Vfrdtyky

I have this loop that continuously checks api data and add it to an array.
Then I calculate an indicator based on one column of the array that I want to add the the array. The issue only is that the indicator need 10 data point to get going.

import numpy as np



list_b = 

i = 0



while True:

    i += 1

    list_a =  



    list_a.append(i)

    list_a.append(33)

    list_a.append(44)

    list_b.append(list_a)

    array = np.array(list_b, dtype=float)



    if len(array) > 10:

        x = indicator_function(array[:,0])

        np.append(x) # in a 4th column



    if i == 15:

        break



print(array.shape)

print(len(array))

print(array)

The issue is that I cant append to the array because it doesnt have the same dimensions as the array before. So how can I extend the array by one column or insert the new indicators values in a new column after 10 rows.

The array should be formed like this:

1 33 44

2 33 44

...

9 33 44

10 33 44

11 33 44 x

12 33 44 x

From what I understand, numpy doesn't allow for jagged arrays (arrays with a changing number of columns). You might have to create an entirely new array. Or, if you want to do something crazy, you could move the if statement up above list_b.append(list_a) and change it to

if (i > 10):

    x = indicator_function(array[:,0])

    list_a.append(x)

else:

    list_a.append(None)

if i < 10:   

    list_a.append(i)

    list_a.append(33)

    list_a.append(44)

    list_a.append(55)

    list_b.append(list_a)

    array = np.array(list_b, dtype=float)



if i > 10:

    list_a.append(i)

    list_a.append(33)

    list_a.append(44)

    list_a.append(66)

    list_b.append(list_a)

    array = np.array(list_b, dtype=float)

@sahil yes that works.

There's no builtin support for proper jagged arrays in numpy. The best bet for your application is probably a masked array:

import itertools

import numpy.ma as ma



array = None

list_b = 



def indicator_function(arr):

    return -1



for i in itertools.count():

    list_a = 

    list_b.append(list_a)



    list_a.append(i)

    list_a.append(33)

    list_a.append(44)

    list_a.append(np.nan)

    array = ma.MaskedArray(list_b)



    # assign mask or indicator_function, as appropriate

    ix = 10 + 1

    array[:ix, -1] = ma.masked

    array[ix:, -1] = indicator_function(array[:,0])



    if i == 15:

        break

This results in a masked array that looks like:

[[0.0, 33.0, 44.0, --],

 [1.0, 33.0, 44.0, --],

 [2.0, 33.0, 44.0, --],

 [3.0, 33.0, 44.0, --],

 [4.0, 33.0, 44.0, --],

 [5.0, 33.0, 44.0, --],

 [6.0, 33.0, 44.0, --],

 [7.0, 33.0, 44.0, --],

 [8.0, 33.0, 44.0, --],

 [9.0, 33.0, 44.0, --],

 [10.0, 33.0, 44.0, --],

 [11.0, 33.0, 44.0, -1.0],

 [12.0, 33.0, 44.0, -1.0],

 [13.0, 33.0, 44.0, -1.0],

 [14.0, 33.0, 44.0, -1.0],

 [15.0, 33.0, 44.0, -1.0]]