from datetime import datetime import numpy as np import pytest from pandas import ( DataFrame, DatetimeIndex, Series, date_range, period_range, to_datetime, ) import pandas._testing as tm from pandas.tseries import offsets class TestAsFreq: def test_asfreq2(self, frame_or_series): ts = frame_or_series( [0.0, 1.0, 2.0], index=DatetimeIndex( [ datetime(2009, 10, 30), datetime(2009, 11, 30), datetime(2009, 12, 31), ], freq="BM", ), ) daily_ts = ts.asfreq("B") monthly_ts = daily_ts.asfreq("BM") tm.assert_equal(monthly_ts, ts) daily_ts = ts.asfreq("B", method="pad") monthly_ts = daily_ts.asfreq("BM") tm.assert_equal(monthly_ts, ts) daily_ts = ts.asfreq(offsets.BDay()) monthly_ts = daily_ts.asfreq(offsets.BMonthEnd()) tm.assert_equal(monthly_ts, ts) result = ts[:0].asfreq("M") assert len(result) == 0 assert result is not ts if frame_or_series is Series: daily_ts = ts.asfreq("D", fill_value=-1) result = daily_ts.value_counts().sort_index() expected = Series([60, 1, 1, 1], index=[-1.0, 2.0, 1.0, 0.0]).sort_index() tm.assert_series_equal(result, expected) def test_asfreq_datetimeindex_empty(self, frame_or_series): # GH#14320 index = DatetimeIndex(["2016-09-29 11:00"]) expected = frame_or_series(index=index, dtype=object).asfreq("H") result = frame_or_series([3], index=index.copy()).asfreq("H") tm.assert_index_equal(expected.index, result.index) @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"]) def test_tz_aware_asfreq_smoke(self, tz, frame_or_series): dr = date_range("2011-12-01", "2012-07-20", freq="D", tz=tz) obj = frame_or_series(np.random.randn(len(dr)), index=dr) # it works! obj.asfreq("T") def test_asfreq_normalize(self, frame_or_series): rng = date_range("1/1/2000 09:30", periods=20) norm = date_range("1/1/2000", periods=20) vals = np.random.randn(20, 3) obj = DataFrame(vals, index=rng) expected = DataFrame(vals, index=norm) if frame_or_series is Series: obj = obj[0] expected = expected[0] result = obj.asfreq("D", normalize=True) tm.assert_equal(result, expected) def test_asfreq_keep_index_name(self, frame_or_series): # GH#9854 index_name = "bar" index = date_range("20130101", periods=20, name=index_name) obj = DataFrame(list(range(20)), columns=["foo"], index=index) obj = tm.get_obj(obj, frame_or_series) assert index_name == obj.index.name assert index_name == obj.asfreq("10D").index.name def test_asfreq_ts(self, frame_or_series): index = period_range(freq="A", start="1/1/2001", end="12/31/2010") obj = DataFrame(np.random.randn(len(index), 3), index=index) obj = tm.get_obj(obj, frame_or_series) result = obj.asfreq("D", how="end") exp_index = index.asfreq("D", how="end") assert len(result) == len(obj) tm.assert_index_equal(result.index, exp_index) result = obj.asfreq("D", how="start") exp_index = index.asfreq("D", how="start") assert len(result) == len(obj) tm.assert_index_equal(result.index, exp_index) def test_asfreq_resample_set_correct_freq(self, frame_or_series): # GH#5613 # we test if .asfreq() and .resample() set the correct value for .freq dti = to_datetime(["2012-01-01", "2012-01-02", "2012-01-03"]) obj = DataFrame({"col": [1, 2, 3]}, index=dti) obj = tm.get_obj(obj, frame_or_series) # testing the settings before calling .asfreq() and .resample() assert obj.index.freq is None assert obj.index.inferred_freq == "D" # does .asfreq() set .freq correctly? assert obj.asfreq("D").index.freq == "D" # does .resample() set .freq correctly? assert obj.resample("D").asfreq().index.freq == "D" def test_asfreq_empty(self, datetime_frame): # test does not blow up on length-0 DataFrame zero_length = datetime_frame.reindex([]) result = zero_length.asfreq("BM") assert result is not zero_length def test_asfreq(self, datetime_frame): offset_monthly = datetime_frame.asfreq(offsets.BMonthEnd()) rule_monthly = datetime_frame.asfreq("BM") tm.assert_frame_equal(offset_monthly, rule_monthly) filled = rule_monthly.asfreq("B", method="pad") # noqa # TODO: actually check that this worked. # don't forget! filled_dep = rule_monthly.asfreq("B", method="pad") # noqa def test_asfreq_datetimeindex(self): df = DataFrame( {"A": [1, 2, 3]}, index=[datetime(2011, 11, 1), datetime(2011, 11, 2), datetime(2011, 11, 3)], ) df = df.asfreq("B") assert isinstance(df.index, DatetimeIndex) ts = df["A"].asfreq("B") assert isinstance(ts.index, DatetimeIndex) def test_asfreq_fillvalue(self): # test for fill value during upsampling, related to issue 3715 # setup rng = date_range("1/1/2016", periods=10, freq="2S") ts = Series(np.arange(len(rng)), index=rng) df = DataFrame({"one": ts}) # insert pre-existing missing value df.loc["2016-01-01 00:00:08", "one"] = None actual_df = df.asfreq(freq="1S", fill_value=9.0) expected_df = df.asfreq(freq="1S").fillna(9.0) expected_df.loc["2016-01-01 00:00:08", "one"] = None tm.assert_frame_equal(expected_df, actual_df) expected_series = ts.asfreq(freq="1S").fillna(9.0) actual_series = ts.asfreq(freq="1S", fill_value=9.0) tm.assert_series_equal(expected_series, actual_series) def test_asfreq_with_date_object_index(self, frame_or_series): rng = date_range("1/1/2000", periods=20) ts = frame_or_series(np.random.randn(20), index=rng) ts2 = ts.copy() ts2.index = [x.date() for x in ts2.index] result = ts2.asfreq("4H", method="ffill") expected = ts.asfreq("4H", method="ffill") tm.assert_equal(result, expected) def test_asfreq_with_unsorted_index(self, frame_or_series): # GH#39805 # Test that rows are not dropped when the datetime index is out of order index = to_datetime(["2021-01-04", "2021-01-02", "2021-01-03", "2021-01-01"]) result = frame_or_series(range(4), index=index) expected = result.reindex(sorted(index)) expected.index = expected.index._with_freq("infer") result = result.asfreq("D") tm.assert_equal(result, expected)