code: def _read_triplets_dataframe(filename):
      """Reads the original dataset TSV as a pandas dataframe"""
      # delay importing this to avoid another dependency
      import pandas
  
      # read in triples of user/artist/playcount from the input dataset
      # get a model based off the input params
      start = time.time()
      log.debug("reading data from %s", filename)
      data = pandas.read_table(
          "train_triplets.txt", names=["user", "track", "plays"]
      )
  
      # map each artist and user to a unique numeric value
      data["user"] = data["user"].astype("category")
      data["track"] = data["track"].astype("category")
  
      # store as a CSR matrix
      log.debug("read data file in %s", time.time() - start)
  
      return data
  
file_path: implicit/implicit/datasets/million_song_dataset.py
imports: import logging
  import os
  import time
  import h5py
  import numpy
  from scipy.sparse import coo_matrix, csr_matrix
  from tqdm.auto import tqdm
  from implicit.datasets import _download
label_desc: Use image node_id 3 for calling a global function with example usage: _read_triplets_dataframe(filename) and returns: data