The document discusses various ways to convert between JSON and XML formats in Python. It describes using the json and xmltodict modules to serialize and deserialize between the two formats. Methods like json.loads(), json.dumps(), xmltodict.unparse() are used to convert between Python dictionaries and JSON/XML strings or files. Both string conversions and file conversions are demonstrated.
A graph search (or traversal) technique visits every node exactly one in a systematic fashion. Two standard graph search techniques have been widely used: Depth-First Search (DFS) Breadth-First Search (BFS)
Erlang is successfully used for game servers. Lua is a friendly, small language that is big in game scripting. And VoltDB is a new high speed ACID database from PostgreSQL inventor Mike Stonebreaker. Add some JSON, Websockets and JavaScript and you have an exciting HTML5 game stack.
This talk is about research and results from the work on a very high throughput game server with embedded Lua for game logic and VoltDB as SQL database backend. The stack aims to maximize robustness, throughput and ease of scripting. Its special feature is linearly scaling true ACID with a goal of a million transactions per second, while being easy on the game logic scripter.
This stack is part of the development effort of games start-up Eonblast for the cross-media Science Fiction franchise Solar Tribes, which will feature film elements merged with cross-platform online games. The stack is created to allow for a unified, non-sharded game world experience, thus for a unified story line and also otherwise transcends current MMO conventions. Players will share the same stories at the same time and be empowered to leave a stronger imprint on their environment. Interaction with other players is taken beyond the limitations currently seen in social games. All of which are technical challenges, asking for fastest possible data access and better data integrity guarantees.
A triple asynchronous server side architecture is described that communicates non-blocking with a fat JS/HTML5 browser client. The related Open Source projects Erlvolt and Fleece are introduced, which were created for this stack. The embedded Lua driver Erlualib and Robert Virding's new project, the Lua VM emulator Luerl are compared and evaluated. A brief overview of capabilities of new databases is given to explain why VoltDB was chosen for the task.
(c) 2012 Eonblast Corporation
A graph search (or traversal) technique visits every node exactly one in a systematic fashion. Two standard graph search techniques have been widely used: Depth-First Search (DFS) Breadth-First Search (BFS)
Erlang is successfully used for game servers. Lua is a friendly, small language that is big in game scripting. And VoltDB is a new high speed ACID database from PostgreSQL inventor Mike Stonebreaker. Add some JSON, Websockets and JavaScript and you have an exciting HTML5 game stack.
This talk is about research and results from the work on a very high throughput game server with embedded Lua for game logic and VoltDB as SQL database backend. The stack aims to maximize robustness, throughput and ease of scripting. Its special feature is linearly scaling true ACID with a goal of a million transactions per second, while being easy on the game logic scripter.
This stack is part of the development effort of games start-up Eonblast for the cross-media Science Fiction franchise Solar Tribes, which will feature film elements merged with cross-platform online games. The stack is created to allow for a unified, non-sharded game world experience, thus for a unified story line and also otherwise transcends current MMO conventions. Players will share the same stories at the same time and be empowered to leave a stronger imprint on their environment. Interaction with other players is taken beyond the limitations currently seen in social games. All of which are technical challenges, asking for fastest possible data access and better data integrity guarantees.
A triple asynchronous server side architecture is described that communicates non-blocking with a fat JS/HTML5 browser client. The related Open Source projects Erlvolt and Fleece are introduced, which were created for this stack. The embedded Lua driver Erlualib and Robert Virding's new project, the Lua VM emulator Luerl are compared and evaluated. A brief overview of capabilities of new databases is given to explain why VoltDB was chosen for the task.
(c) 2012 Eonblast Corporation
Androids' memory management differ from the other operating systems. In this PowerPoint presentation we tried to figure out how it works and how it differs from other operating systems.
A brief overview of caching mechanisms in a web application. Taking a look at the different layers of caching and how to utilize them in a PHP code base. We also compare Redis and MemCached discussing their advantages and disadvantages.
Algorithm Complexity presentation slides. Time Complexity and Space Complexity analysis using Big-O notation with examples that demonstrates how a function complexity effects to algorithm efficiency.
While the Python logging module makes it simple to add flexible logging to your application, wording log messages and choosing the appropriate level to maximize their helpfulness is a topic hardly covered in the documentation. This talk give guidelines on when to choose a certain log level, what information to include and which wording templates to use.
Python too supports file handling and allows users to handle files i.e., to read and write files, along with many other file handling options, to operate on files. The concept of file handling has stretched over various other languages, but the implementation is either complicated or lengthy, but alike other concepts of Python, this concept here is also easy and short. Python treats file differently as text or binary and this is important. Each line of code includes a sequence of characters and they form text file. Each line of a file is terminated with a special character, called the EOL or End of Line characters like comma {,} or newline character. It ends the current line and tells the interpreter a new one has begun. Let’s start with Reading and Writing files.
Androids' memory management differ from the other operating systems. In this PowerPoint presentation we tried to figure out how it works and how it differs from other operating systems.
A brief overview of caching mechanisms in a web application. Taking a look at the different layers of caching and how to utilize them in a PHP code base. We also compare Redis and MemCached discussing their advantages and disadvantages.
Algorithm Complexity presentation slides. Time Complexity and Space Complexity analysis using Big-O notation with examples that demonstrates how a function complexity effects to algorithm efficiency.
While the Python logging module makes it simple to add flexible logging to your application, wording log messages and choosing the appropriate level to maximize their helpfulness is a topic hardly covered in the documentation. This talk give guidelines on when to choose a certain log level, what information to include and which wording templates to use.
Python too supports file handling and allows users to handle files i.e., to read and write files, along with many other file handling options, to operate on files. The concept of file handling has stretched over various other languages, but the implementation is either complicated or lengthy, but alike other concepts of Python, this concept here is also easy and short. Python treats file differently as text or binary and this is important. Each line of code includes a sequence of characters and they form text file. Each line of a file is terminated with a special character, called the EOL or End of Line characters like comma {,} or newline character. It ends the current line and tells the interpreter a new one has begun. Let’s start with Reading and Writing files.
Free Downloadable PDF guide Volume #2 Learn to code JavaScript #javascript
A closure in JavaScript is a function that has access to variables in its parent scope, even after the parent function has returned. Closures are created when a function is defined inside another function, and the inner function retains access to the variables in the outer function’s scope.
Here is an example of a closure in JavaScript:In this example, the innerFunction is a closure because it has access to the variable x and innerVar from the outerFunction even after outerFunction has returned.
A closure has three scope chains:
It has access to its own scope (variables defined between its curly braces {}).
It has access to the outer function’s variables.
It has access to the global variables.
Closures are commonly used in JavaScript for a variety of tasks, such as:
Implementing private methods and variables.
Creating callback functions that retain access to variables from their parent scope.
Creating and returning an object that has access to variables from its parent scope.
JavaScript closures are an important concept and it is important to understand how closures work in JavaScript. It is also important to be aware of the scope chain, and how closures interact with the scope chain.JavaScript Object Notation (JSON) is a lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition – December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.
File Handling Btech computer science and engineering pptpinuadarsh04
Data is very important. Every organization depends on its data for continuing its business operations. If the data is lost, the organization has to be closed. To store data in a computer, we need files. For example, we can store employee data like employee number, name and salary in a file in the computer and later use it whenever we want.
Similarly, we can store student data like student roll number, name and marks in the computer. In computers’ view, a file is nothing but collection of data that is available to a program. Once we store data in a computer file, we can retrieve it and use it depending on our requirements.
This is the reason computers are primarily created for handling data, especially for storing and retrieving data. In later days, programs are developed to process the data that is stored in the computer.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
1. CSV
CSV is an acronym for comma-separated values. It's a file format that you can
use to store tabular data, such as in a spreadsheet. You can also use it to store
data from a tabular database.
You can refer to each row in a CSV file as a data record. Each data record consists
of one or more fields, separated by commas.
The csv module has two classes that you can use in writing data to CSV. These
classes are:
Csv.writer()
Csv.DictWriter()
Csv.writer() class to write data into a CSV file. The class returns a writer object,
which you can then use to convert data into delimited strings.
To ensure that the newline characters inside the quoted fields interpret
correctly, open a CSV file object with newline=''.
The syntax for the csv.writer class is as follows:
2. The csv.writer class has two methods that you can use to write data to CSV files.
The methods are as follows:
import csv
with open('profiles1.csv', 'w', newline='') as file:
writer = csv.writer(file)
field = ["name", "age", "country"]
writer.writerow(field)
writer.writerow(["Oladele Damilola", "40", "Nigeria"])
writer.writerow(["Alina Hricko", "23", "Ukraine"])
writer.writerow(["Isabel Walter", "50", "United Kingdom"])
3. The writerows() method has similar usage to the writerow() method.
The only difference is that while the writerow() method writes a single row to a CSV
file, you can use the writerows() method to write multiple rows to a CSV file.
import csv
with open('profiles2.csv', 'w', newline='') as file:
writer = csv.writer(file)
row_list = [
["name", "age", "country"],
["Oladele Damilola", "40", "Nigeria"],
["Alina Hricko", "23" "Ukraine"],
["Isabel Walter", "50" "United Kingdom"],
]
writer.writerow(row_list)
5. Read()
Python provides various functions to read csv file. Few of them are discussed below as.
To see examples, we must have a csv file.
1. Using csv.reader() function
In Python, the csv.reader() module is used to read the csv file. It takes each row of the
file and makes a list of all the columns.
import csv
def main():
# To Open the CSV file
with open(' python.csv ', newline = '') as csv_file:
csv_read = csv.reader( csv_file, delimiter = ',')
# To Read and display each row
for row in csv_read:
print(row)
main()
6. Append()
from csv import writer
# List that we want to add as a new row
List = [6, 'William', 5532, 1, 'UAE']
# Open our existing CSV file in append mode
# Create a file object for this file
with open('event.csv', 'a') as f_object:
# Pass this file object to csv.writer()
# and get a writer object
writer_object = writer(f_object)
# Pass the list as an argument into
# the writerow()
writer_object.writerow(List)
# Close the file object
f_object.close()
7. JSON
The full form of JSON is JavaScript Object Notation. It means that a script
(executable) file which is made of text in a programming language, is used to
store and transfer the data.
Python supports JSON through a built-in package called JSON. To use this
feature, we import the JSON package in Python script
Deserialize a JSON String to an Object in Python
The Deserialization of JSON means the conversion of JSON objects into their
respective Python objects. The load()/loads() method is used for it.
8. JSON OBJECT PYTHON OBJECT
object dict
array list
string str
null None
number (int) int
number (real) float
true True
false False
9. • json.load() method
• The json.load() accepts the file object, parses the JSON data,
populates a Python dictionary with the data, and returns it back to
you.
• Syntax:
• json.load(file object)
• Parameter: It takes the file object as a parameter.
• Return: It return a JSON Object.
10.
11. • # Python program to read
• # json file
• import json
• # Opening JSON file
• f = open('data.json')
• # returns JSON object as
• data = json.load(f)
• # Iterating through the json
• # list
• for i in data['emp_details']:
• print(i)
• # Closing file
• f.close()
12. • json.loads() Method
• If we have a JSON string, we can parse it by using the json.loads() method.
• json.loads() does not take the file path, but the file contents as a string,
• to read the content of a JSON file we can use fileobject.read() to convert the file
into a string and pass it with json.loads(). This method returns the content of the
file.
• Syntax:
• json.loads(S)
• Parameter: it takes a string, bytes, or byte array instance which contains the JSON
document as a parameter (S).
• Return Type: It returns the Python object.
15. • import json
• with open('path_to_file/person.json', 'r') as f:
• data = json.load(f)
• # Output: {'name': 'Bob', 'languages': ['English', 'French']}
• print(data)
16. • Python Convert to JSON string
• You can convert a dictionary to JSON string using json.dumps() method.
• import json
• person_dict = {'name': 'Bob',
• 'age': 12,
• 'children': None
• }
• person_json = json.dumps(person_dict)
• # Output: {"name": "Bob", "age": 12, "children": null}
• print(person_json)
17. Using XML with Python
• Extensible Mark-up Language is a simple and flexible text format that
is used to exchange different data on the web
• It is a universal format for data on the web
• Why we use XML?
• Reuse: Contents are separated from presentation and we can reuse
• Portability: It is an international platform independent, so developers
can store their files safely
• Interchange: Interoperate and share data seamlessly
• Self-Describing:
18. • XML elements must have a closing tag
• Xml tags are case sensitive
• All XML elements must be properly nested
• All XML documents must have a root elements
• Attribute values must always be quoted
19. XML
• <?xml version="1.0"?>
• <employee>
• <name>John Doe</name>
• <age>35</age>
• <job>
• <title>Software Engineer</title>
• <department>IT</department>
• <years_of_experience>10</years_of_experience>
• </job>
• <address>
• <street>123 Main St.</street>
• <city>San Francisco</city>
• <state>CA</state>
• <zip>94102</zip>
• </address>
• </employee>
• In this XML, we have used the same data shown in the JSON file.
• You can observe that the elements in the XML files are stored using tags.
20. Here is an example of a simple JSON object:
• {
• "employee": {
• "name": "John Doe",
• "age": 35,
• "job": {
• "title": "Software Engineer",
• "department": "IT",
• "years_of_experience": 10
• },
• "address": {
• "street": "123 Main St.",
• "city": "San Francisco",
• "state": "CA",
• "zip": 94102
• }
• }
• }
• This JSON file contains details of an employee. You can observe that the data is stored as key-value pairs.
21. • To convert a JSON string to an XML string, we will first convert the
json string to a python dictionary.
• For this, we will use the loads() method defined in the json module.
• The loads() module takes the json string as its input argument and
returns the dictionary.
• Next, we will convert the python dictionary to XML using the
unparse() method defined in the xmltodict module.
• The unparse() method takes the python dictionary as its input
argument and returns an XML string.
Convert JSON String to XML String in Python
22. • import json
• import xmltodict
• json_string="""{"employee": {"name": "John Doe", "age": "35", "job":
{"title": "Software Engineer", "department": "IT", "years_of_experience":
"10"},"address": {"street": "123 Main St.", "city": "San Francisco", "state":
"CA", "zip": "94102"}}}
• """
• print("The JSON string is:")
• print(json_string)
• python_dict=json.loads(json_string)
• xml_string=xmltodict.unparse(python_dict)
• print("The XML string is:")
• print(xml_string)
• OUT PUT Will be in XML And JSON
23. JSON String to XML File in Python
• Instead of creating a string, we can also convert a JSON string to an XML file in python. For this, we will use
the following steps.
• first, we will convert the JSON string to a python dictionary using the loads() method defined in the json
module.
• Next, we will open an empty XML file using the open() function.
• The open() function takes the file name as its first input argument and the literal “w” as its second input
argument.
• After execution, it returns a file pointer.
• Once we get the file pointer, we will save the python dictionary to an XML file using the unparse() method
defined in the xmltodict module.
• The unparse() method takes the dictionary as its first argument and the file pointer as the argument to the
output parameter.
• After execution, it writes the XML file to the storage.
• Finally, we will close the XML file using the close() method.
25. Convert JSON File to XML String in Python
• To convert a JSON file to an XML string, we will first open the JSON file in
read mode using the open() function.
• The open() function takes the file name as its first input argument and the
python literal “r” as its second input argument. After execution, the open()
function returns a file pointer.
• import json
• import xmltodict
• file=open("person.json","r")
• python_dict=json.load(file)
• xml_string=xmltodict.unparse(python_dict)
• print("The XML string is:")
• print(xml_string)
26. JSON File to XML File in Python
• First, we will open the JSON file in read mode using the open() function.
• For this, we will pass the filename as the first input argument and the
literal “r” as the second input argument to the open() function.
• The open() function returns a file pointer.
• Next, we will load the json file into a python dictionary using the load()
method defined in the json module.
• The load() method takes the file pointer as its input argument and returns
a python dictionary.
• Now, we will open an XML file using the open() function.
• Then, we will save the python dictionary to the XML file using the unparse()
method defined in the xmltodict module.
• Finally, we will close the XML file using the close() method.
27. • import json
• import xmltodict
• file=open("person.json","r")
• python_dict=json.load(file)
• xml_file=open("person.xml","w")
• xmltodict.unparse(python_dict,output=xml_file)
• xml_file.close()
• After executing the above code, the content of the JSON file
"person.json" will be saved as XML in the "person.xml" file.