JSON
- JSON stands for JavaScript Object Notation
- JSON is a lightweight format introduced for easy data exchange
- It is very easy for both humans and machines to read and write
- It is language-independent
- JSON is used in web applications to store and transfer data
SUPPORTED DATA TYPES:
- Number
- Array
- Object (Key-Value pair)
- Boolean
- String
EXAMPLE OF JSON:
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| #Basic example of JSON
{
"website_name":"MYBTECHPROJECTS",
"website_url":"www.mybtechprojects.tech",
"no_of_posts":30
"Categories": ["Arduino","Esp8266","python","NodeMcu","Raspberry PI"]
} |
#Basic example of JSON
{
"website_name":"MYBTECHPROJECTS",
"website_url":"www.mybtechprojects.tech",
"no_of_posts":30
"Categories": ["Arduino","Esp8266","python","NodeMcu","Raspberry PI"]
}
JSON PARSING IN PYTHON:
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| >>>#Parsing JSON data in Python
>>> import json
>>> j='{"website":"MYBTECHPROJECTS","author":"Gowtham","Category":"Python"}'
>>> mbp=json.loads(j)
>>> mbp["website"]
'MYBTECHPROJECTS'
>>> mbp["author"]
'Gowtham'
>>> mbp["Category"]
'Python'
>>> |
>>>#Parsing JSON data in Python
>>> import json
>>> j='{"website":"MYBTECHPROJECTS","author":"Gowtham","Category":"Python"}'
>>> mbp=json.loads(j)
>>> mbp["website"]
'MYBTECHPROJECTS'
>>> mbp["author"]
'Gowtham'
>>> mbp["Category"]
'Python'
>>>
CONVERT DICT TO JSON:
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| >>>#Converting Dict to JSON
>>> dict={}
>>> dict={'website':'MYBTECHPROJECTS','Tagline':'Learn what you like'}
>>> j=json.dumps(dict)
>>> print j
{"website": "MYBTECHPROJECTS", "Tagline": "Learn what you like"}
>>> |
>>>#Converting Dict to JSON
>>> dict={}
>>> dict={'website':'MYBTECHPROJECTS','Tagline':'Learn what you like'}
>>> j=json.dumps(dict)
>>> print j
{"website": "MYBTECHPROJECTS", "Tagline": "Learn what you like"}
>>>
NUMPY :
NumPy is a library in python programming used for scientific programming. It provides
- Support for large N-dimensional array
- Sophisticated functions for easy calculations.
- Useful for performing algebra and Fourier Transform functions.
PROGRAMS:
Numpy Basics
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| >>> import numpy as np
>>> a=np.array([1,2,3,4,5,6,7,8,9,10])
>>> print a[0]
1
>>> print a[9]
10
>>> print type(a)
<type 'numpy.ndarray'>
>>> print a.dtype
int32
>>> print a.shape
(10L,)
>>> |
>>> import numpy as np
>>> a=np.array([1,2,3,4,5,6,7,8,9,10])
>>> print a[0]
1
>>> print a[9]
10
>>> print type(a)
<type 'numpy.ndarray'>
>>> print a.dtype
int32
>>> print a.shape
(10L,)
>>>
Create array with pre-filled values
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| >>> #To create a array filled with zeros
>>> a=np.zeros(10)
>>> a
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
>>> a=np.zeros((2,2))
>>> a
array([[0., 0.],
[0., 0.]])
>>>#To create a array filled with ones
>>> a=np.ones((2,2))
>>> a
array([[1., 1.],
[1., 1.]])
>>># To create a Identity matrix
>>> a=np.eye(2)
>>> a
array([[1., 0.],
[0., 1.]])
>>>
>>> a=np.eye(3)
>>> a
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
>>>#To create a array with a constant value
>>> a=np.full((2,2),10)
>>> a
array([[10, 10],
[10, 10]])
>>># To fill a array with random no
>>> r=np.random.random((2,2))
>>> r
array([[0.52221867, 0.21183574],
[0.07215369, 0.65550072]])
>>>
>>> a=np.arange(4)
>>> a
array([0, 1, 2, 3])
>>> |
>>> #To create a array filled with zeros
>>> a=np.zeros(10)
>>> a
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
>>> a=np.zeros((2,2))
>>> a
array([[0., 0.],
[0., 0.]])
>>>#To create a array filled with ones
>>> a=np.ones((2,2))
>>> a
array([[1., 1.],
[1., 1.]])
>>># To create a Identity matrix
>>> a=np.eye(2)
>>> a
array([[1., 0.],
[0., 1.]])
>>>
>>> a=np.eye(3)
>>> a
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
>>>#To create a array with a constant value
>>> a=np.full((2,2),10)
>>> a
array([[10, 10],
[10, 10]])
>>># To fill a array with random no
>>> r=np.random.random((2,2))
>>> r
array([[0.52221867, 0.21183574],
[0.07215369, 0.65550072]])
>>>
>>> a=np.arange(4)
>>> a
array([0, 1, 2, 3])
>>>
NumPy functions
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| >>> a=np.array([1,2,3,4,5,6,7,8])
>>> b=np.array([8,7,6,5,4,3,2,1])
>>>
>>>#Multiplication
>>> print np.multiply(a,b)
[ 8 14 18 20 20 18 14 8]
>>>#Addition
>>> print np.add(a,b)
[9 9 9 9 9 9 9 9]
>>>#Subtraction
>>> print np.subtract(a,b)
[-7 -5 -3 -1 1 3 5 7]
>>>#Division
>>> print np.divide(a,b)
[0 0 0 0 1 2 3 8]
>>>#Sqare Root
>>> print np.sqrt(a)
[1. 1.41421356 1.73205081 2. 2.23606798 2.44948974
2.64575131 2.82842712]
>>> a=np.array([[1,1],[2,2],[3,3]])
>>> b=np.array([1,1])
>>> y=a+b
>>> y
array([[2, 2],
[3, 3],
[4, 4]])
>>> print y.shape
(3L, 2L)
>>>#Reshaping
>>> print np.reshape(y,(2,3))
[[2 2 3]
[3 4 4]]
>>> |
>>> a=np.array([1,2,3,4,5,6,7,8])
>>> b=np.array([8,7,6,5,4,3,2,1])
>>>
>>>#Multiplication
>>> print np.multiply(a,b)
[ 8 14 18 20 20 18 14 8]
>>>#Addition
>>> print np.add(a,b)
[9 9 9 9 9 9 9 9]
>>>#Subtraction
>>> print np.subtract(a,b)
[-7 -5 -3 -1 1 3 5 7]
>>>#Division
>>> print np.divide(a,b)
[0 0 0 0 1 2 3 8]
>>>#Sqare Root
>>> print np.sqrt(a)
[1. 1.41421356 1.73205081 2. 2.23606798 2.44948974
2.64575131 2.82842712]
>>> a=np.array([[1,1],[2,2],[3,3]])
>>> b=np.array([1,1])
>>> y=a+b
>>> y
array([[2, 2],
[3, 3],
[4, 4]])
>>> print y.shape
(3L, 2L)
>>>#Reshaping
>>> print np.reshape(y,(2,3))
[[2 2 3]
[3 4 4]]
>>>
THANKYOU
