Numpy Data Science PDF

Foundation of Numpy Data Science Lokender Kumar Ph.D.  Numpy NumPy - Numerical Python Advantages of Numpy Arrays: Allows several Mathematical Operations Faster operations Foundation of Data Science Lokender Kum...

Foundation of Numpy Data Science Lokender Kumar Ph.D.  Numpy NumPy - Numerical Python Advantages of Numpy Arrays: Allows several Mathematical Operations Faster operations Foundation of Data Science Lokender Kumar Ph.D. Numpy import numpy as np from time import process_time Foundation of Data Science Lokender Kumar Ph.D. Numpy python_list = [i for i in range(10000)] start_time = process_time() python_list = [i+5 for i in python_list] end_time = process_time() print(end_time - start_time) Foundation of Data Science Lokender Kumar Ph.D. Functions np_array = np.array([i for i in range(10000)]) start_time = process_time() np_array += 5 end_time = process_time() print(end_time - start_time) Foundation of Data Science Lokender Kumar Ph.D. Functions # list list1 = [1,2,3,4,5] print(list1) type(list1) Foundation of Data Science Lokender Kumar Ph.D. Functions np_array = np.array([1,2,3,4,5]) print(np_array) type(np_array) Foundation of Data Science Lokender Kumar Ph.D. Functions # creating a 1 dim array a = np.array([1,2,3,4]) print(a) Foundation of Data Science Lokender Kumar Ph.D. Functions a.shape b = np.array([(1,2,3,4),(5,6,7,8)]) print(b) Foundation of Data Science Lokender Kumar Ph.D. Functions b.shape c = np.array([(1,2,3,4),(5,6,7,8)],dtype=float) print(c) Foundation of Data Science Lokender Kumar Ph.D. Functions Initial Placeholders in numpy arrays # create a numpy array of Zeros x = np.zeros((4,5)) print(x) # create a numpy array of ones y = np.ones((3,3)) print(y) Foundation of Data Science Lokender Kumar Ph.D. Functions Initial Placeholders in numpy arrays # array of a particular value z = np.full((5,4),5) print(z) # create an identity matrix a = np.eye(5) print(a) Foundation of Data Science Lokender Kumar Ph.D. Functions Initial Placeholders in numpy arrays # create a numpy array with random values b = np.random.random((3,4)) print(b) # random integer values array within a specific range c = np.random.randint(10,100,(3,5)) print(c) Foundation of Data Science Lokender Kumar Ph.D. Functions Initial Placeholders in numpy arrays # array of evenly spaced values --> specifying the number of values required d = np.linspace(10,30,5) print(d) # array of evenly spaced values --> specifying the step e = np.arange(10,30,5) print(e) Foundation of Data Science Lokender Kumar Ph.D. Functions Initial Placeholders in numpy arrays # convert a list to a numpy array list2 = [10,20,20,20,50] np_array = np.asarray(list2) print(np_array) type(np_array) Foundation of Data Science Lokender Kumar Ph.D. Functions Analysing a numpy array c = np.random.randint(10,90,(5,5)) print(c) # array dimension print(c.shape) # number of dimensions print(c.ndim) Foundation of Data Science Lokender Kumar Ph.D. Functions # number of elements in an array print(c.size) # checking the data type of the values in the array print(c.dtype) Foundation of Data Science Lokender Kumar Ph.D.