Your GitHub profile as a 3D pixel art building in an interactive city

Create a 3D city from your GitHub contributions 🏙

Integration of TeamCity and GitHub

慕课网 首页 实战 路径 猿问 手记 登录 注册 11.11 Python 手记 \ 史上最全，最详idea搭建springdata+mongoDB+maven+springmvc 史上最全，最详idea搭建springdata+mongoDB+maven+springmvc 原创 2016-10-21 10:54:297759浏览2评论 作为IT届的小弟，本篇作为本人的第一篇手记，还希望各位大牛多多指点，以下均为个人学习所得，如有错误，敬请指正。本着服务IT小白的原则，该手记比较详细。由于最近使用postgre开发大型项目，发现了关系型数据库的弊端及查询效率之慢，苦心钻研之下，对nosql的mongoDB从无知到有了初步了解。 项目环境：win10+IntelliJ IDEA2016+maven3.3.9+MongoDB 3.2+JDK1.7+spring4.1.7 推荐网站（适合学习各种知识的基础）：http://www.runoob.com/ mongo安装请参考http://www.runoob.com/mongodb/mongodb-window-install.html 由于最近osChina的maven仓库挂掉，推荐大家使用阿里的镜像，速度快的飞起 maven配置：<localRepository>F:\.m2\repository</localRepository> <mirrors> <mirror> <id>alimaven</id> <name>aliyun maven</name> <url>http://maven.aliyun.com/nexus/content/groups/public/</url> <mirrorOf>central</mirrorOf> </mirror> </mirrors> 这里不实用idea自带maven插件，改用3.3.9 图片描述 项目结构：图片描述 这里dao与mongoDao分别为mongoDB的两种查询方式： dao为JPA的查询方式（请参考springdataJPA） mongoDao使用mongoTemplate，类似于关系型数据库使用的jdbcTemplate 不罗嗦，上代码 先看配置文件 spring-context.xm为最基本的spring配置 <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:context="http://www.springframework.org/schema/context" xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd"> <!--扫描service包嗲所有使用注解的类型--> <context:component-scan base-package="com.lida.mongo"/> <!-- 导入mongodb的配置文件 --> <import resource="spring-mongo.xml" /> <!-- 开启注解 --> <context:annotation-config /> </beans> spring-web.xml为springmvc的基本配置 <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:mvc="http://www.springframework.org/schema/mvc" xmlns:context="http://www.springframework.org/schema/context" xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/mvc http://www.springframework.org/schema/mvc/spring-mvc-4.0.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context-4.0.xsd"> <!--配置springmvc--> <!--1.开启springmvc注解模式--> <!--简化配置： (1)主动注册DefaultAnnotationHandlerMapping,AnnotationMethodHandlerAdapter (2)提供一系列功能：数据绑定，数字和日期的format @NumberFormt @DataTimeFormat，xml json默认的读写支持--> <mvc:annotation-driven/> <!--servlet-mapping--> <!--2静态资源默认的servlet配置，（1）允许对静态资源的处理：js，gif （2）允许使用“/”做整体映射--> <!-- 容器默认的DefaultServletHandler处理 所有静态内容与无RequestMapping处理的URL--> <mvc:default-servlet-handler/> <!--3:配置jsp 显示viewResolver--> <bean class="org.springframework.web.servlet.view.InternalResourceViewResolver"> <property name="viewClass" value="org.springframework.web.servlet.view.JstlView"/> <property name="prefix" value="/WEB-INF/views/"/> <property name="suffix" value=".jsp"/> </bean> <!-- 4自动扫描且只扫描@Controller --> <context:component-scan base-package="com.lida.mongo.controller" /> <!-- 定义无需Controller的url<->view直接映射 --> <mvc:view-controller path="/" view-name="redirect:/goMongo/list"/> </beans> spring-mongo.xml为mongo配置 <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:context="http://www.springframework.org/schema/context" xmlns:mongo="http://www.springframework.org/schema/data/mongo" xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans-3.0.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd http://www.springframework.org/schema/data/mongo http://www.springframework.org/schema/data/mongo/spring-mongo.xsd"> <!-- 加载mongodb的属性配置文件 --> <context:property-placeholder location="classpath:mongo.properties" /> <!-- spring连接mongodb数据库的配置 --> <mongo:mongo-client replica-set="${mongo.hostport}" id="mongo"> <mongo:client-options connections-per-host="${mongo.connectionsPerHost}" threads-allowed-to-block-for-connection-multiplier="${mongo.threadsAllowedToBlockForConnectionMultiplier}" connect-timeout="${mongo.connectTimeout}" max-wait-time="${mongo.maxWaitTime}" socket-timeout="${mongo.socketTimeout}"/> </mongo:mongo-client> <!-- mongo的工厂，通过它来取得mongo实例,dbname为mongodb的数据库名，没有的话会自动创建 --> <mongo:db-factory id="mongoDbFactory" dbname="mongoLida" mongo-ref="mongo" /> <!-- 只要使用这个调用相应的方法操作 --> <bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate"> <constructor-arg name="mongoDbFactory" ref="mongoDbFactory" /> </bean> <!-- mongodb bean的仓库目录，会自动扫描扩展了MongoRepository接口的接口进行注入 --> <mongo:repositories base-package="com.lida.mongo" /> </beans> mongo.properties #mongoDB连接配置 mongo.hostport=127.0.0.1:27017 mongo.connectionsPerHost=8 mongo.threadsAllowedToBlockForConnectionMultiplier=4 #连接超时时间 mongo.connectTimeout=1000 #等待时间 mongo.maxWaitTime=1500 #Socket超时时间 mongo.socketTimeout=1500 pom.xml <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.liad</groupId> <artifactId>mongo</artifactId> <packaging>war</packaging> <version>1.0-SNAPSHOT</version> <name>mongo Maven Webapp</name> <url>http://maven.apache.org</url> <dependencies> <!--使用junit4，注解的方式测试--> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.11</version> <scope>test</scope> </dependency> <!--日志--> <!--日志 slf4j,log4j,logback,common-logging--> <!--slf4j是规范/接口--> <!--log4j,logback,common-logging是日志实现 本项目使用slf4j + logback --> <dependency> <groupId>org.slf4j</groupId> <artifactId>slf4j-api</artifactId> <version>1.7.12</version> </dependency> <!--实现slf4j并整合--> <dependency> <groupId>ch.qos.logback</groupId> <artifactId>logback-core</artifactId> <version>1.1.1</version> </dependency> <dependency> <groupId>ch.qos.logback</groupId> <artifactId>logback-classic</artifactId> <version>1.1.1</version> </dependency> <!--数据库相关--> <dependency> <groupId>mysql</groupId> <artifactId>mysql-connector-java</artifactId> <version>5.1.22</version> <!--maven工作范围 驱动在真正工作的时候使用，故生命周期改为runtime--> <scope>runtime</scope> </dependency> <!--servlet web相关--> <dependency> <groupId>taglibs</groupId> <artifactId>standard</artifactId> <version>1.1.2</version> </dependency> <dependency> <groupId>jstl</groupId> <artifactId>jstl</artifactId> <version>1.2</version> </dependency> <dependency> <groupId>com.fasterxml.jackson.core</groupId> <artifactId>jackson-databind</artifactId> <version>2.5.4</version> </dependency> <!--spring--> <!--spring核心--> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-core</artifactId> <version>4.1.7.RELEASE</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-beans</artifactId> <version>4.1.7.RELEASE</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-context</artifactId> <version>4.1.7.RELEASE</version> </dependency> <!--spring dao--> <dependency> <groupId>org.springframework.data</groupId> <artifactId>spring-data-mongodb</artifactId> <version>1.8.0.RELEASE</version> </dependency> <dependency> <groupId>org.mongodb</groupId> <artifactId>mongo-java-driver</artifactId> <version>3.2.2</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-tx</artifactId> <version>4.1.7.RELEASE</version> </dependency> <!--spring web--> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-web</artifactId> <version>4.1.7.RELEASE</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-webmvc</artifactId> <version>4.1.7.RELEASE</version> </dependency> <!--spring test--> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-test</artifactId> <version>4.1.7.RELEASE</version> </dependency> <dependency> <groupId>commons-collections</groupId> <artifactId>commons-collections</artifactId> <version>3.2.2</version> </dependency> <dependency> <groupId>commons-fileupload</groupId> <artifactId>commons-fileupload</artifactId> <version>1.3.2</version> </dependency> <dependency> <groupId>commons-codec</groupId> <artifactId>commons-codec</artifactId> <version>1.10</version> </dependency> </dependencies> <dependencyManagement> <dependencies> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-framework-bom</artifactId> <version>${spring.version}</version> <type>pom</type> <scope>import</scope> </dependency> <dependency> <groupId>net.sf.ehcache</groupId> <artifactId>ehcache-core</artifactId> <version>2.6.9</version> </dependency> </dependencies> </dependencyManagement> <build> <finalName>mongo</finalName> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <configuration> <source>1.6</source> <target>1.6</target> </configuration> </plugin> </plugins> </build> </project> 两个实体类： /** * Created by DuLida on 2016/10/20. */ public class Address { private String city; private String street; private int num; public Address() { } public Address(String city, String street, int num) { this.city = city; this.street = street; this.num = num; } public String getCity() { return city; } public void setCity(String city) { this.city = city; } public String getStreet() { return street; } public void setStreet(String street) { this.street = street; } public int getNum() { return num; } public void setNum(int num) { this.num = num; } @Override public String toString() { return "Address{" + "city='" + city + '\'' + ", street='" + street + '\'' + ", num=" + num + '}'; } } /** * Created by DuLida on 2016/10/20. */ @Document(collection="person") public class Person implements Serializable { @Id private ObjectId id; private String name; private int age; private Address address; public Person() { } public Person( String name, int age, Address address) { this.name = name; this.age = age; this.address = address; } public ObjectId getId() { return id; } public void setId(ObjectId id) { this.id = id; } public String getName() { return name; } public void setName(String name) { this.name = name; } public int getAge() { return age; } public void setAge(int age) { this.age = age; } public Address getAddress() { return address; } public void setAddress(Address address) { this.address = address; } @Override public String toString() { return "Person{" + "id=" + id + ", name='" + name + '\'' + ", age=" + age + ", address=" + address + '}'; } } JPA的dao，注意这里只要继承MongoRepository不用写注解spring就能认识这是个Repository，MongoRepository提供了基本的增删改查，不用实现便可直接调用，例如testMongo的personDao.save(persons); public interface PersonDao extends MongoRepository<Person, ObjectId> { @Query(value = "{'age' : {'$gte' : ?0, '$lte' : ?1}, 'name':?2 }",fields="{ 'name' : 1, 'age' : 1}") List<Person> findByAge(int age1, int age2, String name); } mongoTemplate的dao /** * Created by DuLida on 2016/10/21. */ public interface PersonMongoDao { List<Person> findAll(); void insertPerson(Person user); void removePerson(String userName); void updatePerson(); List<Person> findForRequery(String userName); } @Repository("personMongoImpl") public class PersonMongoImpl implements PersonMongoDao { @Resource private MongoTemplate mongoTemplate; @Override public List<Person> findAll() { return mongoTemplate.findAll(Person.class,"person"); } @Override public void insertPerson(Person person) { mongoTemplate.insert(person,"person"); } @Override public void removePerson(String userName) { mongoTemplate.remove(Query.query(Criteria.where("name").is(userName)),"person"); } @Override public void updatePerson() { mongoTemplate.updateMulti(Query.query(Criteria.where("age").gt(3).lte(5)), Update.update("age",3),"person"); } @Override public List<Person> findForRequery(String userName) { return mongoTemplate.find(Query.query(Criteria.where("name").is(userName)),Person.class); } } JPA查询的测试类： /** * Created by DuLida on 2016/10/20. */ @RunWith(SpringJUnit4ClassRunner.class) //告诉junit spring配置文件 @ContextConfiguration({"classpath:spring/spring-context.xml","classpath:spring/spring-mongo.xml"}) public class PersonDaoTest { @Resource private PersonDao personDao; /*先往数据库中插入10个person*/ @Test public void testMongo() { List<Person> persons = new ArrayList<Person>(); for (int i = 0; i < 10; i++) { persons.add(new Person("name"+i,i,new Address("石家庄","裕华路",i))); } personDao.save(persons); } @Test public void findMongo() { System.out.println(personDao.findByAge(2,8,"name6")); } } mongoTemplate查询的测试类 /** * Created by DuLida on 2016/10/21. */ @RunWith(SpringJUnit4ClassRunner.class) //告诉junit spring配置文件 @ContextConfiguration({"classpath:spring/spring-context.xml","classpath:spring/spring-mongo.xml"}) public class MongoTemplateTest { @Resource private PersonMongoImpl personMongo; @Test public void testMongoTemplate() { //personMongo.insertPerson(new Person("wukong",24,new Address("石家庄","鑫达路",20))); //personMongo.removePerson("name3"); //personMongo.updatePerson(); //System.out.println(personMongo.findAll()); System.out.println(personMongo.findForRequery("wukong")); } } 注意测试前请先通过testMongo()向数据库中插入数据。 项目源码Git地址，仅供学习使用：https://github.com/dreamSmile/mongo.git 参考资料http://docs.spring.io/spring-data/mongodb/docs/current/reference/html/ 本文原创发布于慕课网 ，转载请注明出处，谢谢合作！ 相关标签：JAVAMongoDB 时间丶思考 天才小驴 你好小Song 陈词滥调1 4 人推荐 收藏 相关阅读 JAVA第三季1-9（模拟借书系统）作业 用pkp类，players类，playgame类三步教你写扑克牌游戏 Java入门第三季习题，简易扑克牌游戏 java学习第二季哒哒租车系统 Java入门第二季第六章练习题 请登录后，发表评论 评论（Enter+Ctrl） 全部评论2条 你好小Song2F 多数据源如何配置, 比如多个mongodb数据库再加mysql 1天前回复赞同0 时间丶思考 回复 你好小Song： 41分钟前 就在加一个datasource就行啊，原来mysql的datasource怎么加，现在就怎么加上就行，加上直接用。 回复 你好小Song1F 参考一下, 学习了. 2天前回复赞同0 时间丶思考 JAVA开发工程师 情劫难逃。 3篇手记 3推荐 作者的热门手记 神奇的Canvas贝塞尔曲线画心，程序员的表白 1021浏览18推荐3评论 深入探究setTimeout 和setInterval 44浏览1推荐0评论 网站首页企业合作人才招聘联系我们合作专区关于我们讲师招募常见问题意见反馈友情链接 Copyright © 2016 imooc.com All Rights Reserved | 京ICP备 13046642号-2

Documentation for MIT CityScope Project

Your GitHub contributions reimagined as a living 3D city skyline.

Python library to help you to study the GitHub community of a location :part_alternation_mark: :busts_in_silhouette:

Rankings using GitHub API, specially focused on location

Github issue tracker support for TeamCity

No description available

Infoga-Collect Email-Information Infoga is a tool gathering email accounts informations (ip,hostname,country,...) from different public source (search engines, pgp key servers and shodan) and check if emails was leaked using hacked-emails API. Is a really simple tool, but very effective for the early stages of a penetration test or just to know the visibility of your company in the Internet. Installation : $ apt update && apt upgrade $ apt install git $ apt install python2 $ git clone https://github.com/m4ll0k/ Infoga $ cd Infoga $ chmod +x * $ pip2 install requests usage : $ python2 infoga.py Now it shows all options to use this tool $ python2 infoga.py -t gmail.com -s all Now its started collecting emails and e-mails information [hostname, city, organization, longitude and latitude ports..]

Infoga-Collect Email-Information Infoga is a tool gathering email accounts informations (ip,hostname,country,...) from different public source (search engines, pgp key servers and shodan) and check if emails was leaked using hacked-emails API. Is a really simple tool, but very effective for the early stages of a penetration test or just to know the visibility of your company in the Internet. Installation : $ apt update && apt upgrade $ apt install git $ apt install python2 $ git clone https://github.com/m4ll0k/ Infoga $ cd Infoga $ chmod +x * $ pip2 install requests usage : $ python2 infoga.py Now it shows all options to use this tool $ python2 infoga.py -t gmail.com -s all Now its started collecting emails and e-mails information [hostname, city, organization, longitude and latitude ports..]

GITHUB-2022

etri-city-traffic-brain website

Our spartan simple homepage.

CityCamp website

This Git repository contains a Next.js 13 project designed to calculate electric city bills. The project utilizes the Tailwind CSS framework and JavaScript to create an intuitive and responsive user interface for efficient bill calculations.

# Pandas Homework - Pandas, Pandas, Pandas ## Background The data dive continues! Now, it's time to take what you've learned about Python Pandas and apply it to new situations. For this assignment, you'll need to complete **one of two** (not both) Data Challenges. Once again, which challenge you take on is your choice. Just be sure to give it your all -- as the skills you hone will become powerful tools in your data analytics tool belt. ### Before You Begin 1. Create a new repository for this project called `pandas-challenge`. **Do not add this homework to an existing repository**. 2. Clone the new repository to your computer. 3. Inside your local git repository, create a directory for the Pandas Challenge you choose. Use folder names corresponding to the challenges: **HeroesOfPymoli** or **PyCitySchools**. 4. Add your Jupyter notebook to this folder. This will be the main script to run for analysis. 5. Push the above changes to GitHub or GitLab. ## Option 1: Heroes of Pymoli  Congratulations! After a lot of hard work in the data munging mines, you've landed a job as Lead Analyst for an independent gaming company. You've been assigned the task of analyzing the data for their most recent fantasy game Heroes of Pymoli. Like many others in its genre, the game is free-to-play, but players are encouraged to purchase optional items that enhance their playing experience. As a first task, the company would like you to generate a report that breaks down the game's purchasing data into meaningful insights. Your final report should include each of the following: ### Player Count * Total Number of Players ### Purchasing Analysis (Total) * Number of Unique Items * Average Purchase Price * Total Number of Purchases * Total Revenue ### Gender Demographics * Percentage and Count of Male Players * Percentage and Count of Female Players * Percentage and Count of Other / Non-Disclosed ### Purchasing Analysis (Gender) * The below each broken by gender * Purchase Count * Average Purchase Price * Total Purchase Value * Average Purchase Total per Person by Gender ### Age Demographics * The below each broken into bins of 4 years (i.e. &lt;10, 10-14, 15-19, etc.) * Purchase Count * Average Purchase Price * Total Purchase Value * Average Purchase Total per Person by Age Group ### Top Spenders * Identify the the top 5 spenders in the game by total purchase value, then list (in a table): * SN * Purchase Count * Average Purchase Price * Total Purchase Value ### Most Popular Items * Identify the 5 most popular items by purchase count, then list (in a table): * Item ID * Item Name * Purchase Count * Item Price * Total Purchase Value ### Most Profitable Items * Identify the 5 most profitable items by total purchase value, then list (in a table): * Item ID * Item Name * Purchase Count * Item Price * Total Purchase Value As final considerations: * You must use the Pandas Library and the Jupyter Notebook. * You must submit a link to your Jupyter Notebook with the viewable Data Frames. * You must include a written description of three observable trends based on the data. * See [Example Solution](HeroesOfPymoli/HeroesOfPymoli_starter.ipynb) for a reference on expected format. ## Option 2: PyCitySchools  Well done! Having spent years analyzing financial records for big banks, you've finally scratched your idealistic itch and joined the education sector. In your latest role, you've become the Chief Data Scientist for your city's school district. In this capacity, you'll be helping the school board and mayor make strategic decisions regarding future school budgets and priorities. As a first task, you've been asked to analyze the district-wide standardized test results. You'll be given access to every student's math and reading scores, as well as various information on the schools they attend. Your responsibility is to aggregate the data to and showcase obvious trends in school performance. Your final report should include each of the following: ### District Summary * Create a high level snapshot (in table form) of the district's key metrics, including: * Total Schools * Total Students * Total Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### School Summary * Create an overview table that summarizes key metrics about each school, including: * School Name * School Type * Total Students * Total School Budget * Per Student Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Top Performing Schools (By % Overall Passing) * Create a table that highlights the top 5 performing schools based on % Overall Passing. Include: * School Name * School Type * Total Students * Total School Budget * Per Student Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Bottom Performing Schools (By % Overall Passing) * Create a table that highlights the bottom 5 performing schools based on % Overall Passing. Include all of the same metrics as above. ### Math Scores by Grade\*\* * Create a table that lists the average Math Score for students of each grade level (9th, 10th, 11th, 12th) at each school. ### Reading Scores by Grade * Create a table that lists the average Reading Score for students of each grade level (9th, 10th, 11th, 12th) at each school. ### Scores by School Spending * Create a table that breaks down school performances based on average Spending Ranges (Per Student). Use 4 reasonable bins to group school spending. Include in the table each of the following: * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Scores by School Size * Repeat the above breakdown, but this time group schools based on a reasonable approximation of school size (Small, Medium, Large). ### Scores by School Type * Repeat the above breakdown, but this time group schools based on school type (Charter vs. District). As final considerations: * Use the pandas library and Jupyter Notebook. * You must submit a link to your Jupyter Notebook with the viewable Data Frames. * You must include a written description of at least two observable trends based on the data. * See [Example Solution](PyCitySchools/PyCitySchools_starter.ipynb) for a reference on the expected format. ## Hints and Considerations * These are challenging activities for a number of reasons. For one, these activities will require you to analyze thousands of records. Hacking through the data to look for obvious trends in Excel is just not a feasible option. The size of the data may seem daunting, but pandas will allow you to efficiently parse through it. * Second, these activities will also challenge you by requiring you to learn on your feet. Don't fool yourself into thinking: "I need to study pandas more closely before diving in." Get the basic gist of the library and then _immediately_ get to work. When facing a daunting task, it's easy to think: "I'm just not ready to tackle it yet." But that's the surest way to never succeed. Learning to program requires one to constantly tinker, experiment, and learn on the fly. You are doing exactly the _right_ thing, if you find yourself constantly practicing Google-Fu and diving into documentation. There is just no way (or reason) to try and memorize it all. Online references are available for you to use when you need them. So use them! * Take each of these tasks one at a time. Begin your work, answering the basic questions: "How do I import the data?" "How do I convert the data into a DataFrame?" "How do I build the first table?" Don't get intimidated by the number of asks. Many of them are repetitive in nature with just a few tweaks. Be persistent and creative! * Expect these exercises to take time! Don't get discouraged if you find yourself spending hours initially with little progress. Force yourself to deal with the discomfort of not knowing and forge ahead. Consider these hours an investment in your future! * As always, feel encouraged to work in groups and get help from your TAs and Instructor. Just remember, true success comes from mastery and _not_ a completed homework assignment. So challenge yourself to truly succeed! ### Copyright Trilogy Education Services © 2019. All Rights Reserved.

Public site of CityLife Sim

🌡️ This Bash script retrieves weather information from the OpenWeatherMap API for your city, changes the temperature from Kelvin to Celsius, and updates the contents of the README.md file in the Git repository.

A murder has been a committed in ML City at the Pandas Express! To find out whodunit you will need to utilize your new command line, git, and Jupyter notebooks skills.

City of Code: GitHub - THREE.JS representation of GitHub Contributions calendar

DOM City - Learning the DOM-API in a playful way

No description available

C++那些事网站

New York City yellow taxi OD data visualization. git.io/fhvVW

A Qwik City template with TypeScript, TailwindCSS, Qwickest Icons and pre-configured with ESLint, Prettier and Husky git hooks.

# Web Design Homework - Web Visualization Dashboard (Latitude) ## Background Data is more powerful when we share it with others! Let's take what we've learned about HTML and CSS to create a dashboard showing off the analysis we've done.  ### Before You Begin 1. Create a new repository for this project called `Web-Design-Challenge`. **Do not add this homework to an existing repository**. 2. Clone the new repository to your computer. 3. Inside your local git repository, create a directory for the web challenge. Use a folder name to correspond to the challenge: **WebVisualizations**. 4. Add your **html** files to this folder as well as your **assets**, **Resources** and **visualizations** folders. 5. Push the above changes to GitHub or GitLab. 6. Deploy to GitHub pages. ## Latitude - Latitude Analysis Dashboard with Attitude For this homework we'll be creating a visualization dashboard website using visualizations we've created in a past assignment. Specifically, we'll be plotting [weather data](Resources/cities.csv). In building this dashboard, we'll create individual pages for each plot and a means by which we can navigate between them. These pages will contain the visualizations and their corresponding explanations. We'll also have a landing page, a page where we can see a comparison of all of the plots, and another page where we can view the data used to build them. ### Website Requirements For reference, see the ["Screenshots" section](#screenshots) below. The website must consist of 7 pages total, including: * A [landing page](#landing-page) containing: * An explanation of the project. * Links to each visualizations page. There should be a sidebar containing preview images of each plot, and clicking an image should take the user to that visualization. * Four [visualization pages](#visualization-pages), each with: * A descriptive title and heading tag. * The plot/visualization itself for the selected comparison. * A paragraph describing the plot and its significance. * A ["Comparisons" page](#comparisons-page) that: * Contains all of the visualizations on the same page so we can easily visually compare them. * Uses a Bootstrap grid for the visualizations. * The grid must be two visualizations across on screens medium and larger, and 1 across on extra-small and small screens. * A ["Data" page](#data-page) that: * Displays a responsive table containing the data used in the visualizations. * The table must be a bootstrap table component. [Hint](https://getbootstrap.com/docs/4.3/content/tables/#responsive-tables) * The data must come from exporting the `.csv` file as HTML, or converting it to HTML. Try using a tool you already know, pandas. Pandas has a nifty method approprately called `to_html` that allows you to generate a HTML table from a pandas dataframe. See the documentation [here](https://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.to_html.html) The website must, at the top of every page, have a navigation menu that: * Has the name of the site on the left of the nav which allows users to return to the landing page from any page. * Contains a dropdown menu on the right of the navbar named "Plots" that provides a link to each individual visualization page. * Provides two more text links on the right: "Comparisons," which links to the comparisons page, and "Data," which links to the data page. * Is responsive (using media queries). The nav must have similar behavior as the screenshots ["Navigation Menu" section](#navigation-menu) (notice the background color change). Finally, the website must be deployed to GitHub pages. When finished, submit to BootcampSpot the links to 1) the deployed app and 2) the GitHub repository. Ensure your repository has regular commits (i.e. 20+ commits) and a thorough README.md file ### Considerations * You may use the [weather data](Resources/cities.csv) or choose another dataset. Alternatively, you may use the included [cities dataset](Resources/cities.csv) and pull the images from the [assets folder](Resources/assets). * You must use Bootstrap. This includes using the Bootstrap `navbar` component for the header on every page, the bootstrap table component for the data page, and the Bootstrap grid for responsiveness on the comparison page. * You must deploy your website to GitHub pages, with the website working on a live, publicly accessible URL as a result. * Be sure to use a CSS media query for the navigation menu. * Be sure your website works at all window widths/sizes. * Feel free to take some liberty in the visual aspects, but keep the core functionality the same.

This is a Non-Code repo to aid the efficient organisation of GDG(Google developer groups) IIIT Sri City's Git and Github bootcamp

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