I was able to solve this only using NumPy. As NumPy is significantly faster than python's list function (https://towardsdatascience.com/how-fast-numpy-really-is-e9111df44347) I wanted to share my answer as it might be useful to others.
I started with adding np.NaN to make the two arrays the same shape.
import numpy as np
a = np.array([[3,3,3],[3,3,3],[3,3,3]]).astype(float)
b = np.array([[2,2],[2,2],[2,2]]).astype(float)
# Extend each vector in array with Nan to reach same shape
b = np.insert(b, 2, np.nan, axis=1)
# Now vstack the arrays
a = np.vstack([[a], [b]])
print(a)
Output:
[[[ 3. 3. 3.]
[ 3. 3. 3.]
[ 3. 3. 3.]]
[[ 2. 2. nan]
[ 2. 2. nan]
[ 2. 2. nan]]]
Then I wrote a function to unpack each array in a, and remove the nan.
def Unpack_Matrix_with_NaN(Matrix_with_nan, matrix_of_interest):
for first_row in Matrix_with_nan[matrix_of_interest,:1]:
# find shape of matrix row without nan
first_row_without_nan = first_row[~np.isnan(first_row)]
shape = first_row_without_nan.shape[0]
matrix_without_nan = np.arange(shape)
for row in Matrix_with_nan[matrix_of_interest]:
row_without_nan = row[~np.isnan(row)]
matrix_without_nan = np.vstack([matrix_without_nan, row_without_nan])
# Remove vector specifying shape
matrix_without_nan = matrix_without_nan[1:]
return matrix_without_nan
I could then loop through the matrices, find my desired row, and print it.
Matrix_with_nan = a
for matrix in range(len(Matrix_with_nan)):
matrix_of_interest = Unpack_Matrix_with_NaN(a, matrix)
row = matrix_of_interest[1]
print(row)
Output:
[3. 3. 3.]
[2. 2.]
I also made a function to pack matrices when more than one nan needs to be added per row:
import numpy as np
a = np.array([[3,3,3],[3,3,3],[3,3,3]]).astype(float)
b = np.array([[2,2],[2,2],[2,2]]).astype(float)
c = np.array([[4,4,4,4],[4,4,4,4],[4,4,4,4]]).astype(float)
# Extend each vector in array with Nan to reach same shape
def Pack_Matrices_with_NaN(List_of_matrices, Matrix_size):
Matrix_with_nan = np.arange(Matrix_size)
for array in List_of_matrices:
start_position = len(array[0])
for x in range(start_position,Matrix_size):
array = np.insert(array, (x), np.nan, axis=1)
Matrix_with_nan = np.vstack([Matrix_with_nan, array])
Matrix_with_nan = Matrix_with_nan[1:]
return Matrix_with_nan
arrays = [a,b,c]
packed_matrices = Pack_Matrices_with_NaN(arrays, 5)
print(packed_matrices)
Output:
[[ 3. 3. 3. nan nan]
[ 3. 3. 3. nan nan]
[ 3. 3. 3. nan nan]
[ 2. 2. nan nan nan]
[ 2. 2. nan nan nan]
[ 2. 2. nan nan nan]
[ 4. 4. 4. 4. nan]
[ 4. 4. 4. 4. nan]
[ 4. 4. 4. 4. nan]]