Merge branch 'main' into arrow-to-csv

Author: lithomas1

.github/workflows/unit-tests.yml

@@ -73,6 +73,14 @@ jobs:
             env_file: actions-311.yaml
             pattern: "not slow and not network and not single_cpu"
             pandas_copy_on_write: "warn"
+          - name: "Copy-on-Write 3.10 (warnings)"
+            env_file: actions-310.yaml
+            pattern: "not slow and not network and not single_cpu"
+            pandas_copy_on_write: "warn"
+          - name: "Copy-on-Write 3.9 (warnings)"
+            env_file: actions-39.yaml
+            pattern: "not slow and not network and not single_cpu"
+            pandas_copy_on_write: "warn"
           - name: "Pypy"
             env_file: actions-pypy-39.yaml
             pattern: "not slow and not network and not single_cpu"

.pre-commit-config.yaml

@@ -240,8 +240,9 @@ repos:
             # pytest raises without context
             |\s\ pytest.raises
 
+            # TODO
             # pytest.warns (use tm.assert_produces_warning instead)
-            |pytest\.warns
+            # |pytest\.warns
 
             # os.remove
             |os\.remove

asv_bench/benchmarks/algorithms.py

@@ -4,8 +4,6 @@
 
 import pandas as pd
 
-from .pandas_vb_common import tm
-
 for imp in ["pandas.util", "pandas.tools.hashing"]:
     try:
         hashing = import_module(imp)
@@ -47,9 +45,12 @@ def setup(self, unique, sort, dtype):
         elif dtype == "datetime64[ns, tz]":
             data = pd.date_range("2011-01-01", freq="h", periods=N, tz="Asia/Tokyo")
         elif dtype == "object_str":
-            data = tm.makeStringIndex(N)
+            data = pd.Index([f"i-{i}" for i in range(N)], dtype=object)
         elif dtype == "string[pyarrow]":
-            data = pd.array(tm.makeStringIndex(N), dtype="string[pyarrow]")
+            data = pd.array(
+                pd.Index([f"i-{i}" for i in range(N)], dtype=object),
+                dtype="string[pyarrow]",
+            )
         else:
             raise NotImplementedError
 
@@ -88,7 +89,7 @@ def setup(self, unique, keep, dtype):
         elif dtype == "float64":
             data = pd.Index(np.random.randn(N), dtype="float64")
         elif dtype == "string":
-            data = tm.makeStringIndex(N)
+            data = pd.Index([f"i-{i}" for i in range(N)], dtype=object)
         elif dtype == "datetime64[ns]":
             data = pd.date_range("2011-01-01", freq="h", periods=N)
         elif dtype == "datetime64[ns, tz]":
@@ -136,7 +137,9 @@ def setup_cache(self):
         df = pd.DataFrame(
             {
                 "strings": pd.Series(
-                    tm.makeStringIndex(10000).take(np.random.randint(0, 10000, size=N))
+                    pd.Index([f"i-{i}" for i in range(10000)], dtype=object).take(
+                        np.random.randint(0, 10000, size=N)
+                    )
                 ),
                 "floats": np.random.randn(N),
                 "ints": np.arange(N),

asv_bench/benchmarks/algos/isin.py

@@ -8,8 +8,6 @@
     date_range,
 )
 
-from ..pandas_vb_common import tm
-
 
 class IsIn:
     params = [
@@ -60,7 +58,9 @@ def setup(self, dtype):
 
         elif dtype in ["str", "string[python]", "string[pyarrow]"]:
             try:
-                self.series = Series(tm.makeStringIndex(N), dtype=dtype)
+                self.series = Series(
+                    Index([f"i-{i}" for i in range(N)], dtype=object), dtype=dtype
+                )
             except ImportError:
                 raise NotImplementedError
             self.values = list(self.series[:2])

asv_bench/benchmarks/categoricals.py

@@ -6,8 +6,6 @@
 
 import pandas as pd
 
-from .pandas_vb_common import tm
-
 try:
     from pandas.api.types import union_categoricals
 except ImportError:
@@ -189,7 +187,7 @@ def setup(self):
         N = 10**5
         ncats = 15
 
-        self.s_str = pd.Series(tm.makeCategoricalIndex(N, ncats)).astype(str)
+        self.s_str = pd.Series(np.random.randint(0, ncats, size=N).astype(str))
         self.s_str_cat = pd.Series(self.s_str, dtype="category")
         with warnings.catch_warnings(record=True):
             str_cat_type = pd.CategoricalDtype(set(self.s_str), ordered=True)
@@ -242,7 +240,7 @@ def time_categorical_series_is_monotonic_decreasing(self):
 class Contains:
     def setup(self):
         N = 10**5
-        self.ci = tm.makeCategoricalIndex(N)
+        self.ci = pd.CategoricalIndex(np.arange(N))
         self.c = self.ci.values
         self.key = self.ci.categories[0]
 
@@ -325,7 +323,7 @@ def time_sort_values(self):
 class SearchSorted:
     def setup(self):
         N = 10**5
-        self.ci = tm.makeCategoricalIndex(N).sort_values()
+        self.ci = pd.CategoricalIndex(np.arange(N)).sort_values()
         self.c = self.ci.values
         self.key = self.ci.categories[1]
 

asv_bench/benchmarks/ctors.py

@@ -9,8 +9,6 @@
     date_range,
 )
 
-from .pandas_vb_common import tm
-
 
 def no_change(arr):
     return arr
@@ -115,7 +113,7 @@ def time_dtindex_from_index_with_series(self):
 class MultiIndexConstructor:
     def setup(self):
         N = 10**4
-        self.iterables = [tm.makeStringIndex(N), range(20)]
+        self.iterables = [Index([f"i-{i}" for i in range(N)], dtype=object), range(20)]
 
     def time_multiindex_from_iterables(self):
         MultiIndex.from_product(self.iterables)

asv_bench/benchmarks/dtypes.py

@@ -3,7 +3,10 @@
 import numpy as np
 
 import pandas as pd
-from pandas import DataFrame
+from pandas import (
+    DataFrame,
+    Index,
+)
 import pandas._testing as tm
 from pandas.api.types import (
     is_extension_array_dtype,
@@ -73,8 +76,8 @@ class SelectDtypes:
 
     def setup(self, dtype):
         N, K = 5000, 50
-        self.index = tm.makeStringIndex(N)
-        self.columns = tm.makeStringIndex(K)
+        self.index = Index([f"i-{i}" for i in range(N)], dtype=object)
+        self.columns = Index([f"i-{i}" for i in range(K)], dtype=object)
 
         def create_df(data):
             return DataFrame(data, index=self.index, columns=self.columns)

asv_bench/benchmarks/frame_ctor.py

@@ -12,8 +12,6 @@
     date_range,
 )
 
-from .pandas_vb_common import tm
-
 try:
     from pandas.tseries.offsets import (
         Hour,
@@ -30,8 +28,8 @@
 class FromDicts:
     def setup(self):
         N, K = 5000, 50
-        self.index = tm.makeStringIndex(N)
-        self.columns = tm.makeStringIndex(K)
+        self.index = pd.Index([f"i-{i}" for i in range(N)], dtype=object)
+        self.columns = pd.Index([f"i-{i}" for i in range(K)], dtype=object)
         frame = DataFrame(np.random.randn(N, K), index=self.index, columns=self.columns)
         self.data = frame.to_dict()
         self.dict_list = frame.to_dict(orient="records")

asv_bench/benchmarks/frame_methods.py

@@ -5,6 +5,7 @@
 
 from pandas import (
     DataFrame,
+    Index,
     MultiIndex,
     NaT,
     Series,
@@ -14,8 +15,6 @@
     timedelta_range,
 )
 
-from .pandas_vb_common import tm
-
 
 class AsType:
     params = [
@@ -703,8 +702,12 @@ def setup(self, monotonic):
         K = 10
         df = DataFrame(
             {
-                "key1": tm.makeStringIndex(N).values.repeat(K),
-                "key2": tm.makeStringIndex(N).values.repeat(K),
+                "key1": Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(
+                    K
+                ),
+                "key2": Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(
+                    K
+                ),
                 "value": np.random.randn(N * K),
             }
         )

asv_bench/benchmarks/gil.py

@@ -5,15 +5,14 @@
 
 from pandas import (
     DataFrame,
+    Index,
     Series,
     date_range,
     factorize,
     read_csv,
 )
 from pandas.core.algorithms import take_nd
 
-from .pandas_vb_common import tm
-
 try:
     from pandas import (
         rolling_kurt,
@@ -34,7 +33,6 @@
 except ImportError:
     from pandas import algos
 
-
 from .pandas_vb_common import BaseIO  # isort:skip
 
 
@@ -305,7 +303,7 @@ class ParallelFactorize:
     param_names = ["threads"]
 
     def setup(self, threads):
-        strings = tm.makeStringIndex(100000)
+        strings = Index([f"i-{i}" for i in range(100000)], dtype=object)
 
         @test_parallel(num_threads=threads)
         def parallel():