投稿時間:2022-01-15 14:15:44 RSSフィード2022-01-15 14:00 分まとめ(17件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
TECH	Engadget Japanese	アップル製AR/VRヘッドセット、過熱やソフトウェア問題のため2023年まで発表延期か	https://japanese.engadget.com/apple-arvr-headset-delayed-until-2023-041502475.html	過熱	2022-01-15 04:15:02
TECH	Techable（テッカブル）	人ではなくAIが選ぶ「蔦屋家電＋ 大賞」決定！ 時代を反映した注目製品が受賞	https://techable.jp/archives/171087	futurel	2022-01-15 04:00:53
python	Pythonタグが付けられた新着投稿 - Qiita	Pythonのリストのメソッドに慣れる	https://qiita.com/kimtetsu/items/fa97d06b38aac61e0261	listpopi指定の場所を削除i番目を削除を追加します。	2022-01-15 13:18:51
python	Pythonタグが付けられた新着投稿 - Qiita	【日本株】Pythonで日本の株価＋主要指数を取得してDBへ格納する	https://qiita.com/ku_a_i/items/7774793040bdb4921978	「investpygetstockhistoricaldata」だと過去の私の判断と同じように・・searchresultinvestpysearchquotestextcodecountriesjapanproductsstocksnresultshistoricaldatasearchresultretrievehistoricaldatafromdatestarttodateendprinthistoricaldatashape約年分なので千弱日のデータが取れてればOKdisplayhistoricaldatahead最初日displayhistoricaldatatail最後日実行結果DataOpenHighLowCloseVolumeChangePctDataOpenHighLowCloseVolumeChangePctんyfinanceの取得行より行数が少ないのでは→後述yfinanceとinvestpyで数が合わないのは整合性確認日本のyahooファイナンスで確認日本ではからデータが取得されているのに対して・・米国のyahooファイナンス米国での取得開始日は米国市場の開始日に合わせてからになっていることがわかる※だからyfinance米国yahooの方がデータが多くなる※ちなみに出来高のには昨年の最終取引日のデータが入っている株価取得DB格納investpyバージョンさて、前座はここまででここからが前回記事のバージョンアップ版・前回記事は年だけだったが、今回は年今日までなので数が段違い・前回記事のtry文で例外処理した無駄な失敗証券コードで時間をつぶさない存在する証券コードのみ使用・主要指数も取得※今回は入れてないが暗号通貨や商品も取得可能①証券コードリスト作成東証上場銘柄一覧を以下HPのExcelから取得する一部IPO等含まれない可能性ありさらにここに存在しない指数等の情報は個別でlist化しておく※Investingcomでコードやシンボルを調べておく必要あり証券コードリスト取得東証のHPより東証上場銘柄一覧情報を取得し、Excelで保存urlrrequestsgeturlwithopendatajxlswbasoutputoutputwritercontentExcelをデータフレームで開くstocklistpdreadexceldatajxls日本の指数トピマザーズジャスダック東証REIT日経VIindexlistjpJPTOPXMTHRNOTCTREITJNIV指定した日本の指数とデータフレームからコード部分をlist化して統合するindexlistjpextendstocklistコードtolist米国指数ダウspナスラッセルドル円vixWTIindexlistusDJISPXIXICRUTUSDJPYVIXT②株価取得DB格納取得したListを使用して株価指数ポイントを取得し、DB格納する。	2022-01-15 13:10:35
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【Ruby】特定の文字列を検知　check_nameメソッド	https://qiita.com/itosyo4126/items/cb33e8f01c990069c259	includeメソッドincludeメソッドは、指定した値が配列や文字列内に含まれているかを判定するメソッドです。	2022-01-15 13:17:29
AWS	AWSタグが付けられた新着投稿 - Qiita	re:Invent 2021 ストレージ関連の新サービス、アップデートまとめ	https://qiita.com/nidcode/items/076d8a83fd705b66a3b1	reInventストレージ関連の新サービス、アップデートまとめreInventで発表されたストレージ関連サービスのアップデートをまとめてみました。	2022-01-15 13:20:49
Docker	dockerタグが付けられた新着投稿 - Qiita	Nuxt.js+TypeScript+Composition APIの開発環境をDockerで構築	https://qiita.com/ato_fuzi/items/36171b79728d3989cd2e	nuxtconfigjs変更前importcolorsfromvuetifyesutilcolorsexportdefault中略nuxtconfigts変更後importcolorsfromvuetifyesutilcolors追記importNuxtConfigfromnuxttypes変更constconfigNuxtConfig中略追記exportdefaultconfigtsconfigjsonの編集Vuetifyの型定義ファイルの読み込みエラーが発生するので、tsconfigjosnの型定義にvuetifyを追加する。	2022-01-15 13:27:13
Git	Gitタグが付けられた新着投稿 - Qiita	windows git bash ssh接続	https://qiita.com/osorezugoing/items/fabac46348cb975e3016		2022-01-15 13:56:02
海外TECH	DEV Community	Introduction to Amazon Kinesis	https://dev.to/aws-builders/introduction-to-amazon-kinesis-18fh	Introduction to Amazon Kinesis IntroductionAmazon Kinesis is a platform for streaming data on AWS that makes it easy to load and analyze streaming data Amazon Kinesis also enables you to build custom streaming data applications for specialized needs With Kinesis you can ingest real time data such as application logs website clickstreams Internet of Things IoT telemetry data and more into your databases data lakes and data warehouses or build your own real time applications using this data Amazon Kinesis enables you to process and analyze data as it arrives and respond in real time instead of having to wait until all your data is collected before the processing can begin Currently there are four pieces of the Kinesis platform that can be utilized based on your use case Amazon Kinesis Data Streams enables you to build custom applications that process or analyze streaming data Amazon Kinesis Video Streams enables you to build custom applications that process or analyze streaming video Amazon Kinesis Data Firehose enables you to deliver real time streaming data to AWS destinations such as such as Amazon S Amazon Redshift OpenSearch Service and Splunk Amazon Kinesis Data Analytics enables you to process and analyze streaming data with standard SQL or with Java managed Apache Flink Big Data Analytics Options on AWS is a Series containing different articles that provides a basic introduction to different Big Data Analytics Options on AWS Each article covers the detailed guide on how each service is used for collecting processing storing and analyzing big data Kinesis Data Streams and Kinesis Video Streams enable you to build custom applications that process or analyze streaming data in real time Kinesis Data Streams can continuously capture and store terabytes of data per hour from hundreds of thousands of sources such as website clickstreams financial transactions social media feeds IT logs and location tracking events Kinesis Video Streams can continuously capture video data from smartphones security cameras drones satellites dashcams and other edge devices With the Amazon Kinesis Client Library KCL you can build Amazon Kinesis applications and use streaming data to power real time dashboards generate alerts and implement dynamic pricing and advertising You can also emit data from Kinesis Data Streams and Kinesis Video Streams to other AWS services such as Amazon S Amazon Redshift Amazon EMR and AWS Lambda Provision the level of input and output required for your data stream in blocks of one megabyte per second MB sec using the AWS Management Console API or SDKs The size of your stream can be adjusted up or down at any time without restarting the stream and without any impact on the data sources pushing data to the stream Within seconds data put into a stream is available for analysis With Amazon Kinesis Data Firehose you don t need to write applications or manage resources You configure your data producers to send data to Kinesis Data Firehose and it automatically delivers the data to the AWS destination or third party Splunk that you specified You can also configure Kinesis Data Firehose to transform your data before data delivery It is a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration It can also batch compress and encrypt the data before loading it minimizing the amount of storage used at the destination and increasing security Amazon Kinesis Data Analytics is the easiest way to process and analyze real time streaming data With Kinesis Data Analytics you just use standard SQL or Java Flink to process your data streams so you don t have to learn any new programming languages Simply point Kinesis Data Analytics at an incoming data stream write your SQL queries and specify where you want to load the results Kinesis Data Analytics takes care of running your SQL queries continuously on data while it s in transit and sending the results to the destinations For complex data processing applications Amazon Kinesis Data Analytics provides an option use open source libraries such as Apache Flink Apache Beam AWS SDK and AWS service integrations It includes more than ten connectors from Apache Flink and gives you the ability to build custom integrations It s also compatible with the AWS Glue Schema Registry a serverless feature of AWS Glue that enables you to validate and control the evolution of streaming data using registered Apache Avro schemas You can use Apache Flink in Amazon Kinesis Data Analytics to build applications whose processed records affect the results exactly once referred to as exactly once processing This means that even in the case of an application disruption like internal service maintenance or user initiated application update the service will ensure that all data is processed and there is no duplicate data The service stores previous and in progress computations or state in running application storage This enables you to compare real time and past results over any time period and provides fast recovery during application disruptions The subsequent sections focus primarily on Amazon Kinesis Data Streams Ideal usage patternsAmazon Kinesis Data Steams is useful wherever there is a need to move data rapidly off producers data sources and continuously process it That processing can be to transform the data before emitting into another data store drive real time metrics and analytics or derive and aggregate multiple streams into more complex streams or downstream processing The following are typical scenarios for using Kinesis Data Streams for analytics Real time data analytics Kinesis Data Streams enables real time data analytics on streaming data such as analyzing website clickstream data and customer engagement analytics Log and data feed intake and processing With Kinesis Data Streams you can have producers push data directly into an Amazon Kinesis stream For example you can submit system and application logs to Kinesis Data Streams and access the stream for processing within seconds This prevents the log data from being lost if the front end or application server fails and reduces local log storage on the source Kinesis Data Streams provides accelerated data intake because you are not batching up the data on the servers before you submit it for intake Real time metrics and reporting You can use data ingested into Kinesis Data Streams for extracting metrics and generating KPIs to power reports and dashboards at real time speeds This enables data processing application logic to work on data as it is streaming in continuously rather than waiting for data batches to arrive Cost modelAmazon Kinesis Data Streams has simple pay as you go pricing with no upfront costs or minimum fees and you pay only for the resources you consume An Amazon Kinesis stream is made up of one or more shards Each shard gives you a capacity of five read transactions per second up to a maximum total of MB of data read per second Each shard can support up to write transactions per second and up to a maximum total of MB data written per second The data capacity of your stream is a function of the number of shards that you specify for the stream The total capacity of the stream is the sum of the capacity of each shard There are two components to pricing Primary pricing includes an hourly charge per shard and a charge for each one million PUT transactions Pricing for optional components for extended retention and enhanced fan out For more information see Amazon Kinesis Data Streams Pricing Applications that run on Amazon EC and process Amazon Kinesis streams also incur standard Amazon EC costs PerformanceAmazon Kinesis Data Streams enables you to choose the throughput capacity you require in terms of shards With each shard in an Amazon Kinesis stream you can capture up to megabyte per second of data at write transactions per second Your Amazon Kinesis applications can read data from each shard at up to megabytes per second You can provision as many shards as you need to get the throughput capacity you want for example a one gigabyte per second data stream would require shards Additionally there is a new feature Enhanced fan out enables developers to scale up the number of stream consumers applications reading data from a stream in real time by offering each stream consumer their own read throughput Developers can register stream consumers to use enhanced fan out and receive their own MB sec pipe of read throughput per shard This throughput automatically scales with the number of shards in a stream Durability and availabilityAmazon Kinesis Data Streams synchronously replicates data across three Availability Zones in an AWS Region providing high availability and data durability Additionally you can store a cursor in Amazon DynamoDB to durably track what has been read from an Amazon Kinesis stream In the event that your application fails in the middle of reading data from the stream you can restart your application and use the cursor to pick up from the exact spot where the failed application left off Scalability and elasticityYou can increase or decrease the capacity of the stream at any time according to your business or operational needs without any interruption to ongoing stream processing By using API calls or development tools you can automate scaling of your Amazon Kinesis Data Streams environment to meet demand and ensure you only pay for what you need InterfacesThere are two interfaces to Kinesis Data Streams Input which is used by data producers to put data into Kinesis Data StreamsOutput to process and analyze data that comes inProducers can write data using the Amazon Kinesis PUT API an AWS Software Development Kit SDK or toolkit abstraction the Amazon Kinesis Producer Library KPL or the Amazon Kinesis Agent For processing data that has already been put into an Amazon Kinesis stream there are client libraries provided to build and operate real time streaming data processing applications The KCL acts as an intermediary between Amazon Kinesis Data Streams and your business applications which contain the specific processing logic There is also integration to read from an Amazon Kinesis stream into Apache Spark Streaming running on Amazon EMR Anti patternsAmazon Kinesis Data Streams has the following anti patterns Small scale consistent throughput Even though Kinesis Data Streams works for streaming data at KB per second or less it is designed and optimized for larger data throughputs Long term data storage and analytics Kinesis Data Streams is not suited for long term data storage By default data is retained for hours and you can extend the retention period by up to days Hope this guide gives you an Introduction to Amazon Kinesis Let me know your thoughts in the comment section And if you haven t yet make sure to follow me on below handles connect with me on LinkedInconnect with me on Twitter‍follow me on github️Do Checkout my blogs Like share and follow me for more content ltag user id follow action button background color important color fac important border color important Adit ModiFollow Cloud Engineer AWS Community Builder x AWS Certified x Azure Certified Author of Cloud Tech DailyDevOps amp BigDataJournal DEV moderator Reference Notes	2022-01-15 04:35:02
海外TECH	DEV Community	Top 5 Social Media Plugins for WordPress 2022	https://dev.to/elinabey/top-5-social-media-plugins-for-wordpress-2022-161l	Top Social Media Plugins for WordPress Nowadays social media plugins for WordPress have become the best interest of the internet and it is the biggest source of traffic for most websites As we know just started and the smash of social media persists to increase day by day It s time we prepare for destiny and keep social media platforms a step forward from others Best Social Media Plugins for WordPress are Social Warfare Simple Social Icons Smash BalloonSassy Social ShareShared Counts Social Warfare Social Warfare adds beautiful lightning fast social share buttons to your website or blog Easy to use out of the boxSetting up your social sharing buttons has never been easier with its simple setup Simple Social IconsSimple Social Icons is an easy to use customizable way to display icons that link visitors to your various social profiles Smash BalloonSmash Balloon is the best WordPress social feeds plugin because it lets you create completely customizable social media feeds tailored to your website Shared CountsShared Counts is a best Social sharing buttons that look great and load fast If you want to read in detail with their features here Top Social Media Plugins There are many button formats and you can select where to display the button after or before the post You can also allow it for business post types If you want to add social share buttons without any plugin as we have comment below	2022-01-15 04:04:08
Linux	OMG! Ubuntu!	Ubuntu 22.04 Release Date & New Features	https://www.omgubuntu.co.uk/2022/01/ubuntu-22-04-release-features	Ubuntu Release Date amp New FeaturesUbuntu is due for release on April In this post we look at the various new features and key changes that are planned for the release Development of Ubuntu is still at a somewhat early stage but we do have a good idea of what to expect from the update developers have codenamed Jammy Jellyfish In this post we rundown everything that s known so far from the Ubuntu release date to how long it ll be supported for And at the very bottom of this article you will find a link to download the Ubuntu This post Ubuntu Release Date New Features is from OMG Ubuntu Do not reproduce elsewhere without permission	2022-01-15 04:26:00
ニュース	BBC News - Home	Novak Djokovic: Tennis star detained ahead of deportation appeal	https://www.bbc.co.uk/news/world-australia-60004874?at_medium=RSS&at_campaign=KARANGA	appealthe	2022-01-15 04:37:06
ニュース	BBC News - Home	Voter anger mounts over Downing Street parties	https://www.bbc.co.uk/news/uk-politics-60005134?at_medium=RSS&at_campaign=KARANGA	anger	2022-01-15 04:26:24
ニュース	BBC News - Home	The Ashes: 'Stop moving the robot!' - Stuart Broad angered by roving camera	https://www.bbc.co.uk/sport/av/cricket/60006592?at_medium=RSS&at_campaign=KARANGA	The Ashes x Stop moving the robot x Stuart Broad angered by roving cameraEngland s Stuart Broad takes issue with a camera on wheels moving during his attempts to bowl on the second day of the Ashes fifth test	2022-01-15 04:10:00
北海道	北海道新聞	大学共通テスト開始　道内は１万７千人　コロナ下、暴風雪影響	https://www.hokkaido-np.co.jp/article/633737/	大学入学共通テスト	2022-01-15 13:13:43
北海道	北海道新聞	新幹線札樽トンネル　月末にも「札幌工区」工事　振動や地盤、住民から心配の声	https://www.hokkaido-np.co.jp/article/633757/	北海道新幹線	2022-01-15 13:02:00
ニュース	THE BRIDGE	BRIDGE Tokyo 2022「INTRO Showcase」ノミネート企業紹介：BizteX【池田さん確認】	https://thebridge.jp/2022/01/bridge-tokyo-2022-intro-showcase-nominee-biztex	BRIDGETokyo「INTROShowcase」ノミネート企業紹介BizteX【池田さん確認】本稿はBRIDGETokyoの企画をご紹介いたします。	2022-01-15 04:00:44