This document discusses how to connect a Python application to a MySQL database. It explains that the mysql.connector package must be installed first to create the connection bridge between Python and MySQL. It then outlines the steps to connect to a MySQL database from Python code, including importing mysql.connector, opening a connection, creating a cursor, executing queries, and extracting data from the result set. It also provides examples of inserting, updating, and parameterized queries.
Connecting Python to MySQL allows storing and retrieving data from a MySQL database. The mysql.connector module provides a bridge between Python and MySQL. To connect, import mysql.connector, create a connection object specifying login details, then use cursor objects to execute queries and extract result sets. Queries can be parameterized by embedding placeholders in SQL strings. Data is inserted or updated using execute() and changes committed with commit().
Computer science notes of functions chapterGodzilla33
The document discusses functions in Python. It states that large programs are divided into smaller units called functions to make them easier to manage. Functions allow code to be reused by calling the function from different parts of a program. There are several types of functions: functions with no arguments or return value, functions with arguments but no return, and functions with both arguments and a return value. Functions make programs easier to understand, test and maintain. Modularization involves storing functions in a file called a module. Commonly used modules containing generic functions are called libraries.
- SQL (Structured Query Language) enables users to create and operate on relational databases. It is the standard language used by almost all database software.
- SQL allows users to query databases, insert, modify, and delete database contents, as well as create or modify the database structure and security settings.
- The document provides information on various SQL elements like literals, data types, NULL values, and comments. It also discusses SQL commands, creating and using databases and tables, and performing queries and operations.
The document discusses functions in Python. It states that large programs can be divided into smaller and more manageable units called functions. Functions allow code to be reused by calling the function from different parts of a program. The document covers the different types of functions like functions with and without parameters and return values. It also discusses scopes of variables, parameter passing, and composing functions.
Interface Python with MySQL connectivity.pptxBEENAHASSINA1
The document discusses connecting a Python application to a MySQL database. It provides steps to install the mysql.connector package to bridge the connection between Python and MySQL. It explains how to open a connection, create a cursor, execute queries to retrieve and manipulate data, and extract results. Methods shown include using cursors to fetch data row by row, parameterized queries using placeholders, and performing INSERT, UPDATE and DELETE operations with commit.
Kafka Spark Realtime stream processing and analytics in 6 stepsAzmath Mohamad
Realtime stream data processing can be done through Kafka, Spark. In this document we tried to show how we can process stream data in 6 basic steps. SparkML can be applied in pySpark scripts.
The document discusses various SQL constraints that can be applied on columns in MySQL tables. It explains that constraints include primary key, unique, not null, default, check, and foreign key. It demonstrates how to create tables with constraints, alter tables to add or drop constraints, and shows examples of inserting, updating and deleting records with constraints applied. It also discusses naming constraints and getting table structure information.
This document provides an introduction and overview of Cassandra and NoSQL databases. It discusses the challenges faced by modern web applications that led to the development of NoSQL databases. It then describes Cassandra's data model, API, consistency model, and architecture including write path, read path, compactions, and more. Key features of Cassandra like tunable consistency levels and high availability are also highlighted.
Connecting Python to MySQL allows storing and retrieving data from a MySQL database. The mysql.connector module provides a bridge between Python and MySQL. To connect, import mysql.connector, create a connection object specifying login details, then use cursor objects to execute queries and extract result sets. Queries can be parameterized by embedding placeholders in SQL strings. Data is inserted or updated using execute() and changes committed with commit().
Computer science notes of functions chapterGodzilla33
The document discusses functions in Python. It states that large programs are divided into smaller units called functions to make them easier to manage. Functions allow code to be reused by calling the function from different parts of a program. There are several types of functions: functions with no arguments or return value, functions with arguments but no return, and functions with both arguments and a return value. Functions make programs easier to understand, test and maintain. Modularization involves storing functions in a file called a module. Commonly used modules containing generic functions are called libraries.
- SQL (Structured Query Language) enables users to create and operate on relational databases. It is the standard language used by almost all database software.
- SQL allows users to query databases, insert, modify, and delete database contents, as well as create or modify the database structure and security settings.
- The document provides information on various SQL elements like literals, data types, NULL values, and comments. It also discusses SQL commands, creating and using databases and tables, and performing queries and operations.
The document discusses functions in Python. It states that large programs can be divided into smaller and more manageable units called functions. Functions allow code to be reused by calling the function from different parts of a program. The document covers the different types of functions like functions with and without parameters and return values. It also discusses scopes of variables, parameter passing, and composing functions.
Interface Python with MySQL connectivity.pptxBEENAHASSINA1
The document discusses connecting a Python application to a MySQL database. It provides steps to install the mysql.connector package to bridge the connection between Python and MySQL. It explains how to open a connection, create a cursor, execute queries to retrieve and manipulate data, and extract results. Methods shown include using cursors to fetch data row by row, parameterized queries using placeholders, and performing INSERT, UPDATE and DELETE operations with commit.
Kafka Spark Realtime stream processing and analytics in 6 stepsAzmath Mohamad
Realtime stream data processing can be done through Kafka, Spark. In this document we tried to show how we can process stream data in 6 basic steps. SparkML can be applied in pySpark scripts.
The document discusses various SQL constraints that can be applied on columns in MySQL tables. It explains that constraints include primary key, unique, not null, default, check, and foreign key. It demonstrates how to create tables with constraints, alter tables to add or drop constraints, and shows examples of inserting, updating and deleting records with constraints applied. It also discusses naming constraints and getting table structure information.
This document provides an introduction and overview of Cassandra and NoSQL databases. It discusses the challenges faced by modern web applications that led to the development of NoSQL databases. It then describes Cassandra's data model, API, consistency model, and architecture including write path, read path, compactions, and more. Key features of Cassandra like tunable consistency levels and high availability are also highlighted.
PythonWebConference_ Cloud Native Apache Pulsar Development 202 with PythonTimothy Spann
PythonWebConference_ Cloud Native Apache Pulsar Development 202 with Python
Developers want to develop real-time applications that can turn raw text data into analyzed sentences and smart sentiment? Have you heard of real-time analytics, let's leverage your Python skills to make it happen now.
In this talk I will show developers how to develop real-time applications that use Pulsar functions to turn live event data into live NLP results and sentiment analyzed output.
We will walk through how to setup various scenarios to feed live data from apps and webapps to Apache Pulsar for real-time analytics and NLP.
At the end of the talk, developers will be able to use Python for real-time NLP analytics and have gained insight on how and when to use various streaming protocols, platforms, libraries and systems including Apache Pulsar, Apache Kafka, MQTT, Websockets, AMQP and Apache Spark.
https://2023.pythonwebconf.com/presentations/apache-pulsar-development-202-with-python
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. It is written in Java and uses a pluggable backend. Presto is fast due to code generation and runtime compilation techniques. It provides a library and framework for building distributed services and fast Java collections. Plugins allow Presto to connect to different data sources like Hive, Cassandra, MongoDB and more.
Presto generates Java bytecode at runtime to optimize query execution. Key query operations like filtering, projections, joins and aggregations are compiled into efficient Java methods using libraries like ASM and Fastutil. This bytecode generation improves performance by 30% through techniques like compiling row hashing for join lookups directly into machine instructions.
This document discusses future directions for LINQ, including making it work with asynchronous and reactive programming using Rx. It proposes expanding LINQ to handle more types of queries, such as querying over asynchronous data streams, remote data sources, and even non-traditional domains like constraint solving. The goal is to realize a "LINQ to everything" vision by extending LINQ's principles to new programming models and domains.
The document discusses using JDBC (Java Database Connectivity) for object-relational mapping in Java. It covers connecting to databases, executing SQL statements and queries, working with ResultSets, and best practices for managing database connections. Key points include using the DriverManager class to obtain database connections, preparing statements for parameterized queries, and implementing a DAO (Data Access Object) layer to encapsulate data access logic.
How Pony ORM translates Python generators to SQL queriesponyorm
Pony ORM is an Object-Relational Mapper implemented in Python. It uses an unusual approach for writing database queries using Python generators. Pony analyzes the abstract syntax tree of a generator and translates it to its SQL equivalent. The translation process consists of several non-trivial stages.
This talk was given at EuroPython 2014 and reveals the internal details of the translation process.
Tuples are immutable sequences that can store elements of different data types. They are created using parentheses instead of square brackets and cannot be changed once defined. Tuples allow accessing elements by index, slicing, concatenation, membership testing and other operations similar to lists but are more efficient since they are immutable. Individual elements of a tuple cannot be deleted but the entire tuple can be deleted using the del statement.
What is the best full text search engine for Python?Andrii Soldatenko
Nowadays we can see lot’s of benchmarks and performance tests of different web frameworks and Python tools. Regarding to search engines, it’s difficult to find useful information especially benchmarks or comparing between different search engines. It’s difficult to manage what search engine you should select for instance, ElasticSearch, Postgres Full Text Search or may be Sphinx or Whoosh. You face a difficult choice, that’s why I am pleased to share with you my acquired experience and benchmarks and focus on how to compare full text search engines for Python.
KSQL is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. KSQL is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
KSQL offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using KSQL for most part. This will be done in a live demo on a fictitious IoT sample.
The slides I prepared for https://www.meetup.com/Paris-Apache-Kafka-Meetup/events/268164461/ about Apache Kafka integration in Apache Spark Structured Streaming.
A Python module is a file containing Python code such as functions, classes, and variables that can be imported and used in other Python programs. The document discusses several key aspects of Python modules including their structure, importing modules, and built-in modules like the math, random, and statistics modules. The math module contains common mathematical functions, the random module generates random numbers, and the statistics module calculates statistical values like the mean, median, and mode of data sets.
Bring Your Own Apache MXNet and TensorFlow Scripts to Amazon SageMaker (AIM35...Amazon Web Services
Amazon SageMaker enables you to bring your existing Apache MXNet or TensorFlow script for your machine learning models. In this session, we walk through the details of bringing your own script for training your models at scale. We also go into detail on using local containers for repeated experiments for ease of use and scalability.
Creating the PromQL Transpiler for Flux by Julius Volz, Co-Founder | PrometheusInfluxData
Flux is not only a new data scripting and query language — it is also a powerful data processing engine. This talk by Julius Volz will focus on how he worked with the InfluxData team to build PromQL support for the Flux engine. Hear about lessons learned from building the transpiler and recommendations on why and how to use PromQL and Flux. This talk will include a demo and will share the current project progress.
Building Powerful WebSocket, Comet, and RESTful Applications Using Atmosphere
This document discusses the Atmosphere framework for building asynchronous web applications. It introduces key concepts like suspending responses, broadcasting events, scheduling broadcasts, and clustering. It also provides an example of building a real-time Twitter search application with Atmosphere and discusses how Atmosphere allows writing applications once that can run anywhere across different transports without browser workarounds. The document encourages developers to use the simple Atmosphere APIs to build powerful asynchronous applications and to join the Atmosphere community.
Build a Complex, Realtime Data Management App with Postgres 14!Jonathan Katz
Congratulations: you've been selected to build an application that will manage reservations for rooms!
On the surface, this sounds simple, but you are building a system for managing a high traffic reservation web page, so we know that a lot of people will be accessing the system. Therefore, we need to ensure that the system can handle all of the eager users that will be flooding the website checking to see what availability each room has.
Fortunately, PostgreSQL is prepared for this! And even better, we will be using Postgres 14 to make the problem even easier!
We will explore the following PostgreSQL features:
* Data types and their functionality, such as:
* Data/Time types
* Ranges / Multirnages
Indexes such as:
* GiST
* Common Table Expressions and Recursion (though multiranges will make things easier!)
* Set generating functions and LATERAL queries
* Functions and the PL/PGSQL
* Triggers
* Logical decoding and streaming
We will be writing our application primary with SQL, though we will sneak in a little bit of Python and using Kafka to demonstrate the power of logical decoding.
At the end of the presentation, we will have a working application, and you will be happy knowing that you provided a wonderful user experience for all users made possible by the innovation of PostgreSQL!
Kicking off with Zend Expressive and Doctrine ORM (PHPNW2016)James Titcumb
You've heard of Zend's new framework, Expressive, and you've heard it's the new hotness. In this talk, I will introduce the concepts of Expressive, how to bootstrap a simple application with the framework using best practices, and finally how to integrate a third party tool like Doctrine ORM.
Abstract: kaChing powers the largest social investment site on the web with nearly 500,000 registered users. Our mission is to make the investment world open by offering transparent investment vehicles that directly compete with mutual funds.
Over the past year and a half, we have built a large feature set and evolved our software continuously with very short iterations and (almost) no regression. In this talk, I will present our experience building a large test-driven code base from the ground up. Using concrete examples, we will have a look at component based APIs, declarative programming, minimizing the concepts of an API, specific cases of separation of concern and interactions with third-party software. We will look at multiple programming paradigms from languages such as Scala, shell script and Prolog and see how these ideas can be embedded as syntactic sugar in your Java.
Embedded Typesafe Domain Specific Languages for JavaJevgeni Kabanov
The document discusses embedded domain-specific languages (DSLs) for Java and provides two case studies:
1) Building SQL queries using a typesafe DSL that avoids errors and allows type inference.
2) Modifying Java bytecode using the ASM library to define a DSL for bytecode engineering.
The document provides an overview of Node.js and common tools used in Node.js development such as Connect, Express, Mongoose, and Passport. It discusses Node.js features like non-blocking I/O and how frameworks like Connect and Express make building web applications easier. It also covers data storage with Mongoose and authentication with Passport. Conventions for RESTful API design are proposed, including using nouns for resources and verbs for actions, and handling associations and embedded resources.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
PythonWebConference_ Cloud Native Apache Pulsar Development 202 with PythonTimothy Spann
PythonWebConference_ Cloud Native Apache Pulsar Development 202 with Python
Developers want to develop real-time applications that can turn raw text data into analyzed sentences and smart sentiment? Have you heard of real-time analytics, let's leverage your Python skills to make it happen now.
In this talk I will show developers how to develop real-time applications that use Pulsar functions to turn live event data into live NLP results and sentiment analyzed output.
We will walk through how to setup various scenarios to feed live data from apps and webapps to Apache Pulsar for real-time analytics and NLP.
At the end of the talk, developers will be able to use Python for real-time NLP analytics and have gained insight on how and when to use various streaming protocols, platforms, libraries and systems including Apache Pulsar, Apache Kafka, MQTT, Websockets, AMQP and Apache Spark.
https://2023.pythonwebconf.com/presentations/apache-pulsar-development-202-with-python
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. It is written in Java and uses a pluggable backend. Presto is fast due to code generation and runtime compilation techniques. It provides a library and framework for building distributed services and fast Java collections. Plugins allow Presto to connect to different data sources like Hive, Cassandra, MongoDB and more.
Presto generates Java bytecode at runtime to optimize query execution. Key query operations like filtering, projections, joins and aggregations are compiled into efficient Java methods using libraries like ASM and Fastutil. This bytecode generation improves performance by 30% through techniques like compiling row hashing for join lookups directly into machine instructions.
This document discusses future directions for LINQ, including making it work with asynchronous and reactive programming using Rx. It proposes expanding LINQ to handle more types of queries, such as querying over asynchronous data streams, remote data sources, and even non-traditional domains like constraint solving. The goal is to realize a "LINQ to everything" vision by extending LINQ's principles to new programming models and domains.
The document discusses using JDBC (Java Database Connectivity) for object-relational mapping in Java. It covers connecting to databases, executing SQL statements and queries, working with ResultSets, and best practices for managing database connections. Key points include using the DriverManager class to obtain database connections, preparing statements for parameterized queries, and implementing a DAO (Data Access Object) layer to encapsulate data access logic.
How Pony ORM translates Python generators to SQL queriesponyorm
Pony ORM is an Object-Relational Mapper implemented in Python. It uses an unusual approach for writing database queries using Python generators. Pony analyzes the abstract syntax tree of a generator and translates it to its SQL equivalent. The translation process consists of several non-trivial stages.
This talk was given at EuroPython 2014 and reveals the internal details of the translation process.
Tuples are immutable sequences that can store elements of different data types. They are created using parentheses instead of square brackets and cannot be changed once defined. Tuples allow accessing elements by index, slicing, concatenation, membership testing and other operations similar to lists but are more efficient since they are immutable. Individual elements of a tuple cannot be deleted but the entire tuple can be deleted using the del statement.
What is the best full text search engine for Python?Andrii Soldatenko
Nowadays we can see lot’s of benchmarks and performance tests of different web frameworks and Python tools. Regarding to search engines, it’s difficult to find useful information especially benchmarks or comparing between different search engines. It’s difficult to manage what search engine you should select for instance, ElasticSearch, Postgres Full Text Search or may be Sphinx or Whoosh. You face a difficult choice, that’s why I am pleased to share with you my acquired experience and benchmarks and focus on how to compare full text search engines for Python.
KSQL is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. KSQL is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
KSQL offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using KSQL for most part. This will be done in a live demo on a fictitious IoT sample.
The slides I prepared for https://www.meetup.com/Paris-Apache-Kafka-Meetup/events/268164461/ about Apache Kafka integration in Apache Spark Structured Streaming.
A Python module is a file containing Python code such as functions, classes, and variables that can be imported and used in other Python programs. The document discusses several key aspects of Python modules including their structure, importing modules, and built-in modules like the math, random, and statistics modules. The math module contains common mathematical functions, the random module generates random numbers, and the statistics module calculates statistical values like the mean, median, and mode of data sets.
Bring Your Own Apache MXNet and TensorFlow Scripts to Amazon SageMaker (AIM35...Amazon Web Services
Amazon SageMaker enables you to bring your existing Apache MXNet or TensorFlow script for your machine learning models. In this session, we walk through the details of bringing your own script for training your models at scale. We also go into detail on using local containers for repeated experiments for ease of use and scalability.
Creating the PromQL Transpiler for Flux by Julius Volz, Co-Founder | PrometheusInfluxData
Flux is not only a new data scripting and query language — it is also a powerful data processing engine. This talk by Julius Volz will focus on how he worked with the InfluxData team to build PromQL support for the Flux engine. Hear about lessons learned from building the transpiler and recommendations on why and how to use PromQL and Flux. This talk will include a demo and will share the current project progress.
Building Powerful WebSocket, Comet, and RESTful Applications Using Atmosphere
This document discusses the Atmosphere framework for building asynchronous web applications. It introduces key concepts like suspending responses, broadcasting events, scheduling broadcasts, and clustering. It also provides an example of building a real-time Twitter search application with Atmosphere and discusses how Atmosphere allows writing applications once that can run anywhere across different transports without browser workarounds. The document encourages developers to use the simple Atmosphere APIs to build powerful asynchronous applications and to join the Atmosphere community.
Build a Complex, Realtime Data Management App with Postgres 14!Jonathan Katz
Congratulations: you've been selected to build an application that will manage reservations for rooms!
On the surface, this sounds simple, but you are building a system for managing a high traffic reservation web page, so we know that a lot of people will be accessing the system. Therefore, we need to ensure that the system can handle all of the eager users that will be flooding the website checking to see what availability each room has.
Fortunately, PostgreSQL is prepared for this! And even better, we will be using Postgres 14 to make the problem even easier!
We will explore the following PostgreSQL features:
* Data types and their functionality, such as:
* Data/Time types
* Ranges / Multirnages
Indexes such as:
* GiST
* Common Table Expressions and Recursion (though multiranges will make things easier!)
* Set generating functions and LATERAL queries
* Functions and the PL/PGSQL
* Triggers
* Logical decoding and streaming
We will be writing our application primary with SQL, though we will sneak in a little bit of Python and using Kafka to demonstrate the power of logical decoding.
At the end of the presentation, we will have a working application, and you will be happy knowing that you provided a wonderful user experience for all users made possible by the innovation of PostgreSQL!
Kicking off with Zend Expressive and Doctrine ORM (PHPNW2016)James Titcumb
You've heard of Zend's new framework, Expressive, and you've heard it's the new hotness. In this talk, I will introduce the concepts of Expressive, how to bootstrap a simple application with the framework using best practices, and finally how to integrate a third party tool like Doctrine ORM.
Abstract: kaChing powers the largest social investment site on the web with nearly 500,000 registered users. Our mission is to make the investment world open by offering transparent investment vehicles that directly compete with mutual funds.
Over the past year and a half, we have built a large feature set and evolved our software continuously with very short iterations and (almost) no regression. In this talk, I will present our experience building a large test-driven code base from the ground up. Using concrete examples, we will have a look at component based APIs, declarative programming, minimizing the concepts of an API, specific cases of separation of concern and interactions with third-party software. We will look at multiple programming paradigms from languages such as Scala, shell script and Prolog and see how these ideas can be embedded as syntactic sugar in your Java.
Embedded Typesafe Domain Specific Languages for JavaJevgeni Kabanov
The document discusses embedded domain-specific languages (DSLs) for Java and provides two case studies:
1) Building SQL queries using a typesafe DSL that avoids errors and allows type inference.
2) Modifying Java bytecode using the ASM library to define a DSL for bytecode engineering.
The document provides an overview of Node.js and common tools used in Node.js development such as Connect, Express, Mongoose, and Passport. It discusses Node.js features like non-blocking I/O and how frameworks like Connect and Express make building web applications easier. It also covers data storage with Mongoose and authentication with Passport. Conventions for RESTful API design are proposed, including using nouns for resources and verbs for actions, and handling associations and embedded resources.
Similar to 015. Interface Python with MySQL.pdf (20)
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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1. INTERFACE PYTHON WITH
MYSQL
Connecting Python application with MySQL
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
2. Introduction
Every application required data to be stored for future
reference to manipulate data. Today every application
stores data in database for this purpose
For example, reservation system stores passengers
details for reserving the seats and later on for sending
some messages or for printing tickets etc.
In school student details are saved for many reasons
like attendance, fee collections, exams, report card etc.
Python allows us to connect all types of database like
Oracle, SQL Server, MySQL.
In our syllabus we have to understand how to connect
Python programs with MySQL
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
3. Pre-requisite to connect Python with
MySQL
Before we connect python program with any database
like MySQL we need to build a bridge to connect
Python and MySQL.
To build this bridge so that data can travel both ways
we need a connector called “mysql.connector”.
We can install “mysql.connector” by using following
methods:
At command prompt (Administrator login)
Type “pip install mysql.connector” and press enter
(internet connection in required)
This connector will work only for MySQL 5.7.3 or later
Or open
“https://dev.mysql.com/downloads/connector/python/”
and download connector as per OS and Python version
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
4. Connecting to MySQL from Python
Once the connector is installed you are ready to
connect your python program to MySQL.
The following steps to follow while connecting your
python program with MySQL
Open python
Import the package required (import mysql.connector)
Open the connection to database
Create a cursor instance
Execute the query and store it in resultset
Extract data from resultset
Clean up the environment
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
5. Importing mysql.connector
import mysql.connector
Or
import mysql.connector as ms
Here “ms” is an alias, so every time we can use “ms” in
place of “mysql.connector”
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
6. Open a connection to MySQL Database
To create connection, connect() function is used
Its syntax is:
connect(host=<server_name>,user=<user_name>,
passwd=<password>[,database=<database>])
Here server_name means database servername, generally
it is given as “localhost”
User_name means user by which we connect with mysql
generally it is given as “root”
Password is the password of user “root”
Database is the name of database whose data(table) we
want to use
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
7. Example: To establish connection with MySQL
is_connected() function returns
true if connection is established
otherwise false
“mys” is an alias of package “mysql.connector”
“mycon” is connection object which stores connection established with MySQL
“connect()” function is used to connect with mysql by specifying parameters
like host, user, passwd, database
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
8. Table to work (emp)
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
9. Creating Cursor
It is a useful control structure of database connectivity.
When we fire a query to database, it is executed and
resultset (set of records) is sent over he connection in
one go.
We may want to access data one row at a time, but
query processing cannot happens as one row at a time,
so cursor help us in performing this task. Cursor stores
all the data as a temporary container of returned data
and we can fetch data one row at a time from Cursor.
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
10. Creating Cursor and Executing Query
TO CREATE CURSOR
Cursor_name = connectionObject.cursor()
For e.g.
mycursor = mycon.cursor()
TO EXECUTE QUERY
We use execute() function to send query to connection
Cursor_name.execute(query)
For e.g.
mycursor.execute(„select * from emp‟)
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
11. Example - Cursor
Output shows cursor is created and query is fired and stored, but no data is
coming. To fetch data we have to use functions like fetchall(), fetchone(),
fetchmany() are used
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
12. Fetching(extracting) data from ResultSet
To extract data from cursor following functions are used:
fetchall() : it will return all the record in the form of
tuple.
fetchone() : it return one record from the result set. i.e.
first time it will return first record, next time it will return
second record and so on. If no more record it will return
None
fetchmany(n) : it will return n number of records. It no
more record it will return an empty tuple.
rowcount : it will return number of rows retrieved from
the cursor so far.
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
13. Example – fetchall()
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
14. Example 2 – fetchall()
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
15. Example 3 – fetchall()
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
16. Example 4: fetchone()
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
17. Example 5: fetchmany(n)
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
18. Guess the output
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
19. Parameterized Query
We can pass values to query to perform dynamic
search like we want to search for any employee
number entered during runtime or to search any
other column values.
To Create Parameterized query we can use various
methods like:
Concatenating dynamic variable to query in which
values are entered.
String template with % formatting
String template with {} and format function
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
20. Concatenating variable with query
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
21. String template with %s formatting
In this method we will use %s in place of values to
substitute and then pass the value for that place.
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
22. String template with %s formatting
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
23. String template with {} and format()
In this method in place of %s we will use {} and to
pass values for these placeholder format() is used.
Inside we can optionally give 0,1,2… values for e.g.
{0},{1} but its not mandatory. we can also optionally
pass named parameter inside {} so that while passing
values through format function we need not to
remember the order of value to pass. For e.g.
{roll},{name} etc.
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
24. String template with {} and format()
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
25. String template with {} and format()
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
26. Inserting data in MySQL table from Python
INSERT and UPDATE operation are executed in the
same way we execute SELECT query using execute()
but one thing to remember, after executing insert or
update query we must commit our query using
connection object with commit().
For e.g. (if our connection object name is mycon)
mycon.commit()
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
27. Example : inserting data
BEFORE PROGRAM EXECUTION
AFTER PROGRAM EXECUTION
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com
28. Example: Updating record
VINOD KUMAR VERMA, PGT(CS), KV OEF KANPUR &
SACHIN BHARDWAJ, PGT(CS), KV NO.1 TEZPUR
for more updates visit: www.python4csip.com