update documentation

Author: microslaw

doc/source/user_guide/indexing.rst

@@ -700,7 +700,7 @@ to have different probabilities, you can pass the ``sample`` function sampling w
 
     s = pd.Series([0, 1, 2, 3, 4, 5])
     example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4]
-    s.sample(n=3, weights=example_weights)
+    s.sample(n=2, weights=example_weights)
 
     # Weights will be re-normalized automatically
     example_weights2 = [0.5, 0, 0, 0, 0, 0]
@@ -714,7 +714,7 @@ as a string.
 
     df2 = pd.DataFrame({'col1': [9, 8, 7, 6],
                         'weight_column': [0.5, 0.4, 0.1, 0]})
-    df2.sample(n=3, weights='weight_column')
+    df2.sample(n=2, weights='weight_column')
 
 ``sample`` also allows users to sample columns instead of rows using the ``axis`` argument.
 

doc/source/whatsnew/v0.16.1.rst

@@ -196,7 +196,7 @@ facilitate replication. (:issue:`2419`)
 
    # weights are accepted.
    example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4]
-   example_series.sample(n=3, weights=example_weights)
+   example_series.sample(n=2, weights=example_weights)
 
    # weights will also be normalized if they do not sum to one,
    # and missing values will be treated as zeros.
@@ -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=3, 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:

doc/source/whatsnew/v3.0.0.rst

@@ -890,6 +890,7 @@ Other
 - Bug in :meth:`DataFrame.query` where using duplicate column names led to a ``TypeError``. (:issue:`59950`)
 - Bug in :meth:`DataFrame.query` which raised an exception or produced incorrect results when expressions contained backtick-quoted column names containing the hash character ``#``, backticks, or characters that fall outside the ASCII range (U+0001..U+007F). (:issue:`59285`) (:issue:`49633`)
 - Bug in :meth:`DataFrame.query` which raised an exception when querying integer column names using backticks. (:issue:`60494`)
+- Bug in :meth:`DataFrame.sample` with ``replace=False`` and ``(n * max(weights) / sum(weights)) > 1``, the method would return biased results. Now raises ``ValueError``. (:issue:`61516`)
 - Bug in :meth:`DataFrame.shift` where passing a ``freq`` on a DataFrame with no columns did not shift the index correctly. (:issue:`60102`)
 - Bug in :meth:`DataFrame.sort_index` when passing ``axis="columns"`` and ``ignore_index=True`` and ``ascending=False`` not returning a :class:`RangeIndex` columns (:issue:`57293`)
 - Bug in :meth:`DataFrame.sort_values` where sorting by a column explicitly named ``None`` raised a ``KeyError`` instead of sorting by the column as expected. (:issue:`61512`)

pandas/core/generic.py

@@ -5815,6 +5815,8 @@ 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``,
+            in order to avoid biased results.
         random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional
             If int, array-like, or BitGenerator, seed for random number generator.
             If np.random.RandomState or np.random.Generator, use as given.