投稿時間:2023-01-07 22:20:01 RSSフィード2023-01-07 22:00 分まとめ(22件)

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Ruby	Rubyタグが付けられた新着投稿 - Qiita	Ruby on Railsを基本からまとめてみた【APIを構築する方法】	https://qiita.com/kanfutrooper/items/0af160e4d99fb62647c4	apiruby	2023-01-07 21:06:00
Ruby	Railsタグが付けられた新着投稿 - Qiita	Ruby on Railsを基本からまとめてみた【APIを構築する方法】	https://qiita.com/kanfutrooper/items/0af160e4d99fb62647c4	apiruby	2023-01-07 21:06:00
海外TECH	DEV Community	Revolutionizing the Web with WebAssembly: A Comprehensive Guide	https://dev.to/cocoandrew/revolutionizing-the-web-with-webassembly-a-comprehensive-guide-fa5	Revolutionizing the Web with WebAssembly A Comprehensive GuideWebAssembly Wasm is a low level binary format that is designed to be faster and more efficient than traditional JavaScript It allows developers to run code on the web that is compiled from languages like C C and Rust and it can be run in web browsers and other environments that support the WebAssembly standard In this article we will explore how to use WebAssembly in a web application We will start by setting up a basic project then we will write some simple C code and compile it to WebAssembly using the WebAssembly Binary Toolkit WABT Finally we will write some JavaScript code to interact with our WebAssembly module Setting up the projectFirst let s create a new project and install the dependencies we will need mkdir wasm examplecd wasm examplenpm init ynpm install save dev wasm packNext let s create a simple C program that we will compile to WebAssembly Create a file called src main c with the following contents include lt stdio h gt int add int a int b return a b This simple C function takes two integers as arguments and returns their sum To compile C code to WebAssembly WASM we will need to use a WASM compiler such as emscripten Emscripten is an open source toolchain that allows developers to compile C and C code to WASM as well as other target platforms such as JavaScript and OpenGL To use emscripten we will need to install it and set up the necessary build tools This can typically be done using a package manager such as Homebrew or apt get depending on the operating system brew install emscripten emcc vOnce emscripten is installed we can compile C code to WASM by running the emcc compiler with the C source file as input and the desired output file emcc hello c o hello wasmThis will generate a WASM binary file hello wasm that we can then use in our web application To run the WASM binary in the browser we will need to create an HTML page with a JavaScript script that loads and runs the WASM file Here is an example of how to do this Create a file called index html with the following contents lt DOCTYPE html gt lt html gt lt head gt lt title gt WebAssembly Example lt title gt lt head gt lt body gt lt script src index js gt lt script gt lt body gt lt html gt Next create a file called index js with the following contents const wasmUrl pkg main wasm fetch wasmUrl then response gt response arrayBuffer then bytes gt WebAssembly instantiate bytes then results gt const instance results const exports instance console log exports add This JavaScript code loads the main wasm file using the fetch API then it uses the WebAssembly instantiate function to parse the binary and create an instance of the WebAssembly module The instance exports object contains the functions and variables that are exposed by the WebAssembly module and we can call them just like any other JavaScript functions In this case we are calling the add function and printing the result to the console ApplicationWebAssembly can be used in a wide range of web applications including Games WebAssembly can be used to improve the performance of games and other graphics intensive applications Data intensive applications WebAssembly can be used to improve the performance of applications that require a lot of data processing such as data analysis or machine learning Web based tools WebAssembly can be used to create web based versions of desktop tools such as image editors or CAD software Mobile web applications WebAssembly can be used to create web applications that have the performance and capabilities of native mobile apps Virtual reality and augmented reality applications WebAssembly can be used to improve the performance and capabilities of VR and AR applications in the browser AdvantagesThere are several advantages to using WebAssembly in your web applications Improved performance WebAssembly is designed to be faster and more efficient than JavaScript which means that it can offer a significant performance boost for certain types of code This can be especially useful for applications that require a lot of processing power such as games or data intensive applications Language interoperability Because WebAssembly is a compilation target for many different languages it allows developers to use the language that is best suited for a particular task For example developers can use C for performance critical parts of an application and JavaScript for the rest Smaller file sizes WebAssembly modules are typically smaller than their equivalent JavaScript counterparts which can help reduce the size of your application and improve load times Improved security WebAssembly is designed to be safer than JavaScript with features like memory isolation and an explicit memory model that make it more difficult for attackers to exploit vulnerabilities Better support for legacy code If you have an existing codebase written in a language like C you can use WebAssembly to bring that code to the web without having to rewrite it from scratch Overall WebAssembly offers developers a powerful tool for improving the performance security and interoperability of their web applications ConclusionIn conclusion WebAssembly is a powerful tool for running fast efficient code on the web It allows developers to use languages like C C and Rust to write code that can be run in the browser and it can be used in conjunction with JavaScript to create rich interactive web applications By following the steps in this article you should now have a basic understanding of how to use WebAssembly in a web application There are many more advanced features and possibilities with WebAssembly so be sure to explore the documentation and other resources to learn more	2023-01-07 12:40:24
海外TECH	DEV Community	Getting Started with a Web Scraping Project 🕷️🤖	https://dev.to/ken_mwaura1/getting-started-with-a-web-scraping-project-10ej	Getting Started with a Web Scraping Project ️ IntroductionI have worked on and maintained a good number of web scrapers in the past I have also written a few articles on web scraping However I have never written a step by step guide on how to build a web scraper This post will aim to serve a starter guide for myself and anyone for a simple web scraping project Though its not a complete guide it will serve as a good starting point for anyone looking to build a web scraper We will use a combination of technologies to build our web scraper We will be using Python Postgres SQLAlchemy and Docker Audience and ObjectivesThis article at beginner developers hobbyists and DIY folks who are looking to build a web scraper Intermediate developers can also use this article as a refresher on how to build a web scraper This article aims to serve as a step by step guide on how to build a web scraper using Python We will be using the Lifetime Leaderboards UMG Gaming website as our target website We will be scraping the data from the website and storing it in a database Prerequisites to Follow Along To follow along with this article you will need to have the following installed on your machine Python and Pip I am currently using Any version above shoul work Git installed and configured on your machine Instructions vary by Operating System Docker installed and running Docker DocumentationTerminal or Command Line Interface CLI installed on your machine Database Ensure Postgres is installed locally and running on port ORrun a Postgres container on Docker Easy PostgreSQL and pgAdmin Setup with Docker container on port Optional but recommended Visual Studio Code or any other IDE of your choice Github account Initial Setup InstructionsThese instructions will work for most Unix Linux Mac and Windows implementations Kindly refer to the documentation for your Operating System for more information Create a directory for your project and change into it I will be using simple web scraper as my project name mkdir simple web scraper amp amp cd simple web scraperCreate a virtual environment for your project I will be using venv as my virtual environment name python m venv venvActivate the virtual environment source venv bin activateInstall the required dependencies python m pip install requests beautifulsoup psycopg binary SQLAlchemy python dotenv pytest Faker factory boySave the dependencies to a requirements txt file python m pip freeze gt requirements txtCreate a gitignore file and add the following lines to it gitignorevenv pycache vscode env Create a README md file and add the following lines to it simple web scraperSimple web scraper for Lifetime Leaderboards UMG Gaming Installation Clone this RepoRun the following command to initialize a git repository git initAdd the files to the staging area git add Commit the files to the repository git commit m Initial Commit Hopefully you have followed the instructions above and have a working project directory If you have any issues please feel free to reach out to me on Twitter or Github Reconnaissance PhaseThe first step in any web scraping project is to understand the target website As mentioned above we ll be using Lifetime Leaderboards UMG Gaming website as our target website We will be scraping the data from the website and storing it in a database The choice is mostly motivated by the fact that the website is simple and has a good amount of data to scrape and as a bonus it has a leaderboards for the most popular gamers sorted by xp and earnings Understanding the Target Website 🥷Below is a screenshot of the xp and earnings leaderboards We can see that the xp leaderboard has a table with the following columns PlaceUsernameTrophiesSocialXpThe earnings leaderboard is similar with the only difference being the last column which is earnings This is a good starting point for our web scraper We now have a good idea of the data as well as the structure of the data We can now move on to the next step Understanding the Structure of the Target Website ️The next step is to understand the structure of the target website We will be using the Chrome Developer Tools to inspect the target website Press F on your keyboard to open the developer tools Once the developer tools are open navigate to the xp leaderboard Right click on the table and select Inspect This will open the element inspector Here we can see the HTML structure of the table and identify the elements we need to scrape A Game of Codes ‍Now Recon is complete and we have a good idea of the data we need to scrape We can now move on to the next step We will be using Python to scrape the data from the website We will be using the requests library to make HTTP requests to the website We will be using the BeautifulSoup library to parse the HTML response from the website Making HTTP Requests Inside the simple web scraper directory create a new file called xp scrape py Open the file and add the following lines of code xp scrape pyimport requestsfrom bs import BeautifulSoup get the datadata requests get load data into bssoup BeautifulSoup data text html parser leaderboard soup find table id leaderboard table tbody leaderboard find tbody We have imported the requests library and used it to make a GET request to the target website We have also imported the BeautifulSoup library We will be using this library to parse the HTML response from the website We have also used the BeautifulSoup library to find the table with the id leaderboard table We have also found the tbody element inside the table We will be using this element to find the rows in the table Parsing the HTML Response We will now use the BeautifulSoup library to parse the HTML response from the website We will be using the find all method to find all the tr elements inside the tbody element We will then loop through the tr elements and print the text inside each td element xp scrape pyimport requestsfrom bs import BeautifulSoup get the datadata requests get load data into bssoup BeautifulSoup data text html parser leaderboard soup find table id leaderboard table tbody leaderboard find tbody for tr in tbody find all tr place tr find all td text strip username tr find all td find all a text strip xp tr find all td text strip print position username xp sep t print Place Username XP sep print place username xp sep We have used the find all method to find all the tr elements inside the tbody element We have then looped through the tr elements and printed the text inside each td element We have also used the strip method to remove the whitespace from the text We have also used the sep argument to separate the columns with a tab Getting Earnings Leader board The process for getting the earnings leaderboard is similar to the process for getting the xp leaderboard We will be using the find all method to find all the tr elements inside the tbody element We will then loop through the tr elements and print the text inside each td element earnings scrape pyimport requestsfrom bs import BeautifulSoup get the datadata requests get load data into bssoup BeautifulSoup data text html parser leaderboard soup find table id leaderboard table tbody leaderboard find tbody for tr in tbody find all tr place tr find all td text strip username tr find all td find all a text strip earnings tr find all td text strip print position username earnings sep t print Place Username Earnings sep print place username earnings sep We have used the find all method to find all the tr elements inside the tbody element We have then looped through the tr elements and printed the text inside each td element We have also used the strip method to remove the whitespace from the text We have also used the sep argument to separate the columns with spaces Saving the Data to a Postgres DatabaseWe will now save the data to Postgres DB this will give us persistence across sessions as well allow perform analysis on the data To keep things consistent we ll use an Object Relational Mapper ORM SQLAlchemy to create the schema and psycopg to connect to the Postgres DB Creating the Schema Inorder to keep all our files organized we ll create a new directory called db inside our root directory and create a new file called base sql py inside the db directory We ll create a Base class that will be used to create the schema for our database We ll also create a Session class that will be used to create a session to the database Following best security practice we ll also use environment variables to store our database credentials base sql pyimport osfrom sqlalchemy import create enginefrom sqlalchemy ext declarative import declarative basefrom sqlalchemy orm import sessionmakerfrom dotenv import load dotenvload dotenv db os getenv DB postgresql psycopg test testpassword localhost xp db engine create engine db engine connect print engine Session sessionmaker bind engine Base declarative base The Base class is used to create the schema for our database We have used the declarative base method to create the Base class We have also used the create engine method to create an engine that will be used to connect to the database We have also used the sessionmaker method to create a session that will be used to create a session to the database The load dotenv method is used to load the environment variables from the env file We have used the getenv method to get the database credentials from the environment variables If none is found an alternative provided for testing purposes We have also used the connect method to connect to the database Creating the Models ️We ll create two files inside the db directory One file will be used to create the XP model and the other file will be used to create the Earnings model We ll use the Base class to create the schema for the database We ll also use the Column class to create the columns for the database We ll also use the Integer class to create the id column We ll also use the String class to create the username column We ll also use the Float class to create the xp and earnings columns player sql pyfrom sqlalchemy import Column String Integer Datefrom datetime import date as dtfrom base sql import Baseclass Player Base tablename players id Column Integer primary key True username Column String place Column String xp Column Integer date Column Date def init self username place xp self place place self username username self xp xp self date dt today def repr self return f self username self place self xp self date def str self return f self username self place self xp self date if name main print Player class The code above is used to create the Player model We have used the tablename attribute to set the name of the table We have also used the Column class to create the columns for the database We have also used the Integer class to create the id column We have also used the String class to create the username column We have also used the Float class to create the xp and earnings columns We have also used the init method to initialize the Player class We have also used the repr method to return a string representation of the Player class We have also used the str method to return a string representation of the Player class We have also used the if name main statement to check if the file is being run directly If it is being run directly we will print a message top earning players sql pyfrom sqlalchemy import Column String Date Integer Floatfrom datetime import date as dtfrom base sql import Baseclass Earning Player Base tablename paid players id Column Integer primary key True username Column String place Column String earnings Column Float date Column Date def init self username place earnings self place place self username username self earnings earnings self date dt today def repr self return f self username self place self earnings self date def str self return f self username self place self earnings self date if name main print Earning Player class The code above is used to create the Earning Player model It is almost the same as the Player model The only difference is that we have used the Float class to create the earnings column The Date Column is used to store the date the data was scraped By default the Date column will store the date in the format YYYY MM DD We have used the dt today method to get the current date Creating the Database ️We ll create a new file called postgres inserts py inside the db directory We ll use the create all method to create the database We ll also use the Session class to create a session to the database We ll also use the add method to add the data to the database We ll also use the commit method to commit the changes to the database postgres inserts pyfrom base sql import Session engine Basefrom player sql import Playerfrom top earning players sql import Earning Player generate database schemaBase metadata create all engine Base metadata tables values checkfirst True Create a new sessionsession Session session commit session close create playersprint len session query Player all print len session query Earning Player all if name main print Creating tables The code above is used to create the database We have used the create all method to create the database We also query the database to check if the data has been added to the database Data Finally Back to our scrapers we ll add the code to insert the data into the database We ll add the code to insert the data into the database in the xp scraper py file We ll also add the code to insert the data into the database in the top earning players scraper py file xp scraper pyimport requestsfrom bs import BeautifulSoupfrom db base sql import Session engine Basefrom db player sql import Player generate database schemaBase metadata create all engine Create a new sessionsession Session get the datadata requests get load data into bssoup BeautifulSoup data text html parser leaderboard soup find table id leaderboard table tbody leaderboard find tbody for tr in tbody find all tr place tr find all td text strip username tr find all td find all a text strip xp tr find all td text strip print position username xp sep t print Place Username XP sep print place username xp sep leaderboard table gt tbody nth child gt tr nth child gt td nth child create players player Player username username place place xp xp Check if player exists players session query Player all try pl session query Player filter Player username username first if pl session delete pl session commit except Exception as e session rollback print e else session add player session commit session close if name main print Scraping data The code above is the completed code including importing the Player model and the Session class We have also used the Session class to create a session to the database We have also used the add method to add the data to the database We have also used the commit method to commit the changes to the database We first query the database for any existing data if the data exists we delete it and add the new data We also use the try and except statement to handle any errors Finally we commit the changes to the database and close the session Lets do the same for the top earning players scraper py file earning scrape pyimport requestsfrom bs import BeautifulSoupfrom db base sql import Session engine Basefrom db top earning players sql import Earning Player generate database schemaBase metadata create all engine Create a new sessionsession Session get the datadata requests get load data into bssoup BeautifulSoup data text html parser leaderboard soup find table id leaderboard table tbody leaderboard find tbody for tr in tbody find all tr place tr find all td text strip username tr find all td find all a text strip earnings tr find all td text strip print position username earnings sep t print Place Username Earnings sep print place username earnings sep create players player Earning Player username username place place earnings float earnings players session query Earning Player all try if session query Earning Player filter Earning Player id gt all Check if player exists pl session query Earning Player filter Earning Player username username first if pl print pl username pl place pl earnings session delete pl session commit except Exception as e session rollback print e else session add player session commit session close if name main print Scraping data The code above is the completed code including importing the Earning Player model and the Session class We have also used the Session class to create a session to the database We have also used the add method to add the data to the database We have also used the commit method to commit the changes to the database Scrape ️We have everything in place to run our scrapers and save the data accordingly lets walkthrough that process below Database Setup and Insertion Create a new database called xp db in postgresql Then create a env file in the root of the project and add the following code to the env file DB postgresql psycopg test testpassword localhost xp dbThe code above is an example of the env file Replace the test and testpassword with your postgresql username and password Then run the following commands in the terminal cd db python base sql pypython player scraper pypython top earning players scraper pypython postgres inserts py Running the ScrapersRun the scrapers by entering the following commands python xp scrape pypython earnings scrape py Test EverythingInorder to have this project be as well rounded as possible we ll code a series of tests to test the code using pytest Create a new directory called tests and create a new file called test db py in the tests directory Inside create a test players py file test players pyimport pytestfrom db player sql import Playerfrom db top earning players sql import Earning Playerfrom db base sql import Session engine Basefrom db player factory basic import PlayerFactory pytest fixturedef player func return PlayerFactory build pytest fixturedef player func return PlayerFactory build def my func to delete Player session Player id session query Player filter Player id Player id delete def my func to delete Player session Player id session query Earning Player filter Earning Player id Player id delete def test player session player func session add player func assert session query Player my func to delete Player session Player id result session query Player one or none assert result is Nonedef test player session player func session add player func assert session query Earning Player my func to delete Player session Earning Player id result session query Earning Player one or none assert result is NoneThe code above is the completed code for the test players py file We have used the pytest library to create a fixture to create a Player object We have also used the pytest library to create a fixture to create a Earning Player object We have also used the pytest library to create a fixture to create a Session object We have also used the pytest library to create a fixture to create a engine object We have also used the pytest library to create a fixture to create a Base object We have also used the pytest library to create a fixture to create a PlayerFactory object We have also used the pytest library to create a fixture to create a Earning PlayerFactory object Run the TestsTo run the tests open the terminal and run the following command in the root of the project pytest The output should be similar to the following image Completed Code on GitHubAlternatively you can check clone the completed code from KenMwaura simple web scraper Simple web scraper to get player data using beatiful soup and PostgreSQL as a database SQLAlchemy as an ORM simple web scraperThis repository contains code for a webscraper for Lifetime Leaderboards UMG GamingMaking use of the beatifulsoup and requestsPostgres as a DatabaseSQLAlchemy is used as a ORM to insert data into the dbAccompanying Blog PostGet Started with a Web Scraping ProjectInstallationClone this Repogit clone Change into into the simple web scraper foldercd simple web scraperCreate a virtualenvpython m virtualenv envActivate virtualenvsource bin activate OR use pipenv pipenv installInstall the required dependeciespython m pip install r requirements txtDatabaseEnsure Postgres is installed locally and running on port ORrun a Postgres container on Docker Easy PostgreSQL and pgAdmin Setup with Docker container on port The default db credentials are host localhostport user testdb xp dbpassword testpasswordOptionally can also set the db credentials as environment variablesexport DATABASE URL postgres test testpassword localhost xp db or… View on GitHub ConclusionIn this tutorial we have learned how to scrape data from a website using the requests library and BeautifulSoup library We have also learned how to store the scraped data in a database using the SQLAlchemy library We have also learned how to test the code using the pytest library We have also learned how to use the factory boy library to create test data I hope you liked this write up and get inspired to extend it further Keep coding Feel free to leave comments below orreach out on Twitter Ken Mwaura or LinkedIn ResourcesBeautifulSouprequestsSQLAlchemypytest	2023-01-07 12:04:25
海外TECH	Engadget	Samsung might unveil the Galaxy S23 series on February 1st	https://www.engadget.com/samsung-galaxy-s23-series-unpacked-february-1st-120153369.html?src=rss	Samsung might unveil the Galaxy S series on February stSamsung may have inadvertently confirmed that it will unveil its next flagship phones early next month According to toGoogle the company s Colombian website has published a page revealing that its next Galaxy Unpacked event is scheduled for February st Epic moments are approaching it read based on the publication s screenshot of the page which is now no longer viewable on the website While the announcement didn t explicitly say that the event will officially introduce the Galaxy S it shows the flagship series expected triple camera setup nbsp As the publication notes the leaves and flowers in the borders of the teaser reflect the colors of the leaked renders that seemed to show Galaxy S and Galaxy S Ultra units in green and lilac Previous reports also suggested that we ll get to see the upcoming phones in the first week of February at an Unpacked event which is likely to take place in San Francisco In addition an early February Unpacked for the flagship series is consistent with previous unveilings For the Galaxy S series Samsung held an event on February th Breaking Galaxy S series February st pic twitter com ACKfphFLCーIce universe UniverseIce January Samsung is reportedly ditching its Exynos chips and using Qualcomm s Snapdragon Gen SoC to power all the Galaxy S units sold worldwide The Korean tech giant typically equips its Asian and European releases with Exynos chipsets while units sold in the US come with Qualcomm processors Other reports suggested that the Galaxy S will have a megapixel main camera while the base S and Galaxy S Plus models will come with a megapixel main shooter If the leaked Unpacked page is accurate we won t have to wait long to know for sure	2023-01-07 12:01:53
ニュース	BBC News - Home	Newport News: Boy aged six detained after shooting teacher in US	https://www.bbc.co.uk/news/world-us-canada-64194407?at_medium=RSS&at_campaign=KARANGA	virginia	2023-01-07 12:57:32
ニュース	BBC News - Home	Filippo Bernardini: Italian admits stealing unpublished books	https://www.bbc.co.uk/news/world-us-canada-64197625?at_medium=RSS&at_campaign=KARANGA	ethan	2023-01-07 12:25:20
ニュース	BBC News - Home	Novak Djokovic beats Daniil Medvedev to reach Adelaide final	https://www.bbc.co.uk/sport/tennis/64197183?at_medium=RSS&at_campaign=KARANGA	adelaide	2023-01-07 12:22:06
ニュース	BBC News - Home	'Reach out in private' - King Charles' biographer on Prince Harry	https://www.bbc.co.uk/news/uk-64196499?at_medium=RSS&at_campaign=KARANGA	charles	2023-01-07 12:35:34
北海道	北海道新聞	函館港に男性遺体	https://www.hokkaido-np.co.jp/article/784638/	函館市若松町	2023-01-07 21:30:08
北海道	北海道新聞	西岡、負傷で途中棄権　テニス、全豪オープン前哨戦	https://www.hokkaido-np.co.jp/article/784626/	全豪オープン	2023-01-07 21:06:35
北海道	北海道新聞	行政計画の増減、省庁別に公表　政府、自治体訴えで総数抑制へ	https://www.hokkaido-np.co.jp/article/784637/	行政計画	2023-01-07 21:18:00
北海道	北海道新聞	「危害経験ある」国会議員７５人　４割弱不安、元首相銃撃から半年	https://www.hokkaido-np.co.jp/article/784636/	不特定多数	2023-01-07 21:18:00
北海道	北海道新聞	レバンガ３連敗　大阪に８３―９１	https://www.hokkaido-np.co.jp/article/784635/	連敗	2023-01-07 21:17:00
北海道	北海道新聞	高校バレー、決勝は駿台と鎮西　女子は誠英―古川学園	https://www.hokkaido-np.co.jp/article/784599/	古川学園	2023-01-07 21:01:16
北海道	北海道新聞	かるたクイーン３連覇　名人は２連覇、近江神宮	https://www.hokkaido-np.co.jp/article/784634/	小倉百人一首	2023-01-07 21:16:00
北海道	北海道新聞	福島の４人死亡事故で実況見分　容疑者立ち会わせ	https://www.hokkaido-np.co.jp/article/784633/	実況見分	2023-01-07 21:12:00
北海道	北海道新聞	釧路　チーズたっぷりホットサンド　とろけ出す濃厚な味	https://www.hokkaido-np.co.jp/article/784632/	eggcafe	2023-01-07 21:12:00
北海道	北海道新聞	北見　「紅白うどん　めでたいめん」　乾麺、新春や祝い事に	https://www.hokkaido-np.co.jp/article/784631/	北見市豊地	2023-01-07 21:11:00
北海道	北海道新聞	幕別　「２０２３　マクベツ　ツナグ　ランタン」　２千個点灯、幻想世界	https://www.hokkaido-np.co.jp/article/784630/	紙袋	2023-01-07 21:10:00
北海道	北海道新聞	クレインズ接戦制す　ＩＨアジアリーグ、釧路２連戦初日	https://www.hokkaido-np.co.jp/article/784629/	釧路	2023-01-07 21:04:00
北海道	北海道新聞	山本選手「パラ夢だった」　共生社会の催し、釧路町で講演	https://www.hokkaido-np.co.jp/article/784628/	共生社会	2023-01-07 21:03:00