DOC: Update Series.apply docstring (#22510)

pandas/core/series.py

@@ -3288,23 +3288,27 @@ def transform(self, func, axis=0, *args, **kwargs):
 
     def apply(self, 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
+        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
+            Python function or NumPy ufunc to apply.
+        convert_dtype : bool, default True
             Try to find better dtype for elementwise function results. If
-            False, leave as dtype=object
+            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
+            Positional arguments passed to func after the series value.
+        **kwds
+            Additional keyword arguments passed to func.
 
         Returns
         -------
-        y : Series or DataFrame if func returns a Series
+        Series or DataFrame
+            If func returns a Series object the result will be a DataFrame.
 
         See Also
         --------
@@ -3314,12 +3318,11 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
 
         Examples
         --------
-
         Create a series with typical summer temperatures for each city.
 
-        >>> series = pd.Series([20, 21, 12], index=['London',
-        ... 'New York','Helsinki'])
-        >>> series
+        >>> s = pd.Series([20, 21, 12],
+        ...               index=['London', 'New York', 'Helsinki'])
+        >>> s
         London      20
         New York    21
         Helsinki    12
@@ -3329,8 +3332,8 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
         argument to ``apply()``.
 
         >>> def square(x):
-        ...     return x**2
-        >>> series.apply(square)
+        ...     return x ** 2
+        >>> s.apply(square)
         London      400
         New York    441
         Helsinki    144
@@ -3339,7 +3342,7 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
         Square the values by passing an anonymous function as an
         argument to ``apply()``.
 
-        >>> series.apply(lambda x: x**2)
+        >>> s.apply(lambda x: x ** 2)
         London      400
         New York    441
         Helsinki    144
@@ -3350,9 +3353,9 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
         ``args`` keyword.
 
         >>> def subtract_custom_value(x, custom_value):
-        ...     return x-custom_value
+        ...     return x - custom_value
 
-        >>> series.apply(subtract_custom_value, args=(5,))
+        >>> s.apply(subtract_custom_value, args=(5,))
         London      15
         New York    16
         Helsinki     7
@@ -3363,18 +3366,18 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
 
         >>> def add_custom_values(x, **kwargs):
         ...     for month in kwargs:
-        ...         x+=kwargs[month]
+        ...         x += kwargs[month]
         ...     return x
 
-        >>> series.apply(add_custom_values, june=30, july=20, august=25)
+        >>> s.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)
+        >>> s.apply(np.log)
         London      2.995732
         New York    3.044522
         Helsinki    2.484907