CLN: pd. namespace in tests/window (#38095)

Co-authored-by: Matt Roeschke <[email protected]>

Author: mroeschke

pandas/tests/window/moments/test_moments_consistency_rolling.py

@@ -5,8 +5,7 @@
 
 import pandas.util._test_decorators as td
 
-import pandas as pd
-from pandas import DataFrame, DatetimeIndex, Index, Series
+from pandas import DataFrame, DatetimeIndex, Index, MultiIndex, Series
 import pandas._testing as tm
 from pandas.core.window.common import flex_binary_moment
 
@@ -240,7 +239,7 @@ def test_rolling_functions_window_non_shrinkage_binary(f):
     )
     df_expected = DataFrame(
         columns=Index(["A", "B"], name="foo"),
-        index=pd.MultiIndex.from_product([df.index, df.columns], names=["bar", "foo"]),
+        index=MultiIndex.from_product([df.index, df.columns], names=["bar", "foo"]),
         dtype="float64",
     )
     df_result = f(df)
@@ -482,12 +481,10 @@ def test_moment_functions_zero_length_pairwise(f):
     df2["a"] = df2["a"].astype("float64")
 
     df1_expected = DataFrame(
-        index=pd.MultiIndex.from_product([df1.index, df1.columns]), columns=Index([])
+        index=MultiIndex.from_product([df1.index, df1.columns]), columns=Index([])
     )
     df2_expected = DataFrame(
-        index=pd.MultiIndex.from_product(
-            [df2.index, df2.columns], names=["bar", "foo"]
-        ),
+        index=MultiIndex.from_product([df2.index, df2.columns], names=["bar", "foo"]),
         columns=Index(["a"], name="foo"),
         dtype="float64",
     )

pandas/tests/window/moments/test_moments_rolling.py

@@ -3,8 +3,7 @@
 
 import pandas.util._test_decorators as td
 
-import pandas as pd
-from pandas import DataFrame, Series
+from pandas import DataFrame, Series, date_range
 import pandas._testing as tm
 
 
@@ -546,7 +545,7 @@ def test_rolling_quantile_np_percentile():
     # is analogous to Numpy's percentile
     row = 10
     col = 5
-    idx = pd.date_range("20100101", periods=row, freq="B")
+    idx = date_range("20100101", periods=row, freq="B")
     df = DataFrame(np.random.rand(row * col).reshape((row, -1)), index=idx)
 
     df_quantile = df.quantile([0.25, 0.5, 0.75], axis=0)

pandas/tests/window/test_api.py

@@ -1,8 +1,17 @@
 import numpy as np
 import pytest
 
-import pandas as pd
-from pandas import DataFrame, Index, Series, Timestamp, concat
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Period,
+    Series,
+    Timestamp,
+    concat,
+    date_range,
+    timedelta_range,
+)
 import pandas._testing as tm
 from pandas.core.base import SpecificationError
 
@@ -78,7 +87,7 @@ def test_agg():
 
     result = r.aggregate([np.mean, np.std])
     expected = concat([a_mean, a_std, b_mean, b_std], axis=1)
-    expected.columns = pd.MultiIndex.from_product([["A", "B"], ["mean", "std"]])
+    expected.columns = MultiIndex.from_product([["A", "B"], ["mean", "std"]])
     tm.assert_frame_equal(result, expected)
 
     result = r.aggregate({"A": np.mean, "B": np.std})
@@ -88,7 +97,7 @@ def test_agg():
 
     result = r.aggregate({"A": ["mean", "std"]})
     expected = concat([a_mean, a_std], axis=1)
-    expected.columns = pd.MultiIndex.from_tuples([("A", "mean"), ("A", "std")])
+    expected.columns = MultiIndex.from_tuples([("A", "mean"), ("A", "std")])
     tm.assert_frame_equal(result, expected)
 
     result = r["A"].aggregate(["mean", "sum"])
@@ -110,7 +119,7 @@ def test_agg():
     expected = concat([a_mean, a_std, b_mean, b_std], axis=1)
 
     exp_cols = [("A", "mean"), ("A", "std"), ("B", "mean"), ("B", "std")]
-    expected.columns = pd.MultiIndex.from_tuples(exp_cols)
+    expected.columns = MultiIndex.from_tuples(exp_cols)
     tm.assert_frame_equal(result, expected, check_like=True)
 
 
@@ -134,15 +143,15 @@ def test_agg_consistency():
     r = df.rolling(window=3)
 
     result = r.agg([np.sum, np.mean]).columns
-    expected = pd.MultiIndex.from_product([list("AB"), ["sum", "mean"]])
+    expected = MultiIndex.from_product([list("AB"), ["sum", "mean"]])
     tm.assert_index_equal(result, expected)
 
     result = r["A"].agg([np.sum, np.mean]).columns
     expected = Index(["sum", "mean"])
     tm.assert_index_equal(result, expected)
 
     result = r.agg({"A": [np.sum, np.mean]}).columns
-    expected = pd.MultiIndex.from_tuples([("A", "sum"), ("A", "mean")])
+    expected = MultiIndex.from_tuples([("A", "sum"), ("A", "mean")])
     tm.assert_index_equal(result, expected)
 
 
@@ -159,7 +168,7 @@ def test_agg_nested_dicts():
     expected = concat(
         [r["A"].mean(), r["A"].std(), r["B"].mean(), r["B"].std()], axis=1
     )
-    expected.columns = pd.MultiIndex.from_tuples(
+    expected.columns = MultiIndex.from_tuples(
         [("ra", "mean"), ("ra", "std"), ("rb", "mean"), ("rb", "std")]
     )
     with pytest.raises(SpecificationError, match=msg):
@@ -191,21 +200,21 @@ def test_count_nonnumeric_types():
             "int": [1, 2, 3],
             "float": [4.0, 5.0, 6.0],
             "string": list("abc"),
-            "datetime": pd.date_range("20170101", periods=3),
-            "timedelta": pd.timedelta_range("1 s", periods=3, freq="s"),
+            "datetime": date_range("20170101", periods=3),
+            "timedelta": timedelta_range("1 s", periods=3, freq="s"),
             "periods": [
-                pd.Period("2012-01"),
-                pd.Period("2012-02"),
-                pd.Period("2012-03"),
+                Period("2012-01"),
+                Period("2012-02"),
+                Period("2012-03"),
             ],
             "fl_inf": [1.0, 2.0, np.Inf],
             "fl_nan": [1.0, 2.0, np.NaN],
             "str_nan": ["aa", "bb", np.NaN],
             "dt_nat": dt_nat_col,
             "periods_nat": [
-                pd.Period("2012-01"),
-                pd.Period("2012-02"),
-                pd.Period(None),
+                Period("2012-01"),
+                Period("2012-02"),
+                Period(None),
             ],
         },
         columns=cols,
@@ -298,11 +307,11 @@ def test_multiple_agg_funcs(func, window_size, expected_vals):
     else:
         window = f()
 
-    index = pd.MultiIndex.from_tuples(
+    index = MultiIndex.from_tuples(
         [("A", 0), ("A", 1), ("A", 2), ("B", 3), ("B", 4), ("B", 5), ("B", 6)],
         names=["stock", None],
     )
-    columns = pd.MultiIndex.from_tuples(
+    columns = MultiIndex.from_tuples(
         [("low", "mean"), ("low", "max"), ("high", "mean"), ("high", "min")]
     )
     expected = DataFrame(expected_vals, index=index, columns=columns)

pandas/tests/window/test_expanding.py

@@ -3,8 +3,7 @@
 
 from pandas.errors import UnsupportedFunctionCall
 
-import pandas as pd
-from pandas import DataFrame, Series
+from pandas import DataFrame, DatetimeIndex, Series
 import pandas._testing as tm
 from pandas.core.window import Expanding
 
@@ -82,8 +81,8 @@ def test_empty_df_expanding(expander):
 
     # Verifies that datetime and integer expanding windows can be applied
     # to empty DataFrames with datetime index
-    expected = DataFrame(index=pd.DatetimeIndex([]))
-    result = DataFrame(index=pd.DatetimeIndex([])).expanding(expander).sum()
+    expected = DataFrame(index=DatetimeIndex([]))
+    result = DataFrame(index=DatetimeIndex([])).expanding(expander).sum()
     tm.assert_frame_equal(result, expected)