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Hi, my Name is Shreyash

Lets import Numpy first and find the answer of the last exercise

import numpy as np

a=np.ones((5,5))
x=np.zeros((3,3))
a[1:4,1:4]=x
a[2,2]=9
print(a)

[[1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 1.]
 [1. 0. 9. 0. 1.]
 [1. 0. 0. 0. 1.]
 [1. 1. 1. 1. 1.]]

When we assign Numpy array to another variable and the copy of the array isn't created it points to the array meaning

a=np.array([1, 3, 4])
b=a
b[0] =100
print(a)

[100   3   4]

So, if you want to copy b to a and want them to behave separately you may try to use the array's method to make it behaving the way we intend

a=np.array([1, 3, 4])
b=a.copy()
b[0] =100
print(a," and ", b)

[1 3 4]  and  [100   3   4]

Mathmetics

It does element wise operations

Also this jupyter notebook takes excution order no.(Whatever is In[no]) as indicator of the present variable values so if the below line wouldn't have had reinitialization of the array it would've made the array repeated everytime we excute the line and the array would keep growing

a=np.array([1,2,3])
a

array([1, 2, 3])

a-2

array([-1,  0,  1])

a*2

array([2, 4, 6])

a/2

array([0.5, 1. , 1.5])

b=np.array([0,1,0])
a +b

array([1, 3, 3])

a ** 2

array([1, 4, 9])

np.sin(a)

array([0.84147098, 0.90929743, 0.14112001])

np.cos(a)

array([ 0.54030231, -0.41614684, -0.9899925 ])

Linear Algebra

Mtrix Stuff

Creating Identity matrix

c=np.identity(3)
c

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

Matrix multiplication

a=np.ones((2,3))
a
b=np.full((3,2),2)
np.matmul(a,b)

array([[6., 6.],
       [6., 6.]])

Finding the determinent of the matrix

np.linalg.det(c)

1.0

Statistics

stats = np.array([[1,2,3],[4,5,6]])
stats

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

np.min(stats)

1

np.max(stats)

6

Get min or max from each row with axis=1

np.min(stats, axis=1)

array([1, 4])

np.max(stats, axis=1)

array([3, 6])

Get ~Some~ Sum

np.sum(stats)

21

np.sum(stats, axis=1)

array([ 6, 15])

Reorganizing Arrays

before=np.array([[1, 2, 3, 4],[5, 6, 7,8]])
after=before.reshape((1,8))
print ("Before:", before)
print("After:", after)

Before: [[1 2 3 4]
 [5 6 7 8]]
After: [[1 2 3 4 5 6 7 8]]

Stacking vectors

Vertical Stack

v1=np.array([1,2,3,4])
v2=np.array([5,6,7,8])
np.vstack((v1, v2, v1))

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

Horizontal Stack

v1=np.zeros((2,4))
v2=np.ones((2,2))

np.hstack((v1, v2, v2, v1))

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

Miscellaneous

Load data from file

Delimiter defines what seperates the data

Read data.txt
Copy from it

data from: https://github.com/KeithGalli/NumPy

filedata=np.genfromtxt('data.txt', delimiter=',')
filedata

array([[  1.,  13.,  21.,  11., 196.,  75.,   4.,   3.,  34.,   6.,   7.,
          8.,   0.,   1.,   2.,   3.,   4.,   5.],
       [  3.,  42.,  12.,  33., 766.,  75.,   4.,  55.,   6.,   4.,   3.,
          4.,   5.,   6.,   7.,   0.,  11.,  12.],
       [  1.,  22.,  33.,  11., 999.,  11.,   2.,   1.,  78.,   0.,   1.,
          2.,   9.,   8.,   7.,   1.,  76.,  88.]])

change data type of the file

filedata=filedata.astype('int32')
filedata

array([[  1,  13,  21,  11, 196,  75,   4,   3,  34,   6,   7,   8,   0,
          1,   2,   3,   4,   5],
       [  3,  42,  12,  33, 766,  75,   4,  55,   6,   4,   3,   4,   5,
          6,   7,   0,  11,  12],
       [  1,  22,  33,  11, 999,  11,   2,   1,  78,   0,   1,   2,   9,
          8,   7,   1,  76,  88]])

Boolean masking and Advanced indexing

Comparing

filedata>50

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

Get values grater than 50 from the array

filedata[filedata>50]

array([196,  75, 766,  75,  55, 999,  78,  76,  88])

Also we can index with lists

a=np.array([1, 2, 3, 4, 5, 6])
a[[0, 2, 5]]

array([1, 3, 6])

Tells us which colums stisfy the condition of the array

axis =1 will show that data for the rows

np.any(filedata>50, axis=0)

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

np.all(filedata>50, axis=0)

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

Can also use logical conditions for this

here Python's and dosen't work we need to use & for and operation

filedata[(filedata>50) & (filedata<100)]

array([75, 75, 55, 78, 76, 88])

Just like above we can also use the logical or

logical or is | and logical not is ~

filedata[(filedata>50) | (filedata<10)]

array([  1, 196,  75,   4,   3,   6,   7,   8,   0,   1,   2,   3,   4,
         5,   3, 766,  75,   4,  55,   6,   4,   3,   4,   5,   6,   7,
         0,   1, 999,   2,   1,  78,   0,   1,   2,   9,   8,   7,   1,
        76,  88])

filedata[(filedata>50) | ~(filedata<10)]

array([ 13,  21,  11, 196,  75,  34,  42,  12,  33, 766,  75,  55,  11,
        12,  22,  33,  11, 999,  11,  78,  76,  88])

Now, showing valuse grater than fifty or not grater than 10

Another exercise

Solution:

ans = np.ones((1,1))
ans

array([[1.]])

ans=np.hstack((ans,(ans+ans), (ans+ans+ans), (ans+ans+ans+ans), (ans+ans+ans+ans+ans)))
ans

array([[1., 2., 3., 4., 5.]])

ans=np.vstack((ans,ans+5))
ans

array([[ 1.,  2.,  3.,  4.,  5.],
       [ 6.,  7.,  8.,  9., 10.]])

ans=np.vstack((ans,ans[1,:]+5))
ans

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

ans=np.vstack((ans, ans+15))
ans

array([[ 1.,  2.,  3.,  4.,  5.],
       [ 6.,  7.,  8.,  9., 10.],
       [11., 12., 13., 14., 15.],
       [16., 17., 18., 19., 20.],
       [21., 22., 23., 24., 25.],
       [26., 27., 28., 29., 30.]])

Intresting Solutions

ans[2:4, 0:2]

array([[11., 12.],
       [16., 17.]])

ans[[0,1,2,3],[1,2,3,4]]

array([ 2.,  8., 14., 20.])

ans[[0, 4, 5], 3:]

array([[ 4.,  5.],
       [24., 25.],
       [29., 30.]])

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