class_docstr: Basic wrapper around several `DataLoader`s with factory methods for tabular data
docstr: Create `TabularDataLoaders` from `df` in `path` using `procs`
code: def from_df(
      cls,
      df: pd.DataFrame,
      path: str
      | Path = ".",  # Location of `df`, defaults to current working directory
      procs: list = None,  # List of `TabularProc`s
      cat_names: list = None,  # Column names pertaining to categorical variables
      cont_names: list = None,  # Column names pertaining to continuous variables
      y_names: list = None,  # Names of the dependent variables
      y_block: TransformBlock = None,  # `TransformBlock` to use for the target(s)
      valid_idx: list = None,  # List of indices to use for the validation set, defaults to a random split
      **kwargs,
  ):
      "Create `TabularDataLoaders` from `df` in `path` using `procs`"
      if cat_names is None:
          cat_names = []
      if cont_names is None:
          cont_names = list(
              set(df) - set(L(cat_names)) - set(L(y_names))
          )
      splits = (
          RandomSplitter()(df)
          if valid_idx is None
          else IndexSplitter(valid_idx)(df)
      )
      to = TabularPandas(
          df,
          procs,
          cat_names,
          cont_names,
          y_names,
          splits=splits,
          y_block=y_block,
      )
      return to.dataloaders(path=path, **kwargs)
  
file_path: fastai/fastai/tabular/data.py
imports: from __future__ import annotations
  from ..torch_basics import *
  from ..data.all import *
  from .core import *
label_desc: Use image node_id 1 for calling the TabularDataLoaders obj's underlying member method code with example usage: obj.from_df(cls, df, path, procs, cat_names, cont_names, y_names, y_block, valid_idx) and returns: to