Learning Datascience Day 1

After Learning Python( Day 1-Day 10)
https://technologyandarchitecture.blogspot.com/2019/10/learning-python-day-1.html
https://technologyandarchitecture.blogspot.com/2019/10/learning-python-day-2.html
https://technologyandarchitecture.blogspot.com/2019/10/learning-python-day-3.html
https://technologyandarchitecture.blogspot.com/2019/10/learning-python-day-4.html
https://technologyandarchitecture.blogspot.com/2019/10/learning-python-day-5.html
https://technologyandarchitecture.blogspot.com/2019/10/learning-python-day-6.html
https://technologyandarchitecture.blogspot.com/2019/10/learning-python-day-7.html
https://technologyandarchitecture.blogspot.com/2019/11/learning-python-day-8.html
https://technologyandarchitecture.blogspot.com/2019/11/learning-python-day-9.html
https://technologyandarchitecture.blogspot.com/2019/11/learning-python-day-10.html

DATASCIENCE

DAY1

We will use Jupiter notebook. Please check prerequisites of installing jupyter notebook

Today we will start with Numpy module

Remember we need to import number for each notebook

import numpy as np

in above np can be anything even your name, consider as a synonym or shortform for numpy

arr1=np.array([11,22,33,44])

arr1

array([11, 22, 33, 44])

Note: Array is like a list

If we want to make 2 dimensional array

arr2=np.array([[1,2,3],[4,5,6],[7,8,9]])

print(arr2)

[[1 2 3]

 [4 5 6]

 [7 8 9]]

Arrange creates an array within a range, se below example

arr3=np.arange(2,10)

arr3

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

arr4=np.arange(2,10,2)

arr4

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

now with zeroes function

arr5=np.zeros((2,3))

arr5

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

       [0., 0., 0.]])

If we look above example the output is 0. Means it’s a float and if we want to convert float to integer

arr6=np.zeros((3,3),dtype=np.int32)

arr6

array([[0, 0, 0],

       [0, 0, 0],

       [0, 0, 0]])

arr7=np.ones((3,4))

arr7

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

       [1., 1., 1., 1.],

       [1., 1., 1., 1.]])

arr9=np.empty((3,3))

arr9

array([[0.00000000e+000, 0.00000000e+000, 0.00000000e+000],

       [0.00000000e+000, 0.00000000e+000, 6.67976753e-321],

       [2.13622844e-306, 2.33419537e-312, 2.56765117e-312]])

To find the dimension of the array use the command ndim

arr1

array([11, 22, 33, 44])

arr2

array([[1, 2, 3],

       [4, 5, 6],

       [7, 8, 9]])

print(arr1.ndim)

1

print(arr2.ndim)

2

To find the number of elements in the array use size function

print(arr1.size)

to find out item size( size of elements in bytes)

print(arr1.itemsize)

4

Similarly

print(arr1.dtype)

int32

print(arr9.dtype)

float64

print(arr1.shape)

(4,)

arr10=np.arange(1,13)

arr10

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

now we will reshape arr10 from one dimensional to multi dimensional

arr10.reshape((4,3))

array([[ 1,  2,  3],

       [ 4,  5,  6],

       [ 7,  8,  9],

       [10, 11, 12]])

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

a

a+a

array([[ 2,  4,  6],

       [ 8, 10, 12],

       [14, 16, 18]])

Similarly

a-a

a*a

a/a

a**a

b=np.array([[10,11,12,20],[13,14,15,30],[16,17,18,40]])

b

array([[10, 11, 12, 20],

       [13, 14, 15, 30],

       [16, 17, 18, 40]])

In the above example lets clear it up with detail explanation

	0	1	2	3
0	10	11	12	20
1	13	14	15	30
2	16	17	18	40

When we run the command

B[1,2]

Output is 15

So how the output is 15, index 1 that’s row 1 and column 2, and that is 15

b[1]

array([13, 14, 15, 30])

b[0:2,1:4]

array([[11, 12, 20],

       [14, 15, 30]])

In the above example, rows will be 0 and 1 and column will be 1 to 3

c=np.arange(1,12)

c

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

d=c[2:5]

d

now if we change the value of d at any index, then it also automatically changes the value of C also

d[1]=100

d

array([  3, 100,   5])

c

array([  1,   2,   3, 100,   5,   6,   7,   8,   9,  10,  11])

e=c[1:5].copy()

e

array([  2,   3, 100,   5])

Learning Datascience -Day2