create a 3D matrix from 4 columns of a Dataframe

Question

I want to create a 3D matrix from 4 columns of my dataframe

Input:

df = pd.DataFrame({
  "u_id": [55218,55218,55218,55222],
  "i_id": [0,0,1,1],
  "Num": [0,2,1,2]
  "rating":[-1,2,0,2]})

x-axis : 'u_id'; y-axis : 'i_id' z-axis : 'Num'

And the value in the Matrix should be 'rating'

The result should be

[[[NaN,NaN],
  [-1 ,NaN]],
 [[NaN,NaN],
  [  0,NaN]],
 [[  2,NaN],
  [NaN,2]]]

What i tried so far:

x = df['u_id']
y = df['i_id']
z = df['Num']
value = df['rating']
Matrix = [[0 for m in len(z)] for m in len(z)] for c in len(x):

Matrix[c][r][m]= value

but this doesn't work.

Answer 1

I think your expected output doesn't represent the information in your dataframe. But if you want the values of rating placed with the other columns as indices in a 3D array with shape (3,2,2)

Setup the input data

import numpy as np
import pandas as pd

df = pd.DataFrame({
  "u_id": [55218,55218,55218,55222],
  "i_id": [0,0,1,1],
  "Num": [0,2,1,2],      # <-- here was a small typo in your code
  "rating":[-1,2,0,2]})
df

Out:

    u_id  i_id  Num  rating
0  55218     0    0      -1
1  55218     0    2       2
2  55218     1    1       0
3  55222     1    2       2

First convert u_id to suitable indices

df['u_id'] = df['u_id'].astype('category').cat.codes
df[['Num','u_id','i_id','rating']] # order columns to correspond to coordinates

Out:

   Num  u_id  i_id  rating
0    0     0     0      -1
1    2     0     0       2
2    1     0     1       0
3    2     1     1       2

Then create the output array and fill in the rating values

x = np.full(df[['Num','u_id','i_id']].nunique(), np.nan)
x[df['Num'], df['u_id'], df['i_id']] = df['rating']
x

Out:

array([[[-1., nan],
        [nan, nan]],

       [[nan,  0.],
        [nan, nan]],

       [[ 2., nan],
        [nan,  2.]]])