Fundamentals of Numpy

Fundametal of Numpy

Numpy is linear algebra library for python . this is importent library because all pydata ecosystem rely on numpy.

import numpy as np

arr = [1,2,3]
arr

[1, 2, 3]

np.array(arr)

array([1, 2, 3])

my_mat = [[1,2,3],[4,5,6],[7,8,9]]

my_mat

[[1, 2, 3], [4, 5, 6], [7, 8, 9]]

np.array(my_mat)

array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

my_mat[0][1]

Arange

# np.arange(start , last , offset)

np.arange(0,10)

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

np.arange(0,5,1)

array([0, 1, 2, 3, 4])

np.arange(2,12,2)

array([ 2,  4,  6,  8, 10])

Zeros

# Zeros 

np.zeros(3)  # one dimensional

array([0., 0., 0.])

np.zeros((3,3))  # two dimensional

array([[0., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.]])

One's

# one's

np.ones(5)

array([1., 1., 1., 1., 1.])

np.ones((2,2))

array([[1., 1.],
       [1., 1.]])

linespace

# linspace -&gt; (start , end , how many point u want in between start to end)

np.linspace(0,5,10)

array([0.        , 0.55555556, 1.11111111, 1.66666667, 2.22222222,
       2.77777778, 3.33333333, 3.88888889, 4.44444444, 5.        ])

eye

# Diagonal element 

np.eye(3)

array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])

Random Function

# random -&gt; (number of points in between )

np.random.rand(5)

array([[0.93087129, 0.30534035, 0.2508104 ],
       [0.05900399, 0.39901502, 0.00206355],
       [0.10066378, 0.67083509, 0.95297132]])

np.random.rand(3,3)

array([[0.29917792, 0.88770559, 0.0923786 ],
       [0.33748283, 0.56245829, 0.88557374],
       [0.88111555, 0.40227034, 0.28943298]])

# random based on standard normal distrubution

np.random.randn(3)

array([ 0.65926802, -0.18545583, -0.5672595 ])

# random number between start to end anything in interger format

np.random.randint(1,20)

#  start , end , numbers of points in between 
np.random.randint(1,20,5)

array([18, 12, 16,  3,  4])

Reshape

# reshape

# we can reshape the arr into 2 dimesional
# we can't resize 10 into 2 row and 3 column or anything we have to reshape into row 2 and column 5
                
array = np.arange(10)

array.reshape(2,5)

array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

Random

number = np.random.randint(1,20,5);
print(number.max())
print(number.min())
print(number.argmax())  # return location where max is stored
print(number.argmin())  # return location where min is stored

Shape

 array.shape  #because its one dimesional

(10,)

dtype

# type
array.dtype

dtype('int32')

# instead of writing like these -&gt; np.random.randint(1,20)
from numpy.random import randint

# then we can directly call randint which will return random number in between 3 to 10 
randint(3,10)

arr_1 = np.arange(1,20)

mathematical operator

arr_1

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19])

arr_1 + arr_1

array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34,
       36, 38])

arr_1 * arr_1

array([  1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121, 144, 169,
       196, 225, 256, 289, 324, 361])

arr_1 - arr_1

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

arr_1 / arr_1

array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1.])

arr_1*10

array([ 10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110, 120, 130,
       140, 150, 160, 170, 180, 190])

arr_1**2

array([  1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121, 144, 169,
       196, 225, 256, 289, 324, 361], dtype=int32)

np.square(arr_1)

array([  1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121, 144, 169,
       196, 225, 256, 289, 324, 361], dtype=int32)

np.max(arr_1)

np.sqrt(arr_1)

array([1.        , 1.41421356, 1.73205081, 2.        , 2.23606798,
       2.44948974, 2.64575131, 2.82842712, 3.        , 3.16227766,
       3.31662479, 3.46410162, 3.60555128, 3.74165739, 3.87298335,
       4.        , 4.12310563, 4.24264069, 4.35889894])

np.exp(arr_1)

array([2.71828183e+00, 7.38905610e+00, 2.00855369e+01, 5.45981500e+01,
       1.48413159e+02, 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,
       8.10308393e+03, 2.20264658e+04, 5.98741417e+04, 1.62754791e+05,
       4.42413392e+05, 1.20260428e+06, 3.26901737e+06, 8.88611052e+06,
       2.41549528e+07, 6.56599691e+07, 1.78482301e+08])

# mathematical operator
np.sin(arr_1)

array([ 0.84147098,  0.90929743,  0.14112001, -0.7568025 , -0.95892427,
       -0.2794155 ,  0.6569866 ,  0.98935825,  0.41211849, -0.54402111,
       -0.99999021, -0.53657292,  0.42016704,  0.99060736,  0.65028784,
       -0.28790332, -0.96139749, -0.75098725,  0.14987721])

np.log(arr_1)

array([0.        , 0.69314718, 1.09861229, 1.38629436, 1.60943791,
       1.79175947, 1.94591015, 2.07944154, 2.19722458, 2.30258509,
       2.39789527, 2.48490665, 2.56494936, 2.63905733, 2.7080502 ,
       2.77258872, 2.83321334, 2.89037176, 2.94443898])

Numpy indexing and selection

arr_2d = np.array([+.
                   [5,10,15],[20,23,45],[78,54,12]])

arr_2d

array([[ 5, 10, 15],
       [20, 23, 45],
       [78, 54, 12]])

arr_2d[0][1]

#Grab Everything from row 2 which is starting from column 1 to onwords
arr_2d[:2,1:]

array([[10, 15],
       [23, 45]])

arr_2d[1,1:]

array([23, 45])

boolean = arr_2d &gt; 30

boolean

array([[False, False, False],
       [False, False,  True],
       [ True,  True, False]])

arr_2d[boolean]

array([45, 78, 54])

arr_2d[arr_2d&gt;20]

array([23, 45, 78, 54])

What is an algorithm?About us Programming Languages Contribute Contact

GitHub Gitter Twitter Source code Analytics