It appears that I have data in the format of a list of NumPy arrays (type() = np.ndarray
):
[array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]),
array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]),
array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]),
array([[ 0.00353654]]), array([[ 0.00353654]]), array([[ 0.00353654]]),
array([[ 0.00353654]])]
I am trying to put this into a polyfit function:
m1 = np.polyfit(x, y, deg=2)
However, it returns the error: TypeError: expected 1D vector for x
I assume I need to flatten my data into something like:
[0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654 ...]
I have tried a list comprehension which usually works on lists of lists, but this as expected has not worked:
[val for sublist in risks for val in sublist]
What would be the best way to do this?
You could use numpy.concatenate
, which as the name suggests, basically concatenates all the elements of such an input list into a single NumPy array, like so -
import numpy as np
out = np.concatenate(input_list).ravel()
If you wish the final output to be a list, you can extend the solution, like so -
out = np.concatenate(input_list).ravel().tolist()
Sample run -
In [24]: input_list
Out[24]:
[array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]]),
array([[ 0.00353654]])]
In [25]: np.concatenate(input_list).ravel()
Out[25]:
array([ 0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654,
0.00353654, 0.00353654, 0.00353654, 0.00353654, 0.00353654,
0.00353654, 0.00353654, 0.00353654])
Convert to list -
In [26]: np.concatenate(input_list).ravel().tolist()
Out[26]:
[0.00353654,
0.00353654,
0.00353654,
0.00353654,
0.00353654,
0.00353654,
0.00353654,
0.00353654,
0.00353654,
0.00353654,
0.00353654,
0.00353654,
0.00353654]