multi-numpy array from dataframe

Question

I have 5 Pandas DataFrames, its 5 classes of objects in 80,00 images and the coordinates of the objects. Basically, there is more than one class in each image

class 1:

 image_id x y image_0 4835 106 image_0 2609 309 image_0 2891 412 image_0 1823 431 image_0 3309 449 

and ends with:

image_945 950 1238 image_945 34 1362 image_945 821 2059 image_945 1448 2896 

---

class 2:

image_id x y image_0 4835 106 image_0 2609 309 image_0 2891 412 image_0 1823 431 image_0 3309 449 

ends with:

image_945 9530 128 image_945 354 162 image_945 8321 259 image_945 1448 2596 

---

What I'm trying to do is get the x and y to be in the same array while the image_id does not repeat. like so:

 class 1 class 2 class n image_0 [(4240, 41), [(3231, 1086), (3833, 74), (2576, 1372), (4470, 94), (3487, 1462), (2946, 10)] (48...)] image_1 [(4835, 106), [(4532,5438), (2609, 309), (4322,777), (2891, 412), (665,899), (1823,...)] (54..,..)] image_n [(...), [(...), (...)] (...)] 

Basically converting into a multidimensional numpy array. Of course, I've looked before posting.

Answer 1

Create a dictionary of dataframes to pass to pd.concat

clss = {'class 1': cls1, 'class 2': cls2} catted = pd.concat(clss) g = catted.groupby(['image_id', pd.Grouper(level=0)])[['x', 'y']] g.apply(lambda x: list(zip(*x.values.T))).unstack()