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How do I convert a NumPy array to a Python List (for example [[1,2,3],[4,5,6]] ), and do it reasonably fast?
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To convert an array to a dataframe with Python you need to 1) have your NumPy array (e.g., np_array), and 2) use the pd. DataFrame() constructor like this: df = pd. DataFrame(np_array, columns=['Column1', 'Column2']) . Remember, that each column in your NumPy array needs to be named with columns.
The answer is performance. Numpy data structures perform better in: Size - Numpy data structures take up less space. Performance - they have a need for speed and are faster than lists.
NumPy is a Python library that can be used for scientific and numerical applications and is the tool to use for linear algebra operations. The main data structure in NumPy is the ndarray, which is a shorthand name for N-dimensional array. When working with NumPy, data in an ndarray is simply referred to as an array.
Use tolist():
import numpy as np >>> np.array([[1,2,3],[4,5,6]]).tolist() [[1, 2, 3], [4, 5, 6]] Note that this converts the values from whatever numpy type they may have (e.g. np.int32 or np.float32) to the "nearest compatible Python type" (in a list). If you want to preserve the numpy data types, you could call list() on your array instead, and you'll end up with a list of numpy scalars. (Thanks to Mr_and_Mrs_D for pointing that out in a comment.)
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