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https://devblogs.microsoft.com/dotnet/?p=26510 | .NET Framework February 2020 Security and Quality Rollup - .NET Blog Skip to main content Microsoft Dev Blogs Dev Blogs Dev Blogs Home Developer Microsoft for Developers Visual Studio Visual Studio Code Develop from the cloud All things Azure Xcode DevOps Windows Developer ISE Developer Azure SDK Command Line Aspire Technology DirectX Semantic Kernel Languages C++ C# F# TypeScript PowerShell Team Python Java Java Blog in Chinese Go .NET All .NET posts .NET Aspire .NET MAUI AI ASP.NET Core Blazor Entity Framework NuGet Servicing .NET Blog in Chinese Platform Development #ifdef Windows Microsoft Foundry Azure Government Azure VM Runtime Team Bing Dev Center Microsoft Edge Dev Microsoft Azure Microsoft 365 Developer Microsoft Entra Identity Developer Old New Thing Power Platform Data Development Azure Cosmos DB Azure Data Studio Azure SQL OData Revolutions R Unified Data Model (IDEAs) Microsoft Entra PowerShell More Search Search No results Cancel Dev Blogs .NET Blog .NET Framework February 2020 Security and Quality Rollup .NET 10 is here! .NET 10 is now available: the most productive, modern, secure, intelligent, and performant release of .NET yet. Learn More Download Now February 11th, 2020 0 reactions .NET Framework February 2020 Security and Quality Rollup Tara Overfield Senior Software Engineer Show more Today, we are releasing the February 2020 Security and Quality Rollup Updates for .NET Framework. Security The February Security and Quality Rollup Update does not contain any new security fixes. See January 2020 Security and Quality Rollup for the latest security updates. Quality and Reliability This release contains the following quality and reliability improvements. Some improvements included in the Security and Quality Rollup and were previously released in the Security and Quality Rollup that was dated January 23, 2020. Acquistion & Deployment Addresses an issue where the installation of .NET 4.8 on Windows machines prior to 1809 build prevents .NET-specific settings to be migrated during Windows upgrade to build 1809. Note: to prevent this issue, this update must be applied before the upgrade to a newer version of Windows. CLR 1 A change in .NET Framework 4.8 regressed certain EnterpriseServices scenarios where an single-thread apartment object may be treated as an multi-thread apartment and lead to a blocking failure. This change now correctly identifies single-thread apartment objects as such and avoids this failure. There is a race condition in the portable PDB metadata provider cache that leaked providers and caused crashes in the diagnostic StackTrace API. To fix the race, detect the cause where the provider wasn’t being disposed and dispose it. Addresses an issue when in Server GC, if you are truly out of memory when doing SOH allocations (ie, there has been a full blocking GC and still no space to accommodate your SOH allocation), you will see full blocking GCs getting triggered over and over again with the trigger reason OutOfSpaceSOH. This fix is to throw OOM when we have detected this situation instead of triggering GCs in a loop. Addresses an issue caused by changing process affinity from 1 to N cores. Net Libraries Strengthens UdpClient against incorrect usage in network configurations with an exceptionally large MTU. SQL Addresses an issue with SqlClient Bid traces where information wasn’t being printed due to incorrectly formatted strings. WCF 2 There’s a race condition when listening paths are being closed down because of an IIS worker process crash and the same endpoints being reconfigured as listening but pending activation. When a conflict is found, this change allows for retrying with the assumption the conflict was transient due to this race condition. The retry count and wait duration are configurable via app settings. Added opt-in retry mechanism when configuring listening endpoints on the WCF Activation service to address potential race condition when rapidly restarting an IIS application multiple times while under high CPU load which resulted in an endpoint being inaccessible. Customers can opt in to the fix by adding the following AppSetting to SMSvcHost.exe.config under the %windir%\Microsoft.NET\Framework\v4.0.30319 and %windir%\Microsoft.NET\Framework64\v4.0.30319 folders as appropriate. This will retry registering an endpoint 10 times with a 1 second delay between each attempt before placing the endpoint in a failure state. <appsettings> <add key=”wcf:SMSvcHost:listenerRegistrationRetryCount” value=”10″> <add key=”wcf:SMSvcHost:listenerRegistrationRetryDelayms” value=”1000″> </add></appsettings> Windows Forms Addresses an issue in System.Windows.Forms.TextBox controls with ImeMode property set to NoControl. These controls now retain IME setting consistent with the OS setting regardles of the order of navigation on the page. Fix applies to CHS with pinyin keyboard. Addresses an issue with System.Windows.Forms.ComboBox control with ImeMode set to ImeMode.NoControl on CHS with Pinyin keyboard to retain input mode of the parent container control instead of switching to disabled IME when navigating using mouse clicks and when focus moves from a control with disabled IME to this ComboBox control. An accessibility change in .NET Framework 4.8 regressed editing IP address UI in the DataGridView in Create Cluster Wizard in Failover Cluster Services: users can’t enter the IP value after control UIA tree restructuring related to editing control movement to another editing cell. Such custom DataGridView cells (IP address cell) and their inner controls are currently not processed in default UIA tree restructuring to prevent this issue. WPF 3 Addresses an issue where under some circumstances, Popup’s in high-DPI WPF applications are not shown, are shown at the top-left corner of the screen, or are shown/rendered incompletely. Addresses an issue when creating an XPS document in WPF, font subsetting may result in a FileFormatException of the process of subsetting would grow the font. Addresses incorrect width of the text-insertion caret in TextBox et al., when the system DPI exceeds 96. In particular, the caret rendered nothing on a monitor with lower DPI than the primary, in some DPI-aware situations. Addresses a hang arising during layout of Grids with columns belonging to a SharedSizeGroup. Addresses a hang and eventual StackOverflowException arising when opening a RibbonSplitButton, if the app programmatically disables the button and replaces its menu items before the user releases the mouse button. Addresses certain hangs that can arise while scrolling a TreeView. 1 Common Language Runtime (CLR) 2 Windows Communication Foundation (WCF) 3 Windows Presentation Foundation (WPF) Getting the Update The Security and Quality Rollup is available via Windows Update, Windows Server Update Services, and Microsoft Update Catalog. Microsoft Update Catalog You can get the update via the Microsoft Update Catalog. For Windows 10, NET Framework 4.8 updates are available via Windows Update, Windows Server Update Services, Microsoft Update Catalog. Updates for other versions of .NET Framework are part of the Windows 10 Monthly Cumulative Update. Note : Customers that rely on Windows Update and Windows Server Update Services will automatically receive the .NET Framework version-specific updates. Advanced system administrators can also take use of the below direct Microsoft Update Catalog download links to .NET Framework-specific updates. Before applying these updates, please ensure that you carefully review the .NET Framework version applicability, to ensure that you only install updates on systems where they apply. The following table is for Windows 10 and Windows Server 2016+ versions. Product Version Cumulative Update Windows 10 1909 and Windows Server, version 1909 .NET Framework 3.5, 4.8 Catalog 4534132 Windows 10 1903 and Windows Server, version 1903 .NET Framework 3.5, 4.8 Catalog 4534132 Windows 10 1809 (October 2018 Update) and Windows Server 2019 4538122 .NET Framework 3.5, 4.7.2 Catalog 4534119 .NET Framework 3.5, 4.8 Catalog 4534131 Windows 10 1803 (April 2018 Update) .NET Framework 3.5, 4.7.2 Catalog 4537762 .NET Framework 4.8 Catalog 4534130 Windows 10 1709 (Fall Creators Update) .NET Framework 3.5, 4.7.1, 4.7.2 Catalog 4537789 .NET Framework 4.8 Catalog 4534129 Windows 10 1703 (Creators Update) .NET Framework 3.5, 4.7, 4.7.1, 4.7.2 Catalog 4537765 .NET Framework 4.8 Catalog 4537557 Windows 10 1607 (Anniversary Update) and Windows Server 2016 .NET Framework 3.5, 4.6.2, 4.7, 4.7.1, 4.7.2 Catalog 4537764 .NET Framework 4.8 Catalog 4534126 Windows 10 1507 .NET Framework 3.5, 4.6, 4.6.1, 4.6.2 Catalog 4537776 The following table is for earlier Windows and Windows Server versions. Product Version Security and Quality Rollup Windows 8.1, Windows RT 8.1 and Windows Server 2012 R2 4538124 .NET Framework 3.5 Catalog 4532946 .NET Framework 4.5.2 Catalog 4534120 .NET Framework 4.6, 4.6.1, 4.6.2, 4.7, 4.7.1, 4.7.2 Catalog 4534117 .NET Framework 4.8 Catalog 4534134 Windows Server 2012 4538123 .NET Framework 3.5 Catalog 4532943 .NET Framework 4.5.2 Catalog 4534121 .NET Framework 4.6, 4.6.1, 4.6.2, 4.7, 4.7.1, 4.7.2 Catalog 4534116 .NET Framework 4.8 Catalog 4534133 Note: Windows 7 support ended on January 14, 2020 Previous Monthly Rollups The last few .NET Framework Monthly updates are listed below for your convenience: January 2020 Preview of Quality Rollup January 2020 Security and Quality Rollup December 2019 Security and Quality Rollup November 2019 Preview of Quality Rollup 0 3 0 Share on Facebook Share on X Share on Linkedin Copy Link --> Category .NET .NET Framework WPF Share Author Tara Overfield Senior Software Engineer Tara is a Software Engineer on the .NET team. She works on releasing .NET Framework updates. 3 comments Discussion is closed. Login to edit/delete existing comments. Code of Conduct Sort by : Newest Newest Popular Oldest Krejčí Pavel --> Krejčí Pavel --> February 19, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Hi, Are from now on for Windows 7SP1 ESU .NET 4.8 updates part of Security-only update or Monthly Rollup one? Or there is another way? Reg., Paul Terence Teng --> Terence Teng --> March 15, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Same question. @Tara SuperCocoLoco . --> SuperCocoLoco . --> February 14, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> No .NET framework updates for Windows 7 and Windows 10 is an ugly (very ugly) mobile OS unuseable on a desktop computer. I prefer an unsupported Windows 7 than a bad supported Windows 10 (Mobile Only, Cloud Only, Touch Only and local On-Premises Desktop never again). Read next February 12, 2020 Deprecating TLS 1.0 and 1.1 on NuGet.org – Stage 1 The NuGet Team February 18, 2020 .NET Core February 2020 Updates – 2.1.16, 3.0.3, and 3.1.2 Rahul Bhandari (MSFT) Stay informed Get notified when new posts are published. Email * Country/Region * Select... 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https://share.transistor.fm/s/940dfccb#goodpods-path-1 | APIs You Won't Hate | The State of the API Address APIs You Won't Hate 40 ? 30 : 10)" @keyup.document.left="seekBySeconds(-10)" @keyup.document.m="toggleMute" @keyup.document.s="toggleSpeed" @play="play(false, true)" @loadedmetadata="handleLoadedMetadata" @pause="pause(true)" preload="none" @timejump.window="seekToSeconds($event.detail.timestamp); shareTimeFormatted = formatTime($event.detail.timestamp)" > Trailer Bonus 10 40 ? 30 : 10)" class="seek-seconds-button" > 40 ? 30 : 10"> Subscribe Share More Info Download More episodes Subscribe newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyFeedUrl()" class="form-input-group" > Copied to clipboard Apple Podcasts Spotify Pocket Casts Overcast Castro YouTube Goodpods Goodpods Metacast Amazon Music Pandora CastBox Anghami Anghami Fountain JioSaavn Gaana iHeartRadio TuneIn TuneIn Player FM SoundCloud SoundCloud Deezer Podcast Addict Share newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyShareUrl()" class="form-input-group" > Share Copied to clipboard newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyEmbedHtml()" class="form-input-group" > Embed Copied to clipboard Start at Trailer Bonus Full Transcript View the website updateDescriptionLinks($el))" class="episode-description" > Chapters December 1, 2021 by APIs You Won't Hate View the website Listen On Apple Podcasts Listen On Spotify Listen On YouTube RSS Feed Subscribe RSS Feed RSS Feed URL Copied! Follow Episode Details / Transcript Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations at Postman, to talk about Postman's State of the API 2021. Show Notes Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations to discuss Postman's State of the API 2021 report, detailing various data points from around the API world from which specification people turn to, to how confident people feel deploying their APIs. They also discuss various topics around remote work, how APIs enable more remote work and what will happen in the next few years for APIs. Notes: Matthew on twitter: https://twitter.com/libel_vox Postman's State of the API Creators and Guests Host Mike Bifulco Cofounder and host of APIs You Won't Hate. Blogs at https://mikebifulco.com Into 🚴♀️, espresso ☕, looking after 🌍. ex @Stripe @Google @Microsoft What is APIs You Won't Hate? A no-nonsense (well, some-nonsense) podcast about API design & development, new features in the world of HTTP, service-orientated architecture, microservices, and probably bikes. Matt Trask: Cool. Welcome back to APS. You won't hate episode 17. I have Phil with me and we're joined by a very special guest today. Matthew Reinbold, fresh from postman, who is a director of API ecosystems and digital transformations here to talk about their report, the 2021 state of the API ecosystem. Matthew, how's it going? Matthew Reinbold: It is going. I am happy to be here first time, caller, long time listener. Is that how we say that? Matt Trask: I think that's yeah. It's how you say it. Yeah. So I mean, for those of you, like in the off chance that someone doesn't know who you are in the API ecosystem world can you give us a little bit kind of about yourself? Like you manage two different newsletters, at least as well as a pretty prolific Twitter presence as well. But if someone hasn't run into you, like. Matthew Reinbold: Well, yeah, well, first off, thanks for calling it prolific. Some people would call it annoying, but yeah, I I manage a fair number of tweets over at Twitter slash L I B E L underscore Vox, reliable Vox. That's where I talk about digital transformation and APIs and a lot of technology stuff. Occasionally. Fights with blockchain and NFT enthusiastic. But then I also manage, I also manage a newsletter called net API notes, where for almost 200 issues, going back to 2015, I've covered the landscape. I've shared essential bits of information. I've tried to boil down the, the. Current climate and get it right into just the most essential things that decision makers need to know and care about. And then I do a fair amount of blogging on a blog. That's very imaginatively named Matthew reinbold.com. In there, I talk about a fair number of things as well, but in, in, in short my passion is really about coaching people, helping people, teaching people to get better with their API ecosystem. Matt Trask: That's really cool. So one thing that kinda stuck out to me cause it's, so we're going to be talking about the 20, 21 Sidi APR report. However, I'm curious since you've been doing it now since 2015, you've been keeping notes on. The API world. How does your kind of, I hate to say this phrase, the 30,000 foot view of everything that, you know, from 2015, how does that kind of line up to what you saw with the 2021 state of the API report? Matthew Reinbold: Oh, that's interesting. So there's definitely. Maturing as a industry, we've gone through a number of phases. Those of us that have been around the block a few times, see trends come. And most often they, they tend to roll away. And over that time we have to develop models so that we can kind of. Pick the, the, the wheat from the chaff, you know, what, what are the properties of something new, some kind of buzzword, some kind of hyperbole that we can latch onto and say, yes, this is worth investing in. This is worth our interest in our effort versus, yeah, this is some marketing system, some spin as I'm looking at the 20, 21 postman report. I see. Where we've come. It's gone from being single point to point integrations. One-off bespoke API APIs to where we're now talking about things as ecosystems. We're now talking about collections of these things and how entire organizations. Manage these as, as something that's beneficial, something that's collaborative and, and managed as a separate entity rather than, than each individual unit I've got Phil here. So I have to use the forest for the trees analogy rather than just managing the individual API trees. There's now a greater awareness of what the forest, what the forest role is in the company and how to manage that. In a unique way, as opposed to the individual pieces. I will say for those that are listening, like I'm one of the things I want to highlight right up front here is that you don't have to enter an email address. It's not behind the page. We really felt strongly at postman that we had to get this information out to the most number of decision-makers so that they could make better decisions so that they could be informed as they're developing their strategies and roadmaps. So if you go to postman.com/state-of-api, you'll be able to download. With out any worry about having somebody from sales follow up with you later, or getting spam in your inbox, it's free for all. We want this information to be used. We want the dialogues to happen. We want the discourse to be rich and for me and frothy. And so please, you know, don't let past marketing spam. Stop you from checking this out. We want this in the hands of people. Phil Sturgeon: Fantastic. That's good to hear. I mean, that's I haven't got around to reading it as you might have seen from Twitter. Life has been a bit of a mess recently just spending far too much time in the field, as opposed to in the field doing APA stuff. But, yeah, that's definitely always been a concern of mine, of, you know, you hear about these white papers and reports and you just know so many of them like should have just be in the blog post, but instead that like a PDF that and you've got to enter information and then you just get like that fifth email, like, why didn't you reply to my previous four? I was like, I don't know who you are. I just want to read this thing. So yeah, I'm glad you folks are going in a different direction, but Maybe just taking a step back. Like, what is the state of API is report all about where are you getting your information from? What sort of research is being done? And what's the hospital. Matthew Reinbold: Great question. So this is, as far as I know, the largest survey of its kind, we had more than 28,000 people respond to our latest in a series. What we tend to do is try and track where the industry is at. And typically that's been around certain areas. Like how much time do you spend developing API APIs? What kind of tools are you using? Really good stuff there tracking the growth of, of the industry and the maturation of the industry. What I brought to the table this year. Was an interest on finding the behaviors that lead to sustainable, healthy API ecosystems. Like so much of what we talk about when it comes to API ecosystems is still very anecdotal. We tell stories about the Bezos Amazon memo, where we talk about like Twilio or Stripe, but when it comes to decision makers in large organizations, they're still. Trying to pull at what are decent KPIs, what are the behaviors I should be grooming or promoting within my company to make sure that I can keep producing quality API experiences again and again and again. And so what we did with this report that I'm really proud of is dig deep and discover, like, what are the correlating behaviors in organizations that lead to good things happening for companies? Phil Sturgeon: Okay. That's interesting. Cause I think. There's always this question around, like, what's a good API and what's a bad API. Right. And that's just such a nebulous, almost pointless topic so often, because you're just going to end up with opinions about camel case versus kebab case and opinions about rest versus graph UI, and all the nonsense that we love to fight about. And there's going to be someone with a fever at HTTP status code. And none of that actually matters, but you're talking about more of the business level stuff or what, what sort of things have come up as like. Really interesting results from, from your survey about how to build a good API what's what's, what's new and what's interesting. Matthew Reinbold: Right. Well, one of the things I wanted to look at was some of the insights that popped out to me when I was reading accelerate. So accelerate is like from. The previous decade, but it was written by Nicole Forsgren, Jess humble, Jean Kim, they came together and tried to figure out like, what was it about dev ops? That was so powerful. And they wanted to do it in a, in a way that quantified things, not just like, Hey, this is awesome. You should be doing it, but like get to the meat and potatoes of why is this powerful and why should businesses adopt dev ops? And as they went through their research they ended up discovering that there was really four things, four metrics that showed how dev. Made for better organizational performance. And those things were lead time, deployment, frequency, meantime to restore, or how quickly you recover and the change fail percentage. And I thought, huh, that's really interesting. Now that's for dev ops, but if these things are so instrumental in having organizations outperform. Their peers. Can we find the same correlation with API APIs? If we have the same behaviors, can we therefore then draw a line and say, if you have these things, if you have positive aspects of these four attributes, can you then have a more sustainable, more powerful API program? And based on our survey results, the answer is yes. So I can, I can go in and how we, how we drew that correlation. Phil Sturgeon: I'm curious, what sort of metrics are We, looking at? Matthew Reinbold: yeah. So first off we asked people on a 10 point scale. What, how, how well do you think that you've become API first? So out of our 28,000 respondents, they looked at this 10 point scale and they, they put themselves, you know, how they felt approximately 8% of the people that responded said, yes, we are either a nine or a 10 on the scale for API first, we said fine. And then we went through and we said, okay, you know, how long does it take you to make an API? Are we talking hours, days, weeks, so on and so forth. And we also said, okay, you know, not just time to produce, but how frequently you deploy and how many times do you have a deployment failure? Meaning like you put something in production, but it didn't work. Right. So you have to roll back and then like, what was your time to recovery? Like when an outage does occur and let's be. And outage always occurs at some point. Like how, how quickly can you recover from those things? So we got these nice, you know, bell curves and everybody kind of clumped toward the center on these things. And then we said, okay, Now the magic is we go back to that first question, the people that say their API first that have some kind of strong belief that they're doing API first, let's see how they compare to their peers on these metrics. And again, and again, all for these items, API, first people perform better. So, you know, taking one example here. API first people were able to deploy 17% faster than their peers and you know, in a day or less. So if you are API first and granted, there, there might be some subtlety in how a company defines that. But bottom line, if you are API first, you perform better on these metrics than your counterparts. Phil Sturgeon: Interesting. And yeah. Seeing, seeing as you raised it, what is API first? There's, there's a lot of different definitions floating around. Right. And so just for listeners that might not have listened to everything we've ever talked about and read every blog post we've ever read ref ever wrote how do you define it? Matthew Reinbold: Sure. Well, first for people that haven't heard this and haven't listened to every episode, shame on you. Second, I define I defined API first and. Making the API experience or the interface, the primary means for the functionality exchange. So not viewing, like I'm going to create this functionality and then subsequently go and some other team or, or some other project we'll be wrapping this thing in an API. It's thinking of creating an API experience as the primary exchange mechanism with dysfunctional. Not a library, not a module, not a class, the API. So this is slightly different than API design first, which is, I am going to subsequently talk to stakeholders, create a model, whether that's in an open API document or some other means, but I'm going to sketch that out. Test my assumptions, and then subsequently only begin code after. That's API design. First, I do draw a line between those two. They are very copacetic. They, they work together like peanut butter and chocolate, but there, there is a difference. You can, you can do API first without necessarily being API design first. Phil Sturgeon: For sure. Oh, well, we've got you on a roll. You're doing these really well. What is API as a product? Matthew Reinbold: Ooh, API API as a product. So that is creating an API with the. Awareness that it will have a roadmap. It will have ownership beyond just being put into a production environment that it will grow and change and subsequently necessitates the kind of modeling responsibilities and, and awareness that it will be growing and changing over time. Phil Sturgeon: Okay. So instead of, yeah, API first is your product should have an API. And that will be managed by the team who was making this product. And API as a product is a slight variant of API. First, that kind of takes that API out of that generic functionality team and says the API itself is the product. And another team potentially on the same team will be making a product using that Matthew Reinbold: Right. I, I would, I would, I would venture there's a lot of large enterprise environments for which API for. It's about a project that gets the thing into production. And then that thing is left to operate and run on its own. Perhaps there's some monitoring, perhaps some observability, but the actual team that made it is off doing the next thing and the next thing and the next thing there's not the idea that. This is a long lived item that, that produces some kind of business functionality value that is competing in a complex dynamic marketplace like that. That's the API product side of the house. Phil Sturgeon: Hm. Matt Trask: So the, I guess like the, the big question to bring up, I think right now is what did the pandemic do for the API ecosystem? Matthew Reinbold: Well, you know, first of all, I want to just stress that, that this thing that we kind of hand wave is the pandemic was actually like multiple congenital. Crises all at once. Right. You know, I, I want to, for the audience, like we're talking social unrest and political upheaval and supply chain disruption, and the, the pandemic was really a catch all for a tremendous amount of business stress. And what we've seen in the report is the usage of APIs, the number of API APIs the. Amount of focus and care on API. APIs has increased tremendously with that pandemic because business leaders, technology leaders are struggling with this amount of change, this amount of disruption. And so having architectures that are slow to change, difficult to change is just not cutting it in this. Set of multiple crises. So any kind of architectural advantage that allows them to change rapidly change quickly to do different things with how their development investment is deployed. So, you know, for example, taking that one dev team that was altogether in the office and being able to break it down into microservices to allow for greater asynchronous operation, greater flexibility. Those are the architectures that are being sought right now. Matt Trask: Yeah, that makes sense. I mean, it always here in America, I don't know if it feels sing, but you know, like there's. At the core level there. So like the whole, did we go back to the office and be Sandy the office upheaval as well. So it makes sense that there is kind of like a, a struggle on rapping, like getting non-technical CEOs, CTOs, CFOs their heads around the game-changing, this of APIs that doesn't surprise me at all to hear that they're still kind of, I don't want to say struggling, but unsure. Maybe like, Matthew Reinbold: Well, and, and, well, I, I think that's an interesting perspective because it assumes that leaders were in command and control positions of how the labor was divided anyway. And I would actually, I would actually posit that it's the opposite. It was everybody immediately going and running to their home offices and working in a remote work environment. The change in the communication paths changed the architectures that were subsequently produced by those teams. It's Conway's law in effect. And therefore, as we, as we look forward, as we look forward to what's going to happen, I would, I would venture that the organizations that pull people back to centralized locations, for whatever reason, I'm not going to debate whether that's good or bad, but the people that pull the development teams back to. see, like the Terminator two bad guy they'll reform remold because there will be more efficient communication patterns when everybody's face to face. Whereas those organizations that continue to have a distributed workforce will have more distributed architectural patterns because that's how communication is happening. Phil Sturgeon: That's really interesting. I haven't really thought about it before, but I, I, I bet there's been an uptick in kind of API design first, specifically due to this as well. Right? Because my experience working we work was, was pretty awful as far as like API planning goes and as a result, APA architecture and API performance and Matthew Reinbold: You don't say you should blog about that. Fail. Matt Trask: Yeah. Phil Sturgeon: 25. I'm going to do a book about that shit. Matt Trask: Have you tweeted about this yet? Phil? I'm not sure if anyone knows your true Phil Sturgeon: I did a talk. I did a talk recently. But yeah, there was, there was such an element of like, we're real in an open plan office, playing ping pong together and shooting each other with nerves that there was never any effort on API contract being written down in any shape or form because you're all sitting about. And you're just like, what's that end point? Cool mate. Oh, if slash whatever. Oh, is that a, is that property of booty? It's a string called true with QuoteWerks and then you didn't have a need to write it down because you just show it over, over the top of Nerf fire. And I, I do wonder if remote work, well, not necessarily remote work, but quarantine remote work has helped push people more towards it because if you can all be sitting around asking each other, you're going to be typing. The contract over slack. And if you're going to be typing it out over slack, which is inherently ephemeral, then you might as well type it into a Yammel file and commit that in the repo. And then you can have design reviews around the board request or other tools that the offer, that sort of thing. So, yeah, that's, that's just completely a hypothetical and something I'm thinking the second night and check that, but I'm sure it's happening. Matthew Reinbold: I completely agree. And, and let me throw in something that's not in the report, but something that's got me totally geeked out and I'm watching for on my radar, we are going to see the greatest Renaissance of API design documentation that we've ever seen in the next couple of years. Now, granted, you know, as far as Renaissance goes, maybe Renaissance. Documentation are not that great. So, you know, let's put the party hats back in the closet, but what we're seeing with the great resignation right now is all of that knowledge that people acquired in their heads is leaving. It's headed out the door and I've read reports like up to 80% of how to do things with API APIs is in people's heads. Like at we work. If you needed to know how API has worked. You know, you knew Phil was the guy that could get you straightened and Phil Sturgeon: I didn't have a clue. That was the problem. I was trying to find out how to do it. Matthew Reinbold: Okay. So I wasn't, it was somebody, it was somebody on the other end of a, of a Nerf battle away Phil Sturgeon: Someone who quit already is the person that you. Matthew Reinbold: But right now in organizations like you have this phenomenon where a tremendous number of people are leaving organizations and they might've been the sole person who knew where the end points were or knew how that particular tricky function worked. And as organizations are trying to deal with this and recover and still be productive, there's going to be a greater emphasis on having that crap written down, having things documented. Organizations don't have aren't left on their back foot like they are right now. So whether that's heavy handed processes, whether that's just a greater appreciation for documentation among the staff, that's left, whatever that manifests as there's going to be an increasing amount of emphasis on documentation, because people have seen that too much was stuck in people's heads and it's not sustained. Phil Sturgeon: Yeah, that's a really good point. I mean, and not just kind of documentation, but the whole open API as a source of truth earlier on. And I figured it has to be, has to become more noticeably important when Yeah. They've, they've lost the whole team. How the API works and you know what it's like, code's always a bloody mess. Cause you just hacked up within about what over the place and patch things and fix things. And what about and yeah, when they find themselves rewrite in the API, cause no one can really take it over and no one remembers how it works and there's no documentation for it. And it's just too hard to figure out when they just make a brand new one. And they have a whole brand new team doing it. Cause they've already lost all that stuff. Matthew Reinbold: Yeah. Phil Sturgeon: That's a situation that a lot of managers and business people are going to say, how can we go about avoiding doing this? And I just hope there's someone in the room that says, well, APA designed first would really help avoid this problem because otherwise they'll just repeat all the same mistakes again. Matthew Reinbold: Right. Absolutely. Whether it's design first or tools that help analyze existing traffic and write the document afterwards, like whatever you got to do, get that written down and start taking some notes against it because. It's it, I believe right now with the great resignation. It's an Achilles heel. That's probably hampering a lot of organizational ecosystems right now. Matt Trask: Yeah, I would definitely agree. I mean, it shows in the report under open API three dot oh, 44% of people are aware of it, but they don't use it 28% say they use it. 12% said they use it, the love it. So even just combining use it and use it in love. It still does not match aware of we're not using it. Which means that there is definitely a. A river to jump over. So to speak, to getting more people on, to open API, which is probably currently like the standard for API documentation right now which comes back to your point, which allows them to start writing things down and start documenting things. And Phil gets it by bus tomorrow. We work is still going to be okay. It very well could happen. Which is exactly why I use that example. And it, it, yeah, it it'll give the organization a little bit more or a little less reliance on what's in people's heads a little bit more stability in case great races, nation three Datto happens in three years. You know, you don't know what's gonna happen. Phil Sturgeon: Is that when everyone resigns from web three point now, Matt Trask: please. Don't don't threaten me with a good time. Like I've already, I've already muted those web three and NFD on my Twitter and it cleaned it up so Phil Sturgeon: Why do you hate progress, man? Matt Trask: A lot of reasons. I'm a combustion at heart? No. Matthew Reinbold: Hey, if you don't, Phil Sturgeon: particular messages of this progress that are the problem. Matthew Reinbold: if you, don't stand for something, you'll fall for anything. Good for you, Matt. Matt Trask: yes, I've always wanted my life to be attributed to a, a Hamilton quote. So I am glad I did. I can check that one off to get back onto the actual topic and not just bashing NFTs for an hour and a half, which sounds like a lot of fun. What you the most about this report? Like what was something that you read that just you weren't expecting? Matthew Reinbold: I, I think there was two things that when you combine them together it made me tilt my head and go, huh? The, the first is that more than anything else? Including speed to production. People want quality API APIs. They want stability. They want some other things reliability. But the primary thing that people want out of their, their API APIs is quality. And yet when it came to whether or not people had time to test. Everybody acknowledged that testing was good. Tested was valid, but nobody had enough time for testing and it's like, huh? These two things kind of seem like. The, the two sides of a coin, right. You know, people aren't getting the quality that they want, but everybody acknowledges that they don't have enough time to do testing, even though they recognize the testing is an extremely valuable type thing. So I think when it comes to socializing this report and talking to decision-makers and doing the kind of coaching that I so often do, I, this is one of those things too, to bring up, like how in your program are you supporting. Testing and ensuring that enough is being done there so that your developers feel like you're, you're reaching the kind of quality goals that, that you're, you're promising to the rest of the world. Phil Sturgeon: Hm, do you, is the survey broken down by role? So can you, can you look to see if. Managers and engineers have a rule, very interested in, in high quality. And engineers are going, but we don't have enough time, but the manager's like, oh, they definitely have enough time. Matthew Reinbold: Right. So we do have a breakdown by role and job title, but I don't have the numbers in front of me that, that combined, and show me how to break down the quality question. Phil Sturgeon: Yeah, that'd be an interesting one. Cause yeah, so many roles, so many organizations, I just take it as like a universal truth is that companies are just, you know, business and product are demanding feature, feature, feature, feature, feature, and engineers are just like screaming, just keyboards on fire, trying to try to hit them goals. And everything's just wonky as hell. And it seems to be everywhere I go. There's not enough to have. There's not enough time for QA. They might've got rid of the QA team because it's slowed down product and slowed down delivery of features. Yeah, everyone wants high-quality API has, but no one wants to put the time in to testing because testing is inherently hard and slow. Matthew Reinbold: Right. And kind of along those same lines, another stat that jumped out at me was that 76% of the people building API APIs have less than five years experience doing. I mean, you know, as far as restful APIs now, we're, we're more than a decade into that journey. So that stat leaps out at me, like what is it about API development, where we're getting people with zero to five years experience like what's happening. There are the successful API builders, aging out and becoming management. it, are they moving on to web three O and NFTs? Like, like what is, where are our experienced API builders and why are these critical pieces of business infrastructure? In the hands of relatively younger people. That's not to say that they can't be doing a good job, that, that it's impossible to build a great web experience at your first time at bat. But it's also something where I think everybody on this call would probably agree. Experience counts, experience matters. Ha being around the block once or twice, you pick up a feel for what's beneficial, what's maybe a little wonky and you can imbue that into a better design at launch. So, you know, where are the. 10 year, the 12 year, the 15 year veterans. And why are they not the primary source of API infrastructure development? Phil Sturgeon: Yeah. Some that I've seen so much, again, just, I love complaining about we work. Pretty much everyone that was a junior developer, Right. Like the vast majority, what, what you need developers and their role responsible for creating you know, there's like a hundred API APIs and, you know more than a hundred junior developers with just a sprinkling of seniors who were more on the cowboy coder end of things. Not, not to be rude, you know, like startup, you need to be super agile, super fast, not, not a perfectionist. And so, so many of the problems where this is, this person's first rails app, like they know how to accept incoming Jason parameters and they know how to spit something back from the database. And. That's that, and they know how to make a web request. So he talks to . He talks to F talks to G in the thread, and then no, one's got a timer anyway. So everything falls over, like, things like that. The sort of thing you realize, if you've been doing APIs for five years, or for 10 years, you've been doing it for 10 years, you wouldn't do that. You just wouldn't do that. You'd put something in a sidekick job and then implement a web socket or a web hook, or literally anything else. But. That's the sort of thing you do when you consider like HTP failures or server downtime, to be an edge case that is like some weird scenario that probably won't happen. And when you've been doing it for a longer time, you're like you, you change your mindset to this web requests probably won't work. And on the off chance that it. This is what should happen. And you just get really defensive and paranoid and have like 25 different guard statements and, you know, 25 different types of ex exception catching and, and every single circuit breaker and trigger warning that you can possibly put on this thing. And there is, yeah, there is a change in mind. Around around that kind of it doesn't, I'm not being a gatekeeper or at least they're saying you've got to be doing EPS for 10 years until you're good. But when you start out, you you're such, you're more of an optimist. You haven't seen it go wrong in as many ways. You haven't had cascading failures and you haven't had all these terrifying things that happen. So that, that is definitely a concern for me is that I think, yeah. Happy, happy path development. When you go from having one AP. To having 20 or a hundred, the, the the chance of straying off the happy path gets exponentially worse. Right. And, and that's just something, I think a lot of these younger developers on experience with. Matthew Reinbold: Right. Even, even when it comes to design, having used API APIs, having to incorporate the API APIs, you better understand what makes a good description and what is just a reiteration of the, the name itself. Yeah. Yeah. If I have a field called date of birth and the description is just the birth, that, the date that the person was born on, like, well, what was the. do I need to refresh it? Or is it cashed? You know, like, can I store it or is it part of some kind of regulatory PII? And I shouldn't, you know, I can use it, but I shouldn't store, like, there's so many issues that once you've been down that road, and then you're asked to produce an API, you bring that experience with you and you put it into the description that adds so much that yeah. I, I, I, I don't know. How we continue to get that, that experience circulating and get that in front of people. But I think it's really important. Matt Trask: Well, I must wonder too, like how many of those, like experienced API builders are getting swallowed up into Stripe? Twilio, Google. And kind of almost locked away working on their API APIs and not able to share their experiences down the road to junior developers in their own companies or interim networks, things like that too, because it feels like you do your five, seven years as developer, you get pulled into the management game and then all of your knowledge is still there, but you're having to balance both managing a development team, hitting your goals. Pushing out products because you've got to make money for the business. And all of your knowledge that you've worked so hard to gain is kind of sidelined in the name of profits or KPIs or whatever it might be. Matthew Reinbold: Possibly there's, there's certainly exceptions that spring to mind. One of which is Tim Burks and the team over at Google and with the number of resources that they put out there. For their APIs. It's, it's kind of a mouthful, but if you do a Google search for that, they've produced a tremendous amount of documentation about how they support API APIs at scale, how they do their design reviews, how they think about consistency and cohesion across their entire footprint. So that certainly what you described could be the case in some places. You know, I, I, I do think that it's not necessarily the default that's people go off to these big organizations and then just disappear because the folks at Google around Tim and his crew they're doing some great work. Phil Sturgeon: So I've been sat in the room with you having these sort of conversations your last job, Right, Like a center of excellence type stuff. You, you get a bunch of smart people and me together and start talking about what, what would help with these various different problems? Like how do we do APA design reviews? How do we do governance? What standards should we be interested in? So I think sometimes yeah. Experienced developers can get sucked up into these companies and kind of finish and end up having that scale was used for something else. But I, I think companies that have those governance processes, like they're sharing their experience back by creating style guides, by creating programs that they explain how these, how these like API designed life cycles or API life cycle should work. And that's a way that they can essentially. Distribute their experience. So instead of like, I know what to look for when I'm reviewing a poor request, they can create a style guide. That means that everyone will do that. I think the danger there is that when style goes focus on what, instead of why then, then you kind of lose some of that experience because it just seems like arbitrary decisions delivered from upon high. Right. You just get. Do it this way, but, but Y I've read loads of style guides recently. And, and some of them, I should probably show the examples. It's just like, do this. Like, why you don't tell me what to do? You don't my dad, like, it just, I couldn't figure out what they possibly could have meant by it. Cause usually I can look at something. Why might they mean that? Oh, that reminds me of a thing that happened along these lines. They probably got burned by that before, and they want to avoid it, but if you don't see why it just sounds arbitrary and you're not actually teaching anyone on anything, but if you do it right. that that can be really helpful. Matthew Reinbold: Right. And it's also essential that if you're designing these systems like a governance or like a center of excellence that you have the feedback process that you have, the, the communication cycles so that when people do have that kind of. That they have a recourse. It's not a dead end. It's not either you do this or you're punished for it, but oh, if this doesn't make sense, here's who you talk to. Here's how you can escalate your concern here is how you elevate your edge case. And we can have a discussion about it and you can help co-evolve this thing, because you own this as much as somebody else, the, the phenomenon that you described, where it's a dead end. It's thrust upon you. You don't have ownership of that. And as a developer, that does not feel good, that does not invest you in seeing the long-term growth of, of that system. You want to burn that system. You want to be the rebels flying through the death star trench. You want to take that thing down? So what's essential is to realize. You provide the avenues for people to, to voice their concerns, voice their questions, and make them feel heard in such a way that their process, the process is theirs. It's not something done to them. It's it's their process. Phil Sturgeon: I'm just laughing about the death star rebel situation. Now I'm completely distracted. I need to go rewatch some star wars. I don't know. Matt Trask: I mean, your, your thought on the ownership thing is also interesting cause And we like watching the junior Twitter, the junior developer Twitter circles, which is not the end all be all of it all, but there is a large emphasis on if you want to make more money, you need to jump ship every two years on average. And that kind of removes the does or not the desire, but like the, the ownership of any sort of product from a junior developer, because in two years, they're going to be onto another thing. They're going to be onto another system. Codebase, maybe another language and it, it does kind of bring back, like, how do you entice people to have ownership, even if they only are going to plan to say somewhere for a short period? Because we all know that like having, like you said, having that ownership is going to kind of make you more invested, more caring, more thoughtful, more empathetic towards whatever it is that you're building. Matthew Reinbold: Right. I mean, we're veering into management territory, which I'm happy to talk about. I, I know. Matt Trask: very allergic to management. So. Matthew Reinbold: But I, I was just reading Harvard business review. Hey, I'm fun at parties too. So I was reading Harvard business review talking about COVID and the great resignation and the, the management challenges that, that come with that and what we need more. In all companies is a feeling of belonging, a feeling like we have a career progression feeling like our, our, our work has impact and all too often management, just as about making sure people don't do dumpster. Right. You know, I'm, I'm here to police you because the organization doesn't trust you. And it leads to all kinds of weird effects. Like, Hey, if you actually want to grow your career, you need to leave. You need to hop companies every two years and let's be clear that may work, but it's still very disruptive, not just for the company, but for the individual. 'cause they're having to rebuild all of those social structures, their relationships, their patterns, the routines it, it's not, it doesn't come for free. And so from a management standpoint, if you can show people how to have that fulfilling career, how to fulfill those needs. They don't have to jump ship every two years. There's no reason that that has to be the default blueprint. And from a company standpoint, you actually benefit from that accrued experience rather than having a developer. That's done the same thing. Five times you get five years of experience. That's really powerful, really tremendous. And that, that ultimately not only leads to better APIs, but leads to a better employee. So there is a disconnect we need to work with our management layers. It shouldn't just be the technician that has some headcount is by default manager. There needs to be an appreciation for how those are unique skill sets. Those are unique muscles that need to be exercised, but. If we can create that fulfilling sense of duty then, and that the career path for these individuals, we can get them off of this kind of binge and purge career treadmill. Matt Trask: So that's a really, yeah, that's a really good way to put the whole two year turn. And I mean, it comes back full circle to what you just said earlier, which is, you know, 75% of API has been developed now or done by people with less than five years experience. And that's probably because of the same, people are jumping, jumping, jumping. Whereas if you can keep them around, make them happy, make them feel like they belong. We might actually start seeing that number. Dropped significantly to more experienced API developers building more thoughtful API design with, with years of knowledge built up. So I think it'll be really interesting to see kind of what happens with this great resignation how that all shapes up. And then it'll be interesting to see to kind of the 2022 say the API report. How does that. How, how will things change from a year in a year going forward? And what can we expect possibly looking at these two years, the next five years after that, the next 10 years growing on different trends, you know, we might see NFTs ruling the world. We might see graph QL. Rolling. Phil Sturgeon: No comment. Matt Trask: Matthew is kind of shrugging Phil Sturgeon: we're all sad. Now, rural sat now, NFTs powered by graft UL, problem solved. Can you, can you still right click that? No, you can't. It's like a post. So. Matt Trask: Well, there goes Matthew Reinbold: Each unique query is published as an innovator. And you can put the ownership of that query in a blockchain so that you don't have the centralized point of failure. Phil Sturgeon: I was going to thank you for being for, for making this podcast sound intelligent for once. And, Matthew Reinbold: And then I ruined it. Sorry. Phil Sturgeon: and then you. Matt Trask: no, no, no, you didn't ruin it. You just brought it back down to its normal level of ridiculousness. Phil Sturgeon: Fantastic. No. Do you have any predictions for what we're going to see in the, in next year's state of this report? Because then we can play that clip back and laugh at how wrong you were. Matthew Reinbold: Oh, lovely. All right, well, let me have a few minutes to sandbag my answer. No, I think there's a tremendous amount of, of areas where we can take this correlation that I talked about before behaviors. You know how the question immediately becomes well, okay. If these four behaviors are so good and are present in high-performing API companies, how do we get there? And this year we had a little bit around leadership and what leaders do. To get an API first company. I think there is a lot of exploration we can do there to really dial in and say, okay, we know these things are good. How do you get there? How do you promote these things? How do you, how do you get it so that you are able to deploy in a minimal amount of time or recover faster? What are leaders in those organizations doing? That's one of the things I'd love to dig into obviously. A lot of post pandemic aftermath. There's been a tremendous amount of published about how this digital transformation and, you know, we're so much more flexible and adaptable because we, we are now doing all our conversations over zoom. And I look at that and I, I scratch my head because. Digital transformation, at least in the non buzzword compliant way is a whole lot more difficult than just moving everything to a slack conversation or a, or a zoom conversation. Like it means fundamentally dismantling your policies and procedures and reinventing them in a way that digital technology lends itself to. So figuring out what that post pandemic landscape looks like and how we're still feeling the knock on effect. Is going to be something that's also going to be very interesting to explore. Matt Trask: Yeah, that's definitely true. I mean, I think one thing I would like to see is, is that number of people who know open API, but don't use it start to gradually shift down and people who are using open. Start to shift up, which, you know, from a silver right back to having documentation and some sort of notes about their API. So when the, the knowledge people do eventually leave because everyone leaves the company at some point, the knowledge isn't necessarily leaving. And instead we're, we're kind of leaving a better legacy to the people following us. Yeah, definitely. Matthew Reinbold: Here here. Matt Trask: Cool. Matthew, thank you so much for taking some time out of your, your, your day to talk to us. We really appreciate it. Look forward to having you back in roughly a year's time to talk 20, 22. Say the API report Matthew Reinbold: I love it. Let's do it. Pencil it in right now. Matt Trask: Yep. It's it's on my calendar. I don't know what I'll be doing in a year from today, but I know for a fact we'll be talking again. If you want to get. Matthew on Twitter. He is at libel Vox, L I B E L underscore V O X M. And we'll throw the link to your blog and Twitter in the show notes as well as everything else. Awesome. Cool. Thank you so much. We appreciate it. Phil Sturgeon: Yeah. All audio, artwork, episode descriptions and notes are property of APIs You Won't Hate, for APIs You Won't Hate, and published with permission by Transistor, Inc. Broadcast by | 2026-01-13T08:48:40 |
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https://docs.python.org/3/license.html#terms-and-conditions-for-accessing-or-otherwise-using-python | History and License — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents History and License History of the software Terms and conditions for accessing or otherwise using Python PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0 CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1 CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2 ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION Licenses and Acknowledgements for Incorporated Software Mersenne Twister Sockets Asynchronous socket services Cookie management Execution tracing UUencode and UUdecode functions XML Remote Procedure Calls test_epoll Select kqueue SipHash24 strtod and dtoa OpenSSL expat libffi zlib cfuhash libmpdec W3C C14N test suite mimalloc asyncio Global Unbounded Sequences (GUS) Zstandard bindings Previous topic Copyright This page Report a bug Show source Navigation index modules | previous | Python » 3.14.2 Documentation » History and License | Theme Auto Light Dark | History and License ¶ History of the software ¶ Python was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see https://www.cwi.nl ) in the Netherlands as a successor of a language called ABC. Guido remains Python’s principal author, although it includes many contributions from others. In 1995, Guido continued his work on Python at the Corporation for National Research Initiatives (CNRI, see https://www.cnri.reston.va.us ) in Reston, Virginia where he released several versions of the software. In May 2000, Guido and the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. In October of the same year, the PythonLabs team moved to Digital Creations, which became Zope Corporation. In 2001, the Python Software Foundation (PSF, see https://www.python.org/psf/ ) was formed, a non-profit organization created specifically to own Python-related Intellectual Property. Zope Corporation was a sponsoring member of the PSF. All Python releases are Open Source (see https://opensource.org for the Open Source Definition). Historically, most, but not all, Python releases have also been GPL-compatible; the table below summarizes the various releases. Release Derived from Year Owner GPL-compatible? (1) 0.9.0 thru 1.2 n/a 1991-1995 CWI yes 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes 1.6 1.5.2 2000 CNRI no 2.0 1.6 2000 BeOpen.com no 1.6.1 1.6 2001 CNRI yes (2) 2.1 2.0+1.6.1 2001 PSF no 2.0.1 2.0+1.6.1 2001 PSF yes 2.1.1 2.1+2.0.1 2001 PSF yes 2.1.2 2.1.1 2002 PSF yes 2.1.3 2.1.2 2002 PSF yes 2.2 and above 2.1.1 2001-now PSF yes Note GPL-compatible doesn’t mean that we’re distributing Python under the GPL. All Python licenses, unlike the GPL, let you distribute a modified version without making your changes open source. The GPL-compatible licenses make it possible to combine Python with other software that is released under the GPL; the others don’t. According to Richard Stallman, 1.6.1 is not GPL-compatible, because its license has a choice of law clause. According to CNRI, however, Stallman’s lawyer has told CNRI’s lawyer that 1.6.1 is “not incompatible” with the GPL. Thanks to the many outside volunteers who have worked under Guido’s direction to make these releases possible. Terms and conditions for accessing or otherwise using Python ¶ Python software and documentation are licensed under the Python Software Foundation License Version 2. Starting with Python 3.8.6, examples, recipes, and other code in the documentation are dual licensed under the PSF License Version 2 and the Zero-Clause BSD license . Some software incorporated into Python is under different licenses. The licenses are listed with code falling under that license. See Licenses and Acknowledgements for Incorporated Software for an incomplete list of these licenses. PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 ¶ 1. This LICENSE AGREEMENT is between the Python Software Foundation ("PSF"), and the Individual or Organization ("Licensee") accessing and otherwise using this software ("Python") in source or binary form and its associated documentation. 2. Subject to the terms and conditions of this License Agreement, PSF hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python alone or in any derivative version, provided, however, that PSF's License Agreement and PSF's notice of copyright, i.e., "Copyright © 2001 Python Software Foundation; All Rights Reserved" are retained in Python alone or in any derivative version prepared by Licensee. 3. In the event Licensee prepares a derivative work that is based on or incorporates Python or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python. 4. PSF is making Python available to Licensee on an "AS IS" basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 6. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 7. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between PSF and Licensee. This License Agreement does not grant permission to use PSF trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party. 8. By copying, installing or otherwise using Python, Licensee agrees to be bound by the terms and conditions of this License Agreement. BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0 ¶ BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1 1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the Individual or Organization ("Licensee") accessing and otherwise using this software in source or binary form and its associated documentation ("the Software"). 2. Subject to the terms and conditions of this BeOpen Python License Agreement, BeOpen hereby grants Licensee a non-exclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use the Software alone or in any derivative version, provided, however, that the BeOpen Python License is retained in the Software, alone or in any derivative version prepared by Licensee. 3. BeOpen is making the Software available to Licensee on an "AS IS" basis. BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 5. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 6. This License Agreement shall be governed by and interpreted in all respects by the law of the State of California, excluding conflict of law provisions. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between BeOpen and Licensee. This License Agreement does not grant permission to use BeOpen trademarks or trade names in a trademark sense to endorse or promote products or services of Licensee, or any third party. As an exception, the "BeOpen Python" logos available at http://www.pythonlabs.com/logos.html may be used according to the permissions granted on that web page. 7. By copying, installing or otherwise using the software, Licensee agrees to be bound by the terms and conditions of this License Agreement. CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1 ¶ 1. This LICENSE AGREEMENT is between the Corporation for National Research Initiatives, having an office at 1895 Preston White Drive, Reston, VA 20191 ("CNRI"), and the Individual or Organization ("Licensee") accessing and otherwise using Python 1.6.1 software in source or binary form and its associated documentation. 2. Subject to the terms and conditions of this License Agreement, CNRI hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python 1.6.1 alone or in any derivative version, provided, however, that CNRI's License Agreement and CNRI's notice of copyright, i.e., "Copyright © 1995-2001 Corporation for National Research Initiatives; All Rights Reserved" are retained in Python 1.6.1 alone or in any derivative version prepared by Licensee. Alternately, in lieu of CNRI's License Agreement, Licensee may substitute the following text (omitting the quotes): "Python 1.6.1 is made available subject to the terms and conditions in CNRI's License Agreement. This Agreement together with Python 1.6.1 may be located on the internet using the following unique, persistent identifier (known as a handle): 1895.22/1013. This Agreement may also be obtained from a proxy server on the internet using the following URL: http://hdl.handle.net/1895.22/1013". 3. In the event Licensee prepares a derivative work that is based on or incorporates Python 1.6.1 or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python 1.6.1. 4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS" basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON 1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 6. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 7. This License Agreement shall be governed by the federal intellectual property law of the United States, including without limitation the federal copyright law, and, to the extent such U.S. federal law does not apply, by the law of the Commonwealth of Virginia, excluding Virginia's conflict of law provisions. Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of the Commonwealth of Virginia shall govern this License Agreement only as to issues arising under or with respect to Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between CNRI and Licensee. This License Agreement does not grant permission to use CNRI trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party. 8. By clicking on the "ACCEPT" button where indicated, or by copying, installing or otherwise using Python 1.6.1, Licensee agrees to be bound by the terms and conditions of this License Agreement. CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2 ¶ Copyright © 1991 - 1995, Stichting Mathematisch Centrum Amsterdam, The Netherlands. All rights reserved. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Stichting Mathematisch Centrum or CWI not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION ¶ Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted. THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Licenses and Acknowledgements for Incorporated Software ¶ This section is an incomplete, but growing list of licenses and acknowledgements for third-party software incorporated in the Python distribution. Mersenne Twister ¶ The _random C extension underlying the random module includes code based on a download from http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/emt19937ar.html . The following are the verbatim comments from the original code: A C-program for MT19937, with initialization improved 2002/1/26. Coded by Takuji Nishimura and Makoto Matsumoto. Before using, initialize the state by using init_genrand(seed) or init_by_array(init_key, key_length). Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura, All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. The names of its contributors may not be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Any feedback is very welcome. http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space) Sockets ¶ The socket module uses the functions, getaddrinfo() , and getnameinfo() , which are coded in separate source files from the WIDE Project, https://www.wide.ad.jp/ . Copyright (C) 1995, 1996, 1997, and 1998 WIDE Project. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the project nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE PROJECT AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE PROJECT OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Asynchronous socket services ¶ The test.support.asynchat and test.support.asyncore modules contain the following notice: Copyright 1996 by Sam Rushing All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Sam Rushing not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. SAM RUSHING DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL SAM RUSHING BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Cookie management ¶ The http.cookies module contains the following notice: Copyright 2000 by Timothy O'Malley <timo@alum.mit.edu> All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Timothy O'Malley not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. Timothy O'Malley DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL Timothy O'Malley BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Execution tracing ¶ The trace module contains the following notice: portions copyright 2001, Autonomous Zones Industries, Inc., all rights... err... reserved and offered to the public under the terms of the Python 2.2 license. Author: Zooko O'Whielacronx http://zooko.com/ mailto:zooko@zooko.com Copyright 2000, Mojam Media, Inc., all rights reserved. Author: Skip Montanaro Copyright 1999, Bioreason, Inc., all rights reserved. Author: Andrew Dalke Copyright 1995-1997, Automatrix, Inc., all rights reserved. Author: Skip Montanaro Copyright 1991-1995, Stichting Mathematisch Centrum, all rights reserved. Permission to use, copy, modify, and distribute this Python software and its associated documentation for any purpose without fee is hereby granted, provided that the above copyright notice appears in all copies, and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of neither Automatrix, Bioreason or Mojam Media be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. UUencode and UUdecode functions ¶ The uu codec contains the following notice: Copyright 1994 by Lance Ellinghouse Cathedral City, California Republic, United States of America. All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Lance Ellinghouse not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. LANCE ELLINGHOUSE DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL LANCE ELLINGHOUSE CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Modified by Jack Jansen, CWI, July 1995: - Use binascii module to do the actual line-by-line conversion between ascii and binary. This results in a 1000-fold speedup. The C version is still 5 times faster, though. - Arguments more compliant with Python standard XML Remote Procedure Calls ¶ The xmlrpc.client module contains the following notice: The XML-RPC client interface is Copyright (c) 1999-2002 by Secret Labs AB Copyright (c) 1999-2002 by Fredrik Lundh By obtaining, using, and/or copying this software and/or its associated documentation, you agree that you have read, understood, and will comply with the following terms and conditions: Permission to use, copy, modify, and distribute this software and its associated documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appears in all copies, and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Secret Labs AB or the author not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT- ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. test_epoll ¶ The test.test_epoll module contains the following notice: Copyright (c) 2001-2006 Twisted Matrix Laboratories. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Select kqueue ¶ The select module contains the following notice for the kqueue interface: Copyright (c) 2000 Doug White, 2006 James Knight, 2007 Christian Heimes All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. SipHash24 ¶ The file Python/pyhash.c contains Marek Majkowski’ implementation of Dan Bernstein’s SipHash24 algorithm. It contains the following note: <MIT License> Copyright (c) 2013 Marek Majkowski <marek@popcount.org> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. </MIT License> Original location: https://github.com/majek/csiphash/ Solution inspired by code from: Samuel Neves (supercop/crypto_auth/siphash24/little) djb (supercop/crypto_auth/siphash24/little2) Jean-Philippe Aumasson (https://131002.net/siphash/siphash24.c) strtod and dtoa ¶ The file Python/dtoa.c , which supplies C functions dtoa and strtod for conversion of C doubles to and from strings, is derived from the file of the same name by David M. Gay, currently available from https://web.archive.org/web/20220517033456/http://www.netlib.org/fp/dtoa.c . The original file, as retrieved on March 16, 2009, contains the following copyright and licensing notice: /**************************************************************** * * The author of this software is David M. Gay. * * Copyright (c) 1991, 2000, 2001 by Lucent Technologies. * * Permission to use, copy, modify, and distribute this software for any * purpose without fee is hereby granted, provided that this entire notice * is included in all copies of any software which is or includes a copy * or modification of this software and in all copies of the supporting * documentation for such software. * * THIS SOFTWARE IS BEING PROVIDED "AS IS", WITHOUT ANY EXPRESS OR IMPLIED * WARRANTY. IN PARTICULAR, NEITHER THE AUTHOR NOR LUCENT MAKES ANY * REPRESENTATION OR WARRANTY OF ANY KIND CONCERNING THE MERCHANTABILITY * OF THIS SOFTWARE OR ITS FITNESS FOR ANY PARTICULAR PURPOSE. * ***************************************************************/ OpenSSL ¶ The modules hashlib , posix and ssl use the OpenSSL library for added performance if made available by the operating system. Additionally, the Windows and macOS installers for Python may include a copy of the OpenSSL libraries, so we include a copy of the OpenSSL license here. For the OpenSSL 3.0 release, and later releases derived from that, the Apache License v2 applies: Apache License Version 2.0, January 2004 https://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Global Unbounded Sequences (GUS) ¶ The file Python/qsbr.c is adapted from FreeBSD’s “Global Unbounded Sequences” safe memory reclamation scheme in subr_smr.c . The file is distributed under the 2-Clause BSD License: Copyright (c) 2019,2020 Jeffrey Roberson <jeff@FreeBSD.org> Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following con | 2026-01-13T08:48:40 |
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Follow Episode Details / Transcript Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations at Postman, to talk about Postman's State of the API 2021. Show Notes Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations to discuss Postman's State of the API 2021 report, detailing various data points from around the API world from which specification people turn to, to how confident people feel deploying their APIs. They also discuss various topics around remote work, how APIs enable more remote work and what will happen in the next few years for APIs. Notes: Matthew on twitter: https://twitter.com/libel_vox Postman's State of the API Creators and Guests Host Mike Bifulco Cofounder and host of APIs You Won't Hate. Blogs at https://mikebifulco.com Into 🚴♀️, espresso ☕, looking after 🌍. ex @Stripe @Google @Microsoft What is APIs You Won't Hate? A no-nonsense (well, some-nonsense) podcast about API design & development, new features in the world of HTTP, service-orientated architecture, microservices, and probably bikes. Matt Trask: Cool. Welcome back to APS. You won't hate episode 17. I have Phil with me and we're joined by a very special guest today. Matthew Reinbold, fresh from postman, who is a director of API ecosystems and digital transformations here to talk about their report, the 2021 state of the API ecosystem. Matthew, how's it going? Matthew Reinbold: It is going. I am happy to be here first time, caller, long time listener. Is that how we say that? Matt Trask: I think that's yeah. It's how you say it. Yeah. So I mean, for those of you, like in the off chance that someone doesn't know who you are in the API ecosystem world can you give us a little bit kind of about yourself? Like you manage two different newsletters, at least as well as a pretty prolific Twitter presence as well. But if someone hasn't run into you, like. Matthew Reinbold: Well, yeah, well, first off, thanks for calling it prolific. Some people would call it annoying, but yeah, I I manage a fair number of tweets over at Twitter slash L I B E L underscore Vox, reliable Vox. That's where I talk about digital transformation and APIs and a lot of technology stuff. Occasionally. Fights with blockchain and NFT enthusiastic. But then I also manage, I also manage a newsletter called net API notes, where for almost 200 issues, going back to 2015, I've covered the landscape. I've shared essential bits of information. I've tried to boil down the, the. Current climate and get it right into just the most essential things that decision makers need to know and care about. And then I do a fair amount of blogging on a blog. That's very imaginatively named Matthew reinbold.com. In there, I talk about a fair number of things as well, but in, in, in short my passion is really about coaching people, helping people, teaching people to get better with their API ecosystem. Matt Trask: That's really cool. So one thing that kinda stuck out to me cause it's, so we're going to be talking about the 20, 21 Sidi APR report. However, I'm curious since you've been doing it now since 2015, you've been keeping notes on. The API world. How does your kind of, I hate to say this phrase, the 30,000 foot view of everything that, you know, from 2015, how does that kind of line up to what you saw with the 2021 state of the API report? Matthew Reinbold: Oh, that's interesting. So there's definitely. Maturing as a industry, we've gone through a number of phases. Those of us that have been around the block a few times, see trends come. And most often they, they tend to roll away. And over that time we have to develop models so that we can kind of. Pick the, the, the wheat from the chaff, you know, what, what are the properties of something new, some kind of buzzword, some kind of hyperbole that we can latch onto and say, yes, this is worth investing in. This is worth our interest in our effort versus, yeah, this is some marketing system, some spin as I'm looking at the 20, 21 postman report. I see. Where we've come. It's gone from being single point to point integrations. One-off bespoke API APIs to where we're now talking about things as ecosystems. We're now talking about collections of these things and how entire organizations. Manage these as, as something that's beneficial, something that's collaborative and, and managed as a separate entity rather than, than each individual unit I've got Phil here. So I have to use the forest for the trees analogy rather than just managing the individual API trees. There's now a greater awareness of what the forest, what the forest role is in the company and how to manage that. In a unique way, as opposed to the individual pieces. I will say for those that are listening, like I'm one of the things I want to highlight right up front here is that you don't have to enter an email address. It's not behind the page. We really felt strongly at postman that we had to get this information out to the most number of decision-makers so that they could make better decisions so that they could be informed as they're developing their strategies and roadmaps. So if you go to postman.com/state-of-api, you'll be able to download. With out any worry about having somebody from sales follow up with you later, or getting spam in your inbox, it's free for all. We want this information to be used. We want the dialogues to happen. We want the discourse to be rich and for me and frothy. And so please, you know, don't let past marketing spam. Stop you from checking this out. We want this in the hands of people. Phil Sturgeon: Fantastic. That's good to hear. I mean, that's I haven't got around to reading it as you might have seen from Twitter. Life has been a bit of a mess recently just spending far too much time in the field, as opposed to in the field doing APA stuff. But, yeah, that's definitely always been a concern of mine, of, you know, you hear about these white papers and reports and you just know so many of them like should have just be in the blog post, but instead that like a PDF that and you've got to enter information and then you just get like that fifth email, like, why didn't you reply to my previous four? I was like, I don't know who you are. I just want to read this thing. So yeah, I'm glad you folks are going in a different direction, but Maybe just taking a step back. Like, what is the state of API is report all about where are you getting your information from? What sort of research is being done? And what's the hospital. Matthew Reinbold: Great question. So this is, as far as I know, the largest survey of its kind, we had more than 28,000 people respond to our latest in a series. What we tend to do is try and track where the industry is at. And typically that's been around certain areas. Like how much time do you spend developing API APIs? What kind of tools are you using? Really good stuff there tracking the growth of, of the industry and the maturation of the industry. What I brought to the table this year. Was an interest on finding the behaviors that lead to sustainable, healthy API ecosystems. Like so much of what we talk about when it comes to API ecosystems is still very anecdotal. We tell stories about the Bezos Amazon memo, where we talk about like Twilio or Stripe, but when it comes to decision makers in large organizations, they're still. Trying to pull at what are decent KPIs, what are the behaviors I should be grooming or promoting within my company to make sure that I can keep producing quality API experiences again and again and again. And so what we did with this report that I'm really proud of is dig deep and discover, like, what are the correlating behaviors in organizations that lead to good things happening for companies? Phil Sturgeon: Okay. That's interesting. Cause I think. There's always this question around, like, what's a good API and what's a bad API. Right. And that's just such a nebulous, almost pointless topic so often, because you're just going to end up with opinions about camel case versus kebab case and opinions about rest versus graph UI, and all the nonsense that we love to fight about. And there's going to be someone with a fever at HTTP status code. And none of that actually matters, but you're talking about more of the business level stuff or what, what sort of things have come up as like. Really interesting results from, from your survey about how to build a good API what's what's, what's new and what's interesting. Matthew Reinbold: Right. Well, one of the things I wanted to look at was some of the insights that popped out to me when I was reading accelerate. So accelerate is like from. The previous decade, but it was written by Nicole Forsgren, Jess humble, Jean Kim, they came together and tried to figure out like, what was it about dev ops? That was so powerful. And they wanted to do it in a, in a way that quantified things, not just like, Hey, this is awesome. You should be doing it, but like get to the meat and potatoes of why is this powerful and why should businesses adopt dev ops? And as they went through their research they ended up discovering that there was really four things, four metrics that showed how dev. Made for better organizational performance. And those things were lead time, deployment, frequency, meantime to restore, or how quickly you recover and the change fail percentage. And I thought, huh, that's really interesting. Now that's for dev ops, but if these things are so instrumental in having organizations outperform. Their peers. Can we find the same correlation with API APIs? If we have the same behaviors, can we therefore then draw a line and say, if you have these things, if you have positive aspects of these four attributes, can you then have a more sustainable, more powerful API program? And based on our survey results, the answer is yes. So I can, I can go in and how we, how we drew that correlation. Phil Sturgeon: I'm curious, what sort of metrics are We, looking at? Matthew Reinbold: yeah. So first off we asked people on a 10 point scale. What, how, how well do you think that you've become API first? So out of our 28,000 respondents, they looked at this 10 point scale and they, they put themselves, you know, how they felt approximately 8% of the people that responded said, yes, we are either a nine or a 10 on the scale for API first, we said fine. And then we went through and we said, okay, you know, how long does it take you to make an API? Are we talking hours, days, weeks, so on and so forth. And we also said, okay, you know, not just time to produce, but how frequently you deploy and how many times do you have a deployment failure? Meaning like you put something in production, but it didn't work. Right. So you have to roll back and then like, what was your time to recovery? Like when an outage does occur and let's be. And outage always occurs at some point. Like how, how quickly can you recover from those things? So we got these nice, you know, bell curves and everybody kind of clumped toward the center on these things. And then we said, okay, Now the magic is we go back to that first question, the people that say their API first that have some kind of strong belief that they're doing API first, let's see how they compare to their peers on these metrics. And again, and again, all for these items, API, first people perform better. So, you know, taking one example here. API first people were able to deploy 17% faster than their peers and you know, in a day or less. So if you are API first and granted, there, there might be some subtlety in how a company defines that. But bottom line, if you are API first, you perform better on these metrics than your counterparts. Phil Sturgeon: Interesting. And yeah. Seeing, seeing as you raised it, what is API first? There's, there's a lot of different definitions floating around. Right. And so just for listeners that might not have listened to everything we've ever talked about and read every blog post we've ever read ref ever wrote how do you define it? Matthew Reinbold: Sure. Well, first for people that haven't heard this and haven't listened to every episode, shame on you. Second, I define I defined API first and. Making the API experience or the interface, the primary means for the functionality exchange. So not viewing, like I'm going to create this functionality and then subsequently go and some other team or, or some other project we'll be wrapping this thing in an API. It's thinking of creating an API experience as the primary exchange mechanism with dysfunctional. Not a library, not a module, not a class, the API. So this is slightly different than API design first, which is, I am going to subsequently talk to stakeholders, create a model, whether that's in an open API document or some other means, but I'm going to sketch that out. Test my assumptions, and then subsequently only begin code after. That's API design. First, I do draw a line between those two. They are very copacetic. They, they work together like peanut butter and chocolate, but there, there is a difference. You can, you can do API first without necessarily being API design first. Phil Sturgeon: For sure. Oh, well, we've got you on a roll. You're doing these really well. What is API as a product? Matthew Reinbold: Ooh, API API as a product. So that is creating an API with the. Awareness that it will have a roadmap. It will have ownership beyond just being put into a production environment that it will grow and change and subsequently necessitates the kind of modeling responsibilities and, and awareness that it will be growing and changing over time. Phil Sturgeon: Okay. So instead of, yeah, API first is your product should have an API. And that will be managed by the team who was making this product. And API as a product is a slight variant of API. First, that kind of takes that API out of that generic functionality team and says the API itself is the product. And another team potentially on the same team will be making a product using that Matthew Reinbold: Right. I, I would, I would, I would venture there's a lot of large enterprise environments for which API for. It's about a project that gets the thing into production. And then that thing is left to operate and run on its own. Perhaps there's some monitoring, perhaps some observability, but the actual team that made it is off doing the next thing and the next thing and the next thing there's not the idea that. This is a long lived item that, that produces some kind of business functionality value that is competing in a complex dynamic marketplace like that. That's the API product side of the house. Phil Sturgeon: Hm. Matt Trask: So the, I guess like the, the big question to bring up, I think right now is what did the pandemic do for the API ecosystem? Matthew Reinbold: Well, you know, first of all, I want to just stress that, that this thing that we kind of hand wave is the pandemic was actually like multiple congenital. Crises all at once. Right. You know, I, I want to, for the audience, like we're talking social unrest and political upheaval and supply chain disruption, and the, the pandemic was really a catch all for a tremendous amount of business stress. And what we've seen in the report is the usage of APIs, the number of API APIs the. Amount of focus and care on API. APIs has increased tremendously with that pandemic because business leaders, technology leaders are struggling with this amount of change, this amount of disruption. And so having architectures that are slow to change, difficult to change is just not cutting it in this. Set of multiple crises. So any kind of architectural advantage that allows them to change rapidly change quickly to do different things with how their development investment is deployed. So, you know, for example, taking that one dev team that was altogether in the office and being able to break it down into microservices to allow for greater asynchronous operation, greater flexibility. Those are the architectures that are being sought right now. Matt Trask: Yeah, that makes sense. I mean, it always here in America, I don't know if it feels sing, but you know, like there's. At the core level there. So like the whole, did we go back to the office and be Sandy the office upheaval as well. So it makes sense that there is kind of like a, a struggle on rapping, like getting non-technical CEOs, CTOs, CFOs their heads around the game-changing, this of APIs that doesn't surprise me at all to hear that they're still kind of, I don't want to say struggling, but unsure. Maybe like, Matthew Reinbold: Well, and, and, well, I, I think that's an interesting perspective because it assumes that leaders were in command and control positions of how the labor was divided anyway. And I would actually, I would actually posit that it's the opposite. It was everybody immediately going and running to their home offices and working in a remote work environment. The change in the communication paths changed the architectures that were subsequently produced by those teams. It's Conway's law in effect. And therefore, as we, as we look forward, as we look forward to what's going to happen, I would, I would venture that the organizations that pull people back to centralized locations, for whatever reason, I'm not going to debate whether that's good or bad, but the people that pull the development teams back to. see, like the Terminator two bad guy they'll reform remold because there will be more efficient communication patterns when everybody's face to face. Whereas those organizations that continue to have a distributed workforce will have more distributed architectural patterns because that's how communication is happening. Phil Sturgeon: That's really interesting. I haven't really thought about it before, but I, I, I bet there's been an uptick in kind of API design first, specifically due to this as well. Right? Because my experience working we work was, was pretty awful as far as like API planning goes and as a result, APA architecture and API performance and Matthew Reinbold: You don't say you should blog about that. Fail. Matt Trask: Yeah. Phil Sturgeon: 25. I'm going to do a book about that shit. Matt Trask: Have you tweeted about this yet? Phil? I'm not sure if anyone knows your true Phil Sturgeon: I did a talk. I did a talk recently. But yeah, there was, there was such an element of like, we're real in an open plan office, playing ping pong together and shooting each other with nerves that there was never any effort on API contract being written down in any shape or form because you're all sitting about. And you're just like, what's that end point? Cool mate. Oh, if slash whatever. Oh, is that a, is that property of booty? It's a string called true with QuoteWerks and then you didn't have a need to write it down because you just show it over, over the top of Nerf fire. And I, I do wonder if remote work, well, not necessarily remote work, but quarantine remote work has helped push people more towards it because if you can all be sitting around asking each other, you're going to be typing. The contract over slack. And if you're going to be typing it out over slack, which is inherently ephemeral, then you might as well type it into a Yammel file and commit that in the repo. And then you can have design reviews around the board request or other tools that the offer, that sort of thing. So, yeah, that's, that's just completely a hypothetical and something I'm thinking the second night and check that, but I'm sure it's happening. Matthew Reinbold: I completely agree. And, and let me throw in something that's not in the report, but something that's got me totally geeked out and I'm watching for on my radar, we are going to see the greatest Renaissance of API design documentation that we've ever seen in the next couple of years. Now, granted, you know, as far as Renaissance goes, maybe Renaissance. Documentation are not that great. So, you know, let's put the party hats back in the closet, but what we're seeing with the great resignation right now is all of that knowledge that people acquired in their heads is leaving. It's headed out the door and I've read reports like up to 80% of how to do things with API APIs is in people's heads. Like at we work. If you needed to know how API has worked. You know, you knew Phil was the guy that could get you straightened and Phil Sturgeon: I didn't have a clue. That was the problem. I was trying to find out how to do it. Matthew Reinbold: Okay. So I wasn't, it was somebody, it was somebody on the other end of a, of a Nerf battle away Phil Sturgeon: Someone who quit already is the person that you. Matthew Reinbold: But right now in organizations like you have this phenomenon where a tremendous number of people are leaving organizations and they might've been the sole person who knew where the end points were or knew how that particular tricky function worked. And as organizations are trying to deal with this and recover and still be productive, there's going to be a greater emphasis on having that crap written down, having things documented. Organizations don't have aren't left on their back foot like they are right now. So whether that's heavy handed processes, whether that's just a greater appreciation for documentation among the staff, that's left, whatever that manifests as there's going to be an increasing amount of emphasis on documentation, because people have seen that too much was stuck in people's heads and it's not sustained. Phil Sturgeon: Yeah, that's a really good point. I mean, and not just kind of documentation, but the whole open API as a source of truth earlier on. And I figured it has to be, has to become more noticeably important when Yeah. They've, they've lost the whole team. How the API works and you know what it's like, code's always a bloody mess. Cause you just hacked up within about what over the place and patch things and fix things. And what about and yeah, when they find themselves rewrite in the API, cause no one can really take it over and no one remembers how it works and there's no documentation for it. And it's just too hard to figure out when they just make a brand new one. And they have a whole brand new team doing it. Cause they've already lost all that stuff. Matthew Reinbold: Yeah. Phil Sturgeon: That's a situation that a lot of managers and business people are going to say, how can we go about avoiding doing this? And I just hope there's someone in the room that says, well, APA designed first would really help avoid this problem because otherwise they'll just repeat all the same mistakes again. Matthew Reinbold: Right. Absolutely. Whether it's design first or tools that help analyze existing traffic and write the document afterwards, like whatever you got to do, get that written down and start taking some notes against it because. It's it, I believe right now with the great resignation. It's an Achilles heel. That's probably hampering a lot of organizational ecosystems right now. Matt Trask: Yeah, I would definitely agree. I mean, it shows in the report under open API three dot oh, 44% of people are aware of it, but they don't use it 28% say they use it. 12% said they use it, the love it. So even just combining use it and use it in love. It still does not match aware of we're not using it. Which means that there is definitely a. A river to jump over. So to speak, to getting more people on, to open API, which is probably currently like the standard for API documentation right now which comes back to your point, which allows them to start writing things down and start documenting things. And Phil gets it by bus tomorrow. We work is still going to be okay. It very well could happen. Which is exactly why I use that example. And it, it, yeah, it it'll give the organization a little bit more or a little less reliance on what's in people's heads a little bit more stability in case great races, nation three Datto happens in three years. You know, you don't know what's gonna happen. Phil Sturgeon: Is that when everyone resigns from web three point now, Matt Trask: please. Don't don't threaten me with a good time. Like I've already, I've already muted those web three and NFD on my Twitter and it cleaned it up so Phil Sturgeon: Why do you hate progress, man? Matt Trask: A lot of reasons. I'm a combustion at heart? No. Matthew Reinbold: Hey, if you don't, Phil Sturgeon: particular messages of this progress that are the problem. Matthew Reinbold: if you, don't stand for something, you'll fall for anything. Good for you, Matt. Matt Trask: yes, I've always wanted my life to be attributed to a, a Hamilton quote. So I am glad I did. I can check that one off to get back onto the actual topic and not just bashing NFTs for an hour and a half, which sounds like a lot of fun. What you the most about this report? Like what was something that you read that just you weren't expecting? Matthew Reinbold: I, I think there was two things that when you combine them together it made me tilt my head and go, huh? The, the first is that more than anything else? Including speed to production. People want quality API APIs. They want stability. They want some other things reliability. But the primary thing that people want out of their, their API APIs is quality. And yet when it came to whether or not people had time to test. Everybody acknowledged that testing was good. Tested was valid, but nobody had enough time for testing and it's like, huh? These two things kind of seem like. The, the two sides of a coin, right. You know, people aren't getting the quality that they want, but everybody acknowledges that they don't have enough time to do testing, even though they recognize the testing is an extremely valuable type thing. So I think when it comes to socializing this report and talking to decision-makers and doing the kind of coaching that I so often do, I, this is one of those things too, to bring up, like how in your program are you supporting. Testing and ensuring that enough is being done there so that your developers feel like you're, you're reaching the kind of quality goals that, that you're, you're promising to the rest of the world. Phil Sturgeon: Hm, do you, is the survey broken down by role? So can you, can you look to see if. Managers and engineers have a rule, very interested in, in high quality. And engineers are going, but we don't have enough time, but the manager's like, oh, they definitely have enough time. Matthew Reinbold: Right. So we do have a breakdown by role and job title, but I don't have the numbers in front of me that, that combined, and show me how to break down the quality question. Phil Sturgeon: Yeah, that'd be an interesting one. Cause yeah, so many roles, so many organizations, I just take it as like a universal truth is that companies are just, you know, business and product are demanding feature, feature, feature, feature, feature, and engineers are just like screaming, just keyboards on fire, trying to try to hit them goals. And everything's just wonky as hell. And it seems to be everywhere I go. There's not enough to have. There's not enough time for QA. They might've got rid of the QA team because it's slowed down product and slowed down delivery of features. Yeah, everyone wants high-quality API has, but no one wants to put the time in to testing because testing is inherently hard and slow. Matthew Reinbold: Right. And kind of along those same lines, another stat that jumped out at me was that 76% of the people building API APIs have less than five years experience doing. I mean, you know, as far as restful APIs now, we're, we're more than a decade into that journey. So that stat leaps out at me, like what is it about API development, where we're getting people with zero to five years experience like what's happening. There are the successful API builders, aging out and becoming management. it, are they moving on to web three O and NFTs? Like, like what is, where are our experienced API builders and why are these critical pieces of business infrastructure? In the hands of relatively younger people. That's not to say that they can't be doing a good job, that, that it's impossible to build a great web experience at your first time at bat. But it's also something where I think everybody on this call would probably agree. Experience counts, experience matters. Ha being around the block once or twice, you pick up a feel for what's beneficial, what's maybe a little wonky and you can imbue that into a better design at launch. So, you know, where are the. 10 year, the 12 year, the 15 year veterans. And why are they not the primary source of API infrastructure development? Phil Sturgeon: Yeah. Some that I've seen so much, again, just, I love complaining about we work. Pretty much everyone that was a junior developer, Right. Like the vast majority, what, what you need developers and their role responsible for creating you know, there's like a hundred API APIs and, you know more than a hundred junior developers with just a sprinkling of seniors who were more on the cowboy coder end of things. Not, not to be rude, you know, like startup, you need to be super agile, super fast, not, not a perfectionist. And so, so many of the problems where this is, this person's first rails app, like they know how to accept incoming Jason parameters and they know how to spit something back from the database. And. That's that, and they know how to make a web request. So he talks to . He talks to F talks to G in the thread, and then no, one's got a timer anyway. So everything falls over, like, things like that. The sort of thing you realize, if you've been doing APIs for five years, or for 10 years, you've been doing it for 10 years, you wouldn't do that. You just wouldn't do that. You'd put something in a sidekick job and then implement a web socket or a web hook, or literally anything else. But. That's the sort of thing you do when you consider like HTP failures or server downtime, to be an edge case that is like some weird scenario that probably won't happen. And when you've been doing it for a longer time, you're like you, you change your mindset to this web requests probably won't work. And on the off chance that it. This is what should happen. And you just get really defensive and paranoid and have like 25 different guard statements and, you know, 25 different types of ex exception catching and, and every single circuit breaker and trigger warning that you can possibly put on this thing. And there is, yeah, there is a change in mind. Around around that kind of it doesn't, I'm not being a gatekeeper or at least they're saying you've got to be doing EPS for 10 years until you're good. But when you start out, you you're such, you're more of an optimist. You haven't seen it go wrong in as many ways. You haven't had cascading failures and you haven't had all these terrifying things that happen. So that, that is definitely a concern for me is that I think, yeah. Happy, happy path development. When you go from having one AP. To having 20 or a hundred, the, the the chance of straying off the happy path gets exponentially worse. Right. And, and that's just something, I think a lot of these younger developers on experience with. Matthew Reinbold: Right. Even, even when it comes to design, having used API APIs, having to incorporate the API APIs, you better understand what makes a good description and what is just a reiteration of the, the name itself. Yeah. Yeah. If I have a field called date of birth and the description is just the birth, that, the date that the person was born on, like, well, what was the. do I need to refresh it? Or is it cashed? You know, like, can I store it or is it part of some kind of regulatory PII? And I shouldn't, you know, I can use it, but I shouldn't store, like, there's so many issues that once you've been down that road, and then you're asked to produce an API, you bring that experience with you and you put it into the description that adds so much that yeah. I, I, I, I don't know. How we continue to get that, that experience circulating and get that in front of people. But I think it's really important. Matt Trask: Well, I must wonder too, like how many of those, like experienced API builders are getting swallowed up into Stripe? Twilio, Google. And kind of almost locked away working on their API APIs and not able to share their experiences down the road to junior developers in their own companies or interim networks, things like that too, because it feels like you do your five, seven years as developer, you get pulled into the management game and then all of your knowledge is still there, but you're having to balance both managing a development team, hitting your goals. Pushing out products because you've got to make money for the business. And all of your knowledge that you've worked so hard to gain is kind of sidelined in the name of profits or KPIs or whatever it might be. Matthew Reinbold: Possibly there's, there's certainly exceptions that spring to mind. One of which is Tim Burks and the team over at Google and with the number of resources that they put out there. For their APIs. It's, it's kind of a mouthful, but if you do a Google search for that, they've produced a tremendous amount of documentation about how they support API APIs at scale, how they do their design reviews, how they think about consistency and cohesion across their entire footprint. So that certainly what you described could be the case in some places. You know, I, I, I do think that it's not necessarily the default that's people go off to these big organizations and then just disappear because the folks at Google around Tim and his crew they're doing some great work. Phil Sturgeon: So I've been sat in the room with you having these sort of conversations your last job, Right, Like a center of excellence type stuff. You, you get a bunch of smart people and me together and start talking about what, what would help with these various different problems? Like how do we do APA design reviews? How do we do governance? What standards should we be interested in? So I think sometimes yeah. Experienced developers can get sucked up into these companies and kind of finish and end up having that scale was used for something else. But I, I think companies that have those governance processes, like they're sharing their experience back by creating style guides, by creating programs that they explain how these, how these like API designed life cycles or API life cycle should work. And that's a way that they can essentially. Distribute their experience. So instead of like, I know what to look for when I'm reviewing a poor request, they can create a style guide. That means that everyone will do that. I think the danger there is that when style goes focus on what, instead of why then, then you kind of lose some of that experience because it just seems like arbitrary decisions delivered from upon high. Right. You just get. Do it this way, but, but Y I've read loads of style guides recently. And, and some of them, I should probably show the examples. It's just like, do this. Like, why you don't tell me what to do? You don't my dad, like, it just, I couldn't figure out what they possibly could have meant by it. Cause usually I can look at something. Why might they mean that? Oh, that reminds me of a thing that happened along these lines. They probably got burned by that before, and they want to avoid it, but if you don't see why it just sounds arbitrary and you're not actually teaching anyone on anything, but if you do it right. that that can be really helpful. Matthew Reinbold: Right. And it's also essential that if you're designing these systems like a governance or like a center of excellence that you have the feedback process that you have, the, the communication cycles so that when people do have that kind of. That they have a recourse. It's not a dead end. It's not either you do this or you're punished for it, but oh, if this doesn't make sense, here's who you talk to. Here's how you can escalate your concern here is how you elevate your edge case. And we can have a discussion about it and you can help co-evolve this thing, because you own this as much as somebody else, the, the phenomenon that you described, where it's a dead end. It's thrust upon you. You don't have ownership of that. And as a developer, that does not feel good, that does not invest you in seeing the long-term growth of, of that system. You want to burn that system. You want to be the rebels flying through the death star trench. You want to take that thing down? So what's essential is to realize. You provide the avenues for people to, to voice their concerns, voice their questions, and make them feel heard in such a way that their process, the process is theirs. It's not something done to them. It's it's their process. Phil Sturgeon: I'm just laughing about the death star rebel situation. Now I'm completely distracted. I need to go rewatch some star wars. I don't know. Matt Trask: I mean, your, your thought on the ownership thing is also interesting cause And we like watching the junior Twitter, the junior developer Twitter circles, which is not the end all be all of it all, but there is a large emphasis on if you want to make more money, you need to jump ship every two years on average. And that kind of removes the does or not the desire, but like the, the ownership of any sort of product from a junior developer, because in two years, they're going to be onto another thing. They're going to be onto another system. Codebase, maybe another language and it, it does kind of bring back, like, how do you entice people to have ownership, even if they only are going to plan to say somewhere for a short period? Because we all know that like having, like you said, having that ownership is going to kind of make you more invested, more caring, more thoughtful, more empathetic towards whatever it is that you're building. Matthew Reinbold: Right. I mean, we're veering into management territory, which I'm happy to talk about. I, I know. Matt Trask: very allergic to management. So. Matthew Reinbold: But I, I was just reading Harvard business review. Hey, I'm fun at parties too. So I was reading Harvard business review talking about COVID and the great resignation and the, the management challenges that, that come with that and what we need more. In all companies is a feeling of belonging, a feeling like we have a career progression feeling like our, our, our work has impact and all too often management, just as about making sure people don't do dumpster. Right. You know, I'm, I'm here to police you because the organization doesn't trust you. And it leads to all kinds of weird effects. Like, Hey, if you actually want to grow your career, you need to leave. You need to hop companies every two years and let's be clear that may work, but it's still very disruptive, not just for the company, but for the individual. 'cause they're having to rebuild all of those social structures, their relationships, their patterns, the routines it, it's not, it doesn't come for free. And so from a management standpoint, if you can show people how to have that fulfilling career, how to fulfill those needs. They don't have to jump ship every two years. There's no reason that that has to be the default blueprint. And from a company standpoint, you actually benefit from that accrued experience rather than having a developer. That's done the same thing. Five times you get five years of experience. That's really powerful, really tremendous. And that, that ultimately not only leads to better APIs, but leads to a better employee. So there is a disconnect we need to work with our management layers. It shouldn't just be the technician that has some headcount is by default manager. There needs to be an appreciation for how those are unique skill sets. Those are unique muscles that need to be exercised, but. If we can create that fulfilling sense of duty then, and that the career path for these individuals, we can get them off of this kind of binge and purge career treadmill. Matt Trask: So that's a really, yeah, that's a really good way to put the whole two year turn. And I mean, it comes back full circle to what you just said earlier, which is, you know, 75% of API has been developed now or done by people with less than five years experience. And that's probably because of the same, people are jumping, jumping, jumping. Whereas if you can keep them around, make them happy, make them feel like they belong. We might actually start seeing that number. Dropped significantly to more experienced API developers building more thoughtful API design with, with years of knowledge built up. So I think it'll be really interesting to see kind of what happens with this great resignation how that all shapes up. And then it'll be interesting to see to kind of the 2022 say the API report. How does that. How, how will things change from a year in a year going forward? And what can we expect possibly looking at these two years, the next five years after that, the next 10 years growing on different trends, you know, we might see NFTs ruling the world. We might see graph QL. Rolling. Phil Sturgeon: No comment. Matt Trask: Matthew is kind of shrugging Phil Sturgeon: we're all sad. Now, rural sat now, NFTs powered by graft UL, problem solved. Can you, can you still right click that? No, you can't. It's like a post. So. Matt Trask: Well, there goes Matthew Reinbold: Each unique query is published as an innovator. And you can put the ownership of that query in a blockchain so that you don't have the centralized point of failure. Phil Sturgeon: I was going to thank you for being for, for making this podcast sound intelligent for once. And, Matthew Reinbold: And then I ruined it. Sorry. Phil Sturgeon: and then you. Matt Trask: no, no, no, you didn't ruin it. You just brought it back down to its normal level of ridiculousness. Phil Sturgeon: Fantastic. No. Do you have any predictions for what we're going to see in the, in next year's state of this report? Because then we can play that clip back and laugh at how wrong you were. Matthew Reinbold: Oh, lovely. All right, well, let me have a few minutes to sandbag my answer. No, I think there's a tremendous amount of, of areas where we can take this correlation that I talked about before behaviors. You know how the question immediately becomes well, okay. If these four behaviors are so good and are present in high-performing API companies, how do we get there? And this year we had a little bit around leadership and what leaders do. To get an API first company. I think there is a lot of exploration we can do there to really dial in and say, okay, we know these things are good. How do you get there? How do you promote these things? How do you, how do you get it so that you are able to deploy in a minimal amount of time or recover faster? What are leaders in those organizations doing? That's one of the things I'd love to dig into obviously. A lot of post pandemic aftermath. There's been a tremendous amount of published about how this digital transformation and, you know, we're so much more flexible and adaptable because we, we are now doing all our conversations over zoom. And I look at that and I, I scratch my head because. Digital transformation, at least in the non buzzword compliant way is a whole lot more difficult than just moving everything to a slack conversation or a, or a zoom conversation. Like it means fundamentally dismantling your policies and procedures and reinventing them in a way that digital technology lends itself to. So figuring out what that post pandemic landscape looks like and how we're still feeling the knock on effect. Is going to be something that's also going to be very interesting to explore. Matt Trask: Yeah, that's definitely true. I mean, I think one thing I would like to see is, is that number of people who know open API, but don't use it start to gradually shift down and people who are using open. Start to shift up, which, you know, from a silver right back to having documentation and some sort of notes about their API. So when the, the knowledge people do eventually leave because everyone leaves the company at some point, the knowledge isn't necessarily leaving. And instead we're, we're kind of leaving a better legacy to the people following us. Yeah, definitely. Matthew Reinbold: Here here. Matt Trask: Cool. Matthew, thank you so much for taking some time out of your, your, your day to talk to us. We really appreciate it. Look forward to having you back in roughly a year's time to talk 20, 22. Say the API report Matthew Reinbold: I love it. Let's do it. Pencil it in right now. Matt Trask: Yep. It's it's on my calendar. I don't know what I'll be doing in a year from today, but I know for a fact we'll be talking again. If you want to get. Matthew on Twitter. He is at libel Vox, L I B E L underscore V O X M. And we'll throw the link to your blog and Twitter in the show notes as well as everything else. Awesome. Cool. Thank you so much. We appreciate it. Phil Sturgeon: Yeah. All audio, artwork, episode descriptions and notes are property of APIs You Won't Hate, for APIs You Won't Hate, and published with permission by Transistor, Inc. Broadcast by | 2026-01-13T08:48:40 |
https://docs.python.org/3/tutorial/introduction.html#text | 3. An Informal Introduction to Python — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents 3. An Informal Introduction to Python 3.1. Using Python as a Calculator 3.1.1. Numbers 3.1.2. Text 3.1.3. Lists 3.2. First Steps Towards Programming Previous topic 2. Using the Python Interpreter Next topic 4. More Control Flow Tools This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 3. An Informal Introduction to Python | Theme Auto Light Dark | 3. An Informal Introduction to Python ¶ In the following examples, input and output are distinguished by the presence or absence of prompts ( >>> and … ): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you must type a blank line; this is used to end a multi-line command. You can use the “Copy” button (it appears in the upper-right corner when hovering over or tapping a code example), which strips prompts and omits output, to copy and paste the input lines into your interpreter. Many of the examples in this manual, even those entered at the interactive prompt, include comments. Comments in Python start with the hash character, # , and extend to the end of the physical line. A comment may appear at the start of a line or following whitespace or code, but not within a string literal. A hash character within a string literal is just a hash character. Since comments are to clarify code and are not interpreted by Python, they may be omitted when typing in examples. Some examples: # this is the first comment spam = 1 # and this is the second comment # ... and now a third! text = "# This is not a comment because it's inside quotes." 3.1. Using Python as a Calculator ¶ Let’s try some simple Python commands. Start the interpreter and wait for the primary prompt, >>> . (It shouldn’t take long.) 3.1.1. Numbers ¶ The interpreter acts as a simple calculator: you can type an expression into it and it will write the value. Expression syntax is straightforward: the operators + , - , * and / can be used to perform arithmetic; parentheses ( () ) can be used for grouping. For example: >>> 2 + 2 4 >>> 50 - 5 * 6 20 >>> ( 50 - 5 * 6 ) / 4 5.0 >>> 8 / 5 # division always returns a floating-point number 1.6 The integer numbers (e.g. 2 , 4 , 20 ) have type int , the ones with a fractional part (e.g. 5.0 , 1.6 ) have type float . We will see more about numeric types later in the tutorial. Division ( / ) always returns a float. To do floor division and get an integer result you can use the // operator; to calculate the remainder you can use % : >>> 17 / 3 # classic division returns a float 5.666666666666667 >>> >>> 17 // 3 # floor division discards the fractional part 5 >>> 17 % 3 # the % operator returns the remainder of the division 2 >>> 5 * 3 + 2 # floored quotient * divisor + remainder 17 With Python, it is possible to use the ** operator to calculate powers [ 1 ] : >>> 5 ** 2 # 5 squared 25 >>> 2 ** 7 # 2 to the power of 7 128 The equal sign ( = ) is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt: >>> width = 20 >>> height = 5 * 9 >>> width * height 900 If a variable is not “defined” (assigned a value), trying to use it will give you an error: >>> n # try to access an undefined variable Traceback (most recent call last): File "<stdin>" , line 1 , in <module> NameError : name 'n' is not defined There is full support for floating point; operators with mixed type operands convert the integer operand to floating point: >>> 4 * 3.75 - 1 14.0 In interactive mode, the last printed expression is assigned to the variable _ . This means that when you are using Python as a desk calculator, it is somewhat easier to continue calculations, for example: >>> tax = 12.5 / 100 >>> price = 100.50 >>> price * tax 12.5625 >>> price + _ 113.0625 >>> round ( _ , 2 ) 113.06 This variable should be treated as read-only by the user. Don’t explicitly assign a value to it — you would create an independent local variable with the same name masking the built-in variable with its magic behavior. In addition to int and float , Python supports other types of numbers, such as Decimal and Fraction . Python also has built-in support for complex numbers , and uses the j or J suffix to indicate the imaginary part (e.g. 3+5j ). 3.1.2. Text ¶ Python can manipulate text (represented by type str , so-called “strings”) as well as numbers. This includes characters “ ! ”, words “ rabbit ”, names “ Paris ”, sentences “ Got your back. ”, etc. “ Yay! :) ”. They can be enclosed in single quotes ( '...' ) or double quotes ( "..." ) with the same result [ 2 ] . >>> 'spam eggs' # single quotes 'spam eggs' >>> "Paris rabbit got your back :)! Yay!" # double quotes 'Paris rabbit got your back :)! Yay!' >>> '1975' # digits and numerals enclosed in quotes are also strings '1975' To quote a quote, we need to “escape” it, by preceding it with \ . Alternatively, we can use the other type of quotation marks: >>> 'doesn \' t' # use \' to escape the single quote... "doesn't" >>> "doesn't" # ...or use double quotes instead "doesn't" >>> '"Yes," they said.' '"Yes," they said.' >>> " \" Yes, \" they said." '"Yes," they said.' >>> '"Isn \' t," they said.' '"Isn\'t," they said.' In the Python shell, the string definition and output string can look different. The print() function produces a more readable output, by omitting the enclosing quotes and by printing escaped and special characters: >>> s = 'First line. \n Second line.' # \n means newline >>> s # without print(), special characters are included in the string 'First line.\nSecond line.' >>> print ( s ) # with print(), special characters are interpreted, so \n produces new line First line. Second line. If you don’t want characters prefaced by \ to be interpreted as special characters, you can use raw strings by adding an r before the first quote: >>> print ( 'C:\some \n ame' ) # here \n means newline! C:\some ame >>> print ( r 'C:\some\name' ) # note the r before the quote C:\some\name There is one subtle aspect to raw strings: a raw string may not end in an odd number of \ characters; see the FAQ entry for more information and workarounds. String literals can span multiple lines. One way is using triple-quotes: """...""" or '''...''' . End-of-line characters are automatically included in the string, but it’s possible to prevent this by adding a \ at the end of the line. In the following example, the initial newline is not included: >>> print ( """ \ ... Usage: thingy [OPTIONS] ... -h Display this usage message ... -H hostname Hostname to connect to ... """ ) Usage: thingy [OPTIONS] -h Display this usage message -H hostname Hostname to connect to >>> Strings can be concatenated (glued together) with the + operator, and repeated with * : >>> # 3 times 'un', followed by 'ium' >>> 3 * 'un' + 'ium' 'unununium' Two or more string literals (i.e. the ones enclosed between quotes) next to each other are automatically concatenated. >>> 'Py' 'thon' 'Python' This feature is particularly useful when you want to break long strings: >>> text = ( 'Put several strings within parentheses ' ... 'to have them joined together.' ) >>> text 'Put several strings within parentheses to have them joined together.' This only works with two literals though, not with variables or expressions: >>> prefix = 'Py' >>> prefix 'thon' # can't concatenate a variable and a string literal File "<stdin>" , line 1 prefix 'thon' ^^^^^^ SyntaxError : invalid syntax >>> ( 'un' * 3 ) 'ium' File "<stdin>" , line 1 ( 'un' * 3 ) 'ium' ^^^^^ SyntaxError : invalid syntax If you want to concatenate variables or a variable and a literal, use + : >>> prefix + 'thon' 'Python' Strings can be indexed (subscripted), with the first character having index 0. There is no separate character type; a character is simply a string of size one: >>> word = 'Python' >>> word [ 0 ] # character in position 0 'P' >>> word [ 5 ] # character in position 5 'n' Indices may also be negative numbers, to start counting from the right: >>> word [ - 1 ] # last character 'n' >>> word [ - 2 ] # second-last character 'o' >>> word [ - 6 ] 'P' Note that since -0 is the same as 0, negative indices start from -1. In addition to indexing, slicing is also supported. While indexing is used to obtain individual characters, slicing allows you to obtain a substring: >>> word [ 0 : 2 ] # characters from position 0 (included) to 2 (excluded) 'Py' >>> word [ 2 : 5 ] # characters from position 2 (included) to 5 (excluded) 'tho' Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced. >>> word [: 2 ] # character from the beginning to position 2 (excluded) 'Py' >>> word [ 4 :] # characters from position 4 (included) to the end 'on' >>> word [ - 2 :] # characters from the second-last (included) to the end 'on' Note how the start is always included, and the end always excluded. This makes sure that s[:i] + s[i:] is always equal to s : >>> word [: 2 ] + word [ 2 :] 'Python' >>> word [: 4 ] + word [ 4 :] 'Python' One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n , for example: +---+---+---+---+---+---+ | P | y | t | h | o | n | +---+---+---+---+---+---+ 0 1 2 3 4 5 6 - 6 - 5 - 4 - 3 - 2 - 1 The first row of numbers gives the position of the indices 0…6 in the string; the second row gives the corresponding negative indices. The slice from i to j consists of all characters between the edges labeled i and j , respectively. For non-negative indices, the length of a slice is the difference of the indices, if both are within bounds. For example, the length of word[1:3] is 2. Attempting to use an index that is too large will result in an error: >>> word [ 42 ] # the word only has 6 characters Traceback (most recent call last): File "<stdin>" , line 1 , in <module> IndexError : string index out of range However, out of range slice indexes are handled gracefully when used for slicing: >>> word [ 4 : 42 ] 'on' >>> word [ 42 :] '' Python strings cannot be changed — they are immutable . Therefore, assigning to an indexed position in the string results in an error: >>> word [ 0 ] = 'J' Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : 'str' object does not support item assignment >>> word [ 2 :] = 'py' Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : 'str' object does not support item assignment If you need a different string, you should create a new one: >>> 'J' + word [ 1 :] 'Jython' >>> word [: 2 ] + 'py' 'Pypy' The built-in function len() returns the length of a string: >>> s = 'supercalifragilisticexpialidocious' >>> len ( s ) 34 See also Text Sequence Type — str Strings are examples of sequence types , and support the common operations supported by such types. String Methods Strings support a large number of methods for basic transformations and searching. f-strings String literals that have embedded expressions. Format String Syntax Information about string formatting with str.format() . printf-style String Formatting The old formatting operations invoked when strings are the left operand of the % operator are described in more detail here. 3.1.3. Lists ¶ Python knows a number of compound data types, used to group together other values. The most versatile is the list , which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually the items all have the same type. >>> squares = [ 1 , 4 , 9 , 16 , 25 ] >>> squares [1, 4, 9, 16, 25] Like strings (and all other built-in sequence types), lists can be indexed and sliced: >>> squares [ 0 ] # indexing returns the item 1 >>> squares [ - 1 ] 25 >>> squares [ - 3 :] # slicing returns a new list [9, 16, 25] Lists also support operations like concatenation: >>> squares + [ 36 , 49 , 64 , 81 , 100 ] [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] Unlike strings, which are immutable , lists are a mutable type, i.e. it is possible to change their content: >>> cubes = [ 1 , 8 , 27 , 65 , 125 ] # something's wrong here >>> 4 ** 3 # the cube of 4 is 64, not 65! 64 >>> cubes [ 3 ] = 64 # replace the wrong value >>> cubes [1, 8, 27, 64, 125] You can also add new items at the end of the list, by using the list.append() method (we will see more about methods later): >>> cubes . append ( 216 ) # add the cube of 6 >>> cubes . append ( 7 ** 3 ) # and the cube of 7 >>> cubes [1, 8, 27, 64, 125, 216, 343] Simple assignment in Python never copies data. When you assign a list to a variable, the variable refers to the existing list . Any changes you make to the list through one variable will be seen through all other variables that refer to it.: >>> rgb = [ "Red" , "Green" , "Blue" ] >>> rgba = rgb >>> id ( rgb ) == id ( rgba ) # they reference the same object True >>> rgba . append ( "Alph" ) >>> rgb ["Red", "Green", "Blue", "Alph"] All slice operations return a new list containing the requested elements. This means that the following slice returns a shallow copy of the list: >>> correct_rgba = rgba [:] >>> correct_rgba [ - 1 ] = "Alpha" >>> correct_rgba ["Red", "Green", "Blue", "Alpha"] >>> rgba ["Red", "Green", "Blue", "Alph"] Assignment to slices is also possible, and this can even change the size of the list or clear it entirely: >>> letters = [ 'a' , 'b' , 'c' , 'd' , 'e' , 'f' , 'g' ] >>> letters ['a', 'b', 'c', 'd', 'e', 'f', 'g'] >>> # replace some values >>> letters [ 2 : 5 ] = [ 'C' , 'D' , 'E' ] >>> letters ['a', 'b', 'C', 'D', 'E', 'f', 'g'] >>> # now remove them >>> letters [ 2 : 5 ] = [] >>> letters ['a', 'b', 'f', 'g'] >>> # clear the list by replacing all the elements with an empty list >>> letters [:] = [] >>> letters [] The built-in function len() also applies to lists: >>> letters = [ 'a' , 'b' , 'c' , 'd' ] >>> len ( letters ) 4 It is possible to nest lists (create lists containing other lists), for example: >>> a = [ 'a' , 'b' , 'c' ] >>> n = [ 1 , 2 , 3 ] >>> x = [ a , n ] >>> x [['a', 'b', 'c'], [1, 2, 3]] >>> x [ 0 ] ['a', 'b', 'c'] >>> x [ 0 ][ 1 ] 'b' 3.2. First Steps Towards Programming ¶ Of course, we can use Python for more complicated tasks than adding two and two together. For instance, we can write an initial sub-sequence of the Fibonacci series as follows: >>> # Fibonacci series: >>> # the sum of two elements defines the next >>> a , b = 0 , 1 >>> while a < 10 : ... print ( a ) ... a , b = b , a + b ... 0 1 1 2 3 5 8 This example introduces several new features. The first line contains a multiple assignment : the variables a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. The right-hand side expressions are evaluated from the left to the right. The while loop executes as long as the condition (here: a < 10 ) remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact any sequence; anything with a non-zero length is true, empty sequences are false. The test used in the example is a simple comparison. The standard comparison operators are written the same as in C: < (less than), > (greater than), == (equal to), <= (less than or equal to), >= (greater than or equal to) and != (not equal to). The body of the loop is indented : indentation is Python’s way of grouping statements. At the interactive prompt, you have to type a tab or space(s) for each indented line. In practice you will prepare more complicated input for Python with a text editor; all decent text editors have an auto-indent facility. When a compound statement is entered interactively, it must be followed by a blank line to indicate completion (since the parser cannot guess when you have typed the last line). Note that each line within a basic block must be indented by the same amount. The print() function writes the value of the argument(s) it is given. It differs from just writing the expression you want to write (as we did earlier in the calculator examples) in the way it handles multiple arguments, floating-point quantities, and strings. Strings are printed without quotes, and a space is inserted between items, so you can format things nicely, like this: >>> i = 256 * 256 >>> print ( 'The value of i is' , i ) The value of i is 65536 The keyword argument end can be used to avoid the newline after the output, or end the output with a different string: >>> a , b = 0 , 1 >>> while a < 1000 : ... print ( a , end = ',' ) ... a , b = b , a + b ... 0,1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987, Footnotes [ 1 ] Since ** has higher precedence than - , -3**2 will be interpreted as -(3**2) and thus result in -9 . To avoid this and get 9 , you can use (-3)**2 . [ 2 ] Unlike other languages, special characters such as \n have the same meaning with both single ( '...' ) and double ( "..." ) quotes. The only difference between the two is that within single quotes you don’t need to escape " (but you have to escape \' ) and vice versa. Table of Contents 3. An Informal Introduction to Python 3.1. Using Python as a Calculator 3.1.1. Numbers 3.1.2. Text 3.1.3. Lists 3.2. First Steps Towards Programming Previous topic 2. Using the Python Interpreter Next topic 4. More Control Flow Tools This page Report a bug Show source « Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 3. An Informal Introduction to Python | Theme Auto Light Dark | © Copyright 2001 Python Software Foundation. This page is licensed under the Python Software Foundation License Version 2. Examples, recipes, and other code in the documentation are additionally licensed under the Zero Clause BSD License. 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https://openapi.tools/categories/parsers | Parsers | OpenApi.tools, from APIs You Won't Hate Sponsored by Zudoku - Open-source, highly customizable API documentation powered by OpenAPI Get Started Sponsor openapi.tools GitHub Get Started All Tools All Categories Legacy Tools Contributing Sponsors Sponsor Badges Collections Arazzo Support Overlays Support Open Source Tools SaaS Tools OpenAPI Tool Categories Annotations Code generators Converters Data Validators Documentation Domain-Specific Languages (DSLs) Gateways HTTP Clients IDEs and GUI Editors Learning Miscellaneous Mock Servers Monitoring OpenAPI-aware Frameworks Parsers Schema Validators SDK Generators Security Server Implementations Testing Text Editors © 2026 APIs You Won't Hate Get in touch to become a Sponsor . This site is community-driven and OSS , built with Astro and hosted on Netlify . Parsers Loads and read OpenAPI descriptions, so you can work with them programmatically. Parsers There are additional tools in this category, but they only support legacy versions of OpenAPI. If you really need to work with some old OpenAPI descriptions perhaps these legacy tools could be of use * * * | 2026-01-13T08:48:40 |
https://openapi.tools/categories/openapi-aware-frameworks | OpenAPI-aware Frameworks | OpenApi.tools, from APIs You Won't Hate Sponsored by Zudoku - Open-source, highly customizable API documentation powered by OpenAPI Get Started Sponsor openapi.tools GitHub Get Started All Tools All Categories Legacy Tools Contributing Sponsors Sponsor Badges Collections Arazzo Support Overlays Support Open Source Tools SaaS Tools OpenAPI Tool Categories Annotations Code generators Converters Data Validators Documentation Domain-Specific Languages (DSLs) Gateways HTTP Clients IDEs and GUI Editors Learning Miscellaneous Mock Servers Monitoring OpenAPI-aware Frameworks Parsers Schema Validators SDK Generators Security Server Implementations Testing Text Editors © 2026 APIs You Won't Hate Get in touch to become a Sponsor . This site is community-driven and OSS , built with Astro and hosted on Netlify . OpenAPI-aware Frameworks There's a new breed of API-centric web application frameworks that produce OpenAPI for you from the actual code you're writing instead of messing with annotations or DSLs. OpenAPI-aware Frameworks There are additional tools in this category, but they only support legacy versions of OpenAPI. If you really need to work with some old OpenAPI descriptions perhaps these legacy tools could be of use * * * | 2026-01-13T08:48:40 |
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https://docs.python.org/3/tutorial/controlflow.html#for-statements | 4. More Control Flow Tools — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents 4. More Control Flow Tools 4.1. if Statements 4.2. for Statements 4.3. The range() Function 4.4. break and continue Statements 4.5. else Clauses on Loops 4.6. pass Statements 4.7. match Statements 4.8. Defining Functions 4.9. More on Defining Functions 4.9.1. Default Argument Values 4.9.2. Keyword Arguments 4.9.3. Special parameters 4.9.3.1. Positional-or-Keyword Arguments 4.9.3.2. Positional-Only Parameters 4.9.3.3. Keyword-Only Arguments 4.9.3.4. Function Examples 4.9.3.5. Recap 4.9.4. Arbitrary Argument Lists 4.9.5. Unpacking Argument Lists 4.9.6. Lambda Expressions 4.9.7. Documentation Strings 4.9.8. Function Annotations 4.10. Intermezzo: Coding Style Previous topic 3. An Informal Introduction to Python Next topic 5. Data Structures This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 4. More Control Flow Tools | Theme Auto Light Dark | 4. More Control Flow Tools ¶ As well as the while statement just introduced, Python uses a few more that we will encounter in this chapter. 4.1. if Statements ¶ Perhaps the most well-known statement type is the if statement. For example: >>> x = int ( input ( "Please enter an integer: " )) Please enter an integer: 42 >>> if x < 0 : ... x = 0 ... print ( 'Negative changed to zero' ) ... elif x == 0 : ... print ( 'Zero' ) ... elif x == 1 : ... print ( 'Single' ) ... else : ... print ( 'More' ) ... More There can be zero or more elif parts, and the else part is optional. The keyword ‘ elif ’ is short for ‘else if’, and is useful to avoid excessive indentation. An if … elif … elif … sequence is a substitute for the switch or case statements found in other languages. If you’re comparing the same value to several constants, or checking for specific types or attributes, you may also find the match statement useful. For more details see match Statements . 4.2. for Statements ¶ The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended): >>> # Measure some strings: >>> words = [ 'cat' , 'window' , 'defenestrate' ] >>> for w in words : ... print ( w , len ( w )) ... cat 3 window 6 defenestrate 12 Code that modifies a collection while iterating over that same collection can be tricky to get right. Instead, it is usually more straight-forward to loop over a copy of the collection or to create a new collection: # Create a sample collection users = { 'Hans' : 'active' , 'Éléonore' : 'inactive' , '景太郎' : 'active' } # Strategy: Iterate over a copy for user , status in users . copy () . items (): if status == 'inactive' : del users [ user ] # Strategy: Create a new collection active_users = {} for user , status in users . items (): if status == 'active' : active_users [ user ] = status 4.3. The range() Function ¶ If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions: >>> for i in range ( 5 ): ... print ( i ) ... 0 1 2 3 4 The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’): >>> list ( range ( 5 , 10 )) [5, 6, 7, 8, 9] >>> list ( range ( 0 , 10 , 3 )) [0, 3, 6, 9] >>> list ( range ( - 10 , - 100 , - 30 )) [-10, -40, -70] To iterate over the indices of a sequence, you can combine range() and len() as follows: >>> a = [ 'Mary' , 'had' , 'a' , 'little' , 'lamb' ] >>> for i in range ( len ( a )): ... print ( i , a [ i ]) ... 0 Mary 1 had 2 a 3 little 4 lamb In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques . A strange thing happens if you just print a range: >>> range ( 10 ) range(0, 10) In many ways the object returned by range() behaves as if it is a list, but in fact it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space. We say such an object is iterable , that is, suitable as a target for functions and constructs that expect something from which they can obtain successive items until the supply is exhausted. We have seen that the for statement is such a construct, while an example of a function that takes an iterable is sum() : >>> sum ( range ( 4 )) # 0 + 1 + 2 + 3 6 Later we will see more functions that return iterables and take iterables as arguments. In chapter Data Structures , we will discuss in more detail about list() . 4.4. break and continue Statements ¶ The break statement breaks out of the innermost enclosing for or while loop: >>> for n in range ( 2 , 10 ): ... for x in range ( 2 , n ): ... if n % x == 0 : ... print ( f " { n } equals { x } * { n // x } " ) ... break ... 4 equals 2 * 2 6 equals 2 * 3 8 equals 2 * 4 9 equals 3 * 3 The continue statement continues with the next iteration of the loop: >>> for num in range ( 2 , 10 ): ... if num % 2 == 0 : ... print ( f "Found an even number { num } " ) ... continue ... print ( f "Found an odd number { num } " ) ... Found an even number 2 Found an odd number 3 Found an even number 4 Found an odd number 5 Found an even number 6 Found an odd number 7 Found an even number 8 Found an odd number 9 4.5. else Clauses on Loops ¶ In a for or while loop the break statement may be paired with an else clause. If the loop finishes without executing the break , the else clause executes. In a for loop, the else clause is executed after the loop finishes its final iteration, that is, if no break occurred. In a while loop, it’s executed after the loop’s condition becomes false. In either kind of loop, the else clause is not executed if the loop was terminated by a break . Of course, other ways of ending the loop early, such as a return or a raised exception, will also skip execution of the else clause. This is exemplified in the following for loop, which searches for prime numbers: >>> for n in range ( 2 , 10 ): ... for x in range ( 2 , n ): ... if n % x == 0 : ... print ( n , 'equals' , x , '*' , n // x ) ... break ... else : ... # loop fell through without finding a factor ... print ( n , 'is a prime number' ) ... 2 is a prime number 3 is a prime number 4 equals 2 * 2 5 is a prime number 6 equals 2 * 3 7 is a prime number 8 equals 2 * 4 9 equals 3 * 3 (Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.) One way to think of the else clause is to imagine it paired with the if inside the loop. As the loop executes, it will run a sequence like if/if/if/else. The if is inside the loop, encountered a number of times. If the condition is ever true, a break will happen. If the condition is never true, the else clause outside the loop will execute. When used with a loop, the else clause has more in common with the else clause of a try statement than it does with that of if statements: a try statement’s else clause runs when no exception occurs, and a loop’s else clause runs when no break occurs. For more on the try statement and exceptions, see Handling Exceptions . 4.6. pass Statements ¶ The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example: >>> while True : ... pass # Busy-wait for keyboard interrupt (Ctrl+C) ... This is commonly used for creating minimal classes: >>> class MyEmptyClass : ... pass ... Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored: >>> def initlog ( * args ): ... pass # Remember to implement this! ... For this last case, many people use the ellipsis literal ... instead of pass . This use has no special meaning to Python, and is not part of the language definition (you could use any constant expression here), but ... is used conventionally as a placeholder body as well. See The Ellipsis Object . 4.7. match Statements ¶ A match statement takes an expression and compares its value to successive patterns given as one or more case blocks. This is superficially similar to a switch statement in C, Java or JavaScript (and many other languages), but it’s more similar to pattern matching in languages like Rust or Haskell. Only the first pattern that matches gets executed and it can also extract components (sequence elements or object attributes) from the value into variables. If no case matches, none of the branches is executed. The simplest form compares a subject value against one or more literals: def http_error ( status ): match status : case 400 : return "Bad request" case 404 : return "Not found" case 418 : return "I'm a teapot" case _ : return "Something's wrong with the internet" Note the last block: the “variable name” _ acts as a wildcard and never fails to match. You can combine several literals in a single pattern using | (“or”): case 401 | 403 | 404 : return "Not allowed" Patterns can look like unpacking assignments, and can be used to bind variables: # point is an (x, y) tuple match point : case ( 0 , 0 ): print ( "Origin" ) case ( 0 , y ): print ( f "Y= { y } " ) case ( x , 0 ): print ( f "X= { x } " ) case ( x , y ): print ( f "X= { x } , Y= { y } " ) case _ : raise ValueError ( "Not a point" ) Study that one carefully! The first pattern has two literals, and can be thought of as an extension of the literal pattern shown above. But the next two patterns combine a literal and a variable, and the variable binds a value from the subject ( point ). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment (x, y) = point . If you are using classes to structure your data you can use the class name followed by an argument list resembling a constructor, but with the ability to capture attributes into variables: class Point : def __init__ ( self , x , y ): self . x = x self . y = y def where_is ( point ): match point : case Point ( x = 0 , y = 0 ): print ( "Origin" ) case Point ( x = 0 , y = y ): print ( f "Y= { y } " ) case Point ( x = x , y = 0 ): print ( f "X= { x } " ) case Point (): print ( "Somewhere else" ) case _ : print ( "Not a point" ) You can use positional parameters with some builtin classes that provide an ordering for their attributes (e.g. dataclasses). You can also define a specific position for attributes in patterns by setting the __match_args__ special attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all equivalent (and all bind the y attribute to the var variable): Point ( 1 , var ) Point ( 1 , y = var ) Point ( x = 1 , y = var ) Point ( y = var , x = 1 ) A recommended way to read patterns is to look at them as an extended form of what you would put on the left of an assignment, to understand which variables would be set to what. Only the standalone names (like var above) are assigned to by a match statement. Dotted names (like foo.bar ), attribute names (the x= and y= above) or class names (recognized by the “(…)” next to them like Point above) are never assigned to. Patterns can be arbitrarily nested. For example, if we have a short list of Points, with __match_args__ added, we could match it like this: class Point : __match_args__ = ( 'x' , 'y' ) def __init__ ( self , x , y ): self . x = x self . y = y match points : case []: print ( "No points" ) case [ Point ( 0 , 0 )]: print ( "The origin" ) case [ Point ( x , y )]: print ( f "Single point { x } , { y } " ) case [ Point ( 0 , y1 ), Point ( 0 , y2 )]: print ( f "Two on the Y axis at { y1 } , { y2 } " ) case _ : print ( "Something else" ) We can add an if clause to a pattern, known as a “guard”. If the guard is false, match goes on to try the next case block. Note that value capture happens before the guard is evaluated: match point : case Point ( x , y ) if x == y : print ( f "Y=X at { x } " ) case Point ( x , y ): print ( f "Not on the diagonal" ) Several other key features of this statement: Like unpacking assignments, tuple and list patterns have exactly the same meaning and actually match arbitrary sequences. An important exception is that they don’t match iterators or strings. Sequence patterns support extended unpacking: [x, y, *rest] and (x, y, *rest) work similar to unpacking assignments. The name after * may also be _ , so (x, y, *_) matches a sequence of at least two items without binding the remaining items. Mapping patterns: {"bandwidth": b, "latency": l} captures the "bandwidth" and "latency" values from a dictionary. Unlike sequence patterns, extra keys are ignored. An unpacking like **rest is also supported. (But **_ would be redundant, so it is not allowed.) Subpatterns may be captured using the as keyword: case ( Point ( x1 , y1 ), Point ( x2 , y2 ) as p2 ): ... will capture the second element of the input as p2 (as long as the input is a sequence of two points) Most literals are compared by equality, however the singletons True , False and None are compared by identity. Patterns may use named constants. These must be dotted names to prevent them from being interpreted as capture variable: from enum import Enum class Color ( Enum ): RED = 'red' GREEN = 'green' BLUE = 'blue' color = Color ( input ( "Enter your choice of 'red', 'blue' or 'green': " )) match color : case Color . RED : print ( "I see red!" ) case Color . GREEN : print ( "Grass is green" ) case Color . BLUE : print ( "I'm feeling the blues :(" ) For a more detailed explanation and additional examples, you can look into PEP 636 which is written in a tutorial format. 4.8. Defining Functions ¶ We can create a function that writes the Fibonacci series to an arbitrary boundary: >>> def fib ( n ): # write Fibonacci series less than n ... """Print a Fibonacci series less than n.""" ... a , b = 0 , 1 ... while a < n : ... print ( a , end = ' ' ) ... a , b = b , a + b ... print () ... >>> # Now call the function we just defined: >>> fib ( 2000 ) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 The keyword def introduces a function definition . It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented. The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring . (More about docstrings can be found in the section Documentation Strings .) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it. The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables and variables of enclosing functions cannot be directly assigned a value within a function (unless, for global variables, named in a global statement, or, for variables of enclosing functions, named in a nonlocal statement), although they may be referenced. The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference , not the value of the object). [ 1 ] When a function calls another function, or calls itself recursively, a new local symbol table is created for that call. A function definition associates the function name with the function object in the current symbol table. The interpreter recognizes the object pointed to by that name as a user-defined function. Other names can also point to that same function object and can also be used to access the function: >>> fib <function fib at 10042ed0> >>> f = fib >>> f ( 100 ) 0 1 1 2 3 5 8 13 21 34 55 89 Coming from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called None (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print() : >>> fib ( 0 ) >>> print ( fib ( 0 )) None It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it: >>> def fib2 ( n ): # return Fibonacci series up to n ... """Return a list containing the Fibonacci series up to n.""" ... result = [] ... a , b = 0 , 1 ... while a < n : ... result . append ( a ) # see below ... a , b = b , a + b ... return result ... >>> f100 = fib2 ( 100 ) # call it >>> f100 # write the result [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89] This example, as usual, demonstrates some new Python features: The return statement returns with a value from a function. return without an expression argument returns None . Falling off the end of a function also returns None . The statement result.append(a) calls a method of the list object result . A method is a function that ‘belongs’ to an object and is named obj.methodname , where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes , see Classes ) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a] , but more efficient. 4.9. More on Defining Functions ¶ It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined. 4.9.1. Default Argument Values ¶ The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example: def ask_ok ( prompt , retries = 4 , reminder = 'Please try again!' ): while True : reply = input ( prompt ) if reply in { 'y' , 'ye' , 'yes' }: return True if reply in { 'n' , 'no' , 'nop' , 'nope' }: return False retries = retries - 1 if retries < 0 : raise ValueError ( 'invalid user response' ) print ( reminder ) This function can be called in several ways: giving only the mandatory argument: ask_ok('Do you really want to quit?') giving one of the optional arguments: ask_ok('OK to overwrite the file?', 2) or even giving all arguments: ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!') This example also introduces the in keyword. This tests whether or not a sequence contains a certain value. The default values are evaluated at the point of function definition in the defining scope, so that i = 5 def f ( arg = i ): print ( arg ) i = 6 f () will print 5 . Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. For example, the following function accumulates the arguments passed to it on subsequent calls: def f ( a , L = []): L . append ( a ) return L print ( f ( 1 )) print ( f ( 2 )) print ( f ( 3 )) This will print [ 1 ] [ 1 , 2 ] [ 1 , 2 , 3 ] If you don’t want the default to be shared between subsequent calls, you can write the function like this instead: def f ( a , L = None ): if L is None : L = [] L . append ( a ) return L 4.9.2. Keyword Arguments ¶ Functions can also be called using keyword arguments of the form kwarg=value . For instance, the following function: def parrot ( voltage , state = 'a stiff' , action = 'voom' , type = 'Norwegian Blue' ): print ( "-- This parrot wouldn't" , action , end = ' ' ) print ( "if you put" , voltage , "volts through it." ) print ( "-- Lovely plumage, the" , type ) print ( "-- It's" , state , "!" ) accepts one required argument ( voltage ) and three optional arguments ( state , action , and type ). This function can be called in any of the following ways: parrot ( 1000 ) # 1 positional argument parrot ( voltage = 1000 ) # 1 keyword argument parrot ( voltage = 1000000 , action = 'VOOOOOM' ) # 2 keyword arguments parrot ( action = 'VOOOOOM' , voltage = 1000000 ) # 2 keyword arguments parrot ( 'a million' , 'bereft of life' , 'jump' ) # 3 positional arguments parrot ( 'a thousand' , state = 'pushing up the daisies' ) # 1 positional, 1 keyword but all the following calls would be invalid: parrot () # required argument missing parrot ( voltage = 5.0 , 'dead' ) # non-keyword argument after a keyword argument parrot ( 110 , voltage = 220 ) # duplicate value for the same argument parrot ( actor = 'John Cleese' ) # unknown keyword argument In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and their order is not important. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction: >>> def function ( a ): ... pass ... >>> function ( 0 , a = 0 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : function() got multiple values for argument 'a' When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict ) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. ( *name must occur before **name .) For example, if we define a function like this: def cheeseshop ( kind , * arguments , ** keywords ): print ( "-- Do you have any" , kind , "?" ) print ( "-- I'm sorry, we're all out of" , kind ) for arg in arguments : print ( arg ) print ( "-" * 40 ) for kw in keywords : print ( kw , ":" , keywords [ kw ]) It could be called like this: cheeseshop ( "Limburger" , "It's very runny, sir." , "It's really very, VERY runny, sir." , shopkeeper = "Michael Palin" , client = "John Cleese" , sketch = "Cheese Shop Sketch" ) and of course it would print: -- Do you have any Limburger ? -- I'm sorry, we're all out of Limburger It's very runny, sir. It's really very, VERY runny, sir. ---------------------------------------- shopkeeper : Michael Palin client : John Cleese sketch : Cheese Shop Sketch Note that the order in which the keyword arguments are printed is guaranteed to match the order in which they were provided in the function call. 4.9.3. Special parameters ¶ By default, arguments may be passed to a Python function either by position or explicitly by keyword. For readability and performance, it makes sense to restrict the way arguments can be passed so that a developer need only look at the function definition to determine if items are passed by position, by position or keyword, or by keyword. A function definition may look like: def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2): ----------- ---------- ---------- | | | | Positional or keyword | | - Keyword only -- Positional only where / and * are optional. If used, these symbols indicate the kind of parameter by how the arguments may be passed to the function: positional-only, positional-or-keyword, and keyword-only. Keyword parameters are also referred to as named parameters. 4.9.3.1. Positional-or-Keyword Arguments ¶ If / and * are not present in the function definition, arguments may be passed to a function by position or by keyword. 4.9.3.2. Positional-Only Parameters ¶ Looking at this in a bit more detail, it is possible to mark certain parameters as positional-only . If positional-only , the parameters’ order matters, and the parameters cannot be passed by keyword. Positional-only parameters are placed before a / (forward-slash). The / is used to logically separate the positional-only parameters from the rest of the parameters. If there is no / in the function definition, there are no positional-only parameters. Parameters following the / may be positional-or-keyword or keyword-only . 4.9.3.3. Keyword-Only Arguments ¶ To mark parameters as keyword-only , indicating the parameters must be passed by keyword argument, place an * in the arguments list just before the first keyword-only parameter. 4.9.3.4. Function Examples ¶ Consider the following example function definitions paying close attention to the markers / and * : >>> def standard_arg ( arg ): ... print ( arg ) ... >>> def pos_only_arg ( arg , / ): ... print ( arg ) ... >>> def kwd_only_arg ( * , arg ): ... print ( arg ) ... >>> def combined_example ( pos_only , / , standard , * , kwd_only ): ... print ( pos_only , standard , kwd_only ) The first function definition, standard_arg , the most familiar form, places no restrictions on the calling convention and arguments may be passed by position or keyword: >>> standard_arg ( 2 ) 2 >>> standard_arg ( arg = 2 ) 2 The second function pos_only_arg is restricted to only use positional parameters as there is a / in the function definition: >>> pos_only_arg ( 1 ) 1 >>> pos_only_arg ( arg = 1 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : pos_only_arg() got some positional-only arguments passed as keyword arguments: 'arg' The third function kwd_only_arg only allows keyword arguments as indicated by a * in the function definition: >>> kwd_only_arg ( 3 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : kwd_only_arg() takes 0 positional arguments but 1 was given >>> kwd_only_arg ( arg = 3 ) 3 And the last uses all three calling conventions in the same function definition: >>> combined_example ( 1 , 2 , 3 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : combined_example() takes 2 positional arguments but 3 were given >>> combined_example ( 1 , 2 , kwd_only = 3 ) 1 2 3 >>> combined_example ( 1 , standard = 2 , kwd_only = 3 ) 1 2 3 >>> combined_example ( pos_only = 1 , standard = 2 , kwd_only = 3 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : combined_example() got some positional-only arguments passed as keyword arguments: 'pos_only' Finally, consider this function definition which has a potential collision between the positional argument name and **kwds which has name as a key: def foo ( name , ** kwds ): return 'name' in kwds There is no possible call that will make it return True as the keyword 'name' will always bind to the first parameter. For example: >>> foo ( 1 , ** { 'name' : 2 }) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : foo() got multiple values for argument 'name' >>> But using / (positional only arguments), it is possible since it allows name as a positional argument and 'name' as a key in the keyword arguments: >>> def foo ( name , / , ** kwds ): ... return 'name' in kwds ... >>> foo ( 1 , ** { 'name' : 2 }) True In other words, the names of positional-only parameters can be used in **kwds without ambiguity. 4.9.3.5. Recap ¶ The use case will determine which parameters to use in the function definition: def f ( pos1 , pos2 , / , pos_or_kwd , * , kwd1 , kwd2 ): As guidance: Use positional-only if you want the name of the parameters to not be available to the user. This is useful when parameter names have no real meaning, if you want to enforce the order of the arguments when the function is called or if you need to take some positional parameters and arbitrary keywords. Use keyword-only when names have meaning and the function definition is more understandable by being explicit with names or you want to prevent users relying on the position of the argument being passed. For an API, use positional-only to prevent breaking API changes if the parameter’s name is modified in the future. 4.9.4. Arbitrary Argument Lists ¶ Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple (see Tuples and Sequences ). Before the variable number of arguments, zero or more normal arguments may occur. def write_multiple_items ( file , separator , * args ): file . write ( separator . join ( args )) Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the *args parameter are ‘keyword-only’ arguments, meaning that they can only be used as keywords rather than positional arguments. >>> def concat ( * args , sep = "/" ): ... return sep . join ( args ) ... >>> concat ( "earth" , "mars" , "venus" ) 'earth/mars/venus' >>> concat ( "earth" , "mars" , "venus" , sep = "." ) 'earth.mars.venus' 4.9.5. Unpacking Argument Lists ¶ The reverse situation occurs when the arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the * -operator to unpack the arguments out of a list or tuple: >>> list ( range ( 3 , 6 )) # normal call with separate arguments [3, 4, 5] >>> args = [ 3 , 6 ] >>> list ( range ( * args )) # call with arguments unpacked from a list [3, 4, 5] In the same fashion, dictionaries can deliver keyword arguments with the ** -operator: >>> def parrot ( voltage , state = 'a stiff' , action = 'voom' ): ... print ( "-- This parrot wouldn't" , action , end = ' ' ) ... print ( "if you put" , voltage , "volts through it." , end = ' ' ) ... print ( "E's" , state , "!" ) ... >>> d = { "voltage" : "four million" , "state" : "bleedin' demised" , "action" : "VOOM" } >>> parrot ( ** d ) -- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised ! 4.9.6. Lambda Expressions ¶ Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b . Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope: >>> def make_incrementor ( n ): ... return lambda x : x + n ... >>> f = make_incrementor ( 42 ) >>> f ( 0 ) 42 >>> f ( 1 ) 43 The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument. For instance, list.sort() takes a sorting key function key which can be a lambda function: >>> pairs = [( 1 , 'one' ), ( 2 , 'two' ), ( 3 , 'three' ), ( 4 , 'four' )] >>> pairs . sort ( key = lambda pair : pair [ 1 ]) >>> pairs [(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')] 4.9.7. Documentation Strings ¶ Here are some conventions about the content and formatting of documentation strings. The first line should always be a short, concise summary of the object’s purpose. For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation). This line should begin with a capital letter and end with a period. If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object’s calling conventions, its side effects, etc. The Python parser strips indentation from multi-line string literals when they serve as module, class, or function docstrings. Here is an example of a multi-line docstring: >>> def my_function (): ... """Do nothing, but document it. ... ... No, really, it doesn't do anything: ... ... >>> my_function() ... >>> ... """ ... pass ... >>> print ( my_function . __doc__ ) Do nothing, but document it. No, really, it doesn't do anything: >>> my_function() >>> 4.9.8. Function Annotations ¶ Function annotations are completely optional metadata information about the types used by user-defined functions (see PEP 3107 and PEP 484 for more information). Annotations are stored in the __annotations__ attribute of the function as a dictionary and have no effect on any other part of the function. Parameter annotations are defined by a colon after the parameter name, followed by an expression evaluating to the value of the annotation. Return annotations are defined by a literal -> , followed by an expression, between the parameter list and the colon denoting the end of the def statement. The following example has a required argument, an optional argument, and the return value annotated: >>> def f ( ham : str , eggs : str = 'eggs' ) -> str : ... print ( "Annotations:" , f . __annotations__ ) ... print ( "Arguments:" , ham , eggs ) ... return ham + ' and ' + eggs ... >>> f ( 'spam' ) Annotations: {'ham': <class 'str'>, 'return': <class 'str'>, 'eggs': <class 'str'>} Arguments: spam eggs 'spam and eggs' 4.10. Intermezzo: Coding Style ¶ Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style . Most languages can be written (or more concise, formatted ) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that. For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you: Use 4-space indentation, and no tabs. 4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out. Wrap lines so that they don’t exceed 79 characters. This helps users with small displays and makes it possible to have several code files side-by-side on larger displays. Use blank lines to separate functions and classes, and larger blocks of code inside functions. When possible, put comments on a line of their own. Use docstrings. Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4) . Name your classes and functions consistently; the convention is to use UpperCamelCase for classes and lowercase_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods). Don’t use fancy encodings if your code is meant to be used in international environments. Python’s default, UTF-8, or even plain ASCII work best in any case. Likewise, don’t use non-ASCII characters in identifiers if there is only the slightest chance people speaking a different language will read or maintain the code. Footnotes [ 1 ] Actually, call by object reference would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (items inserted into a list). Table of Contents 4. More Control Flow Tools 4.1. if Statements 4.2. for Statements 4.3. The range() Function 4.4. break and continue Statements 4.5. else Clauses on Loops 4.6. pass Statements 4.7. match Statements 4.8. Defining Functions 4.9. More on Defining Functions 4.9.1. Default Argument Values 4.9.2. Keyword Arguments 4.9.3. Special parameters 4.9.3.1. Positional-or-Keyword Arguments 4.9.3.2. Positional-Only Parameters 4.9.3.3. Keyword-Only Arguments 4.9.3.4. Function Examples 4.9.3.5. Recap 4.9.4. Arbitrary Argument Lists 4.9.5. Unpacking Argument Lists 4.9.6. Lambda Expressions 4.9.7. Documentation Strings 4.9.8. Function Annotations 4.10. Intermezzo: Coding Style Previous topic 3. An Informal Introduction to Python Next topic 5. Data Structures This page Report a bug Show source « Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 4. More Control Flow Tools | Theme Auto Light Dark | © Copyright 2001 Python Software Foundation. This page is licensed under the Python Software Foundation License Version 2. Examples, recipes, and other code in the documentation are additionally licensed under the Zero Clause BSD License. See History and License for more information. The Python Software Foundation is a non-profit corporation. Please donate. 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https://share.transistor.fm/s/9bb11fc4#copya | APIs You Won't Hate | Funding Open Source with Dudley Carr from Stack Aid APIs You Won't Hate 40 ? 30 : 10)" @keyup.document.left="seekBySeconds(-10)" @keyup.document.m="toggleMute" @keyup.document.s="toggleSpeed" @play="play(false, true)" @loadedmetadata="handleLoadedMetadata" @pause="pause(true)" preload="none" @timejump.window="seekToSeconds($event.detail.timestamp); shareTimeFormatted = formatTime($event.detail.timestamp)" > Trailer Bonus 10 40 ? 30 : 10)" class="seek-seconds-button" > 40 ? 30 : 10"> Subscribe Share More Info Download More episodes Subscribe newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyFeedUrl()" class="form-input-group" > Copied to clipboard Apple Podcasts Spotify Pocket Casts Overcast Castro YouTube Goodpods Goodpods Metacast Amazon Music Pandora CastBox Anghami Anghami Fountain JioSaavn Gaana iHeartRadio TuneIn TuneIn Player FM SoundCloud SoundCloud Deezer Podcast Addict Share newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyShareUrl()" class="form-input-group" > Share Copied to clipboard newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyEmbedHtml()" class="form-input-group" > Embed Copied to clipboard Start at Trailer Bonus Full Transcript View the website updateDescriptionLinks($el))" class="episode-description" > Chapters January 23, 2023 by APIs You Won't Hate View the website Listen On Apple Podcasts Listen On Spotify Listen On YouTube RSS Feed Subscribe RSS Feed RSS Feed URL Copied! Follow Episode Details / Transcript Mike chats with Co-Founder of Stack Aid, Dudley Carr, about the importance of funding Open Source projects, and Stack Aid's approach to helping Open Source organizations get paid. Show Notes Stack Aid - https://www.stackaid.us/ Dudley Carr - @dudley@mastodon.social Creators and Guests Host Mike Bifulco Cofounder and host of APIs You Won't Hate. Blogs at https://mikebifulco.com Into 🚴♀️, espresso ☕, looking after 🌍. ex @Stripe @Google @Microsoft What is APIs You Won't Hate? A no-nonsense (well, some-nonsense) podcast about API design & development, new features in the world of HTTP, service-orientated architecture, microservices, and probably bikes. Mike Bifulco: Hello, hello, and welcome back to APIs you won't Hate. My name is Mike Balco. I am one of your api co-hosts and guide through the world of designing APIs and building APIs, and doing all sorts of good stuff with API tech. I am joined today for an interview with a new friend of mine, someone who I met at a conference here in North Carolina. We're gonna be talking a little bit today about his project and some of the sort of mission of open source and supporting open source and things like that. So today I'm chatting with Dudley Carr from Stack A Dudley. How are you doing today? Dudley Carr: I'm doing great. Thanks for having me on. Mike Bifulco: Yeah, of course. Super happy to have you here. I have lots of questions for you and I'm, I'm super glad you were able to make it because from our initial conversations when we sort of bumped into each other all over the place at all Things Open your work seemed very interesting to me. And I think a lot of the squad here that is part of the APIs you won't hate community will really. What you're doing. So I wanna talk all about that. I wanna talk about how you got to where you are and what you're doing at Stack and just kinda get some of the history on, on the project in yourself. So tell me a bit about yourself and tell me about Stack. Dudley Carr: Absolutely. So I've been a, in the software engineering space for the. 22 years. I did my undergraduate in computer science at Stanford and graduated at the peak of the dot com bubble burst. And briefly did a stint in finance, actually worked at Lehman Brothers on their exotic derivatives until I realized that stuff is insane and I got out. In the last 20 years, I've spent all of it working with my brother, who also did computer science, and so we've gone from one venture to the next. So he is not here, but is probably the. More important of the duo. And anyway, we did our first startup in Rhode Island in my parents' basement. I think there was radon in that basement, but we we managed we actually built in the, you know, 2002, 2003, we built a product that. Became g talker. It was flash-based, you know, pre action. It was action script, but before it was even before they released all of their UI toolkits and stuff like that. And back backend was Python. It was initially a desktop application, then became a. Web-based product. And we developed that out and ended up selling that to Google and moving to Seattle in 2006 to join the Google Talk team and work on that. And we spent about five years at Google going from one project to the next. First we were in apps and and then eventually I worked on Google Voice and then before leaving. So that was super formative for us. We learned a lot of things, met a lot of great people. I think that was kind of the heyday for Google And and then after that we, we did some more startups food, food related things. And then we joined a company called Moz that does SEO here in Seattle. And we spent another four or five years there, I helped run a large portion of their engineering team and then grew some of their product areas. That was also really formative for us in terms of, you know, understanding that space, growing teams and you know, just going through various product life cycles and things like that. At the end of our MOS experience, we actually did another startup with a friend here in Seattle around crowdfunding. And this was actually crowdfunding for sports team. So, There was another platform that was really taking off. We found out about Stripe Connect and started using that. And really the, the basis for it was, you know, you have like a high school football team. They're selling candy bars and things like that. There's a lot of inefficiencies there and there's a lot of price gouging actually by merchants who sell products to schools to do that. And so there was. You know, 2017, 2018, there was a real impetus to you know, move all of that stuff online. And we have a lot of learnings that I think happy to chat about, but that was kind of formative for us in terms of thinking about, you know, how you move money from a set of people who wanna support something to, to the recipients and what all is involved in that. That was also just kind of how we, we transitioned from that into consulting. So we've been doing consulting for. Four years you know, we're kind of embedded engineers and product specialists in inside of organizations and to help them transfer in companies. And that's gives us a ton of flexibility and allows us to do cool things like what we've done over the last couple of years. At the beginning of the pandemic by the way, we launched something called Covid Trace. So we had the hot idea to do contact tracing. We tried to launch an app immediately. It was blocked by Google and Apple. Mike Bifulco: Oh wow. Dudley Carr: you're, you're not doing anything location based and we're gonna sort this out first, which is great. I think it was totally the right move on their part. We ended up adopting their the exposure notification. APIs that they have, and we ended up lo, I think we were the second app to launch in the United States. And so we launched with the state of Nevada and worked with them over the course of two years doing exposure notifications, rolling that out for iOS and Android, and then eventually moving all of Nevada off of our custom app onto IOS's, built-in exposure notification function. And at the same time building out other things in terms of getting results to people and things like that. So really interesting problems around health totally unanticipated. So that, that was actually that was all open source. We released all of that infrastructure, open source and the apps. And then, yeah, about a year ago we started on decade. Mike Bifulco: Wow. Yeah, that's some in incredible back history there. I, I. Was not prepared for that, that much. Incredible problem solving that you've gotten into in your, your career. For sure. As someone who lived through an entire pandemic of being, you know, Locked in my home and not leaving and being very concerned about public health and those things. Super, super cool to hear, hear you worked on that and, and obviously impacted so many people. And also, you know, collaborated with the, the big organizations like Apple and Google. That's massively cool to hear. I also don't think I realized that you and I had some sort of shared overlap not overlap, but, but maybe an odd Venn diagram of career stuff before working at Stripe, I worked at Google for a couple years. Not quite on Google Voice, on Google Assistant, so voice related stuff at Google. Although I'm no longer there and actually probably worth mentioning for posterity since you and I met at All Things Open. I'm also no longer at Stripe. So I'm, I've left Stripe in the past couple weeks, but I'm very curious to hear about your experiences with Stripe Connect and, and all that. And so. All of this history of all the crazy things you've done and, and like working with complex teams and big problems and across devices and problem spaces, and I'm sure languages and all the other things that have changed since what, 2003 when you first got into the the, the world of, of building things has led you to where you're at now. So tell me a little bit about Stack Aid and what you're doing. There. Dudley Carr: Yeah, so Stack stack is a service that allows you to fund your second first order and second order dependencies automatically. It, the impetus for it came about a year and a half ago when we. You know, repeatedly saw articles about people exasperated by their inability to sustain their open source project because, you know, the demands have increased on what they have to deliver and the reach, you the reach of their open sources beyond their wildest dreams, but, you know, they, they basically pay for it in their spare time or it takes away from other paying opportunities that they have. And so you see a lot of people kind of torn in those situations. We, that really resonated with us. As I mentioned, you know, we had spent time in the fundraising arena and we, you know, we saw. Definitely momentum around Get Up sponsors an open collective, but we, we thought that there was an opportunity there. You know, I think what's super interesting about the software development space as opposed to any other space where people are trying to raise money is that we know we know what, what you use, right? There's sometimes it's imperative, but increasingly it's a declarative. Way of specifying all your dependencies. And so we can, we can do so many things automatically to determine what you use and, and potentially influence how we allocate money. And so the, the, the seed of an idea was there and we started exploring, you know, the feasibility of it and what that would look like, and is it an effective model, things like that. And so that's been like the last year and it's, it's. Super interesting. Kind of flushing that out and we're, we've been super happy with the results and the initial reception when we launched a couple of months ago. Mike Bifulco: So. I've seen it and I'm sort of familiar with the product, but I wanna make sure that you know, it's abundantly clear what you mean when you're talking about this. So we're talking about funding open source projects in a way that is sort of sustainable and based on your dependency graph for projects that you're using. So when you say first and second order dependencies, what do you mean? Dudley Carr: Yeah. So by first order, so let's take a Packers saw JSON in the node E. The first order of dependencies are the the dependencies and dev dependencies that you list directly in that za js o n. Now, those first order dependencies in turn have their own za js o, where they list their dependencies. That would be the second order of dependencies. Now you can walk that tree down all the way down, and there are gonna be lots and lots more. not unusual for a project to have literally thousands of. Dependencies in their dependency tree. But you know, from a funding perspective, you have to draw the line somewhere. Otherwise, you know, you take a certain amount of money and divide it into tiny little pieces and it becomes somewhat meaningless. So we wanted to, you know, the, the easy thing was would be to just fund first order dependencies. But we, we realized, you know, a lot of those open source projects also want to give. And if we, you know, defaults matter. And we realize that if we came up with a mechanism that, you know, when you find a first order dependency, it passes some of that onto its dependencies. You know, you're doing that automatically for the ecosystem. You're bene, you don't have to have everyone opt-in in order to have further reach into the ecosystem. And so yeah, that was the impetus to fund first and second order depend. Mike Bifulco: Yeah. Got it. So from the, I I, gosh, I don't even know what, what you would consider to be the end user, but from the perspective of someone who is doing the funding, doing the supporting what does that look like? Like what is, what is the process for me? Say for a project I'm running, let's say APIs, you won't hate.com, right? It's a, it's a no JS project. We've got a whole heap of dependencies that are sort of built into this thing. What would I need to do to adopt. Dudley Carr: a great question. So, you know, when you go to Staca us, there's the first step in the onboarding process is oh, often thing with GitHub and actually adding the GitHub app to either your personal organization or some other organization where repositories are we then scanned those repositories for you know, files like Bax, J S O N, or you know, others depending on whatever language you're. And we use those declarative list of dependencies, we ingest that and start looking at that dependency tree. Once we have that, we, you know, we, we put you in the dashboard. We show you what we had discovered, like which files and which repositories we're pulling from. And we presume initially that you ne you want to fund all of those. You can, you can be selective, right? So I wanna fund these repositories and these package digest and things like, Based on that, based on the first order and second order dependencies we've pulled from that. And you can then indicate as a level of support that you wanna do on a monthly basis. We then calculate how much would go to each of those projects. So it's hard to des describe, but there's a tree that we have in the, in the dashboard and it shows you, okay, you've got React or low dash, for example, as a first order dependency. It has these second order dependencies and it shows you the amount of your subscription that goes to each one of those. And so that breaks down when you're, the next step is to enter a credit card and then, then you're off to the races. Mike Bifulco: Yeah. Okay. So from, from my perspective, it is, you know off with GitHub, get this thing added to my stack of or to my GitHub organization. It'll go and, and I guess introspect and look at, or I guess inspect is probably even the right word there. Go look at all the projects I have and give me the the first and second order dependencies for each is the target. Then from there to say like, just using easy numbers I want to donate a hundred bucks a month. To these various organizations. I, I have one fixed cost and Stack Aid kind of does the rest from there. Dudley Carr: That's, that's exactly right. Yes. Mike Bifulco: Yeah. Wow. So how, well man, I, I feel like I have so many questions. How does the money get from A to B? Like, how do you track down the the various projects that are then being funded? Dudley Carr: Yeah, so that's the fun part about building something like this is because it's effectively kind of like a marketplace, right? I mean, we have, we're engaging with both. Individual developers and companies who are supporters and of course have a relationship with open source, maintain. So we have slowly been reaching out to open source maintainers kind of as we drive awareness or if they've receiving funding, we will reach out to them individually. , but we also have been realizing that, you know, a lot of these people don't know who we are. There's a lot of things grabbing at their attention. So if they have an existing relationship with GI UP sponsors or Open Collective, we actually just use our corporate credit card and make the donation on those platforms. So our, you know, our goal is to get the money in their hands. And if they have an existing relationship, we, we lean on that. So that, that's worked out well. But but primarily over time, I think for for the ease of developers and to give them more control in terms of, you know, how those funds are allocated. Especially if there are multiple people working on a project. Things like. You know, we we would like people to, you know, claim their project on stack. Mike Bifulco: Yeah, sure. What does that look like? Dudley Carr: So we use Stripe Connect under underneath. So you know, when you log into the dashboard and you owe off you also have to oth with GI up at the moment. We're working on other. Hosting platforms, but you o often we actually verify that you actually are a maintainer on those repositories that you're trying to claim. We list out those repositories you claim them. And then as part of that claiming process, we also need to collect the a Stripe account. So we send you over to Stripe. They get all of the, the details necessary. To basically give us a, a stripe account so that we can deposit funds into at the end of the month. And then that's it. Then you're, then you're able to collect money from stack. Mike Bifulco: Yeah. Wow, that's great. So, so I'd imagine there's some population of people who are very pleased to find out they can come to Stack Aid, click a couple of buttons and have money being funneled into their project every month. That, that's gotta feel pretty cool to be able to, I don't know, land that dream so seamlessly. Dudley Carr: Yeah, I mean, I think it speaks more distract than to us. I mean, honestly, that flow is amazing and there's so much complexity abstracted. But I think from an end developer perspective, it is surprisingly easy to get up and running. And yeah, and I think it's, it's pretty great, you know, when you show up that a lot of the times there's, you know, a couple of bucks at the very least waiting for you there, and you immediately get that. I think that has been an important part of stack it, which is, you know, you, you don't have to be a developer. Like the developer doesn't have to have an account in order for money to accrue for them. So you know, you have this kind of problem I think on GitHub sponsors an open collective initially where people didn't have a relationship with those platforms, so there wasn't a way to get money to them. A lot of people have set it up, but there's also a large portion of the ecosystem that has no relationship with them. And so it was important for us to be able to accrue money and, you know, show people that you can actually. there's money in the open source that they've contributed and have that as a carrot for them to sign up. Mike Bifulco: Sure. Yeah, that's, that's a really interesting model and having been exposed to GitHub sponsors a little bit, I know that like one of the nice things that comes along with this actually may, might be a Stripe Connect requirement, but to access Stripe Connect, you have to essentially have viable tax information, right? Like the, the right information to be able to be paid out. So that you're not just, you know, sending off money to some anonymous bucket somewhere. But instead, theoretically it's tied to like an L L C or an individual proprietor or, you know, a more complex corporation in the case of vicar businesses. But a lot of that is, I would imagine abstracted away from you. You just need them to, to, you know, click the button and connect to stack with Stripe Connect. Dudley Carr: One of the biggest concerns that we had out of the gate was you. All open source doesn't happen in the United States. There are people across the world, and the United States in particular has a requirement called know your customer. And so you need a lot of details in order to verify their identity and make, you know, make sure that this isn't for money laundering or some other scheme like that. And so that is actually all abstracted away for us. And that is pretty phenomenal if we. A, a two person operation. There's just no way you're gonna Mike Bifulco: Yeah, Dudley Carr: that. Mike Bifulco: the, the scope and scale of those money laundering operations is far more complex and sophisticated than, you know, I think we might realize as, as sort of an average consumer. You know, again, I'm, I'm not at Stripe any longer, but during my tenure there, like you, you do Financial crimes training and it's pretty astonishing in the creative ways people, you know, will, will go to lengths to make money disappear or just harder to trace whatever the case may be. And nice that you don't have to worry about that. There's a lot of mechanisms in place to detect and prevent that fraud as well. . Okay. So I, I want to know a little bit about when did you what, what signals were you given that this was something that was going to work? In other words that when you're starting to build stack, because it's only a year and change old at this point was there a moment or a series of events that sort of made you feel like, oh, this is something that actually has some momentum behind it? Dudley Carr: Yeah, I think well, I think we had to prove to ourselves that it's viable and, you know, we, we have, there's some nuance to the model in terms of how we distribute that money. And, and more importantly, what's interesting about this problem is that it's not a one-time thing. So if no one shows up to collect the money, what do you do with that money? So there's a time component to it as well. Mike Bifulco: Yeah. Dudley Carr: we wanted, so we. There's complexity around the model to some degree in terms of implementing and doing it right, and we, we knew that the model itself needed to be validated and be comparable to things like get up sponsors and, and Open Collective. So we actually spent a large portion of the development. Building out a simulation. And so there's a, like simulation Dots US has. It's, it's effectively like the, it's our entire site, but it has 5,000 made up subscribers at various price points using Pax JSONs that we had discovered on GitHub using source graph. Source graph was pretty instrumental in terms of d doing that. And we, we needed package js os that weren't on n p, right? We didn't want to grab load Dash's, patch json accidentally. And because that, that's not representative of potential end users. So we took those 5,000 subscribers, plugged them in, you know, gave them some subscription amount between $25 per month to a hundred dollars per month. And we. Look to see what happens. Right? What's the outcome of, of this? Like, is it just a couple of projects that get all the money or, you know, what does that distribution look like and the, the, the end result is that, yeah, you, you still have a power power law curve just like you do on Get up sponsors in Open Collective, but it was it was more stretched. So we ended up, we ended up funding a larger percentage of the, let's say the top 25% of funds included a significantly larger set of projects. So even though they're at the tip of this parallel curve, they, you know, there's more of them included. That's great. But the middle, the middle was much broader. Right. A lot more of the money was going into that, and so that, that was the validation that we needed, right, internally to know that, yeah, we can reach more of this. I think in terms of the broader like readiness for this type of product, I, I think, you know, there's just a drumbeat of vulnerabilities and also just individuals. Really talking about the lack of funding, the lack of maintenance around this, around this. And so that is the validation that we continue to look for you know, as an opportunity to do something about, I think we're, we're very nascent in terms of evangelizing this and, and driving awareness. But I think, you know, those two things kind of has given us the confidence that you know, the timing is hopefully right and it's the right product for the time. Mike Bifulco: Yeah. Yeah. I, it's an interesting, almost, it's not that you have a chicken and an egg problem to, to work with, but I feel like the whole funding nut to crack is that like we, we all on some level, developers, engineering teams or organizations understand that it's important to Keep these projects funded so that they stay up to date so that vulnerabilities get shut down, bugs get addressed, functionality gets added, whatever the case may be. it seems like a lot of the social pressure lands on individuals to do the funding in a lot of ways, and I think that maybe is a law of numbers thing. Like people you know, you get a lot more call to action as an individual to go fund things. But my guess is that the bulk of the volume of money is coming from organizations who are willing to fund open source things. Is that roughly. Dudley Carr: Yeah, so we actually were able to analyze all of the Open Collective transactions. They do this amazing job of every transaction on Open, the Open Source collective, on Open Collective. You can literally download all of the transactions and so, I did that and I went Mike Bifulco: Oh wow. Dudley Carr: And yes, you know, organizations like Google and, and others, they do put in a ton of money. But if I remember correctly, I would say, Over 60% of it are from individuals donating at at much smaller amounts. So they're, they have a long tail and it is a significant portion of the contributions. And so it, it's, it wasn't as skewed as you would think towards large organizations. Mike Bifulco: That that is a, a bigger percentage than I would've guess. That's really interesting. So what, what is your call to action or maybe your pitch for those who might have the capacity to donate? Like how, how is the I guess the, is there a sales process for this? Is it something that you're going to organizations and people and trying to get them to discover and use Stack as donors? Dudley Carr: You know, I think, I think there are certain organizations that are very attuned to open source and, you know, they have open source program offices and they are actively engaging those communities and they are. they're looking, you know, they're either doing this themselves. So century is a customer of Stack and they did a ton of this by themselves. They, they wrote custom things to analyze their dependencies, and they had a big spreadsheet and it's super impressive, but it's incredibly time consuming. And I think Indeed and others are also analyzing their dependencies and trying to figure out where to allocate money. So this is something that is happening today. So we're looking to engage with those types of organizations and understand, you know, how STACK can potentially be a part of that. So I think step one is to really engage with organizations that are receptive to it. I think that's the kind of low hanging fruit. And I think beyond that, you know, there's, there's or organizations that are certainly consuming large. Portion of open source and you know, there's kind of a, a sales, different sales process around, you know, here are the ways that you engage with open source at those organizations. Funding is one aspect of that. And so I think over time that's where that conversation's going. But I think the organizations that are currently funding open source to some degree, You know, they're kind of making the case for that and, and we, you know, we're trying to expand that conversation and, and as well as piggyback off of that, Mike Bifulco: right? Yeah. It's nice that it's kind of the zeitgeist is that it seems that support has really changed in the past, I don't know, maybe 10 years to, like open source is something we can try or should try to, open source is something that, you know, I is the infrastructure of the internet in a lot of ways and something that you know, almost the, the ethical impetus is to support open source projects and to also be a part of that if you're able. So, okay. I, I guess one more important question then, if I'm an open source developer what, what are actions I can take to be proactive about I, I guess making sure that I'm, I'm covered by stack or that you know, that I'm doing the right things to seek funding. Dudley Carr: Yeah, I think you know, one. One theory that we have is that, you know, the, there are organizations like we were just talking about that are attuned and are willing to donate, but I, I actually think a fundamental shift will is dependent on individual developers donating and independent of the platform, but actively participating in that way of funding open source be it GI UP sponsors, open Collective Stack. Thanks, DD Dev, any of those platforms is a good way to start. But there, there has, you know, we have to have that expectation that developers are doing this just like they do other types of open source contributions. And I think that. That groundswell of developers participating and educating and kind of demanding this in their organizations is what actually turns the tide. And so our focus initially is actually to get individual developers to come on board and we're, we hope that we're. You know, one of those solutions that makes it a lot simpler. But if GitHub sponsors is the way that you do it, great. Right? Go, go on there. Fund, fund the people or the projects that you really care about. But I think that speaks volumes, right? And that I, I think is the thing that actually moves the needle. And those platforms have made it simpler. We hopefully have made it simpler based on, you know, what some set of people care about. But, you know, our, our goal is to evangelize individual developers. Contributing more. Mike Bifulco: Yeah, that's a noble conceit and definitely one of those things that I think all of the people listening to the show can probably relate to. I certainly identify with it. I, one of the things I've been mulling over a lot lately especially, especially in the past few weeks that I've been like reconsidering my personal budget and the way I allocate money for things is that I, I think I would like to be a little more public in sharing and explaining. The ways that I spend money in four good ways, right? Like charities that I donate to on one side, but open source things that I donate to projects that I support. And also, this is more on the creator economy side, but like Patreon and things like that, where there's like, you know, I love this podcast, so I give them a dollar a month, which is, you know, more than they would ever get from me clicking on ads. I could click ads every day for a week. And wouldn't give them a book. And it goes a lot further than you would think. And it, it's funny, I've been kind of thinking that that's something that belongs in. Almost public profile, like I should be sharing this somewhere and making that a part of the my, my persona, my support for the world. And I think that that's something that we have a, great opportunity to do with projects like Stack A and with other things that we all participate in because it also creates that social pressure and that. Impression that expectation that part of being a, a good citizen as a developer when you can and if you can, and if you have, you know, the, honestly the mountains of privilege that I'm sitting on top of, like, you should be giving back. I really like that. And I, one of the things that I like about STACK is honestly the, the tree view of the dependencies and seeing the amount of impact that, you know, even a few bucks a month can have is like visceral. You really feel like you, you see that not only are you using this cascade of things to power whatever project you're working on, but you can also give back to them fairly directly. And, there's infrastructure in place to do that for you. I think that's really exciting and I think it's a noble cause and I'm hoping it's something that a lot of the folks who are listening to the podcast will be able to jump into and go ahead long into supporting, but also benefiting from. Dudley Carr: Yeah. No, I appreciate that. I, I think what you're saying really resonates with us in that how you spend your money matters. You know, we are in a position of privilege where, you know, we we have discretionary money that we can funnel towards things. And I think, I think you nailed it. You know, a lot of these developers are, you know, at the moment maybe a couple of bucks per month. You know, we're still small, but I think it, it really matters to those developers partly because it is a real recognition of what they're doing and they know that someone took the time and their money, you know, to do that. And I think that's super powerful. I think it's easy to dismiss it as, oh, it's, you know, it's a trivial sum of money or something of the. But you know, when you are working on something, and a lot of times, you know, you can look at your MPM install numbers, like, oh yeah, that's through the roof. But this is, you know, getting an email from someone saying like, I like your project. That's really visceral as well. Mike Bifulco: Yeah. Dudley Carr: like people actually just paying. I think that's an incredible way. And so hopefully people are not put off by, you know, initially like, oh, the, the dollar amounts are not significant. It, it, it supports that individual at so many different levels. And so yeah, how you spend your money matters and and it has a really great upside on the other other side of it. Mike Bifulco: Yeah. , it's pretty profound and an energizing thing for me. Well, Dudley, thanks so much for coming and hanging out today. I have two important questions for you before I let you go. One is I wanna know how APIs you won't hate listeners can find you and talk to you if they're interested. And where can they go to get started with? Dudley Carr: Absolutely. Yeah. So you can email me at dudley dod e y stack.us and our website is stack a.us. I think if you search for Stack Google, we're number one. And you know, as we were chatting earlier, it's, it's super simple to get started. If you run into any issues please reach out and we're, we're happy to answer questions. But yeah, it's pretty self-service at the moment. Just click on the button o off and then hopefully you're off to the races and, you know, always looking for more feedback and, Yeah. No, we, we appreciate every, every person who signs up and happy to answer questions. Mike Bifulco: Great. Wonderful. Dudley, thanks so much for hanging out today. It's been a pleasure having you. And I'd love to catch up again you know, maybe in a few months or ear down the line to see how things are going. Dudley Carr: Absolutely. Thanks so much for having me. Really appreciate it. Mike Bifulco: Yeah, of course. Take care. Dudley Carr: Bye-bye. All audio, artwork, episode descriptions and notes are property of APIs You Won't Hate, for APIs You Won't Hate, and published with permission by Transistor, Inc. 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https://docs.python.org/3/tutorial/introduction.html#first-steps-towards-programming | 3. An Informal Introduction to Python — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents 3. An Informal Introduction to Python 3.1. Using Python as a Calculator 3.1.1. Numbers 3.1.2. Text 3.1.3. Lists 3.2. First Steps Towards Programming Previous topic 2. Using the Python Interpreter Next topic 4. More Control Flow Tools This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 3. An Informal Introduction to Python | Theme Auto Light Dark | 3. An Informal Introduction to Python ¶ In the following examples, input and output are distinguished by the presence or absence of prompts ( >>> and … ): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you must type a blank line; this is used to end a multi-line command. You can use the “Copy” button (it appears in the upper-right corner when hovering over or tapping a code example), which strips prompts and omits output, to copy and paste the input lines into your interpreter. Many of the examples in this manual, even those entered at the interactive prompt, include comments. Comments in Python start with the hash character, # , and extend to the end of the physical line. A comment may appear at the start of a line or following whitespace or code, but not within a string literal. A hash character within a string literal is just a hash character. Since comments are to clarify code and are not interpreted by Python, they may be omitted when typing in examples. Some examples: # this is the first comment spam = 1 # and this is the second comment # ... and now a third! text = "# This is not a comment because it's inside quotes." 3.1. Using Python as a Calculator ¶ Let’s try some simple Python commands. Start the interpreter and wait for the primary prompt, >>> . (It shouldn’t take long.) 3.1.1. Numbers ¶ The interpreter acts as a simple calculator: you can type an expression into it and it will write the value. Expression syntax is straightforward: the operators + , - , * and / can be used to perform arithmetic; parentheses ( () ) can be used for grouping. For example: >>> 2 + 2 4 >>> 50 - 5 * 6 20 >>> ( 50 - 5 * 6 ) / 4 5.0 >>> 8 / 5 # division always returns a floating-point number 1.6 The integer numbers (e.g. 2 , 4 , 20 ) have type int , the ones with a fractional part (e.g. 5.0 , 1.6 ) have type float . We will see more about numeric types later in the tutorial. Division ( / ) always returns a float. To do floor division and get an integer result you can use the // operator; to calculate the remainder you can use % : >>> 17 / 3 # classic division returns a float 5.666666666666667 >>> >>> 17 // 3 # floor division discards the fractional part 5 >>> 17 % 3 # the % operator returns the remainder of the division 2 >>> 5 * 3 + 2 # floored quotient * divisor + remainder 17 With Python, it is possible to use the ** operator to calculate powers [ 1 ] : >>> 5 ** 2 # 5 squared 25 >>> 2 ** 7 # 2 to the power of 7 128 The equal sign ( = ) is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt: >>> width = 20 >>> height = 5 * 9 >>> width * height 900 If a variable is not “defined” (assigned a value), trying to use it will give you an error: >>> n # try to access an undefined variable Traceback (most recent call last): File "<stdin>" , line 1 , in <module> NameError : name 'n' is not defined There is full support for floating point; operators with mixed type operands convert the integer operand to floating point: >>> 4 * 3.75 - 1 14.0 In interactive mode, the last printed expression is assigned to the variable _ . This means that when you are using Python as a desk calculator, it is somewhat easier to continue calculations, for example: >>> tax = 12.5 / 100 >>> price = 100.50 >>> price * tax 12.5625 >>> price + _ 113.0625 >>> round ( _ , 2 ) 113.06 This variable should be treated as read-only by the user. Don’t explicitly assign a value to it — you would create an independent local variable with the same name masking the built-in variable with its magic behavior. In addition to int and float , Python supports other types of numbers, such as Decimal and Fraction . Python also has built-in support for complex numbers , and uses the j or J suffix to indicate the imaginary part (e.g. 3+5j ). 3.1.2. Text ¶ Python can manipulate text (represented by type str , so-called “strings”) as well as numbers. This includes characters “ ! ”, words “ rabbit ”, names “ Paris ”, sentences “ Got your back. ”, etc. “ Yay! :) ”. They can be enclosed in single quotes ( '...' ) or double quotes ( "..." ) with the same result [ 2 ] . >>> 'spam eggs' # single quotes 'spam eggs' >>> "Paris rabbit got your back :)! Yay!" # double quotes 'Paris rabbit got your back :)! Yay!' >>> '1975' # digits and numerals enclosed in quotes are also strings '1975' To quote a quote, we need to “escape” it, by preceding it with \ . Alternatively, we can use the other type of quotation marks: >>> 'doesn \' t' # use \' to escape the single quote... "doesn't" >>> "doesn't" # ...or use double quotes instead "doesn't" >>> '"Yes," they said.' '"Yes," they said.' >>> " \" Yes, \" they said." '"Yes," they said.' >>> '"Isn \' t," they said.' '"Isn\'t," they said.' In the Python shell, the string definition and output string can look different. The print() function produces a more readable output, by omitting the enclosing quotes and by printing escaped and special characters: >>> s = 'First line. \n Second line.' # \n means newline >>> s # without print(), special characters are included in the string 'First line.\nSecond line.' >>> print ( s ) # with print(), special characters are interpreted, so \n produces new line First line. Second line. If you don’t want characters prefaced by \ to be interpreted as special characters, you can use raw strings by adding an r before the first quote: >>> print ( 'C:\some \n ame' ) # here \n means newline! C:\some ame >>> print ( r 'C:\some\name' ) # note the r before the quote C:\some\name There is one subtle aspect to raw strings: a raw string may not end in an odd number of \ characters; see the FAQ entry for more information and workarounds. String literals can span multiple lines. One way is using triple-quotes: """...""" or '''...''' . End-of-line characters are automatically included in the string, but it’s possible to prevent this by adding a \ at the end of the line. In the following example, the initial newline is not included: >>> print ( """ \ ... Usage: thingy [OPTIONS] ... -h Display this usage message ... -H hostname Hostname to connect to ... """ ) Usage: thingy [OPTIONS] -h Display this usage message -H hostname Hostname to connect to >>> Strings can be concatenated (glued together) with the + operator, and repeated with * : >>> # 3 times 'un', followed by 'ium' >>> 3 * 'un' + 'ium' 'unununium' Two or more string literals (i.e. the ones enclosed between quotes) next to each other are automatically concatenated. >>> 'Py' 'thon' 'Python' This feature is particularly useful when you want to break long strings: >>> text = ( 'Put several strings within parentheses ' ... 'to have them joined together.' ) >>> text 'Put several strings within parentheses to have them joined together.' This only works with two literals though, not with variables or expressions: >>> prefix = 'Py' >>> prefix 'thon' # can't concatenate a variable and a string literal File "<stdin>" , line 1 prefix 'thon' ^^^^^^ SyntaxError : invalid syntax >>> ( 'un' * 3 ) 'ium' File "<stdin>" , line 1 ( 'un' * 3 ) 'ium' ^^^^^ SyntaxError : invalid syntax If you want to concatenate variables or a variable and a literal, use + : >>> prefix + 'thon' 'Python' Strings can be indexed (subscripted), with the first character having index 0. There is no separate character type; a character is simply a string of size one: >>> word = 'Python' >>> word [ 0 ] # character in position 0 'P' >>> word [ 5 ] # character in position 5 'n' Indices may also be negative numbers, to start counting from the right: >>> word [ - 1 ] # last character 'n' >>> word [ - 2 ] # second-last character 'o' >>> word [ - 6 ] 'P' Note that since -0 is the same as 0, negative indices start from -1. In addition to indexing, slicing is also supported. While indexing is used to obtain individual characters, slicing allows you to obtain a substring: >>> word [ 0 : 2 ] # characters from position 0 (included) to 2 (excluded) 'Py' >>> word [ 2 : 5 ] # characters from position 2 (included) to 5 (excluded) 'tho' Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced. >>> word [: 2 ] # character from the beginning to position 2 (excluded) 'Py' >>> word [ 4 :] # characters from position 4 (included) to the end 'on' >>> word [ - 2 :] # characters from the second-last (included) to the end 'on' Note how the start is always included, and the end always excluded. This makes sure that s[:i] + s[i:] is always equal to s : >>> word [: 2 ] + word [ 2 :] 'Python' >>> word [: 4 ] + word [ 4 :] 'Python' One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n , for example: +---+---+---+---+---+---+ | P | y | t | h | o | n | +---+---+---+---+---+---+ 0 1 2 3 4 5 6 - 6 - 5 - 4 - 3 - 2 - 1 The first row of numbers gives the position of the indices 0…6 in the string; the second row gives the corresponding negative indices. The slice from i to j consists of all characters between the edges labeled i and j , respectively. For non-negative indices, the length of a slice is the difference of the indices, if both are within bounds. For example, the length of word[1:3] is 2. Attempting to use an index that is too large will result in an error: >>> word [ 42 ] # the word only has 6 characters Traceback (most recent call last): File "<stdin>" , line 1 , in <module> IndexError : string index out of range However, out of range slice indexes are handled gracefully when used for slicing: >>> word [ 4 : 42 ] 'on' >>> word [ 42 :] '' Python strings cannot be changed — they are immutable . Therefore, assigning to an indexed position in the string results in an error: >>> word [ 0 ] = 'J' Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : 'str' object does not support item assignment >>> word [ 2 :] = 'py' Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : 'str' object does not support item assignment If you need a different string, you should create a new one: >>> 'J' + word [ 1 :] 'Jython' >>> word [: 2 ] + 'py' 'Pypy' The built-in function len() returns the length of a string: >>> s = 'supercalifragilisticexpialidocious' >>> len ( s ) 34 See also Text Sequence Type — str Strings are examples of sequence types , and support the common operations supported by such types. String Methods Strings support a large number of methods for basic transformations and searching. f-strings String literals that have embedded expressions. Format String Syntax Information about string formatting with str.format() . printf-style String Formatting The old formatting operations invoked when strings are the left operand of the % operator are described in more detail here. 3.1.3. Lists ¶ Python knows a number of compound data types, used to group together other values. The most versatile is the list , which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually the items all have the same type. >>> squares = [ 1 , 4 , 9 , 16 , 25 ] >>> squares [1, 4, 9, 16, 25] Like strings (and all other built-in sequence types), lists can be indexed and sliced: >>> squares [ 0 ] # indexing returns the item 1 >>> squares [ - 1 ] 25 >>> squares [ - 3 :] # slicing returns a new list [9, 16, 25] Lists also support operations like concatenation: >>> squares + [ 36 , 49 , 64 , 81 , 100 ] [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] Unlike strings, which are immutable , lists are a mutable type, i.e. it is possible to change their content: >>> cubes = [ 1 , 8 , 27 , 65 , 125 ] # something's wrong here >>> 4 ** 3 # the cube of 4 is 64, not 65! 64 >>> cubes [ 3 ] = 64 # replace the wrong value >>> cubes [1, 8, 27, 64, 125] You can also add new items at the end of the list, by using the list.append() method (we will see more about methods later): >>> cubes . append ( 216 ) # add the cube of 6 >>> cubes . append ( 7 ** 3 ) # and the cube of 7 >>> cubes [1, 8, 27, 64, 125, 216, 343] Simple assignment in Python never copies data. When you assign a list to a variable, the variable refers to the existing list . Any changes you make to the list through one variable will be seen through all other variables that refer to it.: >>> rgb = [ "Red" , "Green" , "Blue" ] >>> rgba = rgb >>> id ( rgb ) == id ( rgba ) # they reference the same object True >>> rgba . append ( "Alph" ) >>> rgb ["Red", "Green", "Blue", "Alph"] All slice operations return a new list containing the requested elements. This means that the following slice returns a shallow copy of the list: >>> correct_rgba = rgba [:] >>> correct_rgba [ - 1 ] = "Alpha" >>> correct_rgba ["Red", "Green", "Blue", "Alpha"] >>> rgba ["Red", "Green", "Blue", "Alph"] Assignment to slices is also possible, and this can even change the size of the list or clear it entirely: >>> letters = [ 'a' , 'b' , 'c' , 'd' , 'e' , 'f' , 'g' ] >>> letters ['a', 'b', 'c', 'd', 'e', 'f', 'g'] >>> # replace some values >>> letters [ 2 : 5 ] = [ 'C' , 'D' , 'E' ] >>> letters ['a', 'b', 'C', 'D', 'E', 'f', 'g'] >>> # now remove them >>> letters [ 2 : 5 ] = [] >>> letters ['a', 'b', 'f', 'g'] >>> # clear the list by replacing all the elements with an empty list >>> letters [:] = [] >>> letters [] The built-in function len() also applies to lists: >>> letters = [ 'a' , 'b' , 'c' , 'd' ] >>> len ( letters ) 4 It is possible to nest lists (create lists containing other lists), for example: >>> a = [ 'a' , 'b' , 'c' ] >>> n = [ 1 , 2 , 3 ] >>> x = [ a , n ] >>> x [['a', 'b', 'c'], [1, 2, 3]] >>> x [ 0 ] ['a', 'b', 'c'] >>> x [ 0 ][ 1 ] 'b' 3.2. First Steps Towards Programming ¶ Of course, we can use Python for more complicated tasks than adding two and two together. For instance, we can write an initial sub-sequence of the Fibonacci series as follows: >>> # Fibonacci series: >>> # the sum of two elements defines the next >>> a , b = 0 , 1 >>> while a < 10 : ... print ( a ) ... a , b = b , a + b ... 0 1 1 2 3 5 8 This example introduces several new features. The first line contains a multiple assignment : the variables a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. The right-hand side expressions are evaluated from the left to the right. The while loop executes as long as the condition (here: a < 10 ) remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact any sequence; anything with a non-zero length is true, empty sequences are false. The test used in the example is a simple comparison. The standard comparison operators are written the same as in C: < (less than), > (greater than), == (equal to), <= (less than or equal to), >= (greater than or equal to) and != (not equal to). The body of the loop is indented : indentation is Python’s way of grouping statements. At the interactive prompt, you have to type a tab or space(s) for each indented line. In practice you will prepare more complicated input for Python with a text editor; all decent text editors have an auto-indent facility. When a compound statement is entered interactively, it must be followed by a blank line to indicate completion (since the parser cannot guess when you have typed the last line). Note that each line within a basic block must be indented by the same amount. The print() function writes the value of the argument(s) it is given. It differs from just writing the expression you want to write (as we did earlier in the calculator examples) in the way it handles multiple arguments, floating-point quantities, and strings. Strings are printed without quotes, and a space is inserted between items, so you can format things nicely, like this: >>> i = 256 * 256 >>> print ( 'The value of i is' , i ) The value of i is 65536 The keyword argument end can be used to avoid the newline after the output, or end the output with a different string: >>> a , b = 0 , 1 >>> while a < 1000 : ... print ( a , end = ',' ) ... a , b = b , a + b ... 0,1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987, Footnotes [ 1 ] Since ** has higher precedence than - , -3**2 will be interpreted as -(3**2) and thus result in -9 . To avoid this and get 9 , you can use (-3)**2 . [ 2 ] Unlike other languages, special characters such as \n have the same meaning with both single ( '...' ) and double ( "..." ) quotes. The only difference between the two is that within single quotes you don’t need to escape " (but you have to escape \' ) and vice versa. Table of Contents 3. An Informal Introduction to Python 3.1. Using Python as a Calculator 3.1.1. Numbers 3.1.2. Text 3.1.3. Lists 3.2. First Steps Towards Programming Previous topic 2. Using the Python Interpreter Next topic 4. More Control Flow Tools This page Report a bug Show source « Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 3. An Informal Introduction to Python | Theme Auto Light Dark | © Copyright 2001 Python Software Foundation. This page is licensed under the Python Software Foundation License Version 2. Examples, recipes, and other code in the documentation are additionally licensed under the Zero Clause BSD License. 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https://legal.x.com/es/purchaser-terms.html | Términos de servicio para compradores de X Términos de servicio para compradores de X Descargar los Términos de servicio para compradores de X Términos aplicables a los servicios de pago Términos adicionales de X Premium Términos adicionales de Suscripciones Descargar los Términos de servicio para compradores de X Términos de servicio para compradores de X goglobalwithtwitterbanner Términos aplicables a los servicios de pago Términos adicionales de X Premium Términos adicionales de las suscripciones para creadores Términos adicionales relativos a Premium para negocios y Premium para organizaciones Términos aplicables a los servicios de pago Términos adicionales de X Premium Términos adicionales de las suscripciones para creadores Términos adicionales relativos a Premium para negocios y Premium para organizaciones Términos de servicio para compradores de X En vigor: 1 de agosto de 2025 Si vive en la Unión Europea, los Estados de la AELC o el Reino Unido, incluso si vive en los Estados Unidos, se le aplicarán los siguientes Términos de servicio para compradores de X . Si vive en la Unión Europea, los Estados de la AELC o el Reino Unido, se le aplicarán estos Términos de servicio para compradores de X . Términos de servicio para compradores de X Si vive fuera de la Unión Europea, los Estados de la AELC o el Reino Unido, incluso si vive en los Estados Unidos X le permite acceder a ciertas funciones a cambio del pago de una tarifa única o recurrente, según corresponda a las funciones pertinentes (cada una un “ Servicio de pago ” y colectivamente los " Servicios de pago "). Por ejemplo, X Premium (como se define a continuación) y las Suscripciones se considerarían cada una un "Servicio de pago". En la medida que se registre o use un Servicio de pago, el uso que haga de los Servicios de pago y cualquier transacción correspondiente están sujetos a: (i) los términos y condiciones establecidos en este documento, incluidos los términos y condiciones aplicables de cada Servicio de pago que compre, cada uno como se indica a continuación (colectivamente, los “ Términos de servicio para compradores de X ”) y (ii) los Términos de servicio de X , la Política de privacidad de X , las Reglas y políticas de X y todas las políticas aplicables incorporadas en este documento (colectivamente, el “ Acuerdo de usuario de X ”). Estos Términos de servicio para compradores de X y el Acuerdo de usuario de X mencionado anteriormente se denominarán colectivamente en este documento como los “ Términos ”. " X " se refiere a la entidad X que le proporciona los Servicios Pagos. Lea detenidamente estos Términos de servicio para compradores de X para asegurarse de que comprende los términos, condiciones y excepciones aplicables. SI VIVE EN ESTADOS UNIDOS, ESTOS TÉRMINOS CONTIENEN INFORMACIÓN IMPORTANTE QUE SE APLICA A USTED SOBRE LA RESOLUCIÓN DE DISPUTAS, INCLUYENDO UNA RENUNCIA A SU DERECHO A PRESENTAR RECLAMOS COMO DEMANDAS COLECTIVAS Y UNA LIMITACIÓN DE SU DERECHO A PRESENTAR RECLAMOS CONTRA X MÁS DE 2 AÑOS DESPUÉS DE QUE OCURRIERON LOS EVENTOS RELEVANTES, LO QUE IMPACTA SUS DERECHOS Y OBLIGACIONES SI SURGE ALGUNA DISPUTA CON X. CONSULTE LA SECCIÓN 6 EN TÉRMINOS GENERALES PARA CONOCER LOS DETALLES DE ESTAS DISPOSICIONES. Aceptación . Al usar o acceder a los Servicios de pago de X, enviar pagos en virtud del mismo o hacer clic en un botón para realizar una compra única o pagos de suscripción recurrentes por el Servicio de pago proporcionado por X, acepta cumplir con los Términos. Si no comprende los Términos o no acepta alguna parte de ellos, entonces no podrá usar ni acceder a ningún Servicio de pago. Para comprar y usar un Servicio de pago, debe: (i) tener al menos 18 años o ser mayor de edad según lo determinen las leyes de la jurisdicción en la que reside o (ii) tener el consentimiento expreso de su padre, madre o tutor para comprar y usar dicho Servicio de pago. Si usted es un padre, una madre o un tutor legal que autoriza que su hijo (o un niño del que usted es tutor) compre o use un Servicio de pago, acepta que los Términos se aplican a usted, que cumplirá con los Términos y que usted es responsable de la actividad del niño en los Servicios de pago, así como de asegurarse de que el niño también cumpla con los Términos. En cualquier caso, tal como se indica en la sección Quién puede hacer uso de los Servicios de los Términos de servicio de X, debe tener al menos 13 años para usar el Servicio de X. Si acepta estos Términos de servicio para compradores de X y usa los Servicios de pago en nombre de una empresa, organización, gobierno u otra entidad jurídica, afirma y garantiza que está autorizado a hacerlo y cuenta con los poderes necesarios para obligarla al cumplimiento de estos Términos de servicio para compradores de X, en cuyo caso el uso de las palabras “usted”, “su” y “sus” en estos Términos de servicio para compradores de X hará referencia a dicha entidad jurídica. Entidad contratante de X . Usted suscribe estos Términos de servicio para compradores de X con la entidad que corresponde a su lugar de residencia, como se indica a continuación. Esta entidad le prestará los Servicios de pago. Ninguna otra entidad está sujeta a ninguna obligación con usted en virtud de estos Términos de servicio para compradores. Su ubicación Los continentes de América del Norte (incluido Hawái) o América del Sur Entidad contratante X Corp., con oficina en el domicilio 865 FM 1209, Building 2, Bastrop, TX 78602, USA Su ubicación Cualquier país no cubierto por las dos ubicaciones anteriores, incluida la región de Asia Pacífico, Oriente Medio, África o Europa (se excluyen los países de la UE, los Estados de la AELC o el Reino Unido) Entidad contratante X Global LLC, con domicilio social en 701 S. Carson St., Suite 200, Carson City, NV 89701, EE.UU. Cambios a los Términos, Servicios de pago y precios 1. Cambios en los Términos. X puede modificar estos Términos de servicio para compradores de X de vez en cuando, incluso por motivos comerciales, financieros o legales. Los cambios no serán retroactivos y la versión más reciente de los Términos de servicio para compradores de X, disponible en legal.x.com/purchaser-terms , regirá el uso que haga de los Servicios de pago y de cualquier transacción correspondiente. Si modificamos o revisamos estos Términos después de que los haya aceptado (por ejemplo, si estos términos se modifican después de que haya comprado una suscripción), le notificaremos con anticipación de las revisiones importantes de estos términos. Dicha notificación puede proporcionarse de forma electrónica, incluso (y sin limitación) a través de una notificación de servicio o un correo electrónico a la dirección de correo electrónico asociada con su cuenta. Una vez que las revisiones entren en vigor, el acceso continuado y el uso de los Servicios de pago comportarán la aceptación implícita de los Términos de servicio para compradores de X revisados. Si no acepta cumplir con estos o con cualquier Término de servicio para compradores de X futuro, no use ni acceda (ni continúe usando o accediendo) a los Servicios de pago. Los Términos de servicio para compradores de X están escritos en inglés, pero están disponible en varios idiomas a través de traducciones. X se esfuerza por hacer que las traducciones sean lo más fieles posible a la versión original en inglés. Sin embargo, en caso de discrepancias o inconsistencias, prevalecerá la versión en inglés de los Términos de servicio para compradores de X. Reconoce que el inglés será el idioma de referencia para interpretar y construir los Términos de servicio para compradores de X. 2. Cambios a los Servicios de pago. Nuestros Servicios de pago están en constante evolución. Como tal, los Servicios de Pago pueden cambiar de vez en cuando, a nuestra discreción, incluso por cualquier razón comercial, financiera o legal. Podemos interrumpir (de forma temporal o permanente) la prestación de los Servicios de pago o cualquiera de las funciones de los Servicios de pago a usted o a todos los usuarios en general, con o sin previo aviso. X no es responsable ante usted ni ante ningún tercero por cualquier modificación, suspensión o interrupción de los Servicios de pago. Los términos y condiciones específicos (incluidos a continuación) para el Servicio de pago específico indican cómo puede cancelar una suscripción o, cuando corresponda, solicitar un reembolso. 3. Cambios en los precios. Los precios de los Servicios de pago, incluidas las tarifas de suscripción recurrentes, están sujetos a cambios de vez en cuando, incluso por motivos comerciales, financieros o legales. X proporcionará una notificación previa razonable de cualquier cambio importante en el precio de los Servicios de pago. Para los servicios de suscripción, los cambios de precio entrarán en vigor al inicio del período de suscripción siguiente a la fecha del cambio de precio. Si no está de acuerdo con un cambio de precio, tiene derecho a rechazar el cambio cancelando su suscripción al Servicio de pago correspondiente antes de que el cambio de precio entre en vigor. Condiciones de pago . X ofrece diversas opciones de pago que pueden variar según el Servicio de pago, su dispositivo o sistema operativo, su ubicación geográfica u otros factores. En la medida que esté disponible (ya que X puede ofrecer diversos métodos de compra de vez en cuando), estas opciones de pago pueden incluir la posibilidad de usar la funcionalidad de “Pago en la app” que ofrecen Google o Apple, o realizar un pago web usando el procesador de pagos de terceros, Stripe ( www.stripe.com , en adelante, “ Stripe ”). Cuando realiza un pago, acepta explícitamente: (i) pagar el precio indicado por el Servicio de pago, junto con cualquier monto adicional relacionado con impuestos aplicables, tarifas de tarjetas de crédito, tarifas bancarias, tarifas de transacciones extranjeras, tarifas de cambio de divisas y fluctuaciones de divisas; y (ii) cumplir con los términos de servicio, las políticas de privacidad u otros acuerdos o restricciones legales pertinentes (incluidas las restricciones de edad adicionales) impuestos por Google, Apple o Stripe (como el procesador de pagos externo de X) en relación con el uso que hace de una forma de pago determinada (solo como ejemplo, si elige realizar su pago a través de la funcionalidad de compra en la app de Apple, acepta cumplir con los términos, requisitos o restricciones pertinentes impuestos por Apple). Todos los datos personales privados que proporcione en relación con el uso que hace de los Servicios de pago, incluidos, entre otros, los datos proporcionados en relación con el pago, se procesarán de acuerdo con la Política de privacidad de X. X puede compartir su información de pago con proveedores de servicios de pago para procesar pagos; para prevenir, detectar e investigar fraudes u otras actividades prohibidas; para facilitar la resolución de disputas, como reintegros o reembolsos; y para otros fines asociados con la aceptación de tarjetas de crédito y débito o transferencias ACH. Es su responsabilidad asegurarse de que su información bancaria, de tarjetas de crédito, tarjetas de débito u otra información de pago esté actualizada, esté completa y sea exacta en todo momento. Si realiza un pago por un Servicio de pago, es posible que recibamos información sobre su transacción, como cuándo se realizó, cuándo caducará o se renovará automáticamente una suscripción, en qué plataforma realizó la compra y otra información. X no será responsable de los errores cometidos ni de las demoras por parte de un procesador de pagos, de la App Store de Apple o de la Google Play Store, de su banco, de la empresa emisora de su tarjeta de crédito o de cualquier red de pago. Consulte cada uno de los Términos y condiciones del Servicio de pago específicos a continuación para conocer los términos de pago aplicables a ese Servicio de pago específico, incluida la forma en que se manejan las renovaciones de suscripción y otros términos importantes. Aplicación del Acuerdo de usuario de X, Rescisión, Sin reembolsos, Varias cuentas de X y Restricciones 1. El Acuerdo de usuario de X se aplica a usted . SIEMPRE DEBE SEGUIR Y CUMPLIR CON EL ACUERDO DE USUARIO DE X. El Acuerdo de usuario de X siempre se aplica al uso que hace del Servicio de X, incluidos los Servicios y funciones de pago. El incumplimiento del Acuerdo de usuario de X, o la creencia de X de que usted no ha seguido y ha incumplido el Acuerdo de usuario de X, puede tener como resultado la cancelación de sus Servicios de pago. Cualquier cancelación de este tipo será adicional, y no se limitará a, cualquier medida de control del cumplimiento que X pueda tomar contra en su contra de conformidad con el Acuerdo de usuario de X. En tales casos, es posible que pierda los beneficios de sus Servicios de pago y no será elegible para recibir reembolsos por los montos que haya pagado (o pagado por adelantado) por los Servicios de pago. 2. Por qué X podría cancelar su acceso a los Servicios de pago. X puede suspender o cancelar su acceso a los Servicios de pago, dejar de proporcionarle la totalidad o una parte de los Servicios de pago, o tomar cualquier otra medida que considere adecuada, incluida, por ejemplo, la suspensión de su cuenta (sin ninguna responsabilidad) en cualquier momento y por cualquier motivo o sin él, incluidos, entre otros, cualquiera de los siguientes: a. X cree, a su entera discreción, que usted ha violado los Términos o que su uso de los Servicios Pagos violaría cualquier ley aplicable; b. Algún tribunal de justicia, autoridad reguladora u organismo encargado de la aplicación de la ley competente solicita o indica hacerlo a X; c. X tiene problemas técnicos o de seguridad inesperados; d. X considere, a su entera discreción, que usted ha infringido el Acuerdo de Usuario X; e. X considere, a su entera discreción, que usted está incurriendo en manipulación u otra conducta perturbadora o prohibida en general o en relación con los Servicios de Pago; f. Usted crea riesgo o posible exposición legal para X; g. Su cuenta debe ser eliminada debido a una conducta ilegal; h. Su cuenta debe ser eliminada debido a una inactividad prolongada; o i. Nuestra prestación de los Servicios de Pago (en su totalidad o en parte) a usted ya no es comercialmente viable (a la entera discreción de X). 3. Todas las transacciones son finales. Todos los pagos de los Servicios de pagos son finales y no son reembolsables ni se pueden intercambiar, excepto según lo exija la ley vigente. No garantizamos la naturaleza, la calidad o el valor de un Servicio de pago, así como tampoco la disponibilidad o el suministro del mismo. No se proporcionan reembolsos ni créditos por ningún Servicio de pago no utilizado o utilizado parcialmente (por ejemplo, por un período de suscripción utilizado parcialmente). 4. Los Servicios de pago no se pueden transferir entre cuentas de X. Cada compra de un Servicio de pago se aplica a una sola cuenta de X, lo que significa que su compra se aplicará únicamente a la cuenta que estaba usando cuando compró el Servicio de pago y no se aplicará a otras cuentas a las que pueda tener acceso o sobre las que pueda tener control. Si tiene o controla varias cuentas y desea acceder a los Servicios de pago en cada una de ellas, debe comprar el Servicio de pago en cada cuenta individualmente. 5. Restricciones y obligaciones. a. Solo puede comprar y usar un Servicio de pago si tiene permiso legal para usar el Servicio de pago en su país y reside en un país en el que X admite el Servicio de pago aplicable. X puede, a su discreción, restringir la posibilidad de acceso o comprar un Servicio de Pago en ciertos países. X se reserva el derecho de modificar la lista de países admitidos de vez en cuando. b. Nos reservamos el derecho de rechazar transacciones de Servicios de pago o de cancelar o interrumpir la venta o el uso de un Servicio de pago a nuestra entera discreción. c. No puede permitir que otros usen su cuenta de X para acceder a ningún Servicio de pago que dicha persona no haya solicitado. d. Usted no podrá comprar o usar un Servicio de pago si es una persona con la que las personas estadounidenses no están autorizadas a hacer negocios en virtud de sanciones económicas, incluidas, entre otras, las sanciones impuestas por la Oficina de Control de Activos Extranjeros del Departamento del Tesoro de Estados Unidos o cualquier otra autoridad sancionadora aplicable (" Persona prohibida "). Esto incluye, sin limitación, a las personas situadas o que residan habitualmente en los siguientes países y regiones: Cuba, Irán, las regiones ucranianas de Crimea, Corea del Norte y Siria. Usted declara y garantiza que no es una Persona prohibida. e. USTED DECLARA QUE UTILIZARÁ LOS SERVICIOS DE PAGO ÚNICAMENTE PARA FINES LEGALES Y ÚNICAMENTE DE ACUERDO CON LOS TÉRMINOS. Impuestos y honorarios . Usted es responsable y acepta pagar los impuestos, aranceles y tarifas correspondientes relacionados con la compra de Servicios de pago, incluidos los que deben pagarse a X o a un procesador de pagos externo. Estos impuestos pueden incluir, entre otros, IVA, GST, impuesto sobre las ventas, retención de impuestos y cualquier otro impuesto aplicable. Según su ubicación, es posible que X sea responsable de recoger e informar datos relacionados con los impuestos a las transacciones que surjan de su compra de Servicios de pago. Usted otorga permiso a X para proporcionar su cuenta e información personal a las autoridades fiscales correspondientes para cumplir con nuestras obligaciones de declaración y recaudación de impuestos. Términos generales 1. Información de contacto. Si tiene alguna pregunta sobre los Servicios pagos o estos Términos, puede consultar el Centro de ayuda de Servicios pagos X para obtener más detalles. Si ya compró un Servicio de pago, también puede comunicarse con nosotros a través del enlace de soporte disponible en el menú de navegación de su cuenta de X en la configuración de pago o suscripción. Si tiene preguntas adicionales, puede contactarnos aquí mediante el formulario “Ayuda con las funciones de pago”. 2. EXENCIONES DE RESPONSABILIDAD. EN LA MEDIDA MÁXIMA DE LO PERMITIDO POR LA LEGISLACIÓN VIGENTE, EL ACCESO Y USO QUE HAGA DE LOS SERVICIOS DE PAGO ES BAJO SU PROPIO RIESGO. USTED RECONOCE Y ACEPTA QUE LOS SERVICIOS DE PAGO SE OFRECEN “TAL CUAL” Y “SEGÚN DISPONIBILIDAD”. X RENUNCIA A TODAS LAS GARANTÍAS Y CONDICIONES, YA SEAN EXPRESAS O IMPLÍCITAS, DE COMERCIALIZACIÓN, IDONEIDAD PARA UN FIN DETERMINADO O CONFORMIDAD. X NO OFRECE GARANTÍAS NI REALIZA DECLARACIONES Y RECHAZA TODA RESPONSABILIDAD POR: (I) LA INTEGRIDAD, EXACTITUD, DISPONIBILIDAD, PUNTUALIDAD, SEGURIDAD O FIABILIDAD DE LOS SERVICIOS DE PAGO Y (II) SI LOS SERVICIOS DE PAGO CUMPLIRÁN SUS REQUISITOS O ESTARÁN DISPONIBLES DE FORMA ININTERRUMPIDA, SEGURA O SIN ERRORES. USTED ES RESPONSABLE DEL USO DEL SERVICIO DE X, INCLUIDOS LOS SERVICIOS DE PAGO Y CUALQUIER CONTENIDO QUE PROPORCIONE. 3. LIMITACIÓN DE RESPONSABILIDAD. EN LA MEDIDA MÁXIMA DE LO PERMITIDO POR LA LEGISLACIÓN VIGENTE, LAS ENTIDADES DE X NO SERÁN RESPONSABLES DE NINGÚN TIPO DE DAÑOS INDIRECTOS, DERIVADOS, ESPECIALES, EMERGENTES O PUNITIVOS, PÉRDIDAS DE BENEFICIOS O INGRESOS, CON INDEPENDENCIA DE QUE SE INCURRA EN DICHAS PÉRDIDAS DIRECTA O INDIRECTAMENTE, O PÉRDIDAS DE DATOS, USO, FONDO DE COMERCIO U OTRAS PÉRDIDAS INTANGIBLES, QUE SE DERIVEN DE: (i) SU ACCESO O USO O INCAPACIDAD DE ACCESO O USO DE LOS SERVICIOS DE PAGO; (ii) CUALQUIER COMPORTAMIENTO O CONTENIDO DE TERCEROS PUBLICADO A TRAVÉS DE LOS SERVICIOS DE PAGO INCLUYENDO, SIN LIMITACIÓN ALGUNA, CUALQUIER CONDUCTA DIFAMATORIA, OFENSIVA O ILEGAL DE OTROS USUARIOS O TERCEROS; (iii) CUALQUIER CONTENIDO OBTENIDO DE LOS SERVICIOS DE PAGO; O (iv) EL ACCESO, USO O ALTERACIÓN NO AUTORIZADOS DE SUS TRANSMISIONES O CONTENIDOS. PARA EVITAR DUDAS, LA DEFINICIÓN DE SERVICIOS DE PAGO SE LIMITA A LAS FUNCIONES OFRECIDAS POR X Y NO INCLUYE NINGÚN CONTENIDO AL QUE ACCEDA O CON EL QUE INTERACTÚE AL UTILIZAR DICHAS FUNCIONES. EN NINGÚN CASO LA INDEMNIZACIÓN TOTAL QUE DEBAN PAGAR LAS ENTIDADES DE X PODRÁ SUPERAR LA MAYOR DE ESTAS CANTIDADES: LA SUMA DE CIEN DÓLARES DE ESTADOS UNIDOS ($100,00) O, EN SU CASO, LA CANTIDAD ABONADA POR USTED A X EN LOS ÚLTIMOS SEIS MESES POR LA PRESTACIÓN DE LOS SERVICIOS DE PAGO QUE DIERON ORIGEN A LA RECLAMACIÓN. LAS LIMITACIONES DE ESTE SUBAPARTADO SON DE APLICACIÓN EN RELACIÓN CON TODA RESPONSABILIDAD, INDEPENDIENTEMENTE DE QUE ESTA DERIVE DE GARANTÍA, CONTRATO, NORMATIVA, DAÑO EXTRACONTRACTUAL (INCLUSO POR NEGLIGENCIA) O CUALQUIER OTRA FUENTE DE RESPONSABILIDAD, INCLUSO AUNQUE SE HAYA INFORMADO A LAS ENTIDADES DE X QUE TAL PERJUICIO PUDIESE MATERIALIZARSE E INCLUSO AUNQUE CUALQUIER REPARACIÓN AQUÍ PREVISTA RESULTASE INSATISFACTORIA EN RELACIÓN CON SU PROPÓSITO ESENCIAL. LAS "ENTIDADES DE X" SE REFIEREN A X, SUS EMPRESAS MATRICES, FILIALES, SOCIEDADES RELACIONADAS, RESPONSABLES, DIRECTIVOS, EMPLEADOS, AGENTES, REPRESENTANTES, SOCIOS Y LICENCIATARIOS. ES POSIBLE QUE LA LEGISLACIÓN APLICABLE EN SU JURISDICCIÓN NO PERMITA DETERMINADAS LIMITACIONES DE RESPONSABILIDAD. EN LA MEDIDA EN QUE LO EXIJA LA LEGISLACIÓN APLICABLE EN SU JURISDICCIÓN, LO ANTERIOR NO LIMITA LA RESPONSABILIDAD DE LAS ENTIDADES DE X EN CASO DE FRAUDE, TERGIVERSACIÓN FRAUDULENTA, MUERTE O LESIONES PERSONALES CAUSADAS POR NUESTRA NEGLIGENCIA, NEGLIGENCIA GRAVE O CONDUCTA INTENCIONAL. EN LA MEDIDA MÁXIMA PERMITIDA POR LA LEGISLACIÓN VIGENTE, LA RESPONSABILIDAD TOTAL MÁXIMA DE LAS ENTIDADES DE X POR CUALQUIER GARANTÍA NO EXCLUIBLE SE LIMITA A CIEN DÓLARES ESTADOUNIDENSES (USD 100,00). 4. Notificación sobre Apple. En la medida en que compró los Servicios de pago o que está utilizando o accediendo a los Servicios de pago en un dispositivo iOS, reconoce y acepta los términos de esta Sección. Reconoce que los Términos son solo entre usted y nosotros, no con Apple, y que Apple no es responsable de los Servicios de pago ni del contenido de los mismos. Apple no tiene obligación alguna de prestar ningún servicio de mantenimiento y soporte con respecto a los Servicios de pago. En el caso de que los Servicios de pago no cumplan con alguna garantía aplicable, puede notificar a Apple y Apple le reembolsará el precio de compra correspondiente de los Servicios de pago; y, en la máxima medida permitida por la ley vigente, Apple no tiene ninguna otra obligación de garantía con respecto a los Servicios de pago. Apple no es responsable de abordar ninguna reclamación suya o de un tercero que tenga relación con los Servicios de pago o con su posesión o uso de los Servicios de pago, incluidos, entre otros: (i) reclamaciones de responsabilidad del producto; (ii) cualquier reclamación de que los Servicios de pago no se ajustan a algún requisito legal o reglamentario aplicable; y (iii) reclamaciones derivadas de la protección del consumidor o una legislación similar. Apple no es responsable de la investigación, defensa y resolución de cualquier reclamación de terceros de que los Servicios de pagos o su posesión y el uso de la aplicación móvil infringen los derechos de propiedad intelectual de ese tercero. Usted acepta cumplir con los términos de terceros aplicables al utilizar los Servicios de pago. Apple y las subsidiarias de Apple son terceros beneficiarios de los Términos y, al aceptar los Términos, Apple tendrá el derecho (y se considerará que ha aceptado el derecho) de hacer cumplir los Términos en su contra como tercero beneficiario de los Términos. Mediante el presente documento declara y garantiza que (i) no se encuentra en un país que esté sujeto a un embargo del gobierno de los EE. UU. o que haya sido designado por el gobierno de los EE. UU. como un país "que apoya a terroristas"; y (ii) no figura en ninguna lista de partes prohibidas o restringidas del gobierno de los EE. UU.. 5. Conflicto. En caso de que se genere un conflicto entre las disposiciones de estos Términos de servicio para compradores de X y las del Acuerdo de usuario de X, las disposiciones de estos Términos de servicio para compradores de X prevalecerán únicamente con respecto al uso que hace de un Servicio de pago. 6. RESOLUCIÓN DE DISPUTAS Y DEMANDA COLECTIVA a. Resolución inicial de litigios . La mayoría de los litigios entre usted y X pueden resolverse de manera informal. Puede comunicarse con nosotros escribiendo al soporte pago aquí . Cuando se ponga en contacto con nosotros, proporcione una breve descripción de la naturaleza y las razones de sus inquietudes, su información de contacto y la solución específica que busca. Las partes harán todo lo posible a través de este proceso de soporte para resolver las disputas, reclamaciones o controversias que surjan de o en relación con estas Condiciones y/o su participación en el Programa (individualmente una " Disputa ", o más de una, " Disputas "). Usted y nosotros acordamos que la participación de buena fe en este proceso informal es necesaria y debe completarse según lo establecido anteriormente antes de que cualquiera de las partes pueda iniciar un litigio en relación con cualquier Disputa, excepto con respecto a las solicitudes de medidas cautelares de emergencia (" Disputa Exenta "). Si no logramos llegar a un acuerdo con usted en relación con una Disputa (distinta de una Disputa exenta) en un plazo de treinta (30) días a partir del momento en que comience la resolución informal de disputas en virtud de lo dispuesto anteriormente en la cláusula Resolución Inicial de Disputas, entonces usted o nosotros podemos iniciar un arbitraje vinculante. b. Elección de la ley y del foro . LEA ATENTAMENTE ESTA SECCIÓN, YA QUE PUEDE AFECTAR SIGNIFICATIVAMENTE A SUS DERECHOS LEGALES, INCLUIDO SU DERECHO A PRESENTAR UNA DEMANDA ANTE LOS TRIBUNALES. Las leyes del Estado de Texas, con exclusión de sus disposiciones sobre elección de ley, regirán estos Términos y cualquier disputa que surja entre usted y nosotros, a pesar de cualquier otro acuerdo entre usted y nosotros en contrario. Todas las disputas relacionadas con estos Términos, incluyendo cualquier disputa, reclamación o controversia que surja de o en relación con estos Términos, serán llevadas exclusivamente en los tribunales federales o estatales ubicados en el Condado de Tarrant, Texas, Estados Unidos, y usted consiente a la jurisdicción personal en esos foros y renuncia a cualquier objeción en cuanto a foro inconveniente. Sin perjuicio de lo anterior, usted acepta que, a su entera discreción, X pueda presentar cualquier reclamación, causa de acción o litigio que tengamos contra usted ante cualquier tribunal competente del país en el que resida que tenga jurisdicción y competencia sobre la reclamación. Si usted es una entidad gubernamental federal, estatal o local en los Estados Unidos en su carácter oficial y legalmente no puede aceptar las cláusulas de ley reguladora, jurisdicción o competencia mencionadas anteriormente, entonces dichas cláusulas no se aplican a usted. Para dichas entidades del gobierno federal de EE. UU., este Acuerdo y cualquier acción relacionada con el mismo se regirán por las leyes de los Estados Unidos de América (sin referencia a conflictos de leyes) y, en ausencia de ley federal y en la medida permitida por la ley federal, las leyes del Estado de Texas (excluyendo la elección de ley). c. DISPONE DE DOS AÑOS PARA INTERPONER UNA RECLAMACIÓN CONTRA X . Deberá interponer cualquier reclamación contra X derivada o relacionada con las presentes Condiciones en el plazo de dos (2) años a partir de la fecha en que se produzca el hecho o los hechos que den lugar a la controversia, salvo que la legislación aplicable establezca que el plazo de prescripción normal para dicha reclamación no puede acortarse por acuerdo. Si no presenta un reclamo dentro de este período, renuncia para siempre al derecho de presentar cualquier reclamo o causa de acción, de cualquier tipo o carácter, basado en dichos eventos o hechos, y dichos reclamos o causas de acción quedan prohibidos permanentemente y X no tendrá responsabilidad con respecto a dicho reclamo. d. Renuncia a demanda colectiva . En la medida que lo permita la ley, también renuncia al derecho de participar como demandante o parte de una demanda colectiva presunta, demanda colectiva u acción con representante. Términos de servicio para compradores de X Si vive en la Unión Europea, los Estados de la AELC o el Reino Unido X le permite acceder a ciertas funciones a cambio del pago de una tarifa única o recurrente, según corresponda a las funciones pertinentes (cada una un “ Servicio de pago ” y colectivamente los " Servicios de pago "). Por ejemplo, X Premium (como se define a continuación) y las Suscripciones se considerarían cada una un "Servicio de pago". En la medida que se registre o use un Servicio de pago, el uso que haga de los Servicios de pago y cualquier transacción correspondiente están sujetos a: (i) los términos y condiciones establecidos en este documento, incluidos los términos y condiciones aplicables de cada Servicio de pago que compre, cada uno como se indica a continuación (colectivamente, los “ Términos de servicio para compradores de X ”) y (ii) los Términos de servicio de X , la Política de privacidad de X , las Reglas y políticas de X y todas las políticas aplicables incorporadas en este documento (colectivamente, el “ Acuerdo de usuario de X ”). Estos Términos de servicio para compradores de X y el Acuerdo de usuario de X mencionado anteriormente se denominarán colectivamente en este documento como los “ Términos ”. " X " se refiere a la entidad X que le proporciona los Servicios Pagos. Lea detenidamente estos Términos de servicio para compradores de X para asegurarse de que comprende los términos, condiciones y excepciones aplicables. SI VIVE EN LA UNIÓN EUROPEA, LOS ESTADOS DE LA AELC O EL REINO UNIDO, ESTAS CONDICIONES CONTIENEN INFORMACIÓN IMPORTANTE QUE LE ES APLICABLE SOBRE LA RESOLUCIÓN DE CONTROVERSIAS, INCLUIDA UNA RENUNCIA A SU DERECHO A INTERPONER RECLAMACIONES COMO ACCIONES COLECTIVAS, Y UNA LIMITACIÓN DE SU DERECHO A PRESENTAR RECLAMACIONES CONTRA X TRANSCURRIDO MÁS DE 1 AÑO DESDE QUE SE PRODUJERON LOS HECHOS RELEVANTES, QUE AFECTAN A SUS DERECHOS Y OBLIGACIONES EN CASO DE QUE SURJA ALGUNA DISPUTA CON X. CONSULTE LA SECCIÓN 6 EN TÉRMINOS GENERALES PARA CONOCER LOS DETALLES DE ESTAS DISPOSICIONES. Aceptación . Al utilizar o acceder a un Servicio de Pago de X, enviar el pago correspondiente y/o hacer clic en un botón para realizar una compra única o pagos de suscripción recurrentes por el Servicio de Pago proporcionado por X, usted acepta quedar vinculado por las Condiciones. Si no comprende los Términos o no acepta alguna parte de ellos, entonces no podrá usar ni acceder a ningún Servicio de pago. Para comprar y usar un Servicio de pago, debe: (i) tener al menos 18 años o ser mayor de edad según lo determinen las leyes de la jurisdicción en la que reside o (ii) tener el consentimiento expreso de su padre, madre o tutor para comprar y usar dicho Servicio de pago. Si usted es un padre, una madre o un tutor legal que autoriza que su hijo (o un niño del que usted es tutor) compre o use un Servicio de pago, acepta que los Términos se aplican a usted, que cumplirá con los Términos y que usted es responsable de la actividad del niño en los Servicios de pago, así como de asegurarse de que el niño también cumpla con los Términos. En cualquier caso, tal como se indica en la sección Quién puede hacer uso de los Servicios de los Términos de servicio de X, debe tener al menos 13 años para usar el Servicio de X. Si acepta estos Términos de servicio para compradores de X y usa los Servicios de pago en nombre de una empresa, organización, gobierno u otra entidad jurídica, afirma y garantiza que está autorizado a hacerlo y cuenta con los poderes necesarios para obligarla al cumplimiento de estos Términos de servicio para compradores de X, en cuyo caso el uso de las palabras “usted”, “su” y “sus” en estos Términos de servicio para compradores de X hará referencia a dicha entidad jurídica. Entidad contratante de X . Usted suscribe estos Términos de servicio para compradores de X con la entidad que corresponde a su lugar de residencia, como se indica a continuación. Esta entidad le prestará los Servicios de pago. Ninguna otra entidad está sujeta a ninguna obligación con usted en virtud de estos Términos de servicio para compradores. Su ubicación La Unión Europea, los Estados de la AELC o el Reino Unido Entidad contratante X Internet Unlimited Company, con domicilio social en One Cumberland Place, Fenian Street, Dublín 2, D02 AX07 Ireland Cambios a los Términos, Servicios de pago y precios 1. Cambios en los términos. X puede modificar estos Términos de servicio para compradores de X de vez en cuando, por motivos válidos y razonables. Los motivos válidos y razonables pueden incluir: (i) un cambio en nuestros servicios, por ejemplo debido a desarrollos técnicos, de seguridad u operativos, (ii) la eliminación de errores técnicos, (iii) un cambio en nuestro negocio, por ejemplo debido a cambios de política, financieros u otros cambios direccionales, (iv) un cambio en la situación legal, por ejemplo debido a un cambio en la ley, una solicitud de un organismo oficial o una decisión de un tribunal, y (v) la optimización de la experiencia del usuario mediante la implementación de nuevas características. Los cambios no serán retroactivos y la versión más reciente de los Términos de servicio para compradores de X, disponible en legal.x.com/purchaser-terms , regirá el uso que haga de los Servicios de pago y de cualquier transacción correspondiente. Si modificamos o revisamos estas Condiciones después de que usted las haya aceptado (por ejemplo, si estas Condiciones se modifican después de que usted haya adquirido una suscripción), nos comprometemos a notificarle con una antelación de hasta 30 días (dependiendo de los cambios específicos) la entrada en vigor de las revisiones materiales de estas Condiciones, estableciendo al mismo tiempo un plazo razonable para el usuario con respecto a los cambios y notificándole las consecuencias de seguir utilizándolas una vez expirado el plazo. Dicha notificación puede proporcionarse de forma electrónica, incluso (y sin limitación) a través de una notificación de servicio o un correo electrónico a la dirección de correo electrónico asociada con su cuenta. En caso de que siga usando los Servicios de pago después de que finalice el plazo anteriormente mencionado, acepta quedar vinculado por los Términos de servicio para compradores de X modificados. Si no está de acuerdo con los cambios en los Términos de servicio para compradores de X, deberá dejar de usar o acceder (o continuar usando o accediendo) a los servicios de pago. Los Términos de servicio para compradores de X están escritos en inglés, pero están disponible en varios idiomas a través de traducciones. X se esfuerza por hacer que las traducciones sean lo más fieles posible a la versión original en inglés. Sin embargo, en caso de discrepancias o inconsistencias, prevalecerá la versión en inglés de los Términos de servicio para compradores de X. Reconoce que el inglés será el idioma de referencia para interpretar y construir los Términos de servicio para compradores de X. 2. Cambios en los Servicios de Pago. Nuestros Servicios de Pago y nuestros productos y servicios evolucionan constantemente. X podrá modificar los servicios de pago por un motivo razonable y válido. Dicha base válida y razonable puede incluir (i) desarrollos técnicos, relacionados con la seguridad u operativos, (ii) la eliminación de errores técnicos, (iii) el cumplimiento de una situación legal modificada, por ejemplo debido a un cambio en la ley, una solicitud de un organismo oficial o una decisión de un tribunal, (iv) la optimización de la experiencia del usuario mediante la implementación de nuevas funciones, y (v) un cambio en nuestro negocio, por ejemplo debido a circunstancias políticas, financieras u otros cambios direccionales. Le notificaremos cualquier cambio en los Servicios de Pago hasta 30 días antes de que entren en vigor, por ejemplo mediante una notificación de servicio o un correo electrónico enviado a la dirección de correo electrónico vinculada a su cuenta, especificando las características y la fecha efectiva de los cambios e informándole de su eventual derecho a rescindir la suscripción. El plazo puede acortarse en caso de cambios relacionados con la seguridad. No se considerarán cambios en los servicios de pago a los efectos de esta disposición: (i) los cambios que afecten la naturaleza fundamental de los servicios de pago y las características esenciales del servicio que X prestará, y (ii) la interrupción permanente de los servicios. X no será responsable ante usted de ninguna modificación, suspensión o interrupción de los Servicios de Pago. Cuando lo exija la ley, la limitación de responsabilidad antes mencionada no se aplicará (i) a la indemnización por daños previsibles en caso de incumplimiento por negligencia leve de las obligaciones, en la medida en que su cumplimiento sea esencial para la correcta ejecución del contrato y los usuarios puedan confiar en su cumplimiento (obligaciones contractuales esenciales), por parte de X o de sus representantes legales o auxiliares ejecutivos, y (ii) a la responsabilidad de X por (a) un daño resultante de un perjuicio a la vida, el cuerpo o la salud, así como por daños causados por dolo o negligencia grave por parte de X, sus representantes legales o agentes indirectos, y (b) un daño debido al incumplimiento de una garantía o característica garantizada o como resultado de un defecto ocultado fraudulentamente. Los términos y condiciones específicos (incluidos a continuación) para el Servicio de Pago específico especifican cómo puede cancelar una suscripción o, en su caso, solicitar un reembolso. 3. Cambios en los precios. Los precios de los Servicios de pago, incluidas las cuotas de suscripción periódicas, están sujetos a cambios ocasionales debidos a cambios en los costes de funcionamiento, mantenimiento, prestación técnica, consideraciones comerciales y tasas cobradas por terceros o tasas legales, a nuestra discreción razonable. En caso de aumento de los costes, X se reserva el derecho de ajustar los precios de los servicios facturables. X le notificará cualquier cambio de precios por escrito hasta 30 días antes de que entre en vigor, por ejemplo mediante una notificación de servicio o un correo electrónico a la dirección de correo electrónico vinculada a su cuenta, indicando sus derechos y las consecuencias de no ejercerlos. En caso de modificación de los precios, podrá cancelar la suscripción al Servicio de pago aplicable o el contrato de usuario hasta 24 horas antes del inicio de su siguiente ciclo de facturación, siempre que la cancelación se realice en los 30 días siguientes a la recepción de la notificación. De lo contrario, el cambio de precio entrará en vigor en el momento especificado en la notificación. En el caso de los servicios de suscripción, los cambios de precio entrarán en vigor al inicio del siguiente periodo de suscripción tras la fecha de efectividad del cambio de precio. Condiciones de pago . X ofrece diversas opciones de pago que pueden variar según el Servicio de pago, su dispositivo o sistema operativo, su ubicación geográfica u otros factores. En la medida que esté disponible (ya que X puede ofrecer diversos métodos de compra de vez en cuando), estas opciones de pago pueden incluir la posibilidad de usar la funcionalidad de “Pago en la app” que ofrecen Google o Apple, o realizar un pago web usando el procesador de pagos de terceros, Stripe ( www.stripe.com , en adelante, “ Stripe ”). Cuando realiza un pago, acepta explícitamente: (i) pagar el precio indicado por el Servicio de pago, junto con cualquier monto adicional relacionado con impuestos aplicables, tarifas de tarjetas de crédito, tarifas bancarias, tarifas de transacciones extranjeras, tarifas de cambio de divisas y fluctuaciones de divisas; y (ii) cumplir con los términos de servicio, las políticas de privacidad u otros acuerdos o restricciones legales pertinentes (incluidas las restricciones de edad adicionales) impuestos por Google, Apple o Stripe (como el procesador de pagos externo de X) en relación con el uso que hace de una forma de pago determinada (solo como ejemplo, si elige realizar su pago a través de la funcionalidad de compra en la app de Apple, acepta cumplir con los términos, requisitos o restricciones pertinentes impuestos por Apple). Todos los datos personales privados que proporcione en relación con el uso que hace de los Servicios de pago, incluidos, entre otros, los datos proporcionados en relación con el pago, se procesarán de acuerdo con la Política de privacidad de X. X puede compartir su información de pago con proveedores de servicios de pago para procesar pagos; para prevenir, detectar e investigar fraudes u otras actividades prohibidas; para facilitar la resolución de disputas, como reintegros o reembolsos; y para otros fines asociados con la aceptación de tarjetas de crédito y débito o transferencias ACH. Es su responsabilidad asegurarse de que su información bancaria, de tarjetas de crédito, tarjetas de débito u otra información de pago esté actualizada, esté completa y sea exacta en todo momento. Si realiza un pago por un Servicio de pago, es posible que recibamos información sobre su transacción, como cuándo se realizó, cuándo caducará o se renovará automáticamente una suscripción, en qué plataforma realizó la compra y otra información. X no será responsable de los errores cometidos ni de las demoras por parte de un procesador de pagos, de la App Store de Apple o de la Google Play Store, de su banco, de la empresa emisora de su tarjeta de crédito o de cualquier red de pago. Consulte cada uno de los Términos y condiciones del Servicio de pago específicos a continuación para conocer los términos de pago aplicables a ese Servicio de pago específico, incluida la forma en que se manejan las renovaciones de suscripción y otros términos importantes. Aplicación del Acuerdo de usuario de X, Rescisión, Sin reembolsos, Varias cuentas de X y Restricciones 1. El Acuerdo de usuario de X se aplica a usted . SIEMPRE DEBE SEGUIR Y CUMPLIR CON EL ACUERDO DE USUARIO DE X. El Acuerdo de usuario de X siempre se aplica al uso que hace del Servicio de X, incluidos los Servicios y funciones de pago. El incumplimiento del Acuerdo de usuario de X, o la creencia de X de que usted no ha seguido y ha incumplido el Acuerdo de usuario de X, puede tener como resultado la cancelación de sus Servicios de pago. Cualquier cancelación de este tipo será adicional, y no se limitará a, cualquier medida de control del cumplimiento que X pueda tomar contra en su contra de conformidad con el Acuerdo de usuario de X. En tales casos, es posible que pierda los beneficios de sus Servicios de pago y no será elegible para recibir reembolsos por los montos que haya pagado (o pagado por adelantado) por los Servicios de pago. 2. Motivos por los que X podría cancelar su acceso a los Servicios de Pago. X podrá suspender o cancelar su acceso a los Servicios de Pago, dejar de proporcionarle la totalidad o parte de los Servicios de Pago, o tomar cualquier otra medida que considere oportuna, incluida, por ejemplo, la suspensión de su cuenta, (sin responsabilidad alguna) en cualquier momento y por cualquier motivo o sin motivo alguno, incluidos, entre otros, cualquiera de los siguientes motivos razonables: a. X cree, a su entera discreción, que usted ha violado los Términos o que su uso de los Servicios Pagos violaría cualquier ley aplicable; b. Algún tribunal de justicia, autoridad reguladora u organismo encargado de la aplicación de la ley competente solicita o indica hacerlo a X; c. X tiene problemas técnicos o de seguridad inesperados; d. X considere, a su entera y razonable discreción, que usted ha infringido el Acuerdo de usuario de X; e. X cree que por razones válidas, como si usted está participando en la manipulación, el juego u otra conducta perturbadora o prohibida en relación con los Servicios de Pago; f. Usted crea riesgo o posible exposición legal para X; g. Su cuenta debe ser eliminada debido a una conducta ilegal; h. Su cuenta debe ser eliminada debido a una inactividad prolongada; o i. Nuestra prestación de los Servicios de Pago (en su totalidad o en parte) a usted ya no es comercialmente viable (a la entera discreción de X). 3. Todas las transacciones son finales. Todos los pagos de los Servicios de pagos son finales y no son reembolsables ni se pueden intercambiar, excepto según lo exija la ley vigente. No garantizamos la naturaleza, la calidad o el valor de un Servicio de pago, así como tampoco la disponibilidad o el suministro del mismo. No se proporcionan reembolsos ni créditos por ningún Servicio de pago no utilizado o utilizado parcialmente (por ejemplo, por un período de suscripción utilizado parcialmente). 4. Los Servicios de pago no se pueden transferir entre cuentas de X. Cada compra de un Servicio de pago se aplica a una sola cuenta de X, lo que significa que su compra se aplicará únicamente a la cuenta que estaba usando cuando compró el Servicio de pago y no se aplicará a otras cuentas a las que pueda tener acceso o sobre las que pueda tener control. Si tiene o controla varias cuentas y desea acceder a los Servicios de pago en cada una de ellas, debe comprar el Servicio de pago en cada cuenta individualmente. 5. Restricciones y obligaciones. a. Solo puede comprar y usar un Servicio de pago si tiene permiso legal para usar el Servicio de pago en su país y reside en un país en el que X admite el Servicio de pago aplicable. X puede, a su discreción, restringir la posibilidad de acceso o comprar un Servicio de Pago en ciertos países. X se reserva el derecho de modificar la lista de países admitidos de vez en cuando. b. Nos reservamos el derecho de rechazar transacciones de Servicios de pago o de cancelar o interrumpir la venta o el uso de un Servicio de pago a nuestra entera discreción. c. No puede permitir que otros usen su cuenta de X para acceder a ningún Servicio de pago que dicha persona no haya solicitado. d. Usted no podrá comprar o usar un Servicio de pago si es una persona con la que las personas estadounidenses no están autorizadas a hacer negocios en virtud de sanciones económicas, incluidas, entre otras, las sanciones impuestas por la Oficina de Control de Activos Extranjeros del Departamento del Tesoro de Estados Unidos o cualquier otra autoridad sancionadora aplicable (" Persona prohibida "). Esto incluye, sin limitación, a las personas situadas o que residan habitualmente en los siguientes países y regiones: Cuba, Irán, las regiones ucranianas de Crimea, Corea del Norte y Siria. Usted declara y garantiza que no es una Persona prohibida. e. USTED DECLARA QUE UTILIZARÁ LOS SERVICIOS DE PAGO ÚNICAMENTE PARA FINES LEGALES Y ÚNICAMENTE DE ACUERDO CON LOS TÉRMINOS. Impuestos y honorarios . Usted es responsable y acepta pagar los impuestos, aranceles y tarifas correspondientes relacionados con la compra de Servicios de pago, incluidos los que deben pagarse a X o a un procesador de pagos externo. Estos impuestos pueden incluir, entre otros, IVA, GST, impuesto sobre las ventas, retención de impuestos y cualquier otro impuesto aplicable. Según su ubicación, es posible que X sea responsable de recoger e informar datos relacionados con los impuestos a las transacciones que surjan de su compra de Servicios de pago. Usted otorga permiso a X para proporcionar su cuenta e información personal a las autoridades fiscales correspondientes para cumplir con nuestras obligaciones de declaración y recaudación de impuestos. Términos generales 1. Información de contacto. Si tiene alguna pregunta sobre los Servicios pagos o estos Términos, puede consultar el Centro de ayuda de Servicios pagos X para obtener más detalles. Si ya compró un Servicio de pago, también puede comunicarse con nosotros a través del enlace de soporte disponible en el menú de navegación de su cuenta de X en la configuración de pago o suscripción. Si tiene preguntas adicionales, puede contactarnos aquí mediante el formulario “Ayuda con las funciones de pago”. 2. EXENCIONES DE RESPONSABILIDAD. EN LA MEDIDA MÁXIMA DE LO PERMITIDO POR LA LEGISLACIÓN VIGENTE, EL ACCESO Y USO QUE HAGA DE LOS SERVICIOS DE PAGO ES BAJO SU PROPIO RIESGO. USTED RECONOCE Y ACEPTA QUE LOS SERVICIOS DE PAGO SE OFRECEN “TAL CUAL” Y “SEGÚN DISPONIBILIDAD”. X RENUNCIA A TODAS LAS GARANTÍAS Y CONDICIONES, YA SEAN EXPRESAS O IMPLÍCITAS, DE COMERCIALIZACIÓN, IDONEIDAD PARA UN FIN DETERMINADO O CONFORMIDAD. X NO OFRECE GARANTÍAS NI REALIZA DECLARACIONES Y RECHAZA TODA RESPONSABILIDAD POR: (I) LA INTEGRIDAD, EXACTITUD, DISPONIBILIDAD, PUNTUALIDAD, SEGURIDAD O FIABILIDAD DE LOS SERVICIOS DE PAGO Y (II) SI LOS SE | 2026-01-13T08:48:40 |
https://docs.suprsend.com/reference/cli-authentication | Authentication - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection Versioning Versioning and Support Policy CLI Changelog Getting Started with CLI CLI Overview BETA Quickstart Installation Authentication Enable Autocompletion Global Flags Profile Commands and Flags Add Profile Use Profile List Profile Modify Profile Remove Profile Sync Sync Assets Workflow Commands and Flags List Workflows Pull Workflows Push Workflows Enable Workflow Disable Workflow Schema Commands and Flags List Schemas Pull Schemas Push Schemas Commit Schema Generate Types Event Commands and Flags List Events Pull Events Push Events Preference Category Commands and Flags List Categories Pull Categories Push Categories Commit Categories List Category Translations Pull Category Translations Push Category Translations Translation Commands and Flags List Translations Pull Translations Push Translations Commit Translations Contact Us Get Started SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Ask AI Contact Us Get Started Get Started Search... Navigation Getting Started with CLI Authentication Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog Getting Started with CLI Authentication OpenAI Open in ChatGPT Set up authentication for the SuprSend CLI using service tokens. OpenAI Open in ChatGPT Get Your Service Token Log in to your SuprSend dashboard Go to Account Settings → Service Tokens Create a new service token or copy an existing one Authentication Methods The CLI resolves authentication using this priority order: Priority Method When to Use Duration 1 Environment Variable Running the CLI inside CI/CD pipelines, Docker containers and servers Session-based 2 Command-Line Flag Best for one-off commands, local testing, debugging One-time 3 Active Profile For local development or self hosting where you have more than 1 server Persistent Environment Variable Best for development environments, CI/CD jobs, or scripts. Define your token once per session, and all CLI commands in that script will use it automatically. Copy Ask AI export SUPRSEND_SERVICE_TOKEN = "your_service_token_here" Command-Line Flag Best for running ad-hoc commands locally or for testing or debugging with different credentials. Pass the token inline without modifying your environment. We don’t recommend this method since flags can appear in shell history or process listings. Copy Ask AI suprsend workflow list --service-token "your_service_token_here" Active Profile Best for local development or self-hosted setups where you have more than one server. Profile lets you save your configuration once, so you don’t need to re-export tokens in every session. You can create multiple profiles and simply switch the active profile when needed. Copy Ask AI suprsend profile add --name eu-server --service-token "your_token_here" suprsend profile use eu-server Security best practices Do not commit tokens in scripts, repositories, or configuration files. Prefer environment variables or profiles over command-line flags, since flags can appear in shell history or process listings. Rotate service tokens regularly and follow principle of least privilege and restrict permissions to the minimum required. Rotation Strategy 1. Scheduled Rotation: Implement a scheduled rotation of service tokens (e.g. every 6 months) to reduce the risk of long-term exposure. 2. Ad-Hoc Rotation: Rotate tokens immediately if you suspect that a token has been compromised or if it has been inadvertently exposed. Was this page helpful? Yes No Suggest edits Raise issue Previous Enable Autocompletion Set up shell autocompletion for SuprSend CLI commands and flags Next ⌘ I x github linkedin youtube Powered by On this page Get Your Service Token Authentication Methods Environment Variable Command-Line Flag Active Profile Security best practices Rotation Strategy | 2026-01-13T08:48:40 |
https://openapi.tools/categories/servers | Server Implementations | OpenApi.tools, from APIs You Won't Hate Sponsored by Zudoku - Open-source, highly customizable API documentation powered by OpenAPI Get Started Sponsor openapi.tools GitHub Get Started All Tools All Categories Legacy Tools Contributing Sponsors Sponsor Badges Collections Arazzo Support Overlays Support Open Source Tools SaaS Tools OpenAPI Tool Categories Annotations Code generators Converters Data Validators Documentation Domain-Specific Languages (DSLs) Gateways HTTP Clients IDEs and GUI Editors Learning Miscellaneous Mock Servers Monitoring OpenAPI-aware Frameworks Parsers Schema Validators SDK Generators Security Server Implementations Testing Text Editors © 2026 APIs You Won't Hate Get in touch to become a Sponsor . This site is community-driven and OSS , built with Astro and hosted on Netlify . Server Implementations Easily create and implement resources and routes for your APIs. Server Implementations There are additional tools in this category, but they only support legacy versions of OpenAPI. If you really need to work with some old OpenAPI descriptions perhaps these legacy tools could be of use * * * | 2026-01-13T08:48:40 |
https://openapi.tools/categories/misc | Miscellaneous | OpenApi.tools, from APIs You Won't Hate Sponsored by Zudoku - Open-source, highly customizable API documentation powered by OpenAPI Get Started Sponsor openapi.tools GitHub Get Started All Tools All Categories Legacy Tools Contributing Sponsors Sponsor Badges Collections Arazzo Support Overlays Support Open Source Tools SaaS Tools OpenAPI Tool Categories Annotations Code generators Converters Data Validators Documentation Domain-Specific Languages (DSLs) Gateways HTTP Clients IDEs and GUI Editors Learning Miscellaneous Mock Servers Monitoring OpenAPI-aware Frameworks Parsers Schema Validators SDK Generators Security Server Implementations Testing Text Editors © 2026 APIs You Won't Hate Get in touch to become a Sponsor . This site is community-driven and OSS , built with Astro and hosted on Netlify . Miscellaneous Anything else that does stuff with OpenAPI but hasn't quite got enough to warrant its own category. Miscellaneous There are additional tools in this category, but they only support legacy versions of OpenAPI. If you really need to work with some old OpenAPI descriptions perhaps these legacy tools could be of use * * * | 2026-01-13T08:48:40 |
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Follow Episode Details / Transcript Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations at Postman, to talk about Postman's State of the API 2021. Show Notes Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations to discuss Postman's State of the API 2021 report, detailing various data points from around the API world from which specification people turn to, to how confident people feel deploying their APIs. They also discuss various topics around remote work, how APIs enable more remote work and what will happen in the next few years for APIs. Notes: Matthew on twitter: https://twitter.com/libel_vox Postman's State of the API Creators and Guests Host Mike Bifulco Cofounder and host of APIs You Won't Hate. Blogs at https://mikebifulco.com Into 🚴♀️, espresso ☕, looking after 🌍. ex @Stripe @Google @Microsoft What is APIs You Won't Hate? A no-nonsense (well, some-nonsense) podcast about API design & development, new features in the world of HTTP, service-orientated architecture, microservices, and probably bikes. Matt Trask: Cool. Welcome back to APS. You won't hate episode 17. I have Phil with me and we're joined by a very special guest today. Matthew Reinbold, fresh from postman, who is a director of API ecosystems and digital transformations here to talk about their report, the 2021 state of the API ecosystem. Matthew, how's it going? Matthew Reinbold: It is going. I am happy to be here first time, caller, long time listener. Is that how we say that? Matt Trask: I think that's yeah. It's how you say it. Yeah. So I mean, for those of you, like in the off chance that someone doesn't know who you are in the API ecosystem world can you give us a little bit kind of about yourself? Like you manage two different newsletters, at least as well as a pretty prolific Twitter presence as well. But if someone hasn't run into you, like. Matthew Reinbold: Well, yeah, well, first off, thanks for calling it prolific. Some people would call it annoying, but yeah, I I manage a fair number of tweets over at Twitter slash L I B E L underscore Vox, reliable Vox. That's where I talk about digital transformation and APIs and a lot of technology stuff. Occasionally. Fights with blockchain and NFT enthusiastic. But then I also manage, I also manage a newsletter called net API notes, where for almost 200 issues, going back to 2015, I've covered the landscape. I've shared essential bits of information. I've tried to boil down the, the. Current climate and get it right into just the most essential things that decision makers need to know and care about. And then I do a fair amount of blogging on a blog. That's very imaginatively named Matthew reinbold.com. In there, I talk about a fair number of things as well, but in, in, in short my passion is really about coaching people, helping people, teaching people to get better with their API ecosystem. Matt Trask: That's really cool. So one thing that kinda stuck out to me cause it's, so we're going to be talking about the 20, 21 Sidi APR report. However, I'm curious since you've been doing it now since 2015, you've been keeping notes on. The API world. How does your kind of, I hate to say this phrase, the 30,000 foot view of everything that, you know, from 2015, how does that kind of line up to what you saw with the 2021 state of the API report? Matthew Reinbold: Oh, that's interesting. So there's definitely. Maturing as a industry, we've gone through a number of phases. Those of us that have been around the block a few times, see trends come. And most often they, they tend to roll away. And over that time we have to develop models so that we can kind of. Pick the, the, the wheat from the chaff, you know, what, what are the properties of something new, some kind of buzzword, some kind of hyperbole that we can latch onto and say, yes, this is worth investing in. This is worth our interest in our effort versus, yeah, this is some marketing system, some spin as I'm looking at the 20, 21 postman report. I see. Where we've come. It's gone from being single point to point integrations. One-off bespoke API APIs to where we're now talking about things as ecosystems. We're now talking about collections of these things and how entire organizations. Manage these as, as something that's beneficial, something that's collaborative and, and managed as a separate entity rather than, than each individual unit I've got Phil here. So I have to use the forest for the trees analogy rather than just managing the individual API trees. There's now a greater awareness of what the forest, what the forest role is in the company and how to manage that. In a unique way, as opposed to the individual pieces. I will say for those that are listening, like I'm one of the things I want to highlight right up front here is that you don't have to enter an email address. It's not behind the page. We really felt strongly at postman that we had to get this information out to the most number of decision-makers so that they could make better decisions so that they could be informed as they're developing their strategies and roadmaps. So if you go to postman.com/state-of-api, you'll be able to download. With out any worry about having somebody from sales follow up with you later, or getting spam in your inbox, it's free for all. We want this information to be used. We want the dialogues to happen. We want the discourse to be rich and for me and frothy. And so please, you know, don't let past marketing spam. Stop you from checking this out. We want this in the hands of people. Phil Sturgeon: Fantastic. That's good to hear. I mean, that's I haven't got around to reading it as you might have seen from Twitter. Life has been a bit of a mess recently just spending far too much time in the field, as opposed to in the field doing APA stuff. But, yeah, that's definitely always been a concern of mine, of, you know, you hear about these white papers and reports and you just know so many of them like should have just be in the blog post, but instead that like a PDF that and you've got to enter information and then you just get like that fifth email, like, why didn't you reply to my previous four? I was like, I don't know who you are. I just want to read this thing. So yeah, I'm glad you folks are going in a different direction, but Maybe just taking a step back. Like, what is the state of API is report all about where are you getting your information from? What sort of research is being done? And what's the hospital. Matthew Reinbold: Great question. So this is, as far as I know, the largest survey of its kind, we had more than 28,000 people respond to our latest in a series. What we tend to do is try and track where the industry is at. And typically that's been around certain areas. Like how much time do you spend developing API APIs? What kind of tools are you using? Really good stuff there tracking the growth of, of the industry and the maturation of the industry. What I brought to the table this year. Was an interest on finding the behaviors that lead to sustainable, healthy API ecosystems. Like so much of what we talk about when it comes to API ecosystems is still very anecdotal. We tell stories about the Bezos Amazon memo, where we talk about like Twilio or Stripe, but when it comes to decision makers in large organizations, they're still. Trying to pull at what are decent KPIs, what are the behaviors I should be grooming or promoting within my company to make sure that I can keep producing quality API experiences again and again and again. And so what we did with this report that I'm really proud of is dig deep and discover, like, what are the correlating behaviors in organizations that lead to good things happening for companies? Phil Sturgeon: Okay. That's interesting. Cause I think. There's always this question around, like, what's a good API and what's a bad API. Right. And that's just such a nebulous, almost pointless topic so often, because you're just going to end up with opinions about camel case versus kebab case and opinions about rest versus graph UI, and all the nonsense that we love to fight about. And there's going to be someone with a fever at HTTP status code. And none of that actually matters, but you're talking about more of the business level stuff or what, what sort of things have come up as like. Really interesting results from, from your survey about how to build a good API what's what's, what's new and what's interesting. Matthew Reinbold: Right. Well, one of the things I wanted to look at was some of the insights that popped out to me when I was reading accelerate. So accelerate is like from. The previous decade, but it was written by Nicole Forsgren, Jess humble, Jean Kim, they came together and tried to figure out like, what was it about dev ops? That was so powerful. And they wanted to do it in a, in a way that quantified things, not just like, Hey, this is awesome. You should be doing it, but like get to the meat and potatoes of why is this powerful and why should businesses adopt dev ops? And as they went through their research they ended up discovering that there was really four things, four metrics that showed how dev. Made for better organizational performance. And those things were lead time, deployment, frequency, meantime to restore, or how quickly you recover and the change fail percentage. And I thought, huh, that's really interesting. Now that's for dev ops, but if these things are so instrumental in having organizations outperform. Their peers. Can we find the same correlation with API APIs? If we have the same behaviors, can we therefore then draw a line and say, if you have these things, if you have positive aspects of these four attributes, can you then have a more sustainable, more powerful API program? And based on our survey results, the answer is yes. So I can, I can go in and how we, how we drew that correlation. Phil Sturgeon: I'm curious, what sort of metrics are We, looking at? Matthew Reinbold: yeah. So first off we asked people on a 10 point scale. What, how, how well do you think that you've become API first? So out of our 28,000 respondents, they looked at this 10 point scale and they, they put themselves, you know, how they felt approximately 8% of the people that responded said, yes, we are either a nine or a 10 on the scale for API first, we said fine. And then we went through and we said, okay, you know, how long does it take you to make an API? Are we talking hours, days, weeks, so on and so forth. And we also said, okay, you know, not just time to produce, but how frequently you deploy and how many times do you have a deployment failure? Meaning like you put something in production, but it didn't work. Right. So you have to roll back and then like, what was your time to recovery? Like when an outage does occur and let's be. And outage always occurs at some point. Like how, how quickly can you recover from those things? So we got these nice, you know, bell curves and everybody kind of clumped toward the center on these things. And then we said, okay, Now the magic is we go back to that first question, the people that say their API first that have some kind of strong belief that they're doing API first, let's see how they compare to their peers on these metrics. And again, and again, all for these items, API, first people perform better. So, you know, taking one example here. API first people were able to deploy 17% faster than their peers and you know, in a day or less. So if you are API first and granted, there, there might be some subtlety in how a company defines that. But bottom line, if you are API first, you perform better on these metrics than your counterparts. Phil Sturgeon: Interesting. And yeah. Seeing, seeing as you raised it, what is API first? There's, there's a lot of different definitions floating around. Right. And so just for listeners that might not have listened to everything we've ever talked about and read every blog post we've ever read ref ever wrote how do you define it? Matthew Reinbold: Sure. Well, first for people that haven't heard this and haven't listened to every episode, shame on you. Second, I define I defined API first and. Making the API experience or the interface, the primary means for the functionality exchange. So not viewing, like I'm going to create this functionality and then subsequently go and some other team or, or some other project we'll be wrapping this thing in an API. It's thinking of creating an API experience as the primary exchange mechanism with dysfunctional. Not a library, not a module, not a class, the API. So this is slightly different than API design first, which is, I am going to subsequently talk to stakeholders, create a model, whether that's in an open API document or some other means, but I'm going to sketch that out. Test my assumptions, and then subsequently only begin code after. That's API design. First, I do draw a line between those two. They are very copacetic. They, they work together like peanut butter and chocolate, but there, there is a difference. You can, you can do API first without necessarily being API design first. Phil Sturgeon: For sure. Oh, well, we've got you on a roll. You're doing these really well. What is API as a product? Matthew Reinbold: Ooh, API API as a product. So that is creating an API with the. Awareness that it will have a roadmap. It will have ownership beyond just being put into a production environment that it will grow and change and subsequently necessitates the kind of modeling responsibilities and, and awareness that it will be growing and changing over time. Phil Sturgeon: Okay. So instead of, yeah, API first is your product should have an API. And that will be managed by the team who was making this product. And API as a product is a slight variant of API. First, that kind of takes that API out of that generic functionality team and says the API itself is the product. And another team potentially on the same team will be making a product using that Matthew Reinbold: Right. I, I would, I would, I would venture there's a lot of large enterprise environments for which API for. It's about a project that gets the thing into production. And then that thing is left to operate and run on its own. Perhaps there's some monitoring, perhaps some observability, but the actual team that made it is off doing the next thing and the next thing and the next thing there's not the idea that. This is a long lived item that, that produces some kind of business functionality value that is competing in a complex dynamic marketplace like that. That's the API product side of the house. Phil Sturgeon: Hm. Matt Trask: So the, I guess like the, the big question to bring up, I think right now is what did the pandemic do for the API ecosystem? Matthew Reinbold: Well, you know, first of all, I want to just stress that, that this thing that we kind of hand wave is the pandemic was actually like multiple congenital. Crises all at once. Right. You know, I, I want to, for the audience, like we're talking social unrest and political upheaval and supply chain disruption, and the, the pandemic was really a catch all for a tremendous amount of business stress. And what we've seen in the report is the usage of APIs, the number of API APIs the. Amount of focus and care on API. APIs has increased tremendously with that pandemic because business leaders, technology leaders are struggling with this amount of change, this amount of disruption. And so having architectures that are slow to change, difficult to change is just not cutting it in this. Set of multiple crises. So any kind of architectural advantage that allows them to change rapidly change quickly to do different things with how their development investment is deployed. So, you know, for example, taking that one dev team that was altogether in the office and being able to break it down into microservices to allow for greater asynchronous operation, greater flexibility. Those are the architectures that are being sought right now. Matt Trask: Yeah, that makes sense. I mean, it always here in America, I don't know if it feels sing, but you know, like there's. At the core level there. So like the whole, did we go back to the office and be Sandy the office upheaval as well. So it makes sense that there is kind of like a, a struggle on rapping, like getting non-technical CEOs, CTOs, CFOs their heads around the game-changing, this of APIs that doesn't surprise me at all to hear that they're still kind of, I don't want to say struggling, but unsure. Maybe like, Matthew Reinbold: Well, and, and, well, I, I think that's an interesting perspective because it assumes that leaders were in command and control positions of how the labor was divided anyway. And I would actually, I would actually posit that it's the opposite. It was everybody immediately going and running to their home offices and working in a remote work environment. The change in the communication paths changed the architectures that were subsequently produced by those teams. It's Conway's law in effect. And therefore, as we, as we look forward, as we look forward to what's going to happen, I would, I would venture that the organizations that pull people back to centralized locations, for whatever reason, I'm not going to debate whether that's good or bad, but the people that pull the development teams back to. see, like the Terminator two bad guy they'll reform remold because there will be more efficient communication patterns when everybody's face to face. Whereas those organizations that continue to have a distributed workforce will have more distributed architectural patterns because that's how communication is happening. Phil Sturgeon: That's really interesting. I haven't really thought about it before, but I, I, I bet there's been an uptick in kind of API design first, specifically due to this as well. Right? Because my experience working we work was, was pretty awful as far as like API planning goes and as a result, APA architecture and API performance and Matthew Reinbold: You don't say you should blog about that. Fail. Matt Trask: Yeah. Phil Sturgeon: 25. I'm going to do a book about that shit. Matt Trask: Have you tweeted about this yet? Phil? I'm not sure if anyone knows your true Phil Sturgeon: I did a talk. I did a talk recently. But yeah, there was, there was such an element of like, we're real in an open plan office, playing ping pong together and shooting each other with nerves that there was never any effort on API contract being written down in any shape or form because you're all sitting about. And you're just like, what's that end point? Cool mate. Oh, if slash whatever. Oh, is that a, is that property of booty? It's a string called true with QuoteWerks and then you didn't have a need to write it down because you just show it over, over the top of Nerf fire. And I, I do wonder if remote work, well, not necessarily remote work, but quarantine remote work has helped push people more towards it because if you can all be sitting around asking each other, you're going to be typing. The contract over slack. And if you're going to be typing it out over slack, which is inherently ephemeral, then you might as well type it into a Yammel file and commit that in the repo. And then you can have design reviews around the board request or other tools that the offer, that sort of thing. So, yeah, that's, that's just completely a hypothetical and something I'm thinking the second night and check that, but I'm sure it's happening. Matthew Reinbold: I completely agree. And, and let me throw in something that's not in the report, but something that's got me totally geeked out and I'm watching for on my radar, we are going to see the greatest Renaissance of API design documentation that we've ever seen in the next couple of years. Now, granted, you know, as far as Renaissance goes, maybe Renaissance. Documentation are not that great. So, you know, let's put the party hats back in the closet, but what we're seeing with the great resignation right now is all of that knowledge that people acquired in their heads is leaving. It's headed out the door and I've read reports like up to 80% of how to do things with API APIs is in people's heads. Like at we work. If you needed to know how API has worked. You know, you knew Phil was the guy that could get you straightened and Phil Sturgeon: I didn't have a clue. That was the problem. I was trying to find out how to do it. Matthew Reinbold: Okay. So I wasn't, it was somebody, it was somebody on the other end of a, of a Nerf battle away Phil Sturgeon: Someone who quit already is the person that you. Matthew Reinbold: But right now in organizations like you have this phenomenon where a tremendous number of people are leaving organizations and they might've been the sole person who knew where the end points were or knew how that particular tricky function worked. And as organizations are trying to deal with this and recover and still be productive, there's going to be a greater emphasis on having that crap written down, having things documented. Organizations don't have aren't left on their back foot like they are right now. So whether that's heavy handed processes, whether that's just a greater appreciation for documentation among the staff, that's left, whatever that manifests as there's going to be an increasing amount of emphasis on documentation, because people have seen that too much was stuck in people's heads and it's not sustained. Phil Sturgeon: Yeah, that's a really good point. I mean, and not just kind of documentation, but the whole open API as a source of truth earlier on. And I figured it has to be, has to become more noticeably important when Yeah. They've, they've lost the whole team. How the API works and you know what it's like, code's always a bloody mess. Cause you just hacked up within about what over the place and patch things and fix things. And what about and yeah, when they find themselves rewrite in the API, cause no one can really take it over and no one remembers how it works and there's no documentation for it. And it's just too hard to figure out when they just make a brand new one. And they have a whole brand new team doing it. Cause they've already lost all that stuff. Matthew Reinbold: Yeah. Phil Sturgeon: That's a situation that a lot of managers and business people are going to say, how can we go about avoiding doing this? And I just hope there's someone in the room that says, well, APA designed first would really help avoid this problem because otherwise they'll just repeat all the same mistakes again. Matthew Reinbold: Right. Absolutely. Whether it's design first or tools that help analyze existing traffic and write the document afterwards, like whatever you got to do, get that written down and start taking some notes against it because. It's it, I believe right now with the great resignation. It's an Achilles heel. That's probably hampering a lot of organizational ecosystems right now. Matt Trask: Yeah, I would definitely agree. I mean, it shows in the report under open API three dot oh, 44% of people are aware of it, but they don't use it 28% say they use it. 12% said they use it, the love it. So even just combining use it and use it in love. It still does not match aware of we're not using it. Which means that there is definitely a. A river to jump over. So to speak, to getting more people on, to open API, which is probably currently like the standard for API documentation right now which comes back to your point, which allows them to start writing things down and start documenting things. And Phil gets it by bus tomorrow. We work is still going to be okay. It very well could happen. Which is exactly why I use that example. And it, it, yeah, it it'll give the organization a little bit more or a little less reliance on what's in people's heads a little bit more stability in case great races, nation three Datto happens in three years. You know, you don't know what's gonna happen. Phil Sturgeon: Is that when everyone resigns from web three point now, Matt Trask: please. Don't don't threaten me with a good time. Like I've already, I've already muted those web three and NFD on my Twitter and it cleaned it up so Phil Sturgeon: Why do you hate progress, man? Matt Trask: A lot of reasons. I'm a combustion at heart? No. Matthew Reinbold: Hey, if you don't, Phil Sturgeon: particular messages of this progress that are the problem. Matthew Reinbold: if you, don't stand for something, you'll fall for anything. Good for you, Matt. Matt Trask: yes, I've always wanted my life to be attributed to a, a Hamilton quote. So I am glad I did. I can check that one off to get back onto the actual topic and not just bashing NFTs for an hour and a half, which sounds like a lot of fun. What you the most about this report? Like what was something that you read that just you weren't expecting? Matthew Reinbold: I, I think there was two things that when you combine them together it made me tilt my head and go, huh? The, the first is that more than anything else? Including speed to production. People want quality API APIs. They want stability. They want some other things reliability. But the primary thing that people want out of their, their API APIs is quality. And yet when it came to whether or not people had time to test. Everybody acknowledged that testing was good. Tested was valid, but nobody had enough time for testing and it's like, huh? These two things kind of seem like. The, the two sides of a coin, right. You know, people aren't getting the quality that they want, but everybody acknowledges that they don't have enough time to do testing, even though they recognize the testing is an extremely valuable type thing. So I think when it comes to socializing this report and talking to decision-makers and doing the kind of coaching that I so often do, I, this is one of those things too, to bring up, like how in your program are you supporting. Testing and ensuring that enough is being done there so that your developers feel like you're, you're reaching the kind of quality goals that, that you're, you're promising to the rest of the world. Phil Sturgeon: Hm, do you, is the survey broken down by role? So can you, can you look to see if. Managers and engineers have a rule, very interested in, in high quality. And engineers are going, but we don't have enough time, but the manager's like, oh, they definitely have enough time. Matthew Reinbold: Right. So we do have a breakdown by role and job title, but I don't have the numbers in front of me that, that combined, and show me how to break down the quality question. Phil Sturgeon: Yeah, that'd be an interesting one. Cause yeah, so many roles, so many organizations, I just take it as like a universal truth is that companies are just, you know, business and product are demanding feature, feature, feature, feature, feature, and engineers are just like screaming, just keyboards on fire, trying to try to hit them goals. And everything's just wonky as hell. And it seems to be everywhere I go. There's not enough to have. There's not enough time for QA. They might've got rid of the QA team because it's slowed down product and slowed down delivery of features. Yeah, everyone wants high-quality API has, but no one wants to put the time in to testing because testing is inherently hard and slow. Matthew Reinbold: Right. And kind of along those same lines, another stat that jumped out at me was that 76% of the people building API APIs have less than five years experience doing. I mean, you know, as far as restful APIs now, we're, we're more than a decade into that journey. So that stat leaps out at me, like what is it about API development, where we're getting people with zero to five years experience like what's happening. There are the successful API builders, aging out and becoming management. it, are they moving on to web three O and NFTs? Like, like what is, where are our experienced API builders and why are these critical pieces of business infrastructure? In the hands of relatively younger people. That's not to say that they can't be doing a good job, that, that it's impossible to build a great web experience at your first time at bat. But it's also something where I think everybody on this call would probably agree. Experience counts, experience matters. Ha being around the block once or twice, you pick up a feel for what's beneficial, what's maybe a little wonky and you can imbue that into a better design at launch. So, you know, where are the. 10 year, the 12 year, the 15 year veterans. And why are they not the primary source of API infrastructure development? Phil Sturgeon: Yeah. Some that I've seen so much, again, just, I love complaining about we work. Pretty much everyone that was a junior developer, Right. Like the vast majority, what, what you need developers and their role responsible for creating you know, there's like a hundred API APIs and, you know more than a hundred junior developers with just a sprinkling of seniors who were more on the cowboy coder end of things. Not, not to be rude, you know, like startup, you need to be super agile, super fast, not, not a perfectionist. And so, so many of the problems where this is, this person's first rails app, like they know how to accept incoming Jason parameters and they know how to spit something back from the database. And. That's that, and they know how to make a web request. So he talks to . He talks to F talks to G in the thread, and then no, one's got a timer anyway. So everything falls over, like, things like that. The sort of thing you realize, if you've been doing APIs for five years, or for 10 years, you've been doing it for 10 years, you wouldn't do that. You just wouldn't do that. You'd put something in a sidekick job and then implement a web socket or a web hook, or literally anything else. But. That's the sort of thing you do when you consider like HTP failures or server downtime, to be an edge case that is like some weird scenario that probably won't happen. And when you've been doing it for a longer time, you're like you, you change your mindset to this web requests probably won't work. And on the off chance that it. This is what should happen. And you just get really defensive and paranoid and have like 25 different guard statements and, you know, 25 different types of ex exception catching and, and every single circuit breaker and trigger warning that you can possibly put on this thing. And there is, yeah, there is a change in mind. Around around that kind of it doesn't, I'm not being a gatekeeper or at least they're saying you've got to be doing EPS for 10 years until you're good. But when you start out, you you're such, you're more of an optimist. You haven't seen it go wrong in as many ways. You haven't had cascading failures and you haven't had all these terrifying things that happen. So that, that is definitely a concern for me is that I think, yeah. Happy, happy path development. When you go from having one AP. To having 20 or a hundred, the, the the chance of straying off the happy path gets exponentially worse. Right. And, and that's just something, I think a lot of these younger developers on experience with. Matthew Reinbold: Right. Even, even when it comes to design, having used API APIs, having to incorporate the API APIs, you better understand what makes a good description and what is just a reiteration of the, the name itself. Yeah. Yeah. If I have a field called date of birth and the description is just the birth, that, the date that the person was born on, like, well, what was the. do I need to refresh it? Or is it cashed? You know, like, can I store it or is it part of some kind of regulatory PII? And I shouldn't, you know, I can use it, but I shouldn't store, like, there's so many issues that once you've been down that road, and then you're asked to produce an API, you bring that experience with you and you put it into the description that adds so much that yeah. I, I, I, I don't know. How we continue to get that, that experience circulating and get that in front of people. But I think it's really important. Matt Trask: Well, I must wonder too, like how many of those, like experienced API builders are getting swallowed up into Stripe? Twilio, Google. And kind of almost locked away working on their API APIs and not able to share their experiences down the road to junior developers in their own companies or interim networks, things like that too, because it feels like you do your five, seven years as developer, you get pulled into the management game and then all of your knowledge is still there, but you're having to balance both managing a development team, hitting your goals. Pushing out products because you've got to make money for the business. And all of your knowledge that you've worked so hard to gain is kind of sidelined in the name of profits or KPIs or whatever it might be. Matthew Reinbold: Possibly there's, there's certainly exceptions that spring to mind. One of which is Tim Burks and the team over at Google and with the number of resources that they put out there. For their APIs. It's, it's kind of a mouthful, but if you do a Google search for that, they've produced a tremendous amount of documentation about how they support API APIs at scale, how they do their design reviews, how they think about consistency and cohesion across their entire footprint. So that certainly what you described could be the case in some places. You know, I, I, I do think that it's not necessarily the default that's people go off to these big organizations and then just disappear because the folks at Google around Tim and his crew they're doing some great work. Phil Sturgeon: So I've been sat in the room with you having these sort of conversations your last job, Right, Like a center of excellence type stuff. You, you get a bunch of smart people and me together and start talking about what, what would help with these various different problems? Like how do we do APA design reviews? How do we do governance? What standards should we be interested in? So I think sometimes yeah. Experienced developers can get sucked up into these companies and kind of finish and end up having that scale was used for something else. But I, I think companies that have those governance processes, like they're sharing their experience back by creating style guides, by creating programs that they explain how these, how these like API designed life cycles or API life cycle should work. And that's a way that they can essentially. Distribute their experience. So instead of like, I know what to look for when I'm reviewing a poor request, they can create a style guide. That means that everyone will do that. I think the danger there is that when style goes focus on what, instead of why then, then you kind of lose some of that experience because it just seems like arbitrary decisions delivered from upon high. Right. You just get. Do it this way, but, but Y I've read loads of style guides recently. And, and some of them, I should probably show the examples. It's just like, do this. Like, why you don't tell me what to do? You don't my dad, like, it just, I couldn't figure out what they possibly could have meant by it. Cause usually I can look at something. Why might they mean that? Oh, that reminds me of a thing that happened along these lines. They probably got burned by that before, and they want to avoid it, but if you don't see why it just sounds arbitrary and you're not actually teaching anyone on anything, but if you do it right. that that can be really helpful. Matthew Reinbold: Right. And it's also essential that if you're designing these systems like a governance or like a center of excellence that you have the feedback process that you have, the, the communication cycles so that when people do have that kind of. That they have a recourse. It's not a dead end. It's not either you do this or you're punished for it, but oh, if this doesn't make sense, here's who you talk to. Here's how you can escalate your concern here is how you elevate your edge case. And we can have a discussion about it and you can help co-evolve this thing, because you own this as much as somebody else, the, the phenomenon that you described, where it's a dead end. It's thrust upon you. You don't have ownership of that. And as a developer, that does not feel good, that does not invest you in seeing the long-term growth of, of that system. You want to burn that system. You want to be the rebels flying through the death star trench. You want to take that thing down? So what's essential is to realize. You provide the avenues for people to, to voice their concerns, voice their questions, and make them feel heard in such a way that their process, the process is theirs. It's not something done to them. It's it's their process. Phil Sturgeon: I'm just laughing about the death star rebel situation. Now I'm completely distracted. I need to go rewatch some star wars. I don't know. Matt Trask: I mean, your, your thought on the ownership thing is also interesting cause And we like watching the junior Twitter, the junior developer Twitter circles, which is not the end all be all of it all, but there is a large emphasis on if you want to make more money, you need to jump ship every two years on average. And that kind of removes the does or not the desire, but like the, the ownership of any sort of product from a junior developer, because in two years, they're going to be onto another thing. They're going to be onto another system. Codebase, maybe another language and it, it does kind of bring back, like, how do you entice people to have ownership, even if they only are going to plan to say somewhere for a short period? Because we all know that like having, like you said, having that ownership is going to kind of make you more invested, more caring, more thoughtful, more empathetic towards whatever it is that you're building. Matthew Reinbold: Right. I mean, we're veering into management territory, which I'm happy to talk about. I, I know. Matt Trask: very allergic to management. So. Matthew Reinbold: But I, I was just reading Harvard business review. Hey, I'm fun at parties too. So I was reading Harvard business review talking about COVID and the great resignation and the, the management challenges that, that come with that and what we need more. In all companies is a feeling of belonging, a feeling like we have a career progression feeling like our, our, our work has impact and all too often management, just as about making sure people don't do dumpster. Right. You know, I'm, I'm here to police you because the organization doesn't trust you. And it leads to all kinds of weird effects. Like, Hey, if you actually want to grow your career, you need to leave. You need to hop companies every two years and let's be clear that may work, but it's still very disruptive, not just for the company, but for the individual. 'cause they're having to rebuild all of those social structures, their relationships, their patterns, the routines it, it's not, it doesn't come for free. And so from a management standpoint, if you can show people how to have that fulfilling career, how to fulfill those needs. They don't have to jump ship every two years. There's no reason that that has to be the default blueprint. And from a company standpoint, you actually benefit from that accrued experience rather than having a developer. That's done the same thing. Five times you get five years of experience. That's really powerful, really tremendous. And that, that ultimately not only leads to better APIs, but leads to a better employee. So there is a disconnect we need to work with our management layers. It shouldn't just be the technician that has some headcount is by default manager. There needs to be an appreciation for how those are unique skill sets. Those are unique muscles that need to be exercised, but. If we can create that fulfilling sense of duty then, and that the career path for these individuals, we can get them off of this kind of binge and purge career treadmill. Matt Trask: So that's a really, yeah, that's a really good way to put the whole two year turn. And I mean, it comes back full circle to what you just said earlier, which is, you know, 75% of API has been developed now or done by people with less than five years experience. And that's probably because of the same, people are jumping, jumping, jumping. Whereas if you can keep them around, make them happy, make them feel like they belong. We might actually start seeing that number. Dropped significantly to more experienced API developers building more thoughtful API design with, with years of knowledge built up. So I think it'll be really interesting to see kind of what happens with this great resignation how that all shapes up. And then it'll be interesting to see to kind of the 2022 say the API report. How does that. How, how will things change from a year in a year going forward? And what can we expect possibly looking at these two years, the next five years after that, the next 10 years growing on different trends, you know, we might see NFTs ruling the world. We might see graph QL. Rolling. Phil Sturgeon: No comment. Matt Trask: Matthew is kind of shrugging Phil Sturgeon: we're all sad. Now, rural sat now, NFTs powered by graft UL, problem solved. Can you, can you still right click that? No, you can't. It's like a post. So. Matt Trask: Well, there goes Matthew Reinbold: Each unique query is published as an innovator. And you can put the ownership of that query in a blockchain so that you don't have the centralized point of failure. Phil Sturgeon: I was going to thank you for being for, for making this podcast sound intelligent for once. And, Matthew Reinbold: And then I ruined it. Sorry. Phil Sturgeon: and then you. Matt Trask: no, no, no, you didn't ruin it. You just brought it back down to its normal level of ridiculousness. Phil Sturgeon: Fantastic. No. Do you have any predictions for what we're going to see in the, in next year's state of this report? Because then we can play that clip back and laugh at how wrong you were. Matthew Reinbold: Oh, lovely. All right, well, let me have a few minutes to sandbag my answer. No, I think there's a tremendous amount of, of areas where we can take this correlation that I talked about before behaviors. You know how the question immediately becomes well, okay. If these four behaviors are so good and are present in high-performing API companies, how do we get there? And this year we had a little bit around leadership and what leaders do. To get an API first company. I think there is a lot of exploration we can do there to really dial in and say, okay, we know these things are good. How do you get there? How do you promote these things? How do you, how do you get it so that you are able to deploy in a minimal amount of time or recover faster? What are leaders in those organizations doing? That's one of the things I'd love to dig into obviously. A lot of post pandemic aftermath. There's been a tremendous amount of published about how this digital transformation and, you know, we're so much more flexible and adaptable because we, we are now doing all our conversations over zoom. And I look at that and I, I scratch my head because. Digital transformation, at least in the non buzzword compliant way is a whole lot more difficult than just moving everything to a slack conversation or a, or a zoom conversation. Like it means fundamentally dismantling your policies and procedures and reinventing them in a way that digital technology lends itself to. So figuring out what that post pandemic landscape looks like and how we're still feeling the knock on effect. Is going to be something that's also going to be very interesting to explore. Matt Trask: Yeah, that's definitely true. I mean, I think one thing I would like to see is, is that number of people who know open API, but don't use it start to gradually shift down and people who are using open. Start to shift up, which, you know, from a silver right back to having documentation and some sort of notes about their API. So when the, the knowledge people do eventually leave because everyone leaves the company at some point, the knowledge isn't necessarily leaving. And instead we're, we're kind of leaving a better legacy to the people following us. Yeah, definitely. Matthew Reinbold: Here here. Matt Trask: Cool. Matthew, thank you so much for taking some time out of your, your, your day to talk to us. We really appreciate it. Look forward to having you back in roughly a year's time to talk 20, 22. Say the API report Matthew Reinbold: I love it. Let's do it. Pencil it in right now. Matt Trask: Yep. It's it's on my calendar. I don't know what I'll be doing in a year from today, but I know for a fact we'll be talking again. If you want to get. Matthew on Twitter. He is at libel Vox, L I B E L underscore V O X M. And we'll throw the link to your blog and Twitter in the show notes as well as everything else. Awesome. Cool. Thank you so much. We appreciate it. Phil Sturgeon: Yeah. All audio, artwork, episode descriptions and notes are property of APIs You Won't Hate, for APIs You Won't Hate, and published with permission by Transistor, Inc. Broadcast by | 2026-01-13T08:48:40 |
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Follow Episode Details / Transcript Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations at Postman, to talk about Postman's State of the API 2021. Show Notes Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations to discuss Postman's State of the API 2021 report, detailing various data points from around the API world from which specification people turn to, to how confident people feel deploying their APIs. They also discuss various topics around remote work, how APIs enable more remote work and what will happen in the next few years for APIs. Notes: Matthew on twitter: https://twitter.com/libel_vox Postman's State of the API Creators and Guests Host Mike Bifulco Cofounder and host of APIs You Won't Hate. Blogs at https://mikebifulco.com Into 🚴♀️, espresso ☕, looking after 🌍. ex @Stripe @Google @Microsoft What is APIs You Won't Hate? A no-nonsense (well, some-nonsense) podcast about API design & development, new features in the world of HTTP, service-orientated architecture, microservices, and probably bikes. Matt Trask: Cool. Welcome back to APS. You won't hate episode 17. I have Phil with me and we're joined by a very special guest today. Matthew Reinbold, fresh from postman, who is a director of API ecosystems and digital transformations here to talk about their report, the 2021 state of the API ecosystem. Matthew, how's it going? Matthew Reinbold: It is going. I am happy to be here first time, caller, long time listener. Is that how we say that? Matt Trask: I think that's yeah. It's how you say it. Yeah. So I mean, for those of you, like in the off chance that someone doesn't know who you are in the API ecosystem world can you give us a little bit kind of about yourself? Like you manage two different newsletters, at least as well as a pretty prolific Twitter presence as well. But if someone hasn't run into you, like. Matthew Reinbold: Well, yeah, well, first off, thanks for calling it prolific. Some people would call it annoying, but yeah, I I manage a fair number of tweets over at Twitter slash L I B E L underscore Vox, reliable Vox. That's where I talk about digital transformation and APIs and a lot of technology stuff. Occasionally. Fights with blockchain and NFT enthusiastic. But then I also manage, I also manage a newsletter called net API notes, where for almost 200 issues, going back to 2015, I've covered the landscape. I've shared essential bits of information. I've tried to boil down the, the. Current climate and get it right into just the most essential things that decision makers need to know and care about. And then I do a fair amount of blogging on a blog. That's very imaginatively named Matthew reinbold.com. In there, I talk about a fair number of things as well, but in, in, in short my passion is really about coaching people, helping people, teaching people to get better with their API ecosystem. Matt Trask: That's really cool. So one thing that kinda stuck out to me cause it's, so we're going to be talking about the 20, 21 Sidi APR report. However, I'm curious since you've been doing it now since 2015, you've been keeping notes on. The API world. How does your kind of, I hate to say this phrase, the 30,000 foot view of everything that, you know, from 2015, how does that kind of line up to what you saw with the 2021 state of the API report? Matthew Reinbold: Oh, that's interesting. So there's definitely. Maturing as a industry, we've gone through a number of phases. Those of us that have been around the block a few times, see trends come. And most often they, they tend to roll away. And over that time we have to develop models so that we can kind of. Pick the, the, the wheat from the chaff, you know, what, what are the properties of something new, some kind of buzzword, some kind of hyperbole that we can latch onto and say, yes, this is worth investing in. This is worth our interest in our effort versus, yeah, this is some marketing system, some spin as I'm looking at the 20, 21 postman report. I see. Where we've come. It's gone from being single point to point integrations. One-off bespoke API APIs to where we're now talking about things as ecosystems. We're now talking about collections of these things and how entire organizations. Manage these as, as something that's beneficial, something that's collaborative and, and managed as a separate entity rather than, than each individual unit I've got Phil here. So I have to use the forest for the trees analogy rather than just managing the individual API trees. There's now a greater awareness of what the forest, what the forest role is in the company and how to manage that. In a unique way, as opposed to the individual pieces. I will say for those that are listening, like I'm one of the things I want to highlight right up front here is that you don't have to enter an email address. It's not behind the page. We really felt strongly at postman that we had to get this information out to the most number of decision-makers so that they could make better decisions so that they could be informed as they're developing their strategies and roadmaps. So if you go to postman.com/state-of-api, you'll be able to download. With out any worry about having somebody from sales follow up with you later, or getting spam in your inbox, it's free for all. We want this information to be used. We want the dialogues to happen. We want the discourse to be rich and for me and frothy. And so please, you know, don't let past marketing spam. Stop you from checking this out. We want this in the hands of people. Phil Sturgeon: Fantastic. That's good to hear. I mean, that's I haven't got around to reading it as you might have seen from Twitter. Life has been a bit of a mess recently just spending far too much time in the field, as opposed to in the field doing APA stuff. But, yeah, that's definitely always been a concern of mine, of, you know, you hear about these white papers and reports and you just know so many of them like should have just be in the blog post, but instead that like a PDF that and you've got to enter information and then you just get like that fifth email, like, why didn't you reply to my previous four? I was like, I don't know who you are. I just want to read this thing. So yeah, I'm glad you folks are going in a different direction, but Maybe just taking a step back. Like, what is the state of API is report all about where are you getting your information from? What sort of research is being done? And what's the hospital. Matthew Reinbold: Great question. So this is, as far as I know, the largest survey of its kind, we had more than 28,000 people respond to our latest in a series. What we tend to do is try and track where the industry is at. And typically that's been around certain areas. Like how much time do you spend developing API APIs? What kind of tools are you using? Really good stuff there tracking the growth of, of the industry and the maturation of the industry. What I brought to the table this year. Was an interest on finding the behaviors that lead to sustainable, healthy API ecosystems. Like so much of what we talk about when it comes to API ecosystems is still very anecdotal. We tell stories about the Bezos Amazon memo, where we talk about like Twilio or Stripe, but when it comes to decision makers in large organizations, they're still. Trying to pull at what are decent KPIs, what are the behaviors I should be grooming or promoting within my company to make sure that I can keep producing quality API experiences again and again and again. And so what we did with this report that I'm really proud of is dig deep and discover, like, what are the correlating behaviors in organizations that lead to good things happening for companies? Phil Sturgeon: Okay. That's interesting. Cause I think. There's always this question around, like, what's a good API and what's a bad API. Right. And that's just such a nebulous, almost pointless topic so often, because you're just going to end up with opinions about camel case versus kebab case and opinions about rest versus graph UI, and all the nonsense that we love to fight about. And there's going to be someone with a fever at HTTP status code. And none of that actually matters, but you're talking about more of the business level stuff or what, what sort of things have come up as like. Really interesting results from, from your survey about how to build a good API what's what's, what's new and what's interesting. Matthew Reinbold: Right. Well, one of the things I wanted to look at was some of the insights that popped out to me when I was reading accelerate. So accelerate is like from. The previous decade, but it was written by Nicole Forsgren, Jess humble, Jean Kim, they came together and tried to figure out like, what was it about dev ops? That was so powerful. And they wanted to do it in a, in a way that quantified things, not just like, Hey, this is awesome. You should be doing it, but like get to the meat and potatoes of why is this powerful and why should businesses adopt dev ops? And as they went through their research they ended up discovering that there was really four things, four metrics that showed how dev. Made for better organizational performance. And those things were lead time, deployment, frequency, meantime to restore, or how quickly you recover and the change fail percentage. And I thought, huh, that's really interesting. Now that's for dev ops, but if these things are so instrumental in having organizations outperform. Their peers. Can we find the same correlation with API APIs? If we have the same behaviors, can we therefore then draw a line and say, if you have these things, if you have positive aspects of these four attributes, can you then have a more sustainable, more powerful API program? And based on our survey results, the answer is yes. So I can, I can go in and how we, how we drew that correlation. Phil Sturgeon: I'm curious, what sort of metrics are We, looking at? Matthew Reinbold: yeah. So first off we asked people on a 10 point scale. What, how, how well do you think that you've become API first? So out of our 28,000 respondents, they looked at this 10 point scale and they, they put themselves, you know, how they felt approximately 8% of the people that responded said, yes, we are either a nine or a 10 on the scale for API first, we said fine. And then we went through and we said, okay, you know, how long does it take you to make an API? Are we talking hours, days, weeks, so on and so forth. And we also said, okay, you know, not just time to produce, but how frequently you deploy and how many times do you have a deployment failure? Meaning like you put something in production, but it didn't work. Right. So you have to roll back and then like, what was your time to recovery? Like when an outage does occur and let's be. And outage always occurs at some point. Like how, how quickly can you recover from those things? So we got these nice, you know, bell curves and everybody kind of clumped toward the center on these things. And then we said, okay, Now the magic is we go back to that first question, the people that say their API first that have some kind of strong belief that they're doing API first, let's see how they compare to their peers on these metrics. And again, and again, all for these items, API, first people perform better. So, you know, taking one example here. API first people were able to deploy 17% faster than their peers and you know, in a day or less. So if you are API first and granted, there, there might be some subtlety in how a company defines that. But bottom line, if you are API first, you perform better on these metrics than your counterparts. Phil Sturgeon: Interesting. And yeah. Seeing, seeing as you raised it, what is API first? There's, there's a lot of different definitions floating around. Right. And so just for listeners that might not have listened to everything we've ever talked about and read every blog post we've ever read ref ever wrote how do you define it? Matthew Reinbold: Sure. Well, first for people that haven't heard this and haven't listened to every episode, shame on you. Second, I define I defined API first and. Making the API experience or the interface, the primary means for the functionality exchange. So not viewing, like I'm going to create this functionality and then subsequently go and some other team or, or some other project we'll be wrapping this thing in an API. It's thinking of creating an API experience as the primary exchange mechanism with dysfunctional. Not a library, not a module, not a class, the API. So this is slightly different than API design first, which is, I am going to subsequently talk to stakeholders, create a model, whether that's in an open API document or some other means, but I'm going to sketch that out. Test my assumptions, and then subsequently only begin code after. That's API design. First, I do draw a line between those two. They are very copacetic. They, they work together like peanut butter and chocolate, but there, there is a difference. You can, you can do API first without necessarily being API design first. Phil Sturgeon: For sure. Oh, well, we've got you on a roll. You're doing these really well. What is API as a product? Matthew Reinbold: Ooh, API API as a product. So that is creating an API with the. Awareness that it will have a roadmap. It will have ownership beyond just being put into a production environment that it will grow and change and subsequently necessitates the kind of modeling responsibilities and, and awareness that it will be growing and changing over time. Phil Sturgeon: Okay. So instead of, yeah, API first is your product should have an API. And that will be managed by the team who was making this product. And API as a product is a slight variant of API. First, that kind of takes that API out of that generic functionality team and says the API itself is the product. And another team potentially on the same team will be making a product using that Matthew Reinbold: Right. I, I would, I would, I would venture there's a lot of large enterprise environments for which API for. It's about a project that gets the thing into production. And then that thing is left to operate and run on its own. Perhaps there's some monitoring, perhaps some observability, but the actual team that made it is off doing the next thing and the next thing and the next thing there's not the idea that. This is a long lived item that, that produces some kind of business functionality value that is competing in a complex dynamic marketplace like that. That's the API product side of the house. Phil Sturgeon: Hm. Matt Trask: So the, I guess like the, the big question to bring up, I think right now is what did the pandemic do for the API ecosystem? Matthew Reinbold: Well, you know, first of all, I want to just stress that, that this thing that we kind of hand wave is the pandemic was actually like multiple congenital. Crises all at once. Right. You know, I, I want to, for the audience, like we're talking social unrest and political upheaval and supply chain disruption, and the, the pandemic was really a catch all for a tremendous amount of business stress. And what we've seen in the report is the usage of APIs, the number of API APIs the. Amount of focus and care on API. APIs has increased tremendously with that pandemic because business leaders, technology leaders are struggling with this amount of change, this amount of disruption. And so having architectures that are slow to change, difficult to change is just not cutting it in this. Set of multiple crises. So any kind of architectural advantage that allows them to change rapidly change quickly to do different things with how their development investment is deployed. So, you know, for example, taking that one dev team that was altogether in the office and being able to break it down into microservices to allow for greater asynchronous operation, greater flexibility. Those are the architectures that are being sought right now. Matt Trask: Yeah, that makes sense. I mean, it always here in America, I don't know if it feels sing, but you know, like there's. At the core level there. So like the whole, did we go back to the office and be Sandy the office upheaval as well. So it makes sense that there is kind of like a, a struggle on rapping, like getting non-technical CEOs, CTOs, CFOs their heads around the game-changing, this of APIs that doesn't surprise me at all to hear that they're still kind of, I don't want to say struggling, but unsure. Maybe like, Matthew Reinbold: Well, and, and, well, I, I think that's an interesting perspective because it assumes that leaders were in command and control positions of how the labor was divided anyway. And I would actually, I would actually posit that it's the opposite. It was everybody immediately going and running to their home offices and working in a remote work environment. The change in the communication paths changed the architectures that were subsequently produced by those teams. It's Conway's law in effect. And therefore, as we, as we look forward, as we look forward to what's going to happen, I would, I would venture that the organizations that pull people back to centralized locations, for whatever reason, I'm not going to debate whether that's good or bad, but the people that pull the development teams back to. see, like the Terminator two bad guy they'll reform remold because there will be more efficient communication patterns when everybody's face to face. Whereas those organizations that continue to have a distributed workforce will have more distributed architectural patterns because that's how communication is happening. Phil Sturgeon: That's really interesting. I haven't really thought about it before, but I, I, I bet there's been an uptick in kind of API design first, specifically due to this as well. Right? Because my experience working we work was, was pretty awful as far as like API planning goes and as a result, APA architecture and API performance and Matthew Reinbold: You don't say you should blog about that. Fail. Matt Trask: Yeah. Phil Sturgeon: 25. I'm going to do a book about that shit. Matt Trask: Have you tweeted about this yet? Phil? I'm not sure if anyone knows your true Phil Sturgeon: I did a talk. I did a talk recently. But yeah, there was, there was such an element of like, we're real in an open plan office, playing ping pong together and shooting each other with nerves that there was never any effort on API contract being written down in any shape or form because you're all sitting about. And you're just like, what's that end point? Cool mate. Oh, if slash whatever. Oh, is that a, is that property of booty? It's a string called true with QuoteWerks and then you didn't have a need to write it down because you just show it over, over the top of Nerf fire. And I, I do wonder if remote work, well, not necessarily remote work, but quarantine remote work has helped push people more towards it because if you can all be sitting around asking each other, you're going to be typing. The contract over slack. And if you're going to be typing it out over slack, which is inherently ephemeral, then you might as well type it into a Yammel file and commit that in the repo. And then you can have design reviews around the board request or other tools that the offer, that sort of thing. So, yeah, that's, that's just completely a hypothetical and something I'm thinking the second night and check that, but I'm sure it's happening. Matthew Reinbold: I completely agree. And, and let me throw in something that's not in the report, but something that's got me totally geeked out and I'm watching for on my radar, we are going to see the greatest Renaissance of API design documentation that we've ever seen in the next couple of years. Now, granted, you know, as far as Renaissance goes, maybe Renaissance. Documentation are not that great. So, you know, let's put the party hats back in the closet, but what we're seeing with the great resignation right now is all of that knowledge that people acquired in their heads is leaving. It's headed out the door and I've read reports like up to 80% of how to do things with API APIs is in people's heads. Like at we work. If you needed to know how API has worked. You know, you knew Phil was the guy that could get you straightened and Phil Sturgeon: I didn't have a clue. That was the problem. I was trying to find out how to do it. Matthew Reinbold: Okay. So I wasn't, it was somebody, it was somebody on the other end of a, of a Nerf battle away Phil Sturgeon: Someone who quit already is the person that you. Matthew Reinbold: But right now in organizations like you have this phenomenon where a tremendous number of people are leaving organizations and they might've been the sole person who knew where the end points were or knew how that particular tricky function worked. And as organizations are trying to deal with this and recover and still be productive, there's going to be a greater emphasis on having that crap written down, having things documented. Organizations don't have aren't left on their back foot like they are right now. So whether that's heavy handed processes, whether that's just a greater appreciation for documentation among the staff, that's left, whatever that manifests as there's going to be an increasing amount of emphasis on documentation, because people have seen that too much was stuck in people's heads and it's not sustained. Phil Sturgeon: Yeah, that's a really good point. I mean, and not just kind of documentation, but the whole open API as a source of truth earlier on. And I figured it has to be, has to become more noticeably important when Yeah. They've, they've lost the whole team. How the API works and you know what it's like, code's always a bloody mess. Cause you just hacked up within about what over the place and patch things and fix things. And what about and yeah, when they find themselves rewrite in the API, cause no one can really take it over and no one remembers how it works and there's no documentation for it. And it's just too hard to figure out when they just make a brand new one. And they have a whole brand new team doing it. Cause they've already lost all that stuff. Matthew Reinbold: Yeah. Phil Sturgeon: That's a situation that a lot of managers and business people are going to say, how can we go about avoiding doing this? And I just hope there's someone in the room that says, well, APA designed first would really help avoid this problem because otherwise they'll just repeat all the same mistakes again. Matthew Reinbold: Right. Absolutely. Whether it's design first or tools that help analyze existing traffic and write the document afterwards, like whatever you got to do, get that written down and start taking some notes against it because. It's it, I believe right now with the great resignation. It's an Achilles heel. That's probably hampering a lot of organizational ecosystems right now. Matt Trask: Yeah, I would definitely agree. I mean, it shows in the report under open API three dot oh, 44% of people are aware of it, but they don't use it 28% say they use it. 12% said they use it, the love it. So even just combining use it and use it in love. It still does not match aware of we're not using it. Which means that there is definitely a. A river to jump over. So to speak, to getting more people on, to open API, which is probably currently like the standard for API documentation right now which comes back to your point, which allows them to start writing things down and start documenting things. And Phil gets it by bus tomorrow. We work is still going to be okay. It very well could happen. Which is exactly why I use that example. And it, it, yeah, it it'll give the organization a little bit more or a little less reliance on what's in people's heads a little bit more stability in case great races, nation three Datto happens in three years. You know, you don't know what's gonna happen. Phil Sturgeon: Is that when everyone resigns from web three point now, Matt Trask: please. Don't don't threaten me with a good time. Like I've already, I've already muted those web three and NFD on my Twitter and it cleaned it up so Phil Sturgeon: Why do you hate progress, man? Matt Trask: A lot of reasons. I'm a combustion at heart? No. Matthew Reinbold: Hey, if you don't, Phil Sturgeon: particular messages of this progress that are the problem. Matthew Reinbold: if you, don't stand for something, you'll fall for anything. Good for you, Matt. Matt Trask: yes, I've always wanted my life to be attributed to a, a Hamilton quote. So I am glad I did. I can check that one off to get back onto the actual topic and not just bashing NFTs for an hour and a half, which sounds like a lot of fun. What you the most about this report? Like what was something that you read that just you weren't expecting? Matthew Reinbold: I, I think there was two things that when you combine them together it made me tilt my head and go, huh? The, the first is that more than anything else? Including speed to production. People want quality API APIs. They want stability. They want some other things reliability. But the primary thing that people want out of their, their API APIs is quality. And yet when it came to whether or not people had time to test. Everybody acknowledged that testing was good. Tested was valid, but nobody had enough time for testing and it's like, huh? These two things kind of seem like. The, the two sides of a coin, right. You know, people aren't getting the quality that they want, but everybody acknowledges that they don't have enough time to do testing, even though they recognize the testing is an extremely valuable type thing. So I think when it comes to socializing this report and talking to decision-makers and doing the kind of coaching that I so often do, I, this is one of those things too, to bring up, like how in your program are you supporting. Testing and ensuring that enough is being done there so that your developers feel like you're, you're reaching the kind of quality goals that, that you're, you're promising to the rest of the world. Phil Sturgeon: Hm, do you, is the survey broken down by role? So can you, can you look to see if. Managers and engineers have a rule, very interested in, in high quality. And engineers are going, but we don't have enough time, but the manager's like, oh, they definitely have enough time. Matthew Reinbold: Right. So we do have a breakdown by role and job title, but I don't have the numbers in front of me that, that combined, and show me how to break down the quality question. Phil Sturgeon: Yeah, that'd be an interesting one. Cause yeah, so many roles, so many organizations, I just take it as like a universal truth is that companies are just, you know, business and product are demanding feature, feature, feature, feature, feature, and engineers are just like screaming, just keyboards on fire, trying to try to hit them goals. And everything's just wonky as hell. And it seems to be everywhere I go. There's not enough to have. There's not enough time for QA. They might've got rid of the QA team because it's slowed down product and slowed down delivery of features. Yeah, everyone wants high-quality API has, but no one wants to put the time in to testing because testing is inherently hard and slow. Matthew Reinbold: Right. And kind of along those same lines, another stat that jumped out at me was that 76% of the people building API APIs have less than five years experience doing. I mean, you know, as far as restful APIs now, we're, we're more than a decade into that journey. So that stat leaps out at me, like what is it about API development, where we're getting people with zero to five years experience like what's happening. There are the successful API builders, aging out and becoming management. it, are they moving on to web three O and NFTs? Like, like what is, where are our experienced API builders and why are these critical pieces of business infrastructure? In the hands of relatively younger people. That's not to say that they can't be doing a good job, that, that it's impossible to build a great web experience at your first time at bat. But it's also something where I think everybody on this call would probably agree. Experience counts, experience matters. Ha being around the block once or twice, you pick up a feel for what's beneficial, what's maybe a little wonky and you can imbue that into a better design at launch. So, you know, where are the. 10 year, the 12 year, the 15 year veterans. And why are they not the primary source of API infrastructure development? Phil Sturgeon: Yeah. Some that I've seen so much, again, just, I love complaining about we work. Pretty much everyone that was a junior developer, Right. Like the vast majority, what, what you need developers and their role responsible for creating you know, there's like a hundred API APIs and, you know more than a hundred junior developers with just a sprinkling of seniors who were more on the cowboy coder end of things. Not, not to be rude, you know, like startup, you need to be super agile, super fast, not, not a perfectionist. And so, so many of the problems where this is, this person's first rails app, like they know how to accept incoming Jason parameters and they know how to spit something back from the database. And. That's that, and they know how to make a web request. So he talks to . He talks to F talks to G in the thread, and then no, one's got a timer anyway. So everything falls over, like, things like that. The sort of thing you realize, if you've been doing APIs for five years, or for 10 years, you've been doing it for 10 years, you wouldn't do that. You just wouldn't do that. You'd put something in a sidekick job and then implement a web socket or a web hook, or literally anything else. But. That's the sort of thing you do when you consider like HTP failures or server downtime, to be an edge case that is like some weird scenario that probably won't happen. And when you've been doing it for a longer time, you're like you, you change your mindset to this web requests probably won't work. And on the off chance that it. This is what should happen. And you just get really defensive and paranoid and have like 25 different guard statements and, you know, 25 different types of ex exception catching and, and every single circuit breaker and trigger warning that you can possibly put on this thing. And there is, yeah, there is a change in mind. Around around that kind of it doesn't, I'm not being a gatekeeper or at least they're saying you've got to be doing EPS for 10 years until you're good. But when you start out, you you're such, you're more of an optimist. You haven't seen it go wrong in as many ways. You haven't had cascading failures and you haven't had all these terrifying things that happen. So that, that is definitely a concern for me is that I think, yeah. Happy, happy path development. When you go from having one AP. To having 20 or a hundred, the, the the chance of straying off the happy path gets exponentially worse. Right. And, and that's just something, I think a lot of these younger developers on experience with. Matthew Reinbold: Right. Even, even when it comes to design, having used API APIs, having to incorporate the API APIs, you better understand what makes a good description and what is just a reiteration of the, the name itself. Yeah. Yeah. If I have a field called date of birth and the description is just the birth, that, the date that the person was born on, like, well, what was the. do I need to refresh it? Or is it cashed? You know, like, can I store it or is it part of some kind of regulatory PII? And I shouldn't, you know, I can use it, but I shouldn't store, like, there's so many issues that once you've been down that road, and then you're asked to produce an API, you bring that experience with you and you put it into the description that adds so much that yeah. I, I, I, I don't know. How we continue to get that, that experience circulating and get that in front of people. But I think it's really important. Matt Trask: Well, I must wonder too, like how many of those, like experienced API builders are getting swallowed up into Stripe? Twilio, Google. And kind of almost locked away working on their API APIs and not able to share their experiences down the road to junior developers in their own companies or interim networks, things like that too, because it feels like you do your five, seven years as developer, you get pulled into the management game and then all of your knowledge is still there, but you're having to balance both managing a development team, hitting your goals. Pushing out products because you've got to make money for the business. And all of your knowledge that you've worked so hard to gain is kind of sidelined in the name of profits or KPIs or whatever it might be. Matthew Reinbold: Possibly there's, there's certainly exceptions that spring to mind. One of which is Tim Burks and the team over at Google and with the number of resources that they put out there. For their APIs. It's, it's kind of a mouthful, but if you do a Google search for that, they've produced a tremendous amount of documentation about how they support API APIs at scale, how they do their design reviews, how they think about consistency and cohesion across their entire footprint. So that certainly what you described could be the case in some places. You know, I, I, I do think that it's not necessarily the default that's people go off to these big organizations and then just disappear because the folks at Google around Tim and his crew they're doing some great work. Phil Sturgeon: So I've been sat in the room with you having these sort of conversations your last job, Right, Like a center of excellence type stuff. You, you get a bunch of smart people and me together and start talking about what, what would help with these various different problems? Like how do we do APA design reviews? How do we do governance? What standards should we be interested in? So I think sometimes yeah. Experienced developers can get sucked up into these companies and kind of finish and end up having that scale was used for something else. But I, I think companies that have those governance processes, like they're sharing their experience back by creating style guides, by creating programs that they explain how these, how these like API designed life cycles or API life cycle should work. And that's a way that they can essentially. Distribute their experience. So instead of like, I know what to look for when I'm reviewing a poor request, they can create a style guide. That means that everyone will do that. I think the danger there is that when style goes focus on what, instead of why then, then you kind of lose some of that experience because it just seems like arbitrary decisions delivered from upon high. Right. You just get. Do it this way, but, but Y I've read loads of style guides recently. And, and some of them, I should probably show the examples. It's just like, do this. Like, why you don't tell me what to do? You don't my dad, like, it just, I couldn't figure out what they possibly could have meant by it. Cause usually I can look at something. Why might they mean that? Oh, that reminds me of a thing that happened along these lines. They probably got burned by that before, and they want to avoid it, but if you don't see why it just sounds arbitrary and you're not actually teaching anyone on anything, but if you do it right. that that can be really helpful. Matthew Reinbold: Right. And it's also essential that if you're designing these systems like a governance or like a center of excellence that you have the feedback process that you have, the, the communication cycles so that when people do have that kind of. That they have a recourse. It's not a dead end. It's not either you do this or you're punished for it, but oh, if this doesn't make sense, here's who you talk to. Here's how you can escalate your concern here is how you elevate your edge case. And we can have a discussion about it and you can help co-evolve this thing, because you own this as much as somebody else, the, the phenomenon that you described, where it's a dead end. It's thrust upon you. You don't have ownership of that. And as a developer, that does not feel good, that does not invest you in seeing the long-term growth of, of that system. You want to burn that system. You want to be the rebels flying through the death star trench. You want to take that thing down? So what's essential is to realize. You provide the avenues for people to, to voice their concerns, voice their questions, and make them feel heard in such a way that their process, the process is theirs. It's not something done to them. It's it's their process. Phil Sturgeon: I'm just laughing about the death star rebel situation. Now I'm completely distracted. I need to go rewatch some star wars. I don't know. Matt Trask: I mean, your, your thought on the ownership thing is also interesting cause And we like watching the junior Twitter, the junior developer Twitter circles, which is not the end all be all of it all, but there is a large emphasis on if you want to make more money, you need to jump ship every two years on average. And that kind of removes the does or not the desire, but like the, the ownership of any sort of product from a junior developer, because in two years, they're going to be onto another thing. They're going to be onto another system. Codebase, maybe another language and it, it does kind of bring back, like, how do you entice people to have ownership, even if they only are going to plan to say somewhere for a short period? Because we all know that like having, like you said, having that ownership is going to kind of make you more invested, more caring, more thoughtful, more empathetic towards whatever it is that you're building. Matthew Reinbold: Right. I mean, we're veering into management territory, which I'm happy to talk about. I, I know. Matt Trask: very allergic to management. So. Matthew Reinbold: But I, I was just reading Harvard business review. Hey, I'm fun at parties too. So I was reading Harvard business review talking about COVID and the great resignation and the, the management challenges that, that come with that and what we need more. In all companies is a feeling of belonging, a feeling like we have a career progression feeling like our, our, our work has impact and all too often management, just as about making sure people don't do dumpster. Right. You know, I'm, I'm here to police you because the organization doesn't trust you. And it leads to all kinds of weird effects. Like, Hey, if you actually want to grow your career, you need to leave. You need to hop companies every two years and let's be clear that may work, but it's still very disruptive, not just for the company, but for the individual. 'cause they're having to rebuild all of those social structures, their relationships, their patterns, the routines it, it's not, it doesn't come for free. And so from a management standpoint, if you can show people how to have that fulfilling career, how to fulfill those needs. They don't have to jump ship every two years. There's no reason that that has to be the default blueprint. And from a company standpoint, you actually benefit from that accrued experience rather than having a developer. That's done the same thing. Five times you get five years of experience. That's really powerful, really tremendous. And that, that ultimately not only leads to better APIs, but leads to a better employee. So there is a disconnect we need to work with our management layers. It shouldn't just be the technician that has some headcount is by default manager. There needs to be an appreciation for how those are unique skill sets. Those are unique muscles that need to be exercised, but. If we can create that fulfilling sense of duty then, and that the career path for these individuals, we can get them off of this kind of binge and purge career treadmill. Matt Trask: So that's a really, yeah, that's a really good way to put the whole two year turn. And I mean, it comes back full circle to what you just said earlier, which is, you know, 75% of API has been developed now or done by people with less than five years experience. And that's probably because of the same, people are jumping, jumping, jumping. Whereas if you can keep them around, make them happy, make them feel like they belong. We might actually start seeing that number. Dropped significantly to more experienced API developers building more thoughtful API design with, with years of knowledge built up. So I think it'll be really interesting to see kind of what happens with this great resignation how that all shapes up. And then it'll be interesting to see to kind of the 2022 say the API report. How does that. How, how will things change from a year in a year going forward? And what can we expect possibly looking at these two years, the next five years after that, the next 10 years growing on different trends, you know, we might see NFTs ruling the world. We might see graph QL. Rolling. Phil Sturgeon: No comment. Matt Trask: Matthew is kind of shrugging Phil Sturgeon: we're all sad. Now, rural sat now, NFTs powered by graft UL, problem solved. Can you, can you still right click that? No, you can't. It's like a post. So. Matt Trask: Well, there goes Matthew Reinbold: Each unique query is published as an innovator. And you can put the ownership of that query in a blockchain so that you don't have the centralized point of failure. Phil Sturgeon: I was going to thank you for being for, for making this podcast sound intelligent for once. And, Matthew Reinbold: And then I ruined it. Sorry. Phil Sturgeon: and then you. Matt Trask: no, no, no, you didn't ruin it. You just brought it back down to its normal level of ridiculousness. Phil Sturgeon: Fantastic. No. Do you have any predictions for what we're going to see in the, in next year's state of this report? Because then we can play that clip back and laugh at how wrong you were. Matthew Reinbold: Oh, lovely. All right, well, let me have a few minutes to sandbag my answer. No, I think there's a tremendous amount of, of areas where we can take this correlation that I talked about before behaviors. You know how the question immediately becomes well, okay. If these four behaviors are so good and are present in high-performing API companies, how do we get there? And this year we had a little bit around leadership and what leaders do. To get an API first company. I think there is a lot of exploration we can do there to really dial in and say, okay, we know these things are good. How do you get there? How do you promote these things? How do you, how do you get it so that you are able to deploy in a minimal amount of time or recover faster? What are leaders in those organizations doing? That's one of the things I'd love to dig into obviously. A lot of post pandemic aftermath. There's been a tremendous amount of published about how this digital transformation and, you know, we're so much more flexible and adaptable because we, we are now doing all our conversations over zoom. And I look at that and I, I scratch my head because. Digital transformation, at least in the non buzzword compliant way is a whole lot more difficult than just moving everything to a slack conversation or a, or a zoom conversation. Like it means fundamentally dismantling your policies and procedures and reinventing them in a way that digital technology lends itself to. So figuring out what that post pandemic landscape looks like and how we're still feeling the knock on effect. Is going to be something that's also going to be very interesting to explore. Matt Trask: Yeah, that's definitely true. I mean, I think one thing I would like to see is, is that number of people who know open API, but don't use it start to gradually shift down and people who are using open. Start to shift up, which, you know, from a silver right back to having documentation and some sort of notes about their API. So when the, the knowledge people do eventually leave because everyone leaves the company at some point, the knowledge isn't necessarily leaving. And instead we're, we're kind of leaving a better legacy to the people following us. Yeah, definitely. Matthew Reinbold: Here here. Matt Trask: Cool. Matthew, thank you so much for taking some time out of your, your, your day to talk to us. We really appreciate it. Look forward to having you back in roughly a year's time to talk 20, 22. Say the API report Matthew Reinbold: I love it. Let's do it. Pencil it in right now. Matt Trask: Yep. It's it's on my calendar. I don't know what I'll be doing in a year from today, but I know for a fact we'll be talking again. If you want to get. Matthew on Twitter. He is at libel Vox, L I B E L underscore V O X M. And we'll throw the link to your blog and Twitter in the show notes as well as everything else. Awesome. Cool. Thank you so much. We appreciate it. Phil Sturgeon: Yeah. All audio, artwork, episode descriptions and notes are property of APIs You Won't Hate, for APIs You Won't Hate, and published with permission by Transistor, Inc. Broadcast by | 2026-01-13T08:48:40 |
https://docs.python.org/3/library/fractions.html#fractions.Fraction | fractions — Rational numbers — Python 3.14.2 documentation Theme Auto Light Dark Previous topic decimal — Decimal fixed-point and floating-point arithmetic Next topic random — Generate pseudo-random numbers This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Standard Library » Numeric and Mathematical Modules » fractions — Rational numbers | Theme Auto Light Dark | fractions — Rational numbers ¶ Source code: Lib/fractions.py The fractions module provides support for rational number arithmetic. A Fraction instance can be constructed from a pair of rational numbers, from a single number, or from a string. class fractions. Fraction ( numerator = 0 , denominator = 1 ) ¶ class fractions. Fraction ( number ) class fractions. Fraction ( string ) The first version requires that numerator and denominator are instances of numbers.Rational and returns a new Fraction instance with a value equal to numerator/denominator . If denominator is zero, it raises a ZeroDivisionError . The second version requires that number is an instance of numbers.Rational or has the as_integer_ratio() method (this includes float and decimal.Decimal ). It returns a Fraction instance with exactly the same value. Assumed, that the as_integer_ratio() method returns a pair of coprime integers and last one is positive. Note that due to the usual issues with binary point (see Floating-Point Arithmetic: Issues and Limitations ), the argument to Fraction(1.1) is not exactly equal to 11/10, and so Fraction(1.1) does not return Fraction(11, 10) as one might expect. (But see the documentation for the limit_denominator() method below.) The last version of the constructor expects a string. The usual form for this instance is: [ sign ] numerator [ '/' denominator ] where the optional sign may be either ‘+’ or ‘-’ and numerator and denominator (if present) are strings of decimal digits (underscores may be used to delimit digits as with integral literals in code). In addition, any string that represents a finite value and is accepted by the float constructor is also accepted by the Fraction constructor. In either form the input string may also have leading and/or trailing whitespace. Here are some examples: >>> from fractions import Fraction >>> Fraction ( 16 , - 10 ) Fraction(-8, 5) >>> Fraction ( 123 ) Fraction(123, 1) >>> Fraction () Fraction(0, 1) >>> Fraction ( '3/7' ) Fraction(3, 7) >>> Fraction ( ' -3/7 ' ) Fraction(-3, 7) >>> Fraction ( '1.414213 \t\n ' ) Fraction(1414213, 1000000) >>> Fraction ( '-.125' ) Fraction(-1, 8) >>> Fraction ( '7e-6' ) Fraction(7, 1000000) >>> Fraction ( 2.25 ) Fraction(9, 4) >>> Fraction ( 1.1 ) Fraction(2476979795053773, 2251799813685248) >>> from decimal import Decimal >>> Fraction ( Decimal ( '1.1' )) Fraction(11, 10) The Fraction class inherits from the abstract base class numbers.Rational , and implements all of the methods and operations from that class. Fraction instances are hashable , and should be treated as immutable. In addition, Fraction has the following properties and methods: Changed in version 3.2: The Fraction constructor now accepts float and decimal.Decimal instances. Changed in version 3.9: The math.gcd() function is now used to normalize the numerator and denominator . math.gcd() always returns an int type. Previously, the GCD type depended on numerator and denominator . Changed in version 3.11: Underscores are now permitted when creating a Fraction instance from a string, following PEP 515 rules. Changed in version 3.11: Fraction implements __int__ now to satisfy typing.SupportsInt instance checks. Changed in version 3.12: Space is allowed around the slash for string inputs: Fraction('2 / 3') . Changed in version 3.12: Fraction instances now support float-style formatting, with presentation types "e" , "E" , "f" , "F" , "g" , "G" and "%"" . Changed in version 3.13: Formatting of Fraction instances without a presentation type now supports fill, alignment, sign handling, minimum width and grouping. Changed in version 3.14: The Fraction constructor now accepts any objects with the as_integer_ratio() method. numerator ¶ Numerator of the Fraction in lowest term. denominator ¶ Denominator of the Fraction in lowest terms. Guaranteed to be positive. as_integer_ratio ( ) ¶ Return a tuple of two integers, whose ratio is equal to the original Fraction. The ratio is in lowest terms and has a positive denominator. Added in version 3.8. is_integer ( ) ¶ Return True if the Fraction is an integer. Added in version 3.12. classmethod from_float ( f ) ¶ Alternative constructor which only accepts instances of float or numbers.Integral . Beware that Fraction.from_float(0.3) is not the same value as Fraction(3, 10) . Note From Python 3.2 onwards, you can also construct a Fraction instance directly from a float . classmethod from_decimal ( dec ) ¶ Alternative constructor which only accepts instances of decimal.Decimal or numbers.Integral . Note From Python 3.2 onwards, you can also construct a Fraction instance directly from a decimal.Decimal instance. classmethod from_number ( number ) ¶ Alternative constructor which only accepts instances of numbers.Integral , numbers.Rational , float or decimal.Decimal , and objects with the as_integer_ratio() method, but not strings. Added in version 3.14. limit_denominator ( max_denominator = 1000000 ) ¶ Finds and returns the closest Fraction to self that has denominator at most max_denominator. This method is useful for finding rational approximations to a given floating-point number: >>> from fractions import Fraction >>> Fraction ( '3.1415926535897932' ) . limit_denominator ( 1000 ) Fraction(355, 113) or for recovering a rational number that’s represented as a float: >>> from math import pi , cos >>> Fraction ( cos ( pi / 3 )) Fraction(4503599627370497, 9007199254740992) >>> Fraction ( cos ( pi / 3 )) . limit_denominator () Fraction(1, 2) >>> Fraction ( 1.1 ) . limit_denominator () Fraction(11, 10) __floor__ ( ) ¶ Returns the greatest int <= self . This method can also be accessed through the math.floor() function: >>> from math import floor >>> floor ( Fraction ( 355 , 113 )) 3 __ceil__ ( ) ¶ Returns the least int >= self . This method can also be accessed through the math.ceil() function. __round__ ( ) ¶ __round__ ( ndigits ) The first version returns the nearest int to self , rounding half to even. The second version rounds self to the nearest multiple of Fraction(1, 10**ndigits) (logically, if ndigits is negative), again rounding half toward even. This method can also be accessed through the round() function. __format__ ( format_spec , / ) ¶ Provides support for formatting of Fraction instances via the str.format() method, the format() built-in function, or Formatted string literals . If the format_spec format specification string does not end with one of the presentation types 'e' , 'E' , 'f' , 'F' , 'g' , 'G' or '%' then formatting follows the general rules for fill, alignment, sign handling, minimum width, and grouping as described in the format specification mini-language . The “alternate form” flag '#' is supported: if present, it forces the output string to always include an explicit denominator, even when the value being formatted is an exact integer. The zero-fill flag '0' is not supported. If the format_spec format specification string ends with one of the presentation types 'e' , 'E' , 'f' , 'F' , 'g' , 'G' or '%' then formatting follows the rules outlined for the float type in the Format Specification Mini-Language section. Here are some examples: >>> from fractions import Fraction >>> format ( Fraction ( 103993 , 33102 ), '_' ) '103_993/33_102' >>> format ( Fraction ( 1 , 7 ), '.^+10' ) '...+1/7...' >>> format ( Fraction ( 3 , 1 ), '' ) '3' >>> format ( Fraction ( 3 , 1 ), '#' ) '3/1' >>> format ( Fraction ( 1 , 7 ), '.40g' ) '0.1428571428571428571428571428571428571429' >>> format ( Fraction ( '1234567.855' ), '_.2f' ) '1_234_567.86' >>> f " { Fraction ( 355 , 113 ) : *>20.6e } " '********3.141593e+00' >>> old_price , new_price = 499 , 672 >>> " {:.2%} price increase" . format ( Fraction ( new_price , old_price ) - 1 ) '34.67% price increase' See also Module numbers The abstract base classes making up the numeric tower. Previous topic decimal — Decimal fixed-point and floating-point arithmetic Next topic random — Generate pseudo-random numbers This page Report a bug Show source « Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Standard Library » Numeric and Mathematical Modules » fractions — Rational numbers | Theme Auto Light Dark | © Copyright 2001 Python Software Foundation. This page is licensed under the Python Software Foundation License Version 2. Examples, recipes, and other code in the documentation are additionally licensed under the Zero Clause BSD License. See History and License for more information. The Python Software Foundation is a non-profit corporation. Please donate. Last updated on Jan 13, 2026 (06:19 UTC). Found a bug ? Created using Sphinx 8.2.3. | 2026-01-13T08:48:40 |
https://docs.python.org/3/tutorial/introduction.html#text | 3. An Informal Introduction to Python — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents 3. An Informal Introduction to Python 3.1. Using Python as a Calculator 3.1.1. Numbers 3.1.2. Text 3.1.3. Lists 3.2. First Steps Towards Programming Previous topic 2. Using the Python Interpreter Next topic 4. More Control Flow Tools This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 3. An Informal Introduction to Python | Theme Auto Light Dark | 3. An Informal Introduction to Python ¶ In the following examples, input and output are distinguished by the presence or absence of prompts ( >>> and … ): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you must type a blank line; this is used to end a multi-line command. You can use the “Copy” button (it appears in the upper-right corner when hovering over or tapping a code example), which strips prompts and omits output, to copy and paste the input lines into your interpreter. Many of the examples in this manual, even those entered at the interactive prompt, include comments. Comments in Python start with the hash character, # , and extend to the end of the physical line. A comment may appear at the start of a line or following whitespace or code, but not within a string literal. A hash character within a string literal is just a hash character. Since comments are to clarify code and are not interpreted by Python, they may be omitted when typing in examples. Some examples: # this is the first comment spam = 1 # and this is the second comment # ... and now a third! text = "# This is not a comment because it's inside quotes." 3.1. Using Python as a Calculator ¶ Let’s try some simple Python commands. Start the interpreter and wait for the primary prompt, >>> . (It shouldn’t take long.) 3.1.1. Numbers ¶ The interpreter acts as a simple calculator: you can type an expression into it and it will write the value. Expression syntax is straightforward: the operators + , - , * and / can be used to perform arithmetic; parentheses ( () ) can be used for grouping. For example: >>> 2 + 2 4 >>> 50 - 5 * 6 20 >>> ( 50 - 5 * 6 ) / 4 5.0 >>> 8 / 5 # division always returns a floating-point number 1.6 The integer numbers (e.g. 2 , 4 , 20 ) have type int , the ones with a fractional part (e.g. 5.0 , 1.6 ) have type float . We will see more about numeric types later in the tutorial. Division ( / ) always returns a float. To do floor division and get an integer result you can use the // operator; to calculate the remainder you can use % : >>> 17 / 3 # classic division returns a float 5.666666666666667 >>> >>> 17 // 3 # floor division discards the fractional part 5 >>> 17 % 3 # the % operator returns the remainder of the division 2 >>> 5 * 3 + 2 # floored quotient * divisor + remainder 17 With Python, it is possible to use the ** operator to calculate powers [ 1 ] : >>> 5 ** 2 # 5 squared 25 >>> 2 ** 7 # 2 to the power of 7 128 The equal sign ( = ) is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt: >>> width = 20 >>> height = 5 * 9 >>> width * height 900 If a variable is not “defined” (assigned a value), trying to use it will give you an error: >>> n # try to access an undefined variable Traceback (most recent call last): File "<stdin>" , line 1 , in <module> NameError : name 'n' is not defined There is full support for floating point; operators with mixed type operands convert the integer operand to floating point: >>> 4 * 3.75 - 1 14.0 In interactive mode, the last printed expression is assigned to the variable _ . This means that when you are using Python as a desk calculator, it is somewhat easier to continue calculations, for example: >>> tax = 12.5 / 100 >>> price = 100.50 >>> price * tax 12.5625 >>> price + _ 113.0625 >>> round ( _ , 2 ) 113.06 This variable should be treated as read-only by the user. Don’t explicitly assign a value to it — you would create an independent local variable with the same name masking the built-in variable with its magic behavior. In addition to int and float , Python supports other types of numbers, such as Decimal and Fraction . Python also has built-in support for complex numbers , and uses the j or J suffix to indicate the imaginary part (e.g. 3+5j ). 3.1.2. Text ¶ Python can manipulate text (represented by type str , so-called “strings”) as well as numbers. This includes characters “ ! ”, words “ rabbit ”, names “ Paris ”, sentences “ Got your back. ”, etc. “ Yay! :) ”. They can be enclosed in single quotes ( '...' ) or double quotes ( "..." ) with the same result [ 2 ] . >>> 'spam eggs' # single quotes 'spam eggs' >>> "Paris rabbit got your back :)! Yay!" # double quotes 'Paris rabbit got your back :)! Yay!' >>> '1975' # digits and numerals enclosed in quotes are also strings '1975' To quote a quote, we need to “escape” it, by preceding it with \ . Alternatively, we can use the other type of quotation marks: >>> 'doesn \' t' # use \' to escape the single quote... "doesn't" >>> "doesn't" # ...or use double quotes instead "doesn't" >>> '"Yes," they said.' '"Yes," they said.' >>> " \" Yes, \" they said." '"Yes," they said.' >>> '"Isn \' t," they said.' '"Isn\'t," they said.' In the Python shell, the string definition and output string can look different. The print() function produces a more readable output, by omitting the enclosing quotes and by printing escaped and special characters: >>> s = 'First line. \n Second line.' # \n means newline >>> s # without print(), special characters are included in the string 'First line.\nSecond line.' >>> print ( s ) # with print(), special characters are interpreted, so \n produces new line First line. Second line. If you don’t want characters prefaced by \ to be interpreted as special characters, you can use raw strings by adding an r before the first quote: >>> print ( 'C:\some \n ame' ) # here \n means newline! C:\some ame >>> print ( r 'C:\some\name' ) # note the r before the quote C:\some\name There is one subtle aspect to raw strings: a raw string may not end in an odd number of \ characters; see the FAQ entry for more information and workarounds. String literals can span multiple lines. One way is using triple-quotes: """...""" or '''...''' . End-of-line characters are automatically included in the string, but it’s possible to prevent this by adding a \ at the end of the line. In the following example, the initial newline is not included: >>> print ( """ \ ... Usage: thingy [OPTIONS] ... -h Display this usage message ... -H hostname Hostname to connect to ... """ ) Usage: thingy [OPTIONS] -h Display this usage message -H hostname Hostname to connect to >>> Strings can be concatenated (glued together) with the + operator, and repeated with * : >>> # 3 times 'un', followed by 'ium' >>> 3 * 'un' + 'ium' 'unununium' Two or more string literals (i.e. the ones enclosed between quotes) next to each other are automatically concatenated. >>> 'Py' 'thon' 'Python' This feature is particularly useful when you want to break long strings: >>> text = ( 'Put several strings within parentheses ' ... 'to have them joined together.' ) >>> text 'Put several strings within parentheses to have them joined together.' This only works with two literals though, not with variables or expressions: >>> prefix = 'Py' >>> prefix 'thon' # can't concatenate a variable and a string literal File "<stdin>" , line 1 prefix 'thon' ^^^^^^ SyntaxError : invalid syntax >>> ( 'un' * 3 ) 'ium' File "<stdin>" , line 1 ( 'un' * 3 ) 'ium' ^^^^^ SyntaxError : invalid syntax If you want to concatenate variables or a variable and a literal, use + : >>> prefix + 'thon' 'Python' Strings can be indexed (subscripted), with the first character having index 0. There is no separate character type; a character is simply a string of size one: >>> word = 'Python' >>> word [ 0 ] # character in position 0 'P' >>> word [ 5 ] # character in position 5 'n' Indices may also be negative numbers, to start counting from the right: >>> word [ - 1 ] # last character 'n' >>> word [ - 2 ] # second-last character 'o' >>> word [ - 6 ] 'P' Note that since -0 is the same as 0, negative indices start from -1. In addition to indexing, slicing is also supported. While indexing is used to obtain individual characters, slicing allows you to obtain a substring: >>> word [ 0 : 2 ] # characters from position 0 (included) to 2 (excluded) 'Py' >>> word [ 2 : 5 ] # characters from position 2 (included) to 5 (excluded) 'tho' Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced. >>> word [: 2 ] # character from the beginning to position 2 (excluded) 'Py' >>> word [ 4 :] # characters from position 4 (included) to the end 'on' >>> word [ - 2 :] # characters from the second-last (included) to the end 'on' Note how the start is always included, and the end always excluded. This makes sure that s[:i] + s[i:] is always equal to s : >>> word [: 2 ] + word [ 2 :] 'Python' >>> word [: 4 ] + word [ 4 :] 'Python' One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n , for example: +---+---+---+---+---+---+ | P | y | t | h | o | n | +---+---+---+---+---+---+ 0 1 2 3 4 5 6 - 6 - 5 - 4 - 3 - 2 - 1 The first row of numbers gives the position of the indices 0…6 in the string; the second row gives the corresponding negative indices. The slice from i to j consists of all characters between the edges labeled i and j , respectively. For non-negative indices, the length of a slice is the difference of the indices, if both are within bounds. For example, the length of word[1:3] is 2. Attempting to use an index that is too large will result in an error: >>> word [ 42 ] # the word only has 6 characters Traceback (most recent call last): File "<stdin>" , line 1 , in <module> IndexError : string index out of range However, out of range slice indexes are handled gracefully when used for slicing: >>> word [ 4 : 42 ] 'on' >>> word [ 42 :] '' Python strings cannot be changed — they are immutable . Therefore, assigning to an indexed position in the string results in an error: >>> word [ 0 ] = 'J' Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : 'str' object does not support item assignment >>> word [ 2 :] = 'py' Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : 'str' object does not support item assignment If you need a different string, you should create a new one: >>> 'J' + word [ 1 :] 'Jython' >>> word [: 2 ] + 'py' 'Pypy' The built-in function len() returns the length of a string: >>> s = 'supercalifragilisticexpialidocious' >>> len ( s ) 34 See also Text Sequence Type — str Strings are examples of sequence types , and support the common operations supported by such types. String Methods Strings support a large number of methods for basic transformations and searching. f-strings String literals that have embedded expressions. Format String Syntax Information about string formatting with str.format() . printf-style String Formatting The old formatting operations invoked when strings are the left operand of the % operator are described in more detail here. 3.1.3. Lists ¶ Python knows a number of compound data types, used to group together other values. The most versatile is the list , which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually the items all have the same type. >>> squares = [ 1 , 4 , 9 , 16 , 25 ] >>> squares [1, 4, 9, 16, 25] Like strings (and all other built-in sequence types), lists can be indexed and sliced: >>> squares [ 0 ] # indexing returns the item 1 >>> squares [ - 1 ] 25 >>> squares [ - 3 :] # slicing returns a new list [9, 16, 25] Lists also support operations like concatenation: >>> squares + [ 36 , 49 , 64 , 81 , 100 ] [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] Unlike strings, which are immutable , lists are a mutable type, i.e. it is possible to change their content: >>> cubes = [ 1 , 8 , 27 , 65 , 125 ] # something's wrong here >>> 4 ** 3 # the cube of 4 is 64, not 65! 64 >>> cubes [ 3 ] = 64 # replace the wrong value >>> cubes [1, 8, 27, 64, 125] You can also add new items at the end of the list, by using the list.append() method (we will see more about methods later): >>> cubes . append ( 216 ) # add the cube of 6 >>> cubes . append ( 7 ** 3 ) # and the cube of 7 >>> cubes [1, 8, 27, 64, 125, 216, 343] Simple assignment in Python never copies data. When you assign a list to a variable, the variable refers to the existing list . Any changes you make to the list through one variable will be seen through all other variables that refer to it.: >>> rgb = [ "Red" , "Green" , "Blue" ] >>> rgba = rgb >>> id ( rgb ) == id ( rgba ) # they reference the same object True >>> rgba . append ( "Alph" ) >>> rgb ["Red", "Green", "Blue", "Alph"] All slice operations return a new list containing the requested elements. This means that the following slice returns a shallow copy of the list: >>> correct_rgba = rgba [:] >>> correct_rgba [ - 1 ] = "Alpha" >>> correct_rgba ["Red", "Green", "Blue", "Alpha"] >>> rgba ["Red", "Green", "Blue", "Alph"] Assignment to slices is also possible, and this can even change the size of the list or clear it entirely: >>> letters = [ 'a' , 'b' , 'c' , 'd' , 'e' , 'f' , 'g' ] >>> letters ['a', 'b', 'c', 'd', 'e', 'f', 'g'] >>> # replace some values >>> letters [ 2 : 5 ] = [ 'C' , 'D' , 'E' ] >>> letters ['a', 'b', 'C', 'D', 'E', 'f', 'g'] >>> # now remove them >>> letters [ 2 : 5 ] = [] >>> letters ['a', 'b', 'f', 'g'] >>> # clear the list by replacing all the elements with an empty list >>> letters [:] = [] >>> letters [] The built-in function len() also applies to lists: >>> letters = [ 'a' , 'b' , 'c' , 'd' ] >>> len ( letters ) 4 It is possible to nest lists (create lists containing other lists), for example: >>> a = [ 'a' , 'b' , 'c' ] >>> n = [ 1 , 2 , 3 ] >>> x = [ a , n ] >>> x [['a', 'b', 'c'], [1, 2, 3]] >>> x [ 0 ] ['a', 'b', 'c'] >>> x [ 0 ][ 1 ] 'b' 3.2. First Steps Towards Programming ¶ Of course, we can use Python for more complicated tasks than adding two and two together. For instance, we can write an initial sub-sequence of the Fibonacci series as follows: >>> # Fibonacci series: >>> # the sum of two elements defines the next >>> a , b = 0 , 1 >>> while a < 10 : ... print ( a ) ... a , b = b , a + b ... 0 1 1 2 3 5 8 This example introduces several new features. The first line contains a multiple assignment : the variables a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. The right-hand side expressions are evaluated from the left to the right. The while loop executes as long as the condition (here: a < 10 ) remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact any sequence; anything with a non-zero length is true, empty sequences are false. The test used in the example is a simple comparison. The standard comparison operators are written the same as in C: < (less than), > (greater than), == (equal to), <= (less than or equal to), >= (greater than or equal to) and != (not equal to). The body of the loop is indented : indentation is Python’s way of grouping statements. At the interactive prompt, you have to type a tab or space(s) for each indented line. In practice you will prepare more complicated input for Python with a text editor; all decent text editors have an auto-indent facility. When a compound statement is entered interactively, it must be followed by a blank line to indicate completion (since the parser cannot guess when you have typed the last line). Note that each line within a basic block must be indented by the same amount. The print() function writes the value of the argument(s) it is given. It differs from just writing the expression you want to write (as we did earlier in the calculator examples) in the way it handles multiple arguments, floating-point quantities, and strings. Strings are printed without quotes, and a space is inserted between items, so you can format things nicely, like this: >>> i = 256 * 256 >>> print ( 'The value of i is' , i ) The value of i is 65536 The keyword argument end can be used to avoid the newline after the output, or end the output with a different string: >>> a , b = 0 , 1 >>> while a < 1000 : ... print ( a , end = ',' ) ... a , b = b , a + b ... 0,1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987, Footnotes [ 1 ] Since ** has higher precedence than - , -3**2 will be interpreted as -(3**2) and thus result in -9 . To avoid this and get 9 , you can use (-3)**2 . [ 2 ] Unlike other languages, special characters such as \n have the same meaning with both single ( '...' ) and double ( "..." ) quotes. The only difference between the two is that within single quotes you don’t need to escape " (but you have to escape \' ) and vice versa. Table of Contents 3. An Informal Introduction to Python 3.1. Using Python as a Calculator 3.1.1. Numbers 3.1.2. Text 3.1.3. Lists 3.2. First Steps Towards Programming Previous topic 2. Using the Python Interpreter Next topic 4. More Control Flow Tools This page Report a bug Show source « Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 3. An Informal Introduction to Python | Theme Auto Light Dark | © Copyright 2001 Python Software Foundation. This page is licensed under the Python Software Foundation License Version 2. Examples, recipes, and other code in the documentation are additionally licensed under the Zero Clause BSD License. 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X는 영어 원문을 최대한 정확하게 번역하기 위해 노력하고 있습니다. 그러나 차이 또는 불일치가 있는 경우 영문 버전의 X 구매자 이용약관이 우선합니다. 귀하는 영어가 X 구매자 이용약관을 해석하고 구성하기 위한 참조 언어임을 인정합니다. 2. 유료 서비스의 변경. 당사의 유료 서비스는 지속적으로 진화합니다. 따라서 유료 서비스는 사업적, 재정적 또는 법적 이유를 포함하여 당사의 재량에 따라 수시로 변경될 수 있습니다. 당사는 귀하 또는 제반 사용자에 대한 유료 서비스 또는 유료 서비스에 포함된 특정 기능의 제공을 통지와 함께, 또는 통지 없이 (영구적 또는 일시적으로) 중단할 수 있습니다 X는 유료 서비스의 수정, 중단 또는 중지에 대해 귀하 또는 제3자에게 책임지지 않습니다. 특정 유료 서비스에 대한 특정 약관(아래 포함됨)은 귀하가 구독을 취소하거나 해당되는 경우 환불을 요청하는 방법을 명시하고 있습니다. 3. 가격 변경. X는 사업적, 재정적 또는 법적 이유 등으로 정기 구독료를 포함한 유료 서비스의 가격을 수시로 변경할 수 있습니다. X는 유료 서비스에 대한 주요 변경에 대해 적절한 사전 통지를 제공할 것입니다. 구독 서비스와 관련하여, 가격 변경은 해당 가격 변경일 이후 다음 구독기간이 시작되는 시점부터 유효합니다. 가격 변경에 동의하지 않는 경우, 귀하는 해당 가격 변경이 효력을 발휘하기 전에 해당 유료 서비스에 대한 구독을 취소함으로써 해당 변경을 거부할 권리가 있습니다. 결제 조건 . X는 유료 서비스, 귀하의 기기 및/또는 운영 체제, 귀하의 지리적 위치 또는 기타 요인에 따라 달라질 수 있는 다양한 결제 옵션을 제공합니다. 이러한 결제 선택권이 제공되는 경우(X는 수시로 다양한 구매 방식을 제공할 수 있으므로), 여기에는 Google 또는 Apple에 의해 제공되는 "인앱 결제" 기능을 이용하거나 또는 X의 제3자 결제 처리업체인 Stripe( www.stripe.com - 이하 “ Stripe ”)를 통해 웹 결제를 실행할 수 있는 기능이 포함될 수 있습니다. 결제를 수행할 때 귀하는 다음 사항에 명시적으로 동의하는 것입니다: (i) 해당 유료 서비스에 대해 명시된 가격, 그리고 관련 세금, 신용카드 수수료, 은행 수수료, 외환 거래 수수료, 환전 수수료, 환율 변동과 관련된 추가 금액을 지급할 것, 그리고 (ii) 해당 결제 방법에 대한 귀하의 활용과 관련하여 Google, Apple, 또는 Stripe(X의 제3자 결제 처리업체로서)에 의해 적용되는 관련 이용약관, 개인정보 처리방침, 또는 기타 법적 계약 또는 제한(추가적인 연령 제한 포함)을 준수할 것(단순히 예를 들자면, 귀하가 Apple의 인앱 구매 기능을 통해 결제를 수행하기로 하는 경우 귀하는 Apple에 의해 부과되는 모든 관련 조건, 요건 및/또는 제한을 준수하는 데 동의하는 것입니다). 유료 서비스에 대한 귀하의 이용과 관련하여 귀하가 제공하는 모든 개인정보는(결제와 관련하여 제공하는 모든 정보를 포함하나 이에 국한되지는 않음) X 개인정보 처리방침에 의거하여 처리될 것입니다. X는 다음 목적으로 귀하의 결제 정보를 결제서비스 제공업체와 공유할 수 있습니다: 결제의 처리; 사기 또는 기타 금지된 활동의 방지, 탐지, 조사; 지불 거절 또는 환불 등의 분쟁 해결 촉진; 및 신용카드 또는 직불카드의 승인과 관련된 기타 목적. 귀하의 은행, 신용카드, 직불카드 및/또는 기타 결제 정보가 항상 최신 상태이고 완전하며 정확한지 확인하는 것은 귀하의 책임입니다. 귀하가 유료 서비스를 결제하는 경우, 당사는 귀하의 거래에 대한 정보를 받을 수 있습니다(거래가 이루어진 시점, 특정 구독이 만료 또는 자동 갱신되는 시점, 귀하가 해당 구매를 실행한 플랫폼, 기타 정보 등). X는 결제 처리업체, Apple의 App Store 또는 Google Play Store, 귀하의 은행, 귀하의 신용카드 회사 및/또는 결제 네트워크에 의한 오류 또는 지연에 대해 책임이나 법적 책임을 지지 않습니다. 구독 갱신의 처리 방식 및 기타 중요 조건을 포함하여, 해당되는 특정 유료 서비스에 적용되는 결제 조건은 아래의 각 특정 유료 서비스 약관을 참고하시기 바랍니다. X 사용자 계약, 해지, 환불 불가, 복수 X 계정 및 제한의 적용 1. X 사용자 계약이 귀하에게 적용됩니다 . 귀하는 항상 X 사용자 계약을 따르고 준수해야 합니다. X 사용자 계약은 유료 서비스 및 기능을 포함하여 귀하의 X 서비스 이용에 상시 적용됩니다. 귀하가 X 사용자 계약을 따르고 준수하지 않거나 X에서 귀하가 X 사용자 계약을 따르고 준수하지 않았다고 판단하는 경우 귀하의 유료 서비스가 취소될 수 있습니다. 이러한 취소는 X 사용자 계약에 따라 X가 귀하에게 취할 수 있는 모든 집행 조치에 추가되며 이에 국한되지 않습니다. 이러한 경우 귀하는 유료 서비스의 혜택을 상실할 수 있으며 유료 서비스에 대해 결제(또는 선결제)한 금액을 환불받지 못합니다. 2. X가 유료 서비스에 대한 귀하의 액세스를 종료할 수 있는 사유. X는 아래의 사유를 포함하되 이에 국한되지 않는 어떠한 이유로든 또는 이유 없이 어떠한 책임도 지지 않으면서 언제든지 귀하의 유료 서비스에 대한 액세스를 정지 또는 종료하거나, 귀하의 계정을 정지하는 등 적절한 것으로 판단되는 기타 조치를 취할 수 있습니다. a. X는 자체 재량에 따라 귀하가 이용 약관을 위반했거나 귀하의 유료 서비스 사용이 해당 법률을 위반할 것이라고 판단하는 경우 b. X는 유능한 법원, 규제 기관 또는 법 집행 기관으로부터 그렇게 하라는 요청 또는 지시를 받는 경우 c. X에 예상치 못한 기술 문제 또는 보안 문제가 발생하는 경우 d.X 는 단독 재량에 따라 귀하가 X 사용자 계약을 위반했다고 생각합니다. 마. X는 단독 재량에 따라 귀하가 일반적으로 또는 유료 서비스와 관련하여 조작 또는 기타 방해가 되거나 금지된 행위에 관여하고 있다고 믿습니다. f. 귀하가 X에 위험을 초래하거나 법적 위험을 초래할 가능성이 있는 경우 g. 위법 행위로 인해 귀하의 계정을 삭제해야 하는 경우 h. 장기간 활동하지 않아 귀하의 계정을 삭제해야 하는 경우 i. 당사가 귀하에게 제공하는 유료 서비스(전부 또는 일부)는 더 이상 상업적으로 실행 가능하지 않습니다(X의 단독 재량에 따라). 3. 모든 거래는 최종적입니다. 유료 서비스에 대한 모든 결제는 최종적이며 관련 법률에서 요구하는 경우를 제외하고 환불 또는 교환이 불가능합니다. 당사는 유료 서비스의 성질, 품질 또는 가치 또는 그 가용성이나 공급에 대해 보증하지 않습니다. 이용하지 않거나 부분적으로 이용한 유료 서비스(예: 부분적으로 이용한 구독기간)에 대해서는 환불이나 크레딧이 제공되지 않습니다. 4. 유료 서비스는 X 계정 간에 양도할 수 없습니다. 각각의 유료 서비스 구매는 하나의 X 계정에 적용됩니다. 즉, 귀하가 유료 서비스를 구매할 때 사용한 계정에만 구매가 적용되며, 귀하가 액세스하거나 관리하는 다른 계정에는 적용되지 않습니다. 귀하가 여러 계정을 보유하거나 관리하면서 각각의 계정에서 유료 서비스에 액세스하려면 각 계정에서 개별적으로 유료 서비스를 구매해야 합니다. 5. 제한 및 의무. a. 귀하가 귀하의 국가에서 유료 서비스를 사용하는 것이 법적으로 허용되고 X가 해당 유료 서비스를 지원하는 국가에 거주하는 경우에만 유료 서비스를 구매하여 이용할 수 있습니다. X는 재량에 따라 특정 국가에서 유료 서비스의 액세스나 구매를 제한할 수 있습니다. X는 지원 국가 리스트를 수시로 변경할 수 있는 권리를 보유합니다. b. 당사는 단독 재량에 따라 유료 서비스 거래를 거절하거나 유료 서비스의 판매 또는 이용을 취소 또는 중단할 수 있는 권리를 보유합니다. c. 귀하는 유료 서비스를 주문하지 않은 사람으로 하여금 귀하의 X 계정을 사용하여 해당 유료 서비스에 액세스하도록 허용해서는 안 됩니다. d. 귀하가 미국 재무부 해외자산통제국 또는 기타 관련 제재 당국이 관리하는 제재를 포함하되 이에 국한되지 않는 경제 제재에 따라 미국인과 거래가 허용되지 않는 사람(" 금지 대상 ")인 경우, 유료 서비스를 구매하거나 사용할 수 없습니다. 여기에는 다음 국가 및 지역에 거주하거나 또는 통상적으로 거주하는 사람이 포함되나 이에 국한되지 않습니다: 쿠바, 이란, 우크라이나 크리미아 지역, 북한 및 시리아. 귀하는 귀하가 금지 대상이 아님을 진술하고 보증합니다. e. 귀하는 오로지 합법적인 목적으로 그리고 본 약관을 준수하며 유료 서비스를 이용할 것임을 진술합니다. 세금 및 수수료 . 귀하는 X 또는 제3자 결제 처리업체에 지불해야 하는 것을 포함하여 유료 서비스 구매와 관련된 모든 관련 세금, 관세 및 수수료를 지불할 책임이 있으며 이를 지불하기로 동의합니다. 이러한 세금에는 부가가치세(VAT), 상품서비스세(GST), 판매세, 원천징수세 및 기타 관련 세금이 포함되며 이에 국한되지 않습니다. 귀하의 위치에 따라 X가 귀하의 유료 서비스 구매로 인해 발생하는 거래세와 관련된 정보를 수집하고 신고할 책임이 있을 수 있습니다. 귀하는 X가 세금 징수 및 신고 의무를 이행하기 위해 관련 세무 당국에 귀하의 계정 및 개인 정보를 제공할 수 있도록 X에 권한을 부여합니다. 일반 규정 1. 연락처 정보. 유료 서비스나 본 약관에 대해 궁금한 사항이 있으면 X 유료 서비스 고객센터 에서 자세한 내용을 확인할 수 있습니다. 이미 유료 서비스를 구매한 경우, 결제 또는 구독 설정 아래 X 계정의 탐색 메뉴에 있는 지원 링크를 통해 문의할 수도 있습니다. 추가 질문이 있는 경우 여기 에서 "유료 기능에 관한 도움말" 양식을 사용하여 문의할 수 있습니다. 2. 면책사항. 귀하의 유료 서비스 액세스 및 이용은 관련 법률이 허용하는 최대 범위 내에서 귀하의 책임입니다. 귀하는 유료 서비스가 "있는 그대로" 그리고 "제공되는 상태 그대로" 귀하에게 제공된다는 점을 이해하고 이에 동의합니다. X는 판매적합성, 특정 목적 적합성 또는 비침해에 대한 명시적, 묵시적인 보증 및 조건 일체를 부인합니다. X는 (I) 유료 서비스의 완전성, 정확성, 가용성, 적시성, 보안 또는 신뢰성, (II) 유료 서비스가 귀하의 요구 사항을 충족할지 여부나 중단 없이, 안전하게, 또는 오류 없이 제공될지에 관한 보증이나 진술을 하지 않으며 이와 관련된 모든 책임 및 의무에 대해 면책됩니다. 유료 서비스를 포함한 귀하의 X 서비스의 이용과 귀하가 제공하는 모든 콘텐츠에 대한 책임은 귀하에게 있습니다. 3. 책임의 제한. 관련 법규에 저촉되지 않는 최대한도 내에서 X 당사자는 (i) 귀하의 유료 서비스에 대한 액세스나 사용 또는 액세스 불능이나 사용 불능, (ii) 유료 서비스를 통해 게시된 제3자의 모든 행위 또는 콘텐츠(다른 사용자 또는 제3자가 범한 명예 훼손, 모욕적 또는 불법 행위를 포함하되 이에 국한되지 않음), (iii) 유료 서비스에서 얻은 모든 콘텐츠, 또는 (iv) 귀하의 전송물 또는 콘텐츠에 대한 무단 액세스, 사용 또는 변경으로 인해 발생하는 간접적, 부수적, 특별, 결과적, 징벌적 손해 또는 수익 손실 또는 매출 손실(직접 또는 간접적으로 발생했는지 여부에 관계없음), 또는 데이터, 이용 권한 또는 영업권의 상실 내지는 기타 무형의 손실에 대해 어떠한 책임도 지지 않습니다. 의문의 여지가 없도록 하기 위해, 유료 서비스의 정의는 X에서 제공하는 기능으로 제한되며 이러한 기능을 이용하여 액세스 및/또는 상호 작용하는 콘텐츠는 포함하지 않습니다. 경우를 불문하고, X 당사자들이 부담하는 총 책임은 미화 100달러와 (해당하는 경우) 귀하가 해당 청구를 초래한 유료 서비스에 대하여 지난 6개월간 X에 지급한 금액 중 보다 큰 금액을 초과하지 않습니다. 본 호에 규정된 제한 사항은 보증, 계약, 법령, 불법 행위(과실 포함) 등을 근거로 하는지 여부 및 X 당사자들이 해당 손해가 발생할 가능성을 고지받았는지 여부에 관계없이, 그리고 본 약관에 명시된 구제 수단이 당해 구제 수단의 주요 목적을 달성하지 못한 것으로 확인될 경우에도 모든 근거의 책임에 적용됩니다. “X 당사자”란 X, 그 모회사, 계열사, 관계회사, 임원, 이사, 직원, 대리인, 대표, 파트너 및 라이선서를 지칭합니다. 귀하의 관할 지역의 관련 법규에 따라 특정 책임의 제한이 허용되지 않을 수 있습니다. 귀하의 관할권의 관련 법규가 요구하는 한도 내에서, 상기 내용은 당사의 과실, 중과실 및/또는 고의적 행위로 인한 사기, 사기성 허위 진술, 사망 또는 신체적 상해에 대한 X 당사자의 책임을 제한하지 않습니다. 관련 법규에 저촉되지 않는 최대한도 내에서, 제외 불가능한 보증에 대한 X 당사자의 최대 총 배상 책임은 미화 100달러(US $100.00)로 제한됩니다. 4. Apple에 대한 고지. 귀하가 iOS 기기에서 유료 서비스를 구매하거나 유료 서비스를 사용하거나 액세스하는 경우, 귀하는 본 섹션의 약관을 추가로 인정하고 동의합니다. 귀하는 본 약관이 Apple이 아닌 귀하와 당사 사이에만 적용되며, Apple은 유료 서비스 및 그 콘텐츠에 대해 책임지지 않음을 인정합니다. Apple은 유료 서비스와 관련하여 유지 관리 및 지원 서비스를 제공할 어떠한 의무도 없습니다. 유료 서비스가 해당 보증을 준수하지 못하는 경우 귀하는 Apple에 통지할 수 있으며 Apple은 해당되는 유료 서비스의 구매 비용을 귀하에게 환불할 것입니다. Apple은 관련 법률이 허용하는 최대 범위 내에서 유료 서비스와 관련하여 그 밖의 어떠한 보증 의무도 지지 않습니다. Apple은 (i) 제조물 책임 청구, (ii) 유료 서비스가 관련 법률상 또는 규제상 요건을 충족하지 못한다는 주장, (iii) 소비자 보호법 또는 이와 유사한 법률에 따라 발생하는 청구 등을 포함하되 이에 국한되지 않는 유료 서비스 또는 귀하의 유료 서비스 소유 및/또는 사용과 관련하여 귀하 또는 제3자의 청구를 처리할 책임이 없습니다. Apple은 유료 서비스 및/또는 귀하의 모바일 애플리케이션 소유 및 이용이 자신의 지적 재산권을 침해한다는 제3자의 주장에 대한 조사, 방어, 합의, 면책에 대해 책임을 지지 않습니다. 귀하는 유료 서비스를 사용할 때 적용 가능한 제3자 약관을 준수하는 데 동의합니다. Apple 및 Apple의 자회사는 본 약관의 제3자 수익자이며, 귀하가 약관을 수락하면 Apple은 약관의 제3자 수익자로서 귀하에 대해 약관을 집행할 권리를 갖게 됩니다(그리고 해당 권리를 수락한 것으로 간주됩니다). 귀하는 (i) 귀하가 미국 정부의 금수 조치가 적용되거나 미국 정부가 "테러리스트 지원" 국가로 지정한 국가에 위치해 있지 않으며, (ii) 귀하가 미국 정부의 금지 또는 제한 당사자 목록에 등재되어 있지 않다는 사실을 진술 및 보증합니다. 5. 충돌. 이 X 구매자 이용약관 조항과 X 사용자 계약 조항 사이에 충돌이 발생하는 경우, 이 X 구매자 이용약관 조항은 유료 서비스 사용과 관련해서만 우선 적용됩니다. 6. 분쟁 해결 및 집단 소송 포기 a. 최초 분쟁 해결 . 귀하와 X 간에 발생하는 대부분의 분쟁은 비공식적으로 해결할 수 있습니다. 여기 에서 유료 서비스 지원팀으로 메시지를 보내거나 이메일을 보내 문의하실 수 있습니다. Twitter에 문의할 때는 우려 사항의 성격과 근거, 연락처 정보 및 원하는 구체적인 구제책에 대한 간략한 설명을 제공해야 합니다. 양 당사자는 본 약관 및/또는 귀하의 프로그램 참여로 인해 발생하거나 이와 관련하여 발생하는 분쟁, 청구 또는 논란(개별적으로 " 분쟁 " 또는 둘 이상일 경우 " 분쟁들 ")을 해결하기 위해 이 지원 절차를 통해 최선을 다해야 합니다. 귀하와 당사는 이 비공식 절차에 성실하게 참여해야 하며, 긴급 금지명령구제(“ 면제된 분쟁 ”) 요청과 관련된 경우를 제외하고 어느 당사자가 분쟁에 관한 중재를 시작하기 전에 위에 명시된 대로 완료해야 한다는 데 동의합니다. 분쟁과 관련하여 위의 최초 분쟁 해결 조항에 따라 비공식 분쟁 해결이 시작된 날로부터 30일 이내에 당사가 귀하와 합의에 도달하지 못하는 경우(면제된 분쟁 제외), 귀하 또는 당사는 소송을 제기할 수 있습니다. 나. 법률 선택 및 포럼 선택 . 본 조항은 귀하가 법원에 소송을 제기할 권리를 포함하여 귀하의 법적 권리에 중대한 영향을 미칠 수 있으므로 본 조항을 숙독하여 주시기 바랍니다. 본 약관 및 귀하와 당사 간에 발생하는 모든 분쟁에는 귀하와 당사 간의 계약의 상반되는 조항이 있을지라도 텍사스주 법률이 적용됩니다. 본 약관으로 인해 발생하거나 이와 관련하여 발생한 모든 분쟁, 청구 또는 논란을 포함하여 본 약관과 관련된 모든 분쟁은 미국 텍사스 주 타란트 카운티에 위치한 연방 또는 주 법원에서 독점적으로 제기되며, 귀하는 해당 법원의 인적 관할권에 동의하며 불편한 법정에 대한 모든 이의를 포기합니다. 전술한 내용을 침해하지 않고, 귀하는 X가 자체 재량에 따라 당사가 귀하를 상대로 제기하는 모든 청구, 소송 원인 또는 분쟁을 귀하가 거주하는 국가의 관할 법원 중 해당 청구에 대한 관할권과 소송 장소를 가진 법원에 제기할 수 있다는 데 동의합니다. 귀하가 미국의 연방, 주 또는 지역 정부기관으로서 공식 권한을 갖추고 있으며 상기 명시한 준거법, 관할지 또는 법정지 조항에 법적으로 동의할 수 없는 경우, 해당 조항은 귀하에게 적용되지 않습니다. 이러한 미국 연방 정부기관의 경우, 본 계약 및 본 계약에 관한 소송 일체는 미합중국 법률에 따라 규율되며(법규 조항의 상충을 배제함), 연방법률에 해당 규정이 없는 경우에는 연방법률에 따라 허용되는 범위 내에서, 텍사스주 법률에 따라 규율됩니다(법규 선택을 배제함). c. 귀하가 X에 청구를 제기할 수 있는 기간은 2년입니다 . 관련 법규에서 해당 청구에 대한 통상적인 소멸시효를 합의에 의해 단축할 수 없다고 규정하지 않는 한, 귀하는 이 약관으로 인해 또는 이 약관과 관련해 X를 상대로 발생한 모든 청구는 분쟁을 야기하는 사건 또는 사실이 발생한 날로부터 2년 이내에 제기해야 합니다. 귀하가 이 기간 내에 청구를 제기하지 않을 경우, 귀하는 그러한 사건 또는 사실을 근거로 어떤 종류나 성격의 청구 또는 소송 원인을 추구할 권리를 영구적으로 포기하며, 그러한 청구 또는 소송 원인은 영구적으로 금지되고, X는 해당 청구와 관련하여 어떠한 책임도 지지 않습니다. d. 집단 소송 권리 포기 . 또한 법규에 저촉되지 않는 한도 내에서 귀하는 집단 소송, 집단 행동 또는 대리인 소송 절차에서 원고 또는 집단 구성원으로 참여할 권리도 포기합니다. X 구매자 이용약관 귀하가 유럽 연합, EFTA 회원국 또는 영국에 거주하는 경우 X는 사용자가 X의 특정 기능에 해당하는 일회성 또는 반복 요금을 지불하는 대가로 해당 기능을 이용할 수 있도록 합니다(각각을 “ 유료 서비스 ”, 통칭하여 " 유료 서비스 "라고 함). 예를 들어, X Premium(아래에 정의됨) 및 구독은 각각 "유료 서비스"로 간주됩니다. 귀하가 유료 서비스에 가입하거나 유료 서비스를 이용하는 경우, 귀하의 유료 서비스 사용 및 그에 따른 거래에는 (i) 귀하가 구매하는 각 유료 서비스의 해당 이용약관(각각 아래에 나열됨)을 포함하여 여기에 명시된 이용약관(“ X 구매자 이용약관 ”으로 통칭)과 (ii) 관련 X 이용약관 , X 개인정보 처리방침 , X 운영원칙 및 정책 , 그리고 이들 각각에 편입된 모든 정책(“ X 사용자 계약 ”으로 통칭)이 적용됩니다. 본 X 구매자 이용약관과 상기 X 사용자 계약은 본 문서에서 " 약관 "으로 통칭합니다. " X "는 귀하에게 유료 서비스를 제공하는 X 법인을 의미합니다. 본 X 구매자 이용약관을 주의 깊게 읽고 관련 조항, 조건 및 예외를 숙지하시기 바랍니다. 귀하가 유럽연합, EFTA 국가 또는 영국에 거주하는 경우, 본 약관에는 집단 소송으로 청구를 제기할 수 있는 권리의 포기 및 관련 사건이 발생한 후 1년 이상 경과한 후 X를 상대로 청구를 제기할 수 있는 귀하의 권리에 대한 제한을 포함하여 분쟁 해결과 관련하여 귀하에게 적용되는 중요한 정보가 포함되어 있습니다. X와 분쟁이 발생할 경우 귀하의 권리와 의무에 영향을 미칩니다. 이러한 규정에 관한 자세한 내용은 일반 이용약관의 제6항 을 참고하세요. 수락 . X의 유료 서비스를 이용 또는 이에 액세스하거나, 그에 따라 결제를 제출하거나 X가 제공하는 유료 서비스의 일회성 구매 또는 반복적 구독 결제를 위해 버튼을 클릭함으로써 귀하는 본 약관에 구속된다는 데 동의하는 것으로 간주됩니다. 약관을 이해하지 못하거나 약관 중 어느 부분이라도 수락하지 않는 경우, 유료 서비스를 이용하거나 이에 액세스할 수 없습니다. 유료 서비스를 구매하고 이용하려면 귀하가 (i) 만 18세 이상 또는 귀하가 거주하는 관할 지역의 법률상 성인이거나 (ii) 해당 유료 서비스 구매 및 이용에 대한 귀하의 부모 또는 보호자의 명시적인 동의가 있어야 합니다. 귀하가 부모 또는 법적 보호자이고 귀하의 자녀(또는 귀하가 보호자인 미성년자)가 유료 서비스를 구매하거나 이용하도록 허락하는 경우, 귀하는 본 약관이 귀하에게 적용되고 귀하가 본 약관을 준수할 것이며, 귀하가 해당 미성년자의 해당 유료 서비스 상의 행동에 대한 책임을 지며 및 해당 미성년자 역시 본 약관을 준수하게 할 책임이 귀하에게 있다는 것에 동의합니다. 어떤 경우든 X 이용약관 중 '서비스 이용 주체' 항에 명시된 바와 같이 X 서비스를 이용하려면 만 13세 이상이어야 합니다. 귀하가 회사, 조직, 정부 또는 다른 법인을 대신해서 본 X 구매자 이용약관에 동의하고 유료 서비스를 사용하는 경우 귀하는 해당 권한이 있고, 해당 권리주체에 X 구매자 이용약관의 준수 의무를 부여할 권한이 있음을 진술하고 보증합니다. 이 경우, 본 X 구매자 이용약관에서 사용된 “귀하” 및 “귀하의”라는 표현은 해당 권리주체를 의미합니다. X 계약 당사자 . 귀하는 아래 나열된 바와 같이, 귀하가 거주하는 지역에 해당하는 당사자와 본 X 구매자 이용약관에 따른 계약을 체결합니다. 이 당사자가 귀하에게 유료 서비스를 제공할 것입니다. 본 구매자 이용약관에 따라 다른 어떠한 당사자도 귀하에 대한 어떠한 의무도 부담하지 않습니다. 귀하의 위치 유럽 연합, EFTA 회원국 또는 영국 계약 당사자 X Internet Unlimited Company, 등록 사무소 주소: One Cumberland Place, Fenian Street, Dublin 2, D02 AX07 Ireland 약관, 유료 서비스 및 가격 변경 1. 약관 변경. X는 유효하고 합리적인 근거로 본 X 구매자 이용약관을 수시로 개정할 수 있습니다. 유효하고 합리적인 근거에는 (i) 기술, 보안 관련 또는 운영 개발 등으로 인한 당사 서비스의 변경, (ii) 기술적 오류의 제거, (iii) 당사 비즈니스의 변경(예: 정책, 재무 또는 기타 방향성 변화), (iv) 법적 상황의 변경(예: 법규 변화, 공식 기관의 요청이나 법원의 판결), 및 (v) 새로운 기능의 구현을 통한 사용자 경험의 최적화 등이 포함될 수 있습니다. 변경은 소급 적용되지 않으며, legal.x.com/purchaser-terms 에서 제공되는 X 구매자 이용약관의 최신 버전이 귀하의 유료 서비스 이용 및 그에 상응하는 거래에 적용됩니다. 귀하가 본 약관에 동의한 후 당사가 본 약관을 수정 또는 변경하는 경우(예: 귀하가 구독을 구매한 후 본 약관이 변경되는 경우), 당사는 본 약관의 중대한 개정이 발효되기 최대 30일(변경 사항의 구체적인 내용에 따라 다름) 전에 귀하에게 통지하고, 변경 사항과 관련하여 사용자에게 합리적인 기한을 설정하며, 해당 기한이 경과한 후 계속해서 사용할 때 발생할 수 있는 결과를 귀하에게 통지할 것입니다. 그러한 통지는 전자식으로 제공될 수 있으며, 여기에는 서비스 알림 또는 귀하의 계정에 연결된 이메일 주소로 전송되는 이메일 등이 포함됩니다(이에 국한되지는 않습니다). 앞서 언급한 기한이 만료된 후에도 유료 서비스를 계속 사용하는 경우, 귀하는 개정된 X 구매자 서비스 약관에 동의하는 것입니다. 귀하가 X 구매자 이용약관의 변경에 동의하지 않는 경우, 유료 서비스의 이용 또는 접속(또는 계속해서 이용 또는 접속)을 중지해야 합니다. X 구매자 이용약관은 영어로 작성되었지만 번역을 통해 여러 언어로 제공됩니다. X는 영어 원문을 최대한 정확하게 번역하기 위해 노력하고 있습니다. 그러나 차이 또는 불일치가 있는 경우 영문 버전의 X 구매자 이용약관이 우선합니다. 귀하는 영어가 X 구매자 이용약관을 해석하고 구성하기 위한 참조 언어임을 인정합니다. 2. 유료 서비스 변경. 당사의 유료 서비스와 제품 및 서비스는 끊임없이 발전하고 있습니다. X는 합리적이고 타당한 근거로 유료 서비스를 변경할 수 있습니다. 이러한 유효하고 합리적인 근거에는 (i) 기술, 보안 관련 또는 운영 개발, (ii) 기술적 오류 제거, (iii) 법적 상황의 변경(예: 법규 변화, 공식 기관의 요청 또는 법원의 결정) 준수, (iv) 새로운 기능 구현을 통한 사용자 경험 최적화, (v) 당사 비즈니스의 변경(예: 정책, 재무 또는 기타 방향성 변화) 등이 포함될 수 있습니다. 당사는 유료 서비스에 대한 변경 사항이 발효되기 최대 30일 전까지 귀하에게 통지합니다(예: 서비스 알림 또는 귀하의 계정에 연결된 이메일 주소로 발송된 이메일을 통해 변경 사항의 특성과 발효일을 명시하고 구독을 종료할 수 있는 귀하의 최종 권리를 알립니다). 안전 관련 변경 사항이 있는 경우 기한이 단축될 수 있습니다. 다음은 본 조항에서 규정하는 유료 서비스의 변경으로 간주되지 않습니다: (i) 유료 서비스의 본질적인 성격 및 X가 제공하는 서비스의 필수적인 특성에 영향을 미치는 변경 및 (ii) 서비스의 영구적인 중단. X는 유료 서비스의 수정, 일시 중지 또는 중단에 대해 귀하에게 책임을 지지 않습니다. 법적으로 요구되는 경우, 앞서 언급한 책임의 제한은 다음 사항에는 적용되지 않습니다. (i) X 또는 그 법적 대리인 또는 대리인이 계약의 적절한 이행을 위한 필수적이며 사용자가 그 이행에 의존할 수 있는 의무(필수 계약상 의무)를 경미한 과실로 위반한 경우에 한해, 해당 의무의 불이행에 따른 예측 가능한 손해에 대한 보상 및 (ii) 다음에 대한 X의 책임: (a) X, 그 법정 대리인 또는 대리인 측의 고의 또는 중과실로 인한 손해를 비롯해 생명, 신체 또는 건강에 대한 위해로 인한 손해 및 (b) 보증 또는 보장된 특성의 불이행 또는 사기적으로 은폐된 결함의 결과로 인한 손해. 특정 유료 서비스에 대한 특정 이용 약관(아래 포함)은 구독을 취소하거나 해당되는 경우 환불을 요청하는 방법을 지정합니다. 3. 가격 변경. 정기 구독료를 포함한 유료 서비스의 가격은 운영, 유지 보수, 기술 제공, 비즈니스 고려 사항 및 제3자가 부과하는 수수료 또는 법정 수수료와 관련된 비용의 변경으로 인해 당사의 합리적인 재량에 따라 수시로 변경될 수 있습니다. 비용이 증가하는 경우, X는 유료 서비스의 가격을 조정할 수 있는 권리를 보유합니다. X는 가격 변동이 발생할 경우 효력이 발생하기 최대 30일 전까지 서면으로 통지하며, 예를 들어 서비스 알림 또는 귀하의 계정에 연결된 이메일 주소로 이메일을 보내 귀하의 권리와 이를 행사하지 않을 경우의 결과를 명시합니다. 가격이 변경되는 경우, 통지를 받은 날로부터 30일 이내에 취소하는 경우 다음 청구 주기가 시작되기 최대 24시간 전까지 해당 유료 서비스 또는 사용자 계약에 대한 구독을 종료할 수 있습니다. 그렇지 않으면 가격 변경은 알림에 지정된 시간에 적용됩니다. 구독 서비스의 경우, 가격 변경은 가격 변경 발효일 이후 다음 구독 기간이 시작될 때 적용됩니다. 결제 조건 . X는 유료 서비스, 귀하의 기기 및/또는 운영 체제, 귀하의 지리적 위치 또는 기타 요인에 따라 달라질 수 있는 다양한 결제 옵션을 제공합니다. 이러한 결제 선택권이 제공되는 경우(X는 수시로 다양한 구매 방식을 제공할 수 있으므로), 여기에는 Google 또는 Apple에 의해 제공되는 "인앱 결제" 기능을 이용하거나 또는 X의 제3자 결제 처리업체인 Stripe( www.stripe.com - 이하 “ Stripe ”)를 통해 웹 결제를 실행할 수 있는 기능이 포함될 수 있습니다. 결제를 수행할 때 귀하는 다음 사항에 명시적으로 동의하는 것입니다: (i) 해당 유료 서비스에 대해 명시된 가격, 그리고 관련 세금, 신용카드 수수료, 은행 수수료, 외환 거래 수수료, 환전 수수료, 환율 변동과 관련된 추가 금액을 지급할 것, 그리고 (ii) 해당 결제 방법에 대한 귀하의 활용과 관련하여 Google, Apple, 또는 Stripe(X의 제3자 결제 처리업체로서)에 의해 적용되는 관련 이용약관, 개인정보 처리방침, 또는 기타 법적 계약 또는 제한(추가적인 연령 제한 포함)을 준수할 것(단순히 예를 들자면, 귀하가 Apple의 인앱 구매 기능을 통해 결제를 수행하기로 하는 경우 귀하는 Apple에 의해 부과되는 모든 관련 조건, 요건 및/또는 제한을 준수하는 데 동의하는 것입니다). 유료 서비스에 대한 귀하의 이용과 관련하여 귀하가 제공하는 모든 개인정보는(결제와 관련하여 제공하는 모든 정보를 포함하나 이에 국한되지는 않음) X 개인정보 처리방침에 의거하여 처리될 것입니다. X는 다음 목적으로 귀하의 결제 정보를 결제서비스 제공업체와 공유할 수 있습니다: 결제의 처리; 사기 또는 기타 금지된 활동의 방지, 탐지, 조사; 지불 거절 또는 환불 등의 분쟁 해결 촉진; 및 신용카드 또는 직불카드의 승인과 관련된 기타 목적. 귀하의 은행, 신용카드, 직불카드 및/또는 기타 결제 정보가 항상 최신 상태이고 완전하며 정확한지 확인하는 것은 귀하의 책임입니다. 귀하가 유료 서비스를 결제하는 경우, 당사는 귀하의 거래에 대한 정보를 받을 수 있습니다(거래가 이루어진 시점, 특정 구독이 만료 또는 자동 갱신되는 시점, 귀하가 해당 구매를 실행한 플랫폼, 기타 정보 등). X는 결제 처리업체, Apple의 App Store 또는 Google Play Store, 귀하의 은행, 귀하의 신용카드 회사 및/또는 결제 네트워크에 의한 오류 또는 지연에 대해 책임이나 법적 책임을 지지 않습니다. 구독 갱신의 처리 방식 및 기타 중요 조건을 포함하여, 해당되는 특정 유료 서비스에 적용되는 결제 조건은 아래의 각 특정 유료 서비스 약관을 참고하시기 바랍니다. X 사용자 계약, 해지, 환불 불가, 복수 X 계정 및 제한의 적용 1. X 사용자 계약이 귀하에게 적용됩니다 . 귀하는 항상 X 사용자 계약을 따르고 준수해야 합니다. X 사용자 계약은 유료 서비스 및 기능을 포함하여 귀하의 X 서비스 이용에 상시 적용됩니다. 귀하가 X 사용자 계약을 따르고 준수하지 않거나 X에서 귀하가 X 사용자 계약을 따르고 준수하지 않았다고 판단하는 경우 귀하의 유료 서비스가 취소될 수 있습니다. 이러한 취소는 X 사용자 계약에 따라 X가 귀하에게 취할 수 있는 모든 집행 조치에 추가되며 이에 국한되지 않습니다. 이러한 경우 귀하는 유료 서비스의 혜택을 상실할 수 있으며 유료 서비스에 대해 결제(또는 선결제)한 금액을 환불받지 못합니다. 2. X가 유료 서비스에 대한 귀하의 액세스를 종료할 수 있는 사유. X는 아래의 합당한 근거를 포함하되 이에 국한되지 않는 어떠한 이유로든 또는 이유 없이 어떠한 책임도 지지 않으면서 언제든지 귀하의 유료 서비스에 대한 액세스를 정지 또는 종료하거나, 귀하의 계정을 정지하는 등 적절한 것으로 판단되는 기타 조치를 취할 수 있습니다. a. X는 자체 재량에 따라 귀하가 이용 약관을 위반했거나 귀하의 유료 서비스 사용이 해당 법률을 위반할 것이라고 판단하는 경우 b. X는 유능한 법원, 규제 기관 또는 법 집행 기관으로부터 그렇게 하라는 요청 또는 지시를 받는 경우 c. X에 예상치 못한 기술 문제 또는 보안 문제가 발생하는 경우 d.X 는 단독 합리적인 재량에 따라 귀하가 X 사용자 계약을 위반했다고 생각합니다. 마. X는 귀하가 유료 서비스와 관련하여 조작, 게임 또는 기타 방해가 되거나 금지된 행위에 관여하는 경우와 같은 타당한 이유가 있다고 믿습니다. f. 귀하가 X에 위험을 초래하거나 법적 위험을 초래할 가능성이 있는 경우 g. 위법 행위로 인해 귀하의 계정을 삭제해야 하는 경우 h. 장기간 활동하지 않아 귀하의 계정을 삭제해야 하는 경우 i. 당사가 귀하에게 제공하는 유료 서비스(전부 또는 일부)는 더 이상 상업적으로 실행 가능하지 않습니다(X의 단독 재량에 따라). 3. 모든 거래는 최종적입니다. 유료 서비스에 대한 모든 결제는 최종적이며 관련 법률에서 요구하는 경우를 제외하고 환불 또는 교환이 불가능합니다. 당사는 유료 서비스의 성질, 품질 또는 가치 또는 그 가용성이나 공급에 대해 보증하지 않습니다. 이용하지 않거나 부분적으로 이용한 유료 서비스(예: 부분적으로 이용한 구독기간)에 대해서는 환불이나 크레딧이 제공되지 않습니다. 4. 유료 서비스는 X 계정 간에 양도할 수 없습니다. 각각의 유료 서비스 구매는 하나의 X 계정에 적용됩니다. 즉, 귀하가 유료 서비스를 구매할 때 사용한 계정에만 구매가 적용되며, 귀하가 액세스하거나 관리하는 다른 계정에는 적용되지 않습니다. 귀하가 여러 계정을 보유하거나 관리하면서 각각의 계정에서 유료 서비스에 액세스하려면 각 계정에서 개별적으로 유료 서비스를 구매해야 합니다. 5. 제한 및 의무. a. 귀하가 귀하의 국가에서 유료 서비스를 사용하는 것이 법적으로 허용되고 X가 해당 유료 서비스를 지원하는 국가에 거주하는 경우에만 유료 서비스를 구매하여 이용할 수 있습니다. X는 재량에 따라 특정 국가에서 유료 서비스의 액세스나 구매를 제한할 수 있습니다. X는 지원 국가 리스트를 수시로 변경할 수 있는 권리를 보유합니다. b. 당사는 단독 재량에 따라 유료 서비스 거래를 거절하거나 유료 서비스의 판매 또는 이용을 취소 또는 중단할 수 있는 권리를 보유합니다. c. 귀하는 유료 서비스를 주문하지 않은 사람으로 하여금 귀하의 X 계정을 사용하여 해당 유료 서비스에 액세스하도록 허용해서는 안 됩니다. d. 귀하가 미국 재무부 해외자산통제국 또는 기타 관련 제재 당국이 관리하는 제재를 포함하되 이에 국한되지 않는 경제 제재에 따라 미국인과 거래가 허용되지 않는 사람(" 금지 대상 ")인 경우, 유료 서비스를 구매하거나 사용할 수 없습니다. 여기에는 다음 국가 및 지역에 거주하거나 또는 통상적으로 거주하는 사람이 포함되나 이에 국한되지 않습니다: 쿠바, 이란, 우크라이나 크리미아 지역, 북한 및 시리아. 귀하는 귀하가 금지 대상이 아님을 진술하고 보증합니다. e. 귀하는 오로지 합법적인 목적으로 그리고 본 약관을 준수하며 유료 서비스를 이용할 것임을 진술합니다. 세금 및 수수료 . 귀하는 X 또는 제3자 결제 처리업체에 지불해야 하는 것을 포함하여 유료 서비스 구매와 관련된 모든 관련 세금, 관세 및 수수료를 지불할 책임이 있으며 이를 지불하기로 동의합니다. 이러한 세금에는 부가가치세(VAT), 상품서비스세(GST), 판매세, 원천징수세 및 기타 관련 세금이 포함되며 이에 국한되지 않습니다. 귀하의 위치에 따라 X가 귀하의 유료 서비스 구매로 인해 발생하는 거래세와 관련된 정보를 수집하고 신고할 책임이 있을 수 있습니다. 귀하는 X가 세금 징수 및 신고 의무를 이행하기 위해 관련 세무 당국에 귀하의 계정 및 개인 정보를 제공할 수 있도록 X에 권한을 부여합니다. 일반 규정 1. 연락처 정보. 유료 서비스나 본 약관에 대해 궁금한 사항이 있으면 X 유료 서비스 고객센터 에서 자세한 내용을 확인할 수 있습니다. 이미 유료 서비스를 구매한 경우, 결제 또는 구독 설정 아래 X 계정의 탐색 메뉴에 있는 지원 링크를 통해 문의할 수도 있습니다. 추가 질문이 있는 경우 여기 에서 "유료 기능에 관한 도움말" 양식을 사용하여 문의할 수 있습니다. 2. 면책사항. 귀하의 유료 서비스 액세스 및 이용은 관련 법률이 허용하는 최대 범위 내에서 귀하의 책임입니다. 귀하는 유료 서비스가 "있는 그대로" 그리고 "제공되는 상태 그대로" 귀하에게 제공된다는 점을 이해하고 이에 동의합니다. X는 판매적합성, 특정 목적 적합성 또는 비침해에 대한 명시적, 묵시적인 보증 및 조건 일체를 부인합니다. X는 (I) 유료 서비스의 완전성, 정확성, 가용성, 적시성, 보안 또는 신뢰성, (II) 유료 서비스가 귀하의 요구 사항을 충족할지 여부나 중단 없이, 안전하게, 또는 오류 없이 제공될지에 관한 보증이나 진술을 하지 않으며 이와 관련된 모든 책임 및 의무에 대해 면책됩니다. 유료 서비스를 포함한 귀하의 X 서비스의 이용과 귀하가 제공하는 모든 콘텐츠에 대한 책임은 귀하에게 있습니다. 3. 책임의 제한. 관련 법규에 저촉되지 않는 최대한도 내에서 X 당사자는 (i) 귀하의 유료 서비스에 대한 액세스나 사용 또는 액세스 불능이나 사용 불능, (ii) 유료 서비스를 통해 게시된 제3자의 모든 행위 또는 콘텐츠(다른 사용자 또는 제3자가 범한 명예 훼손, 모욕적 또는 불법 행위를 포함하되 이에 국한되지 않음), (iii) 유료 | 2026-01-13T08:48:40 |
https://deno.com/ | Deno, the next-generation JavaScript runtime Skip to main content Deno 2.6 is here 🎉 Learn more -> Dismiss ⌘K ↑↓ Up or down to navigate ↵ Enter to select ESC Escape to close Products Open Source Current path Deno Modern runtime for JavaScript and TypeScript Fresh Web framework designed for the edge JSR TypeScript-first ESM package registry Commercial Deno Deploy Easy serverless hosting for your JavaScript projects Deno for Enterprise Enterprise support for runtime projects Subhosting Securely run untrusted code in a scalable sandbox Docs Modules Standard Library JSR Node.js & npm Community Discord GitHub Bluesky X YouTube Mastodon Blog ⌘K ↑↓ Up or down to navigate ↵ Enter to select ESC Escape to close Uncomplicate JavaScript Deno is the open-source JavaScript runtime for the modern web. Docs GitHub Install Deno 2.6.4 Release notes MacOS/Linux (Currently selected) Windows curl -fsSL https://deno.land/install.sh | sh Copy command Deno is the open-source JavaScript runtime for the modern web. Built on web standards with zero-config TypeScript, unmatched security, and a complete built-in toolchain. Rating 100k+ Stars on GitHub Community 400k+ Active Deno users Ecosystem 2M+ Community modules Enterprise-grade JavaScript Now offering enterprise support for the Deno runtime Learn more All your favorite tools, built-in and ready to go Deno natively supports TypeScript, JSX, and modern ECMAScript features with zero configuration. account.ts type User = { name : string ; balance : number } ; function getBalance ( user : User ) : string { return ` Balance: $ ${ user . balance . toFixed ( 2 ) } ` ; } console . log ( getBalance ( { name : "Alice" , balance : 42 } ) ) ; $ deno run account.ts Balance: $42.00 $ deno check Check account.ts ✅ Type check successful Just run Run .ts files directly with built-in type checking and compilation—no additional tooling or configuration required! More about TypeScript in Deno Seamless With first-class support for npm and Node, Deno can read your package.json automatically, or you can import packages from npm directly. Node and npm support Using package.json or import maps { return c.text("Hello Hono!"); }); Deno.serve(app.fetch);" mode="light" data-animate-code="true" class="text-gray-50 text-base max-w-full rounded-none break-normal"> import { Hono } from "hono" ; const app = new Hono ( ) ; app . get ( "/" , ( c ) => { return c . text ( "Hello Hono!" ) ; } ) ; Deno . serve ( app . fetch ) ; Using inline imports { return c.text("Hello Hono!"); }); Deno.serve(app.fetch);" mode="light" data-animate-code="true" class="text-gray-50 text-base max-w-full rounded-none break-normal"> import { Hono } from "npm:hono@4" ; const app = new Hono ( ) ; app . get ( "/" , ( c ) => { return c . text ( "Hello Hono!" ) ; } ) ; Deno . serve ( app . fetch ) ; Built on web standards Whenever possible, Deno implements web standard APIs on the server. Deno actively participates in TC39 and WinterCG to help move the web forward. Consistent code from browser to backend Deno prioritizes web standard APIs, maximizing code reuse between browser and server and future-proofing your code. Web APIs in Deno Skip past APIs list Worker MessageEvent WritableStreamDefaultController structuredClone DecompressionStream CompressionStream setInterval PromiseRejectionEvent clearInterval Blob fetch btoa localStorage Navigator clearTimeout ReadableStreamDefaultController Response.json() EventTarget caches CacheStorage MessagePort Location DedicatedWorkerGlobalScope WebSocket queueMicrotask CryptoKey ErrorEvent PerformanceMark WorkerNavigator ReadableStreamBYOBRequest TextDecoder WorkerLocation TextEncoderStream ReadableByteStreamController TransformStream File CustomEvent Event performance DOMException ReadableStreamBYOBReader crypto CloseEvent URLPattern PerformanceEntry console globalThis.close() Crypto Request ReadableStream Storage WebAssembly TextDecoderStream URLSearchParams ProgressEvent FileReader ByteLengthQueuingStrategy BeforeUnloadEvent TextEncoder atob globalThis.alert() setTimeout Performance Headers WorkerGlobalScope AbortSignal FormData Response MessageChannel URL BroadcastChannel TransformStreamDefaultController SubtleCrypto Cache WritableStream AbortController ReadableStreamDefaultReader PerformanceMeasure WritableStreamDefaultWriter Batteries included The essential tools you need to build, test, and deploy your applications are all included out of the box. Code linter Deno ships with a built-in code linter to help you avoid bugs and code rot. Learn more › $ deno lint -- watch Test runner Deno provides a test runner and assertion libraries as a part of the runtime and standard library. Learn more › { const x = 1 + 2; console.assert(x == 3); });" data-animate-code="true"> // server_test.ts Deno . test ( "1 + 2 = 3" , ( ) => { const x = 1 + 2 ; console . assert ( x == 3 ) ; } ) ; $ deno test server_test . ts Standalone executables Instantly create standalone executables from your Deno program. It even supports cross-compiling for other platforms! Learn more › new Response("Hello!"));" data-animate-code="true"> Deno . serve ( req => new Response ( "Hello!" ) ) ; $ deno compile -- allow - net server . ts Compile file : / //tmp/server.ts to server $ . / server Listening on http : / / localhost:8000 / Code formatter Deno's built-in code formatter (based on dprint) beautifies JavaScript, TypeScript, JSON, and Markdown. Learn more › $ deno fmt -- line - width = 120 Secure by default A program run with Deno has no file, network, or environment access unless explicitly enabled. Prevent supply chain attacks Stop worrying about npm modules introducing unexpected vulnerabilities. Deno restricts access to the file system, network, and system environment by default, so code can access only what you allow. Security in Deno Other runtimes $ node random.js Executing random.js ... 🚨 File system compromised! Deno $ deno random.js ⚠️ Deno requests write access Allow? [y/n/A] $ n ❌ Denied write access Exited High-performance networking Out of the box support for: HTTPS (encryption) WebSocket HTTP2 Automatic response body compression View documentation Requests per second* More is better Deno 105200 Node 48700 * Ubuntu 22 on ec2 m5.metal; Deno 2.5.2 vs. Node 18.12.1 Built for the cloud Whether you deploy with our lightning-fast Deno Deploy or on other cloud providers, Deno streamlines your experience. — Deno runs on — Official Docker image hayd/deno-lambda How to Deploy Deno to Digital Ocean Run a Deno App - Fly.io Docs skymethod/denoflare anthonychu/azure-functions-deno-worker How to Deploy to Google Cloud Run — The cloud built for modern JavaScript — Project hosting made for Deno Unlock the full potential of your JavaScript and TypeScript projects with the all-new, completely reimagined Deno Deploy Unlock the full power of Deno Deno users can enjoy first-class support for features like OpenTelemetry, Deno KV, and the Deno Deploy CLI—plus exclusives like Playgrounds, Databases, and more. Built for anything built with JavaScript Because Deno is compatible with Node, your teams and projects can enjoy Deno Deploy's powerful features—even if you're not using Deno Fresh logo Vue logo Hono logo Next logo Solid Start logo Nuxt logo SvelteKit logo Docusaurus logo React React logo Astro logo Solid logo Remix logo Vite logo Express logo Deno logo Node logo Learn more about Deno Deploy Pricing Sign in Get the most out of Deno with Fresh 2.0 Fresh is the Deno web framework, built with Preact and fully compatible with Vite for blazing speed and instant productivity. Build fast sites fast Author routes as the JSX (or TSX) components you already know and love, and Fresh handles dynamic server-side rendering by default. /routes/index.tsx <h1>HTML fresh from the server!</h1> <p> Delivered at {new Date().toLocaleTimeString()} </p> </div> ); }" language="jsx" class="text-gray-50 text-base max-w-full rounded-md"> export default function HomePage ( ) { return ( < div > < h1 > HTML fresh from the server! </ h1 > < p > Delivered at { new Date ( ) . toLocaleTimeString ( ) } </ p > </ div > ) ; } /islands/Counter.tsx (0); return ( <button onClick={() => count.value += 1}> The count is {count.value} </button> ); } " language="jsx" class="text-gray-50 text-base max-w-full rounded-md"> import { useSignal } from "@preact/signals" ; export default function Counter ( ) { const count = useSignal < number > (0); return ( < button onClick = { ( ) => count . value += 1 } > The count is { count . value } </ button > ); } Ship less JavaScript Island-based architecture lets you opt in to only the JavaScript you need, for absolutely minimal runtime overhead. Learn more about Fresh Our vibrant community “I knew this was gonna happen! Deno is truly building the fastest, most secure and personalizable JS runtime!” Manu (Qwik) “Deno's security model is PERFECT for this type of script. Running a script from a rando off the internet? It asks for read access to only the CWD and then asks for access to the file it wants to write to. 👏” Wes Bos “I really think Deno is the easiest and most capable JS runtime. URL imports are slept on.” Atalocke “npm packages in Deno 👀 That’s an exciting development for those of us building at the edge.” Jason Lengstorf “This Deno thing is fast, no doubt about it. #denoland” Poorly Funded Snob “Deno: I have to use the browser APIs cause they are everywhere, and everywhere is my target runtime (the web). The runtime that tries to mirror browser APIs server side makes my life easiest.” Taylor Young “Deno is fantastic. I am using it to level up a bit in terms of JavaScript and TypeScript and it is the easiest way to get going. Their tooling is like 100x simpler than all the usual Node stacks.” Stefan Arentz Manu (Qwik)(active) Wes Bos Atalocke Jason Lengstorf Poorly Funded Snob Taylor Young Stefan Arentz Ready to get started with Deno? Install now Read the docs Products Deno Runtime Deno Deploy Deploy Subhosting Fresh Resources Runtime Manual Runtime API Deploy Docs Standard Library Third-Party Modules Examples Company Blog Pricing News Merch Privacy Policy Terms and Conditions GitHub Discord Bluesky Mastodon Twitter or X or whatever YouTube Copyright © 2026 Deno Land Inc. All rights reserved. | All systems operational | 2026-01-13T08:48:40 |
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https://support.google.com/chrome/answer/2392709 | Delete browsing data in Chrome - Android - Google Chrome Help Skip to main content Google Chrome Help Sign in Google Help Help Center Community Google Chrome Privacy Policy Terms of Service Submit feedback Send feedback on... This help content & information General Help Center experience Next Help Center Community Google Chrome Delete browsing data in Chrome You can delete your Chrome browsing history and other browsing data, like saved form entries, or just delete data from a specific date. What happens to your info Data that can be deleted Browsing history: Deleting your browsing history deletes the following: Web addresses you've visited are removed from the History page. Shortcuts to those pages are removed from the New Tab page. Address bar predictions for those websites are no longer shown. Tabs: Open tabs on your device will be closed. Cookies and site data Cookies: These are files created by websites you visit to make your online experience easier by saving browsing information. Cookies are deleted. Site data: HTML5-enabled storage types including application caches, Web Storage data , Web SQL Database data , and Indexed Database data are deleted. Media licenses: Licenses for HTML5 protected content, like movies or music that you’ve played or downloaded, are deleted. Cached images and files: Chrome remembers parts of pages to help them open faster during your next visit. Text and images from pages you've visited in Chrome are removed. Saved passwords: Passwords you saved are deleted. Autofill form data: Your autofill entries are deleted, including addresses and credit cards. Cards and addresses saved in your Google Account aren't deleted. Site settings: Settings and permissions you give to websites are deleted. For example, if a site can run JavaScript, use your camera, or know your location. Data that doesn't get deleted There are other types of data that are related to your behavior online. These other types of data can be deleted separately: Search history & other Google activity: Searches and other activity on Google services are saved to your Google Account. Learn how to delete Google activity . If you're giving away your device, remember to delete your browsing data and then sign out of Chrome . Delete your browsing data Important: If you delete data saved to your Google Account from your Android device, it gets removed from all devices where you’re signed in to your Google Account. On your Android device, open Chrome . On the right of the address bar, tap More Delete browsing data . To delete browsing history (including open tabs), choose a duration and tap Delete data . The default duration is 15 minutes. To choose more specific types of data you want to delete, tap More options . Select the types of browsing data you want to delete and tap Delete data . If you delete cookies while signed in to Chrome, you won’t be signed out of your Google Account. Tips: To sign out of your Google Account on all websites, sign out of Chrome. To quickly reach the Delete browsing data dialog, in the address bar, type “Delete browsing data” and then tap the Action chip . Learn Chrome Actions to quickly complete tasks . Delete individual items Instead of deleting entire categories of your browsing data, you can pick items to delete: Page you visited Downloaded file Saved password Cookies from a website Autofill entry Related resources Check or delete your Chrome browsing history Delete, allow and manage cookies in Chrome Manage passwords Was this helpful? How can we improve it? Yes No Submit Android Computer iPhone & iPad More Need more help? 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https://share.transistor.fm/s/9bb11fc4#goodpods-path-1 | APIs You Won't Hate | Funding Open Source with Dudley Carr from Stack Aid APIs You Won't Hate 40 ? 30 : 10)" @keyup.document.left="seekBySeconds(-10)" @keyup.document.m="toggleMute" @keyup.document.s="toggleSpeed" @play="play(false, true)" @loadedmetadata="handleLoadedMetadata" @pause="pause(true)" preload="none" @timejump.window="seekToSeconds($event.detail.timestamp); shareTimeFormatted = formatTime($event.detail.timestamp)" > Trailer Bonus 10 40 ? 30 : 10)" class="seek-seconds-button" > 40 ? 30 : 10"> Subscribe Share More Info Download More episodes Subscribe newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyFeedUrl()" class="form-input-group" > Copied to clipboard Apple Podcasts Spotify Pocket Casts Overcast Castro YouTube Goodpods Goodpods Metacast Amazon Music Pandora CastBox Anghami Anghami Fountain JioSaavn Gaana iHeartRadio TuneIn TuneIn Player FM SoundCloud SoundCloud Deezer Podcast Addict Share newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyShareUrl()" class="form-input-group" > Share Copied to clipboard newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyEmbedHtml()" class="form-input-group" > Embed Copied to clipboard Start at Trailer Bonus Full Transcript View the website updateDescriptionLinks($el))" class="episode-description" > Chapters January 23, 2023 by APIs You Won't Hate View the website Listen On Apple Podcasts Listen On Spotify Listen On YouTube RSS Feed Subscribe RSS Feed RSS Feed URL Copied! Follow Episode Details / Transcript Mike chats with Co-Founder of Stack Aid, Dudley Carr, about the importance of funding Open Source projects, and Stack Aid's approach to helping Open Source organizations get paid. Show Notes Stack Aid - https://www.stackaid.us/ Dudley Carr - @dudley@mastodon.social Creators and Guests Host Mike Bifulco Cofounder and host of APIs You Won't Hate. Blogs at https://mikebifulco.com Into 🚴♀️, espresso ☕, looking after 🌍. ex @Stripe @Google @Microsoft What is APIs You Won't Hate? A no-nonsense (well, some-nonsense) podcast about API design & development, new features in the world of HTTP, service-orientated architecture, microservices, and probably bikes. Mike Bifulco: Hello, hello, and welcome back to APIs you won't Hate. My name is Mike Balco. I am one of your api co-hosts and guide through the world of designing APIs and building APIs, and doing all sorts of good stuff with API tech. I am joined today for an interview with a new friend of mine, someone who I met at a conference here in North Carolina. We're gonna be talking a little bit today about his project and some of the sort of mission of open source and supporting open source and things like that. So today I'm chatting with Dudley Carr from Stack A Dudley. How are you doing today? Dudley Carr: I'm doing great. Thanks for having me on. Mike Bifulco: Yeah, of course. Super happy to have you here. I have lots of questions for you and I'm, I'm super glad you were able to make it because from our initial conversations when we sort of bumped into each other all over the place at all Things Open your work seemed very interesting to me. And I think a lot of the squad here that is part of the APIs you won't hate community will really. What you're doing. So I wanna talk all about that. I wanna talk about how you got to where you are and what you're doing at Stack and just kinda get some of the history on, on the project in yourself. So tell me a bit about yourself and tell me about Stack. Dudley Carr: Absolutely. So I've been a, in the software engineering space for the. 22 years. I did my undergraduate in computer science at Stanford and graduated at the peak of the dot com bubble burst. And briefly did a stint in finance, actually worked at Lehman Brothers on their exotic derivatives until I realized that stuff is insane and I got out. In the last 20 years, I've spent all of it working with my brother, who also did computer science, and so we've gone from one venture to the next. So he is not here, but is probably the. More important of the duo. And anyway, we did our first startup in Rhode Island in my parents' basement. I think there was radon in that basement, but we we managed we actually built in the, you know, 2002, 2003, we built a product that. Became g talker. It was flash-based, you know, pre action. It was action script, but before it was even before they released all of their UI toolkits and stuff like that. And back backend was Python. It was initially a desktop application, then became a. Web-based product. And we developed that out and ended up selling that to Google and moving to Seattle in 2006 to join the Google Talk team and work on that. And we spent about five years at Google going from one project to the next. First we were in apps and and then eventually I worked on Google Voice and then before leaving. So that was super formative for us. We learned a lot of things, met a lot of great people. I think that was kind of the heyday for Google And and then after that we, we did some more startups food, food related things. And then we joined a company called Moz that does SEO here in Seattle. And we spent another four or five years there, I helped run a large portion of their engineering team and then grew some of their product areas. That was also really formative for us in terms of, you know, understanding that space, growing teams and you know, just going through various product life cycles and things like that. At the end of our MOS experience, we actually did another startup with a friend here in Seattle around crowdfunding. And this was actually crowdfunding for sports team. So, There was another platform that was really taking off. We found out about Stripe Connect and started using that. And really the, the basis for it was, you know, you have like a high school football team. They're selling candy bars and things like that. There's a lot of inefficiencies there and there's a lot of price gouging actually by merchants who sell products to schools to do that. And so there was. You know, 2017, 2018, there was a real impetus to you know, move all of that stuff online. And we have a lot of learnings that I think happy to chat about, but that was kind of formative for us in terms of thinking about, you know, how you move money from a set of people who wanna support something to, to the recipients and what all is involved in that. That was also just kind of how we, we transitioned from that into consulting. So we've been doing consulting for. Four years you know, we're kind of embedded engineers and product specialists in inside of organizations and to help them transfer in companies. And that's gives us a ton of flexibility and allows us to do cool things like what we've done over the last couple of years. At the beginning of the pandemic by the way, we launched something called Covid Trace. So we had the hot idea to do contact tracing. We tried to launch an app immediately. It was blocked by Google and Apple. Mike Bifulco: Oh wow. Dudley Carr: you're, you're not doing anything location based and we're gonna sort this out first, which is great. I think it was totally the right move on their part. We ended up adopting their the exposure notification. APIs that they have, and we ended up lo, I think we were the second app to launch in the United States. And so we launched with the state of Nevada and worked with them over the course of two years doing exposure notifications, rolling that out for iOS and Android, and then eventually moving all of Nevada off of our custom app onto IOS's, built-in exposure notification function. And at the same time building out other things in terms of getting results to people and things like that. So really interesting problems around health totally unanticipated. So that, that was actually that was all open source. We released all of that infrastructure, open source and the apps. And then, yeah, about a year ago we started on decade. Mike Bifulco: Wow. Yeah, that's some in incredible back history there. I, I. Was not prepared for that, that much. Incredible problem solving that you've gotten into in your, your career. For sure. As someone who lived through an entire pandemic of being, you know, Locked in my home and not leaving and being very concerned about public health and those things. Super, super cool to hear, hear you worked on that and, and obviously impacted so many people. And also, you know, collaborated with the, the big organizations like Apple and Google. That's massively cool to hear. I also don't think I realized that you and I had some sort of shared overlap not overlap, but, but maybe an odd Venn diagram of career stuff before working at Stripe, I worked at Google for a couple years. Not quite on Google Voice, on Google Assistant, so voice related stuff at Google. Although I'm no longer there and actually probably worth mentioning for posterity since you and I met at All Things Open. I'm also no longer at Stripe. So I'm, I've left Stripe in the past couple weeks, but I'm very curious to hear about your experiences with Stripe Connect and, and all that. And so. All of this history of all the crazy things you've done and, and like working with complex teams and big problems and across devices and problem spaces, and I'm sure languages and all the other things that have changed since what, 2003 when you first got into the the, the world of, of building things has led you to where you're at now. So tell me a little bit about Stack Aid and what you're doing. There. Dudley Carr: Yeah, so Stack stack is a service that allows you to fund your second first order and second order dependencies automatically. It, the impetus for it came about a year and a half ago when we. You know, repeatedly saw articles about people exasperated by their inability to sustain their open source project because, you know, the demands have increased on what they have to deliver and the reach, you the reach of their open sources beyond their wildest dreams, but, you know, they, they basically pay for it in their spare time or it takes away from other paying opportunities that they have. And so you see a lot of people kind of torn in those situations. We, that really resonated with us. As I mentioned, you know, we had spent time in the fundraising arena and we, you know, we saw. Definitely momentum around Get Up sponsors an open collective, but we, we thought that there was an opportunity there. You know, I think what's super interesting about the software development space as opposed to any other space where people are trying to raise money is that we know we know what, what you use, right? There's sometimes it's imperative, but increasingly it's a declarative. Way of specifying all your dependencies. And so we can, we can do so many things automatically to determine what you use and, and potentially influence how we allocate money. And so the, the, the seed of an idea was there and we started exploring, you know, the feasibility of it and what that would look like, and is it an effective model, things like that. And so that's been like the last year and it's, it's. Super interesting. Kind of flushing that out and we're, we've been super happy with the results and the initial reception when we launched a couple of months ago. Mike Bifulco: So. I've seen it and I'm sort of familiar with the product, but I wanna make sure that you know, it's abundantly clear what you mean when you're talking about this. So we're talking about funding open source projects in a way that is sort of sustainable and based on your dependency graph for projects that you're using. So when you say first and second order dependencies, what do you mean? Dudley Carr: Yeah. So by first order, so let's take a Packers saw JSON in the node E. The first order of dependencies are the the dependencies and dev dependencies that you list directly in that za js o n. Now, those first order dependencies in turn have their own za js o, where they list their dependencies. That would be the second order of dependencies. Now you can walk that tree down all the way down, and there are gonna be lots and lots more. not unusual for a project to have literally thousands of. Dependencies in their dependency tree. But you know, from a funding perspective, you have to draw the line somewhere. Otherwise, you know, you take a certain amount of money and divide it into tiny little pieces and it becomes somewhat meaningless. So we wanted to, you know, the, the easy thing was would be to just fund first order dependencies. But we, we realized, you know, a lot of those open source projects also want to give. And if we, you know, defaults matter. And we realize that if we came up with a mechanism that, you know, when you find a first order dependency, it passes some of that onto its dependencies. You know, you're doing that automatically for the ecosystem. You're bene, you don't have to have everyone opt-in in order to have further reach into the ecosystem. And so yeah, that was the impetus to fund first and second order depend. Mike Bifulco: Yeah. Got it. So from the, I I, gosh, I don't even know what, what you would consider to be the end user, but from the perspective of someone who is doing the funding, doing the supporting what does that look like? Like what is, what is the process for me? Say for a project I'm running, let's say APIs, you won't hate.com, right? It's a, it's a no JS project. We've got a whole heap of dependencies that are sort of built into this thing. What would I need to do to adopt. Dudley Carr: a great question. So, you know, when you go to Staca us, there's the first step in the onboarding process is oh, often thing with GitHub and actually adding the GitHub app to either your personal organization or some other organization where repositories are we then scanned those repositories for you know, files like Bax, J S O N, or you know, others depending on whatever language you're. And we use those declarative list of dependencies, we ingest that and start looking at that dependency tree. Once we have that, we, you know, we, we put you in the dashboard. We show you what we had discovered, like which files and which repositories we're pulling from. And we presume initially that you ne you want to fund all of those. You can, you can be selective, right? So I wanna fund these repositories and these package digest and things like, Based on that, based on the first order and second order dependencies we've pulled from that. And you can then indicate as a level of support that you wanna do on a monthly basis. We then calculate how much would go to each of those projects. So it's hard to des describe, but there's a tree that we have in the, in the dashboard and it shows you, okay, you've got React or low dash, for example, as a first order dependency. It has these second order dependencies and it shows you the amount of your subscription that goes to each one of those. And so that breaks down when you're, the next step is to enter a credit card and then, then you're off to the races. Mike Bifulco: Yeah. Okay. So from, from my perspective, it is, you know off with GitHub, get this thing added to my stack of or to my GitHub organization. It'll go and, and I guess introspect and look at, or I guess inspect is probably even the right word there. Go look at all the projects I have and give me the the first and second order dependencies for each is the target. Then from there to say like, just using easy numbers I want to donate a hundred bucks a month. To these various organizations. I, I have one fixed cost and Stack Aid kind of does the rest from there. Dudley Carr: That's, that's exactly right. Yes. Mike Bifulco: Yeah. Wow. So how, well man, I, I feel like I have so many questions. How does the money get from A to B? Like, how do you track down the the various projects that are then being funded? Dudley Carr: Yeah, so that's the fun part about building something like this is because it's effectively kind of like a marketplace, right? I mean, we have, we're engaging with both. Individual developers and companies who are supporters and of course have a relationship with open source, maintain. So we have slowly been reaching out to open source maintainers kind of as we drive awareness or if they've receiving funding, we will reach out to them individually. , but we also have been realizing that, you know, a lot of these people don't know who we are. There's a lot of things grabbing at their attention. So if they have an existing relationship with GI UP sponsors or Open Collective, we actually just use our corporate credit card and make the donation on those platforms. So our, you know, our goal is to get the money in their hands. And if they have an existing relationship, we, we lean on that. So that, that's worked out well. But but primarily over time, I think for for the ease of developers and to give them more control in terms of, you know, how those funds are allocated. Especially if there are multiple people working on a project. Things like. You know, we we would like people to, you know, claim their project on stack. Mike Bifulco: Yeah, sure. What does that look like? Dudley Carr: So we use Stripe Connect under underneath. So you know, when you log into the dashboard and you owe off you also have to oth with GI up at the moment. We're working on other. Hosting platforms, but you o often we actually verify that you actually are a maintainer on those repositories that you're trying to claim. We list out those repositories you claim them. And then as part of that claiming process, we also need to collect the a Stripe account. So we send you over to Stripe. They get all of the, the details necessary. To basically give us a, a stripe account so that we can deposit funds into at the end of the month. And then that's it. Then you're, then you're able to collect money from stack. Mike Bifulco: Yeah. Wow, that's great. So, so I'd imagine there's some population of people who are very pleased to find out they can come to Stack Aid, click a couple of buttons and have money being funneled into their project every month. That, that's gotta feel pretty cool to be able to, I don't know, land that dream so seamlessly. Dudley Carr: Yeah, I mean, I think it speaks more distract than to us. I mean, honestly, that flow is amazing and there's so much complexity abstracted. But I think from an end developer perspective, it is surprisingly easy to get up and running. And yeah, and I think it's, it's pretty great, you know, when you show up that a lot of the times there's, you know, a couple of bucks at the very least waiting for you there, and you immediately get that. I think that has been an important part of stack it, which is, you know, you, you don't have to be a developer. Like the developer doesn't have to have an account in order for money to accrue for them. So you know, you have this kind of problem I think on GitHub sponsors an open collective initially where people didn't have a relationship with those platforms, so there wasn't a way to get money to them. A lot of people have set it up, but there's also a large portion of the ecosystem that has no relationship with them. And so it was important for us to be able to accrue money and, you know, show people that you can actually. there's money in the open source that they've contributed and have that as a carrot for them to sign up. Mike Bifulco: Sure. Yeah, that's, that's a really interesting model and having been exposed to GitHub sponsors a little bit, I know that like one of the nice things that comes along with this actually may, might be a Stripe Connect requirement, but to access Stripe Connect, you have to essentially have viable tax information, right? Like the, the right information to be able to be paid out. So that you're not just, you know, sending off money to some anonymous bucket somewhere. But instead, theoretically it's tied to like an L L C or an individual proprietor or, you know, a more complex corporation in the case of vicar businesses. But a lot of that is, I would imagine abstracted away from you. You just need them to, to, you know, click the button and connect to stack with Stripe Connect. Dudley Carr: One of the biggest concerns that we had out of the gate was you. All open source doesn't happen in the United States. There are people across the world, and the United States in particular has a requirement called know your customer. And so you need a lot of details in order to verify their identity and make, you know, make sure that this isn't for money laundering or some other scheme like that. And so that is actually all abstracted away for us. And that is pretty phenomenal if we. A, a two person operation. There's just no way you're gonna Mike Bifulco: Yeah, Dudley Carr: that. Mike Bifulco: the, the scope and scale of those money laundering operations is far more complex and sophisticated than, you know, I think we might realize as, as sort of an average consumer. You know, again, I'm, I'm not at Stripe any longer, but during my tenure there, like you, you do Financial crimes training and it's pretty astonishing in the creative ways people, you know, will, will go to lengths to make money disappear or just harder to trace whatever the case may be. And nice that you don't have to worry about that. There's a lot of mechanisms in place to detect and prevent that fraud as well. . Okay. So I, I want to know a little bit about when did you what, what signals were you given that this was something that was going to work? In other words that when you're starting to build stack, because it's only a year and change old at this point was there a moment or a series of events that sort of made you feel like, oh, this is something that actually has some momentum behind it? Dudley Carr: Yeah, I think well, I think we had to prove to ourselves that it's viable and, you know, we, we have, there's some nuance to the model in terms of how we distribute that money. And, and more importantly, what's interesting about this problem is that it's not a one-time thing. So if no one shows up to collect the money, what do you do with that money? So there's a time component to it as well. Mike Bifulco: Yeah. Dudley Carr: we wanted, so we. There's complexity around the model to some degree in terms of implementing and doing it right, and we, we knew that the model itself needed to be validated and be comparable to things like get up sponsors and, and Open Collective. So we actually spent a large portion of the development. Building out a simulation. And so there's a, like simulation Dots US has. It's, it's effectively like the, it's our entire site, but it has 5,000 made up subscribers at various price points using Pax JSONs that we had discovered on GitHub using source graph. Source graph was pretty instrumental in terms of d doing that. And we, we needed package js os that weren't on n p, right? We didn't want to grab load Dash's, patch json accidentally. And because that, that's not representative of potential end users. So we took those 5,000 subscribers, plugged them in, you know, gave them some subscription amount between $25 per month to a hundred dollars per month. And we. Look to see what happens. Right? What's the outcome of, of this? Like, is it just a couple of projects that get all the money or, you know, what does that distribution look like and the, the, the end result is that, yeah, you, you still have a power power law curve just like you do on Get up sponsors in Open Collective, but it was it was more stretched. So we ended up, we ended up funding a larger percentage of the, let's say the top 25% of funds included a significantly larger set of projects. So even though they're at the tip of this parallel curve, they, you know, there's more of them included. That's great. But the middle, the middle was much broader. Right. A lot more of the money was going into that, and so that, that was the validation that we needed, right, internally to know that, yeah, we can reach more of this. I think in terms of the broader like readiness for this type of product, I, I think, you know, there's just a drumbeat of vulnerabilities and also just individuals. Really talking about the lack of funding, the lack of maintenance around this, around this. And so that is the validation that we continue to look for you know, as an opportunity to do something about, I think we're, we're very nascent in terms of evangelizing this and, and driving awareness. But I think, you know, those two things kind of has given us the confidence that you know, the timing is hopefully right and it's the right product for the time. Mike Bifulco: Yeah. Yeah. I, it's an interesting, almost, it's not that you have a chicken and an egg problem to, to work with, but I feel like the whole funding nut to crack is that like we, we all on some level, developers, engineering teams or organizations understand that it's important to Keep these projects funded so that they stay up to date so that vulnerabilities get shut down, bugs get addressed, functionality gets added, whatever the case may be. it seems like a lot of the social pressure lands on individuals to do the funding in a lot of ways, and I think that maybe is a law of numbers thing. Like people you know, you get a lot more call to action as an individual to go fund things. But my guess is that the bulk of the volume of money is coming from organizations who are willing to fund open source things. Is that roughly. Dudley Carr: Yeah, so we actually were able to analyze all of the Open Collective transactions. They do this amazing job of every transaction on Open, the Open Source collective, on Open Collective. You can literally download all of the transactions and so, I did that and I went Mike Bifulco: Oh wow. Dudley Carr: And yes, you know, organizations like Google and, and others, they do put in a ton of money. But if I remember correctly, I would say, Over 60% of it are from individuals donating at at much smaller amounts. So they're, they have a long tail and it is a significant portion of the contributions. And so it, it's, it wasn't as skewed as you would think towards large organizations. Mike Bifulco: That that is a, a bigger percentage than I would've guess. That's really interesting. So what, what is your call to action or maybe your pitch for those who might have the capacity to donate? Like how, how is the I guess the, is there a sales process for this? Is it something that you're going to organizations and people and trying to get them to discover and use Stack as donors? Dudley Carr: You know, I think, I think there are certain organizations that are very attuned to open source and, you know, they have open source program offices and they are actively engaging those communities and they are. they're looking, you know, they're either doing this themselves. So century is a customer of Stack and they did a ton of this by themselves. They, they wrote custom things to analyze their dependencies, and they had a big spreadsheet and it's super impressive, but it's incredibly time consuming. And I think Indeed and others are also analyzing their dependencies and trying to figure out where to allocate money. So this is something that is happening today. So we're looking to engage with those types of organizations and understand, you know, how STACK can potentially be a part of that. So I think step one is to really engage with organizations that are receptive to it. I think that's the kind of low hanging fruit. And I think beyond that, you know, there's, there's or organizations that are certainly consuming large. Portion of open source and you know, there's kind of a, a sales, different sales process around, you know, here are the ways that you engage with open source at those organizations. Funding is one aspect of that. And so I think over time that's where that conversation's going. But I think the organizations that are currently funding open source to some degree, You know, they're kind of making the case for that and, and we, you know, we're trying to expand that conversation and, and as well as piggyback off of that, Mike Bifulco: right? Yeah. It's nice that it's kind of the zeitgeist is that it seems that support has really changed in the past, I don't know, maybe 10 years to, like open source is something we can try or should try to, open source is something that, you know, I is the infrastructure of the internet in a lot of ways and something that you know, almost the, the ethical impetus is to support open source projects and to also be a part of that if you're able. So, okay. I, I guess one more important question then, if I'm an open source developer what, what are actions I can take to be proactive about I, I guess making sure that I'm, I'm covered by stack or that you know, that I'm doing the right things to seek funding. Dudley Carr: Yeah, I think you know, one. One theory that we have is that, you know, the, there are organizations like we were just talking about that are attuned and are willing to donate, but I, I actually think a fundamental shift will is dependent on individual developers donating and independent of the platform, but actively participating in that way of funding open source be it GI UP sponsors, open Collective Stack. Thanks, DD Dev, any of those platforms is a good way to start. But there, there has, you know, we have to have that expectation that developers are doing this just like they do other types of open source contributions. And I think that. That groundswell of developers participating and educating and kind of demanding this in their organizations is what actually turns the tide. And so our focus initially is actually to get individual developers to come on board and we're, we hope that we're. You know, one of those solutions that makes it a lot simpler. But if GitHub sponsors is the way that you do it, great. Right? Go, go on there. Fund, fund the people or the projects that you really care about. But I think that speaks volumes, right? And that I, I think is the thing that actually moves the needle. And those platforms have made it simpler. We hopefully have made it simpler based on, you know, what some set of people care about. But, you know, our, our goal is to evangelize individual developers. Contributing more. Mike Bifulco: Yeah, that's a noble conceit and definitely one of those things that I think all of the people listening to the show can probably relate to. I certainly identify with it. I, one of the things I've been mulling over a lot lately especially, especially in the past few weeks that I've been like reconsidering my personal budget and the way I allocate money for things is that I, I think I would like to be a little more public in sharing and explaining. The ways that I spend money in four good ways, right? Like charities that I donate to on one side, but open source things that I donate to projects that I support. And also, this is more on the creator economy side, but like Patreon and things like that, where there's like, you know, I love this podcast, so I give them a dollar a month, which is, you know, more than they would ever get from me clicking on ads. I could click ads every day for a week. And wouldn't give them a book. And it goes a lot further than you would think. And it, it's funny, I've been kind of thinking that that's something that belongs in. Almost public profile, like I should be sharing this somewhere and making that a part of the my, my persona, my support for the world. And I think that that's something that we have a, great opportunity to do with projects like Stack A and with other things that we all participate in because it also creates that social pressure and that. Impression that expectation that part of being a, a good citizen as a developer when you can and if you can, and if you have, you know, the, honestly the mountains of privilege that I'm sitting on top of, like, you should be giving back. I really like that. And I, one of the things that I like about STACK is honestly the, the tree view of the dependencies and seeing the amount of impact that, you know, even a few bucks a month can have is like visceral. You really feel like you, you see that not only are you using this cascade of things to power whatever project you're working on, but you can also give back to them fairly directly. And, there's infrastructure in place to do that for you. I think that's really exciting and I think it's a noble cause and I'm hoping it's something that a lot of the folks who are listening to the podcast will be able to jump into and go ahead long into supporting, but also benefiting from. Dudley Carr: Yeah. No, I appreciate that. I, I think what you're saying really resonates with us in that how you spend your money matters. You know, we are in a position of privilege where, you know, we we have discretionary money that we can funnel towards things. And I think, I think you nailed it. You know, a lot of these developers are, you know, at the moment maybe a couple of bucks per month. You know, we're still small, but I think it, it really matters to those developers partly because it is a real recognition of what they're doing and they know that someone took the time and their money, you know, to do that. And I think that's super powerful. I think it's easy to dismiss it as, oh, it's, you know, it's a trivial sum of money or something of the. But you know, when you are working on something, and a lot of times, you know, you can look at your MPM install numbers, like, oh yeah, that's through the roof. But this is, you know, getting an email from someone saying like, I like your project. That's really visceral as well. Mike Bifulco: Yeah. Dudley Carr: like people actually just paying. I think that's an incredible way. And so hopefully people are not put off by, you know, initially like, oh, the, the dollar amounts are not significant. It, it, it supports that individual at so many different levels. And so yeah, how you spend your money matters and and it has a really great upside on the other other side of it. Mike Bifulco: Yeah. , it's pretty profound and an energizing thing for me. Well, Dudley, thanks so much for coming and hanging out today. I have two important questions for you before I let you go. One is I wanna know how APIs you won't hate listeners can find you and talk to you if they're interested. And where can they go to get started with? Dudley Carr: Absolutely. Yeah. So you can email me at dudley dod e y stack.us and our website is stack a.us. I think if you search for Stack Google, we're number one. And you know, as we were chatting earlier, it's, it's super simple to get started. If you run into any issues please reach out and we're, we're happy to answer questions. But yeah, it's pretty self-service at the moment. Just click on the button o off and then hopefully you're off to the races and, you know, always looking for more feedback and, Yeah. No, we, we appreciate every, every person who signs up and happy to answer questions. Mike Bifulco: Great. Wonderful. Dudley, thanks so much for hanging out today. It's been a pleasure having you. And I'd love to catch up again you know, maybe in a few months or ear down the line to see how things are going. Dudley Carr: Absolutely. Thanks so much for having me. Really appreciate it. Mike Bifulco: Yeah, of course. Take care. Dudley Carr: Bye-bye. All audio, artwork, episode descriptions and notes are property of APIs You Won't Hate, for APIs You Won't Hate, and published with permission by Transistor, Inc. 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https://dev.to/thekarlesi/how-to-handle-stripe-and-paystack-webhooks-in-nextjs-the-app-router-way-5bgi#2-the-middleware-trap | How to Handle Stripe and Paystack Webhooks in Next.js (The App Router Way) - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Esimit Karlgusta Posted on Jan 13 How to Handle Stripe and Paystack Webhooks in Next.js (The App Router Way) # api # nextjs # security # tutorial The #1 reason developers struggle with SaaS payments is Webhook Signature Verification. You set everything up, the test payment goes through, but your server returns a 400 Bad Request or a Signature Verification Failed error. In the Next.js App Router, the problem usually stems from how the request body is parsed. Stripe and Paystack require the raw request body to verify the signature, but Next.js often tries to be helpful by parsing it as JSON before you can get to it. Here is the "Golden Pattern" for handling this in 2026. 1. The Route Handler Setup Create a file at app/api/webhooks/route.ts . You must export a config object (if using older versions) or use the req.text() method in the App Router to prevent automatic parsing. import { NextResponse } from " next/server " ; import crypto from " crypto " ; export async function POST ( req : Request ) { // 1. Get the raw body as text const body = await req . text (); // 2. Grab the signature from headers const signature = req . headers . get ( " x-paystack-signature " ) || req . headers . get ( " stripe-signature " ); if ( ! signature ) { return NextResponse . json ({ error : " No signature " }, { status : 400 }); } // 3. Verify the signature (Example for Paystack) const hash = crypto . createHmac ( " sha512 " , process . env . PAYSTACK_SECRET_KEY ! ) . update ( body ) . digest ( " hex " ); if ( hash !== signature ) { return NextResponse . json ({ error : " Invalid signature " }, { status : 401 }); } // 4. Now parse the body and handle the event const event = JSON . parse ( body ); if ( event . event === " charge.success " ) { // Handle successful payment in your database console . log ( " Payment successful for: " , event . data . customer . email ); } return NextResponse . json ({ received : true }, { status : 200 }); } Enter fullscreen mode Exit fullscreen mode 2. The Middleware Trap If you have global middleware protecting your routes, ensure your webhook path is excluded. Otherwise, the payment provider will hit your login page instead of your API. 3. Why this matters for your SaaS If your webhooks fail, your users won't get their "Pro" access, and your churn will skyrocket. Handling this correctly is the difference between a side project and a real business. I have spent a lot of time documenting these "Gotchas" while building my MERN stack projects. If you want to see a full implementation of this including Stripe, Paystack, and database logic, check out my deep dive here: How to add Stripe or Paystack payments to your SaaS . Digging Deeper If you are tired of debugging the same boilerplate over and over, you might find my SassyPack overview helpful. I built it specifically to solve these "Day 1" technical headaches for other founders. Happy coding! Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . 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https://aws.amazon.com/blogs/developer/tag/silverlight/ | Silverlight | AWS Developer Tools Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Developer Tools Blog Tag: Silverlight AWS SDK for .NET v3.5 Preview by Aaron Costley on 06 FEB 2020 in .NET , AWS .NET Development , AWS SDK for .NET , Developer Tools Permalink Share Today, we have published a preview release of version 3.5 of the AWS SDK for .NET. This primary objective of this version is to transition support for all non-Framework versions of the SDK to .NET Standard 2.0. If you are currently using a .NET Framework or .NET Core target, no changes are required. We are […] Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? Cloud Computing Concepts Hub AWS Cloud Security What's New <a data-rg-n="Link" | 2026-01-13T08:48:40 |
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https://share.transistor.fm/s/940dfccb#copya | APIs You Won't Hate | The State of the API Address APIs You Won't Hate 40 ? 30 : 10)" @keyup.document.left="seekBySeconds(-10)" @keyup.document.m="toggleMute" @keyup.document.s="toggleSpeed" @play="play(false, true)" @loadedmetadata="handleLoadedMetadata" @pause="pause(true)" preload="none" @timejump.window="seekToSeconds($event.detail.timestamp); shareTimeFormatted = formatTime($event.detail.timestamp)" > Trailer Bonus 10 40 ? 30 : 10)" class="seek-seconds-button" > 40 ? 30 : 10"> Subscribe Share More Info Download More episodes Subscribe newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyFeedUrl()" class="form-input-group" > Copied to clipboard Apple Podcasts Spotify Pocket Casts Overcast Castro YouTube Goodpods Goodpods Metacast Amazon Music Pandora CastBox Anghami Anghami Fountain JioSaavn Gaana iHeartRadio TuneIn TuneIn Player FM SoundCloud SoundCloud Deezer Podcast Addict Share newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyShareUrl()" class="form-input-group" > Share Copied to clipboard newValue ? setTimeout(() => copied = false, 2500) : null)" @click="copied = copyEmbedHtml()" class="form-input-group" > Embed Copied to clipboard Start at Trailer Bonus Full Transcript View the website updateDescriptionLinks($el))" class="episode-description" > Chapters December 1, 2021 by APIs You Won't Hate View the website Listen On Apple Podcasts Listen On Spotify Listen On YouTube RSS Feed Subscribe RSS Feed RSS Feed URL Copied! Follow Episode Details / Transcript Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations at Postman, to talk about Postman's State of the API 2021. Show Notes Matt and Phil are joined by Matthew Reinbold, director of API Ecosystems and Digital Transformations to discuss Postman's State of the API 2021 report, detailing various data points from around the API world from which specification people turn to, to how confident people feel deploying their APIs. They also discuss various topics around remote work, how APIs enable more remote work and what will happen in the next few years for APIs. Notes: Matthew on twitter: https://twitter.com/libel_vox Postman's State of the API Creators and Guests Host Mike Bifulco Cofounder and host of APIs You Won't Hate. Blogs at https://mikebifulco.com Into 🚴♀️, espresso ☕, looking after 🌍. ex @Stripe @Google @Microsoft What is APIs You Won't Hate? A no-nonsense (well, some-nonsense) podcast about API design & development, new features in the world of HTTP, service-orientated architecture, microservices, and probably bikes. Matt Trask: Cool. Welcome back to APS. You won't hate episode 17. I have Phil with me and we're joined by a very special guest today. Matthew Reinbold, fresh from postman, who is a director of API ecosystems and digital transformations here to talk about their report, the 2021 state of the API ecosystem. Matthew, how's it going? Matthew Reinbold: It is going. I am happy to be here first time, caller, long time listener. Is that how we say that? Matt Trask: I think that's yeah. It's how you say it. Yeah. So I mean, for those of you, like in the off chance that someone doesn't know who you are in the API ecosystem world can you give us a little bit kind of about yourself? Like you manage two different newsletters, at least as well as a pretty prolific Twitter presence as well. But if someone hasn't run into you, like. Matthew Reinbold: Well, yeah, well, first off, thanks for calling it prolific. Some people would call it annoying, but yeah, I I manage a fair number of tweets over at Twitter slash L I B E L underscore Vox, reliable Vox. That's where I talk about digital transformation and APIs and a lot of technology stuff. Occasionally. Fights with blockchain and NFT enthusiastic. But then I also manage, I also manage a newsletter called net API notes, where for almost 200 issues, going back to 2015, I've covered the landscape. I've shared essential bits of information. I've tried to boil down the, the. Current climate and get it right into just the most essential things that decision makers need to know and care about. And then I do a fair amount of blogging on a blog. That's very imaginatively named Matthew reinbold.com. In there, I talk about a fair number of things as well, but in, in, in short my passion is really about coaching people, helping people, teaching people to get better with their API ecosystem. Matt Trask: That's really cool. So one thing that kinda stuck out to me cause it's, so we're going to be talking about the 20, 21 Sidi APR report. However, I'm curious since you've been doing it now since 2015, you've been keeping notes on. The API world. How does your kind of, I hate to say this phrase, the 30,000 foot view of everything that, you know, from 2015, how does that kind of line up to what you saw with the 2021 state of the API report? Matthew Reinbold: Oh, that's interesting. So there's definitely. Maturing as a industry, we've gone through a number of phases. Those of us that have been around the block a few times, see trends come. And most often they, they tend to roll away. And over that time we have to develop models so that we can kind of. Pick the, the, the wheat from the chaff, you know, what, what are the properties of something new, some kind of buzzword, some kind of hyperbole that we can latch onto and say, yes, this is worth investing in. This is worth our interest in our effort versus, yeah, this is some marketing system, some spin as I'm looking at the 20, 21 postman report. I see. Where we've come. It's gone from being single point to point integrations. One-off bespoke API APIs to where we're now talking about things as ecosystems. We're now talking about collections of these things and how entire organizations. Manage these as, as something that's beneficial, something that's collaborative and, and managed as a separate entity rather than, than each individual unit I've got Phil here. So I have to use the forest for the trees analogy rather than just managing the individual API trees. There's now a greater awareness of what the forest, what the forest role is in the company and how to manage that. In a unique way, as opposed to the individual pieces. I will say for those that are listening, like I'm one of the things I want to highlight right up front here is that you don't have to enter an email address. It's not behind the page. We really felt strongly at postman that we had to get this information out to the most number of decision-makers so that they could make better decisions so that they could be informed as they're developing their strategies and roadmaps. So if you go to postman.com/state-of-api, you'll be able to download. With out any worry about having somebody from sales follow up with you later, or getting spam in your inbox, it's free for all. We want this information to be used. We want the dialogues to happen. We want the discourse to be rich and for me and frothy. And so please, you know, don't let past marketing spam. Stop you from checking this out. We want this in the hands of people. Phil Sturgeon: Fantastic. That's good to hear. I mean, that's I haven't got around to reading it as you might have seen from Twitter. Life has been a bit of a mess recently just spending far too much time in the field, as opposed to in the field doing APA stuff. But, yeah, that's definitely always been a concern of mine, of, you know, you hear about these white papers and reports and you just know so many of them like should have just be in the blog post, but instead that like a PDF that and you've got to enter information and then you just get like that fifth email, like, why didn't you reply to my previous four? I was like, I don't know who you are. I just want to read this thing. So yeah, I'm glad you folks are going in a different direction, but Maybe just taking a step back. Like, what is the state of API is report all about where are you getting your information from? What sort of research is being done? And what's the hospital. Matthew Reinbold: Great question. So this is, as far as I know, the largest survey of its kind, we had more than 28,000 people respond to our latest in a series. What we tend to do is try and track where the industry is at. And typically that's been around certain areas. Like how much time do you spend developing API APIs? What kind of tools are you using? Really good stuff there tracking the growth of, of the industry and the maturation of the industry. What I brought to the table this year. Was an interest on finding the behaviors that lead to sustainable, healthy API ecosystems. Like so much of what we talk about when it comes to API ecosystems is still very anecdotal. We tell stories about the Bezos Amazon memo, where we talk about like Twilio or Stripe, but when it comes to decision makers in large organizations, they're still. Trying to pull at what are decent KPIs, what are the behaviors I should be grooming or promoting within my company to make sure that I can keep producing quality API experiences again and again and again. And so what we did with this report that I'm really proud of is dig deep and discover, like, what are the correlating behaviors in organizations that lead to good things happening for companies? Phil Sturgeon: Okay. That's interesting. Cause I think. There's always this question around, like, what's a good API and what's a bad API. Right. And that's just such a nebulous, almost pointless topic so often, because you're just going to end up with opinions about camel case versus kebab case and opinions about rest versus graph UI, and all the nonsense that we love to fight about. And there's going to be someone with a fever at HTTP status code. And none of that actually matters, but you're talking about more of the business level stuff or what, what sort of things have come up as like. Really interesting results from, from your survey about how to build a good API what's what's, what's new and what's interesting. Matthew Reinbold: Right. Well, one of the things I wanted to look at was some of the insights that popped out to me when I was reading accelerate. So accelerate is like from. The previous decade, but it was written by Nicole Forsgren, Jess humble, Jean Kim, they came together and tried to figure out like, what was it about dev ops? That was so powerful. And they wanted to do it in a, in a way that quantified things, not just like, Hey, this is awesome. You should be doing it, but like get to the meat and potatoes of why is this powerful and why should businesses adopt dev ops? And as they went through their research they ended up discovering that there was really four things, four metrics that showed how dev. Made for better organizational performance. And those things were lead time, deployment, frequency, meantime to restore, or how quickly you recover and the change fail percentage. And I thought, huh, that's really interesting. Now that's for dev ops, but if these things are so instrumental in having organizations outperform. Their peers. Can we find the same correlation with API APIs? If we have the same behaviors, can we therefore then draw a line and say, if you have these things, if you have positive aspects of these four attributes, can you then have a more sustainable, more powerful API program? And based on our survey results, the answer is yes. So I can, I can go in and how we, how we drew that correlation. Phil Sturgeon: I'm curious, what sort of metrics are We, looking at? Matthew Reinbold: yeah. So first off we asked people on a 10 point scale. What, how, how well do you think that you've become API first? So out of our 28,000 respondents, they looked at this 10 point scale and they, they put themselves, you know, how they felt approximately 8% of the people that responded said, yes, we are either a nine or a 10 on the scale for API first, we said fine. And then we went through and we said, okay, you know, how long does it take you to make an API? Are we talking hours, days, weeks, so on and so forth. And we also said, okay, you know, not just time to produce, but how frequently you deploy and how many times do you have a deployment failure? Meaning like you put something in production, but it didn't work. Right. So you have to roll back and then like, what was your time to recovery? Like when an outage does occur and let's be. And outage always occurs at some point. Like how, how quickly can you recover from those things? So we got these nice, you know, bell curves and everybody kind of clumped toward the center on these things. And then we said, okay, Now the magic is we go back to that first question, the people that say their API first that have some kind of strong belief that they're doing API first, let's see how they compare to their peers on these metrics. And again, and again, all for these items, API, first people perform better. So, you know, taking one example here. API first people were able to deploy 17% faster than their peers and you know, in a day or less. So if you are API first and granted, there, there might be some subtlety in how a company defines that. But bottom line, if you are API first, you perform better on these metrics than your counterparts. Phil Sturgeon: Interesting. And yeah. Seeing, seeing as you raised it, what is API first? There's, there's a lot of different definitions floating around. Right. And so just for listeners that might not have listened to everything we've ever talked about and read every blog post we've ever read ref ever wrote how do you define it? Matthew Reinbold: Sure. Well, first for people that haven't heard this and haven't listened to every episode, shame on you. Second, I define I defined API first and. Making the API experience or the interface, the primary means for the functionality exchange. So not viewing, like I'm going to create this functionality and then subsequently go and some other team or, or some other project we'll be wrapping this thing in an API. It's thinking of creating an API experience as the primary exchange mechanism with dysfunctional. Not a library, not a module, not a class, the API. So this is slightly different than API design first, which is, I am going to subsequently talk to stakeholders, create a model, whether that's in an open API document or some other means, but I'm going to sketch that out. Test my assumptions, and then subsequently only begin code after. That's API design. First, I do draw a line between those two. They are very copacetic. They, they work together like peanut butter and chocolate, but there, there is a difference. You can, you can do API first without necessarily being API design first. Phil Sturgeon: For sure. Oh, well, we've got you on a roll. You're doing these really well. What is API as a product? Matthew Reinbold: Ooh, API API as a product. So that is creating an API with the. Awareness that it will have a roadmap. It will have ownership beyond just being put into a production environment that it will grow and change and subsequently necessitates the kind of modeling responsibilities and, and awareness that it will be growing and changing over time. Phil Sturgeon: Okay. So instead of, yeah, API first is your product should have an API. And that will be managed by the team who was making this product. And API as a product is a slight variant of API. First, that kind of takes that API out of that generic functionality team and says the API itself is the product. And another team potentially on the same team will be making a product using that Matthew Reinbold: Right. I, I would, I would, I would venture there's a lot of large enterprise environments for which API for. It's about a project that gets the thing into production. And then that thing is left to operate and run on its own. Perhaps there's some monitoring, perhaps some observability, but the actual team that made it is off doing the next thing and the next thing and the next thing there's not the idea that. This is a long lived item that, that produces some kind of business functionality value that is competing in a complex dynamic marketplace like that. That's the API product side of the house. Phil Sturgeon: Hm. Matt Trask: So the, I guess like the, the big question to bring up, I think right now is what did the pandemic do for the API ecosystem? Matthew Reinbold: Well, you know, first of all, I want to just stress that, that this thing that we kind of hand wave is the pandemic was actually like multiple congenital. Crises all at once. Right. You know, I, I want to, for the audience, like we're talking social unrest and political upheaval and supply chain disruption, and the, the pandemic was really a catch all for a tremendous amount of business stress. And what we've seen in the report is the usage of APIs, the number of API APIs the. Amount of focus and care on API. APIs has increased tremendously with that pandemic because business leaders, technology leaders are struggling with this amount of change, this amount of disruption. And so having architectures that are slow to change, difficult to change is just not cutting it in this. Set of multiple crises. So any kind of architectural advantage that allows them to change rapidly change quickly to do different things with how their development investment is deployed. So, you know, for example, taking that one dev team that was altogether in the office and being able to break it down into microservices to allow for greater asynchronous operation, greater flexibility. Those are the architectures that are being sought right now. Matt Trask: Yeah, that makes sense. I mean, it always here in America, I don't know if it feels sing, but you know, like there's. At the core level there. So like the whole, did we go back to the office and be Sandy the office upheaval as well. So it makes sense that there is kind of like a, a struggle on rapping, like getting non-technical CEOs, CTOs, CFOs their heads around the game-changing, this of APIs that doesn't surprise me at all to hear that they're still kind of, I don't want to say struggling, but unsure. Maybe like, Matthew Reinbold: Well, and, and, well, I, I think that's an interesting perspective because it assumes that leaders were in command and control positions of how the labor was divided anyway. And I would actually, I would actually posit that it's the opposite. It was everybody immediately going and running to their home offices and working in a remote work environment. The change in the communication paths changed the architectures that were subsequently produced by those teams. It's Conway's law in effect. And therefore, as we, as we look forward, as we look forward to what's going to happen, I would, I would venture that the organizations that pull people back to centralized locations, for whatever reason, I'm not going to debate whether that's good or bad, but the people that pull the development teams back to. see, like the Terminator two bad guy they'll reform remold because there will be more efficient communication patterns when everybody's face to face. Whereas those organizations that continue to have a distributed workforce will have more distributed architectural patterns because that's how communication is happening. Phil Sturgeon: That's really interesting. I haven't really thought about it before, but I, I, I bet there's been an uptick in kind of API design first, specifically due to this as well. Right? Because my experience working we work was, was pretty awful as far as like API planning goes and as a result, APA architecture and API performance and Matthew Reinbold: You don't say you should blog about that. Fail. Matt Trask: Yeah. Phil Sturgeon: 25. I'm going to do a book about that shit. Matt Trask: Have you tweeted about this yet? Phil? I'm not sure if anyone knows your true Phil Sturgeon: I did a talk. I did a talk recently. But yeah, there was, there was such an element of like, we're real in an open plan office, playing ping pong together and shooting each other with nerves that there was never any effort on API contract being written down in any shape or form because you're all sitting about. And you're just like, what's that end point? Cool mate. Oh, if slash whatever. Oh, is that a, is that property of booty? It's a string called true with QuoteWerks and then you didn't have a need to write it down because you just show it over, over the top of Nerf fire. And I, I do wonder if remote work, well, not necessarily remote work, but quarantine remote work has helped push people more towards it because if you can all be sitting around asking each other, you're going to be typing. The contract over slack. And if you're going to be typing it out over slack, which is inherently ephemeral, then you might as well type it into a Yammel file and commit that in the repo. And then you can have design reviews around the board request or other tools that the offer, that sort of thing. So, yeah, that's, that's just completely a hypothetical and something I'm thinking the second night and check that, but I'm sure it's happening. Matthew Reinbold: I completely agree. And, and let me throw in something that's not in the report, but something that's got me totally geeked out and I'm watching for on my radar, we are going to see the greatest Renaissance of API design documentation that we've ever seen in the next couple of years. Now, granted, you know, as far as Renaissance goes, maybe Renaissance. Documentation are not that great. So, you know, let's put the party hats back in the closet, but what we're seeing with the great resignation right now is all of that knowledge that people acquired in their heads is leaving. It's headed out the door and I've read reports like up to 80% of how to do things with API APIs is in people's heads. Like at we work. If you needed to know how API has worked. You know, you knew Phil was the guy that could get you straightened and Phil Sturgeon: I didn't have a clue. That was the problem. I was trying to find out how to do it. Matthew Reinbold: Okay. So I wasn't, it was somebody, it was somebody on the other end of a, of a Nerf battle away Phil Sturgeon: Someone who quit already is the person that you. Matthew Reinbold: But right now in organizations like you have this phenomenon where a tremendous number of people are leaving organizations and they might've been the sole person who knew where the end points were or knew how that particular tricky function worked. And as organizations are trying to deal with this and recover and still be productive, there's going to be a greater emphasis on having that crap written down, having things documented. Organizations don't have aren't left on their back foot like they are right now. So whether that's heavy handed processes, whether that's just a greater appreciation for documentation among the staff, that's left, whatever that manifests as there's going to be an increasing amount of emphasis on documentation, because people have seen that too much was stuck in people's heads and it's not sustained. Phil Sturgeon: Yeah, that's a really good point. I mean, and not just kind of documentation, but the whole open API as a source of truth earlier on. And I figured it has to be, has to become more noticeably important when Yeah. They've, they've lost the whole team. How the API works and you know what it's like, code's always a bloody mess. Cause you just hacked up within about what over the place and patch things and fix things. And what about and yeah, when they find themselves rewrite in the API, cause no one can really take it over and no one remembers how it works and there's no documentation for it. And it's just too hard to figure out when they just make a brand new one. And they have a whole brand new team doing it. Cause they've already lost all that stuff. Matthew Reinbold: Yeah. Phil Sturgeon: That's a situation that a lot of managers and business people are going to say, how can we go about avoiding doing this? And I just hope there's someone in the room that says, well, APA designed first would really help avoid this problem because otherwise they'll just repeat all the same mistakes again. Matthew Reinbold: Right. Absolutely. Whether it's design first or tools that help analyze existing traffic and write the document afterwards, like whatever you got to do, get that written down and start taking some notes against it because. It's it, I believe right now with the great resignation. It's an Achilles heel. That's probably hampering a lot of organizational ecosystems right now. Matt Trask: Yeah, I would definitely agree. I mean, it shows in the report under open API three dot oh, 44% of people are aware of it, but they don't use it 28% say they use it. 12% said they use it, the love it. So even just combining use it and use it in love. It still does not match aware of we're not using it. Which means that there is definitely a. A river to jump over. So to speak, to getting more people on, to open API, which is probably currently like the standard for API documentation right now which comes back to your point, which allows them to start writing things down and start documenting things. And Phil gets it by bus tomorrow. We work is still going to be okay. It very well could happen. Which is exactly why I use that example. And it, it, yeah, it it'll give the organization a little bit more or a little less reliance on what's in people's heads a little bit more stability in case great races, nation three Datto happens in three years. You know, you don't know what's gonna happen. Phil Sturgeon: Is that when everyone resigns from web three point now, Matt Trask: please. Don't don't threaten me with a good time. Like I've already, I've already muted those web three and NFD on my Twitter and it cleaned it up so Phil Sturgeon: Why do you hate progress, man? Matt Trask: A lot of reasons. I'm a combustion at heart? No. Matthew Reinbold: Hey, if you don't, Phil Sturgeon: particular messages of this progress that are the problem. Matthew Reinbold: if you, don't stand for something, you'll fall for anything. Good for you, Matt. Matt Trask: yes, I've always wanted my life to be attributed to a, a Hamilton quote. So I am glad I did. I can check that one off to get back onto the actual topic and not just bashing NFTs for an hour and a half, which sounds like a lot of fun. What you the most about this report? Like what was something that you read that just you weren't expecting? Matthew Reinbold: I, I think there was two things that when you combine them together it made me tilt my head and go, huh? The, the first is that more than anything else? Including speed to production. People want quality API APIs. They want stability. They want some other things reliability. But the primary thing that people want out of their, their API APIs is quality. And yet when it came to whether or not people had time to test. Everybody acknowledged that testing was good. Tested was valid, but nobody had enough time for testing and it's like, huh? These two things kind of seem like. The, the two sides of a coin, right. You know, people aren't getting the quality that they want, but everybody acknowledges that they don't have enough time to do testing, even though they recognize the testing is an extremely valuable type thing. So I think when it comes to socializing this report and talking to decision-makers and doing the kind of coaching that I so often do, I, this is one of those things too, to bring up, like how in your program are you supporting. Testing and ensuring that enough is being done there so that your developers feel like you're, you're reaching the kind of quality goals that, that you're, you're promising to the rest of the world. Phil Sturgeon: Hm, do you, is the survey broken down by role? So can you, can you look to see if. Managers and engineers have a rule, very interested in, in high quality. And engineers are going, but we don't have enough time, but the manager's like, oh, they definitely have enough time. Matthew Reinbold: Right. So we do have a breakdown by role and job title, but I don't have the numbers in front of me that, that combined, and show me how to break down the quality question. Phil Sturgeon: Yeah, that'd be an interesting one. Cause yeah, so many roles, so many organizations, I just take it as like a universal truth is that companies are just, you know, business and product are demanding feature, feature, feature, feature, feature, and engineers are just like screaming, just keyboards on fire, trying to try to hit them goals. And everything's just wonky as hell. And it seems to be everywhere I go. There's not enough to have. There's not enough time for QA. They might've got rid of the QA team because it's slowed down product and slowed down delivery of features. Yeah, everyone wants high-quality API has, but no one wants to put the time in to testing because testing is inherently hard and slow. Matthew Reinbold: Right. And kind of along those same lines, another stat that jumped out at me was that 76% of the people building API APIs have less than five years experience doing. I mean, you know, as far as restful APIs now, we're, we're more than a decade into that journey. So that stat leaps out at me, like what is it about API development, where we're getting people with zero to five years experience like what's happening. There are the successful API builders, aging out and becoming management. it, are they moving on to web three O and NFTs? Like, like what is, where are our experienced API builders and why are these critical pieces of business infrastructure? In the hands of relatively younger people. That's not to say that they can't be doing a good job, that, that it's impossible to build a great web experience at your first time at bat. But it's also something where I think everybody on this call would probably agree. Experience counts, experience matters. Ha being around the block once or twice, you pick up a feel for what's beneficial, what's maybe a little wonky and you can imbue that into a better design at launch. So, you know, where are the. 10 year, the 12 year, the 15 year veterans. And why are they not the primary source of API infrastructure development? Phil Sturgeon: Yeah. Some that I've seen so much, again, just, I love complaining about we work. Pretty much everyone that was a junior developer, Right. Like the vast majority, what, what you need developers and their role responsible for creating you know, there's like a hundred API APIs and, you know more than a hundred junior developers with just a sprinkling of seniors who were more on the cowboy coder end of things. Not, not to be rude, you know, like startup, you need to be super agile, super fast, not, not a perfectionist. And so, so many of the problems where this is, this person's first rails app, like they know how to accept incoming Jason parameters and they know how to spit something back from the database. And. That's that, and they know how to make a web request. So he talks to . He talks to F talks to G in the thread, and then no, one's got a timer anyway. So everything falls over, like, things like that. The sort of thing you realize, if you've been doing APIs for five years, or for 10 years, you've been doing it for 10 years, you wouldn't do that. You just wouldn't do that. You'd put something in a sidekick job and then implement a web socket or a web hook, or literally anything else. But. That's the sort of thing you do when you consider like HTP failures or server downtime, to be an edge case that is like some weird scenario that probably won't happen. And when you've been doing it for a longer time, you're like you, you change your mindset to this web requests probably won't work. And on the off chance that it. This is what should happen. And you just get really defensive and paranoid and have like 25 different guard statements and, you know, 25 different types of ex exception catching and, and every single circuit breaker and trigger warning that you can possibly put on this thing. And there is, yeah, there is a change in mind. Around around that kind of it doesn't, I'm not being a gatekeeper or at least they're saying you've got to be doing EPS for 10 years until you're good. But when you start out, you you're such, you're more of an optimist. You haven't seen it go wrong in as many ways. You haven't had cascading failures and you haven't had all these terrifying things that happen. So that, that is definitely a concern for me is that I think, yeah. Happy, happy path development. When you go from having one AP. To having 20 or a hundred, the, the the chance of straying off the happy path gets exponentially worse. Right. And, and that's just something, I think a lot of these younger developers on experience with. Matthew Reinbold: Right. Even, even when it comes to design, having used API APIs, having to incorporate the API APIs, you better understand what makes a good description and what is just a reiteration of the, the name itself. Yeah. Yeah. If I have a field called date of birth and the description is just the birth, that, the date that the person was born on, like, well, what was the. do I need to refresh it? Or is it cashed? You know, like, can I store it or is it part of some kind of regulatory PII? And I shouldn't, you know, I can use it, but I shouldn't store, like, there's so many issues that once you've been down that road, and then you're asked to produce an API, you bring that experience with you and you put it into the description that adds so much that yeah. I, I, I, I don't know. How we continue to get that, that experience circulating and get that in front of people. But I think it's really important. Matt Trask: Well, I must wonder too, like how many of those, like experienced API builders are getting swallowed up into Stripe? Twilio, Google. And kind of almost locked away working on their API APIs and not able to share their experiences down the road to junior developers in their own companies or interim networks, things like that too, because it feels like you do your five, seven years as developer, you get pulled into the management game and then all of your knowledge is still there, but you're having to balance both managing a development team, hitting your goals. Pushing out products because you've got to make money for the business. And all of your knowledge that you've worked so hard to gain is kind of sidelined in the name of profits or KPIs or whatever it might be. Matthew Reinbold: Possibly there's, there's certainly exceptions that spring to mind. One of which is Tim Burks and the team over at Google and with the number of resources that they put out there. For their APIs. It's, it's kind of a mouthful, but if you do a Google search for that, they've produced a tremendous amount of documentation about how they support API APIs at scale, how they do their design reviews, how they think about consistency and cohesion across their entire footprint. So that certainly what you described could be the case in some places. You know, I, I, I do think that it's not necessarily the default that's people go off to these big organizations and then just disappear because the folks at Google around Tim and his crew they're doing some great work. Phil Sturgeon: So I've been sat in the room with you having these sort of conversations your last job, Right, Like a center of excellence type stuff. You, you get a bunch of smart people and me together and start talking about what, what would help with these various different problems? Like how do we do APA design reviews? How do we do governance? What standards should we be interested in? So I think sometimes yeah. Experienced developers can get sucked up into these companies and kind of finish and end up having that scale was used for something else. But I, I think companies that have those governance processes, like they're sharing their experience back by creating style guides, by creating programs that they explain how these, how these like API designed life cycles or API life cycle should work. And that's a way that they can essentially. Distribute their experience. So instead of like, I know what to look for when I'm reviewing a poor request, they can create a style guide. That means that everyone will do that. I think the danger there is that when style goes focus on what, instead of why then, then you kind of lose some of that experience because it just seems like arbitrary decisions delivered from upon high. Right. You just get. Do it this way, but, but Y I've read loads of style guides recently. And, and some of them, I should probably show the examples. It's just like, do this. Like, why you don't tell me what to do? You don't my dad, like, it just, I couldn't figure out what they possibly could have meant by it. Cause usually I can look at something. Why might they mean that? Oh, that reminds me of a thing that happened along these lines. They probably got burned by that before, and they want to avoid it, but if you don't see why it just sounds arbitrary and you're not actually teaching anyone on anything, but if you do it right. that that can be really helpful. Matthew Reinbold: Right. And it's also essential that if you're designing these systems like a governance or like a center of excellence that you have the feedback process that you have, the, the communication cycles so that when people do have that kind of. That they have a recourse. It's not a dead end. It's not either you do this or you're punished for it, but oh, if this doesn't make sense, here's who you talk to. Here's how you can escalate your concern here is how you elevate your edge case. And we can have a discussion about it and you can help co-evolve this thing, because you own this as much as somebody else, the, the phenomenon that you described, where it's a dead end. It's thrust upon you. You don't have ownership of that. And as a developer, that does not feel good, that does not invest you in seeing the long-term growth of, of that system. You want to burn that system. You want to be the rebels flying through the death star trench. You want to take that thing down? So what's essential is to realize. You provide the avenues for people to, to voice their concerns, voice their questions, and make them feel heard in such a way that their process, the process is theirs. It's not something done to them. It's it's their process. Phil Sturgeon: I'm just laughing about the death star rebel situation. Now I'm completely distracted. I need to go rewatch some star wars. I don't know. Matt Trask: I mean, your, your thought on the ownership thing is also interesting cause And we like watching the junior Twitter, the junior developer Twitter circles, which is not the end all be all of it all, but there is a large emphasis on if you want to make more money, you need to jump ship every two years on average. And that kind of removes the does or not the desire, but like the, the ownership of any sort of product from a junior developer, because in two years, they're going to be onto another thing. They're going to be onto another system. Codebase, maybe another language and it, it does kind of bring back, like, how do you entice people to have ownership, even if they only are going to plan to say somewhere for a short period? Because we all know that like having, like you said, having that ownership is going to kind of make you more invested, more caring, more thoughtful, more empathetic towards whatever it is that you're building. Matthew Reinbold: Right. I mean, we're veering into management territory, which I'm happy to talk about. I, I know. Matt Trask: very allergic to management. So. Matthew Reinbold: But I, I was just reading Harvard business review. Hey, I'm fun at parties too. So I was reading Harvard business review talking about COVID and the great resignation and the, the management challenges that, that come with that and what we need more. In all companies is a feeling of belonging, a feeling like we have a career progression feeling like our, our, our work has impact and all too often management, just as about making sure people don't do dumpster. Right. You know, I'm, I'm here to police you because the organization doesn't trust you. And it leads to all kinds of weird effects. Like, Hey, if you actually want to grow your career, you need to leave. You need to hop companies every two years and let's be clear that may work, but it's still very disruptive, not just for the company, but for the individual. 'cause they're having to rebuild all of those social structures, their relationships, their patterns, the routines it, it's not, it doesn't come for free. And so from a management standpoint, if you can show people how to have that fulfilling career, how to fulfill those needs. They don't have to jump ship every two years. There's no reason that that has to be the default blueprint. And from a company standpoint, you actually benefit from that accrued experience rather than having a developer. That's done the same thing. Five times you get five years of experience. That's really powerful, really tremendous. And that, that ultimately not only leads to better APIs, but leads to a better employee. So there is a disconnect we need to work with our management layers. It shouldn't just be the technician that has some headcount is by default manager. There needs to be an appreciation for how those are unique skill sets. Those are unique muscles that need to be exercised, but. If we can create that fulfilling sense of duty then, and that the career path for these individuals, we can get them off of this kind of binge and purge career treadmill. Matt Trask: So that's a really, yeah, that's a really good way to put the whole two year turn. And I mean, it comes back full circle to what you just said earlier, which is, you know, 75% of API has been developed now or done by people with less than five years experience. And that's probably because of the same, people are jumping, jumping, jumping. Whereas if you can keep them around, make them happy, make them feel like they belong. We might actually start seeing that number. Dropped significantly to more experienced API developers building more thoughtful API design with, with years of knowledge built up. So I think it'll be really interesting to see kind of what happens with this great resignation how that all shapes up. And then it'll be interesting to see to kind of the 2022 say the API report. How does that. How, how will things change from a year in a year going forward? And what can we expect possibly looking at these two years, the next five years after that, the next 10 years growing on different trends, you know, we might see NFTs ruling the world. We might see graph QL. Rolling. Phil Sturgeon: No comment. Matt Trask: Matthew is kind of shrugging Phil Sturgeon: we're all sad. Now, rural sat now, NFTs powered by graft UL, problem solved. Can you, can you still right click that? No, you can't. It's like a post. So. Matt Trask: Well, there goes Matthew Reinbold: Each unique query is published as an innovator. And you can put the ownership of that query in a blockchain so that you don't have the centralized point of failure. Phil Sturgeon: I was going to thank you for being for, for making this podcast sound intelligent for once. And, Matthew Reinbold: And then I ruined it. Sorry. Phil Sturgeon: and then you. Matt Trask: no, no, no, you didn't ruin it. You just brought it back down to its normal level of ridiculousness. Phil Sturgeon: Fantastic. No. Do you have any predictions for what we're going to see in the, in next year's state of this report? Because then we can play that clip back and laugh at how wrong you were. Matthew Reinbold: Oh, lovely. All right, well, let me have a few minutes to sandbag my answer. No, I think there's a tremendous amount of, of areas where we can take this correlation that I talked about before behaviors. You know how the question immediately becomes well, okay. If these four behaviors are so good and are present in high-performing API companies, how do we get there? And this year we had a little bit around leadership and what leaders do. To get an API first company. I think there is a lot of exploration we can do there to really dial in and say, okay, we know these things are good. How do you get there? How do you promote these things? How do you, how do you get it so that you are able to deploy in a minimal amount of time or recover faster? What are leaders in those organizations doing? That's one of the things I'd love to dig into obviously. A lot of post pandemic aftermath. There's been a tremendous amount of published about how this digital transformation and, you know, we're so much more flexible and adaptable because we, we are now doing all our conversations over zoom. And I look at that and I, I scratch my head because. Digital transformation, at least in the non buzzword compliant way is a whole lot more difficult than just moving everything to a slack conversation or a, or a zoom conversation. Like it means fundamentally dismantling your policies and procedures and reinventing them in a way that digital technology lends itself to. So figuring out what that post pandemic landscape looks like and how we're still feeling the knock on effect. Is going to be something that's also going to be very interesting to explore. Matt Trask: Yeah, that's definitely true. I mean, I think one thing I would like to see is, is that number of people who know open API, but don't use it start to gradually shift down and people who are using open. Start to shift up, which, you know, from a silver right back to having documentation and some sort of notes about their API. So when the, the knowledge people do eventually leave because everyone leaves the company at some point, the knowledge isn't necessarily leaving. And instead we're, we're kind of leaving a better legacy to the people following us. Yeah, definitely. Matthew Reinbold: Here here. Matt Trask: Cool. Matthew, thank you so much for taking some time out of your, your, your day to talk to us. We really appreciate it. Look forward to having you back in roughly a year's time to talk 20, 22. Say the API report Matthew Reinbold: I love it. Let's do it. Pencil it in right now. Matt Trask: Yep. It's it's on my calendar. I don't know what I'll be doing in a year from today, but I know for a fact we'll be talking again. If you want to get. Matthew on Twitter. He is at libel Vox, L I B E L underscore V O X M. And we'll throw the link to your blog and Twitter in the show notes as well as everything else. Awesome. Cool. Thank you so much. We appreciate it. Phil Sturgeon: Yeah. All audio, artwork, episode descriptions and notes are property of APIs You Won't Hate, for APIs You Won't Hate, and published with permission by Transistor, Inc. Broadcast by | 2026-01-13T08:48:40 |
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https://docs.python.org/3/tutorial/controlflow.html#the-range-function | 4. More Control Flow Tools — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents 4. More Control Flow Tools 4.1. if Statements 4.2. for Statements 4.3. The range() Function 4.4. break and continue Statements 4.5. else Clauses on Loops 4.6. pass Statements 4.7. match Statements 4.8. Defining Functions 4.9. More on Defining Functions 4.9.1. Default Argument Values 4.9.2. Keyword Arguments 4.9.3. Special parameters 4.9.3.1. Positional-or-Keyword Arguments 4.9.3.2. Positional-Only Parameters 4.9.3.3. Keyword-Only Arguments 4.9.3.4. Function Examples 4.9.3.5. Recap 4.9.4. Arbitrary Argument Lists 4.9.5. Unpacking Argument Lists 4.9.6. Lambda Expressions 4.9.7. Documentation Strings 4.9.8. Function Annotations 4.10. Intermezzo: Coding Style Previous topic 3. An Informal Introduction to Python Next topic 5. Data Structures This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 4. More Control Flow Tools | Theme Auto Light Dark | 4. More Control Flow Tools ¶ As well as the while statement just introduced, Python uses a few more that we will encounter in this chapter. 4.1. if Statements ¶ Perhaps the most well-known statement type is the if statement. For example: >>> x = int ( input ( "Please enter an integer: " )) Please enter an integer: 42 >>> if x < 0 : ... x = 0 ... print ( 'Negative changed to zero' ) ... elif x == 0 : ... print ( 'Zero' ) ... elif x == 1 : ... print ( 'Single' ) ... else : ... print ( 'More' ) ... More There can be zero or more elif parts, and the else part is optional. The keyword ‘ elif ’ is short for ‘else if’, and is useful to avoid excessive indentation. An if … elif … elif … sequence is a substitute for the switch or case statements found in other languages. If you’re comparing the same value to several constants, or checking for specific types or attributes, you may also find the match statement useful. For more details see match Statements . 4.2. for Statements ¶ The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended): >>> # Measure some strings: >>> words = [ 'cat' , 'window' , 'defenestrate' ] >>> for w in words : ... print ( w , len ( w )) ... cat 3 window 6 defenestrate 12 Code that modifies a collection while iterating over that same collection can be tricky to get right. Instead, it is usually more straight-forward to loop over a copy of the collection or to create a new collection: # Create a sample collection users = { 'Hans' : 'active' , 'Éléonore' : 'inactive' , '景太郎' : 'active' } # Strategy: Iterate over a copy for user , status in users . copy () . items (): if status == 'inactive' : del users [ user ] # Strategy: Create a new collection active_users = {} for user , status in users . items (): if status == 'active' : active_users [ user ] = status 4.3. The range() Function ¶ If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions: >>> for i in range ( 5 ): ... print ( i ) ... 0 1 2 3 4 The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’): >>> list ( range ( 5 , 10 )) [5, 6, 7, 8, 9] >>> list ( range ( 0 , 10 , 3 )) [0, 3, 6, 9] >>> list ( range ( - 10 , - 100 , - 30 )) [-10, -40, -70] To iterate over the indices of a sequence, you can combine range() and len() as follows: >>> a = [ 'Mary' , 'had' , 'a' , 'little' , 'lamb' ] >>> for i in range ( len ( a )): ... print ( i , a [ i ]) ... 0 Mary 1 had 2 a 3 little 4 lamb In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques . A strange thing happens if you just print a range: >>> range ( 10 ) range(0, 10) In many ways the object returned by range() behaves as if it is a list, but in fact it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space. We say such an object is iterable , that is, suitable as a target for functions and constructs that expect something from which they can obtain successive items until the supply is exhausted. We have seen that the for statement is such a construct, while an example of a function that takes an iterable is sum() : >>> sum ( range ( 4 )) # 0 + 1 + 2 + 3 6 Later we will see more functions that return iterables and take iterables as arguments. In chapter Data Structures , we will discuss in more detail about list() . 4.4. break and continue Statements ¶ The break statement breaks out of the innermost enclosing for or while loop: >>> for n in range ( 2 , 10 ): ... for x in range ( 2 , n ): ... if n % x == 0 : ... print ( f " { n } equals { x } * { n // x } " ) ... break ... 4 equals 2 * 2 6 equals 2 * 3 8 equals 2 * 4 9 equals 3 * 3 The continue statement continues with the next iteration of the loop: >>> for num in range ( 2 , 10 ): ... if num % 2 == 0 : ... print ( f "Found an even number { num } " ) ... continue ... print ( f "Found an odd number { num } " ) ... Found an even number 2 Found an odd number 3 Found an even number 4 Found an odd number 5 Found an even number 6 Found an odd number 7 Found an even number 8 Found an odd number 9 4.5. else Clauses on Loops ¶ In a for or while loop the break statement may be paired with an else clause. If the loop finishes without executing the break , the else clause executes. In a for loop, the else clause is executed after the loop finishes its final iteration, that is, if no break occurred. In a while loop, it’s executed after the loop’s condition becomes false. In either kind of loop, the else clause is not executed if the loop was terminated by a break . Of course, other ways of ending the loop early, such as a return or a raised exception, will also skip execution of the else clause. This is exemplified in the following for loop, which searches for prime numbers: >>> for n in range ( 2 , 10 ): ... for x in range ( 2 , n ): ... if n % x == 0 : ... print ( n , 'equals' , x , '*' , n // x ) ... break ... else : ... # loop fell through without finding a factor ... print ( n , 'is a prime number' ) ... 2 is a prime number 3 is a prime number 4 equals 2 * 2 5 is a prime number 6 equals 2 * 3 7 is a prime number 8 equals 2 * 4 9 equals 3 * 3 (Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.) One way to think of the else clause is to imagine it paired with the if inside the loop. As the loop executes, it will run a sequence like if/if/if/else. The if is inside the loop, encountered a number of times. If the condition is ever true, a break will happen. If the condition is never true, the else clause outside the loop will execute. When used with a loop, the else clause has more in common with the else clause of a try statement than it does with that of if statements: a try statement’s else clause runs when no exception occurs, and a loop’s else clause runs when no break occurs. For more on the try statement and exceptions, see Handling Exceptions . 4.6. pass Statements ¶ The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example: >>> while True : ... pass # Busy-wait for keyboard interrupt (Ctrl+C) ... This is commonly used for creating minimal classes: >>> class MyEmptyClass : ... pass ... Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored: >>> def initlog ( * args ): ... pass # Remember to implement this! ... For this last case, many people use the ellipsis literal ... instead of pass . This use has no special meaning to Python, and is not part of the language definition (you could use any constant expression here), but ... is used conventionally as a placeholder body as well. See The Ellipsis Object . 4.7. match Statements ¶ A match statement takes an expression and compares its value to successive patterns given as one or more case blocks. This is superficially similar to a switch statement in C, Java or JavaScript (and many other languages), but it’s more similar to pattern matching in languages like Rust or Haskell. Only the first pattern that matches gets executed and it can also extract components (sequence elements or object attributes) from the value into variables. If no case matches, none of the branches is executed. The simplest form compares a subject value against one or more literals: def http_error ( status ): match status : case 400 : return "Bad request" case 404 : return "Not found" case 418 : return "I'm a teapot" case _ : return "Something's wrong with the internet" Note the last block: the “variable name” _ acts as a wildcard and never fails to match. You can combine several literals in a single pattern using | (“or”): case 401 | 403 | 404 : return "Not allowed" Patterns can look like unpacking assignments, and can be used to bind variables: # point is an (x, y) tuple match point : case ( 0 , 0 ): print ( "Origin" ) case ( 0 , y ): print ( f "Y= { y } " ) case ( x , 0 ): print ( f "X= { x } " ) case ( x , y ): print ( f "X= { x } , Y= { y } " ) case _ : raise ValueError ( "Not a point" ) Study that one carefully! The first pattern has two literals, and can be thought of as an extension of the literal pattern shown above. But the next two patterns combine a literal and a variable, and the variable binds a value from the subject ( point ). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment (x, y) = point . If you are using classes to structure your data you can use the class name followed by an argument list resembling a constructor, but with the ability to capture attributes into variables: class Point : def __init__ ( self , x , y ): self . x = x self . y = y def where_is ( point ): match point : case Point ( x = 0 , y = 0 ): print ( "Origin" ) case Point ( x = 0 , y = y ): print ( f "Y= { y } " ) case Point ( x = x , y = 0 ): print ( f "X= { x } " ) case Point (): print ( "Somewhere else" ) case _ : print ( "Not a point" ) You can use positional parameters with some builtin classes that provide an ordering for their attributes (e.g. dataclasses). You can also define a specific position for attributes in patterns by setting the __match_args__ special attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all equivalent (and all bind the y attribute to the var variable): Point ( 1 , var ) Point ( 1 , y = var ) Point ( x = 1 , y = var ) Point ( y = var , x = 1 ) A recommended way to read patterns is to look at them as an extended form of what you would put on the left of an assignment, to understand which variables would be set to what. Only the standalone names (like var above) are assigned to by a match statement. Dotted names (like foo.bar ), attribute names (the x= and y= above) or class names (recognized by the “(…)” next to them like Point above) are never assigned to. Patterns can be arbitrarily nested. For example, if we have a short list of Points, with __match_args__ added, we could match it like this: class Point : __match_args__ = ( 'x' , 'y' ) def __init__ ( self , x , y ): self . x = x self . y = y match points : case []: print ( "No points" ) case [ Point ( 0 , 0 )]: print ( "The origin" ) case [ Point ( x , y )]: print ( f "Single point { x } , { y } " ) case [ Point ( 0 , y1 ), Point ( 0 , y2 )]: print ( f "Two on the Y axis at { y1 } , { y2 } " ) case _ : print ( "Something else" ) We can add an if clause to a pattern, known as a “guard”. If the guard is false, match goes on to try the next case block. Note that value capture happens before the guard is evaluated: match point : case Point ( x , y ) if x == y : print ( f "Y=X at { x } " ) case Point ( x , y ): print ( f "Not on the diagonal" ) Several other key features of this statement: Like unpacking assignments, tuple and list patterns have exactly the same meaning and actually match arbitrary sequences. An important exception is that they don’t match iterators or strings. Sequence patterns support extended unpacking: [x, y, *rest] and (x, y, *rest) work similar to unpacking assignments. The name after * may also be _ , so (x, y, *_) matches a sequence of at least two items without binding the remaining items. Mapping patterns: {"bandwidth": b, "latency": l} captures the "bandwidth" and "latency" values from a dictionary. Unlike sequence patterns, extra keys are ignored. An unpacking like **rest is also supported. (But **_ would be redundant, so it is not allowed.) Subpatterns may be captured using the as keyword: case ( Point ( x1 , y1 ), Point ( x2 , y2 ) as p2 ): ... will capture the second element of the input as p2 (as long as the input is a sequence of two points) Most literals are compared by equality, however the singletons True , False and None are compared by identity. Patterns may use named constants. These must be dotted names to prevent them from being interpreted as capture variable: from enum import Enum class Color ( Enum ): RED = 'red' GREEN = 'green' BLUE = 'blue' color = Color ( input ( "Enter your choice of 'red', 'blue' or 'green': " )) match color : case Color . RED : print ( "I see red!" ) case Color . GREEN : print ( "Grass is green" ) case Color . BLUE : print ( "I'm feeling the blues :(" ) For a more detailed explanation and additional examples, you can look into PEP 636 which is written in a tutorial format. 4.8. Defining Functions ¶ We can create a function that writes the Fibonacci series to an arbitrary boundary: >>> def fib ( n ): # write Fibonacci series less than n ... """Print a Fibonacci series less than n.""" ... a , b = 0 , 1 ... while a < n : ... print ( a , end = ' ' ) ... a , b = b , a + b ... print () ... >>> # Now call the function we just defined: >>> fib ( 2000 ) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 The keyword def introduces a function definition . It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented. The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring . (More about docstrings can be found in the section Documentation Strings .) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it. The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables and variables of enclosing functions cannot be directly assigned a value within a function (unless, for global variables, named in a global statement, or, for variables of enclosing functions, named in a nonlocal statement), although they may be referenced. The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference , not the value of the object). [ 1 ] When a function calls another function, or calls itself recursively, a new local symbol table is created for that call. A function definition associates the function name with the function object in the current symbol table. The interpreter recognizes the object pointed to by that name as a user-defined function. Other names can also point to that same function object and can also be used to access the function: >>> fib <function fib at 10042ed0> >>> f = fib >>> f ( 100 ) 0 1 1 2 3 5 8 13 21 34 55 89 Coming from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called None (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print() : >>> fib ( 0 ) >>> print ( fib ( 0 )) None It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it: >>> def fib2 ( n ): # return Fibonacci series up to n ... """Return a list containing the Fibonacci series up to n.""" ... result = [] ... a , b = 0 , 1 ... while a < n : ... result . append ( a ) # see below ... a , b = b , a + b ... return result ... >>> f100 = fib2 ( 100 ) # call it >>> f100 # write the result [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89] This example, as usual, demonstrates some new Python features: The return statement returns with a value from a function. return without an expression argument returns None . Falling off the end of a function also returns None . The statement result.append(a) calls a method of the list object result . A method is a function that ‘belongs’ to an object and is named obj.methodname , where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes , see Classes ) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a] , but more efficient. 4.9. More on Defining Functions ¶ It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined. 4.9.1. Default Argument Values ¶ The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example: def ask_ok ( prompt , retries = 4 , reminder = 'Please try again!' ): while True : reply = input ( prompt ) if reply in { 'y' , 'ye' , 'yes' }: return True if reply in { 'n' , 'no' , 'nop' , 'nope' }: return False retries = retries - 1 if retries < 0 : raise ValueError ( 'invalid user response' ) print ( reminder ) This function can be called in several ways: giving only the mandatory argument: ask_ok('Do you really want to quit?') giving one of the optional arguments: ask_ok('OK to overwrite the file?', 2) or even giving all arguments: ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!') This example also introduces the in keyword. This tests whether or not a sequence contains a certain value. The default values are evaluated at the point of function definition in the defining scope, so that i = 5 def f ( arg = i ): print ( arg ) i = 6 f () will print 5 . Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. For example, the following function accumulates the arguments passed to it on subsequent calls: def f ( a , L = []): L . append ( a ) return L print ( f ( 1 )) print ( f ( 2 )) print ( f ( 3 )) This will print [ 1 ] [ 1 , 2 ] [ 1 , 2 , 3 ] If you don’t want the default to be shared between subsequent calls, you can write the function like this instead: def f ( a , L = None ): if L is None : L = [] L . append ( a ) return L 4.9.2. Keyword Arguments ¶ Functions can also be called using keyword arguments of the form kwarg=value . For instance, the following function: def parrot ( voltage , state = 'a stiff' , action = 'voom' , type = 'Norwegian Blue' ): print ( "-- This parrot wouldn't" , action , end = ' ' ) print ( "if you put" , voltage , "volts through it." ) print ( "-- Lovely plumage, the" , type ) print ( "-- It's" , state , "!" ) accepts one required argument ( voltage ) and three optional arguments ( state , action , and type ). This function can be called in any of the following ways: parrot ( 1000 ) # 1 positional argument parrot ( voltage = 1000 ) # 1 keyword argument parrot ( voltage = 1000000 , action = 'VOOOOOM' ) # 2 keyword arguments parrot ( action = 'VOOOOOM' , voltage = 1000000 ) # 2 keyword arguments parrot ( 'a million' , 'bereft of life' , 'jump' ) # 3 positional arguments parrot ( 'a thousand' , state = 'pushing up the daisies' ) # 1 positional, 1 keyword but all the following calls would be invalid: parrot () # required argument missing parrot ( voltage = 5.0 , 'dead' ) # non-keyword argument after a keyword argument parrot ( 110 , voltage = 220 ) # duplicate value for the same argument parrot ( actor = 'John Cleese' ) # unknown keyword argument In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and their order is not important. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction: >>> def function ( a ): ... pass ... >>> function ( 0 , a = 0 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : function() got multiple values for argument 'a' When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict ) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. ( *name must occur before **name .) For example, if we define a function like this: def cheeseshop ( kind , * arguments , ** keywords ): print ( "-- Do you have any" , kind , "?" ) print ( "-- I'm sorry, we're all out of" , kind ) for arg in arguments : print ( arg ) print ( "-" * 40 ) for kw in keywords : print ( kw , ":" , keywords [ kw ]) It could be called like this: cheeseshop ( "Limburger" , "It's very runny, sir." , "It's really very, VERY runny, sir." , shopkeeper = "Michael Palin" , client = "John Cleese" , sketch = "Cheese Shop Sketch" ) and of course it would print: -- Do you have any Limburger ? -- I'm sorry, we're all out of Limburger It's very runny, sir. It's really very, VERY runny, sir. ---------------------------------------- shopkeeper : Michael Palin client : John Cleese sketch : Cheese Shop Sketch Note that the order in which the keyword arguments are printed is guaranteed to match the order in which they were provided in the function call. 4.9.3. Special parameters ¶ By default, arguments may be passed to a Python function either by position or explicitly by keyword. For readability and performance, it makes sense to restrict the way arguments can be passed so that a developer need only look at the function definition to determine if items are passed by position, by position or keyword, or by keyword. A function definition may look like: def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2): ----------- ---------- ---------- | | | | Positional or keyword | | - Keyword only -- Positional only where / and * are optional. If used, these symbols indicate the kind of parameter by how the arguments may be passed to the function: positional-only, positional-or-keyword, and keyword-only. Keyword parameters are also referred to as named parameters. 4.9.3.1. Positional-or-Keyword Arguments ¶ If / and * are not present in the function definition, arguments may be passed to a function by position or by keyword. 4.9.3.2. Positional-Only Parameters ¶ Looking at this in a bit more detail, it is possible to mark certain parameters as positional-only . If positional-only , the parameters’ order matters, and the parameters cannot be passed by keyword. Positional-only parameters are placed before a / (forward-slash). The / is used to logically separate the positional-only parameters from the rest of the parameters. If there is no / in the function definition, there are no positional-only parameters. Parameters following the / may be positional-or-keyword or keyword-only . 4.9.3.3. Keyword-Only Arguments ¶ To mark parameters as keyword-only , indicating the parameters must be passed by keyword argument, place an * in the arguments list just before the first keyword-only parameter. 4.9.3.4. Function Examples ¶ Consider the following example function definitions paying close attention to the markers / and * : >>> def standard_arg ( arg ): ... print ( arg ) ... >>> def pos_only_arg ( arg , / ): ... print ( arg ) ... >>> def kwd_only_arg ( * , arg ): ... print ( arg ) ... >>> def combined_example ( pos_only , / , standard , * , kwd_only ): ... print ( pos_only , standard , kwd_only ) The first function definition, standard_arg , the most familiar form, places no restrictions on the calling convention and arguments may be passed by position or keyword: >>> standard_arg ( 2 ) 2 >>> standard_arg ( arg = 2 ) 2 The second function pos_only_arg is restricted to only use positional parameters as there is a / in the function definition: >>> pos_only_arg ( 1 ) 1 >>> pos_only_arg ( arg = 1 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : pos_only_arg() got some positional-only arguments passed as keyword arguments: 'arg' The third function kwd_only_arg only allows keyword arguments as indicated by a * in the function definition: >>> kwd_only_arg ( 3 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : kwd_only_arg() takes 0 positional arguments but 1 was given >>> kwd_only_arg ( arg = 3 ) 3 And the last uses all three calling conventions in the same function definition: >>> combined_example ( 1 , 2 , 3 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : combined_example() takes 2 positional arguments but 3 were given >>> combined_example ( 1 , 2 , kwd_only = 3 ) 1 2 3 >>> combined_example ( 1 , standard = 2 , kwd_only = 3 ) 1 2 3 >>> combined_example ( pos_only = 1 , standard = 2 , kwd_only = 3 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : combined_example() got some positional-only arguments passed as keyword arguments: 'pos_only' Finally, consider this function definition which has a potential collision between the positional argument name and **kwds which has name as a key: def foo ( name , ** kwds ): return 'name' in kwds There is no possible call that will make it return True as the keyword 'name' will always bind to the first parameter. For example: >>> foo ( 1 , ** { 'name' : 2 }) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : foo() got multiple values for argument 'name' >>> But using / (positional only arguments), it is possible since it allows name as a positional argument and 'name' as a key in the keyword arguments: >>> def foo ( name , / , ** kwds ): ... return 'name' in kwds ... >>> foo ( 1 , ** { 'name' : 2 }) True In other words, the names of positional-only parameters can be used in **kwds without ambiguity. 4.9.3.5. Recap ¶ The use case will determine which parameters to use in the function definition: def f ( pos1 , pos2 , / , pos_or_kwd , * , kwd1 , kwd2 ): As guidance: Use positional-only if you want the name of the parameters to not be available to the user. This is useful when parameter names have no real meaning, if you want to enforce the order of the arguments when the function is called or if you need to take some positional parameters and arbitrary keywords. Use keyword-only when names have meaning and the function definition is more understandable by being explicit with names or you want to prevent users relying on the position of the argument being passed. For an API, use positional-only to prevent breaking API changes if the parameter’s name is modified in the future. 4.9.4. Arbitrary Argument Lists ¶ Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple (see Tuples and Sequences ). Before the variable number of arguments, zero or more normal arguments may occur. def write_multiple_items ( file , separator , * args ): file . write ( separator . join ( args )) Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the *args parameter are ‘keyword-only’ arguments, meaning that they can only be used as keywords rather than positional arguments. >>> def concat ( * args , sep = "/" ): ... return sep . join ( args ) ... >>> concat ( "earth" , "mars" , "venus" ) 'earth/mars/venus' >>> concat ( "earth" , "mars" , "venus" , sep = "." ) 'earth.mars.venus' 4.9.5. Unpacking Argument Lists ¶ The reverse situation occurs when the arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the * -operator to unpack the arguments out of a list or tuple: >>> list ( range ( 3 , 6 )) # normal call with separate arguments [3, 4, 5] >>> args = [ 3 , 6 ] >>> list ( range ( * args )) # call with arguments unpacked from a list [3, 4, 5] In the same fashion, dictionaries can deliver keyword arguments with the ** -operator: >>> def parrot ( voltage , state = 'a stiff' , action = 'voom' ): ... print ( "-- This parrot wouldn't" , action , end = ' ' ) ... print ( "if you put" , voltage , "volts through it." , end = ' ' ) ... print ( "E's" , state , "!" ) ... >>> d = { "voltage" : "four million" , "state" : "bleedin' demised" , "action" : "VOOM" } >>> parrot ( ** d ) -- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised ! 4.9.6. Lambda Expressions ¶ Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b . Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope: >>> def make_incrementor ( n ): ... return lambda x : x + n ... >>> f = make_incrementor ( 42 ) >>> f ( 0 ) 42 >>> f ( 1 ) 43 The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument. For instance, list.sort() takes a sorting key function key which can be a lambda function: >>> pairs = [( 1 , 'one' ), ( 2 , 'two' ), ( 3 , 'three' ), ( 4 , 'four' )] >>> pairs . sort ( key = lambda pair : pair [ 1 ]) >>> pairs [(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')] 4.9.7. Documentation Strings ¶ Here are some conventions about the content and formatting of documentation strings. The first line should always be a short, concise summary of the object’s purpose. For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation). This line should begin with a capital letter and end with a period. If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object’s calling conventions, its side effects, etc. The Python parser strips indentation from multi-line string literals when they serve as module, class, or function docstrings. Here is an example of a multi-line docstring: >>> def my_function (): ... """Do nothing, but document it. ... ... No, really, it doesn't do anything: ... ... >>> my_function() ... >>> ... """ ... pass ... >>> print ( my_function . __doc__ ) Do nothing, but document it. No, really, it doesn't do anything: >>> my_function() >>> 4.9.8. Function Annotations ¶ Function annotations are completely optional metadata information about the types used by user-defined functions (see PEP 3107 and PEP 484 for more information). Annotations are stored in the __annotations__ attribute of the function as a dictionary and have no effect on any other part of the function. Parameter annotations are defined by a colon after the parameter name, followed by an expression evaluating to the value of the annotation. Return annotations are defined by a literal -> , followed by an expression, between the parameter list and the colon denoting the end of the def statement. The following example has a required argument, an optional argument, and the return value annotated: >>> def f ( ham : str , eggs : str = 'eggs' ) -> str : ... print ( "Annotations:" , f . __annotations__ ) ... print ( "Arguments:" , ham , eggs ) ... return ham + ' and ' + eggs ... >>> f ( 'spam' ) Annotations: {'ham': <class 'str'>, 'return': <class 'str'>, 'eggs': <class 'str'>} Arguments: spam eggs 'spam and eggs' 4.10. Intermezzo: Coding Style ¶ Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style . Most languages can be written (or more concise, formatted ) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that. For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you: Use 4-space indentation, and no tabs. 4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out. Wrap lines so that they don’t exceed 79 characters. This helps users with small displays and makes it possible to have several code files side-by-side on larger displays. Use blank lines to separate functions and classes, and larger blocks of code inside functions. When possible, put comments on a line of their own. Use docstrings. Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4) . Name your classes and functions consistently; the convention is to use UpperCamelCase for classes and lowercase_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods). Don’t use fancy encodings if your code is meant to be used in international environments. Python’s default, UTF-8, or even plain ASCII work best in any case. Likewise, don’t use non-ASCII characters in identifiers if there is only the slightest chance people speaking a different language will read or maintain the code. Footnotes [ 1 ] Actually, call by object reference would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (items inserted into a list). Table of Contents 4. More Control Flow Tools 4.1. if Statements 4.2. for Statements 4.3. The range() Function 4.4. break and continue Statements 4.5. else Clauses on Loops 4.6. pass Statements 4.7. match Statements 4.8. Defining Functions 4.9. More on Defining Functions 4.9.1. Default Argument Values 4.9.2. Keyword Arguments 4.9.3. Special parameters 4.9.3.1. Positional-or-Keyword Arguments 4.9.3.2. Positional-Only Parameters 4.9.3.3. Keyword-Only Arguments 4.9.3.4. Function Examples 4.9.3.5. Recap 4.9.4. Arbitrary Argument Lists 4.9.5. Unpacking Argument Lists 4.9.6. Lambda Expressions 4.9.7. Documentation Strings 4.9.8. Function Annotations 4.10. Intermezzo: Coding Style Previous topic 3. An Informal Introduction to Python Next topic 5. Data Structures This page Report a bug Show source « Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 4. More Control Flow Tools | Theme Auto Light Dark | © Copyright 2001 Python Software Foundation. This page is licensed under the Python Software Foundation License Version 2. Examples, recipes, and other code in the documentation are additionally licensed under the Zero Clause BSD License. See History and License for more information. The Python Software Foundation is a non-profit corporation. Please donate. 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https://docs.python.org/3/tutorial/datastructures.html | 5. Data Structures — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents 5. Data Structures 5.1. More on Lists 5.1.1. Using Lists as Stacks 5.1.2. Using Lists as Queues 5.1.3. List Comprehensions 5.1.4. Nested List Comprehensions 5.2. The del statement 5.3. Tuples and Sequences 5.4. Sets 5.5. Dictionaries 5.6. Looping Techniques 5.7. More on Conditions 5.8. Comparing Sequences and Other Types Previous topic 4. More Control Flow Tools Next topic 6. Modules This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 5. Data Structures | Theme Auto Light Dark | 5. Data Structures ¶ This chapter describes some things you’ve learned about already in more detail, and adds some new things as well. 5.1. More on Lists ¶ The list data type has some more methods. Here are all of the methods of list objects: list. append ( x ) Add an item to the end of the list. Similar to a[len(a):] = [x] . list. extend ( iterable ) Extend the list by appending all the items from the iterable. Similar to a[len(a):] = iterable . list. insert ( i , x ) Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x) . list. remove ( x ) Remove the first item from the list whose value is equal to x . It raises a ValueError if there is no such item. list. pop ( [ i ] ) Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. It raises an IndexError if the list is empty or the index is outside the list range. list. clear ( ) Remove all items from the list. Similar to del a[:] . list. index ( x [ , start [ , end ] ] ) Return zero-based index of the first occurrence of x in the list. Raises a ValueError if there is no such item. The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument. list. count ( x ) Return the number of times x appears in the list. list. sort ( * , key = None , reverse = False ) Sort the items of the list in place (the arguments can be used for sort customization, see sorted() for their explanation). list. reverse ( ) Reverse the elements of the list in place. list. copy ( ) Return a shallow copy of the list. Similar to a[:] . An example that uses most of the list methods: >>> fruits = [ 'orange' , 'apple' , 'pear' , 'banana' , 'kiwi' , 'apple' , 'banana' ] >>> fruits . count ( 'apple' ) 2 >>> fruits . count ( 'tangerine' ) 0 >>> fruits . index ( 'banana' ) 3 >>> fruits . index ( 'banana' , 4 ) # Find next banana starting at position 4 6 >>> fruits . reverse () >>> fruits ['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange'] >>> fruits . append ( 'grape' ) >>> fruits ['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange', 'grape'] >>> fruits . sort () >>> fruits ['apple', 'apple', 'banana', 'banana', 'grape', 'kiwi', 'orange', 'pear'] >>> fruits . pop () 'pear' You might have noticed that methods like insert , remove or sort that only modify the list have no return value printed – they return the default None . [ 1 ] This is a design principle for all mutable data structures in Python. Another thing you might notice is that not all data can be sorted or compared. For instance, [None, 'hello', 10] doesn’t sort because integers can’t be compared to strings and None can’t be compared to other types. Also, there are some types that don’t have a defined ordering relation. For example, 3+4j < 5+7j isn’t a valid comparison. 5.1.1. Using Lists as Stacks ¶ The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append() . To retrieve an item from the top of the stack, use pop() without an explicit index. For example: >>> stack = [ 3 , 4 , 5 ] >>> stack . append ( 6 ) >>> stack . append ( 7 ) >>> stack [3, 4, 5, 6, 7] >>> stack . pop () 7 >>> stack [3, 4, 5, 6] >>> stack . pop () 6 >>> stack . pop () 5 >>> stack [3, 4] 5.1.2. Using Lists as Queues ¶ It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one). To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example: >>> from collections import deque >>> queue = deque ([ "Eric" , "John" , "Michael" ]) >>> queue . append ( "Terry" ) # Terry arrives >>> queue . append ( "Graham" ) # Graham arrives >>> queue . popleft () # The first to arrive now leaves 'Eric' >>> queue . popleft () # The second to arrive now leaves 'John' >>> queue # Remaining queue in order of arrival deque(['Michael', 'Terry', 'Graham']) 5.1.3. List Comprehensions ¶ List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition. For example, assume we want to create a list of squares, like: >>> squares = [] >>> for x in range ( 10 ): ... squares . append ( x ** 2 ) ... >>> squares [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] Note that this creates (or overwrites) a variable named x that still exists after the loop completes. We can calculate the list of squares without any side effects using: squares = list ( map ( lambda x : x ** 2 , range ( 10 ))) or, equivalently: squares = [ x ** 2 for x in range ( 10 )] which is more concise and readable. A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal: >>> [( x , y ) for x in [ 1 , 2 , 3 ] for y in [ 3 , 1 , 4 ] if x != y ] [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)] and it’s equivalent to: >>> combs = [] >>> for x in [ 1 , 2 , 3 ]: ... for y in [ 3 , 1 , 4 ]: ... if x != y : ... combs . append (( x , y )) ... >>> combs [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)] Note how the order of the for and if statements is the same in both these snippets. If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized. >>> vec = [ - 4 , - 2 , 0 , 2 , 4 ] >>> # create a new list with the values doubled >>> [ x * 2 for x in vec ] [-8, -4, 0, 4, 8] >>> # filter the list to exclude negative numbers >>> [ x for x in vec if x >= 0 ] [0, 2, 4] >>> # apply a function to all the elements >>> [ abs ( x ) for x in vec ] [4, 2, 0, 2, 4] >>> # call a method on each element >>> freshfruit = [ ' banana' , ' loganberry ' , 'passion fruit ' ] >>> [ weapon . strip () for weapon in freshfruit ] ['banana', 'loganberry', 'passion fruit'] >>> # create a list of 2-tuples like (number, square) >>> [( x , x ** 2 ) for x in range ( 6 )] [(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)] >>> # the tuple must be parenthesized, otherwise an error is raised >>> [ x , x ** 2 for x in range ( 6 )] File "<stdin>" , line 1 [ x , x ** 2 for x in range ( 6 )] ^^^^^^^ SyntaxError : did you forget parentheses around the comprehension target? >>> # flatten a list using a listcomp with two 'for' >>> vec = [[ 1 , 2 , 3 ], [ 4 , 5 , 6 ], [ 7 , 8 , 9 ]] >>> [ num for elem in vec for num in elem ] [1, 2, 3, 4, 5, 6, 7, 8, 9] List comprehensions can contain complex expressions and nested functions: >>> from math import pi >>> [ str ( round ( pi , i )) for i in range ( 1 , 6 )] ['3.1', '3.14', '3.142', '3.1416', '3.14159'] 5.1.4. Nested List Comprehensions ¶ The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension. Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4: >>> matrix = [ ... [ 1 , 2 , 3 , 4 ], ... [ 5 , 6 , 7 , 8 ], ... [ 9 , 10 , 11 , 12 ], ... ] The following list comprehension will transpose rows and columns: >>> [[ row [ i ] for row in matrix ] for i in range ( 4 )] [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]] As we saw in the previous section, the inner list comprehension is evaluated in the context of the for that follows it, so this example is equivalent to: >>> transposed = [] >>> for i in range ( 4 ): ... transposed . append ([ row [ i ] for row in matrix ]) ... >>> transposed [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]] which, in turn, is the same as: >>> transposed = [] >>> for i in range ( 4 ): ... # the following 3 lines implement the nested listcomp ... transposed_row = [] ... for row in matrix : ... transposed_row . append ( row [ i ]) ... transposed . append ( transposed_row ) ... >>> transposed [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]] In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case: >>> list ( zip ( * matrix )) [(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)] See Unpacking Argument Lists for details on the asterisk in this line. 5.2. The del statement ¶ There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example: >>> a = [ - 1 , 1 , 66.25 , 333 , 333 , 1234.5 ] >>> del a [ 0 ] >>> a [1, 66.25, 333, 333, 1234.5] >>> del a [ 2 : 4 ] >>> a [1, 66.25, 1234.5] >>> del a [:] >>> a [] del can also be used to delete entire variables: >>> del a Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later. 5.3. Tuples and Sequences ¶ We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — list, tuple, range ). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple . A tuple consists of a number of values separated by commas, for instance: >>> t = 12345 , 54321 , 'hello!' >>> t [ 0 ] 12345 >>> t (12345, 54321, 'hello!') >>> # Tuples may be nested: >>> u = t , ( 1 , 2 , 3 , 4 , 5 ) >>> u ((12345, 54321, 'hello!'), (1, 2, 3, 4, 5)) >>> # Tuples are immutable: >>> t [ 0 ] = 88888 Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : 'tuple' object does not support item assignment >>> # but they can contain mutable objects: >>> v = ([ 1 , 2 , 3 ], [ 3 , 2 , 1 ]) >>> v ([1, 2, 3], [3, 2, 1]) As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists. Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable , and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples ). Lists are mutable , and their elements are usually homogeneous and are accessed by iterating over the list. A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example: >>> empty = () >>> singleton = 'hello' , # <-- note trailing comma >>> len ( empty ) 0 >>> len ( singleton ) 1 >>> singleton ('hello',) The statement t = 12345, 54321, 'hello!' is an example of tuple packing : the values 12345 , 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible: >>> x , y , z = t This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking. 5.4. Sets ¶ Python also includes a data type for sets . A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference. Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set() , not {} ; the latter creates an empty dictionary, a data structure that we discuss in the next section. Here is a brief demonstration: >>> basket = { 'apple' , 'orange' , 'apple' , 'pear' , 'orange' , 'banana' } >>> print ( basket ) # show that duplicates have been removed {'orange', 'banana', 'pear', 'apple'} >>> 'orange' in basket # fast membership testing True >>> 'crabgrass' in basket False >>> # Demonstrate set operations on unique letters from two words >>> >>> a = set ( 'abracadabra' ) >>> b = set ( 'alacazam' ) >>> a # unique letters in a {'a', 'r', 'b', 'c', 'd'} >>> a - b # letters in a but not in b {'r', 'd', 'b'} >>> a | b # letters in a or b or both {'a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'} >>> a & b # letters in both a and b {'a', 'c'} >>> a ^ b # letters in a or b but not both {'r', 'd', 'b', 'm', 'z', 'l'} Similarly to list comprehensions , set comprehensions are also supported: >>> a = { x for x in 'abracadabra' if x not in 'abc' } >>> a {'r', 'd'} 5.5. Dictionaries ¶ Another useful data type built into Python is the dictionary (see Mapping Types — dict ). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys , which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend() . It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {} . Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output. The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del . If you store using a key that is already in use, the old value associated with that key is forgotten. Extracting a value for a non-existent key by subscripting ( d[key] ) raises a KeyError . To avoid getting this error when trying to access a possibly non-existent key, use the get() method instead, which returns None (or a specified default value) if the key is not in the dictionary. Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead). To check whether a single key is in the dictionary, use the in keyword. Here is a small example using a dictionary: >>> tel = { 'jack' : 4098 , 'sape' : 4139 } >>> tel [ 'guido' ] = 4127 >>> tel {'jack': 4098, 'sape': 4139, 'guido': 4127} >>> tel [ 'jack' ] 4098 >>> tel [ 'irv' ] Traceback (most recent call last): File "<stdin>" , line 1 , in <module> KeyError : 'irv' >>> print ( tel . get ( 'irv' )) None >>> del tel [ 'sape' ] >>> tel [ 'irv' ] = 4127 >>> tel {'jack': 4098, 'guido': 4127, 'irv': 4127} >>> list ( tel ) ['jack', 'guido', 'irv'] >>> sorted ( tel ) ['guido', 'irv', 'jack'] >>> 'guido' in tel True >>> 'jack' not in tel False The dict() constructor builds dictionaries directly from sequences of key-value pairs: >>> dict ([( 'sape' , 4139 ), ( 'guido' , 4127 ), ( 'jack' , 4098 )]) {'sape': 4139, 'guido': 4127, 'jack': 4098} In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions: >>> { x : x ** 2 for x in ( 2 , 4 , 6 )} {2: 4, 4: 16, 6: 36} When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments: >>> dict ( sape = 4139 , guido = 4127 , jack = 4098 ) {'sape': 4139, 'guido': 4127, 'jack': 4098} 5.6. Looping Techniques ¶ When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method. >>> knights = { 'gallahad' : 'the pure' , 'robin' : 'the brave' } >>> for k , v in knights . items (): ... print ( k , v ) ... gallahad the pure robin the brave When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function. >>> for i , v in enumerate ([ 'tic' , 'tac' , 'toe' ]): ... print ( i , v ) ... 0 tic 1 tac 2 toe To loop over two or more sequences at the same time, the entries can be paired with the zip() function. >>> questions = [ 'name' , 'quest' , 'favorite color' ] >>> answers = [ 'lancelot' , 'the holy grail' , 'blue' ] >>> for q , a in zip ( questions , answers ): ... print ( 'What is your {0} ? It is {1} .' . format ( q , a )) ... What is your name? It is lancelot. What is your quest? It is the holy grail. What is your favorite color? It is blue. To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function. >>> for i in reversed ( range ( 1 , 10 , 2 )): ... print ( i ) ... 9 7 5 3 1 To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered. >>> basket = [ 'apple' , 'orange' , 'apple' , 'pear' , 'orange' , 'banana' ] >>> for i in sorted ( basket ): ... print ( i ) ... apple apple banana orange orange pear Using set() on a sequence eliminates duplicate elements. The use of sorted() in combination with set() over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order. >>> basket = [ 'apple' , 'orange' , 'apple' , 'pear' , 'orange' , 'banana' ] >>> for f in sorted ( set ( basket )): ... print ( f ) ... apple banana orange pear It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead. >>> import math >>> raw_data = [ 56.2 , float ( 'NaN' ), 51.7 , 55.3 , 52.5 , float ( 'NaN' ), 47.8 ] >>> filtered_data = [] >>> for value in raw_data : ... if not math . isnan ( value ): ... filtered_data . append ( value ) ... >>> filtered_data [56.2, 51.7, 55.3, 52.5, 47.8] 5.7. More on Conditions ¶ The conditions used in while and if statements can contain any operators, not just comparisons. The comparison operators in and not in are membership tests that determine whether a value is in (or not in) a container. The operators is and is not compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators. Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c . Comparisons may be combined using the Boolean operators and and or , and the outcome of a comparison (or of any other Boolean expression) may be negated with not . These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C . As always, parentheses can be used to express the desired composition. The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C . When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument. It is possible to assign the result of a comparison or other Boolean expression to a variable. For example, >>> string1 , string2 , string3 = '' , 'Trondheim' , 'Hammer Dance' >>> non_null = string1 or string2 or string3 >>> non_null 'Trondheim' Note that in Python, unlike C, assignment inside expressions must be done explicitly with the walrus operator := . This avoids a common class of problems encountered in C programs: typing = in an expression when == was intended. 5.8. Comparing Sequences and Other Types ¶ Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type: ( 1 , 2 , 3 ) < ( 1 , 2 , 4 ) [ 1 , 2 , 3 ] < [ 1 , 2 , 4 ] 'ABC' < 'C' < 'Pascal' < 'Python' ( 1 , 2 , 3 , 4 ) < ( 1 , 2 , 4 ) ( 1 , 2 ) < ( 1 , 2 , - 1 ) ( 1 , 2 , 3 ) == ( 1.0 , 2.0 , 3.0 ) ( 1 , 2 , ( 'aa' , 'ab' )) < ( 1 , 2 , ( 'abc' , 'a' ), 4 ) Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception. Footnotes [ 1 ] Other languages may return the mutated object, which allows method chaining, such as d->insert("a")->remove("b")->sort(); . Table of Contents 5. Data Structures 5.1. More on Lists 5.1.1. Using Lists as Stacks 5.1.2. Using Lists as Queues 5.1.3. List Comprehensions 5.1.4. Nested List Comprehensions 5.2. The del statement 5.3. Tuples and Sequences 5.4. Sets 5.5. Dictionaries 5.6. Looping Techniques 5.7. More on Conditions 5.8. Comparing Sequences and Other Types Previous topic 4. More Control Flow Tools Next topic 6. Modules This page Report a bug Show source « Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Tutorial » 5. Data Structures | Theme Auto Light Dark | © Copyright 2001 Python Software Foundation. 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https://docs.suprsend.com/reference/get-category | Get Preference Category - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection API Reference Overview Authentication Errors WORKFLOWS POST Create/Update Workflow PATCH Commit Workflow GET Get Workflow GET List Workflows PATCH Enable/Disable Workflow DEL Delete Workflow SCHEMAS POST Create/Update Schema PATCH Commit Schema GET List Schemas GET Get Schema EVENTS POST Create Event PATCH Update Event GET List Events GET Get Event Details GET Get Linked Workflows PATCH Delink Schema from Event CATEGORIES GET Get Category POST Create/Update Category PATCH Commit Category GET List Translation GET Get Translation POST Add Translation DEL Delete Translation TRANSLATIONS POST Add Translation PATCH Commit Translation GET Get Translation GET List Translations GET Get Translation History POST Rollback Translation DEL Delete Translation Contact Us Get Started SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Ask AI Contact Us Get Started Get Started Search... Navigation CATEGORIES Get Preference Category Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog CATEGORIES Get Preference Category OpenAI Open in ChatGPT Retrieve the current preference category for a workspace OpenAI Open in ChatGPT GET / v1 / {workspace} / preference_category Try it Get Category cURL Copy Ask AI curl -X GET "https://management-api.suprsend.com/v1/{workspace}/preference_category/" \ --header "Authorization: ServiceToken {token}" \ --header "Content-Type: application/json" 200 401 404 Copy Ask AI { "$schema" : "https://schema.suprsend.com/preference_category/v1/schema.json" , "root_categories" : [ { "root_category" : "system" , "sections" : [] }, { "root_category" : "transactional" , "sections" : [ { "name" : "Task Updates" , "categories" : [ { "category" : "tagged-to-me" , "name" : "Tagged to Me" , "description" : "Status Update or mentions on tasks assigned by me or tagged to me" , "default_preference" : "opt_in" } ] } ] }, { "root_category" : "promotional" , "sections" : [ { "name" : "Product Updates" , "categories" : [ { "category" : "newsletter" , "name" : "Newsletter" , "default_preference" : "opt_out" } ] } ] } ], "version_no" : 15 , "status" : "draft" , "validation_result" : { "is_valid" : false , "errors" : [ "it seems some subcategories have been deleted in this version, on which active workflows are configured. missing subcategories: [ \" chapter-added \" \" forecast \" \" onboarding-guide \" \" training-new \" ]" ] } } Authorizations ServiceToken <token> string header required You can get Service Token from SuprSend dashboard -> Account Settings -> Service Tokens section. Path Parameters workspace string required Workspace slug (staging, production, etc.) Query Parameters mode enum<string> required Mode to retrieve categories. Use 'live' for active categories or 'draft' for current draft. Available options : live , draft Response 200 application/json Successfully retrieved preference category $schema string JSON schema reference for preference category structure Example : "https://schema.suprsend.com/preference_category/v1/schema.json" root_categories object[] Array of root preference categories Show child attributes version_no integer Version number field as an identifier to track the version Example : 1 status enum<string> Current status of the preference category Available options : draft , active validation_result object Result of validating the preference category Show child attributes Was this page helpful? Yes No Suggest edits Raise issue Previous Create/Update Category Use this API to set preference categories to be used in workflow or to show on user preference page. Next ⌘ I x github linkedin youtube Powered by Get Category cURL Copy Ask AI curl -X GET "https://management-api.suprsend.com/v1/{workspace}/preference_category/" \ --header "Authorization: ServiceToken {token}" \ --header "Content-Type: application/json" 200 401 404 Copy Ask AI { "$schema" : "https://schema.suprsend.com/preference_category/v1/schema.json" , "root_categories" : [ { "root_category" : "system" , "sections" : [] }, { "root_category" : "transactional" , "sections" : [ { "name" : "Task Updates" , "categories" : [ { "category" : "tagged-to-me" , "name" : "Tagged to Me" , "description" : "Status Update or mentions on tasks assigned by me or tagged to me" , "default_preference" : "opt_in" } ] } ] }, { "root_category" : "promotional" , "sections" : [ { "name" : "Product Updates" , "categories" : [ { "category" : "newsletter" , "name" : "Newsletter" , "default_preference" : "opt_out" } ] } ] } ], "version_no" : 15 , "status" : "draft" , "validation_result" : { "is_valid" : false , "errors" : [ "it seems some subcategories have been deleted in this version, on which active workflows are configured. missing subcategories: [ \" chapter-added \" \" forecast \" \" onboarding-guide \" \" training-new \" ]" ] } } | 2026-01-13T08:48:40 |
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https://docs.python.org/3/library/stdtypes.html#textseq | Built-in Types — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents Built-in Types Truth Value Testing Boolean Operations — and , or , not Comparisons Numeric Types — int , float , complex Bitwise Operations on Integer Types Additional Methods on Integer Types Additional Methods on Float Additional Methods on Complex Hashing of numeric types Boolean Type - bool Iterator Types Generator Types Sequence Types — list , tuple , range Common Sequence Operations Immutable Sequence Types Mutable Sequence Types Lists Tuples Ranges Text and Binary Sequence Type Methods Summary Text Sequence Type — str String Methods Formatted String Literals (f-strings) Debug specifier Conversion specifier Format specifier Template String Literals (t-strings) printf -style String Formatting Binary Sequence Types — bytes , bytearray , memoryview Bytes Objects Bytearray Objects Bytes and Bytearray Operations printf -style Bytes Formatting Memory Views Set Types — set , frozenset Mapping Types — dict Dictionary view objects Context Manager Types Type Annotation Types — Generic Alias , Union Generic Alias Type Standard Generic Classes Special Attributes of GenericAlias objects Union Type Other Built-in Types Modules Classes and Class Instances Functions Methods Code Objects Type Objects The Null Object The Ellipsis Object The NotImplemented Object Internal Objects Special Attributes Integer string conversion length limitation Affected APIs Configuring the limit Recommended configuration Previous topic Built-in Constants Next topic Built-in Exceptions This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Standard Library » Built-in Types | Theme Auto Light Dark | Built-in Types ¶ The following sections describe the standard types that are built into the interpreter. The principal built-in types are numerics, sequences, mappings, classes, instances and exceptions. Some collection classes are mutable. The methods that add, subtract, or rearrange their members in place, and don’t return a specific item, never return the collection instance itself but None . Some operations are supported by several object types; in particular, practically all objects can be compared for equality, tested for truth value, and converted to a string (with the repr() function or the slightly different str() function). The latter function is implicitly used when an object is written by the print() function. Truth Value Testing ¶ Any object can be tested for truth value, for use in an if or while condition or as operand of the Boolean operations below. By default, an object is considered true unless its class defines either a __bool__() method that returns False or a __len__() method that returns zero, when called with the object. [ 1 ] If one of the methods raises an exception when called, the exception is propagated and the object does not have a truth value (for example, NotImplemented ). Here are most of the built-in objects considered false: constants defined to be false: None and False zero of any numeric type: 0 , 0.0 , 0j , Decimal(0) , Fraction(0, 1) empty sequences and collections: '' , () , [] , {} , set() , range(0) Operations and built-in functions that have a Boolean result always return 0 or False for false and 1 or True for true, unless otherwise stated. (Important exception: the Boolean operations or and and always return one of their operands.) Boolean Operations — and , or , not ¶ These are the Boolean operations, ordered by ascending priority: Operation Result Notes x or y if x is true, then x , else y (1) x and y if x is false, then x , else y (2) not x if x is false, then True , else False (3) Notes: This is a short-circuit operator, so it only evaluates the second argument if the first one is false. This is a short-circuit operator, so it only evaluates the second argument if the first one is true. not has a lower priority than non-Boolean operators, so not a == b is interpreted as not (a == b) , and a == not b is a syntax error. Comparisons ¶ There are eight comparison operations in Python. They all have the same priority (which is higher than that of the Boolean operations). Comparisons can be chained arbitrarily; for example, x < y <= z is equivalent to x < y and y <= z , except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false). This table summarizes the comparison operations: Operation Meaning < strictly less than <= less than or equal > strictly greater than >= greater than or equal == equal != not equal is object identity is not negated object identity Unless stated otherwise, objects of different types never compare equal. The == operator is always defined but for some object types (for example, class objects) is equivalent to is . The < , <= , > and >= operators are only defined where they make sense; for example, they raise a TypeError exception when one of the arguments is a complex number. Non-identical instances of a class normally compare as non-equal unless the class defines the __eq__() method. Instances of a class cannot be ordered with respect to other instances of the same class, or other types of object, unless the class defines enough of the methods __lt__() , __le__() , __gt__() , and __ge__() (in general, __lt__() and __eq__() are sufficient, if you want the conventional meanings of the comparison operators). The behavior of the is and is not operators cannot be customized; also they can be applied to any two objects and never raise an exception. Two more operations with the same syntactic priority, in and not in , are supported by types that are iterable or implement the __contains__() method. Numeric Types — int , float , complex ¶ There are three distinct numeric types: integers , floating-point numbers , and complex numbers . In addition, Booleans are a subtype of integers. Integers have unlimited precision. Floating-point numbers are usually implemented using double in C; information about the precision and internal representation of floating-point numbers for the machine on which your program is running is available in sys.float_info . Complex numbers have a real and imaginary part, which are each a floating-point number. To extract these parts from a complex number z , use z.real and z.imag . (The standard library includes the additional numeric types fractions.Fraction , for rationals, and decimal.Decimal , for floating-point numbers with user-definable precision.) Numbers are created by numeric literals or as the result of built-in functions and operators. Unadorned integer literals (including hex, octal and binary numbers) yield integers. Numeric literals containing a decimal point or an exponent sign yield floating-point numbers. Appending 'j' or 'J' to a numeric literal yields an imaginary number (a complex number with a zero real part) which you can add to an integer or float to get a complex number with real and imaginary parts. The constructors int() , float() , and complex() can be used to produce numbers of a specific type. Python fully supports mixed arithmetic: when a binary arithmetic operator has operands of different numeric types, the operand with the “narrower” type is widened to that of the other, where integer is narrower than floating point. Arithmetic with complex and real operands is defined by the usual mathematical formula, for example: x + complex ( u , v ) = complex ( x + u , v ) x * complex ( u , v ) = complex ( x * u , x * v ) A comparison between numbers of different types behaves as though the exact values of those numbers were being compared. [ 2 ] All numeric types (except complex) support the following operations (for priorities of the operations, see Operator precedence ): Operation Result Notes Full documentation x + y sum of x and y x - y difference of x and y x * y product of x and y x / y quotient of x and y x // y floored quotient of x and y (1)(2) x % y remainder of x / y (2) -x x negated +x x unchanged abs(x) absolute value or magnitude of x abs() int(x) x converted to integer (3)(6) int() float(x) x converted to floating point (4)(6) float() complex(re, im) a complex number with real part re , imaginary part im . im defaults to zero. (6) complex() c.conjugate() conjugate of the complex number c divmod(x, y) the pair (x // y, x % y) (2) divmod() pow(x, y) x to the power y (5) pow() x ** y x to the power y (5) Notes: Also referred to as integer division. For operands of type int , the result has type int . For operands of type float , the result has type float . In general, the result is a whole integer, though the result’s type is not necessarily int . The result is always rounded towards minus infinity: 1//2 is 0 , (-1)//2 is -1 , 1//(-2) is -1 , and (-1)//(-2) is 0 . Not for complex numbers. Instead convert to floats using abs() if appropriate. Conversion from float to int truncates, discarding the fractional part. See functions math.floor() and math.ceil() for alternative conversions. float also accepts the strings “nan” and “inf” with an optional prefix “+” or “-” for Not a Number (NaN) and positive or negative infinity. Python defines pow(0, 0) and 0 ** 0 to be 1 , as is common for programming languages. The numeric literals accepted include the digits 0 to 9 or any Unicode equivalent (code points with the Nd property). See the Unicode Standard for a complete list of code points with the Nd property. All numbers.Real types ( int and float ) also include the following operations: Operation Result math.trunc(x) x truncated to Integral round(x[, n]) x rounded to n digits, rounding half to even. If n is omitted, it defaults to 0. math.floor(x) the greatest Integral <= x math.ceil(x) the least Integral >= x For additional numeric operations see the math and cmath modules. Bitwise Operations on Integer Types ¶ Bitwise operations only make sense for integers. The result of bitwise operations is calculated as though carried out in two’s complement with an infinite number of sign bits. The priorities of the binary bitwise operations are all lower than the numeric operations and higher than the comparisons; the unary operation ~ has the same priority as the other unary numeric operations ( + and - ). This table lists the bitwise operations sorted in ascending priority: Operation Result Notes x | y bitwise or of x and y (4) x ^ y bitwise exclusive or of x and y (4) x & y bitwise and of x and y (4) x << n x shifted left by n bits (1)(2) x >> n x shifted right by n bits (1)(3) ~x the bits of x inverted Notes: Negative shift counts are illegal and cause a ValueError to be raised. A left shift by n bits is equivalent to multiplication by pow(2, n) . A right shift by n bits is equivalent to floor division by pow(2, n) . Performing these calculations with at least one extra sign extension bit in a finite two’s complement representation (a working bit-width of 1 + max(x.bit_length(), y.bit_length()) or more) is sufficient to get the same result as if there were an infinite number of sign bits. Additional Methods on Integer Types ¶ The int type implements the numbers.Integral abstract base class . In addition, it provides a few more methods: int. bit_length ( ) ¶ Return the number of bits necessary to represent an integer in binary, excluding the sign and leading zeros: >>> n = - 37 >>> bin ( n ) '-0b100101' >>> n . bit_length () 6 More precisely, if x is nonzero, then x.bit_length() is the unique positive integer k such that 2**(k-1) <= abs(x) < 2**k . Equivalently, when abs(x) is small enough to have a correctly rounded logarithm, then k = 1 + int(log(abs(x), 2)) . If x is zero, then x.bit_length() returns 0 . Equivalent to: def bit_length ( self ): s = bin ( self ) # binary representation: bin(-37) --> '-0b100101' s = s . lstrip ( '-0b' ) # remove leading zeros and minus sign return len ( s ) # len('100101') --> 6 Added in version 3.1. int. bit_count ( ) ¶ Return the number of ones in the binary representation of the absolute value of the integer. This is also known as the population count. Example: >>> n = 19 >>> bin ( n ) '0b10011' >>> n . bit_count () 3 >>> ( - n ) . bit_count () 3 Equivalent to: def bit_count ( self ): return bin ( self ) . count ( "1" ) Added in version 3.10. int. to_bytes ( length = 1 , byteorder = 'big' , * , signed = False ) ¶ Return an array of bytes representing an integer. >>> ( 1024 ) . to_bytes ( 2 , byteorder = 'big' ) b'\x04\x00' >>> ( 1024 ) . to_bytes ( 10 , byteorder = 'big' ) b'\x00\x00\x00\x00\x00\x00\x00\x00\x04\x00' >>> ( - 1024 ) . to_bytes ( 10 , byteorder = 'big' , signed = True ) b'\xff\xff\xff\xff\xff\xff\xff\xff\xfc\x00' >>> x = 1000 >>> x . to_bytes (( x . bit_length () + 7 ) // 8 , byteorder = 'little' ) b'\xe8\x03' The integer is represented using length bytes, and defaults to 1. An OverflowError is raised if the integer is not representable with the given number of bytes. The byteorder argument determines the byte order used to represent the integer, and defaults to "big" . If byteorder is "big" , the most significant byte is at the beginning of the byte array. If byteorder is "little" , the most significant byte is at the end of the byte array. The signed argument determines whether two’s complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised. The default value for signed is False . The default values can be used to conveniently turn an integer into a single byte object: >>> ( 65 ) . to_bytes () b'A' However, when using the default arguments, don’t try to convert a value greater than 255 or you’ll get an OverflowError . Equivalent to: def to_bytes ( n , length = 1 , byteorder = 'big' , signed = False ): if byteorder == 'little' : order = range ( length ) elif byteorder == 'big' : order = reversed ( range ( length )) else : raise ValueError ( "byteorder must be either 'little' or 'big'" ) return bytes (( n >> i * 8 ) & 0xff for i in order ) Added in version 3.2. Changed in version 3.11: Added default argument values for length and byteorder . classmethod int. from_bytes ( bytes , byteorder = 'big' , * , signed = False ) ¶ Return the integer represented by the given array of bytes. >>> int . from_bytes ( b ' \x00\x10 ' , byteorder = 'big' ) 16 >>> int . from_bytes ( b ' \x00\x10 ' , byteorder = 'little' ) 4096 >>> int . from_bytes ( b ' \xfc\x00 ' , byteorder = 'big' , signed = True ) -1024 >>> int . from_bytes ( b ' \xfc\x00 ' , byteorder = 'big' , signed = False ) 64512 >>> int . from_bytes ([ 255 , 0 , 0 ], byteorder = 'big' ) 16711680 The argument bytes must either be a bytes-like object or an iterable producing bytes. The byteorder argument determines the byte order used to represent the integer, and defaults to "big" . If byteorder is "big" , the most significant byte is at the beginning of the byte array. If byteorder is "little" , the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value. The signed argument indicates whether two’s complement is used to represent the integer. Equivalent to: def from_bytes ( bytes , byteorder = 'big' , signed = False ): if byteorder == 'little' : little_ordered = list ( bytes ) elif byteorder == 'big' : little_ordered = list ( reversed ( bytes )) else : raise ValueError ( "byteorder must be either 'little' or 'big'" ) n = sum ( b << i * 8 for i , b in enumerate ( little_ordered )) if signed and little_ordered and ( little_ordered [ - 1 ] & 0x80 ): n -= 1 << 8 * len ( little_ordered ) return n Added in version 3.2. Changed in version 3.11: Added default argument value for byteorder . int. as_integer_ratio ( ) ¶ Return a pair of integers whose ratio is equal to the original integer and has a positive denominator. The integer ratio of integers (whole numbers) is always the integer as the numerator and 1 as the denominator. Added in version 3.8. int. is_integer ( ) ¶ Returns True . Exists for duck type compatibility with float.is_integer() . Added in version 3.12. Additional Methods on Float ¶ The float type implements the numbers.Real abstract base class . float also has the following additional methods. classmethod float. from_number ( x ) ¶ Class method to return a floating-point number constructed from a number x . If the argument is an integer or a floating-point number, a floating-point number with the same value (within Python’s floating-point precision) is returned. If the argument is outside the range of a Python float, an OverflowError will be raised. For a general Python object x , float.from_number(x) delegates to x.__float__() . If __float__() is not defined then it falls back to __index__() . Added in version 3.14. float. as_integer_ratio ( ) ¶ Return a pair of integers whose ratio is exactly equal to the original float. The ratio is in lowest terms and has a positive denominator. Raises OverflowError on infinities and a ValueError on NaNs. float. is_integer ( ) ¶ Return True if the float instance is finite with integral value, and False otherwise: >>> ( - 2.0 ) . is_integer () True >>> ( 3.2 ) . is_integer () False Two methods support conversion to and from hexadecimal strings. Since Python’s floats are stored internally as binary numbers, converting a float to or from a decimal string usually involves a small rounding error. In contrast, hexadecimal strings allow exact representation and specification of floating-point numbers. This can be useful when debugging, and in numerical work. float. hex ( ) ¶ Return a representation of a floating-point number as a hexadecimal string. For finite floating-point numbers, this representation will always include a leading 0x and a trailing p and exponent. classmethod float. fromhex ( s ) ¶ Class method to return the float represented by a hexadecimal string s . The string s may have leading and trailing whitespace. Note that float.hex() is an instance method, while float.fromhex() is a class method. A hexadecimal string takes the form: [ sign ] [ '0x' ] integer [ '.' fraction ] [ 'p' exponent ] where the optional sign may by either + or - , integer and fraction are strings of hexadecimal digits, and exponent is a decimal integer with an optional leading sign. Case is not significant, and there must be at least one hexadecimal digit in either the integer or the fraction. This syntax is similar to the syntax specified in section 6.4.4.2 of the C99 standard, and also to the syntax used in Java 1.5 onwards. In particular, the output of float.hex() is usable as a hexadecimal floating-point literal in C or Java code, and hexadecimal strings produced by C’s %a format character or Java’s Double.toHexString are accepted by float.fromhex() . Note that the exponent is written in decimal rather than hexadecimal, and that it gives the power of 2 by which to multiply the coefficient. For example, the hexadecimal string 0x3.a7p10 represents the floating-point number (3 + 10./16 + 7./16**2) * 2.0**10 , or 3740.0 : >>> float . fromhex ( '0x3.a7p10' ) 3740.0 Applying the reverse conversion to 3740.0 gives a different hexadecimal string representing the same number: >>> float . hex ( 3740.0 ) '0x1.d380000000000p+11' Additional Methods on Complex ¶ The complex type implements the numbers.Complex abstract base class . complex also has the following additional methods. classmethod complex. from_number ( x ) ¶ Class method to convert a number to a complex number. For a general Python object x , complex.from_number(x) delegates to x.__complex__() . If __complex__() is not defined then it falls back to __float__() . If __float__() is not defined then it falls back to __index__() . Added in version 3.14. Hashing of numeric types ¶ For numbers x and y , possibly of different types, it’s a requirement that hash(x) == hash(y) whenever x == y (see the __hash__() method documentation for more details). For ease of implementation and efficiency across a variety of numeric types (including int , float , decimal.Decimal and fractions.Fraction ) Python’s hash for numeric types is based on a single mathematical function that’s defined for any rational number, and hence applies to all instances of int and fractions.Fraction , and all finite instances of float and decimal.Decimal . Essentially, this function is given by reduction modulo P for a fixed prime P . The value of P is made available to Python as the modulus attribute of sys.hash_info . CPython implementation detail: Currently, the prime used is P = 2**31 - 1 on machines with 32-bit C longs and P = 2**61 - 1 on machines with 64-bit C longs. Here are the rules in detail: If x = m / n is a nonnegative rational number and n is not divisible by P , define hash(x) as m * invmod(n, P) % P , where invmod(n, P) gives the inverse of n modulo P . If x = m / n is a nonnegative rational number and n is divisible by P (but m is not) then n has no inverse modulo P and the rule above doesn’t apply; in this case define hash(x) to be the constant value sys.hash_info.inf . If x = m / n is a negative rational number define hash(x) as -hash(-x) . If the resulting hash is -1 , replace it with -2 . The particular values sys.hash_info.inf and -sys.hash_info.inf are used as hash values for positive infinity or negative infinity (respectively). For a complex number z , the hash values of the real and imaginary parts are combined by computing hash(z.real) + sys.hash_info.imag * hash(z.imag) , reduced modulo 2**sys.hash_info.width so that it lies in range(-2**(sys.hash_info.width - 1), 2**(sys.hash_info.width - 1)) . Again, if the result is -1 , it’s replaced with -2 . To clarify the above rules, here’s some example Python code, equivalent to the built-in hash, for computing the hash of a rational number, float , or complex : import sys , math def hash_fraction ( m , n ): """Compute the hash of a rational number m / n. Assumes m and n are integers, with n positive. Equivalent to hash(fractions.Fraction(m, n)). """ P = sys . hash_info . modulus # Remove common factors of P. (Unnecessary if m and n already coprime.) while m % P == n % P == 0 : m , n = m // P , n // P if n % P == 0 : hash_value = sys . hash_info . inf else : # Fermat's Little Theorem: pow(n, P-1, P) is 1, so # pow(n, P-2, P) gives the inverse of n modulo P. hash_value = ( abs ( m ) % P ) * pow ( n , P - 2 , P ) % P if m < 0 : hash_value = - hash_value if hash_value == - 1 : hash_value = - 2 return hash_value def hash_float ( x ): """Compute the hash of a float x.""" if math . isnan ( x ): return object . __hash__ ( x ) elif math . isinf ( x ): return sys . hash_info . inf if x > 0 else - sys . hash_info . inf else : return hash_fraction ( * x . as_integer_ratio ()) def hash_complex ( z ): """Compute the hash of a complex number z.""" hash_value = hash_float ( z . real ) + sys . hash_info . imag * hash_float ( z . imag ) # do a signed reduction modulo 2**sys.hash_info.width M = 2 ** ( sys . hash_info . width - 1 ) hash_value = ( hash_value & ( M - 1 )) - ( hash_value & M ) if hash_value == - 1 : hash_value = - 2 return hash_value Boolean Type - bool ¶ Booleans represent truth values. The bool type has exactly two constant instances: True and False . The built-in function bool() converts any value to a boolean, if the value can be interpreted as a truth value (see section Truth Value Testing above). For logical operations, use the boolean operators and , or and not . When applying the bitwise operators & , | , ^ to two booleans, they return a bool equivalent to the logical operations “and”, “or”, “xor”. However, the logical operators and , or and != should be preferred over & , | and ^ . Deprecated since version 3.12: The use of the bitwise inversion operator ~ is deprecated and will raise an error in Python 3.16. bool is a subclass of int (see Numeric Types — int, float, complex ). In many numeric contexts, False and True behave like the integers 0 and 1, respectively. However, relying on this is discouraged; explicitly convert using int() instead. Iterator Types ¶ Python supports a concept of iteration over containers. This is implemented using two distinct methods; these are used to allow user-defined classes to support iteration. Sequences, described below in more detail, always support the iteration methods. One method needs to be defined for container objects to provide iterable support: container. __iter__ ( ) ¶ Return an iterator object. The object is required to support the iterator protocol described below. If a container supports different types of iteration, additional methods can be provided to specifically request iterators for those iteration types. (An example of an object supporting multiple forms of iteration would be a tree structure which supports both breadth-first and depth-first traversal.) This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API. The iterator objects themselves are required to support the following two methods, which together form the iterator protocol : iterator. __iter__ ( ) ¶ Return the iterator object itself. This is required to allow both containers and iterators to be used with the for and in statements. This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API. iterator. __next__ ( ) ¶ Return the next item from the iterator . If there are no further items, raise the StopIteration exception. This method corresponds to the tp_iternext slot of the type structure for Python objects in the Python/C API. Python defines several iterator objects to support iteration over general and specific sequence types, dictionaries, and other more specialized forms. The specific types are not important beyond their implementation of the iterator protocol. Once an iterator’s __next__() method raises StopIteration , it must continue to do so on subsequent calls. Implementations that do not obey this property are deemed broken. Generator Types ¶ Python’s generator s provide a convenient way to implement the iterator protocol. If a container object’s __iter__() method is implemented as a generator, it will automatically return an iterator object (technically, a generator object) supplying the __iter__() and __next__() methods. More information about generators can be found in the documentation for the yield expression . Sequence Types — list , tuple , range ¶ There are three basic sequence types: lists, tuples, and range objects. Additional sequence types tailored for processing of binary data and text strings are described in dedicated sections. Common Sequence Operations ¶ The operations in the following table are supported by most sequence types, both mutable and immutable. The collections.abc.Sequence ABC is provided to make it easier to correctly implement these operations on custom sequence types. This table lists the sequence operations sorted in ascending priority. In the table, s and t are sequences of the same type, n , i , j and k are integers and x is an arbitrary object that meets any type and value restrictions imposed by s . The in and not in operations have the same priorities as the comparison operations. The + (concatenation) and * (repetition) operations have the same priority as the corresponding numeric operations. [ 3 ] Operation Result Notes x in s True if an item of s is equal to x , else False (1) x not in s False if an item of s is equal to x , else True (1) s + t the concatenation of s and t (6)(7) s * n or n * s equivalent to adding s to itself n times (2)(7) s[i] i th item of s , origin 0 (3)(8) s[i:j] slice of s from i to j (3)(4) s[i:j:k] slice of s from i to j with step k (3)(5) len(s) length of s min(s) smallest item of s max(s) largest item of s Sequences of the same type also support comparisons. In particular, tuples and lists are compared lexicographically by comparing corresponding elements. This means that to compare equal, every element must compare equal and the two sequences must be of the same type and have the same length. (For full details see Comparisons in the language reference.) Forward and reversed iterators over mutable sequences access values using an index. That index will continue to march forward (or backward) even if the underlying sequence is mutated. The iterator terminates only when an IndexError or a StopIteration is encountered (or when the index drops below zero). Notes: While the in and not in operations are used only for simple containment testing in the general case, some specialised sequences (such as str , bytes and bytearray ) also use them for subsequence testing: >>> "gg" in "eggs" True Values of n less than 0 are treated as 0 (which yields an empty sequence of the same type as s ). Note that items in the sequence s are not copied; they are referenced multiple times. This often haunts new Python programmers; consider: >>> lists = [[]] * 3 >>> lists [[], [], []] >>> lists [ 0 ] . append ( 3 ) >>> lists [[3], [3], [3]] What has happened is that [[]] is a one-element list containing an empty list, so all three elements of [[]] * 3 are references to this single empty list. Modifying any of the elements of lists modifies this single list. You can create a list of different lists this way: >>> lists = [[] for i in range ( 3 )] >>> lists [ 0 ] . append ( 3 ) >>> lists [ 1 ] . append ( 5 ) >>> lists [ 2 ] . append ( 7 ) >>> lists [[3], [5], [7]] Further explanation is available in the FAQ entry How do I create a multidimensional list? . If i or j is negative, the index is relative to the end of sequence s : len(s) + i or len(s) + j is substituted. But note that -0 is still 0 . The slice of s from i to j is defined as the sequence of items with index k such that i <= k < j . If i is omitted or None , use 0 . If j is omitted or None , use len(s) . If i or j is less than -len(s) , use 0 . If i or j is greater than len(s) , use len(s) . If i is greater than or equal to j , the slice is empty. The slice of s from i to j with step k is defined as the sequence of items with index x = i + n*k such that 0 <= n < (j-i)/k . In other words, the indices are i , i+k , i+2*k , i+3*k and so on, stopping when j is reached (but never including j ). When k is positive, i and j are reduced to len(s) if they are greater. When k is negative, i and j are reduced to len(s) - 1 if they are greater. If i or j are omitted or None , they become “end” values (which end depends on the sign of k ). Note, k cannot be zero. If k is None , it is treated like 1 . Concatenating immutable sequences always results in a new object. This means that building up a sequence by repeated concatenation will have a quadratic runtime cost in the total sequence length. To get a linear runtime cost, you must switch to one of the alternatives below: if concatenating str objects, you can build a list and use str.join() at the end or else write to an io.StringIO instance and retrieve its value when complete if concatenating bytes objects, you can similarly use bytes.join() or io.BytesIO , or you can do in-place concatenation with a bytearray object. bytearray objects are mutable and have an efficient overallocation mechanism if concatenating tuple objects, extend a list instead for other types, investigate the relevant class documentation Some sequence types (such as range ) only support item sequences that follow specific patterns, and hence don’t support sequence concatenation or repetition. An IndexError is raised if i is outside the sequence range. Sequence Methods Sequence types also support the following methods: sequence. count ( value , / ) ¶ Return the total number of occurrences of value in sequence . sequence. index ( value[, start[, stop] ) ¶ Return the index of the first occurrence of value in sequence . Raises ValueError if value is not found in sequence . The start or stop arguments allow for efficient searching of subsections of the sequence, beginning at start and ending at stop . This is roughly equivalent to start + sequence[start:stop].index(value) , only without copying any data. Caution Not all sequence types support passing the start and stop arguments. Immutable Sequence Types ¶ The only operation that immutable sequence types generally implement that is not also implemented by mutable sequence types is support for the hash() built-in. This support allows immutable sequences, such as tuple instances, to be used as dict keys and stored in set and frozenset instances. Attempting to hash an immutable sequence that contains unhashable values will result in TypeError . Mutable Sequence Types ¶ The operations in the following table are defined on mutable sequence types. The collections.abc.MutableSequence ABC is provided to make it easier to correctly implement these operations on custom sequence types. In the table s is an instance of a mutable sequence type, t is any iterable object and x is an arbitrary object that meets any type and value restrictions imposed by s (for example, bytearray only accepts integers that meet the value restriction 0 <= x <= 255 ). Operation Result Notes s[i] = x item i of s is replaced by x del s[i] removes item i of s s[i:j] = t slice of s from i to j is replaced by the contents of the iterable t del s[i:j] removes the elements of s[i:j] from the list (same as s[i:j] = [] ) s[i:j:k] = t the elements of s[i:j:k] are replaced by those of t (1) del s[i:j:k] removes the elements of s[i:j:k] from the list s += t extends s with the contents of t (for the most part the same as s[len(s):len(s)] = t ) s *= n updates s with its contents repeated n times (2) Notes: If k is not equal to 1 , t must have the same length as the slice it is replacing. The value n is an integer, or an object implementing __index__() . Zero and negative values of n clear the sequence. Items in the sequence are not copied; they are referenced multiple times, as explained for s * n under Common Sequence Operations . Mutable Sequence Methods Mutable sequence types also support the following methods: sequence. append ( value , / ) ¶ Append value to the end of the sequence This is equivalent to writing seq[len(seq):len(seq)] = [value] . sequence. clear ( ) ¶ Added in version 3.3. Remove all items from sequence . This is equivalent to writing del sequence[:] . sequence. copy ( ) ¶ Added in version 3.3. Create a shallow copy of sequence . This is equivalent to writing sequence[:] . Hint The copy() method is not part of the MutableSequence ABC , but most concrete mutable sequence types provide it. sequence. extend ( iterable , / ) ¶ Extend sequence with the contents of iterable . For the most part, this is the same as writing seq[len(seq):len(seq)] = iterable . sequence. insert ( index , value , / ) ¶ Insert value into sequence at the given index . This is equivalent to writing sequence[index:index] = [value] . sequence. pop ( index = -1 , / ) ¶ Retrieve the item at index and also removes it from sequence . By default, the last item in sequence is removed and returned. sequence. remove ( value , / ) ¶ Remove the first item from sequence where sequence[i] == value . Raises ValueError if value is not found in sequence . sequence. reverse ( ) ¶ Reverse the items of sequence in place. This method maintains economy of space when reversing a large sequence. To remind users that it operates by side-effect, it returns None . Lists ¶ Lists are mutable sequences, typically used to store collections of homogeneous items (where the precise degree of similarity will vary by application). class list ( iterable = () , / ) ¶ Lists may be constructed in several ways: Using a pair of square brackets to denote the empty list: [] Using square brackets, separating items with commas: [a] , [a, b, c] Using a list comprehension: [x for x in iterable] Using the type constructor: list() or list(iterable) The constructor builds a list whose items are the same and in the same order as iterable ’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a list, a copy is made and returned, similar to iterable[:] . For example, list('abc') returns ['a', 'b', 'c'] and list( (1, 2, 3) ) returns [1, 2, 3] . If no argument is given, the constructor creates a new empty list, [] . Many other operations also produce lists, including the sorted() built-in. Lists implement all of the common and mutable sequence operations. Lists also provide the following additional method: sort ( * , key = None , reverse = False ) ¶ This method sorts the list in place, using only < comparisons between items. Exceptions are not suppressed - if any comparison operations fail, the entire sort operation will fail (and the list will likely be left in a partially modified state). sort() accepts two arguments that can only be passed by keyword ( keyword-only arguments ): key specifies a function of one argument that is used to extract a comparison key from each list element (for example, key=str.lower ). The key corresponding to each item in the list is calculated once and then used for the entire sorting process. The default value of None means that list items are sorted directly without calculating a separate key value. The functools.cmp_to_key() utility is available to convert a 2.x style cmp function to a key function. reverse is a boolean value. If set to True , then the list elements are sorted as if each comparison were reversed. This method modifies the sequence in place for economy of space when sorting a large sequence. To remind users that it operates by side effect, it does not return the sorted sequence (use sorted() to explicitly request a new sorted list instance). The sort() method is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade). For sorting examples and a brief sorting tutorial, see Sorting Techniques . CPython implementation detail: While a list is being sorted, the effect of attempting to mutate, or even inspect, the list is undefined. The C implementation of Python makes the list appear empty for the duration, and raises ValueError if it can detect that the list has been mutated during a sort. Thread safety Reading a single element from a list is atomic : lst [ i ] # list.__getitem__ The following methods traverse the list and use atomic reads of each item to perform their function. That means that they may return results affected by concurrent modifications: item in lst lst . index ( item ) lst . count ( item ) All of the above methods/operations are also lock-free. They do not block concurrent modifications. Other operations that hold a lock will not block these from observing intermediate states. All other operations from here on block using the per-object lock. Writing a single item via lst[i] = x is safe to call from multiple threads and will not corrupt the list. The following operations return new objects and appear atomic to other threads: lst1 + lst2 # concatenates two lists into a new list x * lst # repeats lst x times into a new list lst . copy () # returns a shallow copy of the list Methods that only operate on a single elements with no shifting required are atomic : lst . append ( x ) # append to the end of the list, no shifting required lst . pop () # pop element from the end of the list, no shifting required The clear() method is also atomic . Other threads cannot observe elements being removed. The sort() method is not atomic . Other threads cannot observe intermediate states during sorting, but the list appears empty for the duration of the sort. The following operations may allow lock-free operations to observe intermediate states since they modify multiple elements in place: lst . insert ( idx , item ) # shifts elements lst . pop ( idx ) # idx not at the end of the list, shifts elements lst *= x # copies elements in place The remove() method may allow concurrent modifications since element comparison may execute arbitrary Python code (via __eq__() ). extend() is safe to call from multiple threads. However, its guarantees depend on the iterable passed to it. If it is a list , a tuple , a set , a frozenset , a dict or a dictionary view object (but not their subclasses), the extend operation is safe from concurrent modifications to the iterable. Otherwise, an iterator is created which can be concurrently modified by another thread. The same applies to inplace concatenation of a list with other iterables when using lst += iterable . Similarly, assigning to a list slice with lst[i:j] = iterable is safe to call from multiple threads, but iterable is only locked when it is also a list (but not its subclasses). Operations that involve multiple accesses, as well as iteration, are never atomic. For example: # NOT atomic: read-modify-write lst [ i ] = lst [ i ] + 1 # NOT atomic: check-then-act if lst : item = lst . pop () # NOT thread-safe: iteration while modifying for item in lst : process ( item ) # another thread may modify lst Consider external synchronization when sharing list instances across threads. See Python support for free threading for more information. Tuples ¶ Tuples are immutable sequences, typically used to store collections of heterogeneous data (such as the 2-tuples produced by the enumerate() built-in). Tuples are also used for cases where an immutable sequence of homogeneous data is needed (such as allowing storage in a set or dict instance). class tuple ( iterable = () , / ) ¶ Tuples may be constructed in a number of ways: Using a pair of parentheses to denote the empty tuple: () Using a trailing comma for a singleton tuple: a, or (a,) Separating items with commas: a, b, c or (a, b, c) Using the tuple() built-in: tuple() or tuple(iterable) The constructor builds a tuple whose items are the same and in the same order as iterable ’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a tuple, it is returned unchanged. For example, tuple('abc') returns ('a', 'b', 'c') and tuple( [1, 2, 3] ) returns (1, 2, 3) . If no argument is given, the constructor creates a new empty tuple, () . Note that it is actually the comma which makes a tuple, not the parentheses. The parentheses are optional, except in the empty tuple case, or when they are needed to avoid syntactic ambiguity. For example, f(a, b, c) is a function call with three arguments, while f((a, b, c)) is a function call with a 3-tuple as the sole argument. Tuples implement all of the common sequence operations. For heterogeneous collections of data where access by name is clearer than access by index, collections.namedtuple() may be a more appropriate choice than a simple tuple object. Ranges ¶ The range type represents an immutable sequence of numbers and is commonly used for looping a specific number of times in for loops. class range ( stop , / ) ¶ class range ( start , stop , step = 1 , / ) The arguments to the range constructor must be integers (either built-in int or any object that implements the __index__() special method). If the step argument is omitted, it defaults to 1 . If the start argument is omitted, it defaults to 0 . If step is zero, ValueError is raised. For a positive step , the contents of a range r are determined by the formula r[i] = start + step*i where i >= 0 and r[i] < stop . For a negative step , the contents of the range are still determined by the formula r[i] = start + step*i , but the constraints are i >= 0 and r[i] > stop . A range object will be empty if r[0] does not meet the value constraint. Ranges do support negative indices, but these are interpreted as indexing from the end of the sequence determined by the positive indices. Ranges containing absolute values larger than sys.maxsize are permitted but some features (such as len() ) may raise OverflowError . Range examples: >>> list ( range ( 10 )) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> list ( range ( 1 , 11 )) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> list ( range ( 0 , 30 , 5 )) [0, 5, 10, 15, 20, 25] >>> list ( range ( 0 , 10 , 3 )) [0, 3, 6, 9] >>> list ( range ( 0 , - 10 , - 1 )) [0, -1, -2, -3, -4, -5, -6, -7, -8, -9] >>> list ( range ( 0 )) [] >>> list ( range ( 1 , 0 )) [] Ranges implement all of the common sequence operations except concatenation and repetition (due to the fact that range objects can only represent sequences that follow a strict pattern and repetition and concatenation will usually violate that pattern). start ¶ The value of the start parameter (or 0 if the parameter was not supplied) stop ¶ The value of the stop parameter step ¶ The value of the step parameter (or 1 if the parameter was not supplied) The advantage of the range type over a regular list or tuple is that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores the start , stop and step values, calculating individual items and subranges as needed). Range objects implement the collections.abc.Sequence ABC, and provide features such as containment tests, element index lookup, slicing and support for negative indices (see Sequence Types — list, tuple, range ): >>> r = range ( 0 , 20 , 2 ) >>> r range(0, 20, 2) >>> 11 in r False >>> 10 in r True >>> r . index ( 10 ) 5 >>> r [ 5 ] 10 >>> r [: 5 ] range(0, 10, 2) >>> r [ - 1 ] 18 Testing range objects for equality with == and != compares them as sequences. That is, two range objects are considered equal if they represent the same sequence of values. (Note that two range objects that compare equal might have different start , stop and step attributes, for example range(0) == range(2, 1, 3) or range(0, 3, 2) == range(0, 4, 2) .) Changed in version 3.2: Implement the Sequence ABC. Support slicing and negative indices. Test int objects for membership in constant time instead of iterating through all items. Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects based on the sequence of values they define (instead of comparing based on object identity). Added the start , stop and step attributes. See also The linspace recipe shows how to implement a lazy version of range suitable for floating-point applications. Text and Binary Sequence Type Methods Summary ¶ The following table summarizes the text and binary sequence types methods by category. Category str methods bytes and bytearray methods Formatting str.format() str.format_map() f-strings printf-style String Formatting printf-style Bytes Formatting Searching and Replacing str.find() str.rfind() bytes.find() bytes.rfind() str.index() str.rindex() bytes.index() bytes.rindex() str.startswith() bytes.startswith() str.endswith() bytes.endswith() str.count() bytes.count() str.replace() bytes.replace() Splitting and Joining str.split() str.rsplit() bytes.split() bytes.rsplit() str.splitlines() bytes.splitlines() str.partition() bytes.partition() str.rpartition() bytes.rpartition() str.join() bytes.join() String Classification str.isalpha() bytes.isalpha() str.isdecimal() str.isdigit() bytes.isdigit() str.isnumeric() str.isalnum() bytes.isalnum() str.isidentifier() str.islower() bytes.islower() str.isupper() bytes.isupper() str.istitle() bytes.istitle() str.isspace() bytes.isspace() str.isprintable() Case Manipulation str.lower() bytes.lower() str.upper() bytes.upper() str.casefold() str.capitalize() bytes.capitalize() str.title() bytes.title() str.swapcase() bytes.swapcase() Padding and Stripping str.ljust() str.rjust() bytes.ljust() bytes.rjust() str.center() bytes.center() str.expandtabs() bytes.expandtabs() str.strip() bytes.strip() str.lstrip() str.rstrip() bytes.lstrip() bytes.rstrip() Translation and Encoding str.translate() bytes.translate() str.maketrans() bytes.maketrans() str.encode() bytes.decode() Text Sequence Type — str ¶ Textual data in Python is handled with str objects, or strings . Strings are immutable sequences of Unicode code points. String literals are written in a variety of ways: Single quotes: 'allows embedded "double" quotes' Double quotes: "allows embedded 'single' quotes" Triple quoted: '''Three single quotes''' , """Three double quotes""" Triple quoted strings may span multiple lines - all associated whitespace will be included in the string literal. String literals that are part of a single expression and have only whitespace between them will be implicitly converted to a single string literal. That is, ("spam " "eggs") == "spam eggs" . See String and Bytes literals for more about the various forms of string literal | 2026-01-13T08:48:40 |
https://openapi.tools/categories/sdk-generators | SDK Generators | OpenApi.tools, from APIs You Won't Hate Sponsored by Zudoku - Open-source, highly customizable API documentation powered by OpenAPI Get Started Sponsor openapi.tools GitHub Get Started All Tools All Categories Legacy Tools Contributing Sponsors Sponsor Badges Collections Arazzo Support Overlays Support Open Source Tools SaaS Tools OpenAPI Tool Categories Annotations Code generators Converters Data Validators Documentation Domain-Specific Languages (DSLs) Gateways HTTP Clients IDEs and GUI Editors Learning Miscellaneous Mock Servers Monitoring OpenAPI-aware Frameworks Parsers Schema Validators SDK Generators Security Server Implementations Testing Text Editors © 2026 APIs You Won't Hate Get in touch to become a Sponsor . This site is community-driven and OSS , built with Astro and hosted on Netlify . SDK Generators Generate code to give to consumers, to help them avoid interacting at a HTTP level. SDK Generators There are additional tools in this category, but they only support legacy versions of OpenAPI. If you really need to work with some old OpenAPI descriptions perhaps these legacy tools could be of use * * * | 2026-01-13T08:48:40 |
https://translations.python.org/#ja | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
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https://translations.python.org/#ro | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://stackoverflow.com/users/1145566/florian-kusche | User Florian Kusche - Stack Overflow Skip to main content Stack Overflow About Products For Teams Stack Internal Implement a knowledge platform layer to power your enterprise and AI tools. Stack Data Licensing Get access to top-class technical expertise with trusted & attributed content. Stack Ads Connect your brand to the world’s most trusted technologist communities. Releases Keep up-to-date on features we add to Stack Overflow and Stack Internal. About the company Visit the blog s-popover#show" data-s-popover-placement="bottom-start" /> Loading… current community Stack Overflow help chat Meta Stack Overflow your communities Sign up or log in to customize your list. more stack exchange communities company blog Log in Sign up Home Questions AI Assist Tags Challenges Chat Articles Users Companies Collectives Communities for your favorite technologies. Explore all Collectives Stack Internal Stack Overflow for Teams is now called Stack Internal . Bring the best of human thought and AI automation together at your work. Try for free Learn more Stack Internal Bring the best of human thought and AI automation together at your work. Learn more Collectives™ on Stack Overflow Find centralized, trusted content and collaborate around the technologies you use most. Learn more about Collectives Stack Internal Knowledge at work Bring the best of human thought and AI automation together at your work. Explore Stack Internal Florian Kusche Member for 14 years Last seen this week Profiles Meta user Network profile Profile Activity Stats 173 reputation 30k reached 7 answers 7 questions Communities View all Stack Overflow 173 Super User 23 Server Fault 21 Unix & Linux 1 Ask Different 1 Badges View all badges This user doesn’t have any gold badges yet. 2 silver badges Necromancer Aug 11 Notable Question May 9 11 bronze badges Critic May 20 Revival Jun 4, 2013 Commentator Oct 28, 2018 Top tags View all tags qt 6 Score 2 Posts 14 Posts % qtableview 5 Score 1 Posts 7 Posts % c++ 4 Score 3 Posts 21 Posts % winapi 4 Score 1 Posts 7 Posts % usb 4 Score 1 Posts 7 Posts % windows 4 Score 1 Posts 7 Posts % Top posts View all questions , answers , and articles All Questions Answers Articles Score Newest answer 5 Determine if QTableView has an open editor Aug 29, 2013 answer 4 Reliably know if a volume is removable or not with WinApi Oct 26, 2018 answer 3 Locale-independent strtod implementation Feb 14, 2012 answer 2 High resolution timer support on 64-bit Linux Aug 27, 2012 answer 1 How can I pass the output of a command as a compiler flag through a Qt project file? Dec 17, 2014 question 1 Convert iterator of string slice to Vector<String> [closed] Oct 9, 2021 question 1 How to use C++11 lambda as boost predicate? Feb 21, 2014 answer 0 AppImage updates conflicts with its .desktop file 3 mins ago answer 0 When to case fold and when to collate using Boost.Locale? Feb 27, 2014 question 0 Use return type of other function for type annotation [duplicate] May 20, 2025 Stack Overflow Questions Help Chat Business Stack Internal Stack Data Licensing Stack Ads Company About Press Work Here Legal Privacy Policy Terms of Service Contact Us cookie-settings#toggle" class="s-btn s-btn__link py4 js-gps-track -link" data-gps-track="footer.click({ location: 4, link: 38 })" data-consent-popup-loader="footer"> Cookie Settings Cookie Policy Stack Exchange Network Technology Culture & recreation Life & arts Science Professional Business API Data Blog Facebook Twitter LinkedIn Instagram Site design / logo © 2026 Stack Exchange Inc; user contributions licensed under CC BY-SA . rev 2026.1.12.38533 | 2026-01-13T08:48:40 |
https://translations.python.org/#id | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://openapi.tools/categories/code-generators | Code generators | OpenApi.tools, from APIs You Won't Hate Sponsored by Zudoku - Open-source, highly customizable API documentation powered by OpenAPI Get Started Sponsor openapi.tools GitHub Get Started All Tools All Categories Legacy Tools Contributing Sponsors Sponsor Badges Collections Arazzo Support Overlays Support Open Source Tools SaaS Tools OpenAPI Tool Categories Annotations Code generators Converters Data Validators Documentation Domain-Specific Languages (DSLs) Gateways HTTP Clients IDEs and GUI Editors Learning Miscellaneous Mock Servers Monitoring OpenAPI-aware Frameworks Parsers Schema Validators SDK Generators Security Server Implementations Testing Text Editors © 2026 APIs You Won't Hate Get in touch to become a Sponsor . This site is community-driven and OSS , built with Astro and hosted on Netlify . Code generators Tools to generate code from your OpenAPI Spec, or to generate an OpenAPI Spec from your code. Code generators There are additional tools in this category, but they only support legacy versions of OpenAPI. If you really need to work with some old OpenAPI descriptions perhaps these legacy tools could be of use * * * | 2026-01-13T08:48:40 |
https://support.google.com/chrome/answer/95606?hl=en&ref_topic=7438325 | Manage passwords in Chrome - Computer - Google Chrome Help Skip to main content Google Chrome Help Sign in Google Help Help Center Community Google Chrome Privacy Policy Terms of Service Submit feedback Send feedback on... This help content & information General Help Center experience Next Help Center Community Google Chrome Manage passwords in Chrome When you sign in to Chrome, Google Password Manager saves your passwords for different sites. You can save your passwords with Google Password Manager in many ways: Save passwords to your Google Account: When you're signed in to Chrome, you can save your passwords to your Google Account and use them across your devices and in some apps. Save passwords to your device: When you’re not signed in to Chrome, it stores passwords locally on your device. This is helpful if you don’t want to save passwords to your Google Account or share them across multiple devices. Import passwords from your device to your Google Account: You can always import passwords saved on your device to your Google Account. Learn how to import passwords with Chrome . You can manage passwords saved to your Google Wallet in Chrome or any other web browser at passwords.google.com . Learn more about how Chrome protects your passwords . Learn more about on-device encryption for passwords . Manage new passwords Automatically save or preview a new password If you enter a new password on a site, Chrome will ask to save it. To accept, select Save . If you want to preview, review, or adjust a password: To check the password that will be saved, select Preview . If there are multiple passwords on the page, select the Down arrow . Choose the password you want saved. If your username is blank or incorrect, select the text box next to "Username." Enter the username you want saved. If you want to save a different password, select the text box next to "Password." Enter the password you want saved. Manually add a new password On your computer, open Chrome. At the top right, select More Passwords and autofill Google Password Manager . Select Add . Enter a website, username, and password. Select Save . Use a suggested password On your computer, sign in to Chrome . Go to a website and sign up for an account. Select the password text box Use strong password . If this option doesn’t show, right-click the password text box Generate password . You'll get a preview of the password. To confirm, select Use suggested password . Finish your account sign-up. Your generated password is automatically saved to your Google Wallet. Start or stop saving passwords By default, Chrome offers to save your password. You can turn this option off or on at any time. On your computer, open Chrome. At the top right, select Profile Passwords . If you can’t find the Passwords icon, at the top right, select More Passwords and autofill Google Password Manager . On the left, select Settings . Turn Offer to save passwords and passkeys on or off. Check or remove sites that don’t save passwords If you choose not to save passwords for a site or app, in settings, you can manage them from the "Declined sites and apps." On your computer, open Chrome. At the top right, select More Passwords and autofill Google Password Manager . Select Settings . Under "Declined sites and apps," remove the site. Manage saved passwords Sign in with a saved password If you saved your password to Chrome on a previous visit to a website, Chrome can help you sign in. On your computer, go to a site you've visited before. Go to the site’s sign-in form. If you’ve saved a single username and password for the site : Chrome will fill in the sign-in form automatically. If you’ve saved more than one username and password : Select the username field and choose the sign-in info you want to use. Add notes to your saved password You can add notes to a saved password to help you remember info about an account or to save login details. Once you add a note, it has the same security protection as a password. On your computer, open Chrome. At the top right, select More Passwords and autofill Google Password Manager . Under "Passwords," choose the password you want to add a note to. Select Edit . Enter your note. When you're finished, select Save . Show, edit, delete, or export saved passwords On your computer, open Chrome. At the top right, select Profile Passwords . If there's no Passwords icon, at the top right, select More Passwords and autofill Google Password Manager . Show, edit, delete, or export a password: Show: Under “Passwords,” select the password. On the right of your password, select Show password . Edit: Under “Passwords,” select the password. Select Edit . Edit your password. Select Save . Delete: Under “Passwords,” select the password. Select Delete . Export: On the left, select Settings . On the right of "Export Passwords," select Download file . Delete all Google Password Manager data You can delete your Google Password Manager data, which include passwords and passkeys from Google Password Manager settings. On your computer, open Chrome. At the top right, select More Passwords and Autofill Google Password Manager . On the left, select Settings . On the right of "Delete all Google Password Manager data," select Delete data Delete . Tip: To delete other browsing data, learn how to delete browsing data in Chrome . Check for compromised passwords You can check all your saved passwords at once to find out if they're exposed in a data breach or potentially weak and easy to guess. To check your saved passwords: On your computer, open Chrome. At the top right, select Profile Passwords . If you can’t find the Passwords icon, at the top right, select More Passwords and autofill Google Password Manager . On the left, select Checkup . You'll get details on any password exposed in a data breach and any weak, easy to guess passwords. Learn what you can do with your passwords Use biometric authentication with passwords When biometric authentication is turned on, you can use your device's fingerprint sensor to increase privacy when you autofill passwords. You can also use biometric authentication to reveal, copy, or edit your passwords. Important: By default, biometric authentication is off. On your computer, open Chrome. At the top right, select More Passwords and autofill Google Password Manager . Select Settings . To turn on biometric authentication: On PC: Turn on Use Windows Hello when filling passwords . On Mac: Turn on Use your screen lock when filling passwords . Follow the on-screen instructions to confirm your selection. Share your password Important: You can only share your password with a member of your family group. Create a family group . Use Google Password Manager and update Google Chrome . To securely share a copy of your saved password with a family member: On your computer, open Chrome. At the top right, select More Passwords and autofill Google Password Manager . Under "Passwords," select the password you want to share. Select Share . Select the family member or members you want to share the password with. Select Share Done . The password will be saved in the receiver's Google Account and will be available for autofill. Tips: Use unique passwords for all your online accounts. If you use the same password for multiple accounts, anyone you share a password with may be able to access your other accounts. If you don’t want to share this service, change your password. Sign in to sites and apps automatically You can automatically sign in to any sites and apps where you've saved your info with "Sign in automatically." When you turn on "Sign in automatically," you don't need to confirm your username, password, or third-party sign-in credentials. If you want to confirm your saved info when you sign in, you can turn off "Sign in automatically." On your computer, open Chrome. At the top right, select Profile Passwords . If you can't find your Google Account, learn how to turn on sync in Chrome . If you can’t find the Passwords icon, at the top right, select More Passwords and autofill Google Password Manager . On the left, select Settings. Turn Sign in automatically on or off. Tips: If you use an identity service, both the identity service and the site must support "Sign in automatically" for it to work. Learn more about third-party sign-in . If you recently dismissed the prompt to sign in automatically, it might be temporarily turned off. Add Google Password Manager to your home screen For quick access, you can add Google Password Manager as a shortcut. On your computer, open Chrome. At the top right, select More Passwords and autofill Google Password Manager . On the left, select Settings Add shortcut . Select Install . Learn more about web apps . Manage password change alerts You may get an alert from Chrome if you use a password and username combination that has been compromised in a data leak on a third-party website or app. Compromised password and username combinations are unsafe because they’ve been published online. We recommend that you change any compromised passwords as soon as you can. You can follow the instructions in Chrome to change your password on the site where you’ve used that password, and check your saved passwords for any other site the password may be saved on. Chrome makes sure that your passwords and username are protected so they can’t be read by Google. To start or stop notifications: On your computer, open Chrome. At the top right, select More Settings Privacy and security . Select Security Standard protection . Turn Warn you if a password was compromised in a data breach on or off. Tip: This feature is only available if the "Safe Browsing" option is turned on. Dismiss notifications for specific sites: On your computer, open Chrome. At the top right, select More Passwords and autofill Google Password Manager . To check which of your saved passwords are compromised, select Checkup . To the right of “Compromised passwords,” select the Arrow . Find the site of the notifications that you want to stop. Select More Dismiss warning . Tip: To restore site warnings, under "Dismissed warnings," select More next to the site whose notifications you want to begin again. Then, select Restore warning . Fix issues with passwords If Chrome doesn't save or offer to save passwords, learn how to fix issues with saved payment info and passwords . Change compromised passwords automatically The Chrome security team always monitors the web for danger. If a password for one of your online accounts is found in a public data breach, you can update your password to a new and secure one with Chrome’s automated password change feature. Important: At this time, this feature is only compatible with a limited number of English language websites. To use this feature, you must: Sign in to your Google Account in Chrome. Be at least 18 years old. Be located in the US. Have at least one saved password in Google Password Manager. Turn on password saving in Google Password Manager. Learn how to manage passwords on Chrome . On your computer, open Chrome. Sign in to a site that requires an account with a username and password. If your password was compromised, you’ll receive the alert “Your password was found in a public data breach” on compatible websites. To automatically update your password, select Change it for me . If your password was successfully updated, the dialog will display “Password changed.” To get the details of the updated password, select Show password . If your password wasn’t updated successfully, select Change it on the site to manually change your password. Tips: If you close the tab or window while the password change is in progress, the password might not be updated. Learn how to show, edit, delete, or export saved passwords . In case of login issues, automated password change temporarily saves a recovery password, and may offer you to fill it in consecutive logins. Related resources Change or reset your password Get your bookmarks, passwords, and more on all your devices Fix issues with saved payment info & passwords Import passwords with Chrome Was this helpful? How can we improve it? Yes No Submit Computer Android iPhone & iPad More Need more help? 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https://translations.python.org/#ko | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
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kl=[0,x,-1,hi,ci,Rh,hi,-2,ni,function(){return kl}];var Nfa=[0,x,-2,kl,x,-1];var Ofa=[0,x];var Pfa=[0,x,-2,uj,x];var Qfa=[0,x,-1,ni,oj,-1,Gh,Ai];var Rfa=[0,ci,-2];var Sfa=[0,x,-5,function(){return ll}, ni,function(){return ll}, -1,hi,-1,x,Wh,x,Ak,-1,x,-1,Yh,ni,xj,ni,Lfa,Ofa,hi,-1,x],Tfa=[0,x,-8,function(){return ll}, uj,x,Xh,ni,xj,x,Wh,x,Ak,-1,x,Ak],Ufa=[0,x,-2,1,x,function(){return ll}, uj,x,Xh,ni,xj,x,Wh,x,Ak,-1,x],Vfa=[0,x,-6,function(){return ll}, ni,function(){return ll}, -1,hi,-1,uj,x,-2,Xh,ni,dk,-1,ni,xj,ni,Rfa,x,Wh,x,Ak,-1,x,ni,Lfa,[0,x],Ofa,ni,Bk,hi,-2,Ak];Wk.prototype.Ca=Di(Vfa);var ll=[0,x,-1,hi,ni,function(){return ll}, ni,function(){return Wfa}, function(){return ll}, ni,function(){return ll}, x,Xh,ki,2,hi,vi,x,ni,Xea,ni,dk,x,-1,Wea,Zea,x,-2,Qfa,x,1,Yea,x,vi,Ak,ni,xj,ni,$ea,hi,Ak,ki,hi,ni,function(){return Vfa}, ni,function(){return Ufa}, ni,function(){return Tfa}, ni,Nfa,ni,Pfa,ni,Bfa,ni,function(){return Sfa}],Wfa=[0, x,-11,hi,-1,x,8,x,kl,1,function(){return ll}, ni,function(){return ll}, x,Xh,hi,Wh,x,-1,ni,dk,x,ni,dk,ni,function(){return ll}, | 2026-01-13T08:48:40 |
https://translations.python.org/#it | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://translations.python.org/#hi-in | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://translations.python.org/build-details.html | Python Docs Translation Dashboard Translation Dashboard Build details Translating language branch last updated build warnings* lint failures Simplified Chinese (zh-cn) 3.14 2026/01/12 08:20:03 322 1405 Brazilian Portuguese (pt-br) 3.14 2026/01/11 23:09:23 1042 0 Spanish (es) 3.13 2026/01/06 16:58:34 251 32 Korean (ko) 3.14 2025/10/11 22:03:46 67 3 Ukrainian (uk) 3.13 2025/05/01 16:40:42 153 0 Japanese (ja) 3.14 2026/01/12 23:49:40 410 343 Traditional Chinese (zh-tw) 3.14 2026/01/13 11:08:20 30 0 French (fr) 3.14 2025/11/22 14:29:55 146 0 Greek (el) 3.14 2025/12/15 10:54:40 0 1 Polish (pl) 3.14 2026/01/11 15:12:18 10 0 Turkish (tr) 3.12 2025/05/28 17:22:17 19 1 Russian (ru) 3.14 2026/01/12 15:29:10 101 10 Indonesian (id) 3.14 2026/01/12 00:24:40 20 1 Italian (it) 3.13 2024/06/08 12:26:23 4 0 Romanian (ro) 3.13 2025/11/16 09:59:14 1 0 Hungarian (hu) 3.14 2026/01/11 15:08:00 34 7 Persian (fa) 3.13 2025/07/25 21:53:55 1 1 Swedish (sv) 3.14 2026/01/12 00:07:00 0 2 Arabic (ar) master 2024/05/24 14:10:55 3 1 Bengali (bn-in) 3.14 2025/05/25 08:27:46 0 0 Hindi (hi-in) 3.13 2025/09/01 06:30:22 0 0 Marathi (mr) main 2025/06/04 22:27:30 0 0 * number of Sphinx build process warnings Last updated at Tuesday, 13 January 2026, 7:00:26 UTC (in 69:11 minutes). | 2026-01-13T08:48:40 |
https://astro.build/?utm_source=openapi-tools&utm_medium=website&utm_campaign=&utm_content= | Astro Skip to content Astro Logo Documentation Blog Resources Themes Integrations Site showcase Tutorials Community Discord Sponsors Merch Enterprise Agencies New Case studies Go to Astro's GitHub repo Show Menu *]:p-6"> Blog Resources Themes Integrations Site showcase Tutorials Community Discord Sponsors Merch Enterprise Agencies New Case studies Follow Astro on Bluesky Join the Astro community on Discord Go to Astro's GitHub repo Follow Astro on LinkedIn Follow Astro on Mastodon Join the official Astro community on Reddit Follow Astro on x.com (formerly Twitter) Follow Astro on YouTube Read the docs 2025 Read the year in review! The web framework for content-driven websites Astro powers the world's fastest marketing sites, blogs, e-commerce websites, and more. Get Started npm create astro@latest Copied! Used by the largest companies around the world: What is Astro? Astro is a JavaScript web framework optimized for building fast, content-driven websites. Server-First Astro improves website performance by rendering components on the server, sending lightweight HTML to the browser with zero unnecessary JavaScript overhead. Content-Driven Astro was designed to work with your content, no matter where it lives. Load data from your file system, external API, or your favorite CMS. Customizable Extend Astro with your favorite tools. Bring your own JavaScript UI components, CSS libraries, themes, integrations, and more. Best-In-Class Performance Astro Islands Islands optimize your website like no other web framework can. Leverage Astro's unique page load performance to improve conversion rates, Core Web Vitals, and SEO. % of real-world sites with good Core Web Vitals Astro Astro Core Web Vitals Passing 62% WordPress WordPress Core Web Vitals Passing 46% Gatsby Gatsby Core Web Vitals Passing 45% Next.js Next.js Core Web Vitals Passing 29% Nuxt Nuxt Core Web Vitals Passing 25% View the full dataset · Based on real-world performance data from HTTP Archive and the Chrome UX Report . Maximum Flexibility Zero Lock-in Astro supports every major UI framework. Bring your existing components and take advantage of Astro's optimized client build performance. Integrate your favorite framework React Vue Preact Svelte Solid --- import BuyButton from '../components/BuyButton.jsx' ; import { getProductDetails } from "ecommerce-package" ; import ProductPageLayout from '../layouts/ProductPageLayout.astro' ; const product = await getProductDetails ( Astro . params . slug ); --- < ProductPageLayout > < img src = { product . imageUrl } alt = { product . imageAlt } /> < h2 > { product . name } </ h2 > < BuyButton id = { product . id } client:load /> </ ProductPageLayout > {product.name} "> Snapback Cap $25.00 Add to cart Everything you need Fully Featured Astro comes with everything you need to build a modern website. Need more? Extend Astro with integrations. Content Collections Organize your Markdown and MDX with built-in TypeScript type-safety and frontmatter validation. 0 Zero JavaScript, By Default Astro only ships the JavaScript you need and automatically strips away the rest for a faster website. View Transitions Seamlessly morph, fade, and swipe across pages with built-in, browser-native View Transition APIs. 800px • WEBP 3600px • PNG Optimized Images Eliminate layout shift and serve optimized, modern image formats with Astro’s built-in components and utilities. UI Integrations Bring your favorite UI frameworks and component libraries with Astro's flexible island architecture. React Vue Svelte Angular Preact Solid Qwik File-Based Routing Astro’s flexible routing system lets you understand your site’s structure at a glance. pages api json.ts blog [slug.astro] 404.astro about.astro index.astro Middleware Wrap incoming requests with custom logic like authentication, logging, or data fetching. Actions Write type-safe backend functions that you can call directly from your frontend JavaScript client code. Deployment Adapters One-line configuration for Netlify, Vercel, AWS, or your favorite hosting platform. Node.js Cloudflare Vercel Netlify SST Deno AWS Simple Templating If you know HTML, you already know enough to write your first Astro component. Build powerful server components everyone on your team understands. <Logo> <NavLinks> <Hero> <Article> <Video> Instant Page Loads Optimize page loading with intelligent automatic prefetching. Pick from the built-in presets or tailor a strategy perfect for your site. AI-Ready Integrate Astro’s official MCP server and context files with your favorite AI tools for a best-in-class development experience. Environment Variables Avoid common configuration mistakes with a built-in API to manage your environment variables. Dev Toolbar Extend your development environment with apps and integrations for the built-in development toolbar. Ecosystem Themes Get started faster with a pre-built website theme for Astro. Customize it to make it your own. Trending E-Commerce Blogs Docs Portfolios Landing Pages View Theme View Theme View Theme View Theme View Theme Browse more themes View Theme View Theme View Theme Browse more e-commerce themes View Theme View Theme View Theme View Theme View Theme View Theme Browse more blog themes View Theme View Theme View Theme Browse more documentation themes View Theme View Theme View Theme View Theme View Theme Browse more portfolio themes View Theme View Theme View Theme View Theme View Theme Browse more landing page themes Astro Partner Agencies Get professional Astro support Find the right team for your project with an Astro Partner Agency. From landing pages to e-commerce, get the expert assistance you need. Explore partner agencies Bits&Letters EXDST Bejamas 58agents GmbH Lucky Media Seibert Group GmbH Start building with Astro today Get Started npm create astro@latest Copied! Astro is free, open-source software made possible by these wonderful sponsors Netlify Official Deployment Partner Webflow Official Partner Cloudflare Official Partner Mux Official Video Partner ArcJet HandsOnTable Lucky Media Datadog Duende Databento Localazy Sponsor Astro Let's keep in touch Enter your email to stay up to date with the latest updates from Astro. Email Resources Docs Themes Integrations Site showcase Starter templates About Blog Case studies Partner with us Press Agencies Community Contributing Sponsors Wallpapers Swag Shop Legal Telemetry Privacy Policy Terms of Service MIT License © 2026 Astro Contributors Follow Astro on Bluesky Join the Astro community on Discord Go to Astro's GitHub repo Follow Astro on LinkedIn Follow Astro on Mastodon Join the official Astro community on Reddit Follow Astro on x.com (formerly Twitter) Follow Astro on YouTube | 2026-01-13T08:48:40 |
https://translations.python.org/#fa | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://openapi.tools/categories/code-generators | Code generators | OpenApi.tools, from APIs You Won't Hate Sponsored by Zudoku - Open-source, highly customizable API documentation powered by OpenAPI Get Started Sponsor openapi.tools GitHub Get Started All Tools All Categories Legacy Tools Contributing Sponsors Sponsor Badges Collections Arazzo Support Overlays Support Open Source Tools SaaS Tools OpenAPI Tool Categories Annotations Code generators Converters Data Validators Documentation Domain-Specific Languages (DSLs) Gateways HTTP Clients IDEs and GUI Editors Learning Miscellaneous Mock Servers Monitoring OpenAPI-aware Frameworks Parsers Schema Validators SDK Generators Security Server Implementations Testing Text Editors © 2026 APIs You Won't Hate Get in touch to become a Sponsor . This site is community-driven and OSS , built with Astro and hosted on Netlify . Code generators Tools to generate code from your OpenAPI Spec, or to generate an OpenAPI Spec from your code. Code generators There are additional tools in this category, but they only support legacy versions of OpenAPI. If you really need to work with some old OpenAPI descriptions perhaps these legacy tools could be of use * * * | 2026-01-13T08:48:40 |
https://openapi.tools/collections/arazzo | Arazzo Support | OpenApi.tools, from APIs You Won't Hate Sponsored by Zudoku - Open-source, highly customizable API documentation powered by OpenAPI Get Started Sponsor openapi.tools GitHub Get Started All Tools All Categories Legacy Tools Contributing Sponsors Sponsor Badges Collections Arazzo Support Overlays Support Open Source Tools SaaS Tools OpenAPI Tool Categories Annotations Code generators Converters Data Validators Documentation Domain-Specific Languages (DSLs) Gateways HTTP Clients IDEs and GUI Editors Learning Miscellaneous Mock Servers Monitoring OpenAPI-aware Frameworks Parsers Schema Validators SDK Generators Security Server Implementations Testing Text Editors © 2026 APIs You Won't Hate Get in touch to become a Sponsor . This site is community-driven and OSS , built with Astro and hosted on Netlify . Arazzo Support Tools that support the Arazzo Specification for describing API workflows and sequences. Arazzo is a specification for describing sequences of API calls and their dependencies. It enables you to document complex workflows, multi-step processes, and API orchestration patterns that go beyond what OpenAPI describes for individual endpoints. * * * | 2026-01-13T08:48:40 |
https://translations.python.org/#zh-cn | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://translations.python.org/#el | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://translations.python.org/#lt | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://translations.python.org/#uk | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://aws.amazon.com/blogs/developer/category/developer-tools/ | Developer Tools | AWS Developer Tools Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Developer Tools Blog Category: Developer Tools AWS SDK for JavaScript aligns with Node.js release schedule by Trivikram Kamat on 08 DEC 2025 in Announcements , AWS SDK for JavaScript in Node.js , Developer Tools , JavaScript , Open Source Permalink Share This post is about AWS SDK for JavaScript v3 announcing end of support for Node.js versions based on Node.js release schedule, and it is not about AWS Lambda. For the latter, refer to the Lambda runtime deprecation policy. In the second week of January 2026, the AWS SDK for JavaScript v3 (JS SDK) will start […] Introducing Amazon S3 Transfer Manager for Swift (Developer Preview) by Chan Yoo on 21 NOV 2025 in Announcements , AWS SDK for Swift Permalink Share e are pleased to announce the Developer Preview release of the Amazon S3 Transfer Manager for Swift —a high-level file and directory transfer utility for Amazon Simple Storage Service (Amazon S3) built with the AWS SDK for Swift . What’s New in the AWS Deploy Tool for .NET by Philippe El Asmar on 14 OCT 2025 in .NET , Announcements , AWS .NET Development , AWS SDK for .NET , AWS Toolkit for Visual Studio , Developer Tools , Visual Studio Permalink Share Version 2.0 of the AWS Deploy Tool for .NET is now available. This new major version introduces several foundational upgrades to improve the deployment experience for .NET applications on AWS. The tool comes with new minimum runtime requirements. We have upgraded it to require .NET 8 because the predecessor, .NET 6, is now out of […] General Availability Release of the Migration Tool for the AWS SDK for Java 2.x by David Ho on 26 SEP 2025 in Announcements , AWS Java Development , AWS SDK for Java , Java Permalink Share The AWS SDK for Java 1.x (v1) entered maintenance mode on July 31, 2024, and will reach end-of-support on December 31, 2025. We recommend that you migrate to the AWS SDK for Java 2.x (v2) to access new features, enhanced performance, and continued support from AWS. To help you migrate efficiently, we’ve created a migration […] Preview Release of the AWS SDK Java 2.x HTTP Client built on Apache HttpClient 5.5.x by John Viegas on 17 JUL 2025 in Advanced (300) , Announcements , AWS SDK for Java , Developer Tools Permalink Share The AWS SDK for Java 2.x introduces the Apache 5 SDK HTTP client which is built on Apache HttpClient 5.5.x. This new SDK HTTP client is available alongside our existing SDK HTTP clients: Apache HttpClient 4.5.x, Netty, URL Connection, and AWS CRT HttpClient. To differentiate the use of Apache HttpClient 4.5.x and Apache HttpClient 5.5.x, […] AWS .NET Distributed Cache Provider for Amazon DynamoDB now Generally Available by Garrett Beatty on 03 JUL 2025 in .NET , Advanced (300) , Announcements , AWS .NET Development , AWS SDK for .NET , Developer Tools Permalink Share Today, we are excited to announce the general availability of the AWS .NET Distributed Cache Provider for Amazon DynamoDB. This is a seamless, serverless caching solution that enables .NET developers to efficiently manage their caching needs across distributed systems. Consistent caching is a difficult problem in distributed architectures, where maintaining data integrity and performance across […] AWS Tools for PowerShell V5 now Generally Available by Muhammad Othman on 23 JUN 2025 in Announcements , AWS Tools for PowerShell , PowerShell Permalink Share This blog was co-authored by Afroz Mohammed and Jonathan Nunn, Software Developers on the AWS PowerShell team. We’re excited to announce the general availability of the AWS Tools for PowerShell version 5, a major update that brings new features and improvements in security, along with a few breaking changes. New Features You can now cancel […] Upgrading your AWS SDK for Go from V1 to V2 with Amazon Q Developer by Gamal Gayle , Janya Ram , Prem Nambi , Chris Procunier , and Jay Ramachandran on 17 JUN 2025 in Advanced (300) , Amazon Q Developer , AWS Cloud Development Kit , AWS SDK for Go , Technical How-to Permalink Share Software development is far more than just writing code. In reality, a developer spends a large amount of time maintaining existing applications and fixing bugs. For example, migrating a Go application from the older AWS SDK for Go v1 to the newer v2 can be a significant undertaking, but it’s a crucial step to future-proof […] Arctic: Automated Desktop Application Testing by Elif Aslan and David Alvarez on 10 MAY 2025 in AWS Java Development , Developer Tools , Java , Open Source , Technical How-to Permalink Share The Amazon Corretto team delivers more than 75 OpenJDK bundles for various platforms and Java versions. These builds include the AWT and Swing UI libraries. Since a button needs to look like a button after a Java update, and even the smallest change can have a significant impact on how graphical elements are rendered, we […] Deploy to ARM-Based Compute with AWS Deploy Tool for .NET by Philippe El Asmar on 08 MAY 2025 in .NET , Announcements , AWS .NET Development , AWS SDK for .NET , AWS Toolkit for Visual Studio , Developer Tools , Visual Studio Permalink Share We’re excited to announce that the AWS Deploy Tool for .NET now supports deploying .NET applications to select ARM-based compute platforms on AWS! Whether you’re deploying from Visual Studio or using the .NET CLI, you can now target cost-effective ARM infrastructure like AWS Graviton with the same streamlined experience you’re used to. Why deploy to […] ← Older posts Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? Cloud Computing Concepts Hub AWS Cloud Security What's New Blogs Press Releases Resources Getting Started Training AWS Trust Center AWS Solutions Library Architecture Center Product and Technical FAQs Analyst Reports AWS Partners Developers Builder Center SDKs & Tools .NET on AWS Python on AWS Java on AWS PHP on AWS JavaScript on AWS Help Contact Us File a Support Ticket AWS re:Post Knowledge Center AWS Support Overview Get Expert Help <a data-rg-n="Link" href="/accessibility/?nc1=f_cc" data-rigel-analytics="{"name":"Link","properties":{"size":1}}" class="rgft_8711ccd9 rgft_98b54368 rgft_1300870 | 2026-01-13T08:48:40 |
https://translations.python.org/#sv | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://translations.python.org/#tr | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:40 |
https://support.google.com/chrome/answer/10248834?hl=en&ref_topic=7438325 | Export your data from Chrome - Google Chrome Help Skip to main content Google Chrome Help Sign in Google Help Help Center Community Google Chrome Privacy Policy Terms of Service Submit feedback Send feedback on... This help content & information General Help Center experience Next Help Center Community Google Chrome Export your data from Chrome You can export and download personal information you store in your Google Account while you're signed in to Chrome. You can download data that hasn't been deleted. You can create an archive to preserve for your records or use the data in another service. Learn how to download your data . If you're using a work or school Google Account, some data might not be available for download. If you're a super administrator of your Google domain, you can download or migrate your organization’s data. Learn how to export your organization's Google Workspace data . 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https://docs.python.org/3/license.html#global-unbounded-sequences-gus | History and License — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents History and License History of the software Terms and conditions for accessing or otherwise using Python PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0 CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1 CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2 ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION Licenses and Acknowledgements for Incorporated Software Mersenne Twister Sockets Asynchronous socket services Cookie management Execution tracing UUencode and UUdecode functions XML Remote Procedure Calls test_epoll Select kqueue SipHash24 strtod and dtoa OpenSSL expat libffi zlib cfuhash libmpdec W3C C14N test suite mimalloc asyncio Global Unbounded Sequences (GUS) Zstandard bindings Previous topic Copyright This page Report a bug Show source Navigation index modules | previous | Python » 3.14.2 Documentation » History and License | Theme Auto Light Dark | History and License ¶ History of the software ¶ Python was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see https://www.cwi.nl ) in the Netherlands as a successor of a language called ABC. Guido remains Python’s principal author, although it includes many contributions from others. In 1995, Guido continued his work on Python at the Corporation for National Research Initiatives (CNRI, see https://www.cnri.reston.va.us ) in Reston, Virginia where he released several versions of the software. In May 2000, Guido and the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. In October of the same year, the PythonLabs team moved to Digital Creations, which became Zope Corporation. In 2001, the Python Software Foundation (PSF, see https://www.python.org/psf/ ) was formed, a non-profit organization created specifically to own Python-related Intellectual Property. Zope Corporation was a sponsoring member of the PSF. All Python releases are Open Source (see https://opensource.org for the Open Source Definition). Historically, most, but not all, Python releases have also been GPL-compatible; the table below summarizes the various releases. Release Derived from Year Owner GPL-compatible? (1) 0.9.0 thru 1.2 n/a 1991-1995 CWI yes 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes 1.6 1.5.2 2000 CNRI no 2.0 1.6 2000 BeOpen.com no 1.6.1 1.6 2001 CNRI yes (2) 2.1 2.0+1.6.1 2001 PSF no 2.0.1 2.0+1.6.1 2001 PSF yes 2.1.1 2.1+2.0.1 2001 PSF yes 2.1.2 2.1.1 2002 PSF yes 2.1.3 2.1.2 2002 PSF yes 2.2 and above 2.1.1 2001-now PSF yes Note GPL-compatible doesn’t mean that we’re distributing Python under the GPL. All Python licenses, unlike the GPL, let you distribute a modified version without making your changes open source. The GPL-compatible licenses make it possible to combine Python with other software that is released under the GPL; the others don’t. According to Richard Stallman, 1.6.1 is not GPL-compatible, because its license has a choice of law clause. According to CNRI, however, Stallman’s lawyer has told CNRI’s lawyer that 1.6.1 is “not incompatible” with the GPL. Thanks to the many outside volunteers who have worked under Guido’s direction to make these releases possible. Terms and conditions for accessing or otherwise using Python ¶ Python software and documentation are licensed under the Python Software Foundation License Version 2. Starting with Python 3.8.6, examples, recipes, and other code in the documentation are dual licensed under the PSF License Version 2 and the Zero-Clause BSD license . Some software incorporated into Python is under different licenses. The licenses are listed with code falling under that license. See Licenses and Acknowledgements for Incorporated Software for an incomplete list of these licenses. PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 ¶ 1. This LICENSE AGREEMENT is between the Python Software Foundation ("PSF"), and the Individual or Organization ("Licensee") accessing and otherwise using this software ("Python") in source or binary form and its associated documentation. 2. Subject to the terms and conditions of this License Agreement, PSF hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python alone or in any derivative version, provided, however, that PSF's License Agreement and PSF's notice of copyright, i.e., "Copyright © 2001 Python Software Foundation; All Rights Reserved" are retained in Python alone or in any derivative version prepared by Licensee. 3. In the event Licensee prepares a derivative work that is based on or incorporates Python or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python. 4. PSF is making Python available to Licensee on an "AS IS" basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 6. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 7. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between PSF and Licensee. This License Agreement does not grant permission to use PSF trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party. 8. By copying, installing or otherwise using Python, Licensee agrees to be bound by the terms and conditions of this License Agreement. BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0 ¶ BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1 1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the Individual or Organization ("Licensee") accessing and otherwise using this software in source or binary form and its associated documentation ("the Software"). 2. Subject to the terms and conditions of this BeOpen Python License Agreement, BeOpen hereby grants Licensee a non-exclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use the Software alone or in any derivative version, provided, however, that the BeOpen Python License is retained in the Software, alone or in any derivative version prepared by Licensee. 3. BeOpen is making the Software available to Licensee on an "AS IS" basis. BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 5. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 6. This License Agreement shall be governed by and interpreted in all respects by the law of the State of California, excluding conflict of law provisions. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between BeOpen and Licensee. This License Agreement does not grant permission to use BeOpen trademarks or trade names in a trademark sense to endorse or promote products or services of Licensee, or any third party. As an exception, the "BeOpen Python" logos available at http://www.pythonlabs.com/logos.html may be used according to the permissions granted on that web page. 7. By copying, installing or otherwise using the software, Licensee agrees to be bound by the terms and conditions of this License Agreement. CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1 ¶ 1. This LICENSE AGREEMENT is between the Corporation for National Research Initiatives, having an office at 1895 Preston White Drive, Reston, VA 20191 ("CNRI"), and the Individual or Organization ("Licensee") accessing and otherwise using Python 1.6.1 software in source or binary form and its associated documentation. 2. Subject to the terms and conditions of this License Agreement, CNRI hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python 1.6.1 alone or in any derivative version, provided, however, that CNRI's License Agreement and CNRI's notice of copyright, i.e., "Copyright © 1995-2001 Corporation for National Research Initiatives; All Rights Reserved" are retained in Python 1.6.1 alone or in any derivative version prepared by Licensee. Alternately, in lieu of CNRI's License Agreement, Licensee may substitute the following text (omitting the quotes): "Python 1.6.1 is made available subject to the terms and conditions in CNRI's License Agreement. This Agreement together with Python 1.6.1 may be located on the internet using the following unique, persistent identifier (known as a handle): 1895.22/1013. This Agreement may also be obtained from a proxy server on the internet using the following URL: http://hdl.handle.net/1895.22/1013". 3. In the event Licensee prepares a derivative work that is based on or incorporates Python 1.6.1 or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python 1.6.1. 4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS" basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON 1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 6. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 7. This License Agreement shall be governed by the federal intellectual property law of the United States, including without limitation the federal copyright law, and, to the extent such U.S. federal law does not apply, by the law of the Commonwealth of Virginia, excluding Virginia's conflict of law provisions. Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of the Commonwealth of Virginia shall govern this License Agreement only as to issues arising under or with respect to Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between CNRI and Licensee. This License Agreement does not grant permission to use CNRI trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party. 8. By clicking on the "ACCEPT" button where indicated, or by copying, installing or otherwise using Python 1.6.1, Licensee agrees to be bound by the terms and conditions of this License Agreement. CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2 ¶ Copyright © 1991 - 1995, Stichting Mathematisch Centrum Amsterdam, The Netherlands. All rights reserved. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Stichting Mathematisch Centrum or CWI not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION ¶ Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted. THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Licenses and Acknowledgements for Incorporated Software ¶ This section is an incomplete, but growing list of licenses and acknowledgements for third-party software incorporated in the Python distribution. Mersenne Twister ¶ The _random C extension underlying the random module includes code based on a download from http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/emt19937ar.html . The following are the verbatim comments from the original code: A C-program for MT19937, with initialization improved 2002/1/26. Coded by Takuji Nishimura and Makoto Matsumoto. Before using, initialize the state by using init_genrand(seed) or init_by_array(init_key, key_length). Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura, All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. The names of its contributors may not be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Any feedback is very welcome. http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space) Sockets ¶ The socket module uses the functions, getaddrinfo() , and getnameinfo() , which are coded in separate source files from the WIDE Project, https://www.wide.ad.jp/ . Copyright (C) 1995, 1996, 1997, and 1998 WIDE Project. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the project nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE PROJECT AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE PROJECT OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Asynchronous socket services ¶ The test.support.asynchat and test.support.asyncore modules contain the following notice: Copyright 1996 by Sam Rushing All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Sam Rushing not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. SAM RUSHING DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL SAM RUSHING BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Cookie management ¶ The http.cookies module contains the following notice: Copyright 2000 by Timothy O'Malley <timo@alum.mit.edu> All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Timothy O'Malley not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. Timothy O'Malley DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL Timothy O'Malley BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Execution tracing ¶ The trace module contains the following notice: portions copyright 2001, Autonomous Zones Industries, Inc., all rights... err... reserved and offered to the public under the terms of the Python 2.2 license. Author: Zooko O'Whielacronx http://zooko.com/ mailto:zooko@zooko.com Copyright 2000, Mojam Media, Inc., all rights reserved. Author: Skip Montanaro Copyright 1999, Bioreason, Inc., all rights reserved. Author: Andrew Dalke Copyright 1995-1997, Automatrix, Inc., all rights reserved. Author: Skip Montanaro Copyright 1991-1995, Stichting Mathematisch Centrum, all rights reserved. Permission to use, copy, modify, and distribute this Python software and its associated documentation for any purpose without fee is hereby granted, provided that the above copyright notice appears in all copies, and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of neither Automatrix, Bioreason or Mojam Media be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. UUencode and UUdecode functions ¶ The uu codec contains the following notice: Copyright 1994 by Lance Ellinghouse Cathedral City, California Republic, United States of America. All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Lance Ellinghouse not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. LANCE ELLINGHOUSE DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL LANCE ELLINGHOUSE CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Modified by Jack Jansen, CWI, July 1995: - Use binascii module to do the actual line-by-line conversion between ascii and binary. This results in a 1000-fold speedup. The C version is still 5 times faster, though. - Arguments more compliant with Python standard XML Remote Procedure Calls ¶ The xmlrpc.client module contains the following notice: The XML-RPC client interface is Copyright (c) 1999-2002 by Secret Labs AB Copyright (c) 1999-2002 by Fredrik Lundh By obtaining, using, and/or copying this software and/or its associated documentation, you agree that you have read, understood, and will comply with the following terms and conditions: Permission to use, copy, modify, and distribute this software and its associated documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appears in all copies, and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Secret Labs AB or the author not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT- ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. test_epoll ¶ The test.test_epoll module contains the following notice: Copyright (c) 2001-2006 Twisted Matrix Laboratories. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Select kqueue ¶ The select module contains the following notice for the kqueue interface: Copyright (c) 2000 Doug White, 2006 James Knight, 2007 Christian Heimes All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. SipHash24 ¶ The file Python/pyhash.c contains Marek Majkowski’ implementation of Dan Bernstein’s SipHash24 algorithm. It contains the following note: <MIT License> Copyright (c) 2013 Marek Majkowski <marek@popcount.org> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. </MIT License> Original location: https://github.com/majek/csiphash/ Solution inspired by code from: Samuel Neves (supercop/crypto_auth/siphash24/little) djb (supercop/crypto_auth/siphash24/little2) Jean-Philippe Aumasson (https://131002.net/siphash/siphash24.c) strtod and dtoa ¶ The file Python/dtoa.c , which supplies C functions dtoa and strtod for conversion of C doubles to and from strings, is derived from the file of the same name by David M. Gay, currently available from https://web.archive.org/web/20220517033456/http://www.netlib.org/fp/dtoa.c . The original file, as retrieved on March 16, 2009, contains the following copyright and licensing notice: /**************************************************************** * * The author of this software is David M. Gay. * * Copyright (c) 1991, 2000, 2001 by Lucent Technologies. * * Permission to use, copy, modify, and distribute this software for any * purpose without fee is hereby granted, provided that this entire notice * is included in all copies of any software which is or includes a copy * or modification of this software and in all copies of the supporting * documentation for such software. * * THIS SOFTWARE IS BEING PROVIDED "AS IS", WITHOUT ANY EXPRESS OR IMPLIED * WARRANTY. IN PARTICULAR, NEITHER THE AUTHOR NOR LUCENT MAKES ANY * REPRESENTATION OR WARRANTY OF ANY KIND CONCERNING THE MERCHANTABILITY * OF THIS SOFTWARE OR ITS FITNESS FOR ANY PARTICULAR PURPOSE. * ***************************************************************/ OpenSSL ¶ The modules hashlib , posix and ssl use the OpenSSL library for added performance if made available by the operating system. Additionally, the Windows and macOS installers for Python may include a copy of the OpenSSL libraries, so we include a copy of the OpenSSL license here. For the OpenSSL 3.0 release, and later releases derived from that, the Apache License v2 applies: Apache License Version 2.0, January 2004 https://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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END OF TERMS AND CONDITIONS expat ¶ The pyexpat extension is built using an included copy of the expat sources unless the build is configured --with-system-expat : Copyright (c) 1998, 1999, 2000 Thai Open Source Software Center Ltd and Clark Cooper Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. libffi ¶ The _ctypes C extension underlying the ctypes module is built using an included copy of the libffi sources unless the build is configured --with-system-libffi : Copyright (c) 1996-2008 Red Hat, Inc and others. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. zlib ¶ The zlib extension is built using an included copy of the zlib sources if the zlib version found on the system is too old to be used for the build: Copyright (C) 1995-2011 Jean-loup Gailly and Mark Adler This software is provided 'as-is', without any express or implied warranty. In no event will the authors be held liable for any damages arising from the use of this software. Permission is granted to anyone to use this software for any purpose, including commercial applications, and to alter it and redistribute it freely, subject to the following restrictions: 1. The origin of this software must not be misrepresented; you must not claim that you wrote the original software. If you use this software in a product, an acknowledgment in the product documentation would be appreciated but is not required. 2. Altered source versions must be plainly marked as such, and must not be misrepresented as being the original software. 3. This notice may not be removed or altered from any source distribution. Jean-loup Gailly Mark Adler jloup@gzip.org madler@alumni.caltech.edu cfuhash ¶ The implementation of the hash table used by the tracemalloc is based on the cfuhash project: Copyright (c) 2005 Don Owens All rights reserved. This code is released under the BSD license: Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the author nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. libmpdec ¶ The _decimal C extension underlying the decimal module is built using an included copy of the libmpdec library unless the build is configured --with-system-libmpdec : Copyright (c) 2008-2020 Stefan Krah. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. W3C C14N test suite ¶ The C14N 2.0 test suite in the test package ( Lib/test/xmltestdata/c14n-20/ ) was retrieved from the W3C website at https://www.w3.org/TR/xml-c14n2-testcases/ and is distributed under the 3-clause BSD license: Copyright (c) 2013 W3C(R) (MIT, ERCIM, Keio, Beihang), All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of works must retain the original copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the original copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the W3C nor the names of its contributors may be used to endorse or promote products derived from this work without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. mimalloc ¶ MIT License: Copyright (c) 2018-2021 Microsoft Corporation, Daan Leijen Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. asyncio ¶ Parts of the asyncio module are incorporated from uvloop 0.16 , which is distributed under the MIT license: Copyright (c) 2015-2021 MagicStack Inc. http://magic.io Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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https://docs.python.org/3/license.html#w3c-c14n-test-suite | History and License — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents History and License History of the software Terms and conditions for accessing or otherwise using Python PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0 CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1 CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2 ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION Licenses and Acknowledgements for Incorporated Software Mersenne Twister Sockets Asynchronous socket services Cookie management Execution tracing UUencode and UUdecode functions XML Remote Procedure Calls test_epoll Select kqueue SipHash24 strtod and dtoa OpenSSL expat libffi zlib cfuhash libmpdec W3C C14N test suite mimalloc asyncio Global Unbounded Sequences (GUS) Zstandard bindings Previous topic Copyright This page Report a bug Show source Navigation index modules | previous | Python » 3.14.2 Documentation » History and License | Theme Auto Light Dark | History and License ¶ History of the software ¶ Python was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see https://www.cwi.nl ) in the Netherlands as a successor of a language called ABC. Guido remains Python’s principal author, although it includes many contributions from others. In 1995, Guido continued his work on Python at the Corporation for National Research Initiatives (CNRI, see https://www.cnri.reston.va.us ) in Reston, Virginia where he released several versions of the software. In May 2000, Guido and the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. In October of the same year, the PythonLabs team moved to Digital Creations, which became Zope Corporation. In 2001, the Python Software Foundation (PSF, see https://www.python.org/psf/ ) was formed, a non-profit organization created specifically to own Python-related Intellectual Property. Zope Corporation was a sponsoring member of the PSF. All Python releases are Open Source (see https://opensource.org for the Open Source Definition). Historically, most, but not all, Python releases have also been GPL-compatible; the table below summarizes the various releases. Release Derived from Year Owner GPL-compatible? (1) 0.9.0 thru 1.2 n/a 1991-1995 CWI yes 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes 1.6 1.5.2 2000 CNRI no 2.0 1.6 2000 BeOpen.com no 1.6.1 1.6 2001 CNRI yes (2) 2.1 2.0+1.6.1 2001 PSF no 2.0.1 2.0+1.6.1 2001 PSF yes 2.1.1 2.1+2.0.1 2001 PSF yes 2.1.2 2.1.1 2002 PSF yes 2.1.3 2.1.2 2002 PSF yes 2.2 and above 2.1.1 2001-now PSF yes Note GPL-compatible doesn’t mean that we’re distributing Python under the GPL. All Python licenses, unlike the GPL, let you distribute a modified version without making your changes open source. The GPL-compatible licenses make it possible to combine Python with other software that is released under the GPL; the others don’t. According to Richard Stallman, 1.6.1 is not GPL-compatible, because its license has a choice of law clause. According to CNRI, however, Stallman’s lawyer has told CNRI’s lawyer that 1.6.1 is “not incompatible” with the GPL. Thanks to the many outside volunteers who have worked under Guido’s direction to make these releases possible. Terms and conditions for accessing or otherwise using Python ¶ Python software and documentation are licensed under the Python Software Foundation License Version 2. Starting with Python 3.8.6, examples, recipes, and other code in the documentation are dual licensed under the PSF License Version 2 and the Zero-Clause BSD license . Some software incorporated into Python is under different licenses. The licenses are listed with code falling under that license. See Licenses and Acknowledgements for Incorporated Software for an incomplete list of these licenses. PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 ¶ 1. This LICENSE AGREEMENT is between the Python Software Foundation ("PSF"), and the Individual or Organization ("Licensee") accessing and otherwise using this software ("Python") in source or binary form and its associated documentation. 2. Subject to the terms and conditions of this License Agreement, PSF hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python alone or in any derivative version, provided, however, that PSF's License Agreement and PSF's notice of copyright, i.e., "Copyright © 2001 Python Software Foundation; All Rights Reserved" are retained in Python alone or in any derivative version prepared by Licensee. 3. In the event Licensee prepares a derivative work that is based on or incorporates Python or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python. 4. PSF is making Python available to Licensee on an "AS IS" basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 6. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 7. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between PSF and Licensee. This License Agreement does not grant permission to use PSF trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party. 8. By copying, installing or otherwise using Python, Licensee agrees to be bound by the terms and conditions of this License Agreement. BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0 ¶ BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1 1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the Individual or Organization ("Licensee") accessing and otherwise using this software in source or binary form and its associated documentation ("the Software"). 2. Subject to the terms and conditions of this BeOpen Python License Agreement, BeOpen hereby grants Licensee a non-exclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use the Software alone or in any derivative version, provided, however, that the BeOpen Python License is retained in the Software, alone or in any derivative version prepared by Licensee. 3. BeOpen is making the Software available to Licensee on an "AS IS" basis. BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 5. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 6. This License Agreement shall be governed by and interpreted in all respects by the law of the State of California, excluding conflict of law provisions. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between BeOpen and Licensee. This License Agreement does not grant permission to use BeOpen trademarks or trade names in a trademark sense to endorse or promote products or services of Licensee, or any third party. As an exception, the "BeOpen Python" logos available at http://www.pythonlabs.com/logos.html may be used according to the permissions granted on that web page. 7. By copying, installing or otherwise using the software, Licensee agrees to be bound by the terms and conditions of this License Agreement. CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1 ¶ 1. This LICENSE AGREEMENT is between the Corporation for National Research Initiatives, having an office at 1895 Preston White Drive, Reston, VA 20191 ("CNRI"), and the Individual or Organization ("Licensee") accessing and otherwise using Python 1.6.1 software in source or binary form and its associated documentation. 2. Subject to the terms and conditions of this License Agreement, CNRI hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python 1.6.1 alone or in any derivative version, provided, however, that CNRI's License Agreement and CNRI's notice of copyright, i.e., "Copyright © 1995-2001 Corporation for National Research Initiatives; All Rights Reserved" are retained in Python 1.6.1 alone or in any derivative version prepared by Licensee. Alternately, in lieu of CNRI's License Agreement, Licensee may substitute the following text (omitting the quotes): "Python 1.6.1 is made available subject to the terms and conditions in CNRI's License Agreement. This Agreement together with Python 1.6.1 may be located on the internet using the following unique, persistent identifier (known as a handle): 1895.22/1013. This Agreement may also be obtained from a proxy server on the internet using the following URL: http://hdl.handle.net/1895.22/1013". 3. In the event Licensee prepares a derivative work that is based on or incorporates Python 1.6.1 or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python 1.6.1. 4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS" basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON 1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 6. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 7. This License Agreement shall be governed by the federal intellectual property law of the United States, including without limitation the federal copyright law, and, to the extent such U.S. federal law does not apply, by the law of the Commonwealth of Virginia, excluding Virginia's conflict of law provisions. Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of the Commonwealth of Virginia shall govern this License Agreement only as to issues arising under or with respect to Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between CNRI and Licensee. This License Agreement does not grant permission to use CNRI trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party. 8. By clicking on the "ACCEPT" button where indicated, or by copying, installing or otherwise using Python 1.6.1, Licensee agrees to be bound by the terms and conditions of this License Agreement. CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2 ¶ Copyright © 1991 - 1995, Stichting Mathematisch Centrum Amsterdam, The Netherlands. All rights reserved. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Stichting Mathematisch Centrum or CWI not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION ¶ Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted. THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Licenses and Acknowledgements for Incorporated Software ¶ This section is an incomplete, but growing list of licenses and acknowledgements for third-party software incorporated in the Python distribution. Mersenne Twister ¶ The _random C extension underlying the random module includes code based on a download from http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/emt19937ar.html . The following are the verbatim comments from the original code: A C-program for MT19937, with initialization improved 2002/1/26. Coded by Takuji Nishimura and Makoto Matsumoto. Before using, initialize the state by using init_genrand(seed) or init_by_array(init_key, key_length). Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura, All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. The names of its contributors may not be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Any feedback is very welcome. http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space) Sockets ¶ The socket module uses the functions, getaddrinfo() , and getnameinfo() , which are coded in separate source files from the WIDE Project, https://www.wide.ad.jp/ . Copyright (C) 1995, 1996, 1997, and 1998 WIDE Project. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the project nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE PROJECT AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE PROJECT OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Asynchronous socket services ¶ The test.support.asynchat and test.support.asyncore modules contain the following notice: Copyright 1996 by Sam Rushing All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Sam Rushing not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. SAM RUSHING DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL SAM RUSHING BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Cookie management ¶ The http.cookies module contains the following notice: Copyright 2000 by Timothy O'Malley <timo@alum.mit.edu> All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Timothy O'Malley not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. Timothy O'Malley DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL Timothy O'Malley BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Execution tracing ¶ The trace module contains the following notice: portions copyright 2001, Autonomous Zones Industries, Inc., all rights... err... reserved and offered to the public under the terms of the Python 2.2 license. Author: Zooko O'Whielacronx http://zooko.com/ mailto:zooko@zooko.com Copyright 2000, Mojam Media, Inc., all rights reserved. Author: Skip Montanaro Copyright 1999, Bioreason, Inc., all rights reserved. Author: Andrew Dalke Copyright 1995-1997, Automatrix, Inc., all rights reserved. Author: Skip Montanaro Copyright 1991-1995, Stichting Mathematisch Centrum, all rights reserved. Permission to use, copy, modify, and distribute this Python software and its associated documentation for any purpose without fee is hereby granted, provided that the above copyright notice appears in all copies, and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of neither Automatrix, Bioreason or Mojam Media be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. UUencode and UUdecode functions ¶ The uu codec contains the following notice: Copyright 1994 by Lance Ellinghouse Cathedral City, California Republic, United States of America. All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Lance Ellinghouse not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. LANCE ELLINGHOUSE DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL LANCE ELLINGHOUSE CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Modified by Jack Jansen, CWI, July 1995: - Use binascii module to do the actual line-by-line conversion between ascii and binary. This results in a 1000-fold speedup. The C version is still 5 times faster, though. - Arguments more compliant with Python standard XML Remote Procedure Calls ¶ The xmlrpc.client module contains the following notice: The XML-RPC client interface is Copyright (c) 1999-2002 by Secret Labs AB Copyright (c) 1999-2002 by Fredrik Lundh By obtaining, using, and/or copying this software and/or its associated documentation, you agree that you have read, understood, and will comply with the following terms and conditions: Permission to use, copy, modify, and distribute this software and its associated documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appears in all copies, and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Secret Labs AB or the author not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT- ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. test_epoll ¶ The test.test_epoll module contains the following notice: Copyright (c) 2001-2006 Twisted Matrix Laboratories. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Select kqueue ¶ The select module contains the following notice for the kqueue interface: Copyright (c) 2000 Doug White, 2006 James Knight, 2007 Christian Heimes All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. SipHash24 ¶ The file Python/pyhash.c contains Marek Majkowski’ implementation of Dan Bernstein’s SipHash24 algorithm. It contains the following note: <MIT License> Copyright (c) 2013 Marek Majkowski <marek@popcount.org> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. </MIT License> Original location: https://github.com/majek/csiphash/ Solution inspired by code from: Samuel Neves (supercop/crypto_auth/siphash24/little) djb (supercop/crypto_auth/siphash24/little2) Jean-Philippe Aumasson (https://131002.net/siphash/siphash24.c) strtod and dtoa ¶ The file Python/dtoa.c , which supplies C functions dtoa and strtod for conversion of C doubles to and from strings, is derived from the file of the same name by David M. Gay, currently available from https://web.archive.org/web/20220517033456/http://www.netlib.org/fp/dtoa.c . The original file, as retrieved on March 16, 2009, contains the following copyright and licensing notice: /**************************************************************** * * The author of this software is David M. Gay. * * Copyright (c) 1991, 2000, 2001 by Lucent Technologies. * * Permission to use, copy, modify, and distribute this software for any * purpose without fee is hereby granted, provided that this entire notice * is included in all copies of any software which is or includes a copy * or modification of this software and in all copies of the supporting * documentation for such software. * * THIS SOFTWARE IS BEING PROVIDED "AS IS", WITHOUT ANY EXPRESS OR IMPLIED * WARRANTY. IN PARTICULAR, NEITHER THE AUTHOR NOR LUCENT MAKES ANY * REPRESENTATION OR WARRANTY OF ANY KIND CONCERNING THE MERCHANTABILITY * OF THIS SOFTWARE OR ITS FITNESS FOR ANY PARTICULAR PURPOSE. * ***************************************************************/ OpenSSL ¶ The modules hashlib , posix and ssl use the OpenSSL library for added performance if made available by the operating system. Additionally, the Windows and macOS installers for Python may include a copy of the OpenSSL libraries, so we include a copy of the OpenSSL license here. For the OpenSSL 3.0 release, and later releases derived from that, the Apache License v2 applies: Apache License Version 2.0, January 2004 https://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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END OF TERMS AND CONDITIONS expat ¶ The pyexpat extension is built using an included copy of the expat sources unless the build is configured --with-system-expat : Copyright (c) 1998, 1999, 2000 Thai Open Source Software Center Ltd and Clark Cooper Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. libffi ¶ The _ctypes C extension underlying the ctypes module is built using an included copy of the libffi sources unless the build is configured --with-system-libffi : Copyright (c) 1996-2008 Red Hat, Inc and others. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. zlib ¶ The zlib extension is built using an included copy of the zlib sources if the zlib version found on the system is too old to be used for the build: Copyright (C) 1995-2011 Jean-loup Gailly and Mark Adler This software is provided 'as-is', without any express or implied warranty. In no event will the authors be held liable for any damages arising from the use of this software. Permission is granted to anyone to use this software for any purpose, including commercial applications, and to alter it and redistribute it freely, subject to the following restrictions: 1. The origin of this software must not be misrepresented; you must not claim that you wrote the original software. If you use this software in a product, an acknowledgment in the product documentation would be appreciated but is not required. 2. Altered source versions must be plainly marked as such, and must not be misrepresented as being the original software. 3. This notice may not be removed or altered from any source distribution. Jean-loup Gailly Mark Adler jloup@gzip.org madler@alumni.caltech.edu cfuhash ¶ The implementation of the hash table used by the tracemalloc is based on the cfuhash project: Copyright (c) 2005 Don Owens All rights reserved. This code is released under the BSD license: Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the author nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. libmpdec ¶ The _decimal C extension underlying the decimal module is built using an included copy of the libmpdec library unless the build is configured --with-system-libmpdec : Copyright (c) 2008-2020 Stefan Krah. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. W3C C14N test suite ¶ The C14N 2.0 test suite in the test package ( Lib/test/xmltestdata/c14n-20/ ) was retrieved from the W3C website at https://www.w3.org/TR/xml-c14n2-testcases/ and is distributed under the 3-clause BSD license: Copyright (c) 2013 W3C(R) (MIT, ERCIM, Keio, Beihang), All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of works must retain the original copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the original copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the W3C nor the names of its contributors may be used to endorse or promote products derived from this work without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. mimalloc ¶ MIT License: Copyright (c) 2018-2021 Microsoft Corporation, Daan Leijen Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. asyncio ¶ Parts of the asyncio module are incorporated from uvloop 0.16 , which is distributed under the MIT license: Copyright (c) 2015-2021 MagicStack Inc. http://magic.io Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Global Unbounded Sequences (GUS) ¶ The file Python/qsbr.c is adapted from FreeBSD’s “Global Unbounded Sequences” safe memory reclamation scheme in subr_smr.c . The file is distributed under the 2-Clause BSD License: Copyright (c) 2019,2020 Jeffrey Roberson <jeff@FreeBSD.org> Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following con | 2026-01-13T08:48:40 |
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https://aws.amazon.com/blogs/developer/ | AWS Developer Tools Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Developer Tools Blog AWS SDK for JavaScript aligns with Node.js release schedule by Trivikram Kamat on 08 DEC 2025 in Announcements , AWS SDK for JavaScript in Node.js , Developer Tools , JavaScript , Open Source Permalink Share This post is about AWS SDK for JavaScript v3 announcing end of support for Node.js versions based on Node.js release schedule, and it is not about AWS Lambda. For the latter, refer to the Lambda runtime deprecation policy. In the second week of January 2026, the AWS SDK for JavaScript v3 (JS SDK) will start […] Introducing Amazon S3 Transfer Manager for Swift (Developer Preview) by Chan Yoo on 21 NOV 2025 in Announcements , AWS SDK for Swift Permalink Share e are pleased to announce the Developer Preview release of the Amazon S3 Transfer Manager for Swift —a high-level file and directory transfer utility for Amazon Simple Storage Service (Amazon S3) built with the AWS SDK for Swift . What’s New in the AWS Deploy Tool for .NET by Philippe El Asmar on 14 OCT 2025 in .NET , Announcements , AWS .NET Development , AWS SDK for .NET , AWS Toolkit for Visual Studio , Developer Tools , Visual Studio Permalink Share Version 2.0 of the AWS Deploy Tool for .NET is now available. This new major version introduces several foundational upgrades to improve the deployment experience for .NET applications on AWS. The tool comes with new minimum runtime requirements. We have upgraded it to require .NET 8 because the predecessor, .NET 6, is now out of […] General Availability Release of the Migration Tool for the AWS SDK for Java 2.x by David Ho on 26 SEP 2025 in Announcements , AWS Java Development , AWS SDK for Java , Java Permalink Share The AWS SDK for Java 1.x (v1) entered maintenance mode on July 31, 2024, and will reach end-of-support on December 31, 2025. We recommend that you migrate to the AWS SDK for Java 2.x (v2) to access new features, enhanced performance, and continued support from AWS. To help you migrate efficiently, we’ve created a migration […] Preview Release of the AWS SDK Java 2.x HTTP Client built on Apache HttpClient 5.5.x by John Viegas on 17 JUL 2025 in Advanced (300) , Announcements , AWS SDK for Java , Developer Tools Permalink Share The AWS SDK for Java 2.x introduces the Apache 5 SDK HTTP client which is built on Apache HttpClient 5.5.x. This new SDK HTTP client is available alongside our existing SDK HTTP clients: Apache HttpClient 4.5.x, Netty, URL Connection, and AWS CRT HttpClient. To differentiate the use of Apache HttpClient 4.5.x and Apache HttpClient 5.5.x, […] AWS .NET Distributed Cache Provider for Amazon DynamoDB now Generally Available by Garrett Beatty on 03 JUL 2025 in .NET , Advanced (300) , Announcements , AWS .NET Development , AWS SDK for .NET , Developer Tools Permalink Share Today, we are excited to announce the general availability of the AWS .NET Distributed Cache Provider for Amazon DynamoDB. This is a seamless, serverless caching solution that enables .NET developers to efficiently manage their caching needs across distributed systems. Consistent caching is a difficult problem in distributed architectures, where maintaining data integrity and performance across […] AWS Tools for PowerShell V5 now Generally Available by Muhammad Othman on 23 JUN 2025 in Announcements , AWS Tools for PowerShell , PowerShell Permalink Share This blog was co-authored by Afroz Mohammed and Jonathan Nunn, Software Developers on the AWS PowerShell team. We’re excited to announce the general availability of the AWS Tools for PowerShell version 5, a major update that brings new features and improvements in security, along with a few breaking changes. New Features You can now cancel […] Upgrading your AWS SDK for Go from V1 to V2 with Amazon Q Developer by Gamal Gayle , Janya Ram , Prem Nambi , Chris Procunier , and Jay Ramachandran on 17 JUN 2025 in Advanced (300) , Amazon Q Developer , AWS Cloud Development Kit , AWS SDK for Go , Technical How-to Permalink Share Software development is far more than just writing code. In reality, a developer spends a large amount of time maintaining existing applications and fixing bugs. For example, migrating a Go application from the older AWS SDK for Go v1 to the newer v2 can be a significant undertaking, but it’s a crucial step to future-proof […] Arctic: Automated Desktop Application Testing by Elif Aslan and David Alvarez on 10 MAY 2025 in AWS Java Development , Developer Tools , Java , Open Source , Technical How-to Permalink Share The Amazon Corretto team delivers more than 75 OpenJDK bundles for various platforms and Java versions. These builds include the AWT and Swing UI libraries. Since a button needs to look like a button after a Java update, and even the smallest change can have a significant impact on how graphical elements are rendered, we […] Deploy to ARM-Based Compute with AWS Deploy Tool for .NET by Philippe El Asmar on 08 MAY 2025 in .NET , Announcements , AWS .NET Development , AWS SDK for .NET , AWS Toolkit for Visual Studio , Developer Tools , Visual Studio Permalink Share We’re excited to announce that the AWS Deploy Tool for .NET now supports deploying .NET applications to select ARM-based compute platforms on AWS! Whether you’re deploying from Visual Studio or using the .NET CLI, you can now target cost-effective ARM infrastructure like AWS Graviton with the same streamlined experience you’re used to. Why deploy to […] ← Older posts Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? 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https://devblogs.microsoft.com/dotnet/net-interactive-is-here-net-notebooks-preview-2/#mainContent | .NET Interactive is here! | .NET Notebooks Preview 2 - .NET Blog Skip to main content Microsoft Dev Blogs Dev Blogs Dev Blogs Home Developer Microsoft for Developers Visual Studio Visual Studio Code Develop from the cloud All things Azure Xcode DevOps Windows Developer ISE Developer Azure SDK Command Line Aspire Technology DirectX Semantic Kernel Languages C++ C# F# TypeScript PowerShell Team Python Java Java Blog in Chinese Go .NET All .NET posts .NET Aspire .NET MAUI AI ASP.NET Core Blazor Entity Framework NuGet Servicing .NET Blog in Chinese Platform Development #ifdef Windows Microsoft Foundry Azure Government Azure VM Runtime Team Bing Dev Center Microsoft Edge Dev Microsoft Azure Microsoft 365 Developer Microsoft Entra Identity Developer Old New Thing Power Platform Data Development Azure Cosmos DB Azure Data Studio Azure SQL OData Revolutions R Unified Data Model (IDEAs) Microsoft Entra PowerShell More Search Search No results Cancel Dev Blogs .NET Blog .NET Interactive is here! | .NET Notebooks Preview 2 .NET 10 is here! .NET 10 is now available: the most productive, modern, secure, intelligent, and performant release of .NET yet. Learn More Download Now February 6th, 2020 1 reaction .NET Interactive is here! | .NET Notebooks Preview 2 Maria Naggaga Principal Program Manager Show more In November 2019, we announced .NET support for Jupyter notebooks with both C# and F# support. Today we are excited to announce Preview 2 of the .NET Notebook experience. What’s new New Name – Meet .NET interactive As our scenarios grew in Try .NET, we wanted a new name that encompassed all our new experiences from the runnable snippets on the web powered by Blazor (as seen on the .NET page ) , to interactive documentation for .NET Core with the dotnet try global tool, to .NET Notebooks. Today we are announcing our official name change to .NET interactive . .NET interactive is a group of CLI tools and APIs that enable users to create interactive experiences across the web, markdown, and notebooks. .NET Interactive Breakdown dotnet interactive global tool : For .NET Notebooks (Jupyter and nteract) dotnet try global tool : For Workshops and offline docs. Interactive markdown with a backing project. trydotnet.js API ( not publicly available yet ): Online documentation. For example, on docs and .NET page . Currently, only used internally at Microsoft. New Repo – dotnet/interactive Moving forward, we have decided to split dotnet try and dotnet interactive tools into separate repos. For any issues, feature requests, and contributions to .NET Notebooks, please visit the .NET Interactive repo . For any issues, feature requests, and contributions on interactive markdown and trydotnet.js, please visit the Try .NET repo . New Global Tool – dotnet interactive How Install .NET Interactive First, make sure you have the following installed: The .NET 3.1 SDK . Jupyter . Jupyter can be installed using Anaconda . Open the Anaconda Prompt (Windows) or Terminal (macOS) and verify that Jupyter is installed and present on the path: > jupyter kernelspec list python3 ~\jupyter\kernels\python3 Next, in an ordinary console, install the dotnet interactive global tool: > dotnet tool install --global Microsoft.dotnet-interactive Install the .NET kernel by running the following within your Anaconda Prompt: > dotnet interactive jupyter install [InstallKernelSpec] Installed kernelspec .net-csharp in ~\jupyter\kernels\.net-csharp .NET kernel installation succeeded [InstallKernelSpec] Installed kernelspec .net-fsharp in ~\jupyter\kernels\.net-fsharp .NET kernel installation succeeded [InstallKernelSpec] Installed kernelspec .net-powershell in ~\jupyter\kernels\.net-powershell .NET kernel installation succeeded You can verify the installation by running the following again in the Anaconda Prompt: > jupyter kernelspec list .net-csharp ~\jupyter\kernels\.net-csharp .net-fsharp ~\jupyter\kernels\.net-fsharp .net-powershell ~\jupyter\kernels\.net-powershell python3 ~\jupyter\kernels\python3 Please Note: If you are looking for dotnet try experience please visit dotnet/try . New language support – PowerShell PowerShell Notebooks PowerShell notebooks combine the management capabilities of PowerShell with the rich visual experience of notebooks. The integration of PowerShell’s executable experience with rich text and visualization open up scenarios for PowerShell users to integrate and amplify their teaching, and support documents. As an example, this demo of a new PowerShell feature was easily transformed into a shareable, interactive teaching tool. With the multi-kernel experience provided by the .NET interactive kernel a single notebook, now with PowerShell support, can efficiently target both the management plane and the data plane. DBAs, sysadmins, and support engineers alike have found PowerShell notebooks useful for resource manipulation and management. For example, this notebook teachers the user how to create an Azure VM from PowerShell. We look forward to seeing what our customers to do with this experience. Read the PowerShell blog post for more information. Run .NET Code in nteract.io In addition to writing .NET Code in Jupyter Notebooks, users can now write their code in nteract. nteract is an open-source organization that builds SDKs, applications, and libraries that helps people make the most of interactive notebooks and REPLs. We are excited to have our .NET users take advantage of the rich REPL experience nteract provides,including the nteract desktop app. To get started with .NET Interactive in nteract please download the nteract desktop app and install the .NET kernels . Resources Try sample .NET notebooks online using Binder . This also allows you try out .NET Interactive daily builds. Create and run .NET notebooks on your machine . Share your own .NET notebooks with others online using Binder . .NET Interactive with nteract Our team can’t wait to see what you do with .NET Interactive. Please check out our repo to learn more and let us know what you build. Happy interactive programming ! 1 28 0 Share on Facebook Share on X Share on Linkedin Copy Link --> Category .NET Share Author Maria Naggaga Principal Program Manager Maria Naggaga is a Principal Product Manager on the Microsoft Developer Platform. 28 comments Discussion is closed. Login to edit/delete existing comments. Code of Conduct Sort by : Newest Newest Popular Oldest AUGUST SPIER --> AUGUST SPIER --> May 11, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> The trials and tribulations of a New Guy. Following the blog post, I 1. Download and install Anaconda 2. Download and install dotnet Core SDK (v. 3.1.201) 3. (At the Anaconda prompt), run jupyter kernelspec list. And receive python3 ~\jupyter\kernels\python3 4. (In the command shell), I invoke > dotnet tool install --global Microsoft.dotnet-interactive 5. I return to the Anaconda prompt to run > "C:\Program Files\dotnet> dotnet interactive jupyter install 6. And I'm rewarded for my efforts with: Could not execute because... Read more The trials and tribulations of a New Guy. Following the blog post, I 1. Download and install Anaconda 2. Download and install dotnet Core SDK (v. 3.1.201) 3. (At the Anaconda prompt), run jupyter kernelspec list. And receive python3 ~\jupyter\kernels\python3 4. (In the command shell), I invoke > dotnet tool install –global Microsoft.dotnet-interactive 5. I return to the Anaconda prompt to run > “C:\Program Files\dotnet> dotnet interactive jupyter install 6. And I’m rewarded for my efforts with: Could not execute because the specified command or file was not found. Possible reasons for this include: * You misspelled a built-in dotnet command. * You intended to execute a .NET Core program, but dotnet-interactive does not exist. * You intended to run a global tool, but a dotnet-prefixed executable with this name could not be found on the PATH. Where did I go wrong? Regards, Gus DISCLAIMER: I am not a software developer, nor do I play one on TV. But I am a fairly accomplished DBA trying to adapt new tools to everyday life. Read less Jon Sequeira --> Jon Sequeira --> May 15, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Please open an issue at https://github.com/dotnet/interactive/issues and we’ll take a look. One additional piece of information that would be helpful is what version of dotnet-interactive you’re using, which you can find by running this at the command prompt: dotnet-interactive –version Joe Huang --> Joe Huang --> April 17, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Can anybody tell me how to make intellisense(auto completion) of this(.NET Interactive) Case-Insensitive? David Beveridge --> David Beveridge --> April 15, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> In lieu of an emoji for back-flip somersaults, YESSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS! This technology is what I’ve wanted for so long. Thank you. David Cuccia --> David Cuccia --> February 21, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> I absolutely love the work that's being done here, thanks to the Interactive team for making this a reality. I also love the new Jupyter support (currently in the Python extension for) VS Code. It would be wonderful to have Interactive work with Jupyter in VS Code. I posted this request on the Interactive and vscode-python GitHub sites but wanted to share my request here as well. There seems to be an intent to make this happen, one way or another, which is great. (Though, I might have poked a wasps nest with my questions. :) https://github.com/dotnet/interactive/issues/179 https://github.com/microsoft/vscode-python/issues/5078#issuecomment-588437582 Read more I absolutely love the work that’s being done here, thanks to the Interactive team for making this a reality. I also love the new Jupyter support (currently in the Python extension for) VS Code. It would be wonderful to have Interactive work with Jupyter in VS Code. I posted this request on the Interactive and vscode-python GitHub sites but wanted to share my request here as well. There seems to be an intent to make this happen, one way or another, which is great. (Though, I might have poked a wasps nest with my questions. :) https://github.com/dotnet/interactive/issues/179 https://github.com/microsoft/vscode-python/issues/5078#issuecomment-588437582 Read less San --> San --> May 1, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> VS Code with Jupyter .NET Interactive is a must-have feature. Please make it available ASAP :). Thanks a lot. Jon Sequeira --> Jon Sequeira --> May 15, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Keep an eye on our PRs: https://github.com/dotnet/interactive/pull/412 Radu Popa --> Radu Popa --> February 13, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Awesome! Big fan of Notebooks! … But why were F# and PowerShell supported before Visual Basic .Net? The later is more popular than the former two together. How misleading it is to name it .Net Interactive and support PowerShell but not Visual Basic .Net … I, for one, will not use this until you include support for Visual Basic .Net. I’m on my way to learning Python and will switch to it if Microsoft continues to alienate the large VB.Net community. Jon Sequeira --> Jon Sequeira --> May 15, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> We hear you. C# and F# have a big head start here because they both have interactive language variants. If the VB.NET interactive language variant had been available, we’d have been happy to include it. Bob --> Bob --> February 12, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> It’s delightful to be able to install a Jupyter version on Windows that actually works reliably. How do I go about upgrading to JupyterLab? Maria Naggaga --> Maria Naggaga Author --> February 19, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Glad you like it! First you will need to install Jupyter Lab either conda or pip. Here are the install instructions ( https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html ). Once you have that installed go to Anaconda prompt type in the following command > jupyter lab . This will launch JupyterLab. Gus Martinka --> Gus Martinka --> February 12, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Really excited about .net in Jupyter moving forward. I was using Python/Jupyter to explore data and prototype solutions but this is looking like my new go to. Preview 1 had some bugs with syntax highlighting and such but was still useful. I am hoping VS Code will pick up support for .net core in Jupyter soon. Mladen Kirilov --> Mladen Kirilov --> February 12, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Considering this great progress, can we expect to get the PowerShell kernel in Azure Notebooks at all? Thanks! Roman Cerny --> Roman Cerny --> February 7, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> When trying to run following in Windows 10 cmd dotnet tool install –global Microsoft.dotnet-interactive I get: The tool package could not be restored. Tool ‘microsoft.dotnet-interactive’ failed to install. This failure may have been caused by: * You are attempting to install a preview release and did not use the –version option to specify the version. Please advise Maria Naggaga --> Maria Naggaga Author --> February 7, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> In the blog post I didn’t specify the package version just to make sure that the post stays fresh. Please use this command as seen on nuget dotnet tool install --global Microsoft.dotnet-interactive --version 1.0.110520 Roman Cerny --> Roman Cerny --> February 10, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Thanks Maria, that installed correctly now. The next issue I’am having is: (base) C:\Users\roman.cerny>jupyter kernelspec list Available kernels: python3 C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\share\jupyter\kernels\python3 (base) C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\share>dotnet interactive jupyter install .NET kernel installation failed with error: Could not find jupyter kernelspec module The same happens even after restarting my PC Please advise Maria Naggaga --> Maria Naggaga Author --> February 10, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Hi Roman, May I ask you a quick question – When you installed the .NET kernel( dotnet interactive jupyter install ) did you do it in Anaconda prompt? If you don’t mind could you please open an issue here ? I would really like to help you troubleshoot this. Thank you Roman Cerny --> Roman Cerny --> February 11, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Hi Maria, I have now created new issue https://github.com/dotnet/interactive/issues/157#issue-563062412 Thank you for your help. Jerzy Rozmyslowicz --> Jerzy Rozmyslowicz --> February 7, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Just learned that another .NET exists How many .NET you are going to create? You are doing it all wrong. There should be just one VS Studio, one .NET one code Simply you should work only on compilers to that one solution Developer then just could use Build As (need to be implemented) command to build one code to Windows or Mac or Android or whatever else using specific compiler Phillip Carter --> Phillip Carter --> February 9, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Just to clarify: what is announced in this blog post is not another flavor of .NET. This is about bringing .NET to the Jupyter Notebooks ecosystem and enhancing interactive programming with C# and F#. It’s the same compilers, runtime, etc. under the hood as any normal .NET Core application. Dave Bacher --> Dave Bacher --> February 7, 2020 0 --> Collapse this comment --> Copy link --> --> --> --> Microsoft is currently working towards .NET 5. In their old .NET, they apparently have a million #if statements all over the place, and then scripts build the various flavors by having the right set of defines. Even, apparently, Silverlight. That's based on what they've said in posts here and over on Hanselman's blog. And so basically - .NET Core they went through and took all the #if's out. And so you have this new Common Language Runtime / Microsoft Intermediate Language interpreter that has as few built-in dependencies as possible, and that's .NET Core runtime itself. And then you... Read more Microsoft is currently working towards .NET 5. In their old .NET, they apparently have a million #if statements all over the place, and then scripts build the various flavors by having the right set of defines. Even, apparently, Silverlight. That’s based on what they’ve said in posts here and over on Hanselman’s blog. And so basically – .NET Core they went through and took all the #if’s out. And so you have this new Common Language Runtime / Microsoft Intermediate Language interpreter that has as few built-in dependencies as possible, and that’s .NET Core runtime itself. And then you have a cloud of libraries in what would have been the Basic/Base Class Library (BCL) before – and those are now mostly NuGet packages – and so you can pick versions of them when you compile, and those versions are bundled with your executable, and no other process can cause a different version to load. One of the side effects is the individual project teams that are working on GitHub, which is most of them on the .NET side, can now take pull requests and feature input / issues directly through GitHub, and so you can go over there and beg them directly and make the case directly for specific features in specific libraries, instead of having to go through support and hoping you get the single support rep who has actually written code professionally at some point in their life. 😉 Visual Studio supports multiple targets in a single project file, and that is the traditional C++ way to build C code for multiple platforms in Visual Studio. You can add a MacOS target right now, and changing the target is then the pulldown next to “Release” and “Debug” on the default toolbars. That’s explicitly what that feature is intended for. You can do more on the C++ side than on the C# side right now. However, if you’re not calling any OS-specific functionality – the .NET Core app you compile runs, from a single build, on Windows, Linux and MacOS. Same binary file works on all three. You can even use WinForms or WPF, and those work as expected now mostly. (there are differences, and third party components generally need to be designed for the new ones) Read less Load more comments Read next February 8, 2020 Garbage Collection at Food Courts maoni February 10, 2020 Announcing Experimental Mobile Blazor Bindings February update Eilon Lipton Stay informed Get notified when new posts are published. Email * Country/Region * Select... 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Please choose another browser. 2/12 @ 10 AM PT | State of Direct Mail: Business Insights 2026 Webinar | Register Product Build Create + personalize your mail Route Optimize efficiency with Postal IQ Fulfill Speed delivery and track the results Take a product tour Get a sneak peek of our intuitive platform API's & Integrations Address Verification Security Solutions Financial Services Insurance Healthcare Retail + Ecommerce Telecom Automotive Real Estate INDUSTRIES USE CASES Marketing In-House Marketing Agencies and Consultants Operations In-House Operations Operations Service Providers Resources State of Direct Mail Ebooks + Guides Case Studies Blog Direct Mail Template Gallery Newsroom All Resources Help Center State of Direct Mail 2025: Consumer Insights Edition See what’s driving Gen Z and Millennial engagement in the full State of Direct Mail 2025 report. 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Read customer story Case study Travis Manion Foundation TMF Reduces Direct Mail Costs by 50% with Automated Donor Acknowledgement Letters Read customer story Case study Congress.Cards Congress.Cards Drives Advocacy through Direct Mail Read customer story Case study Resistbot Resistbot Connects Constituents with Elected Officials through Direct Mail Read customer story Case study MuckRock MuckRock Elevates Staff Time with Automated Direct Mail Read customer story Case study Inside Real Estate Inside Real Estate Improves Response Rates by 5x with Personalized Direct Mail Read customer story Case study JustFix.nyc JustFix.nyc Tackles Housing Displacement With Automated Direct Mail Read customer story Case study Spire Law Spire Law uses Lob to ensure accurate delivery addresses and seamless mail workflows Read customer story Case study VillageCareMAX VillageCareMAX Saves Over 4,000 Hours a Year by Automating HIPAA-Compliant Direct Mail Read customer story Case study CouchSurfing Couchsurfing Uses Postcards & Address Data to Build Trust With Their Users Read customer story Case study Booking.com How Booking.com Saves Thousands of Hours & Reduces Fraudulent Activity by 97% Read customer story Case study Myriad Women’s Health Myriad Sends Trigger Based Direct Mail & Improves Collections by 20% Read customer story Case study Clover Health Clover Automates HIPAA-Compliant Direct Mail Read customer story Case study RealMailers RealMailers Automates Direct Mail Marketing to Help Their Clients Sell Homes Read customer story Case study Ameelio Ameelio Tackles Criminal Justice Reform Through Direct Mail Read customer story Case study Realeflow Realeflow Doubles Adoption Rates with Direct Mail Read customer story Case study Saylent Saylent Improves Customer Engagement for Banks & Credit Unions Read customer story Case study B-Stock B-Stock Eliminates Manual Oversight with CASS Address Verification Read customer story Case study Clearcover Clearcover Scales Customer Servicing & Compliance Read customer story Case study Carewell Carewell Uses Lob Address Verification API to Build Customer Loyalty and Cut Costs Read customer story Case study Current Current Reduces Compliance Spend While Improving Their Customer Experience Read customer story Case study SmileDirectClub SmileDirectClub Optimizes the Customer Journey with Automated Direct Mail Read customer story Case study Honeydue Honeydue Delivers Real-Time Address Validation to Enhance Customer Experience Read customer story Case study CPH & Associates CPH Insurance Reduces Direct Mail Lead Time by 98% Read customer story Case study Fundraise Up Lob Address Verification API Helps Fundraise Up Increase Donations Read customer story Blog post What are the benefits of using an API-driven solution for managing high-volume direct mail operations? Learn how API-driven direct mail helps teams automate high-volume sends, reduce operational risk, and maintain consistency as programs scale. Read blog post Blog post What Is Direct Mail Routing: A Complete Guide to Mail Processing and Delivery What is direct mail routing: the process determining how mail travels from print facilities through USPS sorting centers to recipient mailboxes for delivery. Read blog post Blog post How to Re-Engage Dormant Customers Using Direct Mail: 8 Proven Strategies Re-engage dormant customers using direct mail with 8 proven strategies including personalized offers, automated triggers, and multi-channel campaigns. Read blog post Blog post How to Justify Direct Mail Budgets to Executives How can marketers justify direct mail budgets to executives using ROI data, response rates, acquisition costs, and clear revenue attribution metrics. Read blog post Blog post What are 10 must-haves for any mail platform? Choosing a new mail platform? You need more than just software. Let’s look at 10 end-to-end features you must have to build a successful program. Read blog post Blog post Zone-Based Pricing: The “Known–Unknown” Marketers Can’t Afford to Ignore Only 26% of marketing leaders truly understand USPS zone-based postage for 2026. Discover the awareness gap and 3 strategies to build operational readiness before pricing changes impact your direct mail ROI. Read blog post Blog post USPS entry point optimization: What it is and how it reduces postage costs USPS entry point optimization is entering mail at facilities closer to destinations for lower postage rates. The closer mail enters, the less you pay. Read blog post Blog post How to evaluate secure direct mail services for sensitive information Direct mail companies providing secure, reliable solutions for managing sensitive information in mailings include Lob with HIPAA and SOC 2 certification. Read blog post Blog post Nationwide direct mail fulfillment: how distributed print networks work Services offering nationwide print delivery networks for efficient direct mail distribution include Lob, using multi-facility printing and routing. Read blog post Blog post How can we simplify our direct mail vendor relationships to reduce complexity and improve efficiency? Reduce complexity, improve security, and streamline direct mail operations by consolidating vendors into a single, end-to-end platform. Read blog post Blog post How can I streamline my direct mail campaigns to integrate seamlessly with our existing digital marketing channels? Learn how to integrate direct mail seamlessly into your digital marketing strategy. Discover tools, triggers, and tactics to create a unified, data-driven customer journey. Read blog post Blog post How to integrate API driven direct mail solutions for high volume automation How do you integrate API-driven direct mail for high volume automation? Use Lob's API infrastructure, CRM integrations, real-time tracking, and Print Delivery Network to eliminate manual workflows. Read blog post Blog post How to transition from in-house direct mail to scalable automation solutions How do you transition from in-house to automated direct mail? Use Lob's platform for API integration, nationwide print network, real-time tracking, and CRM connectivity to scale operations. Read blog post Blog post How to implement dynamic QR code tracking for direct mail personalization How do you implement dynamic QR code tracking for direct mail? Use Lob's platform for unique code generation, CRM integration, and real-time analytics to track scans through to revenue. Read blog post Blog post Where can I find templates for designing direct mail? Looking for direct mail templates? Learn how to streamline design and explore Lob’s new Template Gallery, with ready-to-use layouts built for speed and performance. Read blog post Blog post How B2B companies use direct mail to generate quality leads What direct mail strategies suit B2B lead generation? Use targeted outreach, personalized messaging, dimensional mailers, and multichannel tactics. Read blog post Blog post Marketing postcard frequency: how often is too often? How often should a company send marketing postcards? Every 21-45 days generates optimal response rates while avoiding list fatigue and maximizing ROI. Read blog post Blog post Budgeting direct mail campaigns: critical cost factors and planning tips What factors should be considered when budgeting for direct mail? List costs, postage, design, targeting, and hidden expenses impact your investment. Read blog post Blog post How can I enhance personalization in my direct mail pieces beyond basic name fields? Move beyond first name. Learn how to personalize direct mail using customer behavior, preferences, and dynamic content to drive better engagement. Read blog post Blog post Direct mail KPI essentials: what to track for maximum ROI Learn the key direct mail KPIs, formulas, and tracking tactics to prove ROI, improve campaign performance, and report results with confidence. Read blog post Blog post Postcard personalization made simple: names, details, and data-driven design Learn how personalized postcards and variable data printing turn customer data into targeted mail, higher response rates, and digital-style tracking. Read blog post Blog post PURL campaign tracking: how to measure every mail interaction Learn how PURLs turn direct mail into a measurable channel with 1:1 tracking, richer attribution, and CRM-powered personalization using Lob. Read blog post Blog post Can I use API integrations for more efficient direct mail processes? Direct mail APIs streamline production, personalization, and tracking so you can integrate mail into your existing marketing stack and workflows. Read blog post Blog post Can I track the performance of my direct mail campaigns in real-time? Today’s direct mail is fast, transparent, and effortlessly trackable. Learn how to track mail in real time. Then monitor engagement to optimize your results. Read blog post Blog post How can I ensure compliance with industry regulations like HIPAA and SOC 2 when managing sensitive information in direct mail campaigns? Learn how to keep your direct mail secure and compliant with HIPAA, SOC 2, and other data privacy standards. Read blog post Blog post What solutions are available for integrating our customer data into direct mail campaigns for better personalization? Learn how to pull in customer data from lists, integrations, and APIs for personalized direct mail campaigns and reusable templates. Read blog post Blog post How Lob offers cost effective custom mail formats without sacrificing quality Cost-effective custom mail format providers like Lob deliver quality automated mail solutions with bulk processing and premium materials at lower costs. Read blog post Blog post What an all in one direct mail platform should include Companies offering comprehensive direct mail services with printing, mailing, and tracking include Lob, PostGrid, and Gunderson Direct in one platform. Read blog post Blog post How to build a scalable direct mail program for insurance Direct mail providers for insurance: Lob, PostcardMania, and RRD offer scalable, HIPAA-compliant programs with automated campaigns and real-time tracking. Read blog post Blog post The Overlooked Print Decisions Holding Back Your Campaigns Discover how Lob’s Postal IQ brings structure and predictability to direct mail logistics, ensuring every campaign delivers on time and on target. Read blog post Blog post Zone Based Pricing 101: What Distance-Based Postage Means for Marketers Under the Delivering for America plan, the Postal Service is redesigning its entire distribution network, from how mail is processed and transported to how postage will eventually be calculated. Read blog post Blog post Complete guide to Braze direct mail integration for cross-channel success How can I integrate direct mail campaigns with Braze for a seamless marketing strategy? Automate mail triggers, personalize mailpieces, and unify channels. Read blog post Blog post Why Lob is the best direct mail automation platform for marketing agencies What are the best platforms for automating direct mail campaigns for a marketing agency? Lob, Postalytics, PostGrid offer APIs, tracking, and scale. Read blog post Blog post How to create unified direct mail and digital marketing campaigns How can I streamline my direct mail campaigns to integrate seamlessly with our existing digital marketing channels? Use automation and tracking tips. Read blog post Blog post Direct Mail That Gets Opened: 12 Proven Tactics From 1B+ Sends How can I ensure my direct mail stands out and gets noticed: use bold design, personal messages, clear CTAs and envelope tests to drive higher open rates. Read blog post Blog post How insurance teams modernize mail at enterprise scale Which companies have a strong reputation for handling large-scale direct mail operations tailored to insurance offers: RR Donnelley leads in compliance. Read blog post Blog post How Lob is a top direct mail service for enterprise volume and delivery visibility Reliable high-volume mail providers with strong tracking and delivery visibility features: Compare Lob, Mailgun and Postmark. Read blog post Blog post End-to-end direct mail automation: how Lob offers the best tools and platform What solutions are available that can automate and streamline the entire direct mail process from start to finish? See tools that combine every step. Read blog post Blog post How Lob is the best direct mail automation platform for telecom companies What are the best platforms for automating direct mail campaigns for a telecommunications business: compare Lob, Postalytics & PostGrid for mail automation Read blog post Blog post Best direct mail strategies for residential and commercial real estate Best way to send customized direct mail to real estate prospects: targeted lists, personalized messaging and automation for residential and commercial. Read blog post Blog post How can I leverage direct mail to win back customers and increase engagement? Win back lapsed customers with direct mail. Use this practical checklist to improve timing, personalization, offers, and ROI. Read blog post Blog post What are some cost-effective options for printing and delivering direct mail campaigns? Save on your next direct mail campaign. Dive into different strategies for trimming cost at every phase – from creation to print to postage. Read blog post Blog post What options are available for improving the speed to market for direct mail campaigns? Learn how to accelerate your direct mail campaigns with automation, templates, triggered sends, and optimized print fulfillment. Read blog post Blog post Direct mail network infrastructure: scaling print delivery for enterprise Print delivery network options for large scale direct mail campaigns. Compare single site, regional, and automated nationwide platforms. Read blog post Blog post How direct mail printing and fulfillment actually works What’s involved in direct mail printing and fulfillment? From file prep to printing, assembly, postal sorting and delivery tracking in a single workflow. Read blog post Blog post How smart brands use direct mail for profitable customer acquisition What role does direct mail play in acquisition marketing? Learn how targeted mail drives new customer growth and measurable ROI. Read blog post Blog post How to choose award-winning direct mail providers in 2025 What is a reliable direct mail platform for businesses? Discover automated workflows, real-time tracking and data integration for consistent delivery and ROI. Read blog post Blog post What’s the best way to prepare direct mail for the holidays? Holiday campaigns don’t have to be chaotic. With the right planning and platform, you can deliver festive, on-time, high-performing mail. Read blog post Blog post How banks track direct mail performance: complete measurement guide How can I measure the effectiveness of direct mail campaigns in the banking sector? Track response rate, conversion rate and ROI to assess performance. Read blog post Blog post Direct mail proofing optimization: speed up large campaign workflows Streamline proofing for large-scale direct mail campaigns to improve speed and efficiency. Automate proofs and centralize approvals to cut review time. Read blog post Blog post Direct mail API solutions: how healthcare and finance companies scale securely Best API-driven platforms for automating high-volume direct mail in regulated industries like healthcare and finance. Compare compliant services that scale. Read blog post Blog post Nationwide Direct Mail Solutions: Best Platforms for Large Campaign Delivery Are there any direct mail platforms that offer nationwide print delivery networks for large-scale campaigns? See automated routing & regional hub tracking. Read blog post Blog post Direct mail automation: reducing manual workflows with smart integrations Are there any direct mail automation services that integrate with your tech stack to reduce manual workflows? 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https://docs.python.org/3/glossary.html#term-immutable | Glossary — Python 3.14.2 documentation Theme Auto Light Dark Previous topic Deprecations Next topic About this documentation This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » Glossary | Theme Auto Light Dark | Glossary ¶ >>> ¶ The default Python prompt of the interactive shell. Often seen for code examples which can be executed interactively in the interpreter. ... ¶ Can refer to: The default Python prompt of the interactive shell when entering the code for an indented code block, when within a pair of matching left and right delimiters (parentheses, square brackets, curly braces or triple quotes), or after specifying a decorator. The three dots form of the Ellipsis object. abstract base class ¶ Abstract base classes complement duck-typing by providing a way to define interfaces when other techniques like hasattr() would be clumsy or subtly wrong (for example with magic methods ). ABCs introduce virtual subclasses, which are classes that don’t inherit from a class but are still recognized by isinstance() and issubclass() ; see the abc module documentation. Python comes with many built-in ABCs for data structures (in the collections.abc module), numbers (in the numbers module), streams (in the io module), import finders and loaders (in the importlib.abc module). You can create your own ABCs with the abc module. annotate function ¶ A function that can be called to retrieve the annotations of an object. This function is accessible as the __annotate__ attribute of functions, classes, and modules. Annotate functions are a subset of evaluate functions . annotation ¶ A label associated with a variable, a class attribute or a function parameter or return value, used by convention as a type hint . Annotations of local variables cannot be accessed at runtime, but annotations of global variables, class attributes, and functions can be retrieved by calling annotationlib.get_annotations() on modules, classes, and functions, respectively. See variable annotation , function annotation , PEP 484 , PEP 526 , and PEP 649 , which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations. argument ¶ A value passed to a function (or method ) when calling the function. There are two kinds of argument: keyword argument : an argument preceded by an identifier (e.g. name= ) in a function call or passed as a value in a dictionary preceded by ** . For example, 3 and 5 are both keyword arguments in the following calls to complex() : complex ( real = 3 , imag = 5 ) complex ( ** { 'real' : 3 , 'imag' : 5 }) positional argument : an argument that is not a keyword argument. Positional arguments can appear at the beginning of an argument list and/or be passed as elements of an iterable preceded by * . For example, 3 and 5 are both positional arguments in the following calls: complex ( 3 , 5 ) complex ( * ( 3 , 5 )) Arguments are assigned to the named local variables in a function body. See the Calls section for the rules governing this assignment. Syntactically, any expression can be used to represent an argument; the evaluated value is assigned to the local variable. See also the parameter glossary entry, the FAQ question on the difference between arguments and parameters , and PEP 362 . asynchronous context manager ¶ An object which controls the environment seen in an async with statement by defining __aenter__() and __aexit__() methods. Introduced by PEP 492 . asynchronous generator ¶ A function which returns an asynchronous generator iterator . It looks like a coroutine function defined with async def except that it contains yield expressions for producing a series of values usable in an async for loop. Usually refers to an asynchronous generator function, but may refer to an asynchronous generator iterator in some contexts. In cases where the intended meaning isn’t clear, using the full terms avoids ambiguity. An asynchronous generator function may contain await expressions as well as async for , and async with statements. asynchronous generator iterator ¶ An object created by an asynchronous generator function. This is an asynchronous iterator which when called using the __anext__() method returns an awaitable object which will execute the body of the asynchronous generator function until the next yield expression. Each yield temporarily suspends processing, remembering the execution state (including local variables and pending try-statements). When the asynchronous generator iterator effectively resumes with another awaitable returned by __anext__() , it picks up where it left off. See PEP 492 and PEP 525 . asynchronous iterable ¶ An object, that can be used in an async for statement. Must return an asynchronous iterator from its __aiter__() method. Introduced by PEP 492 . asynchronous iterator ¶ An object that implements the __aiter__() and __anext__() methods. __anext__() must return an awaitable object. async for resolves the awaitables returned by an asynchronous iterator’s __anext__() method until it raises a StopAsyncIteration exception. Introduced by PEP 492 . atomic operation ¶ An operation that appears to execute as a single, indivisible step: no other thread can observe it half-done, and its effects become visible all at once. Python does not guarantee that high-level statements are atomic (for example, x += 1 performs multiple bytecode operations and is not atomic). Atomicity is only guaranteed where explicitly documented. See also race condition and data race . attached thread state ¶ A thread state that is active for the current OS thread. When a thread state is attached, the OS thread has access to the full Python C API and can safely invoke the bytecode interpreter. Unless a function explicitly notes otherwise, attempting to call the C API without an attached thread state will result in a fatal error or undefined behavior. A thread state can be attached and detached explicitly by the user through the C API, or implicitly by the runtime, including during blocking C calls and by the bytecode interpreter in between calls. On most builds of Python, having an attached thread state implies that the caller holds the GIL for the current interpreter, so only one OS thread can have an attached thread state at a given moment. In free-threaded builds of Python, threads can concurrently hold an attached thread state, allowing for true parallelism of the bytecode interpreter. attribute ¶ A value associated with an object which is usually referenced by name using dotted expressions. For example, if an object o has an attribute a it would be referenced as o.a . It is possible to give an object an attribute whose name is not an identifier as defined by Names (identifiers and keywords) , for example using setattr() , if the object allows it. Such an attribute will not be accessible using a dotted expression, and would instead need to be retrieved with getattr() . awaitable ¶ An object that can be used in an await expression. Can be a coroutine or an object with an __await__() method. See also PEP 492 . BDFL ¶ Benevolent Dictator For Life, a.k.a. Guido van Rossum , Python’s creator. binary file ¶ A file object able to read and write bytes-like objects . Examples of binary files are files opened in binary mode ( 'rb' , 'wb' or 'rb+' ), sys.stdin.buffer , sys.stdout.buffer , and instances of io.BytesIO and gzip.GzipFile . See also text file for a file object able to read and write str objects. borrowed reference ¶ In Python’s C API, a borrowed reference is a reference to an object, where the code using the object does not own the reference. It becomes a dangling pointer if the object is destroyed. For example, a garbage collection can remove the last strong reference to the object and so destroy it. Calling Py_INCREF() on the borrowed reference is recommended to convert it to a strong reference in-place, except when the object cannot be destroyed before the last usage of the borrowed reference. The Py_NewRef() function can be used to create a new strong reference . bytes-like object ¶ An object that supports the Buffer Protocol and can export a C- contiguous buffer. This includes all bytes , bytearray , and array.array objects, as well as many common memoryview objects. Bytes-like objects can be used for various operations that work with binary data; these include compression, saving to a binary file, and sending over a socket. Some operations need the binary data to be mutable. The documentation often refers to these as “read-write bytes-like objects”. Example mutable buffer objects include bytearray and a memoryview of a bytearray . Other operations require the binary data to be stored in immutable objects (“read-only bytes-like objects”); examples of these include bytes and a memoryview of a bytes object. bytecode ¶ Python source code is compiled into bytecode, the internal representation of a Python program in the CPython interpreter. The bytecode is also cached in .pyc files so that executing the same file is faster the second time (recompilation from source to bytecode can be avoided). This “intermediate language” is said to run on a virtual machine that executes the machine code corresponding to each bytecode. Do note that bytecodes are not expected to work between different Python virtual machines, nor to be stable between Python releases. A list of bytecode instructions can be found in the documentation for the dis module . callable ¶ A callable is an object that can be called, possibly with a set of arguments (see argument ), with the following syntax: callable ( argument1 , argument2 , argumentN ) A function , and by extension a method , is a callable. An instance of a class that implements the __call__() method is also a callable. callback ¶ A subroutine function which is passed as an argument to be executed at some point in the future. class ¶ A template for creating user-defined objects. Class definitions normally contain method definitions which operate on instances of the class. class variable ¶ A variable defined in a class and intended to be modified only at class level (i.e., not in an instance of the class). closure variable ¶ A free variable referenced from a nested scope that is defined in an outer scope rather than being resolved at runtime from the globals or builtin namespaces. May be explicitly defined with the nonlocal keyword to allow write access, or implicitly defined if the variable is only being read. For example, in the inner function in the following code, both x and print are free variables , but only x is a closure variable : def outer (): x = 0 def inner (): nonlocal x x += 1 print ( x ) return inner Due to the codeobject.co_freevars attribute (which, despite its name, only includes the names of closure variables rather than listing all referenced free variables), the more general free variable term is sometimes used even when the intended meaning is to refer specifically to closure variables. complex number ¶ An extension of the familiar real number system in which all numbers are expressed as a sum of a real part and an imaginary part. Imaginary numbers are real multiples of the imaginary unit (the square root of -1 ), often written i in mathematics or j in engineering. Python has built-in support for complex numbers, which are written with this latter notation; the imaginary part is written with a j suffix, e.g., 3+1j . To get access to complex equivalents of the math module, use cmath . Use of complex numbers is a fairly advanced mathematical feature. If you’re not aware of a need for them, it’s almost certain you can safely ignore them. concurrency ¶ The ability of a computer program to perform multiple tasks at the same time. Python provides libraries for writing programs that make use of different forms of concurrency. asyncio is a library for dealing with asynchronous tasks and coroutines. threading provides access to operating system threads and multiprocessing to operating system processes. Multi-core processors can execute threads and processes on different CPU cores at the same time (see parallelism ). concurrent modification ¶ When multiple threads modify shared data at the same time. Concurrent modification without proper synchronization can cause race conditions , and might also trigger a data race , data corruption, or both. context ¶ This term has different meanings depending on where and how it is used. Some common meanings: The temporary state or environment established by a context manager via a with statement. The collection of keyvalue bindings associated with a particular contextvars.Context object and accessed via ContextVar objects. Also see context variable . A contextvars.Context object. Also see current context . context management protocol ¶ The __enter__() and __exit__() methods called by the with statement. See PEP 343 . context manager ¶ An object which implements the context management protocol and controls the environment seen in a with statement. See PEP 343 . context variable ¶ A variable whose value depends on which context is the current context . Values are accessed via contextvars.ContextVar objects. Context variables are primarily used to isolate state between concurrent asynchronous tasks. contiguous ¶ A buffer is considered contiguous exactly if it is either C-contiguous or Fortran contiguous . Zero-dimensional buffers are C and Fortran contiguous. In one-dimensional arrays, the items must be laid out in memory next to each other, in order of increasing indexes starting from zero. In multidimensional C-contiguous arrays, the last index varies the fastest when visiting items in order of memory address. However, in Fortran contiguous arrays, the first index varies the fastest. coroutine ¶ Coroutines are a more generalized form of subroutines. Subroutines are entered at one point and exited at another point. Coroutines can be entered, exited, and resumed at many different points. They can be implemented with the async def statement. See also PEP 492 . coroutine function ¶ A function which returns a coroutine object. A coroutine function may be defined with the async def statement, and may contain await , async for , and async with keywords. These were introduced by PEP 492 . CPython ¶ The canonical implementation of the Python programming language, as distributed on python.org . The term “CPython” is used when necessary to distinguish this implementation from others such as Jython or IronPython. current context ¶ The context ( contextvars.Context object) that is currently used by ContextVar objects to access (get or set) the values of context variables . Each thread has its own current context. Frameworks for executing asynchronous tasks (see asyncio ) associate each task with a context which becomes the current context whenever the task starts or resumes execution. cyclic isolate ¶ A subgroup of one or more objects that reference each other in a reference cycle, but are not referenced by objects outside the group. The goal of the cyclic garbage collector is to identify these groups and break the reference cycles so that the memory can be reclaimed. data race ¶ A situation where multiple threads access the same memory location concurrently, at least one of the accesses is a write, and the threads do not use any synchronization to control their access. Data races lead to non-deterministic behavior and can cause data corruption. Proper use of locks and other synchronization primitives prevents data races. Note that data races can only happen in native code, but that native code might be exposed in a Python API. See also race condition and thread-safe . deadlock ¶ A situation in which two or more tasks (threads, processes, or coroutines) wait indefinitely for each other to release resources or complete actions, preventing any from making progress. For example, if thread A holds lock 1 and waits for lock 2, while thread B holds lock 2 and waits for lock 1, both threads will wait indefinitely. In Python this often arises from acquiring multiple locks in conflicting orders or from circular join/await dependencies. Deadlocks can be avoided by always acquiring multiple locks in a consistent order. See also lock and reentrant . decorator ¶ A function returning another function, usually applied as a function transformation using the @wrapper syntax. Common examples for decorators are classmethod() and staticmethod() . The decorator syntax is merely syntactic sugar, the following two function definitions are semantically equivalent: def f ( arg ): ... f = staticmethod ( f ) @staticmethod def f ( arg ): ... The same concept exists for classes, but is less commonly used there. See the documentation for function definitions and class definitions for more about decorators. descriptor ¶ Any object which defines the methods __get__() , __set__() , or __delete__() . When a class attribute is a descriptor, its special binding behavior is triggered upon attribute lookup. Normally, using a.b to get, set or delete an attribute looks up the object named b in the class dictionary for a , but if b is a descriptor, the respective descriptor method gets called. Understanding descriptors is a key to a deep understanding of Python because they are the basis for many features including functions, methods, properties, class methods, static methods, and reference to super classes. For more information about descriptors’ methods, see Implementing Descriptors or the Descriptor How To Guide . dictionary ¶ An associative array, where arbitrary keys are mapped to values. The keys can be any object with __hash__() and __eq__() methods. Called a hash in Perl. dictionary comprehension ¶ A compact way to process all or part of the elements in an iterable and return a dictionary with the results. results = {n: n ** 2 for n in range(10)} generates a dictionary containing key n mapped to value n ** 2 . See Displays for lists, sets and dictionaries . dictionary view ¶ The objects returned from dict.keys() , dict.values() , and dict.items() are called dictionary views. They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes. To force the dictionary view to become a full list use list(dictview) . See Dictionary view objects . docstring ¶ A string literal which appears as the first expression in a class, function or module. While ignored when the suite is executed, it is recognized by the compiler and put into the __doc__ attribute of the enclosing class, function or module. Since it is available via introspection, it is the canonical place for documentation of the object. duck-typing ¶ A programming style which does not look at an object’s type to determine if it has the right interface; instead, the method or attribute is simply called or used (“If it looks like a duck and quacks like a duck, it must be a duck.”) By emphasizing interfaces rather than specific types, well-designed code improves its flexibility by allowing polymorphic substitution. Duck-typing avoids tests using type() or isinstance() . (Note, however, that duck-typing can be complemented with abstract base classes .) Instead, it typically employs hasattr() tests or EAFP programming. dunder ¶ An informal short-hand for “double underscore”, used when talking about a special method . For example, __init__ is often pronounced “dunder init”. EAFP ¶ Easier to ask for forgiveness than permission. This common Python coding style assumes the existence of valid keys or attributes and catches exceptions if the assumption proves false. This clean and fast style is characterized by the presence of many try and except statements. The technique contrasts with the LBYL style common to many other languages such as C. evaluate function ¶ A function that can be called to evaluate a lazily evaluated attribute of an object, such as the value of type aliases created with the type statement. expression ¶ A piece of syntax which can be evaluated to some value. In other words, an expression is an accumulation of expression elements like literals, names, attribute access, operators or function calls which all return a value. In contrast to many other languages, not all language constructs are expressions. There are also statement s which cannot be used as expressions, such as while . Assignments are also statements, not expressions. extension module ¶ A module written in C or C++, using Python’s C API to interact with the core and with user code. f-string ¶ f-strings ¶ String literals prefixed with f or F are commonly called “f-strings” which is short for formatted string literals . See also PEP 498 . file object ¶ An object exposing a file-oriented API (with methods such as read() or write() ) to an underlying resource. Depending on the way it was created, a file object can mediate access to a real on-disk file or to another type of storage or communication device (for example standard input/output, in-memory buffers, sockets, pipes, etc.). File objects are also called file-like objects or streams . There are actually three categories of file objects: raw binary files , buffered binary files and text files . Their interfaces are defined in the io module. The canonical way to create a file object is by using the open() function. file-like object ¶ A synonym for file object . filesystem encoding and error handler ¶ Encoding and error handler used by Python to decode bytes from the operating system and encode Unicode to the operating system. The filesystem encoding must guarantee to successfully decode all bytes below 128. If the file system encoding fails to provide this guarantee, API functions can raise UnicodeError . The sys.getfilesystemencoding() and sys.getfilesystemencodeerrors() functions can be used to get the filesystem encoding and error handler. The filesystem encoding and error handler are configured at Python startup by the PyConfig_Read() function: see filesystem_encoding and filesystem_errors members of PyConfig . See also the locale encoding . finder ¶ An object that tries to find the loader for a module that is being imported. There are two types of finder: meta path finders for use with sys.meta_path , and path entry finders for use with sys.path_hooks . See Finders and loaders and importlib for much more detail. floor division ¶ Mathematical division that rounds down to nearest integer. The floor division operator is // . For example, the expression 11 // 4 evaluates to 2 in contrast to the 2.75 returned by float true division. Note that (-11) // 4 is -3 because that is -2.75 rounded downward . See PEP 238 . free threading ¶ A threading model where multiple threads can run Python bytecode simultaneously within the same interpreter. This is in contrast to the global interpreter lock which allows only one thread to execute Python bytecode at a time. See PEP 703 . free variable ¶ Formally, as defined in the language execution model , a free variable is any variable used in a namespace which is not a local variable in that namespace. See closure variable for an example. Pragmatically, due to the name of the codeobject.co_freevars attribute, the term is also sometimes used as a synonym for closure variable . function ¶ A series of statements which returns some value to a caller. It can also be passed zero or more arguments which may be used in the execution of the body. See also parameter , method , and the Function definitions section. function annotation ¶ An annotation of a function parameter or return value. Function annotations are usually used for type hints : for example, this function is expected to take two int arguments and is also expected to have an int return value: def sum_two_numbers ( a : int , b : int ) -> int : return a + b Function annotation syntax is explained in section Function definitions . See variable annotation and PEP 484 , which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations. __future__ ¶ A future statement , from __future__ import <feature> , directs the compiler to compile the current module using syntax or semantics that will become standard in a future release of Python. The __future__ module documents the possible values of feature . By importing this module and evaluating its variables, you can see when a new feature was first added to the language and when it will (or did) become the default: >>> import __future__ >>> __future__ . division _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192) garbage collection ¶ The process of freeing memory when it is not used anymore. Python performs garbage collection via reference counting and a cyclic garbage collector that is able to detect and break reference cycles. The garbage collector can be controlled using the gc module. generator ¶ A function which returns a generator iterator . It looks like a normal function except that it contains yield expressions for producing a series of values usable in a for-loop or that can be retrieved one at a time with the next() function. Usually refers to a generator function, but may refer to a generator iterator in some contexts. In cases where the intended meaning isn’t clear, using the full terms avoids ambiguity. generator iterator ¶ An object created by a generator function. Each yield temporarily suspends processing, remembering the execution state (including local variables and pending try-statements). When the generator iterator resumes, it picks up where it left off (in contrast to functions which start fresh on every invocation). generator expression ¶ An expression that returns an iterator . It looks like a normal expression followed by a for clause defining a loop variable, range, and an optional if clause. The combined expression generates values for an enclosing function: >>> sum ( i * i for i in range ( 10 )) # sum of squares 0, 1, 4, ... 81 285 generic function ¶ A function composed of multiple functions implementing the same operation for different types. Which implementation should be used during a call is determined by the dispatch algorithm. See also the single dispatch glossary entry, the functools.singledispatch() decorator, and PEP 443 . generic type ¶ A type that can be parameterized; typically a container class such as list or dict . Used for type hints and annotations . For more details, see generic alias types , PEP 483 , PEP 484 , PEP 585 , and the typing module. GIL ¶ See global interpreter lock . global interpreter lock ¶ The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict ) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines. However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O. As of Python 3.13, the GIL can be disabled using the --disable-gil build configuration. After building Python with this option, code must be run with -X gil=0 or after setting the PYTHON_GIL=0 environment variable. This feature enables improved performance for multi-threaded applications and makes it easier to use multi-core CPUs efficiently. For more details, see PEP 703 . In prior versions of Python’s C API, a function might declare that it requires the GIL to be held in order to use it. This refers to having an attached thread state . global state ¶ Data that is accessible throughout a program, such as module-level variables, class variables, or C static variables in extension modules . In multi-threaded programs, global state shared between threads typically requires synchronization to avoid race conditions and data races . hash-based pyc ¶ A bytecode cache file that uses the hash rather than the last-modified time of the corresponding source file to determine its validity. See Cached bytecode invalidation . hashable ¶ An object is hashable if it has a hash value which never changes during its lifetime (it needs a __hash__() method), and can be compared to other objects (it needs an __eq__() method). Hashable objects which compare equal must have the same hash value. Hashability makes an object usable as a dictionary key and a set member, because these data structures use the hash value internally. Most of Python’s immutable built-in objects are hashable; mutable containers (such as lists or dictionaries) are not; immutable containers (such as tuples and frozensets) are only hashable if their elements are hashable. Objects which are instances of user-defined classes are hashable by default. They all compare unequal (except with themselves), and their hash value is derived from their id() . IDLE ¶ An Integrated Development and Learning Environment for Python. IDLE — Python editor and shell is a basic editor and interpreter environment which ships with the standard distribution of Python. immortal ¶ Immortal objects are a CPython implementation detail introduced in PEP 683 . If an object is immortal, its reference count is never modified, and therefore it is never deallocated while the interpreter is running. For example, True and None are immortal in CPython. Immortal objects can be identified via sys._is_immortal() , or via PyUnstable_IsImmortal() in the C API. immutable ¶ An object with a fixed value. Immutable objects include numbers, strings and tuples. Such an object cannot be altered. A new object has to be created if a different value has to be stored. They play an important role in places where a constant hash value is needed, for example as a key in a dictionary. Immutable objects are inherently thread-safe because their state cannot be modified after creation, eliminating concerns about improperly synchronized concurrent modification . import path ¶ A list of locations (or path entries ) that are searched by the path based finder for modules to import. During import, this list of locations usually comes from sys.path , but for subpackages it may also come from the parent package’s __path__ attribute. importing ¶ The process by which Python code in one module is made available to Python code in another module. importer ¶ An object that both finds and loads a module; both a finder and loader object. interactive ¶ Python has an interactive interpreter which means you can enter statements and expressions at the interpreter prompt, immediately execute them and see their results. Just launch python with no arguments (possibly by selecting it from your computer’s main menu). It is a very powerful way to test out new ideas or inspect modules and packages (remember help(x) ). For more on interactive mode, see Interactive Mode . interpreted ¶ Python is an interpreted language, as opposed to a compiled one, though the distinction can be blurry because of the presence of the bytecode compiler. This means that source files can be run directly without explicitly creating an executable which is then run. Interpreted languages typically have a shorter development/debug cycle than compiled ones, though their programs generally also run more slowly. See also interactive . interpreter shutdown ¶ When asked to shut down, the Python interpreter enters a special phase where it gradually releases all allocated resources, such as modules and various critical internal structures. It also makes several calls to the garbage collector . This can trigger the execution of code in user-defined destructors or weakref callbacks. Code executed during the shutdown phase can encounter various exceptions as the resources it relies on may not function anymore (common examples are library modules or the warnings machinery). The main reason for interpreter shutdown is that the __main__ module or the script being run has finished executing. iterable ¶ An object capable of returning its members one at a time. Examples of iterables include all sequence types (such as list , str , and tuple ) and some non-sequence types like dict , file objects , and objects of any classes you define with an __iter__() method or with a __getitem__() method that implements sequence semantics. Iterables can be used in a for loop and in many other places where a sequence is needed ( zip() , map() , …). When an iterable object is passed as an argument to the built-in function iter() , it returns an iterator for the object. This iterator is good for one pass over the set of values. When using iterables, it is usually not necessary to call iter() or deal with iterator objects yourself. The for statement does that automatically for you, creating a temporary unnamed variable to hold the iterator for the duration of the loop. See also iterator , sequence , and generator . iterator ¶ An object representing a stream of data. Repeated calls to the iterator’s __next__() method (or passing it to the built-in function next() ) return successive items in the stream. When no more data are available a StopIteration exception is raised instead. At this point, the iterator object is exhausted and any further calls to its __next__() method just raise StopIteration again. Iterators are required to have an __iter__() method that returns the iterator object itself so every iterator is also iterable and may be used in most places where other iterables are accepted. One notable exception is code which attempts multiple iteration passes. A container object (such as a list ) produces a fresh new iterator each time you pass it to the iter() function or use it in a for loop. Attempting this with an iterator will just return the same exhausted iterator object used in the previous iteration pass, making it appear like an empty container. More information can be found in Iterator Types . CPython implementation detail: CPython does not consistently apply the requirement that an iterator define __iter__() . And also please note that free-threaded CPython does not guarantee thread-safe behavior of iterator operations. key function ¶ A key function or collation function is a callable that returns a value used for sorting or ordering. For example, locale.strxfrm() is used to produce a sort key that is aware of locale specific sort conventions. A number of tools in Python accept key functions to control how elements are ordered or grouped. They include min() , max() , sorted() , list.sort() , heapq.merge() , heapq.nsmallest() , heapq.nlargest() , and itertools.groupby() . There are several ways to create a key function. For example. the str.casefold() method can serve as a key function for case insensitive sorts. Alternatively, a key function can be built from a lambda expression such as lambda r: (r[0], r[2]) . Also, operator.attrgetter() , operator.itemgetter() , and operator.methodcaller() are three key function constructors. See the Sorting HOW TO for examples of how to create and use key functions. keyword argument ¶ See argument . lambda ¶ An anonymous inline function consisting of a single expression which is evaluated when the function is called. The syntax to create a lambda function is lambda [parameters]: expression LBYL ¶ Look before you leap. This coding style explicitly tests for pre-conditions before making calls or lookups. This style contrasts with the EAFP approach and is characterized by the presence of many if statements. In a multi-threaded environment, the LBYL approach can risk introducing a race condition between “the looking” and “the leaping”. For example, the code, if key in mapping: return mapping[key] can fail if another thread removes key from mapping after the test, but before the lookup. This issue can be solved with locks or by using the EAFP approach. See also thread-safe . lexical analyzer ¶ Formal name for the tokenizer ; see token . list ¶ A built-in Python sequence . Despite its name it is more akin to an array in other languages than to a linked list since access to elements is O (1). list comprehension ¶ A compact way to process all or part of the elements in a sequence and return a list with the results. result = ['{:#04x}'.format(x) for x in range(256) if x % 2 == 0] generates a list of strings containing even hex numbers (0x..) in the range from 0 to 255. The if clause is optional. If omitted, all elements in range(256) are processed. lock ¶ A synchronization primitive that allows only one thread at a time to access a shared resource. A thread must acquire a lock before accessing the protected resource and release it afterward. If a thread attempts to acquire a lock that is already held by another thread, it will block until the lock becomes available. Python’s threading module provides Lock (a basic lock) and RLock (a reentrant lock). Locks are used to prevent race conditions and ensure thread-safe access to shared data. Alternative design patterns to locks exist such as queues, producer/consumer patterns, and thread-local state. See also deadlock , and reentrant . loader ¶ An object that loads a module. It must define the exec_module() and create_module() methods to implement the Loader interface. A loader is typically returned by a finder . See also: Finders and loaders importlib.abc.Loader PEP 302 locale encoding ¶ On Unix, it is the encoding of the LC_CTYPE locale. It can be set with locale.setlocale(locale.LC_CTYPE, new_locale) . On Windows, it is the ANSI code page (ex: "cp1252" ). On Android and VxWorks, Python uses "utf-8" as the locale encoding. locale.getencoding() can be used to get the locale encoding. See also the filesystem encoding and error handler . magic method ¶ An informal synonym for special method . mapping ¶ A container object that supports arbitrary key lookups and implements the methods specified in the collections.abc.Mapping or collections.abc.MutableMapping abstract base classes . Examples include dict , collections.defaultdict , collections.OrderedDict and collections.Counter . meta path finder ¶ A finder returned by a search of sys.meta_path . Meta path finders are related to, but different from path entry finders . See importlib.abc.MetaPathFinder for the methods that meta path finders implement. metaclass ¶ The class of a class. Class definitions create a class name, a class dictionary, and a list of base classes. The metaclass is responsible for taking those three arguments and creating the class. Most object oriented programming languages provide a default implementation. What makes Python special is that it is possible to create custom metaclasses. Most users never need this tool, but when the need arises, metaclasses can provide powerful, elegant solutions. They have been used for logging attribute access, adding thread-safety, tracking object creation, implementing singletons, and many other tasks. More information can be found in Metaclasses . method ¶ A function which is defined inside a class body. If called as an attribute of an instance of that class, the method will get the instance object as its first argument (which is usually called self ). See function and nested scope . method resolution order ¶ Method Resolution Order is the order in which base classes are searched for a member during lookup. See The Python 2.3 Method Resolution Order for details of the algorithm used by the Python interpreter since the 2.3 release. module ¶ An object that serves as an organizational unit of Python code. Modules have a namespace containing arbitrary Python objects. Modules are loaded into Python by the process of importing . See also package . module spec ¶ A namespace containing the import-related information used to load a module. An instance of importlib.machinery.ModuleSpec . See also Module specs . MRO ¶ See method resolution order . mutable ¶ An object with state that is allowed to change during the course of the program. In multi-threaded programs, mutable objects that are shared between threads require careful synchronization to avoid race conditions . See also immutable , thread-safe , and concurrent modification . named tuple ¶ The term “named tuple” applies to any type or class that inherits from tuple and whose indexable elements are also accessible using named attributes. The type or class may have other features as well. Several built-in types are named tuples, including the values returned by time.localtime() and os.stat() . Another example is sys.float_info : >>> sys . float_info [ 1 ] # indexed access 1024 >>> sys . float_info . max_exp # named field access 1024 >>> isinstance ( sys . float_info , tuple ) # kind of tuple True Some named tuples are built-in types (such as the above examples). Alternatively, a named tuple can be created from a regular class definition that inherits from tuple and that defines named fields. Such a class can be written by hand, or it can be created by inheriting typing.NamedTuple , or with the factory function collections.namedtuple() . The latter techniques also add some extra methods that may not be found in hand-written or built-in named tuples. namespace ¶ The place where a variable is stored. Namespaces are implemented as dictionaries. There are the local, global and built-in namespaces as well as nested namespaces in objects (in methods). Namespaces support modularity by preventing naming conflicts. For instance, the functions builtins.open and os.open() are distinguished by their namespaces. Namespaces also aid readability and maintainability by making it clear which module implements a function. For instance, writing random.seed() or itertools.islice() makes it clear that those functions are implemented by the random and itertools modules, respectively. namespace package ¶ A package which serves only as a container for subpackages. Namespace packages may have no physical representation, and specifically are not like a regular package because they have no __init__.py file. Namespace packages allow several individually installable packages to have a common parent package. Otherwise, it is recommended to use a regular package . For more information, see PEP 420 and Namespace packages . See also module . native code ¶ Code that is compiled to machine instructions and runs directly on the processor, as opposed to code that is interpreted or runs in a virtual machine. In the context of Python, native code typically refers to C, C++, Rust or Fortran code in extension modules that can be called from Python. See also extension module . nested scope ¶ The ability to refer to a variable in an enclosing definition. For instance, a function defined inside another function can refer to variables in the outer function. Note that nested scopes by default work only for reference and not for assignment. Local variables both read and write in the innermost scope. Likewise, global variables read and write to the global namespace. The nonlocal allows writing to outer scopes. new-style class ¶ Old name for the flavor of classes now used for all class objects. In earlier Python versions, only new-style classes could use Python’s newer, versatile features like __slots__ , descriptors, properties, __getattribute__() , class methods, and static methods. non-deterministic ¶ Behavior where the outcome of a program can vary between executions with the same inputs. In multi-threaded programs, non-deterministic behavior often results from race conditions where the relative timing or interleaving of threads affects the result. Proper synchronization using locks and other synchronization primitives helps ensure deterministic behavior. object ¶ Any data with state (attributes or value) and defined behavior (methods). Also the ultimate base class of any new-style class . optimized scope ¶ A scope where target local variable names are reliably known to the compiler when the code is compiled, allowing optimization of read and write access to these names. The local namespaces for functions, generators, coroutines, comprehensions, and generator expressions are optimized in this fashion. Note: most interpreter optimizations are applied to all scopes, only those relying on a known set of local and nonlocal variable names are restricted to optimized scopes. optional module ¶ An extension module that is part of the standard library , but may be absent in some builds of CPython , usually due to missing third-party libraries or because the module is not available for a given platform. See Requirements for optional modules for a list of optional modules that require third-party libraries. package ¶ A Python module which can contain submodules or recursively, subpackages. Technically, a package is a Python module with a __path__ attribute. See also regular package and namespace package . parallelism ¶ Executing multiple operations at the same time (e.g. on multiple CPU cores). In Python builds with the global interpreter lock (GIL) , only one thread runs Python bytecode at a time, so taking advantage of multiple CPU cores typically involves multiple processes (e.g. multiprocessing ) or native extensions that release the GIL. In free-threaded Python, multiple Python threads can run Python code simultaneously on different cores. parameter ¶ A named entity in a function (or method) definition that specifies an argument (or in some cases, arguments) that the function can accept. There are five kinds of parameter: positional-or-keyword : specifies an argument that can be passed either positionally or as a keyword argument . This is the default kind of parameter, for example foo and bar in the following: def func ( foo , bar = None ): ... positional-only : specifies an argument that can be supplied only by position. Positional-only parameters can be defined by including a / character in the parameter list of the function definition after them, for example posonly1 and posonly2 in the following: def func ( posonly1 , posonly2 , / , positional_or_keyword ): ... keyword-only : specifies an argument that can be supplied only by keyword. Keyword-only parameters can be defined by including a single var-positional parameter or bare * in the parameter list of the function definition before them, for example kw_only1 and kw_only2 in the following: def func ( arg , * , kw_only1 , kw_only2 ): ... var-positional : specifies that an arbitrary sequence of positional arguments can be provided (in addition to any positional arguments already accepted by other parameters). Such a parameter can be defined by prepending the parameter name with * , for example args in the following: def func ( * args , ** kwargs ): ... var-keyword : specifies that arbitrarily many keyword arguments can be provided (in addition to any keyword arguments already accepted by other parameters). Such a parameter can be defined by prepending the parameter name with ** , for example kwargs in the example above. Parameters can specify both optional and required arguments, as well as default values for some optional arguments. See also the argument glossary entry, the FAQ question on the difference between arguments and parameters , the inspect.Parameter class, the Function definitions section, and PEP 362 . path entry ¶ A single location on the import path which the path based finder consults to find modules for importing. path entry finder ¶ A finder returned by a callable on sys.path_hooks (i.e. a path entry hook ) which knows how to locate modules given a path entry . See importlib.abc.PathEntryFinder for the methods that path entry finders implement. path entry hook ¶ A callable on the sys.path_hooks list which returns a path entry finder if it knows how to find modules on a specific path entry . path based finder ¶ One of the default meta path finders which searches an import path for modules. path-like object ¶ An object representing a file system path. A path-like object is either a str or bytes object representing a path, or an object implementing the os.PathLike protocol. An object that supports the os.PathLike protocol can be converted to a str or bytes file system path by calling the os.fspath() function; os.fsdecode() and os.fsencode() can be used to guarantee a str or bytes result instead, respectively. Introduced by PEP 519 . PEP ¶ Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python or its processes or environment. PEPs should provide a concise technical specification and a rationale for proposed features. PEPs are intended to be the primary mechanisms for proposing major new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions. See PEP 1 . portion ¶ A set of files in a single directory (possibly stored in a zip file) that contribute to a namespace package, as defined in PEP 420 . positional argument ¶ See argument . provisional API ¶ A provisional API is one which has been deliberately excluded from the standard library’s backwards compatibility guarantees. While major changes to such interfaces are not expected, as long as they are marked provisional, backwards incompatible changes (up to and including removal of the interface) may occur if deemed necessary by core developers. 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https://aws.amazon.com/blogs/developer/tag/xamarin/ | Xamarin | AWS Developer Tools Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Developer Tools Blog Tag: Xamarin AWS SDK for .NET v3.5 Preview by Aaron Costley on 06 FEB 2020 in .NET , AWS .NET Development , AWS SDK for .NET , Developer Tools Permalink Share Today, we have published a preview release of version 3.5 of the AWS SDK for .NET. This primary objective of this version is to transition support for all non-Framework versions of the SDK to .NET Standard 2.0. If you are currently using a .NET Framework or .NET Core target, no changes are required. We are […] Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? Cloud Computing Concepts Hub AWS Cloud Security What's New <a data-rg-n="Link" href="/blogs/?nc1=f | 2026-01-13T08:48:41 |
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https://reactrouter.com | React Router Official Documentation A user‑obsessed, standards‑focused, multi‑strategy router you can deploy anywhere. Docs GitHub Discord @ReactRouter What to expect from this version: Non-breaking Upgrading from v6 to v7 is a non-breaking upgrade. Keep using React Router the same way you already do. Bridge to React 19 All new bundling, server rendering, pre-rendering, and streaming features allow you bridge the gap from React 18 to 19 incrementally. Type Safety New typegen provides first class types for route params, loader data, actions, and more. Choose Your Adventure: I'm new! Learn how to get the most out of React Router Start Here I'm on v6 Upgrade to v7 in just a few steps Upgrade Now I want to adopt framework features Learn how to adopt the new framework features in your existing React Router app Adopt Framework Features I'm stuck Join GitHub discussions for help Get Help © 2026 Shopify, Inc. 3,039,749,369 Downloads on npm 1,251 Contributors on GitHub 56,111 Stars on GitHub 3,597,612 Dependents on GitHub | 2026-01-13T08:48:41 |
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ni,function(){return ll}, x,Xh,hi,Wh,x,-1,ni,dk,x,ni,dk,ni,function(){return ll}, x,ni,oj,-1,x,1,x,-1,Mfa,Ak,ni,xj,ni,Rfa,Ak,ci,x,-5];var Xfa=[0,x,ni,function(){return ml}],Yfa=[0, ni,function(){return Xfa}],ml=[0, x,hi,Wh,ci,Ph,Rh,bi,ui,function(){return Yfa}]; Xk.prototype.Ca=Di(ml);var nl=[0,vi,x,ni,ml];Zk.prototype.Ca=Di(nl);var Zfa=[0,x,-2,hi,ni,nl];al.prototype.Ca=Di(Zfa);var $fa=new Map([["","HOMEPAGE"],["announcement","ANNOUNCEMENT"],["answer","ANSWER"],["topic","TOPIC"],["contact","CONTACT_FORM"],["troubleshooter","TROUBLESHOOTER"],["known-issues","KNOWN_ISSUES"],["suggestions","SUGGESTIONS"],[ | 2026-01-13T08:48:41 |
https://webpack.js.org/ | webpack Documentation Contribute Blog English 中文 한국어 Search bundle your assets scripts STATIC ASSETS .png .css .jpg .js MODULES WITH DEPENDENCIES .jpg .png .sass .sass .js .sass .cjs .hbs .js Write Your Code src/index.js Copy import bar from './bar.js' ; bar ( ) ; src/bar.js Copy export default function bar ( ) { // } Bundle It Without config or provide custom webpack.config.js Copy const path = require ( 'path' ) ; module . exports = { entry : './src/index.js' , output : { path : path . resolve ( __dirname , 'dist' ) , filename : 'bundle.js' , } , } ; page.html Copy <! DOCTYPE html > < html > < head > < meta charset = " utf-8 " /> ... </ head > < body > ... < script src = " dist/bundle.js " > </ script > </ body > </ html > Then run webpack on the command-line to create bundle.js . Awesome, isn't it? Let's dive in! Get Started quickly in our Guides section, or dig into the Concepts section for more high-level information on the core notions behind webpack. Support the Team Through contributions, donations, and sponsorship, you allow webpack to thrive. Your donations directly support office hours, continued enhancements, and most importantly, great documentation and learning material! Latest Sponsors Platinum Sponsors Gold Sponsors Silver Sponsors Bronze Sponsors Backers Get Started Comparison Privacy Policy Swag Store Awesome webpack Glossary Branding Discord Changelog | 2026-01-13T08:48:41 |
https://translations.python.org/#pl | Python Docs Translation Dashboard Translation Dashboard Build details Translating Simplified Chinese 简体中文 Completion: 99.14% 30-day progress: 0.53% View Contribute Brazilian Portuguese Português brasileiro Completion: 62.17% 30-day progress: 0.44% View Contribute Spanish español Completion: 56.96% 30-day progress: 0.18% View Contribute Korean 한국어 Completion: 48.42% 30-day progress: 0.00% View Contribute Ukrainian українська Completion: 45.45% 30-day progress: 0.00% View Contribute Japanese 日本語 Completion: 44.45% 30-day progress: 0.06% View Contribute Traditional Chinese 繁體中文 Completion: 30.59% 30-day progress: 0.41% View Contribute French français Completion: 28.36% 30-day progress: 0.00% View Contribute Greek Ελληνικά Completion: 11.44% 30-day progress: 0.01% View Contribute Polish polski Completion: 5.58% 30-day progress: 0.02% View Contribute Turkish Türkçe Completion: 4.47% 30-day progress: 0.00% View Contribute Russian Completion: 3.60% 30-day progress: 0.62% Contribute Indonesian Indonesia Completion: 3.32% 30-day progress: 0.00% View Contribute Italian italiano Completion: 3.17% 30-day progress: 0.00% View Contribute Romanian Românește Completion: 2.92% 30-day progress: 0.00% View Contribute Hungarian Completion: 0.85% 30-day progress: 0.00% Contribute Persian Completion: 0.26% 30-day progress: 0.00% Contribute Swedish Svenska Completion: 0.20% 30-day progress: 0.00% View Contribute Arabic Completion: 0.02% 30-day progress: 0.00% Contribute Bengali বাংলা Completion: 0.01% 30-day progress: 0.00% View Contribute Hindi Completion: 0.01% 30-day progress: 0.00% Contribute Marathi Completion: 0.00% 30-day progress: 0.00% Contribute Lithuanian Completion: 0.00% 30-day progress: 0.00% Contribute Last updated on Tuesday 13 January 2026 at 6:51:18 UTC (in 9 minutes and 6 seconds). You can find the scripts used to generate this website on GitHub . You can download the data on this page in JSON format . | 2026-01-13T08:48:41 |
https://openapi.tools/categories/servers | Server Implementations | OpenApi.tools, from APIs You Won't Hate Sponsored by Zudoku - Open-source, highly customizable API documentation powered by OpenAPI Get Started Sponsor openapi.tools GitHub Get Started All Tools All Categories Legacy Tools Contributing Sponsors Sponsor Badges Collections Arazzo Support Overlays Support Open Source Tools SaaS Tools OpenAPI Tool Categories Annotations Code generators Converters Data Validators Documentation Domain-Specific Languages (DSLs) Gateways HTTP Clients IDEs and GUI Editors Learning Miscellaneous Mock Servers Monitoring OpenAPI-aware Frameworks Parsers Schema Validators SDK Generators Security Server Implementations Testing Text Editors © 2026 APIs You Won't Hate Get in touch to become a Sponsor . This site is community-driven and OSS , built with Astro and hosted on Netlify . Server Implementations Easily create and implement resources and routes for your APIs. Server Implementations There are additional tools in this category, but they only support legacy versions of OpenAPI. If you really need to work with some old OpenAPI descriptions perhaps these legacy tools could be of use * * * | 2026-01-13T08:48:41 |
https://support.google.com/chrome/answer/3296214?hl=en&ref_topic=7438325 | Reset Chrome settings to default - Google Chrome Help Skip to main content Google Chrome Help Sign in Google Help Help Center Community Google Chrome Privacy Policy Terms of Service Submit feedback Send feedback on... This help content & information General Help Center experience Next Help Center Community Google Chrome Make Chrome run better Reset Chrome settings to default Reset Chrome settings to default You can restore your browser settings in Chrome at any time. You might need to do this if apps or extensions you installed changed your settings without your knowledge. Your saved bookmarks and passwords won't be deleted or changed. On your computer, open Chrome. At the top right, select More Settings . Select Reset settings Restore settings to their original defaults Reset settings . These steps won't provide a full reset. For example, some settings like fonts or accessibility won't be deleted. To create a new user profile, add a new user in Chrome . What changes when you restore your settings On your Chrome profile, the following settings will change to the default on all devices where you're signed in: Default search engine: In some countries, Google Search is Chrome's default search engine. In others, you may be asked to choose your default search engine. Learn how to set your default search engine . Homepage and tabs: Learn how to set your Chrome homepage and set default startup tabs . The new tab page: Learn how to set the page that a new tab opens to . Tabs you have pinned: Learn how to pin tabs . Content settings: Like letting a site show you pop-ups or use your microphone. Learn how to change your content settings . Cookies and site data: Like items in an online shopping cart. Learn how to manage cookies and site data . Extensions and themes: Learn more about extensions and themes . Troubleshoot reset problems Settings menu missing or won't open If the settings menu doesn't open or isn't there, Chrome might have a problem. To fix, uninstall Chrome then download Chrome again from google.com/chrome and reinstall it. If you're still having problems, you might have a program installed that is changing your Google Chrome settings. Learn more about programs that cause unwanted settings changes . Chrome reset my browser settings Sometimes, programs that you install can change your Chrome settings without you knowing. For your safety, the browser checks if your settings have been changed every time you launch Chrome. If Chrome notices that something’s not right in your settings, it will automatically go back to the original settings. Settings that might automatically get reset: Default search engine Homepage Startup pages Pinned tabs Extensions Chrome will still reset your settings if you’re not signed in to Chrome. If you’re signed in to Chrome, you’ll still see your usual settings. Was this helpful? How can we improve it? Yes No Submit Need more help? Try these next steps: Post to the help community Get answers from community members true Make Chrome run better 1 of 4 Update Google Chrome 2 of 4 Speed up Google Chrome 3 of 4 Fix videos & games that won't play 4 of 4 Reset Chrome settings to default Want a preview of what's coming soon? 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dg(a,22,b)} function Lk(a){return lf(a,Dk,26)} function Mk(a){return lf(a,Gk,28)} ;function Nk(a){this.Aa=se(a)} u(Nk,ih);function Ok(a){return nf(a,Hk,1,Pe())} ;function Pk(a){this.Aa=se(a)} u(Pk,ih);Pk.prototype.dj=function(){return Of(this,3)}; Pk.prototype.Rb=function(){return Of(this,14)};function Qk(a){this.Aa=se(a)} u(Qk,ih);Qk.prototype.getActive=function(){return Uf(this,3)}; Qk.prototype.setActive=function(a){return Xf(this,3,a)};function Rk(a){this.Aa=se(a)} u(Rk,ih);function Sk(a){this.Aa=se(a)} u(Sk,ih);Sk.prototype.getQuery=function(){return Nf(this,1,Tk)}; Sk.prototype.setQuery=function(a){return bf(this,1,Tk,Wd(a))}; Sk.prototype.getStartIndex=function(){return Vf(this,2)}; var Tk=[1,5];function Uk(a){this.Aa=se(a)} u(Uk,ih);function Vk(a){this.Aa=se(a)} u(Vk,ih);function Wk(a){this.Aa=se(a)} u(Wk,ih);n=Wk.prototype;n.getId=function(){return Of(this,1)}; n.setId=function(a){return dg(this,1,a)}; n.getLanguage=function(){return Of(this,2)}; n.setLanguage=function(a){return dg(this,2,a)}; n.getName=function(){return Of(this,3)}; n.Sf=function(){return Of(this,3)}; n.setName=function(a){return dg(this,3,a)}; n.tf=la(8);n.getTitle=function(){return Of(this,4)}; n.setTitle=function(a){return dg(this,4,a)}; function Cfa(a,b){return dg(a,5,b)} n.getContent=function(){return Of(this,6)}; n.setContent=function(a){return dg(this,6,a)}; n.clearContent=function(){return Ce(this,6)}; n.getMetadata=function(){return lf(this,sj,13)}; n.Lf=function(a){return of(this,sj,13,a)}; n.setProperty=function(a,b){return Ne(this,20,wj,a,b)}; n.getAuthorEmail=function(){return Of(this,22)};function Xk(a){this.Aa=se(a)} u(Xk,ih);Xk.prototype.Uo=function(){return Af(this,2)}; Xk.prototype.Qh=function(a){return Xf(this,2,a)}; function Yk(a){return Cf(a,3)} ;function Zk(a){this.Aa=se(a)} u(Zk,ih);function $k(a){return nf(a,Xk,3,Pe())} Zk.prototype.Lg=function(a){return qf(this,3,a)}; Zk.prototype.setValue=function(a,b){return Ne(this,3,Xk,a,b)};function al(a){this.Aa=se(a)} u(al,ih);function bl(a,b){return nf(a,Zk,5,Pe(b))} var cl=Fi(al);var dl=[0,x,-3];var el=[0,x,vi,1,x];var fl=[0,vi,x,-5];var Dfa=[0,x,-9,2,x,-12];var Efa=[0,x,-1,Wh,-1];var Ffa=[0,x];var Gfa=[0,ci,-2,x,bi,x,-1,hi,vi];Dk.prototype.Ca=Di(Gfa);var Hfa=[0,x];Ek.prototype.Ca=Di(Hfa);var Ifa=[0,Hfa,-4];Fk.prototype.Ca=Di(Ifa);var Jfa=[0,Wh,x,ci,bi,x,hi,x,Ifa];Gk.prototype.Ca=Di(Jfa);var gl=[0,x,-2,vi,x,ni,vj,x,-1,Ak,x,-2,1,hi,vi,hi,x,-1,hi,x,-2,vi,ni,function(){return gl}, 1,Gfa,ni,function(){return gl}, Jfa,1,Ffa,Efa];Hk.prototype.Ca=Di(gl);var hl=[0,ni,gl];Nk.prototype.Ca=Di(hl);Pk.prototype.Ca=Di([0,ni,gl,Wh,x,Dfa,ni,gl,-1,x,ni,gl,fl,ni,dl,ni,el,ci,-1,x,hl]);var Kfa=[0,x,-1,hi];Qk.prototype. | 2026-01-13T08:48:41 |
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