How To Manage Libraries in Python And MySQL

Library management using Python & MySQL

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Dev/Editor: Pavan Ananth Sharma & MK Akash

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Introduction:

This is an intermediate project which is a user-friendly library management system that allows you to:

• Add book with details
• Delete books
• View listed books
• Issue books to students/anyone
• Return books

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Output:

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Dependencies:

• tkinter
• pillow
• pymysql
• MySQL should be instaled

Code to download dependencies:

pip install tk
pip install pillow
pip install pymysql

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Note: we need to install tk which is tkinter and pillow which is PIL, so dont worry and install these dependencies using the above code.

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❗ IMPORTANT STEP ❗

• Make sure you have installed MySQL for your OS, if not follow this video : https://www.youtube.com/watch?v=N3B3OonC2AU&t=343s Not sponcered
• Follow the above video carefully and we recommend to use the password as root
• After you have successfully installed and tested now you will need to run a few commands before running the main.py
• Open MySQL 8.0(or any version) Command Line Client using windows key and searching it if you use windows
• Enter the password in this case we use root
• then we run show databases; to show the existing databases
• We add our data based which we will call db code for it : create database db;
• Then we run : create table books(bid varchar(20) primary key, title varchar(30), author varchar(30), status varchar(30));
• Last but not the least we enter: create table books_issued(bid varchar(20) primary key, issuedto varchar(30));
• So basically we created 2 tables inside a database named db with multiple access elements
• Now to enter into the database you have created use teh code: use db;
• Now to ckeck the tables you have created you can say ener : show tablesl;
• To see the elements inside the tables (for example books) code: select * from books;
• Similarly to use the elements insie the table books_issued run the code: select * from books_issued;

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Usecase / run:

if you want to run the code you will need to run : main.py

• If you have followed all the steps correctly as soon as you run main.py the UI will popup then as as soon as you add a book by filling the information(case sensitive), you can now get to the CLI of MYSql and run: select * from books; and this will display if you have added any book into the database.
• Similarly after you have deleted any book you can run the same code to verify if it has been deleted from the database system.
• When you issue a book then if you want to check who have you given it to enter the code: select * fom books_issued; to find the name and book ID (bid).
• After you have created a book and then issued it to anyone you can see the status of the book change by again going into the books_issued table for that we use the code: select * from books; if you have followed you shouold see the change in the status of the book(if Avail before now it would have updated to issued).
• If you have got all of them then congratulations you have done an amazing job

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Wallet: 0x592eF244F8924be9F3F8803f8d639392f465Ac51

Instagram: pavan_ananth

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Author: PavanAnanthSharma
Source Code: https://github.com/PavanAnanthSharma/Library-Management-Using-Python-and-MySQL
License: GPL-3.0 License

#python #mysql 

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Best MySQL DigitalOcean Performance – ScaleGrid vs. DigitalOcean Managed Databases

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MySQL is the all-time number one open source database in the world, and a staple in RDBMS space. DigitalOcean is quickly building its reputation as the developers cloud by providing an affordable, flexible and easy to use cloud platform for developers to work with. MySQL on DigitalOcean is a natural fit, but what’s the best way to deploy your cloud database? In this post, we are going to compare the top two providers, DigitalOcean Managed Databases for MySQL vs. ScaleGrid MySQL hosting on DigitalOcean.

At a glance – TLDR
ScaleGrid Blog - At a glance overview - 1st pointCompare Throughput
ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads. Read now

ScaleGrid Blog - At a glance overview - 2nd pointCompare Latency
On average, ScaleGrid achieves almost 30% lower latency over DigitalOcean for the same deployment configurations. Read now

ScaleGrid Blog - At a glance overview - 3rd pointCompare Pricing
ScaleGrid provides 30% more storage on average vs. DigitalOcean for MySQL at the same affordable price. Read now

MySQL DigitalOcean Performance Benchmark
In this benchmark, we compare equivalent plan sizes between ScaleGrid MySQL on DigitalOcean and DigitalOcean Managed Databases for MySQL. We are going to use a common, popular plan size using the below configurations for this performance benchmark:

Comparison Overview
ScaleGridDigitalOceanInstance TypeMedium: 4 vCPUsMedium: 4 vCPUsMySQL Version8.0.208.0.20RAM8GB8GBSSD140GB115GBDeployment TypeStandaloneStandaloneRegionSF03SF03SupportIncludedBusiness-level support included with account sizes over $500/monthMonthly Price$120$120

As you can see above, ScaleGrid and DigitalOcean offer the same plan configurations across this plan size, apart from SSD where ScaleGrid provides over 20% more storage for the same price.

To ensure the most accurate results in our performance tests, we run the benchmark four times for each comparison to find the average performance across throughput and latency over read-intensive workloads, balanced workloads, and write-intensive workloads.

Throughput
In this benchmark, we measure MySQL throughput in terms of queries per second (QPS) to measure our query efficiency. To quickly summarize the results, we display read-intensive, write-intensive and balanced workload averages below for 150 threads for ScaleGrid vs. DigitalOcean MySQL:

ScaleGrid MySQL vs DigitalOcean Managed Databases - Throughput Performance Graph

For the common 150 thread comparison, ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads.

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Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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Top 20 Most Useful Python Modules or Packages

 March 25, 2021  Deepak@321  0 Comments

Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.

Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:

1. Web Development
2. Data Science
3. Machine Learning
4. AI and graphical user interfaces.

Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.

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Top 7 Python Libraries Used For Hacking

python is one of the most go-for languages among the developers due to the availability of open-source libraries and frameworks. According to a survey report, Python is the top language preferred for Statistical Modelling, and an overwhelming majority of practitioners prefer Python as the language for statistical works.

Python has become a favourite language for hackers these days. The reason is the presence of pre-built tools and libraries, which makes hacking easy. In fact, the language is adequate for ethical hacking as ethical hackers need to develop smaller scripts, and Python fulfils this criterion.

Below here, we listed down the top 7 Python libraries used in hacking.

1| Requests

Stars: 43.3k

**About: **Requests is a simple HTTP library for Python that allows a user to send HTTP/1.1 requests extremely easily. This library helps in building robust HTTP applications and includes intuitive features such as automatic content decompression and decoding, connection timeouts, basic & digits authentication, among others.

Know more here.

2| Scapy

Stars: 5.5k

About: Scapy is a powerful Python-based interactive packet manipulation program and library. This library is able to forge or decode packets of a wide number of protocols, send them on the wire, capture them, store or read them using pcap files, match requests, and more. It allows the construction of tools that can easily scan or attack networks. It is designed to allow fast packet prototyping by using default values that work. It can also perform tasks such as sending invalid frames, injecting your own 802.11 frames, combining techniques, such as VLAN hopping with ARP cache poisoning, VOIP decoding on WEP encrypted channel, etc., which most other tools cannot.

Know more here.

3| IMpacket

**Stars: **5.3k

**About: **IMpacket is a library that includes a collection of Python classes for working with network protocols. It is focused on providing low-level programmatic access to network packets. It allows Python developers to craft and decode network packets in a simple and consistent manner. The library provides a set of tools as examples of what can be done within the context of this library.

Know more here.

4| Cryptography

**Stars: **3.5k

**About: **Cryptography is a package which provides cryptographic recipes and primitives to Python developers. It includes both high-level recipes and low-level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests and key derivation functions. This library is broadly divided into two levels. One is with safe cryptographic recipes that require little to no configuration choices. The other level is low-level cryptographic primitives, which are often dangerous and can be used incorrectly.

Know more here.

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