Flattening a list of NumPy arrays?

问题:

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?

回答1:

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]

回答2:

I came across this same issue and found a solution that combines 1-D numpy arrays of variable length:

np.column_stack(input_list).ravel()

See numpy.column_stack for more info.

Example with variable-length arrays with your example data:

In [135]: input_list
Out[135]: 
[array([[ 0.00353654,  0.00353654]]),
 array([[ 0.00353654]]),
 array([[ 0.00353654]]),
 array([[ 0.00353654,  0.00353654,  0.00353654]])]

In [136]: [i.size for i in input_list]    # variable size arrays
Out[136]: [2, 1, 1, 3]

In [137]: np.column_stack(input_list).ravel()
Out[137]: 
array([ 0.00353654,  0.00353654,  0.00353654,  0.00353654,  0.00353654,
        0.00353654,  0.00353654])

Note: Only tested on Python 2.7.12

回答3:

Can also be done by

np.array(list_of_arrays).flatten().tolist()

resulting in

[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]