Create an array from a list
my_list = [1, 2, 3] my_array = np.array(my_list)
Create an array with arrange
my_array = np.arange(0, 10) my_array2 = np.arange(0, 10, 2) # with step 2
Create an array with zeros
my_array = np.zeros((2, 3)) # default type if float my_array2 = np.zeros((2, 3), np.int32) # with type int
Create an array with ones
my_array = np.ones((10, 8), np.int32)
Create an array from random
np.random.seed(101) array1 = np.random.randint(0, 100, 10) array2=np.random.randint(0,100,10)
Max, Min, Mean
np.random.seed(101) array1 = np.random.randint(0, 100, 10) print(array1.max()) print(array1.argmax()) # index of max value print(array1.min()) print(array1.argmin()) # index of min value print(array1.mean())
Indexing an array
matrix = np.arange(0, 100).reshape(10, 10) print(matrix) print(matrix[2,3]) row = 1 col = 4 print(matrix[row,col])
Manipulating an array
matrix = np.arange(0, 100).reshape(10, 10) print(matrix) matrix[0:3, 0:3] = 0 print(matrix)
Copying an array
matrix: np.ndarray = np.arange(0, 100).reshape(10, 10) print(matrix) matrix2 = matrix.copy() matrix2[0:3, 0:3] = 0 print(matrix2)
Slicing an array
matrix = np.arange(0, 100).reshape(10, 10) print(matrix[1, :]) # whole row 1 print(matrix[:, 1]) # whole col 1 print(matrix[1, 1:4]) # row 1 from col 1 to 4 print(matrix[1:4, 1]) # col 1 from row 1 to 4 new_matrix: np.ndarray = matrix[1, :] print(new_matrix.reshape(5, 2))
Reshaping an array
np.random.seed(101) array1 = np.random.randint(0, 100, 10) print(array1) print(array1.shape) array2 = array1.reshape((2, 5)) print(array2) print(array2.shape)