DOC: fix DataFrame.isin docstring and doctests (#22767)

Author: Moisan

ci/doctests.sh

@@ -21,7 +21,7 @@ if [ "$DOCTEST" ]; then
 
     # DataFrame / Series docstrings
     pytest --doctest-modules -v pandas/core/frame.py \
-        -k"-axes -combine -isin -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_dict -to_stata"
+        -k"-axes -combine -itertuples -join -nlargest -nsmallest -nunique -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack -to_dict -to_stata"
 
     if [ $? -ne "0" ]; then
         RET=1

pandas/core/frame.py

@@ -7451,52 +7451,66 @@ def to_period(self, freq=None, axis=0, copy=True):
 
     def isin(self, values):
         """
-        Return boolean DataFrame showing whether each element in the
-        DataFrame is contained in values.
+        Whether each element in the DataFrame is contained in values.
 
         Parameters
         ----------
-        values : iterable, Series, DataFrame or dictionary
+        values : iterable, Series, DataFrame or dict
             The result will only be true at a location if all the
             labels match. If `values` is a Series, that's the index. If
-            `values` is a dictionary, the keys must be the column names,
+            `values` is a dict, the keys must be the column names,
             which must match. If `values` is a DataFrame,
             then both the index and column labels must match.
 
         Returns
         -------
+        DataFrame
+            DataFrame of booleans showing whether each element in the DataFrame
+            is contained in values.
 
-        DataFrame of booleans
+        See Also
+        --------
+        DataFrame.eq: Equality test for DataFrame.
+        Series.isin: Equivalent method on Series.
+        Series.str.contains: Test if pattern or regex is contained within a
+            string of a Series or Index.
 
         Examples
         --------
-        When ``values`` is a list:
-
-        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']})
-        >>> df.isin([1, 3, 12, 'a'])
-               A      B
-        0   True   True
-        1  False  False
-        2   True  False
-
-        When ``values`` is a dict:
-
-        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 4, 7]})
-        >>> df.isin({'A': [1, 3], 'B': [4, 7, 12]})
-               A      B
-        0   True  False  # Note that B didn't match the 1 here.
-        1  False   True
-        2   True   True
-
-        When ``values`` is a Series or DataFrame:
-
-        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']})
-        >>> df2 = pd.DataFrame({'A': [1, 3, 3, 2], 'B': ['e', 'f', 'f', 'e']})
-        >>> df.isin(df2)
-               A      B
-        0   True  False
-        1  False  False  # Column A in `df2` has a 3, but not at index 1.
-        2   True   True
+
+        >>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
+        ...                   index=['falcon', 'dog'])
+        >>> df
+                num_legs  num_wings
+        falcon         2          2
+        dog            4          0
+
+        When ``values`` is a list check whether every value in the DataFrame
+        is present in the list (which animals have 0 or 2 legs or wings)
+
+        >>> df.isin([0, 2])
+                num_legs  num_wings
+        falcon      True       True
+        dog        False       True
+
+        When ``values`` is a dict, we can pass values to check for each
+        column separately:
+
+        >>> df.isin({'num_wings': [0, 3]})
+                num_legs  num_wings
+        falcon     False      False
+        dog        False       True
+
+        When ``values`` is a Series or DataFrame the index and column must
+        match. Note that 'falcon' does not match based on the number of legs
+        in df2.
+
+        >>> other = pd.DataFrame({'num_legs': [8, 2],'num_wings': [0, 2]},
+        ...                      index=['spider', 'falcon'])
+        >>> df.isin(other)
+                num_legs  num_wings
+        falcon      True       True
+        dog        False      False
         """
         if isinstance(values, dict):
             from pandas.core.reshape.concat import concat