DOC: Adjusted DataFrame.assign docstring

pandas/core/frame.py

@@ -3220,7 +3220,7 @@ def assign(self, **kwargs):
 
         Parameters
         ----------
-        kwargs : keyword, value pairs
+        **kwargs : dict of {str: callable or series}
             The column names are keywords. If the values are
             callable, they are computed on the DataFrame and
             assigned to the new columns. The callable must not
@@ -3230,7 +3230,7 @@ def assign(self, **kwargs):
 
         Returns
         -------
-        df : DataFrame
+        DataFrame
             A new DataFrame with the new columns in addition to
             all the existing columns.
 
@@ -3250,8 +3250,9 @@ def assign(self, **kwargs):
 
         Examples
         --------
-        >>> df = pd.DataFrame({'temp_c': (17.0, 25.0)},
-                                index=['Portland', 'Berkeley'])
+        >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
+        ...                   index=['Portland', 'Berkeley'])
+        >>> df
                   temp_c
         Portland    17.0
         Berkeley    25.0
@@ -3264,8 +3265,7 @@ def assign(self, **kwargs):
 
         Alternatively, the same behavior can be achieved by directly
         referencing an existing Series or list-like:
-        >>> newcol = df['temp_c'] * 9 / 5 + 32
-        >>> df.assign(temp_f=newcol)
+        >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
                   temp_c  temp_f
         Portland    17.0    62.6
         Berkeley    25.0    77.0
@@ -3274,7 +3274,7 @@ def assign(self, **kwargs):
         where one of the columns depends on another one defined within the same
         assign:
         >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
-                        temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
+        ...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
                   temp_c  temp_f  temp_k
         Portland    17.0    62.6  290.15
         Berkeley    25.0    77.0  298.15