Convert json to csv python pandas

EasyXLS

2016.07.17 11:53 Laurian EasyXLS

EasyXLS™ is the best .NET and Java Excel library to read, write or convert Microsoft Excel files, an Excel reporting tool for programmers, a library to export and import Excel files. File formats: XLSX, XLS, XLSM, XLSB, CSV, TXT, XML, HTML. - C# Excel library - VB.NET Excel library - PHP Excel library - Classic ASP Excel library - Java Excel library - ColdFusion Excel library - C++ Excel library - VB6 Excel library - VBScript Excel library - Python Excel library
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2023.06.07 13:11 throwaway84483994 Having a real hard time learning Python (Java background)

I come from a strong object-oriented programming background. I started off with C++ and Java during my Bachelor’s and then stuck to Java for becoming an Android Developer. I have a rock solid understanding of Java and how OOP works. Recently I did my Master’s and am looking to get into Data Science and Machine Learning so I began learning Python.
The main problem that I face is understanding the object type or the data type whenever I return a value from a function etc. I think the reason being because Python is dynamically-typed where as I am very used to statically-typed formats. For example, say you have an object of a Class A in Java. Let’s call it obj. Now obj has a method which returns a string value. So if I’m calling this function elsewhere in my program I know that the value that will be assigned is going to be 100% a string value (considering there are no errors/exceptions).
Now in python there are times when I don’t know what the return type of a function is gonna be. This is especially evident whenever I’m working on a library like say pandas. One example is: I have a DataFrame that I have stored as the name df1. Now df1.columns returns an object of the type pandas.core.indexes.base.Index. Now when I iterate over this returned Index value using
for i in df1.columns: print(type(i))
Now this returns a string value. So does this mean that and Index object is an array-like(?) object of string values? Is that why it returns a string value when I iterate over it? I thought that the for-each loop can only iterate over collections(?). Or can it iterate over objects as well? Or am I not understanding the working of the for-each loop in Python?
I literally cannot wrap my head around this. Can someone please help/advise?
submitted by throwaway84483994 to CodingHelp [link] [comments]


2023.06.07 13:08 throwaway84483994 Having a real hard time learning Python.

I come from a strong object-oriented programming background. I started off with C++ and Java during my Bachelor’s and then stuck to Java for becoming an Android Developer. I have a rock solid understanding of Java and how OOP works. Recently I did my Master’s and am looking to get into Data Science and Machine Learning so I began learning Python.
The main problem that I face is understanding the object type or the data type whenever I return a value from a function etc. I think the reason being because Python is dynamically-typed where as I am very used to statically-typed formats. For example, say you have an object of a Class A in Java. Let’s call it obj. Now obj has a method which returns a string value. So if I’m calling this function elsewhere in my program I know that the value that will be assigned is going to be 100% a string value (considering there are no errors/exceptions).
Now in python there are times when I don’t know what the return type of a function is gonna be. This is especially evident whenever I’m working on a library like say pandas. One example is: I have a DataFrame that I have stored as the name df1. Now df1.columns returns an object of the type pandas.core.indexes.base.Index. Now when I iterate over this returned Index value using
for i in df1.columns: print(type(i))
Now this returns a string value. So does this mean that and Index object is an array-like(?) object of string values? Is that why it returns a string value when I iterate over it? I thought that the for-each loop can only iterate over collections(?). Or can it iterate over objects as well? Or am I not understanding the working of the for-each loop in Python?
I literally cannot wrap my head around this. Can someone please help/advise?
submitted by throwaway84483994 to pythontips [link] [comments]


2023.06.07 13:03 throwaway84483994 I feel defeated. Having a hard time learning Python (Java background)

I come from a strong object-oriented programming background. I started off with C++ and Java during my Bachelor’s and then stuck to Java for becoming an Android Developer. I have a rock solid understanding of Java and how OOP works. Recently I did my Master’s and am looking to get into Data Science and Machine Learning so I began learning Python.
The main problem that I face is understanding the object type or the data type whenever I return a value from a function etc. I think the reason being because Python is dynamically-typed where as I am very used to statically-typed formats. For example, say you have an object of a Class A in Java. Let’s call it obj. Now obj has a method which returns a string value. So if I’m calling this function elsewhere in my program I know that the value that will be assigned is going to be 100% a string value (considering there are no errors/exceptions).
Now in python there are times when I don’t know what the return type of a function is gonna be. This is especially evident whenever I’m working on a library like say pandas. One example is: I have a DataFrame that I have stored as the name df1. Now df1.columns returns an object of the type pandas.core.indexes.base.Index. Now when I iterate over this returned Index value using
for i in df1.columns: print(type(i))
Now this returns a string value. So does this mean that and Index object is an array-like(?) object of string values? Is that why it returns a string value when I iterate over it? I thought that the for-each loop can only iterate over collections(?). Or can it iterate over objects as well? Or am I not understanding the working of the for-each loop in Python?
I literally cannot wrap my head around this. Can someone please help/advise?
submitted by throwaway84483994 to learnpython [link] [comments]


2023.06.07 11:50 johntheh4cker Trying to build docker image using jenkins getting strange error, please help me.

Pipelines are failing and in logs it is showing no such method found, i am pasting the logs below. Please help me-
Started by user Zen Admin > git rev-parse --resolve-git-dir /valib/jenkins/caches/git-4a497cbeca7fbace311ce2fe6be29d0e/.git # timeout=10 Setting origin to https://gitlab.com/org/no-framework.git > git config remote.origin.url https://gitlab.com/org/no-framework.git # timeout=10 Fetching origin... Fetching upstream changes from origin > git --version # timeout=10 > git --version # 'git version 2.34.1' > git config --get remote.origin.url # timeout=10 using GIT_ASKPASS to set credentials gitlab-credentials > git fetch --tags --force --progress -- origin +refs/heads/*:refs/remotes/origin/* # timeout=10 Seen branch in repository origin/AddField/AttachEmail Seen branch in repository origin/Feature/Postman_collection Seen branch in repository origin/add-client_name-and-customer_name Seen branch in repository origin/add-customer_name-client_name Seen branch in repository origin/adding_qa_image_promotion_stage Seen branch in repository origin/aws_from_dev_config Seen branch in repository origin/aws_from_master Seen branch in repository origin/ayush_sphinxDocumentation Seen branch in repository origin/char-limit Seen branch in repository origin/cicd_rules Seen branch in repository origin/correct-loggers-notifications Seen branch in repository origin/defectdojo_jobs Seen branch in repository origin/dev-env-testing Seen branch in repository origin/dev-sc-POC Seen branch in repository origin/dev_config Seen branch in repository origin/dockerfile-non-root-user Seen branch in repository origin/email-char-limit-problem-notifications Seen branch in repository origin/emailID-limit-change-migration-test Seen branch in repository origin/endpoint_update Seen branch in repository origin/final-sms-daily-reporting Seen branch in repository origin/final-sms-daily-reporting-with-dev-config Seen branch in repository origin/fix-build-image Seen branch in repository origin/fix-migration-tracking Seen branch in repository origin/fix-notification-upgrade Seen branch in repository origin/fix-security-notifications Seen branch in repository origin/fix-ses-email-exception Seen branch in repository origin/fix-sonarqube-defectdojo-notitifications Seen branch in repository origin/fix-whitenoise Seen branch in repository origin/helm-fix Seen branch in repository origin/helm_security Seen branch in repository origin/integrate-swagger Seen branch in repository origin/integrate-vodafone-api Seen branch in repository origin/international_isd_code_support Seen branch in repository origin/local_test Seen branch in repository origin/logger-error-fix Seen branch in repository origin/logger-handle-bounce Seen branch in repository origin/master Seen branch in repository origin/new_pg_chart_15.1 Seen branch in repository origin/non_root_poc Seen branch in repository origin/notification-dev Seen branch in repository origin/notification_qa Seen branch in repository origin/pg_test_15 Seen branch in repository origin/python-django-upgrade Seen branch in repository origin/remove-extra-added-loggers Seen branch in repository origin/remove-extra-loggers Seen branch in repository origin/remove-international_isd_code_support Seen branch in repository origin/rishi_logging_duplicate_messageContent Seen branch in repository origin/s3_secret_update Seen branch in repository origin/sms-daily-reporting-satyam Seen branch in repository origin/sms-reporting-enhancement Seen branch in repository origin/sms_daily_reporting Seen branch in repository origin/sms_daily_reporting-with-dev-config Seen branch in repository origin/sonarqube-changes Seen branch in repository origin/subchart_integration Seen branch in repository origin/subchart_update Seen branch in repository origin/test Seen branch in repository origin/test-image-local Seen branch in repository origin/test-migration Seen branch in repository origin/turn-off-mail-admins Seen branch in repository origin/upgrade-postgres Seen branch in repository origin/vault_v2 Seen 61 remote branches Obtained Jenkinsfile.Build from efd6ca96c51d2bbebf5f523c28d0f9eb0e4f4b01 Loading library [email protected] Attempting to resolve testsonaar from remote references... > git --version # timeout=10 > git --version # 'git version 2.34.1' using GIT_ASKPASS to set credentials gitlab-credentials > git ls-remote -h -- https://gitlab.com/org/common-library.git # timeout=10 Found match: refs/heads/testsonaar revision bc3f74b58072bd6f208c2b55aad11e59bd17f957 The recommended git tool is: NONE using credential gitlab-credentials > git rev-parse --resolve-git-dir /valib/jenkins/workspace/[email protected]/19e1f074e72b9c95c7402648f84f95c2705487f5be8df1aa9345c2affee7bf38/.git # timeout=10 Fetching changes from the remote Git repository > git config remote.origin.url https://gitlab.com/org/common-library.git # timeout=10 Fetching without tags Fetching upstream changes from https://gitlab.com/org/common-library.git > git --version # timeout=10 > git --version # 'git version 2.34.1' using GIT_ASKPASS to set credentials gitlab-credentials > git fetch --no-tags --force --progress -- https://gitlab.zenatix.com/devops/common-library.git +refs/heads/*:refs/remotes/origin/* # timeout=10 Checking out Revision bc3f74b58072bd6f208c2b55aad11e59bd17f957 (testsonaar) > git config core.sparsecheckout # timeout=10 > git checkout -f bc3f74b58072bd6f208c2b55aad11e59bd17f957 # timeout=10 Commit message: "commented sonarqube" > git rev-list --no-walk bc3f74b58072bd6f208c2b55aad11e59bd17f957 # timeout=10 [Pipeline] Start of Pipeline [Pipeline] node Running on Jenkins in /valib/jenkins/workspace/ramework_01_-_Build-Image_master [Pipeline] { [Pipeline] checkout The recommended git tool is: git using credential gitlab-credentials > git rev-parse --resolve-git-dir /valib/jenkins/workspace/ramework_01_-_Build-Image_maste.git # timeout=10 Fetching changes from the remote Git repository > git config remote.origin.url https://gitlab.com/org/no-framework.git # timeout=10 Fetching without tags Fetching upstream changes from https://gitlab.com/org/no-framework.git > git --version # timeout=10 > git --version # 'git version 2.34.1' using GIT_ASKPASS to set credentials gitlab-credentials > git fetch --no-tags --force --progress -- https://gitlab.com/org/no-framework.git +refs/heads/*:refs/remotes/origin/* # timeout=10 Checking out Revision efd6ca96c51d2bbebf5f523c28d0f9eb0e4f4b01 (master) > git config core.sparsecheckout # timeout=10 > git checkout -f efd6ca96c51d2bbebf5f523c28d0f9eb0e4f4b01 # timeout=10 Commit message: "Merge branch 'adding_qa_image_promotion_stage' into 'master'" > git rev-list --no-walk efd6ca96c51d2bbebf5f523c28d0f9eb0e4f4b01 # timeout=10 [Pipeline] stage [Pipeline] { (Executing pylint on python scripts) [Pipeline] libraryResource [Pipeline] writeFile [Pipeline] libraryResource [Pipeline] writeFile [Pipeline] sh + chmod +x pylint.sh + ./pylint.sh + execute_pylint ++ find . -type f -name '*.py' + python_files='./notification_app/models.py ./notification_app/tests.py ./notification_app/migrations/0002_auto_20210819_0612.py ./notification_app/migrations/0007_auto_20230117_0756.py ./notification_app/migrations/0005_auto_20221107_0659.py ./notification_app/migrations/__init__.py ./notification_app/migrations/0003_auto_20210819_0636.py ./notification_app/migrations/0004_auto_20220309_1344.py ./notification_app/migrations/0006_auto_20221209_0859.py ./notification_app/migrations/0001_initial.py ./notification_app/email_utilities.py ./notification_app/apps.py ./notification_app/email_views.py ./notification_app/admin.py ./notification_app/serializers.py ./notification_app/__init__.py ./notification_app/views.py ./notification_app/utils.py ./notification_app/urls.py ./notification_app/management/__init__.py ./notification_app/management/commands/zenmigrate.py ./notification_app/management/commands/sync_customer_am_emails.py ./notification_app/management/commands/__init__.py ./notification_app/management/commands/daily_sms_reporting.py ./Dockeanalytics/jupyter_notebook_config.py ./Docke__init__.py ./notifications/wsgi.py ./notifications/settings.py ./notifications/asgi.py ./notifications/__init__.py ./notifications/urls.py ./manage.py ./docs/source/conf.py' + for file in $python_files + true + pylint --rcfile=./pylintrc ./notification_app/models.py + true [Pipeline] } [Pipeline] // stage [Pipeline] } [Pipeline] // node [Pipeline] End of Pipeline Also: org.jenkinsci.plugins.workflow.actions.ErrorAction$ErrorId: 23031cc0-2923-4598-b1ad-e5b49d42a449 java.lang.NoSuchMethodError: No such DSL method 'pyLint' found among steps [acceptGitLabMR, addGitLabMRComment, ansiColor, ansiblePlaybook, ansibleVault, archive, bat, build, catchError, checkout, compareVersions, container, containerLog, deleteDir, dir, dockerFingerprintFrom, dockerFingerprintRun, dockerNode, echo, emailext, emailextrecipients, envVarsForTool, error, fileExists, findBuildScans, findFiles, getContext, git, gitlabBuilds, gitlabCommitStatus, input, isUnix, jiraComment, jiraIssueSelector, jiraSearch, junit, library, libraryResource, load, lock, mail, milestone, node, nodesByLabel, parallel, podTemplate, powershell, prependToFile, properties, publishChecks, pwd, pwsh, readCSV, readFile, readJSON, readManifest, readMavenPom, readProperties, readTrusted, readYaml, resolveScm, retry, script, sh, sha1, sha256, slackSend, slackUploadFile, slackUserIdFromEmail, slackUserIdsFromCommitters, sleep, sshagent, stage, stash, step, tar, tee, timeout, timestamps, tm, tool, touch, unarchive, unstable, unstash, untar, unzip, updateGitlabCommitStatus, validateDeclarativePipeline, verifySha1, verifySha256, waitForBuild, waitForQualityGate, waitUntil, warnError, withChecks, withContext, withCredentials, withDockerContainer, withDockerRegistry, withDockerServer, withEnv, withGradle, withGroovy, withMaven, withNPM, wrap, writeCSV, writeFile, writeJSON, writeMavenPom, writeYaml, ws, zip] or symbols [GitUsernamePassword, agent, all, allBranchesSame, allOf, always, ansible, ant, antFromApache, antOutcome, antTarget, any, anyOf, apiToken, apiTokenProperty, aquaMicroscanner, architecture, archiveArtifacts, artifactManager, artifactsPublisher, assembla, attach, authorInChangelog, authorizationMatrix, batchFile, bitbucket, bitbucketPush, bitbucketServer, booleanParam, branch, brokenBuildSuspects, brokenTestsSuspects, browser, buildButton, buildDiscarder, buildDiscarders, buildRetention, buildSingleRevisionOnly, buildUser, buildingTag, builtInNode, caseInsensitive, caseSensitive, certificate, cgit, changeRequest, changelog, changelogBase, changelogToBranch, changeset, checkoutOption, checkoutToSubdirectory, choice, choiceParam, cleanAfterCheckout, cleanBeforeCheckout, cleanWs, clock, cloneOption, command, concordionPublisher, configFile, configFileProvider, configMapVolume, containerEnvVar, containerLivenessProbe, containerTemplate, contributor, credentials, cron, crumb, culprits, default, defaultFolderConfiguration, defaultView, demand, dependenciesFingerprintPublisher, developers, disableConcurrentBuilds, disableRestartFromStage, disableResume, discoverOtherRefs, discoverOtherRefsTrait, docker, dockerCert, dockerServer, dockerTool, dockerfile, downstream, dumb, durabilityHint, dynamicPVC, email-ext, emptyDirVolume, emptyDirWorkspaceVolume, envVar, envVars, envVarsFilter, environment, equals, expression, extendedEmailPublisher, file, fileParam, filePath, findbugsPublisher, fingerprint, fingerprints, fisheye, frameOptions, freeStyle, freeStyleJob, fromDocker, fromScm, fromSource, git, gitBranchDiscovery, gitHooks, gitHubBranchDiscovery, gitHubBranchHeadAuthority, gitHubExcludeArchivedRepositories, gitHubExcludeForkedRepositories, gitHubExcludePrivateRepositories, gitHubExcludePublicRepositories, gitHubForkDiscovery, gitHubIgnoreDraftPullRequestFilter, gitHubPullRequestDiscovery, gitHubSshCheckout, gitHubTagDiscovery, gitHubTopicsFilter, gitHubTrustContributors, gitHubTrustEveryone, gitHubTrustNobody, gitHubTrustPermissions, gitLab, gitLabConnection, gitList, gitSCM, gitTagDiscovery, gitTool, gitUsernamePassword, gitWeb, gitblit, github, githubProjectProperty, githubPush, gitiles, gitlab, globalConfigFiles, gogs, gradle, groovy, headRegexFilter, headWildcardFilter, hostPathVolume, hostPathWorkspaceVolume, http, hyperlink, hyperlinkToModels, ignoreOnPush, inbound, inheriting, inheritingGlobal, installSource, invokerPublisher, isRestartedRun, issueCommentTrigger, jacocoPublisher, jdk, jdkInstaller, jgit, jgitapache, jgivenPublisher, jnlp, jobBuildDiscarder, jobDsl, jobName, junitPublisher, junitTestResultStorage, kiln, kubeconfig, kubernetes, kubernetesAgent, label, lastDuration, lastFailure, lastGrantedAuthorities, lastStable, lastSuccess, legacy, legacySCM, lfs, list, local, localBranch, localBranchTrait, locale, location, logRotator, loggedInUsersCanDoAnything, mailer, masterBuild, maven, maven3Mojos, mavenErrors, mavenGlobalConfig, mavenLinkerPublisher, mavenMojos, mavenWarnings, merge, modernSCM, multibranch, myView, namedBranchesDifferent, never, newContainerPerStage, nfsVolume, nfsWorkspaceVolume, node, nodeProperties, nonInheriting, none, nonresumable, not, onFailure, openTasksPublisher, organizationFolder, override, overrideIndexTriggers, paneStatus, parallelsAlwaysFailFast, parameters, password, pattern, perBuildTag, permanent, persistentVolumeClaim, persistentVolumeClaimWorkspaceVolume, phabricator, pipeline, pipeline-model, pipeline-model-docker, pipelineGraphPublisher, pipelineMaven, pipelineTriggers, plainText, plugin, podAnnotation, podEnvVar, podLabel, pollSCM, portMapping, preserveStashes, previous, projectNamingStrategy, proxy, pruneStaleBranch, pruneStaleTag, pruneTags, pullRequestReview, queueItemAuthenticator, quietPeriod, rateLimit, rateLimitBuilds, recipients, redmine, refSpecs, remoteName, requestor, resourceRoot, retainOnlyVariables, rhodeCode, run, runParam, sSHLauncher, schedule, scmGit, scmRetryCount, script, scriptApproval, scriptApprovalLink, search, secretEnvVar, secretVolume, security, shell, simpleBuildDiscarder, skipDefaultCheckout, skipStagesAfterUnstable, slackNotifier, slave, snapshotDependencies, sourceRegexFilter, sourceWildcardFilter, sparseCheckoutPaths, spotbugsPublisher, ssh, sshPublicKey, sshUserPrivateKey, standard, status, string, stringParam, submodule, submoduleOption, suppressAutomaticTriggering, suppressFolderAutomaticTriggering, swapSpace, tag, teamFoundation, teamSlugFilter, text, textParam, timestamper, timestamperConfig, timezone, tmpSpace, toolLocation, triggeredBy, unsecured, untrusted, upstream, upstreamDevelopers, url, userIdentity, userSeed, usernameColonPassword, usernamePassword, viewgit, viewsTabBar, weather, withAnt, withSonarQubeEnv, x509ClientCert, zip] or globals [airflowCron, buildImage, commonUtils, configureLocustMasterSlave, createCluster, currentBuild, deployAirflow, deployControl, deployDashboard, deployDeviceMonitor, deployMakerSuite, deployServerMonitor, deployV2Server, deployWeatherDriver, deployWebTNT, docker, dockerOperation, env, fetchDynamicImage, helm, imagePromotion, inspec, issuesDeploy, locustDeploy, nodeJSPipeline, notification, params, pipeline, pythonCIPipeline, scm, setupDruid, setupDruidPub, setupFlask, setupIstio, setupKafka, setupKafkaAlertDriver, setupKeycloak, setupKubernetesDashboard, setupLogging, setupMonitoringStack, setupRedis, sonnarScanner, terraform, zenalytixBuildImage, zenalytixDeploy] at org.jenkinsci.plugins.workflow.cps.DSL.invokeMethod(DSL.java:219) at org.jenkinsci.plugins.workflow.cps.CpsScript.invokeMethod(CpsScript.java:124) at groovy.lang.MetaClassImpl.invokeMethodOnGroovyObject(MetaClassImpl.java:1295) at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1184) at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1034) at org.codehaus.groovy.runtime.callsite.PogoMetaClassSite.call(PogoMetaClassSite.java:41) at org.codehaus.groovy.runtime.callsite.CallSiteArray.defaultCall(CallSiteArray.java:47) at org.codehaus.groovy.runtime.callsite.AbstractCallSite.call(AbstractCallSite.java:116) at com.cloudbees.groovy.cps.sandbox.DefaultInvoker.methodCall(DefaultInvoker.java:20) at pythonCIPipeline.executePylint(pythonCIPipeline.groovy:114) at ___cps.transform___(Native Method) at com.cloudbees.groovy.cps.impl.ContinuationGroup.methodCall(ContinuationGroup.java:90) at com.cloudbees.groovy.cps.impl.FunctionCallBlock$ContinuationImpl.dispatchOrArg(FunctionCallBlock.java:116) at com.cloudbees.groovy.cps.impl.FunctionCallBlock$ContinuationImpl.fixArg(FunctionCallBlock.java:85) at jdk.internal.reflect.GeneratedMethodAccessor155.invoke(Unknown Source) at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.base/java.lang.reflect.Method.invoke(Method.java:566) at com.cloudbees.groovy.cps.impl.ContinuationPtr$ContinuationImpl.receive(ContinuationPtr.java:72) at com.cloudbees.groovy.cps.impl.CollectionLiteralBlock$ContinuationImpl.dispatch(CollectionLiteralBlock.java:55) at com.cloudbees.groovy.cps.impl.CollectionLiteralBlock$ContinuationImpl.item(CollectionLiteralBlock.java:45) at jdk.internal.reflect.GeneratedMethodAccessor152.invoke(Unknown Source) at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.base/java.lang.reflect.Method.invoke(Method.java:566) at com.cloudbees.groovy.cps.impl.ContinuationPtr$ContinuationImpl.receive(ContinuationPtr.java:72) at com.cloudbees.groovy.cps.impl.ConstantBlock.eval(ConstantBlock.java:21) at com.cloudbees.groovy.cps.Next.step(Next.java:83) at com.cloudbees.groovy.cps.Continuable$1.call(Continuable.java:152) at com.cloudbees.groovy.cps.Continuable$1.call(Continuable.java:146) at org.codehaus.groovy.runtime.GroovyCategorySupport$ThreadCategoryInfo.use(GroovyCategorySupport.java:136) at org.codehaus.groovy.runtime.GroovyCategorySupport.use(GroovyCategorySupport.java:275) at com.cloudbees.groovy.cps.Continuable.run0(Continuable.java:146) at org.jenkinsci.plugins.workflow.cps.SandboxContinuable.access$001(SandboxContinuable.java:18) at org.jenkinsci.plugins.workflow.cps.SandboxContinuable.run0(SandboxContinuable.java:51) at org.jenkinsci.plugins.workflow.cps.CpsThread.runNextChunk(CpsThread.java:187) at org.jenkinsci.plugins.workflow.cps.CpsThreadGroup.run(CpsThreadGroup.java:420) at org.jenkinsci.plugins.workflow.cps.CpsThreadGroup$2.call(CpsThreadGroup.java:330) at org.jenkinsci.plugins.workflow.cps.CpsThreadGroup$2.call(CpsThreadGroup.java:294) at org.jenkinsci.plugins.workflow.cps.CpsVmExecutorService$2.call(CpsVmExecutorService.java:67) at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264) at hudson.remoting.SingleLaneExecutorService$1.run(SingleLaneExecutorService.java:139) at jenkins.util.ContextResettingExecutorService$1.run(ContextResettingExecutorService.java:28) at jenkins.security.ImpersonatingExecutorService$1.run(ImpersonatingExecutorService.java:68) at jenkins.util.ErrorLoggingExecutorService.lambda$wrap$0(ErrorLoggingExecutorService.java:51) at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515) at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:829) Finished: FAILURE 
Please help me.
submitted by johntheh4cker to sysadmin [link] [comments]


2023.06.07 11:07 Meece156 Maternal Mortality Rates in the United States, 2021

Hey Everyone!
I'm having issues attempting to decode the information provided by the CDC. I downloaded the Mortality Multiple Cause File for 2021, and the .txt file is - not only over 2GB, but also incomprehensible. I followed the accompanying .pdf file and was even more confused by its "List of File Data Elements and Tape Locations", and how I'm supposed to use the file to comprehend a list of codes upon codes upon codes? Especially, when the .txt file has no structure, and when I try to follow a top down approach, codes don't seem to match.
I wanted to ask if there is a common approach to this, or if I am missing something?
Additional Info:
I am using R for statistical analysis. I wanted the raw data for this reason. I attempted to convert the .txt file to a .csv file format using Python, and it helped by structuring the data a little, but I still don't know what I am looking at in terms of what it all means.
This is how the rows look now: 11 7101 F1080 422210 4D1 2021U7CN C851129 039 13 0511I509 21I518 31I513 41C851 61M481 05 C851 I509 I513 I518 M481 100 01 184005949020
I would appreciate any, and all help. Thank you all very much in advance.
submitted by Meece156 to datasets [link] [comments]


2023.06.07 10:24 ollivierre Do you require Devices to be marked as compliant?

We're contemplating enabling the conditional access policy for compliant devices. Surely the report only mode shows that there will be lots of folks who will be blocked by such policy if it was to be enforced. We downloaded the JSON export from sign in logs and converted that to a CSV using PowerShell to avoid having to pay for Log Analytics for now.
The report will be sent off to management to show all users that must enroll before policy enforcement. Many of which are using BYOD/unmanaged devices and will only be allowed via Browser.
Just curious what others are doing.
submitted by ollivierre to Intune [link] [comments]


2023.06.07 10:12 nocgod Removing default values while serializing using Newtonsoft.Json

So, I trying to check how much money would it save us if we will not write default values to the DB. meaning, if it is a worthwhile project to do in the next quarter or not.
So I've got some documents from the DB (around the average document size) and wrote some simple code I thought would work
```csharp var original = """{"notDef":true, "defBool":false, "defInt":0, "defObj":null, "defArr":[]}"""; var json = JObject.Parse(original);
var opts = new JsonSerializerSettings() { NullValueHandling = NullValueHandling.Ignore, DefaultValueHandling = DefaultValueHandling.Ignore, Formatting = Formatting.None };
var str = JsonConvert.SerializeObject(json, opts);
Console.WriteLine($"original size: {Encoding.UTF8.GetByteCount(original)}"); Console.WriteLine($"new size: {Encoding.UTF8.GetByteCount(str)}"); Console.WriteLine($"saving: {CalculateSavings(original, str)}%");
static double CalculateSavings(string original, string str) { var originalSize = Encoding.UTF8.GetByteCount(original); var newSize = Encoding.UTF8.GetByteCount(str); return (originalSize - newSize)/ (double)originalSize * 100; } ```
low and behold the output was:
original size: 72 {"notDef":true, "defBool":false, "defInt":0, "defObj":null, "defArr":[]} new size: 68 {"notDef":true,"defBool":false,"defInt":0,"defObj":null,"defArr":[]} a bit unexpected.
so I had to add something like this: ```csharp var json = JObject.Parse(original);
json .Descendants() .OfType() .Where(x => x.Value.Type == JTokenType.Null x.Value is JArray arr && arr.Count == 0 x.Value.Type == JTokenType.Integer && x.Value.Value() == 0 x.Value.Type == JTokenType.Float && x.Value.Value() == 0 x.Value.Type == JTokenType.Boolean && x.Value.Value() == false) .ToList() .ForEach(x => x.Remove()); ``` and it worked as expected.
I'm still left with the question - why didn't it work?
code: https://dotnetfiddle.net/wruWtF
submitted by nocgod to dotnet [link] [comments]


2023.06.07 09:34 Artistic_Cicada_4553 How to leverage the state-of-the-art NLP models in Rust

In this tutorial we will show you how to use rust-bert library to utilize the state-of-the-art natural language processing models in Rust, and we are specifically tested on macOS environments.
Rust crate rust_bert implementation of the BERT language model (https://arxiv.org/abs/1810.04805 Devlin, Chang, Lee, Toutanova, 2018). The base model is implemented in the bert_model::BertModel struct. Several language model heads have also been implemented, including:

Transformers

Transformers is a State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Before installing the transformers:
``` python3 -m venv .env source .env/bin/activate
brew install cmake brew install pkg-config brew install sentencepiece
pip install sentencepiece pip install transformers pip install 'transformers[torch]' pip install 'transformers[tf-cpu]' pip install 'transformers[flax]' pip install onnxruntime ```
Verify the installation:
(.env) ➜ transformers git:(main) python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))" No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english). Using a pipeline without specifying a model name and revision in production is not recommended. Downloading (…)lve/main/config.json: 100%████████████████████████████████████████████████████████████████████████████████████████████████ 629/629 [00:00<00:00, 1.13MB/s] Downloading model.safetensors: 100%████████████████████████████████████████████████████████████████████████████████████████████████████ 268M/268M [00:26<00:00, 10.0MB/s] Downloading (…)okenizer_config.json: 100%███████████████████████████████████████████████████████████████████████████████████████████████ 48.0/48.0 [00:00<00:00, 158kB/s] Downloading (…)solve/main/vocab.txt: 100%███████████████████████████████████████████████████████████████████████████████████████████████ 232k/232k [00:00<00:00, 554kB/s] Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers pip install xformers. [{'label': 'POSITIVE', 'score': 0.9998704195022583}]
Install the huggineface's transformers:
git clone https://github.com/huggingface/transformers.git cd transformers pip install -e .
🤗 Transformers is tested on Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, and Flax. Follow the installation instructions below for the deep learning library you are using:
So we will need to install these 3 dependend projects as follow.

Install PyTorch

pip3 install torch torchvision torchaudio

Install TensorFlow

```

There is currently no official GPU support for MacOS.

python3 -m pip install tensorflow

Verify install:

python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))" ```

Install Flax

Flax delivers an end-to-end and flexible user experience for researchers who use JAX with neural networks. Flax exposes the full power of JAX. It is made up of loosely coupled libraries.
JAX is a project designed for High-Performance Array Computing,
python3.11 -m pip install --upgrade pip pip install flax

Init Rust BERT

``` brew install libtorch brew link libtorch brew ls --verbose libtorch grep dylib export LIBTORCH=$(brew --cellar pytorch)/$(brew info --json pytorch jq -r '.[0].installed[0].version') export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH
git clone https://github.com/guillaume-be/rust-bert.git cd rust-bert ORT_STRATEGY=system cargo run --example sentence_embeddings ```
Also better to add the following to bash/zsh environment in case you met exception like "libtch/torch_api_generated.cpp" with args "c++" did not execute successfully (status code exit status: 1).: export LIBTORCH=$(brew --cellar pytorch)/$(brew info --json pytorch jq -r '.[0].installed[0].version') export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH

In Rust's project

Import the example code into Pizza's module:
``` use log::*; use rust_bert::pipelines::translation::{Language, TranslationModelBuilder}; pub(crate) fn translation() -> anyhow::Result<()> { info!("start translation:"); let model = TranslationModelBuilder::new() .with_source_languages(vec![Language::English]) .with_target_languages(vec![ Language::Spanish, Language::French, Language::Italian, ]) .create_model() .unwrap();
let input_text = "Hello world!"; let output = model .translate(&[input_text], None, Language::Spanish) .unwrap(); for sentence in output { info!("Output: {}", sentence); } Ok(()) 
}
```
And we will get the result as follows: ```
/ _ \_ / _ / _ / /\ / /)/ / //// /// / //\ / _// / / /// /// _ \ / _/ //__/\/ _/
[PIZZA] The Next-Gen Real-Time Hybrid Search & AI-Native Innovation Engine. [2023-06-03 19:00:32] [INFO] [pizza:96] PIZZA now starting. [2023-06-03 19:00:32] [INFO] [pizza::modules::api:71] api listen at: http://0.0.0.0:2900 [2023-06-03 19:00:32] [INFO] [pizza::modules:37] started module [api] [2023-06-03 19:00:32] [INFO] [pizza::modules::bert::translation:4] start translation: [2023-06-03 19:00:32] [INFO] [actix_server::builder:200] starting 8 workers [2023-06-03 19:00:32] [INFO] [actix_server::server:197] Tokio runtime found; starting in existing Tokio runtime [2023-06-03 19:00:33] [INFO] [cached_path::cache:414] Cached version of https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE/resolve/main/vocab.json is up-to-date [2023-06-03 19:00:33] [INFO] [cached_path::cache:414] Cached version of https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE/resolve/main/source.spm is up-to-date [2023-06-03 19:00:34] [INFO] [cached_path::cache:414] Cached version of https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE/resolve/main/config.json is up-to-date [2023-06-03 19:00:36] [INFO] [cached_path::cache:414] Cached version of https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE/resolve/main/rust_model.ot is up-to-date [2023-06-03 19:00:36] [INFO] [pizza::modules::bert::translation:21] Output: ¡Hola mundo! [2023-06-03 19:00:36] [INFO] [pizza::modules:37] started module [bert] [2023-06-03 19:00:36] [INFO] [pizza::modules:39] all modules are started [2023-06-03 19:00:36] [INFO] [pizza:116] PIZZA is up and running now. PID: 37426 ```
Wow, Hello World was successfully translated to ¡Hola mundo!, which is great!
As you can see, creating a translation application is incredibly simple just in a few lines of code. And this is just the beginning! By harnessing the power of these pre-trained language models, we can accomplish so much more.

Links

submitted by Artistic_Cicada_4553 to infinilabs [link] [comments]


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submitted by CKangel to udemyfreebies [link] [comments]


2023.06.07 06:22 Dankmemexplorer maybe there are vacancies in cosmetology schools

maybe there are vacancies in cosmetology schools submitted by Dankmemexplorer to okbuddyml [link] [comments]


2023.06.07 05:46 Visual-Imagination19 use node to fetch api, when improper data is being sent.

I'm working on a project using node, express, HTML, and js, and a 3rd party API. I need to use an API key( the only reason I'm creating my own server is to .env the key) but I'm getting errors when I fetch the api data. it worked fine in the front end because I could
 let response = await fetch(url, options).text(); let results = await JSON.parse(response) 
but I can't use .text() in node, and JSON.stringify(response) will throw the error 'TypeError: Converting circular structure to JSON.'
I don't believe there's a way to safely use an API key in the front end, and not sure how to receive the API data.
submitted by Visual-Imagination19 to learnjavascript [link] [comments]


2023.06.07 05:06 j-kaleb Pycharm+GitHub saying no module named ‘matplotlib’, 'sympy', 'ezdxf' and others even after installing with pip and on python packages

I recently moved a project of mine to GitHub which was working perfectly. A few pushes and pulls later a ModuleNotFoundError: began showing up on some packages, but not all of them (Pandas and Numpy work). Looking through my changes history. I think it stopped working after this push to the iml file.
- +
I didn't do this consciously, but surely an update to python wouldn't have caused this? Ive tried reverting this change but it still doesnt work.
Anyway, all of these modules are in the "Python packages" tab under "installed", all of these packages say "requirement already satisfied" when going to the terminal and inputting "pip install x". And the most annoying thing is, if i copy these files BACK to the original local folder I built this project in, it works!
And, I'm pretty sure I have the same interpreter on both environments (its got the same path).
For more context, what I did to create the GitHub repository was
Any insight into why this might be happening would very much be appreciated. And sorry if this has an easy solution
submitted by j-kaleb to learnpython [link] [comments]


2023.06.07 05:04 wazazzz High level library for interfacing with open source and OpenAI LLMs

Working on this project to use scikitlearn style APIs to easily do prompt engineering (using python lists and functions) to design explicit chains. Also includes fine tuning your own model on open source LLMs. This works for data in the pandas dataframe format easily as well. Looking for feedback and collaboration:
https://github.com/Pan-ML/panml
submitted by wazazzz to PromptEngineering [link] [comments]


2023.06.07 04:46 algusdark Help me to get the best python Neovim environment

Hi community. I've been trying to setup the ultimate python environment, trying to make it as nice as PyCharm. I'm kind of new on this and I'm using LazyVim for my vim setup.
I've been reading several posts about how to just use pyright and ruff... but I feel like there is a lot of things missing for a nicer experience. For not selecting the venv I'm using workon.
If you have something as an experienced python developer using neovim, could you share me some tips and repos on how to setup this?
Things I've tried: - ruff + pyright - I think this is ok-ish. The problem is that there are no code actions - pylsp + ruff + pyright - I think this is the best combo... but I think I'm not good at configuring it as I need to do some extra configs on lsp to remove some capabilities and to setup correctly to place nicely.
Thanks!
This is my current config: ```lua return { { "mfusseneggenvim-dap", },
{ "mfusseneggenvim-dap-python", ft = "python", dependencies = { "mfusseneggenvim-dap", }, },
{ "nvim-treesittenvim-treesitter", opts = function(_, opts) vim.list_extend(opts.ensure_installed, { "python", }) end, },
{ "williamboman/mason.nvim", opts = function(_, opts) vim.list_extend(opts.ensure_installed, { "pyright", "mypy", "flake8", "black", "ruff", "isort", "python-lsp-server", }) end, },
{ "neovim/nvim-lspconfig", opts = { servers = { pylsp = { settings = { pylsp = { plugins = { jedi_definition = { enabled = true, follow_imports = true, follow_builtin_imports = true, follow_builtin_definitions = true, }, jedi_rename = { enabled = true }, jedi_completion = { enabled = true, include_params = true, cache_labels_for = { "pandas", "numpy", "pydantic", "fastapi", "flask", "sqlalchemy", "dagster", }, }, jedi_hover = { enabled = true }, pylsp_mypy = { enabled = true, live_mode = false, dmypy = false, report_progress = false, -- args = { -- "--sqlite-cache", -- Use an SQLite database to store the cache. -- "--cache-fine-grained", -- Include fine-grained dependency information in the cache for the mypy daemon. -- }, }, -- Disabled ones: flake8 = { enabled = false }, mccabe = { enabled = false }, preload = { enabled = false }, pycodestyle = { enabled = false }, pyflakes = { enabled = false }, pylint = { enabled = false }, rope = { enabled = true }, rope_completion = { enabled = true }, rope_rename = { enabled = false }, yapf = { enabled = false }, -- still unsure: -- jedi_completion = { enabled = false }, -- done better by pyright ? -- Formatting is taken care of by null-ls ["pylsp_black"] = { enabled = false }, ["pyls_isort"] = { enabled = false }, autopep8 = { enabled = false }, }, }, }, },
 pyright = { settings = { python = { disableOrganizeImports = true, analysis = { indexing = true, typeCheckingMode = "basic", diagnosticMode = "openFilesOnly", autoImportCompletions = false, autoSearchPaths = false, }, }, }, }, }, setup = { pylsp = function() require("lazyvim.util").on_attach(function(client, _) if client.name == "pyslp" then client.server_capabilities.documentFormattingProvider = false client.server_capabilities.documentRangeFormattingProvider = false end end) end, pyright = function() require("lazyvim.util").on_attach(function(client, _) if client.name == "pyright" then client.server_capabilities.renameProvider = false -- rope is ok client.server_capabilities.hoverProvider = false -- pylsp includes also docstrings client.server_capabilities.signatureHelpProvider = false -- pyright typing of signature is weird client.server_capabilities.definitionProvider = false -- pyright does not follow imports correctly client.server_capabilities.referencesProvider = false -- pylsp does it client.server_capabilities.completionProvider = { resolveProvider = true, triggerCharacters = { "." }, } end end) end, }, }, 
}, { "jose-elias-alvarez/null-ls.nvim", opts = function(_, opts) local nls = require("null-ls") vim.list_extend(opts.sources, { nls.builtins.diagnostics.ruff,
 nls.builtins.formatting.black.with({ args = { "--fast", "--quiet", "--stdin-filename", "$FILENAME", "-", }, }), nls.builtins.formatting.isort, }) end, 
}, } ```
submitted by algusdark to neovim [link] [comments]


2023.06.07 03:48 wtfwafflezor (Selling) 800 Titles Creed III (2023) (Vudu/4K) $9 Shazam! Fury of the Gods (2023) (MA/4K) $9

Prices FIRM - CashApp/Venmo/PayPal Friends & Family
Disney/Marvel titles are split codes. Only redeem what you pay for. Thank you.
12 Monkeys (1995) (MA/4K) $3.50
12 Years a Slave (2013) (MA/HD) $3.50
13 Hours: Secret Soldiers of Benghazi (2016) (Vudu/4K) $5.50 (Vudu/HD) $2 (iTunes/4K) $3
1917 (2019) (MA/4K) $6 (MA/HD) $3.50
2 Guns (2013) (MA/HD) $4.75 (iTunes/HD) $3.50
2012 (2009) (MA/4K) $6.50
21 Jump Street (2012) (MA/4K) $6.75 (MA/HD) $2.75
22 Jump Street (2014) (MA/HD) $4.50
355, The (2022) (MA/HD) $5.75
47 Meters Down (2017) (Vudu/HD) (iTunes/HD) $4.50
47 Ronin (2013) (MA/4K) $5.75 (iTunes/4K) $4 (MA/HD) $3.50
A Beautiful Day in the Neighborhood (2019) (MA/HD) $5.75
A Clockwork Orange (1972) (MA/4K) $6.50
A Man Called Otto (2022) (MA/HD) $7.25
A Million Ways to Die in the West (2014) (iTunes/HD) Ports to MA $4.25
A Monster Calls (2016) (MA/HD) (iTunes/HD) $3.75
A Vigilante (2018) (Vudu/HD) (iTunes/HD) $5.50
A Wrinkle in Time (2018) (MA/HD) $3
Abominable (2019) (MA/4K) $7.50 (MA/HD) $6.25
Action Point (2018) (Vudu/HD) $2.25 (iTunes/4K) $1.50
Ad Astra (2019) (MA/HD) $4.75
Adventures of Tintin (2011) (Vudu/HD) (iTunes/HD) $4.50
After Earth (2013) (MA/HD) $2.50
Aladdin (1992) (MA/4K) $6.75 (MA/HD) $3.25 (GP/HD) $2.25
Aladdin (2019) (MA/4K) $6 (iTunes/4K) (MA/HD) $4.25 (GP/HD) $1.50
Alice in Wonderland (1951) (GP/HD) $5.50
Alien (1979) (MA/4K) $7.25 (iTunes/4K) (MA/HD) $5
Alien 3 (1992) (MA/HD) $5.50
Alien Collection 1-6 (MA/HD) $19.50 1-4 (MA/SD) $9
Alien Resurrection (1997) (MA/HD) $5.50
Aliens (1986) (MA/HD) $5.50
Alita: Battle Angel (2019) (MA/4K) $5.50 (MA/HD) $4
All The Money In The World (2017) (MA/HD) $4.25
All the Way (2016) (GP/HD) $3.50 No Port
Aloha (2015) (MA/HD) $2.50
Amazing Spider-Man (2012) (MA/4K) $6.50 (MA/HD) $4.50
Amazing Spider-Man 2 (2014) (MA/4K) $6.50 (MA/HD) $5.50
Ambulance (2022) (MA/4K) $6.75 (MA/HD) $4
American Beauty (1999) (Vudu/HD) (iTunes/HD) $6
American Gangster (Extended Edition) (2007) (MA/4K) $7.25 (iTunes/4K) (MA/HD) $6.25
American Made (2017) (MA/4K) $7.25 (MA/HD) $4.25
American Sniper (2014) (MA/4K) $6.50
American Underdog (2021) (iTunes/4K) (Vudu/HD) $4.75
Amsterdam (2022) (MA/HD) $5 (GP/HD) $3.75
Anchorman 2: The Legend Continues (2013) (iTunes/HD) $2
Angry Birds Movie (2016) (MA/HD) $3.75
Antlers (2021) (MA/HD) $5.50 (GP/HD) $4
Ant-Man (2015) (MA/4K) $6.25 (iTunes/4K) $5 (MA/HD) $4.50 (GP/HD) $2.25
Ant-Man and the Wasp (2018) (MA/4K) $8 (iTunes/4K) $6.25 (GP/HD) $3.25
Apollo 11 (2019) (MA/HD) $6.25
Apollo 13 (1995) (MA/4K) $6 (MA/HD) $4.75
Aqua Teen Forever: Plantasm (2022) (MA/HD) $4.75
Artist, The (2011) (MA/HD) $6
Atomic Blonde (2017) (MA/4K) $5 (iTunes/4K) $3.25 (MA/HD) $2.25
Avengers 1-4 (MA/4K) $25 (iTunes/4K) $20 (GP/HD) $7.75
Babylon (2022) (Vudu/HD) (iTunes/4K) $6.25
Back to the Future (1985) (iTunes/4K) (MA/HD) $4.25
Back to the Future Collection 1-3 (MA/4K) $15 (MA/HD) $7.50
Bad Boys Collection 1-3 (MA/HD) $12
Bad Boys for Life (2020) (MA/4K) $5.75 (MA/HD) $3.50
Bad Guys, The (2022) (MA/4K) $8 (MA/HD) $4.25
Bad Moms (2016) (MA/HD) $3.50 (iTunes/HD) $2.75
Bad Times at The El Royale (2018) (MA/4K) $7.75 (MA/HD) $5.75
Bambi (1942) (MA/HD) $6.25 (GP/HD) $4.50
Bambi II (2006) (MA/HD) $6.25 (GP/HD) $4.50
Band of Brothers (2001) (GP/HD) $3.75 No Port
Bank Job, The (2008) (Vudu/HD) $3.25
Banshees of Inisherin (2022) (GP/HD) $4.50
Batman and Superman: Battle of the Super Sons (2022) (MA/4K) $6 (MA/HD) $4.50
Batman Year One (2011) (MA/4K) $5
Batman, The (2022) (MA/4K) $5.25 (MA/HD) $3
Batman: Soul of the Dragon (2021) (MA/4K) $6
Batman: The Doom That Came to Gotham (2022) (MA/4K) $7.50
Batman: The Long Halloween Deluxe Edition (2022) (MA/HD) $6
Battle: Los Angeles (2011) (MA/4K) $6.50
Battleship (2012) (MA/4K) $4.50 (MA/HD) $1.75 (iTunes/4K) $3
Beast (2022) (MA/HD) $5.75
Beauty and the Beast (1991) (MA/4K) $7 (MA/HD) $4.75 (GP/HD) $2
Beauty and the Beast (2017) (MA/4K) $7 (MA/HD) $3.25 (GP/HD) $2
Bedknobs and Broomsticks (1971) (MA/HD) $5 (GP/HD) $3.50
Beguiled, The (2017) (MA/HD) (iTunes/HD) $4
Being John Malkovich (1999) (MA/HD) $3.50
Beirut (2018) (MA/HD) $4.75
Ben-Hur (2016) (Vudu/HD) $2.50
BFG, The (2016) (MA/HD) $5.25 (GP/HD) $3.50
Big (1988) (MA/HD) $5.75
Big Wedding (2013) (Vudu/HD) (iTunes/HD) $3.75
Billy Elliot (2000) (MA/HD) (iTunes/HD) $4.25
Black Adam (2022) (MA/4K) $7 (MA/HD) $4.25
Black Panther (2018) (MA/4K) $6 (iTunes/4K) $4.25 (GP/HD) $1.75
Black Panther: Wakanda Forever (2022) (MA/4K) $6.50 (MA/HD) $3.75 (GP/HD) $2.50
Black Phone, The (2021) (MA/HD) $5
Black Swan (2010) (MA/HD) $4.50
Black Widow (2021) (MA/4K) $6.50 (MA/HD) $4.75 (GP/HD) $3.25
Blacklight (2022) (MA/HD) $4.25
Bleed for This (2016) (MA/HD) (iTunes/HD) $4
Blockers (2018) (MA/HD) $3
Blood Father (2016) (Vudu/HD) $4
Bloodshot (2020) (MA/HD) $4
Blues Brothers + Unrated (1980) (MA/4K) $7
Bob's Burgers Movie (2022) (MA/HD) $3.25 (GP/HD) $2.25
Bodies Bodies Bodies (2022) (Vudu/4K) $6.75
Bodyguard, The (1992) (MA/HD) $5
Bohemian Rhapsody (2018) (MA/4K) $5 (MA/HD) $3.25
Bolt (2008) (MA/HD) $8 (GP/HD) $5.50
Bond: Goldfinger (1964) (Vudu/HD) $7
Bond: Skyfall (2012) (Vudu/HD) $1
Bond: Spectre (2015) (iTunes/4K) (Vudu/HD) $5.50
Book of Henry (2017) (iTunes/HD) Ports $5
Book of Life (2014) (MA/HD) (iTunes/HD) $2.50
Booksmart (2019) (MA/HD) $5.25
Boss Baby (2017) & Family Business (2021) (MA/HD) $5.75
Boss Baby: Family Business (2021) (MA/HD) $4.50
Bourne Collection 1-5 (MA/4K) $25 (iTunes/HD) $19 (MA/HD) $15
Boy Next Door, The (2015) (iTunes/HD) Ports to MA $5.25
Bram Stoker's Dracula (1992) (MA/4K) $7
Braven (2018) (Vudu/HD) $3.75
Breakdown (1997) (Vudu/HD) (iTunes/HD) $5.75
Breakfast Club (1985), Weird Science (2008), Sixteen Candles (1984) (MA/HD) $11.50
Breakthrough (2019) (MA/4K) $6.50
Brian Banks (2019) (MA/HD) $4.25
Bridge of Spies (2015) (MA/HD) $5.25 (GP/HD) $3.75
Bring It On: Worldwide #Cheersmack (2017) (MA/HD) $3.25 (iTunes/HD) $1.25
Broken City (2013) (MA/HD) $3.50 (iTunes/SD) $1.25
Broken Hearts Gallery (2020) (MA/HD) $3.75
Brothers (2009) (Vudu/HD) $5.50
Buffy, the Vampire Slayer (1992) (MA/HD) $5.75
Bullet Train (2022) (MA/4K) $5.75 (MA/HD) $4.25
Cake (2014) (MA/HD) $5.25
Call Me by Your Name (2017) (MA/HD) $5.75
Call of the Wild (2020) (MA/4K) $7 (MA/HD) $1.50 (GP/HD) $1.25
Call, The (2013) (MA/HD) $4.50
Captain America: Civil War (2016) (MA/4K) $6 (iTunes/4K) $5 (GP/HD) $2.25
Captain America: Winter Soldier (2014) (MA/4K) $7.50 (iTunes/4K) (MA/HD) $6 (GP/HD) $2.25
Captain Marvel (2019) (MA/4K) $5 (iTunes/4K) $4 (GP/HD) $1.75
Captain Underpants: The First Epic Movie (2017) (MA/HD) $3.25
Carrie (2013) (Vudu/HD) $5.50
Cars 1-3 (iTunes/4K) (MA/HD) $15.50 (GP/HD) $9
Casablanca (1943) (MA/4K) $6.25
Casper (1995) (iTunes/HD) Ports to MA $3.75
Catch the Bullet (2021) (Vudu/HD) $4.75
Change-Up, The (2011) (Unrated) (2011) (iTunes/HD) Ports to MA $5.50
Chappie (2015) (MA/4K) $6.50 (MA/HD) $3.75
Charlie's Angels (2000) (MA/4K) $7.75
Charlie's Angels (2019) (MA/4K) $6.50 (MA/HD) $5.50
Chronicles of Riddick (Unrated Director's Cut) (2004) (MA/HD) (iTunes/HD) $5
Cinderella (1950) (MA/HD) $5.75 (GP/HD) $3.75
Cinderella (2015) (MA/4K) $7.50 (iTunes/4K) (MA/HD) $5.50 (GP/HD) $2.50
Cinderella 'Camila Cabello' (2021) (MA/HD) $4.50
Cinderella II: Dreams Come True (2002) (MA/HD) $6.50
Citizenfour (2014) (Vudu/HD) $5.50
Clerks III (2022) (Vudu/4K) (iTunes/4K) $5.50
Clifford the Big Red Dog (2021) (iTunes/4K) (Vudu/HD) $3.50
Clueless (1995) (Vudu/HD) (iTunes/HD) $4.25
Coal Miner's Daughter (1980) (MA/HD) $6.25
Coco (2017) (MA/4K) $6.50 (iTunes/4K) $5.25 (GP/HD) $2.25
Colombiana (Unrated) (2011) (MA/HD) $4.25
Concussion (2015) (MA/HD) $3
Constantine: The House of Mystery (2022) (MA/HD) $3.50
Contraband (2012) (iTunes/HD) Ports to MA $2
Contractor (2022) (Vudu/4K) $7 (iTunes/4K) (Vudu/HD) $4.50
Counselor, The (2013) (MA/HD) $3
Creed Collection 1-3 (Vudu/HD) $12
Creed III (2023) (Vudu/4K) $9
Croods (2013) & A New Age (2020) (MA/HD) $6.75
Croods (2013) (MA/HD) $3.50
Croods: A New Age (2020) (MA/HD) $5
Crouching Tiger, Hidden Dragon (2001) (MA/4K) $7.75
Cruella (2021) (MA/4K) $5.75 (MA/HD) $3.50 (GP/HD) $2.50
Cult of Chucky (Unrated) (2017) (MA/HD) $3.75 (iTunes/HD) $2.50
Daddy's Home 1-2 (iTunes/4K) (Vudu/HD) $5.25
Daddy's Home 2 (2017) (Vudu/4K) $4.50 (iTunes/4K) $2 (Vudu/HD) $2.25
Daniel Craig Collection 5-Movie (Vudu/4K) $20
Darkest Minds, The (2018) (MA/HD) $4.75
DC League of Super-Pets (2022) (MA/4K) $8 (MA/HD) $5
Dead Man Down (2013) (MA/HD) $4.75
Deadpool (2016) (MA/4K) $6 (iTunes/4K) (MA/HD) $2
Deadpool 2 (2018) (MA/4K) $6.25 (MA/HD) $3.25
Dear Evan Hansen (2021) (MA/HD) $4.25
Death on the Nile (2022) (MA/4K) $7.50 (MA/HD) $5 (GP/HD) $3.50
Death Wish (2018) (Vudu/HD) $2.25
Dentist Collection 1-2 (1996-1998) (Vudu/HD) $5.25
Descent, The (2005) (Vudu/HD) $5.25
Detective Knight Collection 1-3 (iTunes/4K) (Vudu/HD) $15 $5.75 Each
Detroit (2017) (iTunes/4K) Ports to MA $4.75
Devil Wears Prada (2006) (MA/HD) $5.75
Devil's Due (2014) (MA/HD) $2.75
Diary of a Wimpy Kid: The Long Haul (2017) (MA/HD) $2
Die Hard (1988) (MA/4K) $7 (MA/HD) $4
Die Hard 1-5 (MA/HD) $16 $4.75 Each
Disaster Artist, The (2017) (Vudu/HD) $6.25
Disneynature Born in China (2017) (MA/HD) $5.25
DisneyNature: Bears (2014) (MA/HD) $5.50 (GP/HD) $3.50
District 9 (2009) (MA/4K) $6.50
Do the Right Thing (1989) (MA/4K) $6
Doctor Strange (2016) (MA/4K) $6.50 (iTunes/4K) $4 (MA/HD) $3.50 (GP/HD) $1.75
Doctor Strange in the Multiverse of Madness (2022) (MA/4K) $6 (MA/HD) $2.75 (GP/HD) $2
Dog (2022) (Vudu/HD) $3
Dolittle (2020) (MA/4K) $6 (MA/HD) $3.50
Don't Breathe 2 (2021) (MA/HD) $7.50
Don't Let Go (2019) (MA/HD) $4
Don't Worry Darling (2022) (MA/HD) $5.50
Downton Abbey: A New Era (2022) (MA/HD) $3.75
Dr. Seuss' Horton Hears a Who (2008) (MA/HD) $6.50
Dr. Seuss' The Lorax (2012) (MA/HD) $3.25 (iTunes/HD) $2.25
Dracula Untold (2014) (MA/4K) $7 (MA/HD) $2.75 (iTunes/4K) $4
Dragonheart 5-Movie (MA/HD) $15
Dredd (2012) (iTunes/4K) (Vudu/HD) $3.50
Drive (2011) (MA/HD) $4.50
Duff, The (2015) (Vudu/HD) (iTunes/HD) $2.75
Dumbo (2019) (MA/4K) $6 (iTunes/4K) $5.25 (GP/HD) $2.50
Dune (2021) (MA/4K) $5.50
Dunkirk (2017) (MA/4K) $6.50
E.T. the Extra-Terrestrial (1982) (MA/4K) $6 (iTunes/4K) $5 (MA/HD) $3
Early Man (2018) (Vudu/HD) (iTunes/HD) $5.25
Echo Boomers (2020) (Vudu/HD) (iTunes/HD) $4
Eddie the Eagle (2016) (MA/HD) (iTunes/4K) $6
Elvis (2022) (MA/4K) $6.75 (MA/HD) $4
Elysium (2013) (MA/4K) $6.50 (MA/HD) $3.25
Empire State (2013) (Vudu/HD) $4.75
Encanto (2021) (MA/4K) $6.50 (MA/HD) (GP/4K) $3.50
Equalizer (2014) (MA/HD) $3.75
Equalizer 2 (2018) (MA/4K) $7.25 (MA/HD) $2.75
Escape Plan: The Extractors (2019) (Vudu/HD) $3.75
Eternals (2021) (MA/4K) $6.75 (MA/HD) $4.50 (GP/HD) $3
Everest (2015) (MA/4K) $6.50 (MA/HD) $3 (iTunes/4K) $4
Everything Everywhere All at Once (2022) (Vudu/4K) $7.50
Expendables 1-3 (Vudu/HD) $4.50
Extreme Prejudice (1987) (Vudu/HD) $5.25
F9: The Fast Saga + Director's Cut (2021) (MA/4K) $5.25 (MA/HD) $3.25
Fabelmans (2022) (MA/HD) $6.50
Faculty, The (1998) (Vudu/HD) (iTunes/HD) $6.50
Fantastic Beasts Collection 1-3 (MA/HD) $7.75
Fantastic Beasts: The Secrets of Dumbledore (2022) (MA/4K) $5.25 (MA/HD) $3
Fantasy Island (2020) (MA/HD) $7
Fast & Furious Collection 1-9 (MA/HD) $10
Father Stu (2022) (MA/HD) $5.50
Ferdinand (2017) (MA/HD) $3.50
Field of Dreams (1989) (MA/4K) $7.50 (iTunes/4K) (MA/HD) $6
Fifth Element (1997) (MA/4K) $6.50 (MA/HD) $6
Fifty Shades of Black (2016) (iTunes/HD) Ports to MA $3.50
Fifty Shades of Grey 3-Movie + Unrated (MA/HD) $9.75
Finding Dory (2016) (MA/4K) $5.75 (iTunes/4K) $3.50 (GP/HD) $1.25
Finding Nemo (2003) (MA/4K) $6.75 (iTunes/4K) $5.25 (GP/HD) $3
First Man (2018) (MA/4K) $6.75 (MA/HD) $4.25
First Purge (2018) (MA/HD) $4.50
Five Feet Apart (2019) (iTunes/4K) (Vudu/HD) $3
Forbidden Kingdom (2008) (Vudu/HD) $5
Ford v Ferrari (2019) (MA/4K) $7.75 (MA/HD) $4.75
Founder, The (2017) (Vudu/HD) $5 (iTunes/HD) $5.50
Fox and the Hound 2, The (2006) (MA/HD) $4 (GP/HD) $3
Foxcatcher (2014) (MA/HD) $4.50
Frank & Lola (2016) (MA/HD) $4.75
Frankenstein (1931) (MA/4K) $6.50 (MA/HD) (iTunes/HD) $4.50
Free Guy (2021) (MA/4K) $7.50 (MA/HD) $4.75 (GP/HD) $3.25
Frozen (2013) (MA/4K) $5.50 (MA/HD) $3.50 (GP/HD) $1.50
Frozen 2 (2019) (MA/4K) $4.50 (MA/HD) $4 (GP/HD) $1.75
Frozen Sing-Along Edition (2014) (MA/HD) $3.75 (GP/HD) $1.75
Full Metal Jacket (1987) (MA/4K) $6.50
Fury (2014) (MA/4K) $6.50 (MA/HD) $4.50
Future World (2018) (Vudu/HD) $4
Galaxy Quest (1999) (Vudu/HD) (iTunes/HD) $6
Gambler (2014) (Vudu/HD) $3.50 (iTunes/HD) $2.75
Gangs of New York (2002) (Vudu/HD) (iTunes/HD) $5.50
Gate, The (1987) (Vudu/SD) $4.25
Get on Up (2014) (iTunes/HD) Ports to MA $4.50
Get Out (2017) (iTunes/4K) (MA/HD) $3.75
Ghost In The Shell (2017) (Vudu/HD) $2.25 (iTunes/4K) $2.75
Ghostbusters (1984) (MA/HD) $3.50
Ghostbusters + Extended (2016) (MA/HD) $3
Ghostbusters II (1989) (MA/HD) $3.50
Ghostbusters: Afterlife (2021) (MA/4K) $7.50 (MA/HD) $3.50
Gifted (2017) (MA/HD) $5
Girl with the Dragon Tattoo (2011) (MA/HD) $6
Girls Trip (2017) (MA/HD) $1.50 (iTunes/HD) $1
Glass (2019) (MA/4K) $6.50 (MA/HD) $4.50
Glory (1989) (MA/4K) $7.75
Godfather (1972) (Vudu/HD) (iTunes/4K) $4.75
Godfather Trilogy (iTunes/4K) (Vudu/HD) $14
Godzilla (1998) (MA/4K) $6.50
Gone Baby Gone (2007) (Vudu/HD) $5.25
Good Boys (2019) (MA/HD) $3.75
Good Dinosaur (2015) (iTunes/4K) (MA/HD) $5.50 (GP/HD) $3
Goodbye Christopher Robin (2017) (MA/HD) $6.50
Goosebumps (2015) (MA/HD) $5
Goosebumps 2 (2018) (MA/4K) $7.50 (MA/HD) $6.50
Grease (1978), 2 (1982), Live! (2016) (Vudu/HD) (iTunes/HD) $14
Great Wall (2016) (MA/HD) $2.50
Green Book (2018) (MA/4K) $7.25 (MA/HD) $5
Green Hornet (2011) (MA/HD) $6.50
Green Lantern: Beware My Power (2022) (MA/HD) $3
Green Mile, The (1999) (MA/4K) $6
Groundhog Day (1993) (MA/4K) $8
Guardians of the Galaxy (2014) (MA/4K) $7.25 (iTunes/4K) $4.75 (MA/HD) $4 (GP/HD) $1.75
Guardians of the Galaxy Vol. 2 (2017) (MA/4K) $6.75 (iTunes/4K) $4.25 (GP/HD) $1.25
Half Brothers (2020) (MA/HD) $5.75
Halloween (2018) (MA/4K) $6.25 (MA/HD) $4.25
Halloween Ends (2022) (MA/4K) $6.50 (MA/HD) $4.50
Halloween Kills (2021) (MA/4K) $6 (MA/HD) $4.25
Hancock (2008) (MA/4K) $6.50
Happy Death Day (2017) (MA/HD) $6
Happy Death Day 2U (2019) (MA/HD) $6
Hate U Give (2018) (MA/4K) $7.75 (MA/HD) $4.50
Hateful Eight (2015) (Vudu/HD) $2
Heat: Director's Definitive Edition (1995) (MA/4K) $6 (MA/HD) $5.25
Heavy Metal (1981) (MA/4K) $6.50
Hellboy (Director's Cut) (2004) (MA/4K) $6.50
Hercules (1997) (MA/HD) $6.50 (GP/HD) $5.50
Hidden Figures (2016) (iTunes/4K) (MA/HD) $2
Hitman: Agent 47 (2015) (MA/HD) $4.50
Hitman's Wife's Bodyguard (2021) (Vudu/4K) (iTunes/4K) $5
Hobbs & Shaw (2019) (MA/4K) $5.75 (MA/HD) $3.75
Hocus Pocus (1993) (MA/4K) $6.75 (iTunes/4K) (MA/HD) $4.75 (GP/HD) $2.25
Holiday Inn (1942) (iTunes/HD) Ports to MA $3
Home Alone (1990) (MA/HD) $4
Home Alone 2: Lost in New York (1992) (MA/HD) $3.50
Hotel Transylvania (2012) (MA/HD) $6
Hotel Transylvania 2 (2015) (MA/HD) $6.75
Hotel Transylvania 3: Summer Vacation (2018) (MA/4K) $6.75 (MA/HD) $4.50
House of 1,000 Corpses (2003), Devil's Rejects (2005), 3 From Hell (2019) (Vudu/HD) $6
House of Gucci (2021) (iTunes/4K) $5
House of the Dragon: Season 1 (2022) (Vudu/4K) $9 (Vudu/HD) $5.50
House with a Clock in Its Walls (2018) (MA/4K) $6 (MA/HD) $3.75
How to Train Your Dragon (2010) (MA/4K) $6.50
How to Train Your Dragon Collection 1-3 (MA/HD) $7.50 $4.75 Each
How to Train Your Dragon: The Hidden World (2019) (MA/4K) $6 (MA/HD) $4.25
Howard the Duck (1986) (MA/4K) $7
Hugo (2011) (Vudu/HD) (iTunes/HD) $3.75
Hulk, The (2003) (iTunes/HD) Ports to MA $6.25
Hunger Games Collection 1-4 (Vudu/HD) $6 (iTunes/4K) $12
Hunt, The (2019) (MA/HD) $5.75
Huntsman: Winter's War - Extended Edition (2016) (iTunes/4K) (MA/HD) $3
I Can Only Imagine (2018) (Vudu/HD) (iTunes/HD) $2.75
I Now Pronounce You Chuck & Larry (2007) (MA/HD) $3.50
Ice Age (2002) (MA/HD) $5
Ice Age: Collision Course (2016) (iTunes/4K) (MA/HD) $4.25
Ice Age: Continental Drift (2012) (MA/HD) $4.50
Ice Age: The Meltdown (2006) (MA/HD) $6
Identity Thief (2013) (iTunes/HD) Ports to MA $3.75
Ides of March (2011) (MA/HD) $5.25
If Beale Street Could Talk (2018) (MA/HD) $5.75
Impossible, The (2013) (Vudu/HD) (iTunes/HD) $4.75
In the Heights (2021) (MA/4K) $5
Incredible Hulk (2008) (MA/4K) $7 (MA/HD) $5.25
Incredibles (2004) (MA/4K) $7.75 (iTunes/4K) $6.25 (GP/HD) $4.75
Incredibles 2 (2018) (MA/4K) $6.25 (iTunes/4K) $4.50 (GP/HD) $2
Independence Day (1996) (MA/4K) $7.75 (iTunes/4K) (MA/HD) $5.50
Independence Day: Resurgence (2014) (iTunes/4K) $2 (MA/HD) $1.50
Indiana Jones 1-4 (iTunes/4K) (Vudu/HD) $20
Inferno (2016) (MA/HD) $3.25
Inglorious Bastards (2009) (MA/4K) $7
Inside Llewyn Davis (2013) (MA/HD) $6
Inside Out (2015) (MA/4K) $5.75 (iTunes/4K) $4.25 (GP/HD) $1.50
Insidious: Chapter 2 (2013) (MA/HD) $5.25
Insidious: Chapter 3 (2015) (MA/HD) $6.50
Instructions Not Included (2013) (Vudu/HD) $3.75
Internship (2013) (MA/HD) $3.25
Interview, The (2014) (MA/HD) $3.50
Into the Woods (2014) (MA/HD) $4 (GP/HD) $2.25
Invisible Man (2020) (MA/4K) $6.50 (MA/HD) $3.75
Iron Man (2008) (MA/4K) $7.25 (iTunes/4K) $7 (GP/HD) $3
Iron Man 1-3 (MA/4K) $21 (iTunes/4K) $16 (GP/HD) $7.50
Iron Man 2 (2010) (MA/4K) $7.25 (iTunes/4K) $6.50 (GP/HD) $3
Iron Man 3 (2013) (MA/4K) $7.25 (iTunes/4K) $3 (MA/HD) $2.25 (GP/HD) $1.50
Isle of Dogs (2018) (MA/HD) $4.75
It Comes at Night (2017) (Vudu/HD) $6.25
It Follows (2015) (Vudu/HD) $4.25
Jack Reacher Collection 1-2 (iTunes/4K) $7
Jackass Forever (2022) (iTunes/4K) (Vudu/HD) $4
Jackass Presents: Bad Grandpa (2013) (Vudu/HD) (iTunes/HD) $3
Jackie (2016) (MA/HD) $4.25
Jacob's Ladder (1990) (Vudu/HD) $3.50
Jane Got a Gun (2016) (Vudu/HD) $5.75
Jason Statham 6-Movie (Wild Card, War, Bank Job, Transporter 3, Crank, Crank 2) (Vudu/HD) $11.50
Jaws (1975) (MA/4K) $6.25 (iTunes/4K) (MA/HD) $4.75
Jaws (1975) Jaws 2 (1978) Jaws 3 (1983) Jaws: The Revenge (1987) (MA/HD) $15.50
Jay and Silent Bob Strike Back (2001) (Vudu/HD) (iTunes/HD) $5.50
Jingle All the Way (1996) (MA/HD) $5.25
John Wick Collection 1-3 (Vudu/4K) $16 (iTunes/4K) $14 (Vudu/HD) $8
John Wick: Chapter 4 (2023) (Vudu/4K) (iTunes/4K) $15
Jojo Rabbit (2019) (MA/HD) $6.75
Joy (2015) (MA/HD) (iTunes/4K) $4
Jumanji (1995) (MA/4K) $6.50 (MA/HD) $6
Jumanji: Next Level (2019) & Welcome to the Jungle (2017) (MA/HD) $7.50
Jumanji: The Next Level (2019) (MA/4K) $7.75 (MA/HD) $5.50
Jumanji: Welcome To The Jungle (2017) (MA/4K) $5.50 (MA/HD) $2 (MA/SD) $1
Jungle Cruise (2021) (MA/4K) $6 (MA/HD) $3.75 (GP/HD) $3
Jurassic Park (1993) (MA/4K) $5.25 (iTunes/4K) $3.75 (MA/HD) $3
Jurassic Park III (2001) (MA/4K) $6.50 (iTunes/4K) $3.75 (MA/HD) $3.50
Jurassic Park: The Lost World (1997) (MA/4K) $6.50 (iTunes/4K) $3.75 (MA/HD) $3
Jurassic World (2015) (MA/4K) $5.25 (iTunes/4K) $3.75 (MA/HD) $2.75
Jurassic World Collection 1-5 (MA/4K) $20 (iTunes/4K) $17.50 (MA/HD) $10
Jurassic World Collection 1-6 (MA/4K) $23.50 (MA/HD) $11.50
Jurassic World: Dominion + Extended Cut (2022) (MA/4K) $6.50 (MA/HD) $4.25
Jurassic World: Fallen Kingdom (2018) (MA/4K) $5.75 (MA/HD) $1.75
Justice League x RWBY Super Heroes and Huntsmen Part One (2023) (MA/HD) $4
Justice Society: World War II (2021) (MA/4K) $5.50
Katy Perry: Part of Me (2012) (Vudu/HD) (iTunes/HD) $3.75
Kick-Ass 2 (2013) (MA/HD) $5.25 (iTunes/HD) $5
Kicks (2016) (iTunes/HD) Ports to MA $5
Kid Who Would Be King (2019) (MA/4K) $5.75 (MA/HD) $4.75
Kidnap (2017) (MA/HD) (iTunes/HD) $3.25
Kill the Messenger (2014) (iTunes/HD) Ports to MA $5
Killer Elite (2011) (iTunes/HD) Ports to MA $3.25
Killerman (2019) (Vudu/HD) $2.25 (iTunes/HD) $1.75
Killing Lincoln (2013) (MA/HD) $5.25
King Kong (2005) (MA/4K) $5.75 (iTunes/4K) $3.75 (MA/HD) $3.50
King of Staten Island (2020) (MA/HD) $4.75
King's Man (2021) (MA/HD) $4.50 (GP/HD) $3.50
Kung Fu Panda 3 (2016) (MA/HD) $2.50
Kung Fu Panda Collection 1-3 (MA/HD) $12.50
L.A. Confidential (1997) (MA/HD) $5.75
Last Christmas (2019) (MA/HD) $6.50
Last Full Measure (2020) (Vudu/HD) (iTunes/HD) $5.50
Last Night in Soho (2021) (MA/4K) $7 (MA/HD) $5.75
Last Vegas (2013) (MA/HD) $3
Lawless (2012) (Vudu/HD) $3.75
Lee Daniels' The Butler (2013) (Vudu/HD) $2.25
Legion of Super Heroes (2023) (MA/HD) $5.50
Les Miserables (2012) (MA/HD) (iTunes/HD) $2.75
Let Him Go (2020) (MA/HD) $3.75
Let's Be Cops (2014) (iTunes/4K) (MA/HD) $2.50
Life (2017) (MA/HD) $2.50
Light of My Life (2019) (Vudu/HD) $2.50 (iTunes/HD) $2
Lightyear (2022) (MA/4K) $5 (MA/HD) $2.75 (GP/HD) $2
Like a Boss (2020) (iTunes/4K) (Vudu/HD) $2.25
Lion (2016) (Vudu/HD) $4.50
Lion King (1994) (MA/4K) $7 (iTunes/4K) $4.50 (GP/HD) $2.75
Lion King (2019) (MA/4K) $6 (iTunes/4K) $4 (GP/HD) $1.25
Lion King 1 1/2 (2004) (MA/HD) $6.50
Lion King 2: Simba's Pride (1998) (MA/HD) $6.75 (GP/HD) $5.25
Little Mermaid (1989) (MA/4K) $7.25 (iTunes/4K) (MA/HD) $5.75 (GP/HD) $3.75
Little Monsters (1989) (Vudu/HD) $5.50
Live Die Repeat: Edge Of Tomorrow (2014) (MA/4K) $6.50
Lodge, The (2019) (MA/HD) $5.75
Logan Lucky (2017) (MA/HD) $1.50 (iTunes/4K) $2.25
London Has Fallen (2016) (iTunes/HD) Ports to MA $3.75
Lone Survivor (2013) (MA/4K) $6.75 (iTunes/4K) $2 (MA/HD) $1.50
Long Shot (2019) (iTunes/4K) (Vudu/HD) $4
Longest Ride (2015) (iTunes/4K) (MA/HD) $1.50
Looper (2012) (MA/4K) $5.75 (MA/HD) $3
Lords of Salem, The (2012) (Vudu/HD) $4.75
Lost City, The (2022) (Vudu/4K) (iTunes/4K) $6
Love Actually (2003) (MA/HD) $5.50
Love, Simon (2018) (MA/HD) $3
Luca (2021) (MA/4K) $6.50 (MA/HD) $3.75 (GP/HD) $3.25
Lucy (2014) (MA/HD) $2
Lyle, Lyle, Crocodile (2022) (MA/HD) $5.50
Ma (2019) (MA/HD) $5.25
Mad Max Collection 1-4 (Vudu/4K) $20
Madagascar 3: Europe's Most Wanted (2012) (MA/HD) (iTunes/HD) $2
Madagascar Collection 1-4 (MA/HD) $14
Maleficent (2014) (MA/4K) $5.75 (iTunes/4K) (MA/HD) $3 (GP/HD) $1.25
Maleficent: Mistress of Evil (2019) (MA/4K) $6 (iTunes/4K) $3.75 (GP/HD) $1.75
Mama (2013) (iTunes/HD) Ports to MA $3.50
Mamma Mia! Here We Go Again (2018) (MA/4K) $6.50 (MA/HD) $1.75
Mamma Mia! The Movie (2008) & Here We Go Again (2018) (MA/HD) $6.50 $4.50 Each
Martian - Extended Cut (2015) (MA/4K) $7.75 (MA/HD) $5.25
Martian (Theatrical) (2015) (MA/4K) $7.25 (MA/HD) $3.25
Mary Poppins (1964) (MA/HD) $4.50 (GP/HD) $3
Mary Poppins Returns (2018) (MA/4K) $6.50 (iTunes/4K) (MA/HD) $4.50 (GP/HD) $2
Matrix Collection 1-4 (MA/4K) $18.50
Matrix: Resurrections (2021) (MA/4K) $5
Maze Runner: The Death Cure (2018) (MA/HD) $5.75
McFarland, USA (2015) (MA/HD) $6.25 (GP/HD) $4.50
Memory (2022) (MA/HD) $3.50
Men (2022) (Vudu/HD) $3.75
Men in Black Collection 1-3 (MA/HD) $15.50
Menu (2022) (MA/HD) $5.50 (GP/HD) $4
MIB: International (2019) (MA/4K) $5.75 (MA/HD) $4.75
Mickey & Minnie 10 Classic Shorts - Volume 1 (2023) (MA/HD) $5.75 (GP/HD) $4
Midsommar (2019) (Vudu/HD) $5.75
Mike and Dave Need Wedding Dates (2016) (MA/HD) (iTunes/4K) $4.50
Million Dollar Arm (2014) (MA/HD) $4
Minions (2015) (iTunes/4K) (MA/HD) $2.75
Minions: The Rise of Gru (2022) & Minions (2015) (MA/HD) $8
Minions: The Rise of Gru (2022) (MA/4K) $6.75 (MA/HD) $5.25
Miracles From Heaven (2016) (MA/HD) $4.50
Mission: Impossible Collection 1-6 (Vudu/4K) $25 (iTunes/4K) (Vudu/HD) $20
Mitchells Vs. The Machines (2021) (MA/HD) $4.50
Moana (2016) (MA/4K) $7 (iTunes/4K) (MA/HD) $4.75 (GP/HD) $2
Money Monster (2016) (MA/HD) $3.25
Monster Hunter (2020) (MA/4K) $6.75 (MA/HD) $4.25
Monster Trucks (2016) (Vudu/HD) $2.25
Monster's Ball (2001) (Vudu/HD) $6.25
Monsters University (2013) (MA/4K) $6.75 (iTunes/4K) $5.75 (GP/HD) $3.50
Monty Python's The Meaning of Life (1983) (MA/4K) $7.25
Monuments Men (2014) (MA/HD) $2
Moonfall (2022) (Vudu/4K) (iTunes/4K) $5
Morbius (2022) (MA/4K) $5.25 (MA/HD) $3.25 (MA/SD) $2.25
Mortal Engines (2018) (MA/4K) $7 (MA/HD) $3.25
Mortal Kombat Legends: Snow Blind (2022) (MA/HD) $5.50
Mother! (2017) (Vudu/HD) $3.50
Mountain Between Us (2017) (iTunes/4K) (MA/HD) $1.50
Mr Popper's Penguins (2011) (MA/HD) $6
Much Ado About Nothing (2013) (Vudu/HD) $4.25
Mulan (1998) (MA/4K) $6.75 (iTunes/4K) $5.75 (GP/HD) $3
Mulan (2020) (MA/4K) $6.25 (MA/HD) $4 (GP/HD) $2.25
Muppet Movie (1979) (MA/HD) $7.50 (GP/HD) $6
My Big Fat Greek Wedding 2 (2016) (iTunes/HD) Ports to MA $4
My Dinner with Herve (2018) (Vudu/HD) (iTunes/HD) $4.50 (GP/HD) $3
National Lampoon's Animal House (1978) (MA/4K) $6.25 (MA/HD) $5.25
Natural, The (1984) (MA/4K) $5
Nebraska (2013) (Vudu/HD) (iTunes/HD) $2.25
Neighbors (2014) (iTunes/HD) Ports to MA $2
New Mutants (2020) (MA/4K) $6.75 (MA/HD) $5.25 (GP/HD) $2.75
News of the World (2020) (MA/4K) $6.25 (MA/HD) $3.75
Night at the Museum 3-Movie (MA/HD) $13.50 $6 Each (MA/SD) $9
Night Before (2015) (MA/HD) $4.75
Night House, The (2021) (MA/HD) $5 (GP/HD) $3
Night School (Extended) (2018) (MA/4K) $7 (MA/HD) $3.75
Ninth Gate, The (1999) (Vudu/HD) $4.50
No Country For Old Men (2007) (Vudu/HD) $5.50
No Time to Die (2021) (iTunes/4K) $3.50
Nobody (2021) (MA/HD) $5.25
Non-Stop (2014) (MA/HD) (iTunes/HD) $2.75
Nope (2022) (MA/HD) $5.75
Nope (2022), Get Out (2017) & Us (2019) (MA/HD) $10
Norm of the North (2016) (Vudu/HD) (iTunes/HD) $2.75
Northman (2022) (MA/4K) $7.25 (MA/HD) $4.50
Nutcracker and the Four Realms (2018) (MA/4K) $6 (MA/HD) $3 (GP/HD) $2.50
Oblivion (2013) (MA/4K) $7 (iTunes/4K) $3.50 (MA/HD) $2.25
Olaf's Frozen Adventure Plus 6 Disney Tales (2017) (MA/HD) $4.50 (GP/HD) $3
Olympus Has Fallen (2013) (MA/HD) $5
On the Basis of Sex (2019) (MA/HD) $4.50
Once Upon A Time... In Hollywood (2019) (MA/4K) $6.75 (MA/HD) $5
Onward (2020) (MA/4K) $5.50 (MA/HD) $4 (GP/HD) $2.25
Oranges, The (2011) (MA/HD) $4.50
Other Woman (2014) (MA/HD) $2.25
Ouija (2014) & Origin of Evil (2016) (MA/HD) (iTunes/HD) $9
Overboard (2016) (Vudu/HD) (iTunes/HD) $5.75
Pacific Rim Uprising (2018) (MA/4K) $7.75 (MA/HD) $4.50
Pain & Gain (2013) (Vudu/HD) (iTunes/HD) $3.25
Paper Towns (2011) (iTunes/4K) (MA/HD) $4.50
ParaNorman (2012) (iTunes/HD) $5
Passengers (2016) (MA/4K) $6.50 (MA/HD) $2.75
Passion of the Christ (2004) (MA/HD) $10
Paul (2011) (iTunes/HD) Ports to MA $4.50
Paul Blart: Mall Cop 2 (2015) (MA/HD) $4.25
Paw Patrol: The Movie (2021) (iTunes/4K) (Vudu/HD) $5
Paws of Fury: The Legend of Hank (2022) (iTunes/4K) (Vudu/HD) $6
Pearl (2022) (Vudu/HD) $5.50
Peppermint (2018) (iTunes/HD) $1.75
Percy Jackson: Sea of Monsters (2013) (MA/HD) $2.25
Perks of Being a Wallflower (2012) (Vudu/HD) (iTunes/HD) $1.75
Peter Rabbit (2018) & 2 (2021) (MA/HD) $8.50 $4.75 Each
Peter Rabbit (2018) (MA/4K) $5.75 (MA/HD) $4.75
Phantom Thread (2017) (MA/HD) $3.75
Philadelphia (1993) (MA/4K) $7.75
Philomena (2013) (Vudu/HD) $2
Pinocchio (1940) (MA/HD) $5.50 (GP/HD) $3.75
Pirate Fairy (2014) (MA/HD) $3.25
Pitch Perfect (2012) (MA/4K) $5.75 (MA/HD) $2.75 (iTunes/4K) $3.75
Pitch Perfect Collection 1-3 (MA/HD) $11.50
Pixels (2015) (MA/4K) $6.50 (MA/HD) $5.50
Planes, Trains and Automobiles (1987) (iTunes/HD) $3.75
Planes: Fire & Rescue (2014) (MA/HD) $4 (GP/HD) $2
Planet of the Apes 1-3 (Newer) (iTunes/4K) (MA/HD) $12
Playing with Fire (2019) (iTunes/4K) $1.50 (Vudu/HD) $2
Pocahontas (1995) (MA/HD) $6.50 (GP/HD) $5
Pompeii (2014) (MA/HD) $3.50
Poms (2019) (iTunes/HD) $2.50
Post, The (2017) (MA/HD) $2.75
Predator (1987), 2 (1990), Predators (2009), Predator (2018) (MA/HD) $11
Predator (2018) (MA/4K) $6.25 (MA/HD) $3.50
Premium Rush (2012) (MA/HD) $3.25
Prey for the Devil (2022) (Vudu/4K) (iTunes/4K) $6
Princess and the Frog (2009) (iTunes/4K) $5.50 (GP/HD) $3.25
Prometheus (2012) (MA/HD) $1.75
Prophecy Collection 1-5 (Vudu/HD) (iTunes/HD) $14.50
Psycho (1960) (MA/HD) (iTunes/4K) $5
Psycho (1960), Rear Window (1954), The Birds (1963), Vertigo (1958) (MA/4K) $17
Public Enemies (2009) (MA/HD) (iTunes/HD) $6.25
Purge, The (2013) (MA/4K) $6.50 (iTunes/4K) (MA/HD) $3
Purge: Anarchy (2014) (MA/4K) $5.75 (iTunes/4K) (MA/HD) $4.75
Purge: Election Year (2016) (MA/4K $5.50 (iTunes/4K) (MA/HD) $3
Puss in Boots (2011) (MA/4K) $6.75
Puss in Boots: The Last Wish (2022) (MA/HD) $7.50
Queen & Slim (2019) (MA/HD) $4.50
R.I.P.D. (2013) (MA/HD) $3.25 (iTunes/HD) $3
Race (2016) (iTunes/HD) Ports to MA $2.75
Ralph Breaks the Internet (2018) (MA/4K) $5.25 (iTunes/4K) $4.50 (GP/HD) $1.50
Rambo Collection 1-5 (Vudu/HD) $14
Rambo: First Blood (1982) (Vudu/4K) (iTunes/4K) $6
Rambo: First Blood Part II (1985) (Vudu/4K) $6.75 (iTunes/4K) (Vudu/HD) $5.50
Raya and the Last Dragon (2021) (MA/4K) $6.25 (MA/HD) $4.50 (GP/HD) $2.50
Rescuers Down Under (1990) (MA/HD) $6.50 (GP/HD) $4
Resident Evil: Retribution (2012) (MA/HD) $2.25
Resident Evil: The Final Chapter (2017) (MA/4K) $7.25 (MA/HD) $3.25
Resident Evil: Welcome to Raccoon City (2021) (MA/4K) $6.50 (MA/HD) $4.50
Respect (2021) (iTunes/4K) $4.75
Revenant, The (2015) (MA/4K) $5.25 (iTunes/4K) (MA/HD) $3.25
Ricki And The Flash (2015) (MA/HD) $4.50
Riddick - Unrated Director's Cut (2013) (MA/HD) (iTunes/HD) $4
Riddick Collection 1-3 (Unrated) (MA/HD) $14
Ride Along 1-2 (MA/HD) (iTunes/HD) $5 $2.75 Each
Rio 2 (2014) (MA/HD) $2.25
Risen (2016) (MA/HD) $4.50
Road to El Dorado (2000) (MA/HD) $5.50
Robin Hood (2010) (MA/4K) $6.25
Robin Hood (Animated) (1973) (MA/HD) $3.75 (GP/HD) $2.75
RoboCop (1987) (Vudu/HD) $7.25
Robots (2005) (MA/HD) $6.75
Rock Dog (2016) (Vudu/HD) $4.25
Ron's Gone Wrong (2021) (MA/4K) $7 (MA/HD) $5.25 (GP/HD) $3.50
Rumble (2022) (iTunes/4K) (Vudu/HD) $5.75
Run Lola Run (1998) (MA/HD) $6.50
Rush (2013) (MA/HD) $2.75 (iTunes/HD) $3.25
Russell Madness (2015) (MA/HD) $4
Safe (2012) (Vudu/HD) (iTunes/HD) $1.75
Saint Maud (2020) (Vudu/HD) $6
Santa Clause (1994), 2 (2002), 3 (2006) (iTunes/4K) (MA/HD) $10.50 (GP/HD) $6.50
Saving Mr. Banks (2013) (MA/HD) $4.75 (GP/HD) $2.75
Saw Collection 1-7 (Vudu/HD) $10
Scott Pilgrim vs. The World (2010) (MA/4K) (iTunes/4K) $5.75 (MA/HD) $5.25
Scream 5 (2022) (iTunes/4K) (Vudu/HD) $5.50
Scream Collection 1-3 (Vudu/HD) (iTunes/HD) $13.50
Second Act (2018) (iTunes/HD) $1.50
Secret Garden, The (2020) (iTunes/4K) $4.25
Secret Headquarters (2022) (Vudu/HD) (iTunes/4K) $6
Secret Life of Pets 1-2 (MA/HD) $7.50
Secret Life of Pets 2 (2019) (MA/4K) $6.25 (MA/HD) $5
Seeking a Friend for the End of the World (2012) (iTunes/HD) Ports to MA $4.50
Seriously Red (2022) (Vudu/HD) $6.75
Sessions, The (2012) (MA/HD) $4.50
Sex Tape (2014) (MA/HD) $3
Shallows, The (2016) (MA/4K) $6.75 (MA/HD $4
Shang-Chi (2021) (MA/4K) $6.25 (MA/HD) $4.75 (GP/HD) $3
Shaun of the Dead (2004) (MA/4K) $4
Shaun of the Dead (2004), Hot Fuzz (2007), World's End (2013) (MA/HD) $10
Shaun the Sheep Movie (2015) (Vudu/HD) $4
Shawshank Redemption (1994) (MA/4K) $6
Shazam! Fury of the Gods (2023) & Shazam! (2019) (MA/HD) $10.50
Shazam! Fury of the Gods (2023) (MA/4K) $9 (MA/HD) $8
She's Having a Baby (1988) (Vudu/HD) (iTunes/HD) $4.50
Shooter (2007) (Vudu/HD) $5.75
Sicario: Day of the Soldado (2018) (MA/4K) $7.75 (MA/HD) $3.75
Sideways (2004) (MA/HD) $5.25
Silent Night, Deadly Night: 3-Film Collection (1989-1991) (Vudu/HD) $6
Silver Linings Playbook (2012) (Vudu/HD) $2
Sin City: A Dame to Kill For (2014) (Vudu/HD) $6.25
Sing 2 (2021) (MA/4K) $6.50 (MA/HD) $3.50
Sing Collection 1-2 (MA/HD) $6
Singin' in the Rain (1952) (MA/4K) $6.50
Sinister (2012) (Vudu/HD) $3 (iTunes/HD) $2.75
Sisters (Unrated) (2015) (MA/HD) $4 (iTunes/HD) $3.25
Sixteen Candles (1984) (MA/HD) $5.25 (iTunes/HD) $4.25
Skeleton Twins (2014) (Vudu/HD) $4.50
Skyscraper (2018) (MA/4K) $5.25 (MA/HD) $1.75
Sleepless (2017) (iTunes/HD) Ports to MA $1
Smile (2022) (Vudu/HD) (iTunes/4K) $6.75
Smokey and the Bandit (1977) (MA/4K) $6.50 (MA/HD) $3.75
Smurfs 2 (2013) (MA/HD) $3.25
Smurfs: The Lost Village (2017) (MA/HD) $3.25
Snake Eyes (2021) (iTunes/4K) (Vudu/HD) $4.25
Snatched (2017) (iTunes/4K) (MA/HD) $1
Snitch (2013) (iTunes/4K) (Vudu/HD) $1.75
Snow White and the Huntsman (Extended) (2012) (iTunes/4K) $3.50 (MA/HD) $2.50
Son of God (2014) (MA/HD) $1.25
Sonic the Hedgehog 2 (2022) (Vudu/4K) $6.50 (iTunes/4K) (Vudu/HD) $4.50
Soul (2020) (MA/4K) $6.25 (MA/HD) $3.75 (GP/HD) $2.25
Southpaw (2015) (Vudu/HD) $4.50
Southside With You (2016) (Vudu/HD) $3.50
Space Between Us, The (2017) (iTunes/HD) Ports to MA $5
Space Jam (1996) (MA/4K) $5
Space Jam: A New Legacy (2021) (MA/4K) $5
Speed (1994) (MA/4K) $5.25
Spider-Man (2002) (MA/4K) $6.50 (MA/HD) $5.50
Spider-Man 2 (2004) (MA/4K) $6.50 (MA/HD) $5.50
Spider-Man 3 (2007) (MA/4K) $6.50 (MA/HD) $5.50
Spider-Man Collection 1-8 (MA/HD) $26
Spider-Man: Far From Home (2019) (MA/4K) $8 (MA/HD) $4
Spider-Man: Homecoming (2017) (MA/4K) $8 (MA/HD) $1.75
Spider-Man: No Way Home (2021) (MA/4K) $6.75 (MA/HD) $3.75
Spirit: Stallion of the Cimarron (2003) (MA/HD) $5
Split (2017) (MA/4K) $6.75 (iTunes/4K) (MA/HD) $2.75
Spy (Unrated) (2015) (MA/HD) $2
Spy Game (2001) (iTunes/HD) Ports to MA $5.75
Stand Up Guys (2012) (Vudu/HD) $2.75
Star Trek 1-3 (Vudu/4K) $18 (Vudu/HD) $9.50 (iTunes/4K) $13.50
Star Trek Beyond (2016) (Vudu/HD) $1.75 (iTunes/4K) $3.25
Star Trek Into Darkness (2013) (Vudu/HD) $1.75 (iTunes/4K) $3.25
Starship Troopers (1997) (MA/4K) $6.50
Still Alice (2015) (MA/HD) $3
Stillwater (2021) (MA/HD) $5
Straight Outta Compton (Unrated Director’s Cut) (2015) (MA/4K) $7.50 (MA/HD) (iTunes/HD) $2.50
Strange World (2022) (GP/HD) Ports to MA $4.25
Strangers: Prey at Night (2018) (MA/HD) $3.50
Stronger (2017) (Vudu/HD) (iTunes/HD) $4.50
Stuber (2019) (MA/HD) $4.75
Studio 666 (2022) (MA/HD) $6.75
Suicide Squad, The (2021) (MA/4K) $5
Super Troopers (2002) (MA/HD) $5.75
SW: A New Hope (1977) (MA/4K) $7 (iTunes/4K) $6.25 (GP/HD) $3.50
SW: Empire Strikes Back (1980) (MA/4K) $7 (iTunes/4K) $6.50 (GP/HD) $3.50
SW: Force Awakens (2015) (MA/4K) $5.25 (iTunes/4K) $3.50 (GP/HD) $1
SW: Last Jedi (2017) (MA/4K) $5.75 (iTunes/4K) $3.75 (GP/HD) $1
SW: Phantom Menace (1999) (MA/4K) $7.50 (iTunes/4K) $6.50 (GP/HD) $3.50
SW: Return of the Jedi (1983) (MA/4K) $7.25 (iTunes/4K) $6.50 (GP/HD) $3.50
SW: Revenge of the Sith (2005) (MA/4K) $7.50 (GP/HD) $3.50
SW: Rise of Skywalker (2019) (MA/4K) $6 (iTunes/4K) $4.75 (GP/HD) $2.25
SW: Rogue One: A Star Wars Story (2016) (MA/4K) $6.50 (iTunes/4K) $3.75 (GP/HD) $1.25
SW: Solo: A Star Wars Story (2018) (MA/4K) $7 (iTunes/4K) $5 (GP/HD) $3.50
Sword in the Stone (1963) (MA/HD) $6.25 (GP/HD) $3.75
Taken Collection 1-3 (MA/HD) $9
Tangled (2010) (MA/4K) $8 (MA/HD) $5 (GP/HD) $3.75
Tarzan (1999) (MA/HD) $6.50 (GP/HD) $5
Teen Titans Go! & DC Super Hero Girls: Mayhem in the Multiverse (2022) (MA/HD) $4.75
Terminator (1984) (Vudu/HD) $7
Terminator: Genisys (2015) (Vudu/HD) $1.75 (iTunes/4K) $3
Theory Of Everything (2014) (iTunes/HD) Ports to MA $4
Think Like a Man (2012) & Two (2014) (MA/HD) $9
This Is 40 (2012) (MA/HD) $3.75 (iTunes/HD) $2.50
Thor (2011) (iTunes/4K) (MA/HD) $7 (GP/HD) $3.50
Thor: Love and Thunder (2022) (MA/4K) $6.75 (MA/HD) $3.25 (GP/HD) $2
Thor: The Dark World (2013) (MA/4K) $7 (iTunes/4K) $4.50 (GP/HD) $2.25
Till (2022) (iTunes/4K) $6.50
Tinker Bell and the Legend of the NeverBeast (2014) (MA/HD) $6 (GP/HD) $4
Tinker, Tailor, Soldier, Spy (2011) (iTunes/HD) Ports to MA $4.50
Titanic (1997) (Vudu/HD) (iTunes/HD) $4.75
TMNT Out of the Shadows (2016) (iTunes/4K) $4
To Kill a Mockingbird (1962) (MA/4K) $6.25 (iTunes/HD) $4.50
Tomorrowland (2015) (MA/HD) $5.50 (GP/HD) $3.50
Top Gun: Maverick (2022) (Vudu/4K) $6 (iTunes/4K) (Vudu/HD) $5.75
Total Recall + Extended (2012) (MA/HD) $5 (Theatrical) $4
Toy Story 1-4 (MA/4K) $23 (iTunes/4K) $21 (GP/HD) $11.50
Toy Story of Terror! (2013) (MA/HD) $4.50 (GP/HD) $3
Trading Places (1983) (Vudu/HD) $4.50
Training Day (2001) (MA/4K) $6.50
Trainwreck (2015) (iTunes/HD) Ports to MA $1.50
Transformers 1-5 (Vudu/4K) $30 (Vudu/HD) $23
Trauma Center (2019) (iTunes/4K) $3.25
Triple 9 (2016) (iTunes/HD) Ports to MA $2.50
Trolls (2016) (MA/HD) $1.25
Trolls Collection 1-2 (MA/HD) $6
Tully (2018) (MA/HD) $5.75
Turning Red (2022) (MA/4K) $6.25 (MA/HD) $4 (GP/HD) $2.75
Umma (2022) (MA/HD) $4.75
Unbearable Weight of Massive Talent (2022) (Vudu/4K) (iTunes/4K) $6.75
Unbreakable (2000) (MA/4K) $6 (GP/HD) $3.75
Unbroken (2014) (MA/HD) (iTunes/HD) $3
Uncharted (2022) (MA/4K) $5.50 (MA/HD) $3.25
Under the Skin (2014) (Vudu/HD) $5.25
Underworld: Blood Wars (2016) (MA/4K) $5.75 (MA/HD) $2.25
Unforgiven (1992) (MA/4K) $6.50
Unhinged (2020) (Vudu/HD) $4.75
Up (2009) (iTunes/4K) $7.50 (MA/HD) $6.75
Up in Smoke ‘Cheech and Chong’ (1978) (Vudu/HD) $3.50 (iTunes/HD) $2.75
Upside, The (2017) (iTunes/HD) $2
Us (2019) (MA/HD) $5.25
Van Helsing (2004) (MA/4K) $6 (iTunes/4K) (MA/HD) $4.75
Venom (2018) (MA/4K) $6.75 (MA/HD) $3.25
Venom: Let There Be Carnage (2021) (MA/4K) $7 (MA/HD) $3.50
Vice (2015) 'Bruce Willis' (Vudu/HD) $2.50
WALL-E (2008) (iTunes/4K) $8 (GP/HD) $5.50
Walt Disney Animation Studios Shorts Collection (2015) (MA/HD) $5.50 (GP/HD) $4
Warcraft (2016) (MA/4K) $5 (iTunes/4K) (MA/HD) $2.25
Watch, The (2012) (MA/HD) $4.25
Waterworld (1995) (MA/4K) $6.75 (MA/HD) $6
Way, Way Back, The (2013) (MA/HD) $5.25
Weird Science (2008) (MA/HD) (iTunes/HD) $6
Welcome to Marwen (2018) (MA/4K) $3.50
West Side Story (2021) (MA/4K) $5.75 (MA/HD) (GP/HD) $2.50
What to Expect When You're Expecting (2012) (Vudu/HD) (iTunes/HD) $2.25
When the Game Stands Tall (2014) (MA/HD) $4.50 (MA/SD) $1.75
Where the Crawdads Sing (2022) (MA/HD) $4.50
Whiplash (2014) (MA/HD) $5.75
White House Down (2013) (MA/HD) $3.50
Whitney Houston: I Wanna Dance With Somebody (2022) (MA/HD) $5.75
Widows (2018) (MA/4K) $6.50 (MA/HD) $1.75
Willy Wonka and the Chocolate Factory (1971) (MA/4K) $5
Wind River (2017) (Vudu/HD) (iTunes/HD) $5
Wings (1927) (Vudu/HD) (iTunes/HD) $4
Witch, The (2016) (Vudu/HD) $3.50
Wolf Man (1941) (MA/4K) $6.50
Wolverine (Unrated) (2013) (MA/HD) $3.75
Woman King (2022) (MA/4K) $6.50 (MA/HD) $5.50
Wonder Woman 1984 (2020) (MA/4K) $5
Won't Back Down (2012) (MA/HD) $4
Wreck-It Ralph (2012) (MA/4K) $8 (GP/HD) $4.25
X (2022) (Vudu/HD) $6.75
X2: X-Men United (2003) (MA/HD) $6.25
X-Men (2000), X2 (2003), X-Men: The Last Stand (2006) (MA/HD) $15
X-Men: Apocalypse (2016) (iTunes/4K) (MA/HD) $2.50
X-Men: First Class (2010), Days of Future Past (2004), Apocalypse (2014) (MA/HD) $11
Yesterday (2019) (MA/HD) $4.50
Zathura (2005) (MA/HD) $7
Zero Dark Thirty (2012) (MA/HD) $3
Zootopia (2016) (MA/4K) $7.25 (iTunes/4K) $5 (MA/HD) $4.50 (GP/HD) $3.25
submitted by wtfwafflezor to DigitalCodeSELL [link] [comments]


2023.06.07 01:43 onewatt The Coming Revolution in Gospel Study - or "how nerds read scriptures"

The Coming Revolution in Gospel Study - or
My hype for AI-related tools combined with gospel study is very high right now.

AI Interpretation of hype levels in the church when we get semantic search!
Beep boop beep.
Despite the vast amounts of hype-vomit related to "AI" in recent months, a lot of which is unwarranted, there are some really exciting possibilities out there in technology land. I've gone back to AI and AI-adjacent tools to solve a few problems related to study and research in the gospel setting. What I found is surprising and thrilling. In fact, it sucked me in HARD the past few weeks.
I'm so excited about what is possible I want to write a super long post about it! Here is my report.
Young people will be powerful speakers with AI help! Also apparently ice cream will appear at the pulpit!

Writing a talk.

I was asked to speak in church recently and, on a whim, decided to "talk" with ChatGPT for part of that process. I asked it for advice on a certain subject, tried outlining, etc. The results were very boilerplate and uninspiring. I would say this could be a good tool to get started, brainstorming ideas, creating a first outline, or distilling points from a talk. I imagine youth speakers could get a lot of mileage out of using it! :)
Use cases for talk writing:
  • Brainstorm: Paste the text of your assigned conference talk and tell ChatGPT to distill it down to its main principles or points. See if any of those resonate with stories from your life.
  • Outline: explain you are giving a talk on your given subject and ask it to provide an outline for a X minute talk. You can then use that as a starting point, cutting and replacing sections you don't like.
  • Expand ideas: Try asking the program to expand a concept. For example: "How could I explain Christ's Healing Power in a talk? Are there scriptures that I could cite which relate?" If you do this in a conversation where you have already made it clear this is for a talk in the LDS church, the program will know to include our unique doctrines and scriptures in its response.
What it can't do: Write a joke.

According to AI, this is what \"Latter-day Saint News Reporter\" looks like. Nice.

Eliminate clickbait from Latter-day Saint News? Check.

What is it?
For many years I have been aware of dozens, perhaps hundreds, of Latter-day Saint focused blogs, news sites, online magazines, etc. The problem, of course, is that ain't nobody got time for that.
I've tried a few different solutions to this, relying on google alerts to find me relevant articles, but it often gets overwhelmed when something hits the news, or by false positives like obituaries, church building construction announcements in regional news outlets, or even ads. I tweaked and tweaked and finally gave up on that, resorting to just a simple feed aggregator.
The downsides of a feed aggregator are that A) many sites nowadays don't have feeds, and B) It can still be way way way too much since you still have clickbait titles, unclear titles, and, well, a decades-old technology that isn't really approachable.
So I tried an experiment. Could ChatGPT read the article for me and let me know if it's something I actually care about?
The AI News Flow:
  • A website posts an article.
  • New articles are sent to ChatGPT with the instructions to summarize the article in the style of a news roundup email. That summary is saved in a google sheet.
  • Once per day, all the new rows of the google sheet are put together as a single "post" and sent to Chat GPT with instructions to:
    • Read the post and generate a fun, social-media style title based on the content.
    • Read the post and generate a welcome paragraph or two based on the content.
  • The daily roundup post is emailed to me with the title as the subject, and the body as the welcome plus the list of summaries.
This works pretty ok. It could work great if I was willing to pay for ChatGPT4 to do the work but that would cost me about 20 dollars per month instead of a buck or two in processing time. By using ChatGPT 3.5 it costs me next to nothing, but it often forgets to return the link to the original post or to format properly. I'm not too fussed about that. You can see the results of this experiment in /mormonism where I have MoroniBot post the results daily.
I really like this because I can skim the email or the post in /mormonism once per day and get a pretty good idea of what's available to read without dealing with clickbaity headlines. I'd love your feedback.
What would be neat:
If I were feeling bold I would work on a system where ChatGPT actually ranks each article based on relevance and other factors. That ranking could then be used to eliminate false-positives, repetitive content, etc. and allow me to go back to using Google Alerts which would make the feed more news-like instead of just blog focused. The downside, of course, is that this would cost a lot (maybe an extra 20 - 30 bucks per month?) since GPT4 is really the best for this kind of qualitative task.
Second, while I can gather videos from youtube, if they don't include a lot of details in the video description then there's not much for ChatGPT to go on. It would be cool to send any new videos that hit the feed to Assemblyai or some other transcription tool before GPT analysis. Then even videos with blank descriptions could be included. Same with podcasts. This, of course, would add significantly to the cost for a casual user like me, but it's crazy to think that the possibility is out there!
Tools used:
  • Pipedream - for automation, cron, email, etc. Cost: free
  • Google sheets - to store article summaries. I could also use pipedream for this, but eh. Cost: free
  • OpenAI API - used to connect to ChatGPT. Cost: couple dollars per month based on processing time
  • Reddit API - used to post on reddit. Cost: free
  • FreshRSS - used to gather blog posts. Cost: free
This little experiment shows just how disruptive LLM enhanced algorithms will become in the future. Imagine if Youtube, which already HAS the transcripts for its countless videos, is able to implement GPT-style analysis at scale. It suddenly stops being a case of "here's videos that people LIKE you also watched" but instead "Here are more videos which are like the ones you already enjoy." Ideally this will eliminate those annoying false-positives like when searching for Latter-day Saint related subjects and you also get bombarded with all the anti-mormon content. (The scary truth side of that is: ideology bubbles would only be made stronger. You'd have to actually want to see a different perspective before finding it.)
Speaking of improved searches:

Massive-handed President Nelson cavorts through data using Semantic Search!

Semantic Search

What is it?
Of everything on my list, this excites me the most!!
Underpinning the recent AI developments is a technology called "vectors." This is not new tech. In fact it's decades old. For our purposes, think of it this way: Instead of storing a word in a database, you convert that word into a shape. (That's the vector) Every letter, word, sentence, and paragraph could be turned into a unique shape depending on how your program works.
Why is this useful? Well, computers are bad at comparing words, or making sense of natural language. But they are VERY good at math problems like comparing shapes. See where we're going? So if you use vectors, you might be able to get a computer to identify when words are "pretty close" to other words.
Stick a bunch of words, phrases, and terms that mean "hate" in a vector database and a computer might be able to compare Yelp reviews to that database and figure out which reviewers hated their meals - even if they never used that word - simply because the shape of the review was mathematically similar or "close" to the shape of hate-related terms.
What makes the latest AI so powerful is that they are very very very good at generating vectors based on meaning or "semantics." So, for example, the words "hot dog" and "mustard" will be more similar than "hot dog" and "dog."
But what about in a gospel setting?
If this works right, it will mean much deeper studying and learning for us. Imagine l jump into the gospel library and search for "How can I access physical healing?" and get some great results in conference talks and articles which talk about that exact issue. But if I search "semantically" I might also get things like the gospel topic essay on death, and the one on adversity, since those are closely related semantically to the need for physical healing.
  • A search for "What is the purpose of suffering" will return not just articles about that subject but also topics like:
    • Adversity
    • Council in Heaven
    • Atonement
    • Conscience
    • Heavenly Parents
    • Original Sin
    • Mortality
  • Complex questions in conversation style can still get relevant results
  • Well trained AI can translate your human-style question into a more targeted query if needed
  • Well trained AI can examine all the returned results and summarize them for you
  • Scriptures can be connected not just by footnote, but by meaning
My tests
I tested this out in an app using "streamlit" and a database of general conference talks, scriptures, and other documents. I fed large chunks of the talks to OpenAI which created a vector for each. Then the app uses a user query to search those vectors for similarity. You can try it out here: https://topical-guide.streamlit.app/ It's not well done, and crashes often, (just try again if it happens to you) but I have had a great experience using it.
In the last few weeks I have used this tool to research reddit questions on this subreddit. Inevitably I find resources and insights I hadn't thought of before.
What's crazy is that I have ZERO programming and engineering experience and I was able to make something that I feel gets me some great results. A whole gospel search engine made from scratch by a total amateur in a matter of a couple of days!
I expect that real programmers and engineers will be able to create tools that will let members search through our vast gospel library MUCH more powerfully. Especially in the scriptures, where finding answers can be difficult, and meaning isn't always clear. I look forward to see what people create.
What would be neat:
There are incredible libraries out there that we ought to be searching through. The reality is that we just don't have the time to read the text, categorize it, and find places to include it in the existing gospel library. A semantic search engine fixes all of that!
I added BYU Studies and BYU Devotional texts to my app, but there's so much more. It would not take much to create a database that searches through much more than we currently have in the gospel library, but still be focused on faith. I hope to add more documents, to my test and if you have any ideas of resources that should be in my database let me know. I'd also love to see an app that can return results based on date of the talk, maybe even sorting by weight for "authoritativeness" in some way. There's some very interesting opportunities here.
Tools used:
  • ChatGPT - to tell me how to write python scripts that will do what I want, and to generate vectors. - 20 / month for gpt4
  • Python - you know, for doing things. - free
  • Streamlit - creates an "app" from my hokey code -free
  • Supabase - database for the text and vectors - free

A perfectly average Latter-day Saint volunteer answers questions in chat, circa 1997

Trained AI

What is it
A process called "Fine Tuning" is how you train an AI into acting the way you want. ChatGPT is a large language model that has been fine tuned by having tens of thousands of conversations with real humans who then correct it and give it examples of the "right" way to talk. While this cost millions of dollars, some companies are creating trained AI for as little as 600 dollars by simply letting the big AI do all the training instead of actual humans.
Ideally you will upload a large database of questions and responses to your model, thousands and thousands of examples of how you hope it will behave, the types of answers it should give, the temperament it should have, etc. But creating that database is a big challenge. For example, if I want to create a chat bot for a law firm, I'd have to spend months and months finding and inserting common legal questions and the right kind of non-legal-advice responses that would be appropriate. So these fine-tuned models are still only options for bleeding-edge tech companies.
Right?
The Gospel Angle
For decades, we've been chatting online, human-to-human, about gospel questions, needs and desires. I can think of two massive databases:
  1. The Church Missionary Department, which ran live chats with missionaries for many years
  2. FAIR Latter-day Saints, which has been offering email-based human responses to questions for decades.
I'd be very very interested to see how a Fine Tuned model could be adapted to answer simple questions for websites like FAIRLatterDaySaints.org and ChurchofJesusChrist.org. FAIR in particular could really power up their search function by combining semantic search and a Fine-Tuned model to summarize search results. And the crazy thing is it wouldn't cost a lot. It might be a couple thousand to do the fine-tuning, but the database and the individual queries would be only a few dollars per month.
In essence, we could end up with a search function where the article you find isn't the static subject matter page with additional resources, but a custom response written specifically for your need and presented in the style of the thousands of faithful members who have volunteered their time to help people just like you. It could express sympathy for your unique struggles, and offer encouragement tailored to your perspective.
Perhaps most importantly, it could be trained to offer a gentle prompt in the right direction for many, encouraging users to reach out for help.

Thanks for reading. Let me know your thoughts.
submitted by onewatt to latterdaysaints [link] [comments]


2023.06.07 01:30 throwaway682749273 I’m a data… wannabe?

Ok Reddit, help me figure out what I am.
I’ve got about 6 years of SQL experience, querying, inserting, deleting, and updating data in live production SSMS databases on a daily basis.
I’ve migrated data from one db to another with a matching schema with various methods, but the most streamlined of which included a Python script/program to retrieve the data from database(s) make any necessary manipulations with mapping files or utilizing pandas data frame merges, and inserting it into a new database.
It’s as terrifying as it sounds.
I’ve been in a new role the last year that is far less technical and far more business, mostly using sql to build looker dashboards, and it’s boring. I’m hoping I can move into DE but my concern is my skills will fall short as I have almost no experience with big data/cloud infrastructure, and really have no clue how to bridge that gap.
Any inputs on what kind of positions I could fit my current skill set into? My previous position seems fairly niche, and I can’t seem to find something similar.
submitted by throwaway682749273 to dataengineering [link] [comments]


2023.06.07 01:09 datatulga I need advice

I need advice on picking a cloud data storage/server (AWS, Azure, Google Cloud) to store roughly 50GB of data in parquet, csv, and pickle. I run python scripts hourly and daily to update them. Which service is fastecheapereliable. I run batch scripts hourly and daily to update that data and then push to Salesforce, so the data flow is non-stop. I am thinking of storing the python scripts into the cloud to have the whole ETL process in cloud. Please give me advice. I am new to Cloud Engineering/Architecture
submitted by datatulga to dataengineering [link] [comments]


2023.06.06 23:36 FleXx27 How to allow user to import a .csv file

I am pretty new to python and coding in general and I am trying to allow the user to import a .csv file in my "def importfile(all_sales):" but im struggling on how to get that done and I need to make it allow users to import sales data from a CSV file that contains sales amount, year, month, and day. Use a text file to keep track of imported files so a user can't import a file more than once.

for reference this is what it needs to look like https://imgur.com/gallery/rmIfU4E

import sales import csv with open('all_sales.csv', newline='') as f: data = list(csv.reader(f)) #display a title def display_welcome_message(): print("Welcome to data viewer program") #display a title def display_welcome_message(): print("Welcome to data viewer program") def display_menu(): print("enter sales data") print() print("COMMAND MENU") print("View - View All Sales") print("add - Add sales") print("Menu - Show menu") print("Import - Import file") print("Exit - Exit Program") print() total_amount=0 FILENAME = "all_sales.csv" def read_sales(): all_sales = [] with open(FILENAME, newline="") as file: reader = csv.reader(file) for row in reader: all_sales.append(row) return all_sales def write_sales(all_sales): with open(FILENAME, "w", newline="") as file: writer = csv.writer(file) writer.writerows(all_sales) #def create_file(all_sales): #with open(FILENAME, "w") as file: #file.write() def add(all_sales): amount1 = sales.get_amount() year1 = sales.get_year() month1 = sales.get_month() day1 = sales.get_day() #month1, day1, year1, print(" "), "amount:", amount1 add_sales = [] add_sales.append(month1) add_sales.append(day1) add_sales.append(year1) add_sales.append(amount1) all_sales.append(add_sales) write_sales(all_sales) print(str(month1), " - ", day1, " - ", year1, "was added") def importfile(all_sales): filename = input("Import file name: ") with open(filename, 'r') as file: csvreader = csv.reader(file) for row in csvreader: print(str(i+1),". ",all_sales[i][2],"-",all_sales[i][0],"-",all_sales[i][1]," "," $",all_sales[i][3]) total += all_sales[i][3] i += 1 print("---------------------------------------------------------") print("TOTAL: $", total) def view(all_sales): i = 0 total = 0 if len(all_sales) == 0: print("there are no sales data in the list. \n") return else: print(f" Date Quarter Amount") print("---------------------------------------------------------") while i < len(all_sales): print(str(i+1),". ",all_sales[i][2],"-",all_sales[i][0],"-",all_sales[i][1]," "," $",all_sales[i][3]) total += all_sales[i][3] i += 1 print("---------------------------------------------------------") print("TOTAL: $", total) def calculate_quarter(all_sales): if month1 <= 3: quarter1 = 1 elif month1 > 3 and month1 <= 6: quarter1 = 2 elif month1 > 6 and month1 <=9: quarter1 = 3 elif month1 > 9 and month1 <=12: quarter1 = 4 def main(): display_welcome_message() display_menu() all_sales = [] sales_list = [] while True: command = input('enter command: ') if command == 'view': view(sales_list) elif command == 'add': add(sales_list) elif command == 'menu': display_menu() elif command == 'import': importfile(sales_list) elif command == 'exit': break else: print("not a valid command. Please try again. \n") print("Program Exit") if __name__ == "__main__": main() 
submitted by FleXx27 to codereview [link] [comments]


2023.06.06 22:54 devler_io 🌍How Startups Support Green Tech Initiatives for a Sustainable Future?

🌍How Startups Support Green Tech Initiatives for a Sustainable Future?
Top interesting #apps and #startups.
Devler.io
Every year on June 5th we celebrate World Environment Day 🌿 Supporting a sustainable future is no longer a choice; it is a requirement. Startups have emerged as potent change agents in the digital age, strongly supporting green tech initiatives to #design a more sustainable future. Here's a look at how startups are leading the move to a greener, more environmentally responsible future:
🌱JouleBug Programming Language: JavaScript United States JouleBug is an app that encourages sustainable habits and behaviors through gamification. Users can track their eco-friendly actions, earn points, and compete with friends to make positive environmental changes.
🌱 SolarGaps Programming Language: JavaScript, Python, C and C++ Ukraine SolarGaps is a company that develops and manufactures smart solar blinds. These blinds are equipped with solar panels that capture sunlight and convert it into electricity. The generated energy can be used to power various devices and appliances within a home or office.
🌱 Ecosia Programming Language: JavaScript Germany Ecosia is a search engine that uses its revenue to plant trees. Built with JavaScript, Ecosia directs its profits towards reforestation projects, helping to combat deforestation and promote a greener planet.
🌱FoodKeeper Programming Language: Swift, Kotlin Malaysia FoodKeeper is an app developed by the USDA that helps users reduce food waste. It provides information on proper storage methods, shelf life, and safety guidelines for various food items. The app is built using #Swift for iOS and Kotlin for Android.
🌱Powerpal Programming Language: Python Australia Powerpal is an energy-saving app that allows users to track and optimize their energy consumption. It provides insights into energy usage, suggests energy-saving actions, and rewards users for reducing their carbon footprint. Powerpal is built using #Python.
🌱 GridPoint Programming Language: C++, Java United States GridPoint is a startup that focuses on smart grid #technology. They provide software and hardware solutions to optimize energy distribution and consumption. Their applications are typically developed using a combination of C++ and Java.
Are you enthusiastic and passionate about supporting green initiatives by your business idea? Devler.io's experts in software development are here to help you transform your software idea into a reality while adhering to sustainable standards.
Have you come across any interesting #software applications or startups that are actively supporting green initiatives? How are they making a positive impact on sustainability?
submitted by devler_io to u/devler_io [link] [comments]


2023.06.06 22:53 JSkywalker93 What is Software?

Inquisitive Universe: This evening, we're going to be making our way tentatively through the world of software. We'll start with software in general and over time we'll end up at smartphone software.
Now I know very well that software can be very contentious and subjective so I'll stick mostly with the objective stuff. Anything that is subjective that I touch are mostly my personal views which I am entitled to.
With that being said, let's cast off.
When we were younger, our teacher stood in front of a dead HP Compaq desktop and declared that a computer is divided into two parts.
  1. Hardware and
  2. Software
She went on to state that hardware are those parts that we can see and touch but software are those parts that we can see but cannot touch.
Even tho, I never saw that desktop come on 😂😂😂, I swallowed the definition hook, line and sinker.
I'm sure that some of us may have been fed that definition as well. However, it's time to upgrade.
Software at its most basic is a collection of instructions and data that tells a computer how to work.
It can also be defined as all the information and data processed by a computer.
If you've ever bricked a phone or laptop, you'll understand this part very clearly. Because without software to tell the hardware what to do, the hardware is absolutely useless.
How does software work?
Oh that's easy. Software acts as the middle man between you the user and the hardware.
How?
Simple, it does this by converting human commands into machine language that the hardware understands.
A good example is the camera app on your phone and the cameras at the back of your phone. One is software while the other is hardware.
You tell your camera app that you want to take a picture, your camera app (software) tells the camera (hardware) what to do (i.e. take a picture).
If the software and hardware work as intended, a picture should appear on you phone.
Software helps humans and computers communicate or at least understand each other by converting human commands into machine language that the computer understands.
How is software created?
Well in the past, software was originally written by hand and each type of computer had its own special type of software that was written for it.
Thankfully, these days, the process of creating software has been standardized and you can do it on your computer instead of by hand.
The process of writing software from scratch is known as programming. The people who write software programs (software) are known as programmers or software developers.
Side note: Coding and programming are not the same. A programmer writes programmes from scratch. A coder converts software from one software language to another e.g from Java to PHP.
In your smartphone or any other general multi-purpose PC, there are generally two levels of software.
Low level language: machine language High level language: programing language
Machine language is the software code that the hardware understands. This is usually in binary code e.g. 1010101
This is the code that CPUs work with.
Programming language is the language that humans use and understand. There's a lot of them. Examples include: C, C++, Pascal, Java, PHP, Cobol, HTML, Python etc.
These are the languages that are used to build the apps, games and operating system software that we all use today.
These programming languages are translated into machine language by a compiler, interpreter or assembler.
This is how instructions pass from you to the software, then the interpreter, machine language and finally the hardware (CPU, GPU, Cameras etc.).
One more definition of software I forgot to add is that software can also be defined as any entity that you interact with on a screen or display of any sort.
So lets quickly move on to...
Types of software
Now, I won't go into details much. I'll list them out and leave links at the end.
There are many types of software but they can be divided into two main groups. The first is the purpose of use and the second is the nature or device/platform of use.
  1. Purpose of use
a. Application software b. System software c. Malicious software
  1. Nature or Device/platform
a. Platform i.e. desktop, mobile, console b. Internet/webpage c. Servers d. Embedded systems
This is a quick crash course on what software is. I hope that I have made it as clear and as concise as possible.
So with that lemme pause for this evening. Enjoy the rest of your evening.
https://inquisitiveuniverse.com/2021/07/14/software/
https://inquisitiveuniverse.com/2021/07/18/types-of-software/
submitted by JSkywalker93 to smartphone_specs_edu [link] [comments]