doc: minior adjustments

Author: microslaw

doc/source/whatsnew/v0.16.1.rst

@@ -209,8 +209,8 @@ when sampling from rows.
 
 .. ipython:: python
 
-      df = pd.DataFrame({"col1": [9, 8, 7, 6], "weight_column": [0.5, 0.4, 0.1, 0]})
-      df.sample(n=2, weights="weight_column")
+   df = pd.DataFrame({"col1": [9, 8, 7, 6], "weight_column": [0.5, 0.4, 0.1, 0]})
+   df.sample(n=2, weights="weight_column")
 
 
 .. _whatsnew_0161.enhancements.string:

pandas/core/generic.py

@@ -5814,7 +5814,7 @@ def sample(
             If weights do not sum to 1, they will be normalized to sum to 1.
             Missing values in the weights column will be treated as zero.
             Infinite values not allowed.
-            When replace = False will not allow ``(n * max(weights) / sum(weights)) > 1``,
+            When replace = False will not allow ``(n * max(weights) / sum(weights)) > 1``
             in order to avoid biased results. See the Notes below for more details.
         random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional
             If int, array-like, or BitGenerator, seed for random number generator.
@@ -5853,8 +5853,9 @@ def sample(
         If `frac` > 1, `replacement` should be set to `True`.
 
         When replace = False will not allow ``(n * max(weights) / sum(weights)) > 1``,
-        since that would cause results to be biased. E.g. sampling 2 items without replacement,
-        with weights [100, 1, 1] would yield two last items in 1/2 of cases, instead of 1/102
+        since that would cause results to be biased. E.g. sampling 2 items without replacement
+        with weights [100, 1, 1] would yield two last items in 1/2 of cases, instead of 1/102.
+        This is similar to specifying `n=4` without replacement on a Series with 3 elements.
 
         Examples
         --------