DOC: Set value for undefined variables in examples

pandas/core/apply.py

@@ -1323,17 +1323,23 @@ def relabel_result(
     columns: New columns name for relabelling
     order: New order for relabelling
 
-    Examples:
-    ---------
-    >>> result = DataFrame({"A": [np.nan, 2, np.nan],
-    ...       "C": [6, np.nan, np.nan], "B": [np.nan, 4, 2.5]})  # doctest: +SKIP
+    Examples
+    --------
+    >>> from pandas.core.apply import relabel_result
+    >>> result = pd.DataFrame(
+    ...     {"A": [np.nan, 2, np.nan], "C": [6, np.nan, np.nan], "B": [np.nan, 4, 2.5]},
+    ...     index=["max", "mean", "min"]
+    ... )
     >>> funcs = {"A": ["max"], "C": ["max"], "B": ["mean", "min"]}
     >>> columns = ("foo", "aab", "bar", "dat")
     >>> order = [0, 1, 2, 3]
-    >>> _relabel_result(result, func, columns, order)  # doctest: +SKIP
-    dict(A=Series([2.0, NaN, NaN, NaN], index=["foo", "aab", "bar", "dat"]),
-         C=Series([NaN, 6.0, NaN, NaN], index=["foo", "aab", "bar", "dat"]),
-         B=Series([NaN, NaN, 2.5, 4.0], index=["foo", "aab", "bar", "dat"]))
+    >>> result_in_dict = relabel_result(result, funcs, columns, order)
+    >>> pd.DataFrame(result_in_dict, index=columns)
+           A    C    B
+    foo  2.0  NaN  NaN
+    aab  NaN  6.0  NaN
+    bar  NaN  NaN  4.0
+    dat  NaN  NaN  2.5
     """
     from pandas.core.indexes.base import Index
 

pandas/core/arrays/datetimelike.py

@@ -258,8 +258,9 @@ def _unbox_scalar(
 
         Examples
         --------
-        >>> self._unbox_scalar(Timedelta("10s"))  # doctest: +SKIP
-        10000000000
+        >>> arr = pd.arrays.DatetimeArray(np.array(['1970-01-01'], 'datetime64[ns]'))
+        >>> arr._unbox_scalar(arr[0])
+        numpy.datetime64('1970-01-01T00:00:00.000000000')
         """
         raise AbstractMethodError(self)
 

pandas/core/dtypes/base.py

@@ -261,6 +261,7 @@ def construct_from_string(
         For extension dtypes with arguments the following may be an
         adequate implementation.
 
+        >>> import re
         >>> @classmethod
         ... def construct_from_string(cls, string):
         ...     pattern = re.compile(r"^my_type\[(?P<arg_name>.+)\]$")

pandas/core/generic.py

@@ -2204,7 +2204,7 @@ def to_excel(
 
         >>> with pd.ExcelWriter('output.xlsx',
         ...                     mode='a') as writer:  # doctest: +SKIP
-        ...     df.to_excel(writer, sheet_name='Sheet_name_3')
+        ...     df1.to_excel(writer, sheet_name='Sheet_name_3')
 
         To set the library that is used to write the Excel file,
         you can pass the `engine` keyword (the default engine is
@@ -5864,9 +5864,9 @@ def pipe(
             Alternatively a ``(callable, data_keyword)`` tuple where
             ``data_keyword`` is a string indicating the keyword of
             ``callable`` that expects the {klass}.
-        args : iterable, optional
+        *args : iterable, optional
             Positional arguments passed into ``func``.
-        kwargs : mapping, optional
+        **kwargs : mapping, optional
             A dictionary of keyword arguments passed into ``func``.
 
         Returns
@@ -5883,25 +5883,70 @@ def pipe(
         Notes
         -----
         Use ``.pipe`` when chaining together functions that expect
-        Series, DataFrames or GroupBy objects. Instead of writing
+        Series, DataFrames or GroupBy objects.
 
-        >>> func(g(h(df), arg1=a), arg2=b, arg3=c)  # doctest: +SKIP
+        Examples
+        --------
+        Constructing a 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)  # doctest: +SKIP
 
         You can write
 
-        >>> (df.pipe(h)
-        ...    .pipe(g, arg1=a)
-        ...    .pipe(func, arg2=b, arg3=c)
-        ... )  # doctest: +SKIP
+        >>> (
+        ...     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 ``func`` takes its data as ``arg2``:
-
-        >>> (df.pipe(h)
-        ...    .pipe(g, arg1=a)
-        ...    .pipe((func, 'arg2'), arg1=a, arg3=c)
-        ...  )  # doctest: +SKIP
+        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
         """
         if using_copy_on_write():
             return common.pipe(self.copy(deep=None), func, *args, **kwargs)

pandas/io/formats/style_render.py

@@ -1272,7 +1272,7 @@ def format_index(
 
         >>> df = pd.DataFrame([[1, 2, 3]],
         ...     columns=pd.MultiIndex.from_arrays([["a", "a", "b"],[2, np.nan, 4]]))
-        >>> df.style.format_index({0: lambda v: upper(v)}, axis=1, precision=1)
+        >>> df.style.format_index({0: lambda v: v.upper()}, axis=1, precision=1)
         ...  # doctest: +SKIP
                        A       B
               2.0    nan     4.0