diff --git a/asv_bench/benchmarks/rolling.py b/asv_bench/benchmarks/rolling.py
index bd4da00bfd2ad..f9a5f38c2e349 100644
--- a/asv_bench/benchmarks/rolling.py
+++ b/asv_bench/benchmarks/rolling.py
@@ -10,7 +10,19 @@ class Methods:
["DataFrame", "Series"],
[("rolling", {"window": 10}), ("rolling", {"window": 1000}), ("expanding", {})],
["int", "float"],
- ["median", "mean", "max", "min", "std", "count", "skew", "kurt", "sum", "sem"],
+ [
+ "median",
+ "mean",
+ "max",
+ "min",
+ "std",
+ "count",
+ "skew",
+ "kurt",
+ "sum",
+ "sem",
+ "nunique",
+ ],
)
param_names = ["constructor", "window_kwargs", "dtype", "method"]
diff --git a/doc/source/reference/window.rst b/doc/source/reference/window.rst
index 2aeb57faac112..2bd63f02faf69 100644
--- a/doc/source/reference/window.rst
+++ b/doc/source/reference/window.rst
@@ -42,6 +42,7 @@ Rolling window functions
Rolling.quantile
Rolling.sem
Rolling.rank
+ Rolling.nunique
.. _api.functions_window:
@@ -86,6 +87,7 @@ Expanding window functions
Expanding.quantile
Expanding.sem
Expanding.rank
+ Expanding.nunique
.. _api.functions_ewm:
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index 9fab1d12fc6a5..99b5d4e0ac9eb 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -62,6 +62,7 @@ Other enhancements
- :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
- :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)
- :class:`DataFrameGroupBy` and :class:`SeriesGroupBy` methods ``sum``, ``mean``, ``median``, ``prod``, ``min``, ``max``, ``std``, ``var`` and ``sem`` now accept ``skipna`` parameter (:issue:`15675`)
+- :class:`Rolling` and :class:`Expanding` now support ``nunique`` (:issue:`26958`)
- :class:`Rolling` and :class:`Expanding` now support aggregations ``first`` and ``last`` (:issue:`33155`)
- :func:`read_parquet` accepts ``to_pandas_kwargs`` which are forwarded to :meth:`pyarrow.Table.to_pandas` which enables passing additional keywords to customize the conversion to pandas, such as ``maps_as_pydicts`` to read the Parquet map data type as python dictionaries (:issue:`56842`)
- :meth:`.DataFrameGroupBy.transform`, :meth:`.SeriesGroupBy.transform`, :meth:`.DataFrameGroupBy.agg`, :meth:`.SeriesGroupBy.agg`, :meth:`.SeriesGroupBy.apply`, :meth:`.DataFrameGroupBy.apply` now support ``kurt`` (:issue:`40139`)
diff --git a/pandas/_libs/window/aggregations.pyi b/pandas/_libs/window/aggregations.pyi
index ee735761e3dc6..b4bdd7e05cf0e 100644
--- a/pandas/_libs/window/aggregations.pyi
+++ b/pandas/_libs/window/aggregations.pyi
@@ -89,6 +89,12 @@ def roll_rank(
method: WindowingRankType,
ascending: bool,
) -> np.ndarray: ... # np.ndarray[float]
+def roll_nunique(
+ values: np.ndarray, # const float64_t[:]
+ start: np.ndarray, # np.ndarray[np.int64]
+ end: np.ndarray, # np.ndarray[np.int64]
+ minp: int, # int64_t
+) -> np.ndarray: ... # np.ndarray[float]
def roll_apply(
obj: object,
start: np.ndarray, # np.ndarray[np.int64]
diff --git a/pandas/_libs/window/aggregations.pyx b/pandas/_libs/window/aggregations.pyx
index d33c840371d2a..2baed13cbd7be 100644
--- a/pandas/_libs/window/aggregations.pyx
+++ b/pandas/_libs/window/aggregations.pyx
@@ -6,6 +6,7 @@ from libc.math cimport (
sqrt,
)
from libcpp.deque cimport deque
+from libcpp.unordered_map cimport unordered_map
from pandas._libs.algos cimport TiebreakEnumType
@@ -1470,6 +1471,66 @@ def roll_rank(const float64_t[:] values, ndarray[int64_t] start,
return np.asarray(output)
+def roll_nunique(const float64_t[:] values, ndarray[int64_t] start,
+ ndarray[int64_t] end, int64_t minp) -> np.ndarray:
+ """
+ Rolling number of unique elements in the window
+ """
+ cdef:
+ Py_ssize_t i, j, s, e, N = len(start)
+ int64_t nobs = 0
+ float64_t val
+ float64_t[::1] output
+ unordered_map[float64_t, int64_t] value_counts
+
+ is_monotonic_increasing_bounds = is_monotonic_increasing_start_end_bounds(
+ start, end
+ )
+ output = np.empty(N, dtype=np.float64)
+ value_counts = unordered_map[float64_t, int64_t]()
+
+ with nogil:
+ for i in range(N):
+ s = start[i]
+ e = end[i]
+
+ if i == 0 or not is_monotonic_increasing_bounds or s >= end[i - 1]:
+ if i != 0:
+ nobs = 0
+ value_counts.clear()
+
+ # setup
+ for j in range(s, e):
+ val = values[j]
+ if val == val:
+ nobs += 1
+ value_counts[val] += 1
+
+ else:
+ # calculate deletes
+ for j in range(start[i - 1], s):
+ val = values[j]
+ if val == val:
+ value_counts[val] -= 1
+ if value_counts[val] == 0:
+ value_counts.erase(val)
+ nobs -= 1
+
+ # calculate adds
+ for j in range(end[i - 1], e):
+ val = values[j]
+ if val == val:
+ nobs += 1
+ value_counts[val] += 1
+
+ if nobs >= minp:
+ output[i] = value_counts.size()
+ else:
+ output[i] = NaN
+
+ return np.asarray(output)
+
+
def roll_apply(object obj,
ndarray[int64_t] start, ndarray[int64_t] end,
int64_t minp,
diff --git a/pandas/core/window/expanding.py b/pandas/core/window/expanding.py
index 81c89e1ef5428..bff3485c9cb86 100644
--- a/pandas/core/window/expanding.py
+++ b/pandas/core/window/expanding.py
@@ -927,6 +927,41 @@ def rank(
numeric_only=numeric_only,
)
+ @doc(
+ template_header,
+ ".. versionadded:: 3.0.0 \n\n",
+ create_section_header("Parameters"),
+ kwargs_numeric_only,
+ create_section_header("Returns"),
+ template_returns,
+ create_section_header("See Also"),
+ template_see_also,
+ create_section_header("Examples"),
+ dedent(
+ """
+ >>> s = pd.Series([1, 4, 2, 3, 5, 3])
+ >>> s.expanding().nunique()
+ 0 1.0
+ 1 2.0
+ 2 3.0
+ 3 4.0
+ 4 5.0
+ 5 5.0
+ dtype: float64
+ """
+ ).replace("\n", "", 1),
+ window_method="expanding",
+ aggregation_description="nunique",
+ agg_method="nunique",
+ )
+ def nunique(
+ self,
+ numeric_only: bool = False,
+ ):
+ return super().nunique(
+ numeric_only=numeric_only,
+ )
+
@doc(
template_header,
create_section_header("Parameters"),
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index 69fce8cf2137e..03534bbee4c58 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -1799,6 +1799,16 @@ def rank(
return self._apply(window_func, name="rank", numeric_only=numeric_only)
+ def nunique(
+ self,
+ numeric_only: bool = False,
+ ):
+ window_func = partial(
+ window_aggregations.roll_nunique,
+ )
+
+ return self._apply(window_func, name="nunique", numeric_only=numeric_only)
+
def cov(
self,
other: DataFrame | Series | None = None,
@@ -2855,6 +2865,43 @@ def rank(
numeric_only=numeric_only,
)
+ @doc(
+ template_header,
+ ".. versionadded:: 3.0.0 \n\n",
+ create_section_header("Parameters"),
+ kwargs_numeric_only,
+ create_section_header("Returns"),
+ template_returns,
+ create_section_header("See Also"),
+ template_see_also,
+ create_section_header("Examples"),
+ dedent(
+ """
+ >>> s = pd.Series([1, 4, 2, np.nan, 3, 3, 4, 5])
+ >>> s.rolling(3).nunique()
+ 0 NaN
+ 1 NaN
+ 2 3.0
+ 3 NaN
+ 4 NaN
+ 5 NaN
+ 6 2.0
+ 7 3.0
+ dtype: float64
+ """
+ ).replace("\n", "", 1),
+ window_method="rolling",
+ aggregation_description="nunique",
+ agg_method="nunique",
+ )
+ def nunique(
+ self,
+ numeric_only: bool = False,
+ ):
+ return super().nunique(
+ numeric_only=numeric_only,
+ )
+
@doc(
template_header,
create_section_header("Parameters"),
diff --git a/pandas/tests/window/test_cython_aggregations.py b/pandas/tests/window/test_cython_aggregations.py
index feb25a294c540..39811ea3ec5b9 100644
--- a/pandas/tests/window/test_cython_aggregations.py
+++ b/pandas/tests/window/test_cython_aggregations.py
@@ -32,6 +32,7 @@ def _get_rolling_aggregations():
("roll_min", window_aggregations.roll_min),
("roll_first", window_aggregations.roll_first),
("roll_last", window_aggregations.roll_last),
+ ("roll_nunique", window_aggregations.roll_nunique),
]
+ [
(
diff --git a/pandas/tests/window/test_expanding.py b/pandas/tests/window/test_expanding.py
index 39cedc3b692da..2c96ce01c6328 100644
--- a/pandas/tests/window/test_expanding.py
+++ b/pandas/tests/window/test_expanding.py
@@ -255,6 +255,43 @@ def test_rank(window, method, pct, ascending, test_data):
tm.assert_series_equal(result, expected)
+@pytest.mark.parametrize("window", [1, 3, 10, 20])
+@pytest.mark.parametrize("test_data", ["default", "duplicates", "nans", "precision"])
+def test_nunique(window, test_data):
+ length = 20
+ if test_data == "default":
+ ser = Series(data=np.random.default_rng(2).random(length))
+ elif test_data == "duplicates":
+ ser = Series(data=np.random.default_rng(2).choice(3, length))
+ elif test_data == "nans":
+ ser = Series(
+ data=np.random.default_rng(2).choice(
+ [1.0, 0.25, 0.75, np.nan, np.inf, -np.inf], length
+ )
+ )
+ elif test_data == "precision":
+ ser = Series(
+ data=[
+ 0.3,
+ 0.1 * 3, # Not necessarily exactly 0.3
+ 0.6,
+ 0.2 * 3, # Not necessarily exactly 0.6
+ 0.9,
+ 0.3 * 3, # Not necessarily exactly 0.9
+ 0.5,
+ 0.1 * 5, # Not necessarily exactly 0.5
+ 0.8,
+ 0.2 * 4, # Not necessarily exactly 0.8
+ ],
+ dtype=np.float64,
+ )
+
+ expected = ser.expanding(window).apply(lambda x: x.nunique())
+ result = ser.expanding(window).nunique()
+
+ tm.assert_series_equal(result, expected)
+
+
def test_expanding_corr(series):
A = series.dropna()
B = (A + np.random.default_rng(2).standard_normal(len(A)))[:-5]
diff --git a/pandas/tests/window/test_groupby.py b/pandas/tests/window/test_groupby.py
index 392239b8adadd..1dcdad2bfd73d 100644
--- a/pandas/tests/window/test_groupby.py
+++ b/pandas/tests/window/test_groupby.py
@@ -96,6 +96,7 @@ def test_getitem_multiple(self, roll_frame):
"count",
"kurt",
"skew",
+ "nunique",
],
)
def test_rolling(self, f, roll_frame):
@@ -1034,7 +1035,19 @@ def frame(self):
return DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
@pytest.mark.parametrize(
- "f", ["sum", "mean", "min", "max", "first", "last", "count", "kurt", "skew"]
+ "f",
+ [
+ "sum",
+ "mean",
+ "min",
+ "max",
+ "first",
+ "last",
+ "count",
+ "kurt",
+ "skew",
+ "nunique",
+ ],
)
def test_expanding(self, f, frame):
g = frame.groupby("A", group_keys=False)
diff --git a/pandas/tests/window/test_rolling.py b/pandas/tests/window/test_rolling.py
index 2aaa35ec5ec2c..8c57781c1447c 100644
--- a/pandas/tests/window/test_rolling.py
+++ b/pandas/tests/window/test_rolling.py
@@ -1586,6 +1586,43 @@ def test_rank(window, method, pct, ascending, test_data):
tm.assert_series_equal(result, expected)
+@pytest.mark.parametrize("window", [1, 3, 10, 20])
+@pytest.mark.parametrize("test_data", ["default", "duplicates", "nans", "precision"])
+def test_nunique(window, test_data):
+ length = 20
+ if test_data == "default":
+ ser = Series(data=np.random.default_rng(2).random(length))
+ elif test_data == "duplicates":
+ ser = Series(data=np.random.default_rng(2).choice(3, length))
+ elif test_data == "nans":
+ ser = Series(
+ data=np.random.default_rng(2).choice(
+ [1.0, 0.25, 0.75, np.nan, np.inf, -np.inf], length
+ )
+ )
+ elif test_data == "precision":
+ ser = Series(
+ data=[
+ 0.3,
+ 0.1 * 3, # Not necessarily exactly 0.3
+ 0.6,
+ 0.2 * 3, # Not necessarily exactly 0.6
+ 0.9,
+ 0.3 * 3, # Not necessarily exactly 0.9
+ 0.5,
+ 0.1 * 5, # Not necessarily exactly 0.5
+ 0.8,
+ 0.2 * 4, # Not necessarily exactly 0.8
+ ],
+ dtype=np.float64,
+ )
+
+ expected = ser.rolling(window).apply(lambda x: x.nunique())
+ result = ser.rolling(window).nunique()
+
+ tm.assert_series_equal(result, expected)
+
+
def test_rolling_quantile_np_percentile():
# #9413: Tests that rolling window's quantile default behavior
# is analogous to Numpy's percentile