DOC: Set value for undefined variables in examples

pandas/core/apply.py

@@ -1330,7 +1330,7 @@ def relabel_result(
     >>> 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
+    >>> relabel_result(result, funcs, 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"]))

pandas/core/arrays/datetimelike.py

@@ -258,7 +258,11 @@ def _unbox_scalar(
 
         Examples
         --------
-        >>> self._unbox_scalar(Timedelta("10s"))  # doctest: +SKIP
+        >>> obj = DatetimeLikeArrayMixin(
+        ...    OpsMixin(),
+        ...    NDArrayBackedExtensionArray(NDArrayBacked(), ExtensionArray())
+        ...    )
+        >>> obj._unbox_scalar(Timedelta("10s"))  # doctest: +SKIP
         10000000000
         """
         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
@@ -5885,24 +5885,58 @@ 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]]
+        >>> income = pd.DataFrame(data, columns=['Salary', 'Others'])
+           Salary  Others
+        0    8000  1000.0
+        1    9500     NaN
+        2    5000  2000.0
+
+        Functions that perform tax reductions on an income DataFrame.
+
+        >>> def federal_tax(income):
+        ...     return income * 0.9
+        >>> def state_tax(income, rate):
+        ...     return income * (1 - rate)
+        >>> def national_insurance(income, rate, rate_increase):
+        ...     new_rate = rate + rate_increase
+        ...     return income * (1 - new_rate)
+
+        Instead of writing
+
+        >>> (national_insurance(state_tax(federal_tax(income), 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)
+        >>> (income.pipe(federal_tax)
+        ...    .pipe(state_tax, rate=0.12)
+        ...    .pipe(national_insurance, rate=0.05, rate_increase=0.02)
         ... )  # doctest: +SKIP
+            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)
+        data. For example, suppose ``national_insurance`` takes its data as ``income``
+        in the second argument:
+
+        >>> def national_insurance(rate, income, rate_increase):
+        ...     new_rate = rate + rate_increase
+        ...     return income * (1 - new_rate)
+        >>> (income.pipe(federal_tax)
+        ...    .pipe(state_tax, rate=0.12)
+        ...    .pipe((national_insurance, 'income'), rate=0.05, rate_increase=0.02)
         ...  )  # doctest: +SKIP
         """
         if using_copy_on_write():

pandas/io/formats/style_render.py

@@ -1275,7 +1275,7 @@ 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