DOC: fix doc to understand fillna with second dataframe (#43529)

* DOC: added missing value in column D for better understanding fillna with second dataframe

* add note about column D

Author: erfannariman

pandas/core/generic.py

@@ -6216,64 +6216,66 @@ def fillna(
         --------
         >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
         ...                    [3, 4, np.nan, 1],
-        ...                    [np.nan, np.nan, np.nan, 5],
+        ...                    [np.nan, np.nan, np.nan, np.nan],
         ...                    [np.nan, 3, np.nan, 4]],
         ...                   columns=list("ABCD"))
         >>> df
-             A    B   C  D
-        0  NaN  2.0 NaN  0
-        1  3.0  4.0 NaN  1
-        2  NaN  NaN NaN  5
-        3  NaN  3.0 NaN  4
+             A    B   C    D
+        0  NaN  2.0 NaN  0.0
+        1  3.0  4.0 NaN  1.0
+        2  NaN  NaN NaN  NaN
+        3  NaN  3.0 NaN  4.0
 
         Replace all NaN elements with 0s.
 
         >>> df.fillna(0)
-            A   B   C   D
-        0   0.0 2.0 0.0 0
-        1   3.0 4.0 0.0 1
-        2   0.0 0.0 0.0 5
-        3   0.0 3.0 0.0 4
+             A    B    C    D
+        0  0.0  2.0  0.0  0.0
+        1  3.0  4.0  0.0  1.0
+        2  0.0  0.0  0.0  0.0
+        3  0.0  3.0  0.0  4.0
 
         We can also propagate non-null values forward or backward.
 
         >>> df.fillna(method="ffill")
-            A   B   C   D
-        0   NaN 2.0 NaN 0
-        1   3.0 4.0 NaN 1
-        2   3.0 4.0 NaN 5
-        3   3.0 3.0 NaN 4
+             A    B   C    D
+        0  NaN  2.0 NaN  0.0
+        1  3.0  4.0 NaN  1.0
+        2  3.0  4.0 NaN  1.0
+        3  3.0  3.0 NaN  4.0
 
         Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
         2, and 3 respectively.
 
         >>> values = {{"A": 0, "B": 1, "C": 2, "D": 3}}
         >>> df.fillna(value=values)
-            A   B   C   D
-        0   0.0 2.0 2.0 0
-        1   3.0 4.0 2.0 1
-        2   0.0 1.0 2.0 5
-        3   0.0 3.0 2.0 4
+             A    B    C    D
+        0  0.0  2.0  2.0  0.0
+        1  3.0  4.0  2.0  1.0
+        2  0.0  1.0  2.0  3.0
+        3  0.0  3.0  2.0  4.0
 
         Only replace the first NaN element.
 
         >>> df.fillna(value=values, limit=1)
-            A   B   C   D
-        0   0.0 2.0 2.0 0
-        1   3.0 4.0 NaN 1
-        2   NaN 1.0 NaN 5
-        3   NaN 3.0 NaN 4
+             A    B    C    D
+        0  0.0  2.0  2.0  0.0
+        1  3.0  4.0  NaN  1.0
+        2  NaN  1.0  NaN  3.0
+        3  NaN  3.0  NaN  4.0
 
         When filling using a DataFrame, replacement happens along
         the same column names and same indices
 
         >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
         >>> df.fillna(df2)
-            A   B   C   D
-        0   0.0 2.0 0.0 0
-        1   3.0 4.0 0.0 1
-        2   0.0 0.0 0.0 5
-        3   0.0 3.0 0.0 4
+             A    B    C    D
+        0  0.0  2.0  0.0  0.0
+        1  3.0  4.0  0.0  1.0
+        2  0.0  0.0  0.0  NaN
+        3  0.0  3.0  0.0  4.0
+
+        Note that column D is not affected since it is not present in df2.
         """
         inplace = validate_bool_kwarg(inplace, "inplace")
         value, method = validate_fillna_kwargs(value, method)