Threads are like busy workers trying to use shared tools or enter a room. But sometimes, only a few workers can use the tool or fit in the room at the same time. Without control, things get messy and confusing. A Semaphore acts like a smart gatekeeper. It counts how many workers are inside and […]
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Sometimes, threads need to pause and wait for something important to happen before moving on. Condition variables act like traffic lights for threads—they tell them when to stop and when it’s safe to go. In this article, we’ll learn how to use condition variables to make threads wait for a signal and then continue safely,
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Threads are like busy helpers working together. Sometimes, they need to share the same tools — like a hammer or a paintbrush. If two helpers try to use the same tool at once, things get messy! Locks act like a tool locker that lets only one helper use a tool at a time, keeping things
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Imagine your program is a busy workshop. It has little helpers — threads — that take care of different jobs. Some helpers are serious: they stay until every task is done. Others are more easygoing — they vanish quietly when the main shift ends. These are daemon threads. In Python, daemon threads are like night-shift
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Imagine a bunch of kids trying to play with the same toy at the same time. One grabs it, another pulls, and suddenly it’s chaos — the toy’s broken, and everyone’s crying. Threads in Python can be just like those kids. They run at the same time and often need to use the same stuff
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Think of threads as tiny workers inside your Python program. Each one has a job to do, like fetching water or delivering messages. Just like people, threads have a lifecycle—they start out new, get to work running their tasks, and then finish when their job is done. In this article, we’ll follow these little workers
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Have you ever wanted your Python program to do two things at the same time — like download cat photos and play your favorite tune in the background? That’s exactly what threads are for. In Python, threads let your program multitask — kind of like a circus juggler handling flaming swords while riding a unicycle.
Python Threads: The Basics Read More »
Filtering a stream means picking only some events from it based on rules you set. Instead of using every value that comes, you choose which ones to keep. This helps you work with just the data you want, making your program cleaner and easier to control. For example, you can filter to get only even
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Reducing a stream means taking many pieces of data that come from the stream and combining them into one single value. Imagine you have a list of numbers flowing one by one. Reducing lets you add them all up or join them into one string. It’s like gathering small parts to make a whole. In
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In Dart, “mapping” a stream means changing the data that flows through it. You take each item in the stream and transform it into something else. For example, you can take a stream of numbers like 1, 2, 3 and turn them into emojis like ⭐️, ⭐️⭐️, ⭐️⭐️⭐️. This is done using the .map() method.
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