Since pandas.DataFrame uses numpy internally, you could create PyTorch tensors via tensor = torch.from_numpy(df[...]) (and call .clone() additionally if you don’t want to share the underlying numpy array).
The input shape depends on the used module and the majority of modules expect an input in the shape [batch_size, *], where * denotes additional dimensions. You can check the docs to see the expected shape input. In case you want to use RNNs, make sure to check the shapes, as the default setup is to use the sequential dimension in dim0.