Merge remote-tracking branch 'upstream/master' into css_multiple_selectors

Author: attack68

.github/workflows/ci.yml

@@ -154,6 +154,7 @@ jobs:
         source activate pandas-dev
         pytest pandas/tests/frame/methods --array-manager
         pytest pandas/tests/arithmetic/ --array-manager
+        pytest pandas/tests/reshape/merge --array-manager
 
         # indexing subset (temporary since other tests don't pass yet)
         pytest pandas/tests/frame/indexing/test_indexing.py::TestDataFrameIndexing::test_setitem_boolean --array-manager

Makefile

@@ -25,16 +25,3 @@ doc:
 	cd doc; \
 	python make.py clean; \
 	python make.py html
-
-check:
-	python3 scripts/validate_unwanted_patterns.py \
-		--validation-type="private_function_across_module" \
-		--included-file-extensions="py" \
-		--excluded-file-paths=pandas/tests,asv_bench/ \
-		pandas/
-
-	python3 scripts/validate_unwanted_patterns.py \
-		--validation-type="private_import_across_module" \
-		--included-file-extensions="py" \
-		--excluded-file-paths=pandas/tests,asv_bench/,doc/
-		pandas/

asv_bench/benchmarks/algos/__init__.py

@@ -0,0 +1,12 @@
+"""
+algos/ directory is intended for individual functions from core.algorithms
+
+In many cases these algorithms are reachable in multiple ways:
+   algos.foo(x, y)
+   Series(x).foo(y)
+   Index(x).foo(y)
+   pd.array(x).foo(y)
+
+In most cases we profile the Series variant directly, trusting the performance
+of the others to be highly correlated.
+"""

asv_bench/benchmarks/algos/isin.py

@@ -0,0 +1,317 @@
+import numpy as np
+
+from pandas.compat.numpy import np_version_under1p20
+
+from pandas import (
+    Categorical,
+    NaT,
+    Series,
+    date_range,
+)
+
+
+class IsIn:
+
+    params = [
+        "int64",
+        "uint64",
+        "object",
+        "Int64",
+        "boolean",
+        "bool",
+        "datetime64[ns]",
+        "category[object]",
+        "category[int]",
+    ]
+    param_names = ["dtype"]
+
+    def setup(self, dtype):
+        N = 10000
+
+        self.mismatched = [NaT.to_datetime64()] * 2
+
+        if dtype in ["boolean", "bool"]:
+            self.series = Series(np.random.randint(0, 2, N)).astype(dtype)
+            self.values = [True, False]
+
+        elif dtype == "datetime64[ns]":
+            # Note: values here is much larger than non-dt64ns cases
+
+            # dti has length=115777
+            dti = date_range(start="2015-10-26", end="2016-01-01", freq="50s")
+            self.series = Series(dti)
+            self.values = self.series._values[::3]
+            self.mismatched = [1, 2]
+
+        elif dtype in ["category[object]", "category[int]"]:
+            # Note: sizes are different in this case than others
+            np.random.seed(1234)
+
+            n = 5 * 10 ** 5
+            sample_size = 100
+
+            arr = list(np.random.randint(0, n // 10, size=n))
+            if dtype == "category[object]":
+                arr = [f"s{i:04d}" for i in arr]
+
+            self.values = np.random.choice(arr, sample_size)
+            self.series = Series(arr).astype("category")
+
+        else:
+            self.series = Series(np.random.randint(1, 10, N)).astype(dtype)
+            self.values = [1, 2]
+
+        self.cat_values = Categorical(self.values)
+
+    def time_isin(self, dtype):
+        self.series.isin(self.values)
+
+    def time_isin_categorical(self, dtype):
+        self.series.isin(self.cat_values)
+
+    def time_isin_empty(self, dtype):
+        self.series.isin([])
+
+    def time_isin_mismatched_dtype(self, dtype):
+        self.series.isin(self.mismatched)
+
+
+class IsinAlmostFullWithRandomInt:
+    params = [
+        [np.float64, np.int64, np.uint64, np.object_],
+        range(10, 21),
+        ["inside", "outside"],
+    ]
+    param_names = ["dtype", "exponent", "title"]
+
+    def setup(self, dtype, exponent, title):
+        M = 3 * 2 ** (exponent - 2)
+        # 0.77-the maximal share of occupied buckets
+        np.random.seed(42)
+        self.series = Series(np.random.randint(0, M, M)).astype(dtype)
+
+        values = np.random.randint(0, M, M).astype(dtype)
+        if title == "inside":
+            self.values = values
+        elif title == "outside":
+            self.values = values + M
+        else:
+            raise ValueError(title)
+
+    def time_isin(self, dtype, exponent, title):
+        self.series.isin(self.values)
+
+
+class IsinWithRandomFloat:
+    params = [
+        [np.float64, np.object],
+        [
+            1_300,
+            2_000,
+            7_000,
+            8_000,
+            70_000,
+            80_000,
+            750_000,
+            900_000,
+        ],
+        ["inside", "outside"],
+    ]
+    param_names = ["dtype", "size", "title"]
+
+    def setup(self, dtype, size, title):
+        np.random.seed(42)
+        self.values = np.random.rand(size)
+        self.series = Series(self.values).astype(dtype)
+        np.random.shuffle(self.values)
+
+        if title == "outside":
+            self.values = self.values + 0.1
+
+    def time_isin(self, dtype, size, title):
+        self.series.isin(self.values)
+
+
+class IsinWithArangeSorted:
+    params = [
+        [np.float64, np.int64, np.uint64, np.object],
+        [
+            1_000,
+            2_000,
+            8_000,
+            100_000,
+            1_000_000,
+        ],
+    ]
+    param_names = ["dtype", "size"]
+
+    def setup(self, dtype, size):
+        self.series = Series(np.arange(size)).astype(dtype)
+        self.values = np.arange(size).astype(dtype)
+
+    def time_isin(self, dtype, size):
+        self.series.isin(self.values)
+
+
+class IsinWithArange:
+    params = [
+        [np.float64, np.int64, np.uint64, np.object],
+        [
+            1_000,
+            2_000,
+            8_000,
+        ],
+        [-2, 0, 2],
+    ]
+    param_names = ["dtype", "M", "offset_factor"]
+
+    def setup(self, dtype, M, offset_factor):
+        offset = int(M * offset_factor)
+        np.random.seed(42)
+        tmp = Series(np.random.randint(offset, M + offset, 10 ** 6))
+        self.series = tmp.astype(dtype)
+        self.values = np.arange(M).astype(dtype)
+
+    def time_isin(self, dtype, M, offset_factor):
+        self.series.isin(self.values)
+
+
+class IsInFloat64:
+
+    params = [
+        [np.float64, "Float64"],
+        ["many_different_values", "few_different_values", "only_nans_values"],
+    ]
+    param_names = ["dtype", "title"]
+
+    def setup(self, dtype, title):
+        N_many = 10 ** 5
+        N_few = 10 ** 6
+        self.series = Series([1, 2], dtype=dtype)
+
+        if title == "many_different_values":
+            # runtime is dominated by creation of the lookup-table
+            self.values = np.arange(N_many, dtype=np.float64)
+        elif title == "few_different_values":
+            # runtime is dominated by creation of the lookup-table
+            self.values = np.zeros(N_few, dtype=np.float64)
+        elif title == "only_nans_values":
+            # runtime is dominated by creation of the lookup-table
+            self.values = np.full(N_few, np.nan, dtype=np.float64)
+        else:
+            raise ValueError(title)
+
+    def time_isin(self, dtype, title):
+        self.series.isin(self.values)
+
+
+class IsInForObjects:
+    """
+    A subset of the cartesian product of cases have special motivations:
+
+    "nans" x "nans"
+        if nan-objects are different objects,
+        this has the potential to trigger O(n^2) running time
+
+    "short" x "long"
+        running time dominated by the preprocessing
+
+    "long" x "short"
+        running time dominated by look-up
+
+    "long" x "long"
+        no dominating part
+
+    "long_floats" x "long_floats"
+        because of nans floats are special
+        no dominating part
+
+    """
+
+    variants = ["nans", "short", "long", "long_floats"]
+
+    params = [variants, variants]
+    param_names = ["series_type", "vals_type"]
+
+    def setup(self, series_type, vals_type):
+        N_many = 10 ** 5
+
+        if series_type == "nans":
+            ser_vals = np.full(10 ** 4, np.nan)
+        elif series_type == "short":
+            ser_vals = np.arange(2)
+        elif series_type == "long":
+            ser_vals = np.arange(N_many)
+        elif series_type == "long_floats":
+            ser_vals = np.arange(N_many, dtype=np.float_)
+
+        self.series = Series(ser_vals).astype(object)
+
+        if vals_type == "nans":
+            values = np.full(10 ** 4, np.nan)
+        elif vals_type == "short":
+            values = np.arange(2)
+        elif vals_type == "long":
+            values = np.arange(N_many)
+        elif vals_type == "long_floats":
+            values = np.arange(N_many, dtype=np.float_)
+
+        self.values = values.astype(object)
+
+    def time_isin(self, series_type, vals_type):
+        self.series.isin(self.values)
+
+
+class IsInLongSeriesLookUpDominates:
+    params = [
+        ["int64", "int32", "float64", "float32", "object", "Int64", "Float64"],
+        [5, 1000],
+        ["random_hits", "random_misses", "monotone_hits", "monotone_misses"],
+    ]
+    param_names = ["dtype", "MaxNumber", "series_type"]
+
+    def setup(self, dtype, MaxNumber, series_type):
+        N = 10 ** 7
+
+        if not np_version_under1p20 and dtype in ("Int64", "Float64"):
+            raise NotImplementedError
+
+        if series_type == "random_hits":
+            np.random.seed(42)
+            array = np.random.randint(0, MaxNumber, N)
+        if series_type == "random_misses":
+            np.random.seed(42)
+            array = np.random.randint(0, MaxNumber, N) + MaxNumber
+        if series_type == "monotone_hits":
+            array = np.repeat(np.arange(MaxNumber), N // MaxNumber)
+        if series_type == "monotone_misses":
+            array = np.arange(N) + MaxNumber
+
+        self.series = Series(array).astype(dtype)
+        self.values = np.arange(MaxNumber).astype(dtype)
+
+    def time_isin(self, dtypes, MaxNumber, series_type):
+        self.series.isin(self.values)
+
+
+class IsInLongSeriesValuesDominate:
+    params = [
+        ["int64", "int32", "float64", "float32", "object", "Int64", "Float64"],
+        ["random", "monotone"],
+    ]
+    param_names = ["dtype", "series_type"]
+
+    def setup(self, dtype, series_type):
+        N = 10 ** 7
+        if series_type == "random":
+            np.random.seed(42)
+            vals = np.random.randint(0, 10 * N, N)
+        if series_type == "monotone":
+            vals = np.arange(N)
+
+        self.values = vals.astype(dtype)
+        M = 10 ** 6 + 1
+        self.series = Series(np.arange(M)).astype(dtype)
+
+    def time_isin(self, dtypes, series_type):
+        self.series.isin(self.values)

asv_bench/benchmarks/categoricals.py

@@ -220,25 +220,6 @@ def time_rank_int_cat_ordered(self):
         self.s_int_cat_ordered.rank()
 
 
-class Isin:
-
-    params = ["object", "int64"]
-    param_names = ["dtype"]
-
-    def setup(self, dtype):
-        np.random.seed(1234)
-        n = 5 * 10 ** 5
-        sample_size = 100
-        arr = list(np.random.randint(0, n // 10, size=n))
-        if dtype == "object":
-            arr = [f"s{i:04d}" for i in arr]
-        self.sample = np.random.choice(arr, sample_size)
-        self.series = pd.Series(arr).astype("category")
-
-    def time_isin_categorical(self, dtype):
-        self.series.isin(self.sample)
-
-
 class IsMonotonic:
     def setup(self):
         N = 1000