class_docstr: Handles MovieLens-100K
docstr: Convert the input dataframe into a coo matrix
  
  Args:
      df (pandas.DataFrame): DataFrame containing the target matrix information.
docstr_tok: ["Convert","the","input","dataframe","into","a","coo","matrix","Args",":","df","(","pandas.DataFrame",")",":","DataFrame","containing","the","target","matrix","information","."]
code: def df2coo(self, df):
      """Convert the input dataframe into a coo matrix
  
      Args:
          df (pandas.DataFrame): DataFrame containing the target matrix information.
      """
      data = []
      row = list(df["user id"] - 1)
      col = list(df["item id"] - 1)
      for idx in range(0, len(df)):
          val = df["rating"].iloc[idx]
          data += [val]
  
      if self.target_transform == "normalize":
          data = data / np.sqrt(
              np.sum(
                  np.arange(self.min_rating, self.max_rating + 1) ** 2
              )
          )
      elif self.target_transform == "binarize":
          data = binarize(np.array(data), 3)
  
      # TODO: Get this from `u.info`
      return coo_matrix((data, (row, col)), shape=(943, 1682))
  
imports: import logging
  import pandas
  import numpy
  from scipy.sparse import coo_matrix, isspmatrix_csr
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import normalize
  from recommenders.utils.python_utils import binarize
  from .geoimc_utils import length_normalize, reduce_dims
label_desc: Use image node_id 2 for calling the ML_100K obj's underlying member method code with example usage: obj.df2coo(df) and returns: coo_matrix