How to build a keylogger in python
- #How to build a keylogger in python how to#
- #How to build a keylogger in python generator#
- #How to build a keylogger in python code#
So far we have covered the most common uses and constructions of generators, but there are a few more things to cover. This is because generators, like all iterators, can be exhausted, and if you try to call next() after that happens you will get this error. When we now call next() for the 3rd time, we get an error. The second time next() is called, the execution pointer continues from line 3, hitting the second yield statement on line 4 and returning “My second time here”, though technically was only in that line once ?. The first time we executed the function, the execution pointer was at the beginning and thus we hit the first yield on line 2, thus the statement “I’m here for the first time” is printed on the screen. I 'm here for the first time > print(next(multi_gen)) Pretty neat! Let’s see an example to understand this better
#How to build a keylogger in python generator#
When the generator is once again called, the state is restored and the function continues from the last yield statement it hit, like if the previous yield wouldn’t have been called and the function wouldn’t have been suspended. When the function gets suspended, the state of that function is saved, this includes data like any variable bindings, the instruction pointer, the internal stack, and any exception handling. When the Python yield statement is hit, the program suspends the function execution and returns the yielded value to the caller. But what do we mean to save the local state? well… here is where it comes very interesting.
![how to build a keylogger in python how to build a keylogger in python](https://i.ytimg.com/vi/q6EoHVzCKzw/maxresdefault.jpg)
So far we looked at simple cases for generators, and the yield statement, however, as with all Python things, it doesn’t end there, there are more things around it, though the idea of it is what you learned so far.Īs we already discussed, when we use yield we are saving the local state for the function and returning the value expression to the caller function.
#How to build a keylogger in python code#
Let’s say we need to count how many rows there are on a text file, our code could look something like: One of the advantages of generator functions to iterators is the amount of code that is required to code.Īfter that introduction, let’s see some examples of generators in action: Some use cases of generators Reading large filesĪ common use case of generators is to work with large files or data streams, like for example, CSV files. There are objects that you can loop over like a list, however, unlike lists, lazy iterators do not store their contents in memory. , generator functions are a special kind of function that returns some sort of lazy iterator. These are some of the scenarios where generator can be really useful and simple. Have you ever run into a situation where you would need to read large datasets or files, and those were too overwhelming to load into memory? Or maybe you wanted to build an iterator, but the producer function was so simple that most of your code is just around building the iterator other than producing the desired values?
![how to build a keylogger in python how to build a keylogger in python](https://img.wonderhowto.com/img/63/42/63585064182577/0/create-python-remote-keylogger-for-facebook.w1456.jpg)
Today we are going to talk about generators in Python, how are they different from normal functions, and why you should use them.
#How to build a keylogger in python how to#
How to Use Generator and yield in Python Updated on Jun 1, 2020