How can I train a neural network by reading different csv files


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I am currently working on an RNN LSTM project. I have 60.000 csv files as data to be trained. These csv files consist of outputs from a simulation, each giving a different Vx Vy Vz coordinator. (You can think of it as throwing a ball many times and drawing a trajectory related to it.) My aim is for the model I trained to acquire the characteristics of this ball and predict the path it will draw in 3d space by only giving the velocity and direction of fire. I thought it would be right to use LSTM RNN since it is a time dependent data. Before asking my question, I wanted to give information about the project.

My question is; What method should I apply when giving the data I have to the model? Is there a method to read this data separately and give it to the model?

I thought of a way to parse these files first and then train them with the help of the pandas library, but this time I wasn't sure if the shots were kept separate from each other.

In the attachment you can see what the data looks like:
 

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