class_docstr: Test behaviour of the from_pandas method
docstr: Providing an empty data set should result in a Value Error explaining that data must not be empty.
  This error is useful to catch and handle gracefully, because otherwise the user would experience
  misleading division by zero, or unpacking errors.
docstr_tok: ["Providing","an","empty","data","set","should","result","in","a","Value","Error","explaining","that","data","must","not","be","empty",".","This","error","is","useful","to","catch","and","handle","gracefully",",","because","otherwise","the","user","would","experience","misleading","division","by","zero",",","or","unpacking","errors","."]
code: def test_empty_data_raises_error(self):
      """
      Providing an empty data set should result in a Value Error explaining that data must not be empty.
      This error is useful to catch and handle gracefully, because otherwise the user would experience
      misleading division by zero, or unpacking errors.
      """
  
      with pytest.raises(ValueError):
          from_pandas(pd.DataFrame(data=[], columns=["a"]))
  
imports: import logging
  import networkx
  import numpy
  import pandas
  import pytest
  import scipy.optimize
  from mock import patch
  from causalnex.structure import StructureModel
  from causalnex.structure.data_generators import generate_binary_data, generate_binary_dataframe, generate_continuous_dataframe, generate_count_dataframe, generate_structure
  from causalnex.structure.pytorch.notears import from_numpy, from_pandas
label_desc: Use image node_id 4 for calling the TestFromPandas obj's underlying member method code with example usage: obj.test_empty_data_raises_error() without return types