# pandas.Series.apply

`Series.``apply`(func, convert_dtype=True, args=(), **kwds)

  Invoke function on values of Series. Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values

  Parameters:

  func : function

  convert_dtype : boolean, default True

  > Try to find better dtype for elementwise function results. If False, leave as dtype=object

  args : tuple

  > Positional arguments to pass to function in addition to the value

  Additional keyword arguments will be passed as keywords to the function

  Returns: | y : Series or DataFrame if func returns a Series

  See also

  `Series.map`
    For element-wise operations

  Examples

  Create a series with typical summer temperatures for each city.

      >>> import pandas as pd
      >>> import numpy as np
      >>> series = pd.Series([20, 21, 12], index=['London',
      ... 'New York','Helsinki'])
      London      20
      New York    21
      Helsinki    12
      dtype: int64

  Square the values by defining a function and passing it as an argument to `apply()`.

      >>> def square(x):
      ...     return x**2
      >>> series.apply(square)
      London      400
      New York    441
      Helsinki    144
      dtype: int64

  Square the values by passing an anonymous function as an argument to `apply()`.

      >>> series.apply(lambda x: x**2)
      London      400
      New York    441
      Helsinki    144
      dtype: int64

  Define a custom function that needs additional positional arguments and pass these additional arguments using the `args` keyword.

      >>> def subtract_custom_value(x, custom_value):
      ...     return x-custom_value

      >>> series.apply(subtract_custom_value, args=(5,))
      London      15
      New York    16
      Helsinki     7
      dtype: int64

  Define a custom function that takes keyword arguments and pass these arguments to `apply`.

      >>> def add_custom_values(x, **kwargs):
      ...     for month in kwargs:
      ...         x+=kwargs[month]
      ...         return x

      >>> series.apply(add_custom_values, june=30, july=20, august=25)
      London      95
      New York    96
      Helsinki    87
      dtype: int64

  Use a function from the Numpy library.

      >>> series.apply(np.log)
      London      2.995732
      New York    3.044522
      Helsinki    2.484907
      dtype: float64