Before we start: This Python tutorial is a part of our series of Python Package tutorials. You can find other Numpy related topics too!
It is easy to transform a numpy array into a Python list using the numpy tolist() function or the list() function. Let’s take a look at the list() function first:
a = np.arange(4) print(type(list(b)[0])) print(type(list(b)))
Which will print the following.
<class 'numpy.int64'> <class 'list'>
As you can see, the numpy array was transformed into a Python list, but the elements are still numpy built-in integer values. This is because using the list() function doesn’t recursively transform all elements. As a result, you can only use the list() function on one dimensional numpy arrays.
Now let’s look at the tolist() function. Using our example above, we can apply the tolist() function as follows:
a = np.arange(4) print(type(b.tolist()[0])) print(type(b.tolist()))
The tolist() function can be used for any dimension numpy array of any shape:
a = np.arange(4).reshape(2,2) a.tolist()
The above code snippet will create a 1D numpy array with digits from 0 to 3, reshape it to be a 2 by 2 array, and then print it out as a Python list.
Compiling from source can get quite complex, what with environment setup, scripts and patches, not to mention resolving any dependency conflicts or errors that may occur. Instead, consider using the ActivateState Platform to automatically build and package it for you.
The following tutorials will provide you with step-by-step instructions on how to work with Numpy, including:
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