# Author: OMKAR PATHAK # NumPy (Numeric Python) is a Python package used for building multi dimensional arrays and performing # various operations # In this program we will walk through various concepts and see available functions in the NumPy package. # For installing: pip3 install numpy import numpy as np # we have a function arange() which makes an array of the specified dimension. Example: myArray = np.arange(20) print(myArray) # [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19] # an array from 10 to 20 myArray = np.arange(10, 20) # [10 11 12 13 14 15 16 17 18 19] print(myArray) # an array from 10 to 20 with 2 steps myArray = np.arange(10, 20, 2) print(myArray) # [10 12 14 16 18] # reshape() helps to reshape our NumPy array myArray = np.arange(20) # syntax: reshape(number_of_rows, number_of_columns) myArray = myArray.reshape(4, 5) print(myArray) # [[ 0  1  2  3  4] #  [ 5  6  7  8  9] #  [10 11 12 13 14] #  [15 16 17 18 19]] myArray = myArray.reshape(10, 2) print(myArray) # [[ 0  1] #  [ 2  3] #  [ 4  5] #  [ 6  7] #  [ 8  9] #  [10 11] #  [12 13] #  [14 15] #  [16 17] #  [18 19]] # shape returns the shape of the array. The length of shape tuple is called as rank (or dimension) print(myArray.shape) # (10, 2) # ndim returns the dimension (rank) of the array print(myArray.ndim) # 2 # size returns the total number of elements in the array print(myArray.size) # 20 # to check the data we have dtype. print(myArray.dtype) # int64 # zeros creates an array will all zeros myArray = np.zeros((3, 4)) print(myArray) # [[ 0.  0.  0.  0.] #  [ 0.  0.  0.  0.] #  [ 0.  0.  0.  0.]] # ones creates an array with all ones myArray = np.ones((3, 4)) print(myArray) # [[ 1.  1.  1.  1.] #  [ 1.  1.  1.  1.] #  [ 1.  1.  1.  1.]] # numpy random module helps to initialize array with random values myArray = np.random.rand(3, 4) print(myArray) # [[ 0.54808903  0.08750717  0.23886267  0.93589283] #  [ 0.90750146  0.31197039  0.54013725  0.91092763] #  [ 0.38827674  0.04647878  0.15997665  0.94909537]]