KeyError when enumerating over dataloader
I'm trying to iterate over a pytorch dataloader initialized as follows:

trainDL = torch.utils.data.DataLoader(X_train,batch_size=BATCH_SIZE, shuffle=True, **kwargs)

where X_train is a pandas dataframe like this one:


So, I'm not being able to do the following statement, since I'm getting a KeyError in the 'enumerate':

for batch_idx, (data, _) in enumerate(trainDL):
    {stuff}


has anyone a clue of what's happening?

EDIT:

The error I get is:

KeyError                                  Traceback (most recent call last)
~/.local/share/virtualenvs/Pipenv-l_wD1rT4/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2896             try:
-&gt; 2897                 return self._engine.get_loc(key)
   2898             except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 40592

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
&lt;ipython-input-63-95142e0748bb&gt; in &lt;module&gt;
----&gt; 1 for batch_idx, (data, _) in enumerate(trainDL):
      2     print(".")

~/.local/share/virtualenvs/Pipenv-l_wD1rT4/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)
    344     def __next__(self):
    345         index = self._next_index()  # may raise StopIteration
--&gt; 346         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    347         if self._pin_memory:
    348             data = _utils.pin_memory.pin_memory(data)

~/.local/share/virtualenvs/Pipenv-l_wD1rT4/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---&gt; 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

~/.local/share/virtualenvs/Pipenv-l_wD1rT4/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py in &lt;listcomp&gt;(.0)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---&gt; 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

~/.local/share/virtualenvs/Pipenv-l_wD1rT4/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)
   2993             if self.columns.nlevels &gt; 1:
   2994                 return self._getitem_multilevel(key)
-&gt; 2995             indexer = self.columns.get_loc(key)
   2996             if is_integer(indexer):
   2997                 indexer = [indexer]

~/.local/share/virtualenvs/Pipenv-l_wD1rT4/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2897                 return self._engine.get_loc(key)
   2898             except KeyError:
-&gt; 2899                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2900         indexer = self.get_indexer([key], method=method, tolerance=tolerance)
   2901         if indexer.ndim &gt; 1 or indexer.size &gt; 1:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 40592


You have to create torch.utils.data.Dataset wrapping your dataset.
For example:
from torch.utils.data import Dataset

class PandasDataset(Dataset):
    def __init__(self, dataframe):
        self.dataframe = dataframe

    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, index):
        return self.dataframe.iloc[index]

Pass this object to DataLoader instantiated by your pandas dataframe and you should be fine.
Example usage with DataLoader:
import pandas as pd

df = pd.read_csv(&quot;data.csv&quot;)
dataset = PandasDataset(df)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=16)
for sample in dataloader:
    ...

