DOC: Updated the DataFrame.assign docstring

Author: aeltanawy

pandas/core/frame.py

@@ -3354,38 +3354,39 @@ def assign(self, **kwargs):
 
         Examples
         --------
-        >>> df = pd.DataFrame({'A': range(1, 11), 'B': np.random.randn(10)})
+        >>> df = pd.DataFrame({'A': range(1, 11),
+        ...                    'B':np.arange(-1.0, 2.0, 0.3)})
 
         Where the value is a callable, evaluated on `df`:
 
         >>> df.assign(ln_A = lambda x: np.log(x.A))
-            A         B      ln_A
-        0   1  0.426905  0.000000
-        1   2 -0.780949  0.693147
-        2   3 -0.418711  1.098612
-        3   4 -0.269708  1.386294
-        4   5 -0.274002  1.609438
-        5   6 -0.500792  1.791759
-        6   7  1.649697  1.945910
-        7   8 -1.495604  2.079442
-        8   9  0.549296  2.197225
-        9  10 -0.758542  2.302585
+            A    B      ln_A
+        0   1 -1.0  0.000000
+        1   2 -0.7  0.693147
+        2   3 -0.4  1.098612
+        3   4 -0.1  1.386294
+        4   5  0.2  1.609438
+        5   6  0.5  1.791759
+        6   7  0.8  1.945910
+        7   8  1.1  2.079442
+        8   9  1.4  2.197225
+        9  10  1.7  2.302585
 
         Where the value already exists and is inserted:
 
         >>> newcol = np.log(df['A'])
         >>> df.assign(ln_A=newcol)
-            A         B      ln_A
-        0   1  0.426905  0.000000
-        1   2 -0.780949  0.693147
-        2   3 -0.418711  1.098612
-        3   4 -0.269708  1.386294
-        4   5 -0.274002  1.609438
-        5   6 -0.500792  1.791759
-        6   7  1.649697  1.945910
-        7   8 -1.495604  2.079442
-        8   9  0.549296  2.197225
-        9  10 -0.758542  2.302585
+            A    B      ln_A
+        0   1 -1.0  0.000000
+        1   2 -0.7  0.693147
+        2   3 -0.4  1.098612
+        3   4 -0.1  1.386294
+        4   5  0.2  1.609438
+        5   6  0.5  1.791759
+        6   7  0.8  1.945910
+        7   8  1.1  2.079442
+        8   9  1.4  2.197225
+        9  10  1.7  2.302585
 
         Where the keyword arguments depend on each other