I can't completely understand your problem as your code is not formatted properly in your question but the error is just an expected datatype error.

You need to convert your dataframe to a np array. Just add .values at the end of your dataframe. So if your input was a sample dataframe like so:

df = pd.DataFrame({"Col1":[1,2,3,4], "Col2":[2,2,3,4]})

Convert the whole thing to a numpy array like so:

sample_array = df.values

or convert one column to a np array like so:

sample_array_2 = df["Col1"].values

Update: As mentioned in the comments, pandas recommends .to_numpy() instead, so use something like:

sample_array = df.to_numpy()

Can you post the full traceback? It appears that your error isn't occurring in the code you posted as I don't see any argument called tuples.

I do see a possible problem that will be thrown later if the error isn't in the code you posted.

This line of code, is what's in the column Close also a Dataframe or structured data type? It appears you are trying to access df["Closes"][column name] new_df [t] = ema(6,df["Close"][t])

I'm a bit tired so I may have misunderstood what you're doing on this line.

This is a known error, you need to uninstall numpy by:

pip uninstall numpy

then install "numpy<1.26.4" afresh.

pip install "numpy<1.26.4"

References:

https://github.com/huggingface/diffusers/issues/9069#issuecomment-2267596351

https://github.com/pytorch/pytorch/issues/135836#issuecomment-2347209235

This can give you a rough idea. There are multiple checks on data argument. If data argument is a list of items then the below code will execute under the hood. The first element of list is checked. If it's a list then it proceeds into listlike block but if its ndarray it proceeds into ndarray block. The lists block cannot handle ndarray but ndarray block can handle lists. This is why you don't get errors.

elif isinstance(data, (list, types.GeneratorType)):
    if isinstance(data, types.GeneratorType):
        data = list(data)
    if len(data) > 0:
-------> #IF FIRST ELEMENT IS LIST ENTER THIS BLOCK
        if is_list_like(data[0]) and getattr(data[0], 'ndim', 1) == 1:
            if is_named_tuple(data[0]) and columns is None:
                columns = data[0]._fields
            arrays, columns = _to_arrays(data, columns, dtype=dtype)
            columns = _ensure_index(columns)

            # set the index
            if index is None:
                if isinstance(data[0], Series):
                    index = _get_names_from_index(data)
                elif isinstance(data[0], Categorical):
                    index = _default_index(len(data[0]))
                else:
                    index = _default_index(len(data))

            mgr = _arrays_to_mgr(arrays, columns, index, columns,
                                 dtype=dtype)
------> #ELSE ENTER THIS BLOCK
        else:
            mgr = self._init_ndarray(data, index, columns, dtype=dtype,
                                     copy=copy)
    else:
        mgr = self._init_dict({}, index, columns, dtype=dtype)

Here is what I would do:

import torch
import numpy as np
n = np.arange(10)
print(n) #[0 1 2 3 4 5 6 7 8 9]
t1 = torch.Tensor(n)  # as torch.float32
print(t1) #tensor([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
t2 = torch.from_numpy(n)  # as torch.int32
print(t2) #tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)