class_docstr: Normalize the continuous variables.
class_docstr_tok: ["Normalize","the","continuous","variables","."]
docstr: Compute the means and stds of `self.cont_names` columns to normalize them.
docstr_tok: ["Compute","the","means","and","stds","of","`","self.cont_names","`","columns","to","normalize","them","."]
code: def apply_train(self, df):
      "Compute the means and stds of `self.cont_names` columns to normalize them."
      self.means, self.stds = {}, {}
      for n in self.cont_names:
          assert is_numeric_dtype(
              df[n]
          ), f"""Cannot normalize '{n}' column as it isn't numerical.
                  Are you sure it doesn't belong in the categorical set of columns?"""
          self.means[n], self.stds[n] = df[n].mean(), df[n].std()
          df[n] = (df[n] - self.means[n]) / (1e-7 + self.stds[n])
  
imports: from ..None import utils
  from ..imports import *
  from ..preprocessor import Preprocessor
  from numbers import Number
  from types import SimpleNamespace
  from typing import Any, AnyStr, Callable, Collection, Dict, Hashable, Iterator, List, Mapping, NewType, Optional, Sequence, Tuple, TypeVar, Union
  from pandas.api.types import is_categorical_dtype, is_numeric_dtype
label_desc: Use image node_id 2 for calling the Normalize obj's underlying member method code with example usage: obj.apply_train(df) without return types