code: def _determine_case_and_prepare_df(
      df, x, y, sample=5_000, random_seed=123
  ):
      """Return str with the name of the determined case based on the columns x and y."""
      if x == y:
          return df, "predict_itself"
  
      df = df[[x, y]]
      # IDEA: log.warning when values have been dropped
      df = df.dropna()
  
      if len(df) == 0:
          return df, "empty_dataframe_after_dropping_na"
          # IDEA: show warning
          # raise Exception(
          #     "After dropping missing values, there are no valid rows left"
          # )
  
      df = _maybe_sample(df, sample, random_seed=random_seed)
  
      if _feature_is_id(df, x):
          return df, "feature_is_id"
  
      category_count = df[y].value_counts().count()
      if category_count == 1:
          # it is helpful to separate this case in order to save unnecessary calculation time
          return df, "target_is_constant"
      if _dtype_represents_categories(df[y]) and (
          category_count == len(df[y])
      ):
          # it is important to separate this case in order to save unnecessary calculation time
          return df, "target_is_id"
  
      if _dtype_represents_categories(df[y]):
          return df, "classification"
      if is_numeric_dtype(df[y]):
          # this check needs to be after is_bool_dtype (which is part of _dtype_represents_categories) because bool is
          # considered numeric by pandas
          return df, "regression"
  
      if is_datetime64_any_dtype(df[y]) or is_timedelta64_dtype(df[y]):
          # IDEA: show warning
          # raise TypeError(
          #     f"The target column {y} has the dtype {df[y].dtype} which is not supported. A possible solution might be
          #     to convert {y} to a string column"
          # )
          return df, "target_is_datetime"
  
      # IDEA: show warning
      # raise Exception(
      #     f"Could not infer a valid task based on the target {y}. The dtype {df[y].dtype} is not yet supported"
      # )  # pragma: no cover
      return df, "target_data_type_not_supported"
  
imports: import warnings
  import numpy
  import pandas
  from pandas.api.types import is_bool_dtype, is_categorical_dtype, is_datetime64_any_dtype, is_numeric_dtype, is_object_dtype, is_string_dtype, is_timedelta64_dtype
  from sklearn import preprocessing, tree
  from sklearn.metrics import f1_score, mean_absolute_error
  from sklearn.model_selection import cross_val_score
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 7 for calling a global function with example usage: _determine_case_and_prepare_df(df, x, y, sample, random_seed) and returns: df,  str, df,  str, df,  str, df,  str, df,  str, df,  str, df,  str, df,  str, df,  str