Convert list of two dimensional DataFrame to Torch Tensor
Goal: I am working with RNNs in PyTorch, and my data is given by a list of DataFrames, where each DataFrame means one observation like:

import numpy as np
data = [pd.DataFrame(np.zeros((5,50))) for x in range(100)]


which means 100 observation, with 50 parameters and 5 timesteps each. For my Model i need a tensor of shape (100,5,50). 

Issue: I tried a lot of things but nothing seems to work, does anyone know how this is done?
This approaches doesn't work:

import torch
torch.tensor(np.array(data))


I thing the problem is to convert the DataFrames into Arrays and the List into a Tensor at the same time.

I don't think you can convert the list of dataframes in a single command, but you can convert the list of dataframes into a list of tensors and then concatenate the list.

E.g.

import pandas as pd
import numpy as np
import torch

data = [pd.DataFrame(np.zeros((5,50))) for x in range(100)]

list_of_arrays = [np.array(df) for df in data]
torch.tensor(np.stack(list_of_arrays))

#or

list_of_tensors = [torch.tensor(np.array(df)) for df in data]
torch.stack(list_of_tensors)

