I use scipy for mathematical operation (such as log10()) and numpy for array creation/operations (such as np.zeros()). The example above is for macOS using pyenv. Right now, my code starts with: import numpy as np To make sure SciPy is installed, run Python in your terminal and try to import SciPy: > import scipy > print(scipy.file) /./lib/python3.7/site-packages/scipy/init.py In this code, you’ve imported scipy and printed the location of the file from where scipy is loaded. What is the recommended way to work with SciPy and NumPy? Being a scientist, sqrt(-1) should return a complex number, so I'm inclined to go with SciPy only. On the other hand, SciPy imports every Numpy functions in its main namespace, such that scipy.array() is the same thing as numpy.array() ( see this question), so NumPy should only be used when SciPy is not being used, as they are duplicates. So NumPy should be used for array operation and SciPy for everything else. Call numpy.genfromtxt(filename, dtypeNone, encodingNone, delimiterNone) with filename as the specified file and. Dense Array: is the opposite of a sparse array: most of the values are not zero. It can be an array like this one: 1, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0 Sparse Data: is a data set where most of the item values are zero. I know there is no strict guideline and I can do it the way I want, but from time to time, I still find contradictory instructions.įor example, I've read somewhere that NumPy is meant to only implement the array object, while SciPy is there for every other scientific algorithms. Sparse data is data that has mostly unused elements (elements that don't carry any information ). In an effort to clean up my code, I have been looking for a standard convention for importing SciPy and NumPy in my programs. I trust you'll let me know if I missed something!

Notice: I checked for duplicate and nothing clearly answers my question.

Import scipy as