BUG: Require sample weights to sum to less than 1 when replace = True

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

@@ -891,6 +891,7 @@ 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.

pandas/core/sample.py

@@ -150,6 +150,14 @@ def sample(
         else:
             raise ValueError("Invalid weights: weights sum to zero")
 
+    if weights is not None:
+        is_max_weight_dominating = size * weights.max() > 1
+        if is_max_weight_dominating and not replace:
+            raise ValueError(
+                "Invalid weights: If `replace`=False, "
+                "total unit probabilities have to be less than 1"
+            )
+
     return random_state.choice(obj_len, size=size, replace=replace, p=weights).astype(
         np.intp, copy=False
     )

pandas/tests/frame/methods/test_sample.py

@@ -113,9 +113,6 @@ def test_sample_invalid_weight_lengths(self, obj):
         with pytest.raises(ValueError, match=msg):
             obj.sample(n=3, weights=[0.5] * 11)
 
-        with pytest.raises(ValueError, match="Fewer non-zero entries in p than size"):
-            obj.sample(n=4, weights=Series([0, 0, 0.2]))
-
     def test_sample_negative_weights(self, obj):
         # Check won't accept negative weights
         bad_weights = [-0.1] * 10
@@ -137,6 +134,28 @@ def test_sample_inf_weights(self, obj):
         with pytest.raises(ValueError, match=msg):
             obj.sample(n=3, weights=weights_with_ninf)
 
+    def test_sample_unit_probabilities_raises(self, obj):
+        # GH#61516
+        high_variance_weights = [1] * 10
+        high_variance_weights[0] = 100
+        msg = (
+            "Invalid weights: If `replace`=False, "
+            "total unit probabilities have to be less than 1"
+        )
+        with pytest.raises(ValueError, match=msg):
+            obj.sample(n=2, weights=high_variance_weights, replace=False)
+
+        # edge case, n*max(weights)/sum(weights) == 1
+        edge_variance_weights = [1] * 10
+        edge_variance_weights[0] = 9
+        # should not raise
+        obj.sample(n=2, weights=edge_variance_weights, replace=False)
+
+        low_variance_weights = [1] * 10
+        low_variance_weights[0] = 8
+        # should not raise
+        obj.sample(n=2, weights=low_variance_weights, replace=False)
+
     def test_sample_zero_weights(self, obj):
         # All zeros raises errors