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# pandas.DataFrame.pipe

DataFrame.pipe(func, *args, **kwargs)

  Apply chainable functions that expect Series or DataFrames.

  Passes the object as the first argument to each function, so that `df.pipe(f).pipe(g)` is equivalent to `g(f(df))`. Improves readability when chaining several transformations.

  Parameters:
    func function

      Function to apply to the Series/DataFrame. `args`, and `kwargs` are passed into `func`. Alternatively a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the Series/DataFrame.

    *args iterable, optional

      Positional arguments passed into `func`.

    **kwargs mapping, optional

      A dictionary of keyword arguments passed into `func`.

  Returns:
    The return type of `func`.

      The result of applying `func` to the Series or DataFrame.

  See also

  `DataFrame.apply`

    Apply a function along input axis of DataFrame.

  `DataFrame.map`

    Apply a function elementwise on a whole DataFrame.

  `Series.map`

    Apply a mapping correspondence on a `Series`.

  Notes

  Use `.pipe` when chaining together functions that expect Series, DataFrames or GroupBy objects.

  Examples

  Constructing an income DataFrame from a dictionary.

      >>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
      >>> df = pd.DataFrame(data, columns=["Salary", "Others"])
      >>> df
         Salary  Others
      0    8000  1000.0
      1    9500     NaN
      2    5000  2000.0

  Functions that perform tax reductions on an income DataFrame.

      >>> def subtract_federal_tax(df):
      ...     return df * 0.9
      >>> def subtract_state_tax(df, rate):
      ...     return df * (1 - rate)
      >>> def subtract_national_insurance(df, rate, rate_increase):
      ...     new_rate = rate + rate_increase
      ...     return df * (1 - new_rate)

  Instead of writing

      >>> subtract_national_insurance(
      ...     subtract_state_tax(subtract_federal_tax(df), rate=0.12),
      ...     rate=0.05,
      ...     rate_increase=0.02,
      ... )

  You can write

      >>> (
      ...     df.pipe(subtract_federal_tax)
      ...     .pipe(subtract_state_tax, rate=0.12)
      ...     .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
      ... )
          Salary   Others
      0  5892.48   736.56
      1  6997.32      NaN
      2  3682.80  1473.12

  If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose `national_insurance` takes its data as `df` in the second argument:

      >>> def subtract_national_insurance(rate, df, rate_increase):
      ...     new_rate = rate + rate_increase
      ...     return df * (1 - new_rate)
      >>> (
      ...     df.pipe(subtract_federal_tax)
      ...     .pipe(subtract_state_tax, rate=0.12)
      ...     .pipe(
      ...         (subtract_national_insurance, "df"), rate=0.05, rate_increase=0.02
      ...     )
      ... )
          Salary   Others
      0  5892.48   736.56
      1  6997.32      NaN
      2  3682.80  1473.12