It depends on the reason that the data are separated in different files in the first place and what you want to achieve.
If each file contains observations for a different entity AND you want to predict observations about EACH specific known entity, you can build a model for each entity. In this case, the entities with more training data will of course have better results.
Still, if the difference between those entities can be described with numerical values, depending on the exact problem, you can also try adding those to the training data and then concatenating everything. In this case, the added features will make the final classification to better classify the observations of each different entity without building 100 models. Note however that this could work only if the "qualities" of the entities actually affect the observations in some degree, otherwise (if the observations are randomly distributed amongst the entities) the results will probably be worse.
If however the observations of different entities are needed to train a model that works for any entity (including unknown ones), the data can be concatenated to a single table (pandas DataFrames were mentioned in the question) and then train your model with this combined dataset.
I would like to create a CNN model in Python and I have organized my data in such a way that I have 100 csv files with different sizes (all of them have 141 colunms but some have 33 rows and others have 70 rows). All of those files can be categorized in 6 different categories. All the examples that I have seen so far for buiding a CNN model are using either just one dataset in pandas or using several images of the same size. So the question would be, Can I use my data for creating a CNN model in this fashion? If yes, Can anyone give me some tricks or/and tips of how to?
Thanks a lot in advance!
I have seen some Tensorflow or PyTorch examples but I dont know how to use them with my data
Using several csv files of different sizes to build a CNN model in Python