投稿時間:2022-06-22 06:24:33 RSSフィード2022-06-22 06:00 分まとめ(35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
AWS	AWS Machine Learning Blog	Accelerate your career with ML skills through the AWS Machine Learning Engineer Scholarship	https://aws.amazon.com/blogs/machine-learning/accelerate-your-career-with-ml-skills-through-the-aws-machine-learning-engineer-scholarship/	Accelerate your career with ML skills through the AWS Machine Learning Engineer ScholarshipAmazon Web Services and Udacity are partnering to offer free services to educate developers of all skill levels on machine learning ML concepts with the AWS Machine Learning Engineer Scholarship program The program offers free enrollment to the AWS Machine Learning Foundations course and scholarships awarded to the AWS Machine Learning Engineer Nanodegree a …	2022-06-21 20:11:45
AWS	AWS	How do I empty an Amazon S3 bucket using a lifecycle configuration rule?	https://www.youtube.com/watch?v=JfK9vamen9I	How do I empty an Amazon S bucket using a lifecycle configuration rule For more details see the Knowledge Center article with this video Shaji shows you how to empty an Amazon S bucket using a lifecycle configuration rule Introduction Chapter Chapter ClosingSubscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWS AmazonWebServices CloudComputing	2022-06-21 20:36:03
AWS	AWS	AWS Leadership - Meet Eileen, Director of Security Marketing | Amazon Web Services	https://www.youtube.com/watch?v=9qNjiNjs1-0	AWS Leadership Meet Eileen Director of Security Marketing Amazon Web ServicesAs an Executive Leader at AWS you ll collaborate with some of the world s top organizations to extend their capabilities with the AWS Cloud You ll choose innovation over the status quo dive deep to solve complex customer challenges and discover opportunities to grow your career in ways you won t find elsewhere View open roles at AWS Learn about AWS culture Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWSCareers AWSExecutiveLeadership AWS AmazonWebServices CloudComputing	2022-06-21 20:27:48
AWS	AWS	AWS Leadership - Meet Inbal, General Manager, Elastic Containers | Amazon Web Services	https://www.youtube.com/watch?v=TELw8SxoER4	AWS Leadership Meet Inbal General Manager Elastic Containers Amazon Web ServicesAs an Executive Leader at AWS you ll collaborate with some of the world s top organizations to extend their capabilities with the AWS Cloud You ll choose innovation over the status quo dive deep to solve complex customer challenges and discover opportunities to grow your career in ways you won t find elsewhere View open roles at AWS Learn about AWS culture Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWSCareers AWSExecutiveLeadership AWS AmazonWebServices CloudComputing	2022-06-21 20:26:47
AWS	AWS	AWS Leadership - Meet Daryl, General Manager, Demand Generation | Amazon Web Services	https://www.youtube.com/watch?v=dIga6kYx56w	AWS Leadership Meet Daryl General Manager Demand Generation Amazon Web ServicesAs an Executive Leader at AWS you ll collaborate with some of the world s top organizations to extend their capabilities with the AWS Cloud You ll choose innovation over the status quo dive deep to solve complex customer challenges and discover opportunities to grow your career in ways you won t find elsewhere View open roles at AWS Learn about AWS culture Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWSCareers AWSExecutiveLeadership AWS AmazonWebServices CloudComputing	2022-06-21 20:25:25
AWS	AWS	Why enlist in AWS? The transferable skills from the Military to AWS | Amazon Web Services	https://www.youtube.com/watch?v=dxI2dG50FdU	Why enlist in AWS The transferable skills from the Military to AWS Amazon Web ServicesWe hear from Daryl Hammett GM of Demand Generation on why the AWS culture is a good fit for ex miltary and the transerable skills that can support your career View open roles at AWS Learn about AWS culture Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWSCareers AWSExecutiveLeadership AWS AmazonWebServices CloudComputing	2022-06-21 20:23:49
AWS	AWS - Webinar Channel	Getting Hands on with Amazon GuardDuty - AWS Virtual Workshop	https://www.youtube.com/watch?v=eq3_H-aiHhk	Getting Hands on with Amazon GuardDuty AWS Virtual WorkshopThis session walks you through a scenario covering threat detection and remediation using Amazon GuardDuty a managed threat detection service The scenario simulates an event that spans a few threat vectors representing just a small sample of the threats that GuardDuty is able to detect including Amazon S protection features To participate all you need is your laptop AWS provides an AWS account Learning Objectives Objective View and analyze GuardDuty findings Objective Send alerts based on the findings Objective Remediate findings To learn more about the services featured in this talk please visit	2022-06-21 20:31:13
python	Pythonタグが付けられた新着投稿 - Qiita	知識ゼロから始める具体的なプログラミング入門	https://qiita.com/lamrongol/items/f838fd4231a8e17d220f	試し	2022-06-22 05:49:04
海外TECH	MakeUseOf	How to Create Bold Text in Photoshop: 4 Ways	https://www.makeuseof.com/photoshop-how-to-create-bold-text/	photoshop	2022-06-21 20:45:14
海外TECH	DEV Community	25 Years of Indian Headlines: A Data Opportunity	https://dev.to/subh_2111/25-years-of-indian-headlines-a-data-opportunity-20on	Years of Indian Headlines A Data OpportunityWould you be interested in knowing which politician has made the most headlines Do you want to see which celebrity has best held the attention of journalists over the years Are you intrigued about the difference in crime news grouped by states If your answer is yes to any of those questions then we share the same curiosity Looking at archives maintained by Newspapers I wondered who would go through over twenty five thousand pages of news headlines sorted by date and make any heads or tails of the big picture around it How could someone analyse trends or gains insights from an almost endless list of headlines dating decades ago by just reading them on a website But I could not stop myself from doing something about this problem I had at hand I needed to devise a way to reliably efficiently and easily comb through the data these archives made available To accomplish this goal I set out with this project In this writeup I will go over how I created a fundamental tool or method for anyone to easily analyse understand and research the Indian Headlines data of the past years PART The DataTo start with my project I thought I could easily find a dataset which contained all the data I needed properly formatted in a csv file and would only leave me to write scripts to analyse that data If only life was that easy Unfortunately I could not find any dataset suitable to my needs and thus had to dedicate a significant portion of work time of this project on procuring and cleaning the data myself Ideally we would need a data file with an exhaustive credible and unbiased collection of headlines from a large enough timeline Sourcing the DataIn the absence of pre prepared datasets I had to find a credible source to collect headlines data stretching for a significant duration of time So what better place than the websites of major journalistic organisations like The Times The Hindu Deccan Herald or the Indian Express But these organisations do not publicly provide an easy way to procure the data we need So the most obvious way was to read the data off their own websites Fortunately almost all of these organisations provide a public archive of their journalism which is better option than sitting and recording all the headlines myself for the next decade But the archives that these organisations provide do not allow you to easily export the data they make available themselves in a format which can be utilized by us So the best way to obtain the data we need is to utilize Web Scraping Web ScrapingWeb Scraping is essentially the process of automatically extract the data we need from websites by utilizing tools which can parse and read data from the source code of these websites Mostly HTML For this project I use the following python modules packages which are extremely suited for the task BeautifulSoup A python library that makes it easy to scrape content from web pages It sits atop an HTML or XML parser providing Pythonic idioms for iterating searching and modifying the parse tree Requests The requests module allows you to send HTTP requests using Python The HTTP request returns a Response Object with all the response data the web page code I have not covered the Ethics of using Web Scraping Techniques in the scope of this project Please do your research before putting into practice any code instructions present here A screenshot of the Indian Express Archives page I scraped for the purpose of this project I decided to use the Indian Express News Archives because it generates its content statically and the URL to change pages is easily iterable through code Certain other public archives generate their web pages dynamically through JavaScript while some others make it very difficult to traverse the content for the scraping script Now lets look at the code for the scraping script I wrote Code ImplementationThis is the basic code needed to obtain the source HTML code from the Indian Express pages The pageNumber is the variable iterated through a for loop in the range of the pages we need to scrape url str pageNumber page request requests get url data page request contentsoup BeautifulSoup data html parser The final obtained source code is stored in the soup variable Now the actual headlines in the code are actually stored inside lt h gt tags with the class name as title So all we need to do is use the BeautifulSoup library and filter the obtained code to read off the lt a gt tags from the filtered title headline tags Then we further use get text function to get the headline string for divtag in soup find all h class title for atag in divtag find all a HEADLINES hl atag get text print Headline hl This prints the scraped headline It is much more convenient for us to store the scraped data in a text file rather than print it on the terminal A solution to write all of this data is easily implemented with the following code file open r lt ABSOLUTE PATH gt headlines txt a encoding utf file write hl lower stored in lower for easier analysisfile write n file close When this code is properly looped it can be used to scrape and obtain the headline data from the complete archive rather than just one page In this case the pages extend to Page and thus a the above code can simply be put into a loop iterating over all the pages When I ran the final script for the first time I realised that scraping over thousand pages with this script would take me over multiple days running my the script x while any interruption to the process could erase all the progress Thus for the sake of accessibility ease of use and practicality of the process I implemented the code in a fashion where it could be paused at any moment and would save and remember its progress While this addressed the problem of interruptions and being able to pause the script execution it was still projected to take me a very long time to just be able to scrape this data To decrease the time it would take for the complete scraping of the archive I divided the thousand pages into equal groups This allowed my workstation to simultaneously run twenty instances of the same script running parallel workloads and reducing the time needed over twentyfold PART The FrameworkAll of the above described process finally netted me a list of over lac headlines in a text file in the following format covid up govt extends closure of all schools colleges till january renotify seats reserved for obcs in local body polls sc tells maharashtra sectalks fail over action against karnal ex sdm siege continuesjagan promotes andhra as favoured investment destination defends scrapping ppasmurder accused husband of mla visits house bjp slams congressover cops cctvs drones to secure gujarat rath yatra on july in bhutan teachers medical staff will now be highest paid civil servants cyclone man mrutyunjay mohapatra appointed imd chiefpakistan hotel attack four killed as armed militants storm star hotel in gwadar port city says policeindian man convicted in dubai for hacking client websites continued These headlines encompass a time period of years Thus the title from Keyword AnalysisIt is obviously in a much more accessible format now but how do we run any kind of analysis on this data file The simple answer is Keyword Analysis In the context of this project it is the method of classifying categorizing labelling and analysing these headlines by the presence or lack of certain words in them For example if a certain headline has the word Mahatma Gandhi Bapu or Gandhi Ji it is very likely to be a headline reporting something about Gandhi We will be using this form of deduction in the scope of this project to analyse the dataset that we have formed Therefore to exercise this idea we need to implement certain tools to be on our disposal In the scope of this project these tools are basically methods functions which find aggregate the headlines depending on the presence of keywords Multiple different need based cases can be imagined but overall they can all be divided into the following fundamental implementations Finding the headlines where any of the keyword from a group is found Finding the headlines where all of the keywords from a group are found Finding the headlines where any of the keywords from each of the multiple groups are found Any other needed use case can be made to fit work with these fundamental functions For example if we need to find the headlines where all of the keywords from each of the multiple groups in found we can just merge the groups and use the second function for them On the other hand if we need to find the headlines where the keywords do not occur we can just eliminate the headlines where those keywords do occur using the first function Code ImplementationThe above mentioned three fundamental functions are implemented as follows Finding the headlines where any of the keyword from a group is found Here the code iterates through all the headlines in the headLinesList object and if any of the keyword provided in the list is found then the counter maintained is bumped and finally returned FUNCTION TO COUNT HEADLINES WHERE ANY OF THE WORD S OCCURS wordsToSearch is a list of the form word word word def countOccurancesAny wordsToSearch count for headline in headLinesList for eachWord in wordsToSearch if headline find eachWord lower count break return countFinding the headlines where all of the keywords from a group are found The premise here is almost the same as the st function but instead of searching for one word and then bumping the counter this function has to wait and make sure all the elements of the list are found in the headline For optimisation purposes the loop skips cycles if any of the element is not found FUNCTION TO COUNT HEADLINES WHERE ALL OF THE WORD S OCCURS wordsToSearch is a list of the form word word word def countOccurancesAll wordsToSearch mainCount wordCount totalWords len wordsToSearch for headline in headLinesList wordCount for eachWord in wordsToSearch if headline find eachWord lower break else wordCount if wordCount totalWords mainCount return mainCountFinding the headlines where any of the keywords from each of the multiple groups are found This function basically nests the first function one more time finding out if a keyword from the groups passed off as D lists are present There is also optimization similar to function present here FUNCTION TO COUNT HEADLINES WHERE ANY OF THE WORD S FROM MULTIPLE GROUPS OCCURS wordsToSearch is a list of lists each of the form word word word def countOccurancesGroupedAny wordsToSearch count countMain for headline in headLinesList countMain for eachGroup in wordsToSearch optimiseCheck for eachWord in eachGroup if headline find eachWord lower optimiseCheck countMain break if optimiseCheck break if countMain len wordsToSearch count return countWell now that we have looked at the tools and platforms I developed for this project lets look at how these can be used to analyse and interpret data PART The AnalysisAn analysis was not so much the focus of this project than actually developing a framework allowing for easy analysis of this dataset But I have still done some analysis and visualizations to demonstrate the usability of the above described code I will be using the word Popular to denote how much the entity has been in news It has unreliable correlation with actual popularity Most Popular Political PartyFirst we create lists with the keywords that will be used to identify appropriate headlines These lists contain keywords that I think are enough to identify if a headline is aimed at these parties Then the data is obtained and arranged as a pandas dataframe and then printed sorted by headlines count bjp bjp Bharatiya Janata Party inc INC Congress aap AAP Aam Aadmi Party cpi CPI Communist Party of India bsp BSP Bahujan Samaj Party dmk DMK Dravida Munnetra Kazhagam shivsena sena shiv sena data Party BJP INC AAP CPI BSP DMK SHIV SENA Reported hd countOccurancesAny bjp hd countOccurancesAny inc hd countOccurancesAny aap hd countOccurancesAny cpi hd countOccurancesAny bsp hd countOccurancesAny dmk hd countOccurancesAny shivsena df pd DataFrame data print df sort values Reported ascending False The result is visualised in the following graph As is clearly discernible INC is the most reported on by part followed very closely by BJP Most Reported PoliticianThe code to crunch the numbers is very similar to the one we saw above modi Modi Narendra Modi Narendra Damodardas Modi amitshah amit shah shah rajnath rajnath singh jaishankar jaishankar mamata mamata didi kejriwal aravind kejriwal yogi adityanath yogi ajay bisht rahul rahul stalin stalin akhilesh akhilesh owaisi owaisi gehlot gehlot biswa himanta biswa biswa scindia scindia sibal sibal manmohan manmohan singh mayavati mayavati mulayam mulayam naveen patnaik nitish nitish sonia sonia uddhav uddhav data Political Narendra Modi Amit Shah Rajnath Singh S Jaishankar Mamata Bannerjee Aravind Kejrival Yogi Adityanath Rahul Gandhi MK Stalin Akhilesh Yadav Asaduddin Owaisi Ashok Gehlot Himanta Biswa Sarma Jotiraditya Scindia Kapil Sibal Manmohan Singh Mayavati Mulayam Singh Yadav Naveen Patnaik Nitish Kumar Sonia Gandhi Uddhav Thackeray Reported hd countOccurancesAny modi hd countOccurancesAny amitshah hd countOccurancesAny rajnath hd countOccurancesAny jaishankar hd countOccurancesAny mamata hd countOccurancesAny kejriwal hd countOccurancesAny yogi hd countOccurancesAny rahul hd countOccurancesAny stalin hd countOccurancesAny akhilesh hd countOccurancesAny owaisi hd countOccurancesAny gehlot hd countOccurancesAny biswa hd countOccurancesAny scindia hd countOccurancesAny sibal hd countOccurancesAny manmohan hd countOccurancesAny mayavati hd countOccurancesAny mulayam hd countOccurancesAny naveen hd countOccurancesAny nitish hd countOccurancesAny sonia hd countOccurancesAny uddhav df pd DataFrame data print df sort values Reported ascending False For the visualization aspect of this I thought of using a Circle Packing Graph I explored my options with external visualization options but none of them offered what I was looking for After a lot of research I stumbled on a way to generate what I wanted through python code and the Circlify module So I wrote the code to generate the graph I was looking for But it turned out to not be as polished or presentable as I wanted it to be But I am still including the code snippet I wrote compute circle positions circles circlify circlify df Reported tolist show enclosure False target enclosure circlify Circle x y r Create just a figure and only one subplotfig ax plt subplots figsize Remove axesax axis off Find axis boundarieslim max max abs circle x circle r abs circle y circle r for circle in circles plt xlim lim lim plt ylim lim lim list of labelslabels df Reported print circlesfor circle label in zip circles labels x y r circle ax add patch plt Circle x y r alpha linewidth plt annotate label x y va center ha center plt show Instead of the Circle Packing Graph I decided to create the following visualization which presents the data generated by my above code Crime Statistics By StatesBefore diving right into the the crime news statistics lets first write individual code for calculating State statistics and crime statistics Crime Statisticsrobbery robbery thief thieves chori sexual rape rapist sexual assault dowry dowry dahej drugs drug traffick traffick cyber hack cyber crime phish murder murder data Crime Robbery Sexual Dowry Drugs Trafficking Cyber Murder Reported hd countOccurancesAny robbery hd countOccurancesAny sexual hd countOccurancesAny dowry hd countOccurancesAny drugs hd countOccurancesAny traffick hd countOccurancesAny cyber hd countOccurancesAny murder df pd DataFrame data print df sort values Reported ascending False Crime Reported Murder Sexual Cyber Drugs Robbery Dowry Trafficking States StatsUttarPradesh Uttar Pradesh UP Agra Aligarh Ambedkar Nagar Amethi Amroha Auraiya Ayodhya Azamgarh Baghpat Bahraich Ballia Balrampur Barabanki Bareilly Bhadohi Bijnor Budaun Bulandshahr Chandauli Chitrakoot Deoria Etah Etawah Farrukhabad Fatehpur Firozabad Gautam Buddha Nagar Ghaziabad Ghazipur Gorakhpur Hamirpur Hardoi Hathras Jalaun Jaunpur Jhansi Kannauj Kanpur Dehat Kanpur Nagar Kasganj Kaushambi Kushinagar Lalitpur Lucknow Maharajganj Mahoba Mainpuri Mathura Meerut Mirzapur Moradabad Muzaffarnagar Pilibhit Pratapgarh Prayagraj Raebareli Rampur Saharanpur Sambhal Sant Kabir Nagar Shahjahanpur Shamli Shravasti Siddharthnagar Sitapur Sonbhadra Sultanpur Unnao Varanasi AndamanNicobar Andaman Nicobar AndhraPradesh Andhra Anantapur Chittoor East Godavari Alluri Sitarama Raju Anakapalli Annamaya Bapatla Eluru Guntur Kadapa Kakinada Konaseema Krishna Kurnool Manyam N T Rama Rao Nandyal Nellore Palnadu Prakasam Sri Balaji Sri Satya Sai Srikakulam Visakhapatnam Vizianagaram West Godavari ArunachalPradesh Arunachal Anjaw Changlang Dibang Valley East Kameng East Siang Kamle Kra Daadi Kurung Kumey Lepa Rada Lohit Longding Lower Dibang Valley Lower Siang Lower Subansiri Namsai Pakke Kessang Papum Pare Shi Yomi Siang Tawang Tirap Upper Siang Upper Subansiri West Kameng West Siang Assam assam Bajali Baksa Barpeta Biswanath Bongaigaon Cachar Charaideo Chirang Darrang Dhemaji Dhubri Dibrugarh Dima Hasao Goalpara Golaghat Hailakandi Hojai Jorhat Kamrup Kamrup Metropolitan Karbi Anglong Karimganj Kokrajhar Lakhimpur Majuli Morigaon Nagaon Nalbari Sivasagar Sonitpur South Salmara Mankachar Tinsukia Udalguri West Karbi Anglong Bihar bihar Araria Arwal Aurangabad Banka Begusarai Bhagalpur Bhojpur Buxar Darbhanga East Champaran Gaya Gopalganj Jamui Jehanabad Kaimur Katihar Khagaria Kishanganj Lakhisarai Madhepura Madhubani Munger Muzaffarpur Nalanda Nawada Patna Purnia Rohtas Saharsa Samastipur Saran Sheikhpura Sheohar Sitamarhi Siwan Supaul Vaishali West Champaran Chandigarh Chandigarh Chhattisgarh Chhattisgarh Balod Baloda Bazar Balrampur Bastar Bemetara Bijapur Bilaspur Dantewada Dhamtari Durg Gariaband Gaurela Pendra Marwahi Janjgir Champa Jashpur Kabirdham Kanker Kondagaon Korba Koriya Mahasamund Manendragarh Mohla Manpur Mungeli Narayanpur Raigarh Raipur Rajnandgaon Sakti Sarangarh Bilaigarh Sukma Surajpur Dadra Dadra Daman Diu Delhi Delhi Goa Goa Gujrat Gujrat Ahmedabad Amreli Anand Aravalli Banaskantha Bharuch Bhavnagar Botad Chhota Udaipur Dahod Dang Devbhoomi Dwarka Gandhinagar Gir Somnath Jamnagar Junagadh Kheda Kutch Mahisagar Mehsana Morbi Narmada Navsari Panchmahal Patan Porbandar Rajkot Sabarkantha Surat Surendranagar Tapi Vadodara Valsad Haryana Haryana Ambala Bhiwani Charkhi Dadri Faridabad Fatehabad Gurugram Hisar Jhajjar Jind Kaithal Karnal Kurukshetra Mahendragarh Mewat Palwal Panchkula Panipat Rewari Rohtak Sirsa Sonipat Yamunanagar HimachalPradesh Himachal Bilaspur Chamba Hamirpur Kangra Kinnaur Kullu Lahaul Spiti Mandi Shimla Sirmaur Solan Una JammuKashmir Jammu Kashmir J amp K JK Anantnag Bandipora Baramulla Budgam Doda Ganderbal Jammu Kathua Kishtwar Kulgam Kupwara Poonch Pulwama Rajouri Ramban Reasi Samba Shopian Srinagar Udhampur Jharkand Jharkand Bokaro Chatra Deoghar Dhanbad Dumka East Singhbhum Garhwa Giridih Godda Gumla Hazaribagh Jamtara Khunti Koderma Latehar Lohardaga Pakur Palamu Ramgarh Ranchi Sahebganj Seraikela Kharsawan Simdega West Singhbhum Karnataka Karnataka Bagalkot Bangalore Rural Bangalore Urban Belgaum Bellary Bidar Chamarajanagar Chikkaballapur Chikkamagaluru Chitradurga Dakshina Kannada Davanagere Dharwad Gadag Gulbarga Hassan Haveri Kodagu Kolar Koppal Mandya Mysore Raichur Ramanagara Shimoga Tumkur Udupi Uttara Kannada Vijayanagara Vijayapura Yadgir Kerala Kerala Alappuzha Ernakulam Idukki Kannur Kasaragod Kollam Kottayam Kozhikode Malappuram Palakkad Pathanamthitta Thiruvananthapuram Thrissur Wayanad Ladakh Ladakh leh kargil Lakshadweep Lakshadweep MadhyaPradesh Madhya Agar Malwa Alirajpur Anuppur Ashoknagar Balaghat Barwani Betul Bhind Bhopal Burhanpur Chachaura Chhatarpur Chhindwara Damoh Datia Dewas Dhar Dindori Guna Gwalior Harda Hoshangabad Indore Jabalpur Jhabua Katni Khandwa Khargone Maihar Mandla Mandsaur Morena Nagda Narsinghpur Neemuch Niwari Panna Raisen Rajgarh Ratlam Rewa Sagar Satna Sehore Seoni Shahdol Shajapur Sheopur Shivpuri Sidhi Singrauli Tikamgarh Ujjain Umaria Vidisha Maharashtra Maharashtra Bombay Ahmednagar Akola Amravati Aurangabad Beed Bhandara Buldhana Chandrapur Dhule Gadchiroli Gondia Hingoli Jalgaon Jalna Kolhapur Latur Mumbai Mumbai Suburban Nagpur Nanded Nandurbar Nashik Osmanabad Palghar Parbhani Pune Raigad Ratnagiri Sangli Satara Sindhudurg Solapur Thane Wardha Washim Yavatmal Manipur Manipur Bishnupur Chandel Churachandpur Imphal East Imphal West Jiribam Kakching Kamjong Kangpokpi Noney Pherzawl Senapati Tamenglong Tengnoupal Thoubal Ukhrul Meghalaya Megh East Garo Hills East Jaintia Hills East Khasi Hills Mairang North Garo Hills Ri Bhoi South Garo Hills South West Garo Hills South West Khasi Hills West Garo Hills West Jaintia Hills West Khasi Hills Mizoram Mizoram Aizawl Champhai Hnahthial Khawzawl Kolasib Lawngtlai Lunglei Mamit Saiha Saitual Serchhip Nagaland Nagaland Chumukedima Dimapur Kiphire Kohima Longleng Mokokchung Niuland Noklak Peren Phek Tseminyu Tuensang Wokha Zunheboto Odisha Odisha Angul Balangir Balasore Bargarh Bhadrak Boudh Cuttack Debagarh Dhenkanal Gajapati Ganjam Jagatsinghpur Jajpur Jharsuguda Kalahandi Kandhamal Kendrapara Kendujhar Khordha Koraput Malkangiri Mayurbhanj Nabarangpur Nayagarh Nuapada Puri Rayagada Sambalpur Subarnapur Sundergarh Puducherry Puducherry Karaikal Mahe Puducherry Yanam Punjab Punjab Amritsar Barnala Bathinda Faridkot Fatehgarh Sahib Fazilka Firozpur Gurdaspur Hoshiarpur Jalandhar Kapurthala Ludhiana Malerkotla Mansa Moga Mohali Muktsar Pathankot Patiala Rupnagar Sangrur Shaheed Bhagat Singh Nagar Tarn Taran Rajasthan Rajasthan Ajmer Alwar Banswara Baran Barmer Bharatpur Bhilwara Bikaner Bundi Chittorgarh Churu Dausa Dholpur Dungarpur Hanumangarh Jaipur Jaisalmer Jalore Jhalawar Jhunjhunu Jodhpur Karauli Kota Nagaur Pali Pratapgarh Rajsamand Sawai Madhopur Sikar Sirohi Sri Ganganagar Tonk Udaipur Sikkim Sikkim Soreng Pakyong TamilNadu Tamil Nadu Ariyalur Chengalpattu Chennai Coimbatore Cuddalore Dharmapuri Dindigul Erode Kallakurichi Kanchipuram Kanyakumari Karur Krishnagiri Madurai Mayiladuthurai Nagapattinam Namakkal Nilgiris Perambalur Pudukkottai Ramanathapuram Ranipet Salem Sivaganga Tenkasi Thanjavur Theni Thoothukudi Tiruchirappalli Tirunelveli Tirupattur Tiruppur Tiruvallur Tiruvannamalai Tiruvarur Vellore Viluppuram Virudhunagar Telangana Telangana Adilabad Bhadradri Kothagudem Hanamkonda Hyderabad Jagtial Jangaon Jayashankar Jogulamba Kamareddy Karimnagar Khammam Komaram Bheem Mahabubabad Mahbubnagar Mancherial Medak Medchal Mulugu Nagarkurnool Nalgonda Narayanpet Nirmal Nizamabad Peddapalli Rajanna Sircilla Ranga Reddy Sangareddy Siddipet Suryapet Vikarabad Wanaparthy Warangal Yadadri Bhuvanagiri Tripura Tripura Dhalai Gomati Khowai North Tripura Sepahijala South Tripura Unakoti West Tripura Uttarakhand Uttarakhand Almora Bageshwar Chamoli Champawat Dehradun Haridwar Nainital Pauri Pithoragarh Rudraprayag Tehri Udham Singh Nagar Uttarkashi WestBengal Bengal Alipurduar Bankura Birbhum Cooch Behar Dakshin Dinajpur Darjeeling Hooghly Howrah Jalpaiguri Jhargram Kalimpong Kolkata Malda Murshidabad Nadia North Parganas Paschim Bardhaman Paschim Medinipur Purba Bardhaman Purba Medinipur Purulia South Parganas Uttar Dinajpur data States Andaman Nicobar Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh Dadra Nagar Delhi Goa Gujarat Haryana Himachal Pradesh Jammu Kashmir Jharkhand Karnataka Kerala Ladakh Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Telangana Tripura Uttar Pradesh Uttarakhand West Bengal Reported hd countOccurancesAny AndamanNicobar hd countOccurancesAny AndhraPradesh hd countOccurancesAny ArunachalPradesh hd countOccurancesAny Assam hd countOccurancesAny Bihar hd countOccurancesAny Chandigarh hd countOccurancesAny Chhattisgarh hd countOccurancesAny Dadra hd countOccurancesAny Delhi hd countOccurancesAny Goa hd countOccurancesAny Gujrat hd countOccurancesAny Haryana hd countOccurancesAny HimachalPradesh hd countOccurancesAny JammuKashmir hd countOccurancesAny Jharkand hd countOccurancesAny Karnataka hd countOccurancesAny Kerala hd countOccurancesAny Ladakh hd countOccurancesAny Lakshadweep hd countOccurancesAny MadhyaPradesh hd countOccurancesAny Maharashtra hd countOccurancesAny Manipur hd countOccurancesAny Meghalaya hd countOccurancesAny Mizoram hd countOccurancesAny Nagaland hd countOccurancesAny Odisha hd countOccurancesAny Puducherry hd countOccurancesAny Punjab hd countOccurancesAny Rajasthan hd countOccurancesAny Sikkim hd countOccurancesAny TamilNadu hd countOccurancesAny Telangana hd countOccurancesAny Tripura hd countOccurancesAny UttarPradesh hd countOccurancesAny Uttarakhand hd countOccurancesAny WestBengal df pd DataFrame data print df sort values Reported ascending False print df States Reported Uttar Pradesh Maharashtra Delhi Jammu Kashmir Gujarat Himachal Pradesh Bihar Punjab West Bengal Madhya Pradesh Rajasthan Goa Haryana Andhra Pradesh Kerala Chhattisgarh Chandigarh Karnataka Tamil Nadu Assam Odisha Telangana Ladakh Dadra Nagar Manipur Uttarakhand Meghalaya Arunachal Pradesh Jharkhand Puducherry Tripura Nagaland Sikkim Mizoram Andaman Nicobar Lakshadweep Now we will proceed to combine the above code to produce statistics for state wise crime reports grouped by type of crime I used the following code to derive values for the analysis we are performing replacing crimeMerged with the combination of crimes I am analysing crimeMerged murderdata States Andaman Nicobar Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh Dadra Nagar Delhi Goa Gujarat Haryana Himachal Pradesh Jammu Kashmir Jharkhand Karnataka Kerala Ladakh Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Telangana Tripura Uttar Pradesh Uttarakhand West Bengal Reported hd countOccurancesGroupedAny AndamanNicobar crimeMerged hd countOccurancesGroupedAny AndhraPradesh crimeMerged hd countOccurancesGroupedAny ArunachalPradesh crimeMerged hd countOccurancesGroupedAny Assam crimeMerged hd countOccurancesGroupedAny Bihar crimeMerged hd countOccurancesGroupedAny Chandigarh crimeMerged hd countOccurancesGroupedAny Chhattisgarh crimeMerged hd countOccurancesGroupedAny Dadra crimeMerged hd countOccurancesGroupedAny Delhi crimeMerged hd countOccurancesGroupedAny Goa crimeMerged hd countOccurancesGroupedAny Gujrat crimeMerged hd countOccurancesGroupedAny Haryana crimeMerged hd countOccurancesGroupedAny HimachalPradesh crimeMerged hd countOccurancesGroupedAny JammuKashmir crimeMerged hd countOccurancesGroupedAny Jharkand crimeMerged hd countOccurancesGroupedAny Karnataka crimeMerged hd countOccurancesGroupedAny Kerala crimeMerged hd countOccurancesGroupedAny Ladakh crimeMerged hd countOccurancesGroupedAny Lakshadweep crimeMerged hd countOccurancesGroupedAny MadhyaPradesh crimeMerged hd countOccurancesGroupedAny Maharashtra crimeMerged hd countOccurancesGroupedAny Manipur crimeMerged hd countOccurancesGroupedAny Meghalaya crimeMerged hd countOccurancesGroupedAny Mizoram crimeMerged hd countOccurancesGroupedAny Nagaland crimeMerged hd countOccurancesGroupedAny Odisha crimeMerged hd countOccurancesGroupedAny Puducherry crimeMerged hd countOccurancesGroupedAny Punjab crimeMerged hd countOccurancesGroupedAny Rajasthan crimeMerged hd countOccurancesGroupedAny Sikkim crimeMerged hd countOccurancesGroupedAny TamilNadu crimeMerged hd countOccurancesGroupedAny Telangana crimeMerged hd countOccurancesGroupedAny Tripura crimeMerged hd countOccurancesGroupedAny UttarPradesh crimeMerged hd countOccurancesGroupedAny Uttarakhand crimeMerged hd countOccurancesGroupedAny WestBengal crimeMerged df pd DataFrame data print df sort values Reported ascending False The visualization in forms of a heat map for the heavies in terms of the range of dataset is as follows Here darker colours represent heavier intensity of the labelled crime This is a similar heatmap which encompasses a general image of crime intensity over the Indian states Map of India Warmer colours represent heavier intensity The crime news statistics actually correspond to the real crime stats of the states as per government data The only exception are the southern states The southern states show much lower numbers in journalistic data compared to actual govt data This raises an interesting question on what the reason can be Is it journalistic bias Unfortunately I am not qualified to answer that question but I do encourage you to find the answer to this question The Three Khans of BollywoodI still remember when a famous saying used to be Bollywood is ruled by the Three Khans Well to take a lighter away from crime and politics look at the data lets see which khan ends up the most popular amongst the three Here is the code which was run to find the most popular amongst the three srk srk shah rukh salman salman sallu amir aamir data Actor SRK Salman Amir Reported hd countOccurancesAny srk hd countOccurancesAny salman hd countOccurancesAny amir df pd DataFrame data print df sort values Reported ascending False Actor Reported Salman SRK Amir As it turns out its the Bhai of the industry To be very honest it makes sense with how crazy his fans are PART EpilogueThe aim of this endeavour was to make available a tool to be able to perform analysis like the ones shown above And in that I have succeeded The scripts for the Web Scraper and the Headline Analysis Functions is available on the project repository It was extremely fun and informative working on this project I learned and gained experience with the following Web Scraping BeautifulSoup requests Keyword AnalysisData VisualizationData Analysis pandas matplotlib Open Source and Version Control Thankyou very much for reading through the whole write up I really hope you found it interesting Please leave a comment sharing any feedback	2022-06-21 20:11:34
Apple	AppleInsider - Frontpage News	Apple iPhone sales in China strongly rebound in May after downtick	https://appleinsider.com/articles/22/06/21/apple-iphone-sales-in-china-strongly-rebound-in-may-after-downtick?utm_medium=rss	Apple iPhone sales in China strongly rebound in May after downtickSmartphone sales data out of China suggests that Apple s iPhone is selling better than previous years in the month of May alongside an uptick in total shipments iPhone Pro modelsIn a new note to investors seen by AppleInsider JP Morgan analyst Samik Chatterjee takes a look at the latest smartphone shipment data released by the China Academy of Information and Communications Technology Read more	2022-06-21 20:51:15
Apple	AppleInsider - Frontpage News	Apple's 35W Dual USB-C Port power adapter is uniquely engineered, demonstrates teardown	https://appleinsider.com/articles/22/06/21/apples-35w-dual-usb-c-port-power-adapter-is-uniquely-engineered-demonstrates-teardown?utm_medium=rss	Apple x s W Dual USB C Port power adapter is uniquely engineered demonstrates teardownApple s new W Dual USB C Port Compact Power Adapter has a special design that makes it easy to change prongs during mass production a teardown of the accessory has revealed First introduced during WWDC the latest power adapter from Apple is a unique addition to the lineup due to its inclusion of two USB C ports A teardown of the device has revealed how Apple designed the adapter to enable two devices to recharge from the same small charger The adapter uses clips to attach the PCB module as well as a custom section on the top to hold it into position ChargerLab s reveals after cutting it in half A thermal pad is also hidden away helping to cool the adapter under load Read more	2022-06-21 20:51:32
海外TECH	Engadget	Robots learn to shape letters using Play-Doh	https://www.engadget.com/robot-play-doh-letters-robocraft-202710608.html?src=rss	Robots learn to shape letters using Play DohHumans aren t the only ones working with Play Doh MIT CSAIL researchers have created a system RoboCraft that teaches robots how to work with the kid friendly goo The platform first takes the image of a shape in this case a letter of the alphabet and reinterprets it as a cluster of interlocking particles The bot then uses a physics oriented neural network to predict how its two quot fingers quot can manipulate those spheres to match the desired outcome A predictive algorithm helps the machine plan its actions The technology doesn t require much time to produce usable results It took just ten minutes of practice for an robot to perform roughly as well and in some cases better than humans remote controlling the same hardware That s not the same as having a human shape the Play Doh by hand but it s no mean feat for a machine discovering how to perform the task for the first time Robots frequently struggle with soft objects where they tend to thrive with firm shapes RoboCraft trained bots aren t about to produce elaborate sculptures The results are still imprecise and the machine works slowly using just two fingers The team is already developing a method of making dumplings though and plans to teach robots to use additional tools such as a rolling pin to prep the food The CSAIL scientists already have an idea of where the technology might be deployed Kitchen robots could take over more responsibilities while artistic automatons might create pottery Eventually technology like this could help the elderly and people with mobility issues by taking over household duties that require subtle motor skills	2022-06-21 20:27:10
海外科学	NYT > Science	NASA Artemis Moon Rocket Fueling Test Is Partly Completed	https://www.nytimes.com/2022/06/21/science/nasa-artemis-moon-rocket.html	NASA Artemis Moon Rocket Fueling Test Is Partly CompletedThe agency mostly completed a dress rehearsal of the fueling and countdown of its rocket a crucial step before it can launch an uncrewed capsule around the moon	2022-06-21 20:52:48
海外科学	NYT > Science	Finding Traces of Harriet Tubman on Maryland’s Eastern Shore	https://www.nytimes.com/2022/06/21/travel/harriet-tubman-maryland.html	Finding Traces of Harriet Tubman on Maryland s Eastern ShoreA historian marks the th birthday of a fearless conductor of the Underground Railroad with a visit to her birthplace only to learn how climate change is washing away memories of “the ultimate outdoors woman	2022-06-21 20:15:45
海外TECH	WIRED	The Power and Pitfalls of AI for US Intelligence	https://www.wired.com/story/ai-machine-learning-us-intelligence-community/	secret	2022-06-21 20:14:03
海外TECH	WIRED	NASA’s Giant SLS Rocket Is One Step Closer to Launch	https://www.wired.com/story/nasas-giant-sls-rocket-is-one-step-closer-to-launch/	liquid	2022-06-21 20:03:06
ニュース	BBC News - Home	Rail strike: Millions faced delays and congestion on first day	https://www.bbc.co.uk/news/uk-61879766?at_medium=RSS&at_campaign=KARANGA	union	2022-06-21 20:13:38
ニュース	BBC News - Home	Capitol riot hearing: Poll officials detail Trump voters' death threats	https://www.bbc.co.uk/news/world-us-canada-61889593?at_medium=RSS&at_campaign=KARANGA	arizona	2022-06-21 20:41:16
ニュース	BBC News - Home	Ukraine round-up: Medal sale millions to help refugees, and Ben Stiller visits Zelensky	https://www.bbc.co.uk/news/world-europe-61884212?at_medium=RSS&at_campaign=KARANGA	ukraine	2022-06-21 20:12:54
ニュース	BBC News - Home	Serena Williams wins alongside Ons Jabeur in comeback at Eastbourne	https://www.bbc.co.uk/sport/tennis/61884663?at_medium=RSS&at_campaign=KARANGA	eastbourne	2022-06-21 20:26:31
ニュース	BBC News - Home	London Underground strike: Fourth 24-hour Tube strike of 2022 disrupts capital	https://www.bbc.co.uk/news/uk-england-london-61873467?at_medium=RSS&at_campaign=KARANGA	national	2022-06-21 20:28:39
ビジネス	ダイヤモンド・オンライン - 新着記事	神宮外苑の大地主・明治神宮と三井不動産、急接近の訳【神社界＆政官財のマル秘人脈図付き】 - Diamond Premium News	https://diamond.jp/articles/-/305089	神宮外苑の大地主・明治神宮と三井不動産、急接近の訳【神社界政官財のマル秘人脈図付き】DiamondPremiumNews東京都心のオアシス、明治神宮外苑。	2022-06-22 05:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	エムスリー“1強体制”にJMDCが挑む！医療IT業界の5年後、300社合従連衡の先を大胆予測！ - 円安・金利高・インフレで明暗くっきり！ 株価・給料・再編 5年後の業界地図	https://diamond.jp/articles/-/304955		2022-06-22 05:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	新生・東電の下に東芝、日立、三菱重工が結集!?原発再稼働の次に待つ「原発超再編」構想 - 熾烈なるエネルギー大戦	https://diamond.jp/articles/-/304942	三菱重工	2022-06-22 05:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	親子上場企業に巣くう「“追認”社外取」、アクティビストとの泥沼闘争で浮き彫りに - 社外取「欺瞞のバブル」9400人の全序列	https://diamond.jp/articles/-/304223	少数株主	2022-06-22 05:10:00
ビジネス	ダイヤモンド・オンライン - 新着記事	キヤノン・ニコン・HOYA…唯一「2桁増収」となった企業とその要因は？ - ダイヤモンド 決算報	https://diamond.jp/articles/-/305139		2022-06-22 05:05:00
ビジネス	電通報 | 広告業界動向とマーケティングのコラム・ニュース	「パーパス」とは「ウチの会社らしさ」の究極形である	https://dentsu-ho.com/articles/8237	存在意義	2022-06-22 06:00:00
ビジネス	電通報 | 広告業界動向とマーケティングのコラム・ニュース	そのDX、組織への「熱量」は足りていますか？	https://dentsu-ho.com/articles/8231	電通グループ	2022-06-22 06:00:00
ビジネス	電通報 | 広告業界動向とマーケティングのコラム・ニュース	アスリートブレーンズ為末大の「緩急自在」vol.21	https://dentsu-ho.com/articles/8200	緩急自在	2022-06-22 06:00:00
北海道	北海道新聞	大谷はアＤＨ部門で２位　オールスター戦中間発表	https://www.hokkaido-np.co.jp/article/696484/	大リーグ	2022-06-22 05:47:00
北海道	北海道新聞	ネットカフェで立てこもり　４２歳男逮捕、人質は無事	https://www.hokkaido-np.co.jp/article/696483/	埼玉県川越市脇田新町	2022-06-22 05:37:02
北海道	北海道新聞	室蘭市内の中学校で不登校急増　昨年度１３３人、前年度比１・８倍　コロナの制約影響か	https://www.hokkaido-np.co.jp/article/696432/	室蘭市内	2022-06-22 05:02:00
北海道	北海道新聞	街頭募金で使用のスタンド修繕　室工高建築科３年の５人が取り組み	https://www.hokkaido-np.co.jp/article/696361/	取り組み	2022-06-22 05:01:00
ビジネス	東洋経済オンライン	初公開!｢住みよさランキング2022｣【子育て編】 全国と東京･名古屋･大阪の3大都市圏別に集計 | 住みよさランキング | 東洋経済オンライン	https://toyokeizai.net/articles/-/597665?utm_source=rss&utm_medium=http&utm_campaign=link_back	千代田区	2022-06-22 05:40:00