From f29c969d17971268aeb3697b7c57bfad6aa5b492 Mon Sep 17 00:00:00 2001
From: thomasyu888 <thomas.yu@sagebase.org>
Date: Thu, 4 Mar 2021 17:06:26 +0800
Subject: [PATCH 1/3] Fix styling

---
 pandas/tests/resample/test_datetime_index.py  | 62 +++++++++----------
 pandas/tests/resample/test_deprecated.py      |  4 +-
 pandas/tests/resample/test_period_index.py    | 24 +++----
 pandas/tests/resample/test_resample_api.py    | 28 ++++-----
 .../tests/resample/test_resampler_grouper.py  | 22 +++----
 pandas/tests/resample/test_time_grouper.py    |  8 +--
 pandas/tests/resample/test_timedelta.py       | 16 ++---
 7 files changed, 82 insertions(+), 82 deletions(-)

diff --git a/pandas/tests/resample/test_datetime_index.py b/pandas/tests/resample/test_datetime_index.py
index cbf69696d5801..ea8b8fc7aa6a2 100644
--- a/pandas/tests/resample/test_datetime_index.py
+++ b/pandas/tests/resample/test_datetime_index.py
@@ -125,12 +125,12 @@ def test_resample_basic(series, closed, expected):
 def test_resample_integerarray():
     # GH 25580, resample on IntegerArray
     ts = Series(
-        range(9), index=pd.date_range("1/1/2000", periods=9, freq="T"), dtype="Int64"
+        range(9), index=date_range("1/1/2000", periods=9, freq="T"), dtype="Int64"
     )
     result = ts.resample("3T").sum()
     expected = Series(
         [3, 12, 21],
-        index=pd.date_range("1/1/2000", periods=3, freq="3T"),
+        index=date_range("1/1/2000", periods=3, freq="3T"),
         dtype="Int64",
     )
     tm.assert_series_equal(result, expected)
@@ -138,7 +138,7 @@ def test_resample_integerarray():
     result = ts.resample("3T").mean()
     expected = Series(
         [1, 4, 7],
-        index=pd.date_range("1/1/2000", periods=3, freq="3T"),
+        index=date_range("1/1/2000", periods=3, freq="3T"),
         dtype="Float64",
     )
     tm.assert_series_equal(result, expected)
@@ -529,8 +529,8 @@ def test_resample_ohlc(series):
 def test_resample_ohlc_result():
 
     # GH 12332
-    index = pd.date_range("1-1-2000", "2-15-2000", freq="h")
-    index = index.union(pd.date_range("4-15-2000", "5-15-2000", freq="h"))
+    index = date_range("1-1-2000", "2-15-2000", freq="h")
+    index = index.union(date_range("4-15-2000", "5-15-2000", freq="h"))
     s = Series(range(len(index)), index=index)
 
     a = s.loc[:"4-15-2000"].resample("30T").ohlc()
@@ -805,7 +805,7 @@ def test_resample_bad_offset(offset):
 def test_resample_origin_prime_freq():
     # GH 31809
     start, end = "2000-10-01 23:30:00", "2000-10-02 00:30:00"
-    rng = pd.date_range(start, end, freq="7min")
+    rng = date_range(start, end, freq="7min")
     ts = Series(np.random.randn(len(rng)), index=rng)
 
     exp_rng = date_range("2000-10-01 23:14:00", "2000-10-02 00:22:00", freq="17min")
@@ -863,7 +863,7 @@ def test_resample_origin_with_tz():
 def test_resample_origin_epoch_with_tz_day_vs_24h():
     # GH 34474
     start, end = "2000-10-01 23:30:00+0500", "2000-12-02 00:30:00+0500"
-    rng = pd.date_range(start, end, freq="7min")
+    rng = date_range(start, end, freq="7min")
     random_values = np.random.randn(len(rng))
     ts_1 = Series(random_values, index=rng)
 
@@ -880,7 +880,7 @@ def test_resample_origin_epoch_with_tz_day_vs_24h():
 
     # check that we have the similar results with two different timezones (+2H and +5H)
     start, end = "2000-10-01 23:30:00+0200", "2000-12-02 00:30:00+0200"
-    rng = pd.date_range(start, end, freq="7min")
+    rng = date_range(start, end, freq="7min")
     ts_2 = Series(random_values, index=rng)
     result_5 = ts_2.resample("D", origin="epoch").mean()
     result_6 = ts_2.resample("24H", origin="epoch").mean()
@@ -903,7 +903,7 @@ def _create_series(values, timestamps, freq="D"):
     # test classical behavior of origin in a DST context
     start = Timestamp("2013-11-02", tz=tz)
     end = Timestamp("2013-11-03 23:59", tz=tz)
-    rng = pd.date_range(start, end, freq="1h")
+    rng = date_range(start, end, freq="1h")
     ts = Series(np.ones(len(rng)), index=rng)
 
     expected = _create_series([24.0, 25.0], ["2013-11-02", "2013-11-03"])
@@ -914,7 +914,7 @@ def _create_series(values, timestamps, freq="D"):
     # test complex behavior of origin/offset in a DST context
     start = Timestamp("2013-11-03", tz=tz)
     end = Timestamp("2013-11-03 23:59", tz=tz)
-    rng = pd.date_range(start, end, freq="1h")
+    rng = date_range(start, end, freq="1h")
     ts = Series(np.ones(len(rng)), index=rng)
 
     expected_ts = ["2013-11-02 22:00-05:00", "2013-11-03 22:00-06:00"]
@@ -969,7 +969,7 @@ def test_period_with_agg():
     # aggregate a period resampler with a lambda
     s2 = Series(
         np.random.randint(0, 5, 50),
-        index=pd.period_range("2012-01-01", freq="H", periods=50),
+        index=period_range("2012-01-01", freq="H", periods=50),
         dtype="float64",
     )
 
@@ -1003,7 +1003,7 @@ def test_resample_dtype_preservation():
 
     df = DataFrame(
         {
-            "date": pd.date_range(start="2016-01-01", periods=4, freq="W"),
+            "date": date_range(start="2016-01-01", periods=4, freq="W"),
             "group": [1, 1, 2, 2],
             "val": Series([5, 6, 7, 8], dtype="int32"),
         }
@@ -1022,7 +1022,7 @@ def test_resample_dtype_coercion():
 
     # GH 16361
     df = {"a": [1, 3, 1, 4]}
-    df = DataFrame(df, index=pd.date_range("2017-01-01", "2017-01-04"))
+    df = DataFrame(df, index=date_range("2017-01-01", "2017-01-04"))
 
     expected = df.astype("float64").resample("H").mean()["a"].interpolate("cubic")
 
@@ -1056,12 +1056,12 @@ def test_nanosecond_resample_error():
     # Resampling using pd.tseries.offsets.Nano as period
     start = 1443707890427
     exp_start = 1443707890400
-    indx = pd.date_range(start=pd.to_datetime(start), periods=10, freq="100n")
+    indx = date_range(start=pd.to_datetime(start), periods=10, freq="100n")
     ts = Series(range(len(indx)), index=indx)
     r = ts.resample(pd.tseries.offsets.Nano(100))
     result = r.agg("mean")
 
-    exp_indx = pd.date_range(start=pd.to_datetime(exp_start), periods=10, freq="100n")
+    exp_indx = date_range(start=pd.to_datetime(exp_start), periods=10, freq="100n")
     exp = Series(range(len(exp_indx)), index=exp_indx)
 
     tm.assert_series_equal(result, exp)
@@ -1128,8 +1128,8 @@ def test_resample_anchored_multiday():
     #
     # See: https://github.com/pandas-dev/pandas/issues/8683
 
-    index1 = pd.date_range("2014-10-14 23:06:23.206", periods=3, freq="400L")
-    index2 = pd.date_range("2014-10-15 23:00:00", periods=2, freq="2200L")
+    index1 = date_range("2014-10-14 23:06:23.206", periods=3, freq="400L")
+    index2 = date_range("2014-10-15 23:00:00", periods=2, freq="2200L")
     index = index1.union(index2)
 
     s = Series(np.random.randn(5), index=index)
@@ -1174,7 +1174,7 @@ def test_anchored_lowercase_buglet():
 
 def test_upsample_apply_functions():
     # #1596
-    rng = pd.date_range("2012-06-12", periods=4, freq="h")
+    rng = date_range("2012-06-12", periods=4, freq="h")
 
     ts = Series(np.random.randn(len(rng)), index=rng)
 
@@ -1183,7 +1183,7 @@ def test_upsample_apply_functions():
 
 
 def test_resample_not_monotonic():
-    rng = pd.date_range("2012-06-12", periods=200, freq="h")
+    rng = date_range("2012-06-12", periods=200, freq="h")
     ts = Series(np.random.randn(len(rng)), index=rng)
 
     ts = ts.take(np.random.permutation(len(ts)))
@@ -1255,12 +1255,12 @@ def test_resample_consistency():
     # GH 6418
     # resample with bfill / limit / reindex consistency
 
-    i30 = pd.date_range("2002-02-02", periods=4, freq="30T")
+    i30 = date_range("2002-02-02", periods=4, freq="30T")
     s = Series(np.arange(4.0), index=i30)
     s[2] = np.NaN
 
     # Upsample by factor 3 with reindex() and resample() methods:
-    i10 = pd.date_range(i30[0], i30[-1], freq="10T")
+    i10 = date_range(i30[0], i30[-1], freq="10T")
 
     s10 = s.reindex(index=i10, method="bfill")
     s10_2 = s.reindex(index=i10, method="bfill", limit=2)
@@ -1364,8 +1364,8 @@ def test_resample_nunique_preserves_column_level_names():
 
 def test_resample_nunique_with_date_gap():
     # GH 13453
-    index = pd.date_range("1-1-2000", "2-15-2000", freq="h")
-    index2 = pd.date_range("4-15-2000", "5-15-2000", freq="h")
+    index = date_range("1-1-2000", "2-15-2000", freq="h")
+    index2 = date_range("4-15-2000", "5-15-2000", freq="h")
     index3 = index.append(index2)
     s = Series(range(len(index3)), index=index3, dtype="int64")
     r = s.resample("M")
@@ -1462,7 +1462,7 @@ def test_groupby_with_dst_time_change():
 
     df = DataFrame([1, 2], index=index)
     result = df.groupby(Grouper(freq="1d")).last()
-    expected_index_values = pd.date_range(
+    expected_index_values = date_range(
         "2016-11-02", "2016-11-24", freq="d", tz="America/Chicago"
     )
 
@@ -1587,11 +1587,11 @@ def test_downsample_across_dst_weekly():
     )
     tm.assert_frame_equal(result, expected)
 
-    idx = pd.date_range("2013-04-01", "2013-05-01", tz="Europe/London", freq="H")
+    idx = date_range("2013-04-01", "2013-05-01", tz="Europe/London", freq="H")
     s = Series(index=idx, dtype=np.float64)
     result = s.resample("W").mean()
     expected = Series(
-        index=pd.date_range("2013-04-07", freq="W", periods=5, tz="Europe/London"),
+        index=date_range("2013-04-07", freq="W", periods=5, tz="Europe/London"),
         dtype=np.float64,
     )
     tm.assert_series_equal(result, expected)
@@ -1601,7 +1601,7 @@ def test_downsample_dst_at_midnight():
     # GH 25758
     start = datetime(2018, 11, 3, 12)
     end = datetime(2018, 11, 5, 12)
-    index = pd.date_range(start, end, freq="1H")
+    index = date_range(start, end, freq="1H")
     index = index.tz_localize("UTC").tz_convert("America/Havana")
     data = list(range(len(index)))
     dataframe = DataFrame(data, index=index)
@@ -1681,7 +1681,7 @@ def f(data, add_arg):
     tm.assert_series_equal(result, expected)
 
     # Testing dataframe
-    df = DataFrame({"A": 1, "B": 2}, index=pd.date_range("2017", periods=10))
+    df = DataFrame({"A": 1, "B": 2}, index=date_range("2017", periods=10))
     result = df.groupby("A").resample("D").agg(f, multiplier)
     expected = df.groupby("A").resample("D").mean().multiply(multiplier)
     tm.assert_frame_equal(result, expected)
@@ -1707,7 +1707,7 @@ def test_resample_equivalent_offsets(n1, freq1, n2, freq2, k):
     # GH 24127
     n1_ = n1 * k
     n2_ = n2 * k
-    s = Series(0, index=pd.date_range("19910905 13:00", "19911005 07:00", freq=freq1))
+    s = Series(0, index=date_range("19910905 13:00", "19911005 07:00", freq=freq1))
     s = s + range(len(s))
 
     result1 = s.resample(str(n1_) + freq1).mean()
@@ -1797,10 +1797,10 @@ def test_resample_calendar_day_with_dst(
     first: str, last: str, freq_in: str, freq_out: str, exp_last: str
 ):
     # GH 35219
-    ts = Series(1.0, pd.date_range(first, last, freq=freq_in, tz="Europe/Amsterdam"))
+    ts = Series(1.0, date_range(first, last, freq=freq_in, tz="Europe/Amsterdam"))
     result = ts.resample(freq_out).pad()
     expected = Series(
-        1.0, pd.date_range(first, exp_last, freq=freq_out, tz="Europe/Amsterdam")
+        1.0, date_range(first, exp_last, freq=freq_out, tz="Europe/Amsterdam")
     )
     tm.assert_series_equal(result, expected)
 
diff --git a/pandas/tests/resample/test_deprecated.py b/pandas/tests/resample/test_deprecated.py
index d3672b3d32be1..fdb3a7872ad67 100644
--- a/pandas/tests/resample/test_deprecated.py
+++ b/pandas/tests/resample/test_deprecated.py
@@ -52,7 +52,7 @@ def _create_index(*args, **kwargs):
 def test_deprecating_on_loffset_and_base():
     # GH 31809
 
-    idx = pd.date_range("2001-01-01", periods=4, freq="T")
+    idx = date_range("2001-01-01", periods=4, freq="T")
     df = DataFrame(data=4 * [range(2)], index=idx, columns=["a", "b"])
 
     with tm.assert_produces_warning(FutureWarning):
@@ -243,7 +243,7 @@ def test_loffset_returns_datetimeindex(frame, kind, agg_arg):
 )
 def test_resample_with_non_zero_base(start, end, start_freq, end_freq, base, offset):
     # GH 23882
-    s = Series(0, index=pd.period_range(start, end, freq=start_freq))
+    s = Series(0, index=period_range(start, end, freq=start_freq))
     s = s + np.arange(len(s))
     with tm.assert_produces_warning(FutureWarning):
         result = s.resample(end_freq, base=base).mean()
diff --git a/pandas/tests/resample/test_period_index.py b/pandas/tests/resample/test_period_index.py
index 5dab0cbafbc59..c311ad71fba0b 100644
--- a/pandas/tests/resample/test_period_index.py
+++ b/pandas/tests/resample/test_period_index.py
@@ -224,9 +224,9 @@ def test_resample_basic(self):
     )
     def test_resample_count(self, freq, expected_vals):
         # GH12774
-        series = Series(1, index=pd.period_range(start="2000", periods=100))
+        series = Series(1, index=period_range(start="2000", periods=100))
         result = series.resample(freq).count()
-        expected_index = pd.period_range(
+        expected_index = period_range(
             start="2000", freq=freq, periods=len(expected_vals)
         )
         expected = Series(expected_vals, index=expected_index)
@@ -236,7 +236,7 @@ def test_resample_same_freq(self, resample_method):
 
         # GH12770
         series = Series(
-            range(3), index=pd.period_range(start="2000", periods=3, freq="M")
+            range(3), index=period_range(start="2000", periods=3, freq="M")
         )
         expected = series
 
@@ -250,7 +250,7 @@ def test_resample_incompat_freq(self):
         )
         with pytest.raises(IncompatibleFrequency, match=msg):
             Series(
-                range(3), index=pd.period_range(start="2000", periods=3, freq="M")
+                range(3), index=period_range(start="2000", periods=3, freq="M")
             ).resample("W").mean()
 
     def test_with_local_timezone_pytz(self):
@@ -261,7 +261,7 @@ def test_with_local_timezone_pytz(self):
         # 1 day later
         end = datetime(year=2013, month=11, day=2, hour=0, minute=0, tzinfo=pytz.utc)
 
-        index = pd.date_range(start, end, freq="H")
+        index = date_range(start, end, freq="H")
 
         series = Series(1, index=index)
         series = series.tz_convert(local_timezone)
@@ -270,14 +270,14 @@ def test_with_local_timezone_pytz(self):
         # Create the expected series
         # Index is moved back a day with the timezone conversion from UTC to
         # Pacific
-        expected_index = pd.period_range(start=start, end=end, freq="D") - offsets.Day()
+        expected_index = period_range(start=start, end=end, freq="D") - offsets.Day()
         expected = Series(1, index=expected_index)
         tm.assert_series_equal(result, expected)
 
     def test_resample_with_pytz(self):
         # GH 13238
         s = Series(
-            2, index=pd.date_range("2017-01-01", periods=48, freq="H", tz="US/Eastern")
+            2, index=date_range("2017-01-01", periods=48, freq="H", tz="US/Eastern")
         )
         result = s.resample("D").mean()
         expected = Series(
@@ -302,7 +302,7 @@ def test_with_local_timezone_dateutil(self):
             year=2013, month=11, day=2, hour=0, minute=0, tzinfo=dateutil.tz.tzutc()
         )
 
-        index = pd.date_range(start, end, freq="H", name="idx")
+        index = date_range(start, end, freq="H", name="idx")
 
         series = Series(1, index=index)
         series = series.tz_convert(local_timezone)
@@ -312,7 +312,7 @@ def test_with_local_timezone_dateutil(self):
         # Index is moved back a day with the timezone conversion from UTC to
         # Pacific
         expected_index = (
-            pd.period_range(start=start, end=end, freq="D", name="idx") - offsets.Day()
+            period_range(start=start, end=end, freq="D", name="idx") - offsets.Day()
         )
         expected = Series(1, index=expected_index)
         tm.assert_series_equal(result, expected)
@@ -343,7 +343,7 @@ def test_resample_nonexistent_time_bin_edge(self):
 
     def test_resample_ambiguous_time_bin_edge(self):
         # GH 10117
-        idx = pd.date_range(
+        idx = date_range(
             "2014-10-25 22:00:00", "2014-10-26 00:30:00", freq="30T", tz="Europe/London"
         )
         expected = Series(np.zeros(len(idx)), index=idx)
@@ -827,7 +827,7 @@ def test_resample_with_only_nat(self):
     )
     def test_resample_with_offset(self, start, end, start_freq, end_freq, offset):
         # GH 23882 & 31809
-        s = Series(0, index=pd.period_range(start, end, freq=start_freq))
+        s = Series(0, index=period_range(start, end, freq=start_freq))
         s = s + np.arange(len(s))
         result = s.resample(end_freq, offset=offset).mean()
         result = result.to_timestamp(end_freq)
@@ -869,7 +869,7 @@ def test_get_period_range_edges(self, first, last, freq, exp_first, exp_last):
 
     def test_sum_min_count(self):
         # GH 19974
-        index = pd.date_range(start="2018", freq="M", periods=6)
+        index = date_range(start="2018", freq="M", periods=6)
         data = np.ones(6)
         data[3:6] = np.nan
         s = Series(data, index).to_period()
diff --git a/pandas/tests/resample/test_resample_api.py b/pandas/tests/resample/test_resample_api.py
index 219e407c3e999..48a41c30b0c4e 100644
--- a/pandas/tests/resample/test_resample_api.py
+++ b/pandas/tests/resample/test_resample_api.py
@@ -57,7 +57,7 @@ def test_groupby_resample_api():
     # when appropriate
     df = DataFrame(
         {
-            "date": pd.date_range(start="2016-01-01", periods=4, freq="W"),
+            "date": date_range(start="2016-01-01", periods=4, freq="W"),
             "group": [1, 1, 2, 2],
             "val": [5, 6, 7, 8],
         }
@@ -65,8 +65,8 @@ def test_groupby_resample_api():
 
     # replication step
     i = (
-        pd.date_range("2016-01-03", periods=8).tolist()
-        + pd.date_range("2016-01-17", periods=8).tolist()
+        date_range("2016-01-03", periods=8).tolist()
+        + date_range("2016-01-17", periods=8).tolist()
     )
     index = pd.MultiIndex.from_arrays([[1] * 8 + [2] * 8, i], names=["group", "date"])
     expected = DataFrame({"val": [5] * 7 + [6] + [7] * 7 + [8]}, index=index)
@@ -82,7 +82,7 @@ def test_groupby_resample_on_api():
     df = DataFrame(
         {
             "key": ["A", "B"] * 5,
-            "dates": pd.date_range("2016-01-01", periods=10),
+            "dates": date_range("2016-01-01", periods=10),
             "values": np.random.randn(10),
         }
     )
@@ -146,7 +146,7 @@ def test_api_compat_before_use():
     # make sure that we are setting the binner
     # on these attributes
     for attr in ["groups", "ngroups", "indices"]:
-        rng = pd.date_range("1/1/2012", periods=100, freq="S")
+        rng = date_range("1/1/2012", periods=100, freq="S")
         ts = Series(np.arange(len(rng)), index=rng)
         rs = ts.resample("30s")
 
@@ -175,12 +175,12 @@ def tests_skip_nuisance(test_frame):
 def test_downsample_but_actually_upsampling():
 
     # this is reindex / asfreq
-    rng = pd.date_range("1/1/2012", periods=100, freq="S")
+    rng = date_range("1/1/2012", periods=100, freq="S")
     ts = Series(np.arange(len(rng), dtype="int64"), index=rng)
     result = ts.resample("20s").asfreq()
     expected = Series(
         [0, 20, 40, 60, 80],
-        index=pd.date_range("2012-01-01 00:00:00", freq="20s", periods=5),
+        index=date_range("2012-01-01 00:00:00", freq="20s", periods=5),
     )
     tm.assert_series_equal(result, expected)
 
@@ -191,7 +191,7 @@ def test_combined_up_downsampling_of_irregular():
     # ts2.resample('2s').mean().ffill()
     # preserve these semantics
 
-    rng = pd.date_range("1/1/2012", periods=100, freq="S")
+    rng = date_range("1/1/2012", periods=100, freq="S")
     ts = Series(np.arange(len(rng)), index=rng)
     ts2 = ts.iloc[[0, 1, 2, 3, 5, 7, 11, 15, 16, 25, 30]]
 
@@ -252,7 +252,7 @@ def test_transform():
 def test_fillna():
 
     # need to upsample here
-    rng = pd.date_range("1/1/2012", periods=10, freq="2S")
+    rng = date_range("1/1/2012", periods=10, freq="2S")
     ts = Series(np.arange(len(rng), dtype="int64"), index=rng)
     r = ts.resample("s")
 
@@ -289,7 +289,7 @@ def test_agg_consistency():
     # similar aggregations with and w/o selection list
     df = DataFrame(
         np.random.randn(1000, 3),
-        index=pd.date_range("1/1/2012", freq="S", periods=1000),
+        index=date_range("1/1/2012", freq="S", periods=1000),
         columns=["A", "B", "C"],
     )
 
@@ -304,7 +304,7 @@ def test_agg_consistency_int_str_column_mix():
     # GH#39025
     df = DataFrame(
         np.random.randn(1000, 2),
-        index=pd.date_range("1/1/2012", freq="S", periods=1000),
+        index=date_range("1/1/2012", freq="S", periods=1000),
         columns=[1, "a"],
     )
 
@@ -591,7 +591,7 @@ def test_agg_with_datetime_index_list_agg_func(col_name):
     # We catch these errors and move on to the correct branch.
     df = DataFrame(
         list(range(200)),
-        index=pd.date_range(
+        index=date_range(
             start="2017-01-01", freq="15min", periods=200, tz="Europe/Berlin"
         ),
         columns=[col_name],
@@ -599,7 +599,7 @@ def test_agg_with_datetime_index_list_agg_func(col_name):
     result = df.resample("1d").aggregate(["mean"])
     expected = DataFrame(
         [47.5, 143.5, 195.5],
-        index=pd.date_range(
+        index=date_range(
             start="2017-01-01", freq="D", periods=3, tz="Europe/Berlin"
         ),
         columns=pd.MultiIndex(levels=[[col_name], ["mean"]], codes=[[0], [0]]),
@@ -609,7 +609,7 @@ def test_agg_with_datetime_index_list_agg_func(col_name):
 
 def test_resample_agg_readonly():
     # GH#31710 cython needs to allow readonly data
-    index = pd.date_range("2020-01-01", "2020-01-02", freq="1h")
+    index = date_range("2020-01-01", "2020-01-02", freq="1h")
     arr = np.zeros_like(index)
     arr.setflags(write=False)
 
diff --git a/pandas/tests/resample/test_resampler_grouper.py b/pandas/tests/resample/test_resampler_grouper.py
index 50775b9ef3a47..999d8a6c90ba2 100644
--- a/pandas/tests/resample/test_resampler_grouper.py
+++ b/pandas/tests/resample/test_resampler_grouper.py
@@ -72,7 +72,7 @@ def f(x):
 
     df = DataFrame(
         {
-            "date": pd.date_range(start="2016-01-01", periods=4, freq="W"),
+            "date": date_range(start="2016-01-01", periods=4, freq="W"),
             "group": [1, 1, 2, 2],
             "val": [5, 6, 7, 8],
         }
@@ -106,7 +106,7 @@ def test_getitem_multiple():
     # GH 13174
     # multiple calls after selection causing an issue with aliasing
     data = [{"id": 1, "buyer": "A"}, {"id": 2, "buyer": "B"}]
-    df = DataFrame(data, index=pd.date_range("2016-01-01", periods=2))
+    df = DataFrame(data, index=date_range("2016-01-01", periods=2))
     r = df.groupby("id").resample("1D")
     result = r["buyer"].count()
     expected = Series(
@@ -126,7 +126,7 @@ def test_getitem_multiple():
 def test_groupby_resample_on_api_with_getitem():
     # GH 17813
     df = DataFrame(
-        {"id": list("aabbb"), "date": pd.date_range("1-1-2016", periods=5), "data": 1}
+        {"id": list("aabbb"), "date": date_range("1-1-2016", periods=5), "data": 1}
     )
     exp = df.set_index("date").groupby("id").resample("2D")["data"].sum()
     result = df.groupby("id").resample("2D", on="date")["data"].sum()
@@ -140,7 +140,7 @@ def test_groupby_with_origin():
     start, end = "1/1/2000 00:00:00", "1/31/2000 00:00"
     middle = "1/15/2000 00:00:00"
 
-    rng = pd.date_range(start, end, freq="1231min")  # prime number
+    rng = date_range(start, end, freq="1231min")  # prime number
     ts = Series(np.random.randn(len(rng)), index=rng)
     ts2 = ts[middle:end]
 
@@ -178,7 +178,7 @@ def test_nearest():
 
     # GH 17496
     # Resample nearest
-    index = pd.date_range("1/1/2000", periods=3, freq="T")
+    index = date_range("1/1/2000", periods=3, freq="T")
     result = Series(range(3), index=index).resample("20s").nearest()
 
     expected = Series(
@@ -263,7 +263,7 @@ def f(x):
 
 def test_apply_with_mutated_index():
     # GH 15169
-    index = pd.date_range("1-1-2015", "12-31-15", freq="D")
+    index = date_range("1-1-2015", "12-31-15", freq="D")
     df = DataFrame(data={"col1": np.random.rand(len(index))}, index=index)
 
     def f(x):
@@ -338,7 +338,7 @@ def test_median_duplicate_columns():
     df = DataFrame(
         np.random.randn(20, 3),
         columns=list("aaa"),
-        index=pd.date_range("2012-01-01", periods=20, freq="s"),
+        index=date_range("2012-01-01", periods=20, freq="s"),
     )
     df2 = df.copy()
     df2.columns = ["a", "b", "c"]
@@ -352,11 +352,11 @@ def test_apply_to_one_column_of_df():
     # GH: 36951
     df = DataFrame(
         {"col": range(10), "col1": range(10, 20)},
-        index=pd.date_range("2012-01-01", periods=10, freq="20min"),
+        index=date_range("2012-01-01", periods=10, freq="20min"),
     )
     result = df.resample("H").apply(lambda group: group.col.sum())
     expected = Series(
-        [3, 12, 21, 9], index=pd.date_range("2012-01-01", periods=4, freq="H")
+        [3, 12, 21, 9], index=date_range("2012-01-01", periods=4, freq="H")
     )
     tm.assert_series_equal(result, expected)
     result = df.resample("H").apply(lambda group: group["col"].sum())
@@ -402,7 +402,7 @@ def test_resample_groupby_agg():
 @pytest.mark.parametrize("keys", [["a"], ["a", "b"]])
 def test_empty(keys):
     # GH 26411
-    df = pd.DataFrame([], columns=["a", "b"], index=TimedeltaIndex([]))
+    df = DataFrame([], columns=["a", "b"], index=TimedeltaIndex([]))
     result = df.groupby(keys).resample(rule=pd.to_timedelta("00:00:01")).mean()
     expected = DataFrame(columns=["a", "b"]).set_index(keys, drop=False)
     if len(keys) == 1:
@@ -415,7 +415,7 @@ def test_empty(keys):
 def test_resample_groupby_agg_object_dtype_all_nan(consolidate):
     # https://github.com/pandas-dev/pandas/issues/39329
 
-    dates = pd.date_range("2020-01-01", periods=15, freq="D")
+    dates = date_range("2020-01-01", periods=15, freq="D")
     df1 = DataFrame({"key": "A", "date": dates, "col1": range(15), "col_object": "val"})
     df2 = DataFrame({"key": "B", "date": dates, "col1": range(15)})
     df = pd.concat([df1, df2], ignore_index=True)
diff --git a/pandas/tests/resample/test_time_grouper.py b/pandas/tests/resample/test_time_grouper.py
index e62c907032938..5dc64a33098f3 100644
--- a/pandas/tests/resample/test_time_grouper.py
+++ b/pandas/tests/resample/test_time_grouper.py
@@ -60,7 +60,7 @@ def test_numpy_reduction():
 def test_apply_iteration():
     # #2300
     N = 1000
-    ind = pd.date_range(start="2000-01-01", freq="D", periods=N)
+    ind = date_range(start="2000-01-01", freq="D", periods=N)
     df = DataFrame({"open": 1, "close": 2}, index=ind)
     tg = Grouper(freq="M")
 
@@ -173,7 +173,7 @@ def test_aggregate_normal(request, resample_method):
     ],
 )
 def test_resample_entirely_nat_window(method, method_args, unit):
-    s = Series([0] * 2 + [np.nan] * 2, index=pd.date_range("2017", periods=4))
+    s = Series([0] * 2 + [np.nan] * 2, index=date_range("2017", periods=4))
     result = methodcaller(method, **method_args)(s.resample("2d"))
     expected = Series(
         [0.0, unit], index=pd.DatetimeIndex(["2017-01-01", "2017-01-03"], freq="2D")
@@ -284,7 +284,7 @@ def test_repr():
     ],
 )
 def test_upsample_sum(method, method_args, expected_values):
-    s = Series(1, index=pd.date_range("2017", periods=2, freq="H"))
+    s = Series(1, index=date_range("2017", periods=2, freq="H"))
     resampled = s.resample("30T")
     index = pd.DatetimeIndex(
         ["2017-01-01T00:00:00", "2017-01-01T00:30:00", "2017-01-01T01:00:00"],
@@ -301,7 +301,7 @@ def test_groupby_resample_interpolate():
 
     df = DataFrame(d)
 
-    df["week_starting"] = pd.date_range("01/01/2018", periods=3, freq="W")
+    df["week_starting"] = date_range("01/01/2018", periods=3, freq="W")
 
     result = (
         df.set_index("week_starting")
diff --git a/pandas/tests/resample/test_timedelta.py b/pandas/tests/resample/test_timedelta.py
index ee08dac09695b..e602b53a9ee2c 100644
--- a/pandas/tests/resample/test_timedelta.py
+++ b/pandas/tests/resample/test_timedelta.py
@@ -49,7 +49,7 @@ def test_resample_with_timedeltas():
 
     expected = DataFrame({"A": np.arange(1480)})
     expected = expected.groupby(expected.index // 30).sum()
-    expected.index = pd.timedelta_range("0 days", freq="30T", periods=50)
+    expected.index = timedelta_range("0 days", freq="30T", periods=50)
 
     df = DataFrame(
         {"A": np.arange(1480)}, index=pd.to_timedelta(np.arange(1480), unit="T")
@@ -65,10 +65,10 @@ def test_resample_with_timedeltas():
 
 def test_resample_single_period_timedelta():
 
-    s = Series(list(range(5)), index=pd.timedelta_range("1 day", freq="s", periods=5))
+    s = Series(list(range(5)), index=timedelta_range("1 day", freq="s", periods=5))
     result = s.resample("2s").sum()
     expected = Series(
-        [1, 5, 4], index=pd.timedelta_range("1 day", freq="2s", periods=3)
+        [1, 5, 4], index=timedelta_range("1 day", freq="2s", periods=3)
     )
     tm.assert_series_equal(result, expected)
 
@@ -76,7 +76,7 @@ def test_resample_single_period_timedelta():
 def test_resample_timedelta_idempotency():
 
     # GH 12072
-    index = pd.timedelta_range("0", periods=9, freq="10L")
+    index = timedelta_range("0", periods=9, freq="10L")
     series = Series(range(9), index=index)
     result = series.resample("10L").mean()
     expected = series
@@ -146,10 +146,10 @@ def test_resample_timedelta_values():
 def test_resample_timedelta_edge_case(start, end, freq, resample_freq):
     # GH 33498
     # check that the timedelta bins does not contains an extra bin
-    idx = pd.timedelta_range(start=start, end=end, freq=freq)
+    idx = timedelta_range(start=start, end=end, freq=freq)
     s = Series(np.arange(len(idx)), index=idx)
     result = s.resample(resample_freq).min()
-    expected_index = pd.timedelta_range(freq=resample_freq, start=start, end=end)
+    expected_index = timedelta_range(freq=resample_freq, start=start, end=end)
     tm.assert_index_equal(result.index, expected_index)
     assert result.index.freq == expected_index.freq
     assert not np.isnan(result[-1])
@@ -159,13 +159,13 @@ def test_resample_with_timedelta_yields_no_empty_groups():
     # GH 10603
     df = DataFrame(
         np.random.normal(size=(10000, 4)),
-        index=pd.timedelta_range(start="0s", periods=10000, freq="3906250n"),
+        index=timedelta_range(start="0s", periods=10000, freq="3906250n"),
     )
     result = df.loc["1s":, :].resample("3s").apply(lambda x: len(x))
 
     expected = DataFrame(
         [[768.0] * 4] * 12 + [[528.0] * 4],
-        index=pd.timedelta_range(start="1s", periods=13, freq="3s"),
+        index=timedelta_range(start="1s", periods=13, freq="3s"),
     )
     tm.assert_frame_equal(result, expected)
 

From ee5ed79b231c0e6bd88a576acb5700ee1a9db67c Mon Sep 17 00:00:00 2001
From: thomasyu888 <thomas.yu@sagebase.org>
Date: Thu, 4 Mar 2021 17:21:57 +0800
Subject: [PATCH 2/3] Fix other linting issues

---
 .pre-commit-config.yaml                    | 4 +++-
 pandas/tests/resample/test_period_index.py | 4 +---
 pandas/tests/resample/test_resample_api.py | 4 +---
 pandas/tests/resample/test_timedelta.py    | 4 +---
 4 files changed, 6 insertions(+), 10 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3966e8931162c..78b9d9ad05ec6 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -140,7 +140,9 @@ repos:
         entry: python scripts/check_for_inconsistent_pandas_namespace.py
         language: python
         types: [python]
-        files: ^pandas/tests/frame/
+        files: ^pandas/tests/(frame|tseries)/
+        additional_dependencies: [tokenize-rt]
+        args: [--replace]
     -   id: FrameOrSeriesUnion
         name: Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias
         entry: Union\[.*(Series,.*DataFrame|DataFrame,.*Series).*\]
diff --git a/pandas/tests/resample/test_period_index.py b/pandas/tests/resample/test_period_index.py
index c311ad71fba0b..e2b13f6a00677 100644
--- a/pandas/tests/resample/test_period_index.py
+++ b/pandas/tests/resample/test_period_index.py
@@ -235,9 +235,7 @@ def test_resample_count(self, freq, expected_vals):
     def test_resample_same_freq(self, resample_method):
 
         # GH12770
-        series = Series(
-            range(3), index=period_range(start="2000", periods=3, freq="M")
-        )
+        series = Series(range(3), index=period_range(start="2000", periods=3, freq="M"))
         expected = series
 
         result = getattr(series.resample("M"), resample_method)()
diff --git a/pandas/tests/resample/test_resample_api.py b/pandas/tests/resample/test_resample_api.py
index 48a41c30b0c4e..76ac86d798086 100644
--- a/pandas/tests/resample/test_resample_api.py
+++ b/pandas/tests/resample/test_resample_api.py
@@ -599,9 +599,7 @@ def test_agg_with_datetime_index_list_agg_func(col_name):
     result = df.resample("1d").aggregate(["mean"])
     expected = DataFrame(
         [47.5, 143.5, 195.5],
-        index=date_range(
-            start="2017-01-01", freq="D", periods=3, tz="Europe/Berlin"
-        ),
+        index=date_range(start="2017-01-01", freq="D", periods=3, tz="Europe/Berlin"),
         columns=pd.MultiIndex(levels=[[col_name], ["mean"]], codes=[[0], [0]]),
     )
     tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/resample/test_timedelta.py b/pandas/tests/resample/test_timedelta.py
index e602b53a9ee2c..c6ee295208607 100644
--- a/pandas/tests/resample/test_timedelta.py
+++ b/pandas/tests/resample/test_timedelta.py
@@ -67,9 +67,7 @@ def test_resample_single_period_timedelta():
 
     s = Series(list(range(5)), index=timedelta_range("1 day", freq="s", periods=5))
     result = s.resample("2s").sum()
-    expected = Series(
-        [1, 5, 4], index=timedelta_range("1 day", freq="2s", periods=3)
-    )
+    expected = Series([1, 5, 4], index=timedelta_range("1 day", freq="2s", periods=3))
     tm.assert_series_equal(result, expected)
 
 

From ecb4d51c6c12d0e844249e5fdb2cb202eb0e575b Mon Sep 17 00:00:00 2001
From: thomasyu888 <thomas.yu@sagebase.org>
Date: Thu, 4 Mar 2021 17:22:33 +0800
Subject: [PATCH 3/3] Revert

---
 .pre-commit-config.yaml | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 78b9d9ad05ec6..3966e8931162c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -140,9 +140,7 @@ repos:
         entry: python scripts/check_for_inconsistent_pandas_namespace.py
         language: python
         types: [python]
-        files: ^pandas/tests/(frame|tseries)/
-        additional_dependencies: [tokenize-rt]
-        args: [--replace]
+        files: ^pandas/tests/frame/
     -   id: FrameOrSeriesUnion
         name: Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias
         entry: Union\[.*(Series,.*DataFrame|DataFrame,.*Series).*\]