Why Numpy over Lists?

1. Speed of computation is faster
2. Designed for data analysis
3. Vector operations can be performed
4. Concise and easy to read/write

Learn By Examples

Create a 3*3 array using predefined lists

import numpy as np list_1 = [1,2,3] list_2 = [4,5,6] list_3 = [7,8,9] out = np.array([list_1, list_2, list_3]) print(out)

Element Wise Multiplication

import numpy as np list_1 = [1,2,3] list_2 = [4,5,6] arr1 = np.array(list_1) arr2 = np.array(list_2) mul = arr1 * arr2 print(mul) list_mul = list(mul) print(list_mul)

Create array of given dimension filled with given integer value

import numpy as np rows = 5 columns = 3 value = 15 # using np.full arr = np.full((rows, columns), value) print(arr) # using np.tile arr = np.tile(value, (rows, columns)) print(arr) # using np.ones + multiplicationm # note that here we need to specify dtype=int as float is default for this function arr = np.ones((rows, columns), dtype=int)*value print(arr)

Create array filled with multiples of given number

import numpy as np start = 5 stop = 51 step = 5 arr = np.arange(start, stop, step) print(arr)

Create a checkerboard matrix of size n*n

import numpy as np board_size = 5 out = np.tile([[0, 1], [1, 0]], (board_size//2, board_size//2)) print(out)

Explanation

• np.tile can be used to repeat an array any number of times across any number of dimensions

• Checkerboard's smallest unit is

  [0, 1], [1, 0]
• Since smallest unit is already 2x2, whatever is this required board_size, we will divide it by 2

Reference

• https://stackoverflow.com/a/56887785/7048915

Print the shape and dimension of an array

import numpy as np list_1 = [10,11,12,13] list_2 = [15,12,13,14] arr = np.array([list_1, list_2]) print(arr) print('shape:', np.shape(arr)) print('dimension:', np.ndim(arr)) print('data type:', arr.dtype) print('length of one array element (bytes):', arr.itemsize)

Extract all elements of 2nd column in 2D array

arr = np.arange(24).reshape(6, 4) print(arr) print(arr[:, 1])

Extract border row, column of numpy array

import numpy as np array_2d = np.arange(24).reshape(6, 4) col_first = array_2d[:, 0] row_first = array_2d[0, :] col_last = array_2d[:, array_2d.shape[1]-1] row_last = array_2d[array_2d.shape[0]-1, :] print(col_first) print(row_first) print(col_last) print(row_last)

Binomial Distribution

Problem Statement

Generate a binomial distribution, tested 10 times, given the number of trials(n) and probability(p) of each trial.

Input

The input will contain seed, n and p in the same order.

Output

The output should contain a numpy array with 10 numbers representing the required binomial distribution.

import numpy as np # seed=int(input()) # n=int(input()) # p=float(input()) seed=10 n=10 p=0.5 np.random.seed(seed) s = np.random.binomial(n, p, 10) print(s)

Conclusion

1. Numpy arrays are significantly faster than lists when it comes to mathematical operations
2. Numpy operations are concise, and easy to read & write.
3. Numpy arrays are built for vector mathematics so that element wise calculations can be easily carried out.
4. While in lists, one would have to write a for-loop or list comprehension for basic things like squaring all numbers in the list, the equivalent on a numpy array will simply be "array_name * 2".

References

• https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html
• https://numpy.org/doc/stable/reference/random/index.html
• https://numpy.org/doc/stable/reference/random/generated/numpy.random.binomial.html