from datetime import ( UTC, datetime, ) import dateutil.tz import numpy as np import pytest import pandas as pd import pandas._testing as tm def test_get_values_for_csv_sub_minute_utc_offset(): # GH#66547 array formatting routes through Timestamp.isoformat, so a UTC # offset carrying seconds used to have the fraction spliced into it dti = pd.DatetimeIndex(["1800-01-01 00:00:00.000000001"], tz="UTC").tz_convert( "Asia/Tokyo" ) expected = np.array(["1800-01-01 09:18:59.000000001+09:18:59"], dtype=object) tm.assert_numpy_array_equal(dti._get_values_for_csv(), expected) tm.assert_numpy_array_equal(pd.Series(dti).astype(str).to_numpy(), expected) assert expected[0] in repr(dti) def test_get_values_for_csv(): index = pd.date_range(freq="1D", periods=3, start="2017-01-01") # First, with no arguments. expected = np.array(["2017-01-01", "2017-01-02", "2017-01-03"], dtype=object) result = index._get_values_for_csv() tm.assert_numpy_array_equal(result, expected) # No NaN values, so na_rep has no effect result = index._get_values_for_csv(na_rep="pandas") tm.assert_numpy_array_equal(result, expected) # Make sure date formatting works expected = np.array(["01-2017-01", "01-2017-02", "01-2017-03"], dtype=object) result = index._get_values_for_csv(date_format="%m-%Y-%d") tm.assert_numpy_array_equal(result, expected) # NULL object handling should work index = pd.DatetimeIndex(["2017-01-01", pd.NaT, "2017-01-03"]) expected = np.array(["2017-01-01", "NaT", "2017-01-03"], dtype=object) result = index._get_values_for_csv(na_rep="NaT") tm.assert_numpy_array_equal(result, expected) expected = np.array(["2017-01-01", "pandas", "2017-01-03"], dtype=object) result = index._get_values_for_csv(na_rep="pandas") tm.assert_numpy_array_equal(result, expected) result = index._get_values_for_csv(na_rep="NaT", date_format="%Y-%m-%d %H:%M:%S.%f") expected = np.array( ["2017-01-01 00:00:00.000000", "NaT", "2017-01-03 00:00:00.000000"], dtype=object, ) tm.assert_numpy_array_equal(result, expected) # invalid format result = index._get_values_for_csv(na_rep="NaT", date_format="foo") expected = np.array(["foo", "NaT", "foo"], dtype=object) tm.assert_numpy_array_equal(result, expected) class TestDatetimeIndexRendering: @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"]) def test_dti_with_timezone_repr(self, tzstr): rng = pd.date_range("4/13/2010", "5/6/2010") rng_eastern = rng.tz_localize(tzstr) rng_repr = repr(rng_eastern) assert "2010-04-13 00:00:00" in rng_repr def test_dti_repr_dates(self): text = str(pd.to_datetime([datetime(2013, 1, 1), datetime(2014, 1, 1)])) assert "['2013-01-01'," in text assert ", '2014-01-01']" in text def test_dti_repr_mixed(self): text = str( pd.to_datetime( [datetime(2013, 1, 1), datetime(2014, 1, 1, 12), datetime(2014, 1, 1)] ) ) assert "'2013-01-01 00:00:00'," in text assert "'2014-01-01 00:00:00']" in text def test_dti_repr_short(self): dr = pd.date_range(start="1/1/2012", periods=1) repr(dr) dr = pd.date_range(start="1/1/2012", periods=2) repr(dr) dr = pd.date_range(start="1/1/2012", periods=3) repr(dr) @pytest.mark.parametrize( "dates, freq, expected_repr", [ ( ["2012-01-01 00:00:00"], "60min", ( "DatetimeIndex(['2012-01-01 00:00:00'], " "dtype='datetime64[ns]', freq='60min')" ), ), ( ["2012-01-01 00:00:00", "2012-01-01 01:00:00"], "60min", "DatetimeIndex(['2012-01-01 00:00:00', " "'2012-01-01 01:00:00'],\n" " dtype='datetime64[ns]', freq='60min')", ), ( ["2012-01-01"], "24h", "DatetimeIndex(['2012-01-01'], dtype='datetime64[ns]', freq='24h')", ), ], ) def test_dti_repr_time_midnight(self, dates, freq, expected_repr, unit): # GH53634 dti = pd.DatetimeIndex(dates, freq).as_unit(unit) actual_repr = repr(dti) assert actual_repr == expected_repr.replace("[ns]", f"[{unit}]") def test_dti_repr_wraps_at_display_width(self): # GH#11552 dti = pd.date_range("2011-01-01", periods=3, freq="D", name="dates") result = repr(dti) expected = ( "DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'],\n" " dtype='datetime64[us]', name='dates', freq='D')" ) assert result == expected def test_dti_repr_two_values_wraps_at_display_width(self): # GH#16334 dti = pd.DatetimeIndex( ["2017-01-01 12:00:00.000000001", "2017-01-02 12:00:00.000000001"] ).tz_localize("US/Eastern") result = repr(dti) expected = ( "DatetimeIndex(['2017-01-01 12:00:00.000000001-05:00',\n" " '2017-01-02 12:00:00.000000001-05:00'],\n" " dtype='datetime64[ns, US/Eastern]', freq=None)" ) assert result == expected def test_dti_representation(self, unit): idxs = [] idxs.append(pd.DatetimeIndex([], freq="D")) idxs.append(pd.DatetimeIndex(["2011-01-01"], freq="D")) idxs.append(pd.DatetimeIndex(["2011-01-01", "2011-01-02"], freq="D")) idxs.append( pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D") ) idxs.append( pd.DatetimeIndex( ["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"], freq="h", tz="Asia/Tokyo", ) ) idxs.append( pd.DatetimeIndex( ["2011-01-01 09:00", "2011-01-01 10:00", pd.NaT], tz="US/Eastern" ) ) idxs.append( pd.DatetimeIndex(["2011-01-01 09:00", "2011-01-01 10:00", pd.NaT], tz="UTC") ) exp = [] exp.append("DatetimeIndex([], dtype='datetime64[ns]', freq='D')") exp.append("DatetimeIndex(['2011-01-01'], dtype='datetime64[ns]', freq='D')") exp.append( "DatetimeIndex(['2011-01-01', '2011-01-02'], " "dtype='datetime64[ns]', freq='D')" ) exp.append( "DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], " "dtype='datetime64[ns]', freq='D')" ) exp.append( "DatetimeIndex(['2011-01-01 09:00:00+09:00', " "'2011-01-01 10:00:00+09:00', '2011-01-01 11:00:00+09:00']" ", dtype='datetime64[ns, Asia/Tokyo]', freq='h')" ) exp.append( "DatetimeIndex(['2011-01-01 09:00:00-05:00', " "'2011-01-01 10:00:00-05:00', 'NaT'], " "dtype='datetime64[ns, US/Eastern]', freq=None)" ) exp.append( "DatetimeIndex(['2011-01-01 09:00:00+00:00', " "'2011-01-01 10:00:00+00:00', 'NaT'], " "dtype='datetime64[ns, UTC]', freq=None)" "" ) with pd.option_context("display.width", 300): for index, expected in zip(idxs, exp, strict=True): index = index.as_unit(unit) expected = expected.replace("[ns", f"[{unit}") result = repr(index) assert result == expected result = str(index) assert result == expected # TODO: this is a Series.__repr__ test def test_dti_representation_to_series(self, unit): idx1 = pd.DatetimeIndex([], freq="D") idx2 = pd.DatetimeIndex(["2011-01-01"], freq="D") idx3 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], freq="D") idx4 = pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D") idx5 = pd.DatetimeIndex( ["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"], freq="h", tz="Asia/Tokyo", ) idx6 = pd.DatetimeIndex( ["2011-01-01 09:00", "2011-01-01 10:00", pd.NaT], tz="US/Eastern" ) idx7 = pd.DatetimeIndex(["2011-01-01 09:00", "2011-01-02 10:15"]) exp1 = """Series([], dtype: datetime64[ns])""" exp2 = "0 2011-01-01\ndtype: datetime64[ns]" exp3 = "0 2011-01-01\n1 2011-01-02\ndtype: datetime64[ns]" exp4 = "0 2011-01-01\n1 2011-01-02\n2 2011-01-03\ndtype: datetime64[ns]" exp5 = ( "0 2011-01-01 09:00:00+09:00\n" "1 2011-01-01 10:00:00+09:00\n" "2 2011-01-01 11:00:00+09:00\n" "dtype: datetime64[ns, Asia/Tokyo]" ) exp6 = ( "0 2011-01-01 09:00:00-05:00\n" "1 2011-01-01 10:00:00-05:00\n" "2 NaT\n" "dtype: datetime64[ns, US/Eastern]" ) exp7 = "0 2011-01-01 09:00:00\n1 2011-01-02 10:15:00\ndtype: datetime64[ns]" with pd.option_context("display.width", 300): for idx, expected in zip( [idx1, idx2, idx3, idx4, idx5, idx6, idx7], [exp1, exp2, exp3, exp4, exp5, exp6, exp7], strict=True, ): ser = pd.Series(idx.as_unit(unit)) result = repr(ser) assert result == expected.replace("[ns", f"[{unit}") def test_dti_summary(self): # GH#9116 idx1 = pd.DatetimeIndex([], freq="D") idx2 = pd.DatetimeIndex(["2011-01-01"], freq="D") idx3 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], freq="D") idx4 = pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D") idx5 = pd.DatetimeIndex( ["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"], freq="h", tz="Asia/Tokyo", ) idx6 = pd.DatetimeIndex( ["2011-01-01 09:00", "2011-01-01 10:00", pd.NaT], tz="US/Eastern" ) exp1 = "DatetimeIndex: 0 entries\nFreq: D" exp2 = "DatetimeIndex: 1 entries, 2011-01-01 to 2011-01-01\nFreq: D" exp3 = "DatetimeIndex: 2 entries, 2011-01-01 to 2011-01-02\nFreq: D" exp4 = "DatetimeIndex: 3 entries, 2011-01-01 to 2011-01-03\nFreq: D" exp5 = ( "DatetimeIndex: 3 entries, 2011-01-01 09:00:00+09:00 " "to 2011-01-01 11:00:00+09:00\n" "Freq: h" ) exp6 = """DatetimeIndex: 3 entries, 2011-01-01 09:00:00-05:00 to NaT""" for idx, expected in zip( [idx1, idx2, idx3, idx4, idx5, idx6], [exp1, exp2, exp3, exp4, exp5, exp6], strict=True, ): result = idx._summary() assert result == expected @pytest.mark.parametrize("tz", [None, UTC, dateutil.tz.tzutc()]) @pytest.mark.parametrize("freq", ["B", "C"]) def test_dti_business_repr_etc_smoke(self, tz, freq): # only really care that it works dti = pd.bdate_range( datetime(2009, 1, 1), datetime(2010, 1, 1), tz=tz, freq=freq ) repr(dti) dti._summary() dti[2:2]._summary()