required dimension automatically.

Stacking arrays horizontally and vertically

import numpy as np arr_1 = np.arange(25).reshape(5, -1) arr_2 = np.arange(15).reshape(5, -1) arr_3 = np.arange(15).reshape(-1, 3) print(arr_1) print(arr_2) print(arr_3) hstack = np.hstack([arr_1, arr_2]) vstack = np.vstack([arr_2, arr_3]) print(hstack) print(vstack)

Mathematical Operations on arrays

import numpy as np arr_1 = np.arange(10) print(arr_1) sin_arr = np.sin(arr_1) print(sin_arr) cos_arr = np.cos(arr_1) print(cos_arr) # Custom vectorized function fn = np.vectorize(lambda x: x/5) print(fn(arr_1))

Note: If you want to apply a customized function on arrays, make sure to vectorize the function and apply it like inbuilt functions. If you use for loops the power of numpy is lost.

Linear Algebra - Inverse, Determinant, Eigen Values, Dot Product

import numpy as np matrix_1 = np.array([[1, 2, 3], [0, 1, 4], [5, 1, 0]]) matrix_2 = np.arange(12).reshape(3, 4) inverse = np.linalg.inv(matrix_1) determinant = np.linalg.det(matrix_1) eigen = np.linalg.eig(matrix_1) dot_product = np.dot(matrix_1, matrix_2) print('inverse:', inverse) print('determinant:', inverse) print('eigen:', eigen) print('dot product:', dot_product)

Swapping elements of numpy array

import numpy as np m = 0 n = 2 a = np.arange(11*12).reshape(11, 12) + 1 print(a) a[[m,n]] = a[[n,m]] print(a)

Print Matrix such that borders are filled with ones, and rest of elements are filled with zero

import numpy as np n = 5 # Method One a = np.ones(n*n, dtype=int).reshape(n,n) a[1:a.shape[0]-1, 1:a.shape[1]-1] = 0 print(a) # Method Two border_array = np.ones((n, n), dtype = int) border_array[1:-1, 1:-1] = 0 print(border_array)

Transpose of Matrix

If we have a set of data points $x_1, x_2, x_3$ upto $x_n$, stored in array, we can create a design matrix using vstack and transpose.

import numpy as np X = [5, 6, 7] print(np.vstack((np.ones(len(X)), X)).T)