Corrections - Tom
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- Tom Reclik authored
+ 6
− 6
* ```from my_module import my_func```: If we want to make only specific functions or classes available, we can import them explicitly. If we now want to use the imported functions, classes, etc. imported this way, we do not need to prepend the name of the module. We say that we have imported them into our current *namespace*.
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One advantage of python is its rich ecosystem where many modules are made avaiable publicly as open-source. Many packages can be obtained from the central [Python Package Index](https://pypi.org/). At the moment, there are more than 400.000 projects that allow you to use modules for scientific computing, machine learning, deep learning, statistical analysis, visulalisation of data, image processing, language processing, web development, working with databases, and many many more.
One advantage of python is its rich ecosystem where many modules are made available publicly as open-source. Many packages can be obtained from the central [Python Package Index](https://pypi.org/). At the moment, there are more than 400.000 projects that allow you to use modules for scientific computing, machine learning, deep learning, statistical analysis, visulalisation of data, image processing, language processing, web development, working with databases, and many many more.
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As a first step, we need to create the $x$ values at which we want to evaluate and plot the function. In the end, we cannot have infinite values but we need to decide on a range and the number of evaluation points. The latter depends, in particular, how much the function we want to visualise changes, and how large the range is over which we want to show the function. \
Rather than doing the loop ourselves (which would be slow), NumPy can take the $x$ array, apply our function to each of the values in "bulk", and return a corresponding array with the $y$ values. These are called (in NumPy jargon): vector operations. These are much faster than if we were to implement this ourselves.
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