""" Test cases for time series specific (freq conversion, etc) """ from datetime import ( date, datetime, time, timedelta, ) import pickle import numpy as np import pytest from pandas._libs.tslibs import ( BaseOffset, to_offset, ) import pandas.util._test_decorators as td from pandas import ( DataFrame, Index, NaT, Series, concat, isna, to_datetime, ) import pandas._testing as tm from pandas.core.indexes.datetimes import ( DatetimeIndex, bdate_range, date_range, ) from pandas.core.indexes.period import ( Period, PeriodIndex, period_range, ) from pandas.core.indexes.timedeltas import timedelta_range from pandas.tests.plotting.common import TestPlotBase try: from pandas.plotting._matplotlib.compat import mpl_ge_3_6_0 except ImportError: mpl_ge_3_6_0 = lambda: True from pandas.tseries.offsets import WeekOfMonth @td.skip_if_no_mpl class TestTSPlot(TestPlotBase): @pytest.mark.filterwarnings("ignore::UserWarning") def test_ts_plot_with_tz(self, tz_aware_fixture): # GH2877, GH17173, GH31205, GH31580 tz = tz_aware_fixture index = date_range("1/1/2011", periods=2, freq="H", tz=tz) ts = Series([188.5, 328.25], index=index) _check_plot_works(ts.plot) ax = ts.plot() xdata = list(ax.get_lines())[0].get_xdata() # Check first and last points' labels are correct assert (xdata[0].hour, xdata[0].minute) == (0, 0) assert (xdata[-1].hour, xdata[-1].minute) == (1, 0) def test_fontsize_set_correctly(self): # For issue #8765 df = DataFrame(np.random.randn(10, 9), index=range(10)) fig, ax = self.plt.subplots() df.plot(fontsize=2, ax=ax) for label in ax.get_xticklabels() + ax.get_yticklabels(): assert label.get_fontsize() == 2 def test_frame_inferred(self): # inferred freq idx = date_range("1/1/1987", freq="MS", periods=100) idx = DatetimeIndex(idx.values, freq=None) df = DataFrame(np.random.randn(len(idx), 3), index=idx) _check_plot_works(df.plot) # axes freq idx = idx[0:40].union(idx[45:99]) df2 = DataFrame(np.random.randn(len(idx), 3), index=idx) _check_plot_works(df2.plot) # N > 1 idx = date_range("2008-1-1 00:15:00", freq="15T", periods=10) idx = DatetimeIndex(idx.values, freq=None) df = DataFrame(np.random.randn(len(idx), 3), index=idx) _check_plot_works(df.plot) def test_is_error_nozeroindex(self): # GH11858 i = np.array([1, 2, 3]) a = DataFrame(i, index=i) _check_plot_works(a.plot, xerr=a) _check_plot_works(a.plot, yerr=a) def test_nonnumeric_exclude(self): idx = date_range("1/1/1987", freq="A", periods=3) df = DataFrame({"A": ["x", "y", "z"], "B": [1, 2, 3]}, idx) fig, ax = self.plt.subplots() df.plot(ax=ax) # it works assert len(ax.get_lines()) == 1 # B was plotted self.plt.close(fig) msg = "no numeric data to plot" with pytest.raises(TypeError, match=msg): df["A"].plot() @pytest.mark.parametrize("freq", ["S", "T", "H", "D", "W", "M", "Q", "A"]) def test_tsplot_period(self, freq): idx = period_range("12/31/1999", freq=freq, periods=100) ser = Series(np.random.randn(len(idx)), idx) _, ax = self.plt.subplots() _check_plot_works(ser.plot, ax=ax) @pytest.mark.parametrize( "freq", ["S", "T", "H", "D", "W", "M", "Q-DEC", "A", "1B30Min"] ) def test_tsplot_datetime(self, freq): idx = date_range("12/31/1999", freq=freq, periods=100) ser = Series(np.random.randn(len(idx)), idx) _, ax = self.plt.subplots() _check_plot_works(ser.plot, ax=ax) def test_tsplot(self): ts = tm.makeTimeSeries() _, ax = self.plt.subplots() ts.plot(style="k", ax=ax) color = (0.0, 0.0, 0.0, 1) assert color == ax.get_lines()[0].get_color() def test_both_style_and_color(self): ts = tm.makeTimeSeries() msg = ( "Cannot pass 'style' string with a color symbol and 'color' " "keyword argument. Please use one or the other or pass 'style' " "without a color symbol" ) with pytest.raises(ValueError, match=msg): ts.plot(style="b-", color="#000099") s = ts.reset_index(drop=True) with pytest.raises(ValueError, match=msg): s.plot(style="b-", color="#000099") @pytest.mark.parametrize("freq", ["ms", "us"]) def test_high_freq(self, freq): _, ax = self.plt.subplots() rng = date_range("1/1/2012", periods=100, freq=freq) ser = Series(np.random.randn(len(rng)), rng) _check_plot_works(ser.plot, ax=ax) def test_get_datevalue(self): from pandas.plotting._matplotlib.converter import get_datevalue assert get_datevalue(None, "D") is None assert get_datevalue(1987, "A") == 1987 assert get_datevalue(Period(1987, "A"), "M") == Period("1987-12", "M").ordinal assert get_datevalue("1/1/1987", "D") == Period("1987-1-1", "D").ordinal def test_ts_plot_format_coord(self): def check_format_of_first_point(ax, expected_string): first_line = ax.get_lines()[0] first_x = first_line.get_xdata()[0].ordinal first_y = first_line.get_ydata()[0] assert expected_string == ax.format_coord(first_x, first_y) annual = Series(1, index=date_range("2014-01-01", periods=3, freq="A-DEC")) _, ax = self.plt.subplots() annual.plot(ax=ax) check_format_of_first_point(ax, "t = 2014 y = 1.000000") # note this is added to the annual plot already in existence, and # changes its freq field daily = Series(1, index=date_range("2014-01-01", periods=3, freq="D")) daily.plot(ax=ax) check_format_of_first_point(ax, "t = 2014-01-01 y = 1.000000") tm.close() @pytest.mark.parametrize("freq", ["S", "T", "H", "D", "W", "M", "Q", "A"]) def test_line_plot_period_series(self, freq): idx = period_range("12/31/1999", freq=freq, periods=100) ser = Series(np.random.randn(len(idx)), idx) _check_plot_works(ser.plot, ser.index.freq) @pytest.mark.parametrize( "frqncy", ["1S", "3S", "5T", "7H", "4D", "8W", "11M", "3A"] ) def test_line_plot_period_mlt_series(self, frqncy): # test period index line plot for series with multiples (`mlt`) of the # frequency (`frqncy`) rule code. tests resolution of issue #14763 idx = period_range("12/31/1999", freq=frqncy, periods=100) s = Series(np.random.randn(len(idx)), idx) _check_plot_works(s.plot, s.index.freq.rule_code) @pytest.mark.parametrize( "freq", ["S", "T", "H", "D", "W", "M", "Q-DEC", "A", "1B30Min"] ) def test_line_plot_datetime_series(self, freq): idx = date_range("12/31/1999", freq=freq, periods=100) ser = Series(np.random.randn(len(idx)), idx) _check_plot_works(ser.plot, ser.index.freq.rule_code) @pytest.mark.parametrize("freq", ["S", "T", "H", "D", "W", "M", "Q", "A"]) def test_line_plot_period_frame(self, freq): idx = date_range("12/31/1999", freq=freq, periods=100) df = DataFrame(np.random.randn(len(idx), 3), index=idx, columns=["A", "B", "C"]) _check_plot_works(df.plot, df.index.freq) @pytest.mark.parametrize( "frqncy", ["1S", "3S", "5T", "7H", "4D", "8W", "11M", "3A"] ) def test_line_plot_period_mlt_frame(self, frqncy): # test period index line plot for DataFrames with multiples (`mlt`) # of the frequency (`frqncy`) rule code. tests resolution of issue # #14763 idx = period_range("12/31/1999", freq=frqncy, periods=100) df = DataFrame(np.random.randn(len(idx), 3), index=idx, columns=["A", "B", "C"]) freq = df.index.asfreq(df.index.freq.rule_code).freq _check_plot_works(df.plot, freq) @pytest.mark.parametrize( "freq", ["S", "T", "H", "D", "W", "M", "Q-DEC", "A", "1B30Min"] ) def test_line_plot_datetime_frame(self, freq): idx = date_range("12/31/1999", freq=freq, periods=100) df = DataFrame(np.random.randn(len(idx), 3), index=idx, columns=["A", "B", "C"]) freq = df.index.to_period(df.index.freq.rule_code).freq _check_plot_works(df.plot, freq) @pytest.mark.parametrize( "freq", ["S", "T", "H", "D", "W", "M", "Q-DEC", "A", "1B30Min"] ) def test_line_plot_inferred_freq(self, freq): idx = date_range("12/31/1999", freq=freq, periods=100) ser = Series(np.random.randn(len(idx)), idx) ser = Series(ser.values, Index(np.asarray(ser.index))) _check_plot_works(ser.plot, ser.index.inferred_freq) ser = ser[[0, 3, 5, 6]] _check_plot_works(ser.plot) def test_fake_inferred_business(self): _, ax = self.plt.subplots() rng = date_range("2001-1-1", "2001-1-10") ts = Series(range(len(rng)), index=rng) ts = concat([ts[:3], ts[5:]]) ts.plot(ax=ax) assert not hasattr(ax, "freq") def test_plot_offset_freq(self): ser = tm.makeTimeSeries() _check_plot_works(ser.plot) dr = date_range(ser.index[0], freq="BQS", periods=10) ser = Series(np.random.randn(len(dr)), index=dr) _check_plot_works(ser.plot) def test_plot_multiple_inferred_freq(self): dr = Index([datetime(2000, 1, 1), datetime(2000, 1, 6), datetime(2000, 1, 11)]) ser = Series(np.random.randn(len(dr)), index=dr) _check_plot_works(ser.plot) @pytest.mark.xfail(mpl_ge_3_6_0(), reason="Api changed") def test_uhf(self): import pandas.plotting._matplotlib.converter as conv idx = date_range("2012-6-22 21:59:51.960928", freq="L", periods=500) df = DataFrame(np.random.randn(len(idx), 2), index=idx) _, ax = self.plt.subplots() df.plot(ax=ax) axis = ax.get_xaxis() tlocs = axis.get_ticklocs() tlabels = axis.get_ticklabels() for loc, label in zip(tlocs, tlabels): xp = conv._from_ordinal(loc).strftime("%H:%M:%S.%f") rs = str(label.get_text()) if len(rs): assert xp == rs def test_irreg_hf(self): idx = date_range("2012-6-22 21:59:51", freq="S", periods=100) df = DataFrame(np.random.randn(len(idx), 2), index=idx) irreg = df.iloc[[0, 1, 3, 4]] _, ax = self.plt.subplots() irreg.plot(ax=ax) diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff() sec = 1.0 / 24 / 60 / 60 assert (np.fabs(diffs[1:] - [sec, sec * 2, sec]) < 1e-8).all() _, ax = self.plt.subplots() df2 = df.copy() df2.index = df.index.astype(object) df2.plot(ax=ax) diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff() assert (np.fabs(diffs[1:] - sec) < 1e-8).all() def test_irregular_datetime64_repr_bug(self): ser = tm.makeTimeSeries() ser = ser[[0, 1, 2, 7]] _, ax = self.plt.subplots() ret = ser.plot(ax=ax) assert ret is not None for rs, xp in zip(ax.get_lines()[0].get_xdata(), ser.index): assert rs == xp def test_business_freq(self): bts = tm.makePeriodSeries() _, ax = self.plt.subplots() bts.plot(ax=ax) assert ax.get_lines()[0].get_xydata()[0, 0] == bts.index[0].ordinal idx = ax.get_lines()[0].get_xdata() assert PeriodIndex(data=idx).freqstr == "B" def test_business_freq_convert(self): bts = tm.makeTimeSeries(300).asfreq("BM") ts = bts.to_period("M") _, ax = self.plt.subplots() bts.plot(ax=ax) assert ax.get_lines()[0].get_xydata()[0, 0] == ts.index[0].ordinal idx = ax.get_lines()[0].get_xdata() assert PeriodIndex(data=idx).freqstr == "M" def test_freq_with_no_period_alias(self): # GH34487 freq = WeekOfMonth() bts = tm.makeTimeSeries(5).asfreq(freq) _, ax = self.plt.subplots() bts.plot(ax=ax) idx = ax.get_lines()[0].get_xdata() msg = "freq not specified and cannot be inferred" with pytest.raises(ValueError, match=msg): PeriodIndex(data=idx) def test_nonzero_base(self): # GH2571 idx = date_range("2012-12-20", periods=24, freq="H") + timedelta(minutes=30) df = DataFrame(np.arange(24), index=idx) _, ax = self.plt.subplots() df.plot(ax=ax) rs = ax.get_lines()[0].get_xdata() assert not Index(rs).is_normalized def test_dataframe(self): bts = DataFrame({"a": tm.makeTimeSeries()}) _, ax = self.plt.subplots() bts.plot(ax=ax) idx = ax.get_lines()[0].get_xdata() tm.assert_index_equal(bts.index.to_period(), PeriodIndex(idx)) def test_axis_limits(self): def _test(ax): xlim = ax.get_xlim() ax.set_xlim(xlim[0] - 5, xlim[1] + 10) result = ax.get_xlim() assert result[0] == xlim[0] - 5 assert result[1] == xlim[1] + 10 # string expected = (Period("1/1/2000", ax.freq), Period("4/1/2000", ax.freq)) ax.set_xlim("1/1/2000", "4/1/2000") result = ax.get_xlim() assert int(result[0]) == expected[0].ordinal assert int(result[1]) == expected[1].ordinal # datetime expected = (Period("1/1/2000", ax.freq), Period("4/1/2000", ax.freq)) ax.set_xlim(datetime(2000, 1, 1), datetime(2000, 4, 1)) result = ax.get_xlim() assert int(result[0]) == expected[0].ordinal assert int(result[1]) == expected[1].ordinal fig = ax.get_figure() self.plt.close(fig) ser = tm.makeTimeSeries() _, ax = self.plt.subplots() ser.plot(ax=ax) _test(ax) _, ax = self.plt.subplots() df = DataFrame({"a": ser, "b": ser + 1}) df.plot(ax=ax) _test(ax) df = DataFrame({"a": ser, "b": ser + 1}) axes = df.plot(subplots=True) for ax in axes: _test(ax) def test_get_finder(self): import pandas.plotting._matplotlib.converter as conv assert conv.get_finder(to_offset("B")) == conv._daily_finder assert conv.get_finder(to_offset("D")) == conv._daily_finder assert conv.get_finder(to_offset("M")) == conv._monthly_finder assert conv.get_finder(to_offset("Q")) == conv._quarterly_finder assert conv.get_finder(to_offset("A")) == conv._annual_finder assert conv.get_finder(to_offset("W")) == conv._daily_finder def test_finder_daily(self): day_lst = [10, 40, 252, 400, 950, 2750, 10000] xpl1 = xpl2 = [Period("1999-1-1", freq="B").ordinal] * len(day_lst) rs1 = [] rs2 = [] for n in day_lst: rng = bdate_range("1999-1-1", periods=n) ser = Series(np.random.randn(len(rng)), rng) _, ax = self.plt.subplots() ser.plot(ax=ax) xaxis = ax.get_xaxis() rs1.append(xaxis.get_majorticklocs()[0]) vmin, vmax = ax.get_xlim() ax.set_xlim(vmin + 0.9, vmax) rs2.append(xaxis.get_majorticklocs()[0]) self.plt.close(ax.get_figure()) assert rs1 == xpl1 assert rs2 == xpl2 def test_finder_quarterly(self): yrs = [3.5, 11] xpl1 = xpl2 = [Period("1988Q1").ordinal] * len(yrs) rs1 = [] rs2 = [] for n in yrs: rng = period_range("1987Q2", periods=int(n * 4), freq="Q") ser = Series(np.random.randn(len(rng)), rng) _, ax = self.plt.subplots() ser.plot(ax=ax) xaxis = ax.get_xaxis() rs1.append(xaxis.get_majorticklocs()[0]) (vmin, vmax) = ax.get_xlim() ax.set_xlim(vmin + 0.9, vmax) rs2.append(xaxis.get_majorticklocs()[0]) self.plt.close(ax.get_figure()) assert rs1 == xpl1 assert rs2 == xpl2 def test_finder_monthly(self): yrs = [1.15, 2.5, 4, 11] xpl1 = xpl2 = [Period("Jan 1988").ordinal] * len(yrs) rs1 = [] rs2 = [] for n in yrs: rng = period_range("1987Q2", periods=int(n * 12), freq="M") ser = Series(np.random.randn(len(rng)), rng) _, ax = self.plt.subplots() ser.plot(ax=ax) xaxis = ax.get_xaxis() rs1.append(xaxis.get_majorticklocs()[0]) vmin, vmax = ax.get_xlim() ax.set_xlim(vmin + 0.9, vmax) rs2.append(xaxis.get_majorticklocs()[0]) self.plt.close(ax.get_figure()) assert rs1 == xpl1 assert rs2 == xpl2 def test_finder_monthly_long(self): rng = period_range("1988Q1", periods=24 * 12, freq="M") ser = Series(np.random.randn(len(rng)), rng) _, ax = self.plt.subplots() ser.plot(ax=ax) xaxis = ax.get_xaxis() rs = xaxis.get_majorticklocs()[0] xp = Period("1989Q1", "M").ordinal assert rs == xp def test_finder_annual(self): xp = [1987, 1988, 1990, 1990, 1995, 2020, 2070, 2170] xp = [Period(x, freq="A").ordinal for x in xp] rs = [] for nyears in [5, 10, 19, 49, 99, 199, 599, 1001]: rng = period_range("1987", periods=nyears, freq="A") ser = Series(np.random.randn(len(rng)), rng) _, ax = self.plt.subplots() ser.plot(ax=ax) xaxis = ax.get_xaxis() rs.append(xaxis.get_majorticklocs()[0]) self.plt.close(ax.get_figure()) assert rs == xp @pytest.mark.slow def test_finder_minutely(self): nminutes = 50 * 24 * 60 rng = date_range("1/1/1999", freq="Min", periods=nminutes) ser = Series(np.random.randn(len(rng)), rng) _, ax = self.plt.subplots() ser.plot(ax=ax) xaxis = ax.get_xaxis() rs = xaxis.get_majorticklocs()[0] xp = Period("1/1/1999", freq="Min").ordinal assert rs == xp def test_finder_hourly(self): nhours = 23 rng = date_range("1/1/1999", freq="H", periods=nhours) ser = Series(np.random.randn(len(rng)), rng) _, ax = self.plt.subplots() ser.plot(ax=ax) xaxis = ax.get_xaxis() rs = xaxis.get_majorticklocs()[0] xp = Period("1/1/1999", freq="H").ordinal assert rs == xp def test_gaps(self): ts = tm.makeTimeSeries() ts.iloc[5:25] = np.nan _, ax = self.plt.subplots() ts.plot(ax=ax) lines = ax.get_lines() assert len(lines) == 1 line = lines[0] data = line.get_xydata() data = np.ma.MaskedArray(data, mask=isna(data), fill_value=np.nan) assert isinstance(data, np.ma.core.MaskedArray) mask = data.mask assert mask[5:25, 1].all() self.plt.close(ax.get_figure()) # irregular ts = tm.makeTimeSeries() ts = ts[[0, 1, 2, 5, 7, 9, 12, 15, 20]] ts.iloc[2:5] = np.nan _, ax = self.plt.subplots() ax = ts.plot(ax=ax) lines = ax.get_lines() assert len(lines) == 1 line = lines[0] data = line.get_xydata() data = np.ma.MaskedArray(data, mask=isna(data), fill_value=np.nan) assert isinstance(data, np.ma.core.MaskedArray) mask = data.mask assert mask[2:5, 1].all() self.plt.close(ax.get_figure()) # non-ts idx = [0, 1, 2, 5, 7, 9, 12, 15, 20] ser = Series(np.random.randn(len(idx)), idx) ser.iloc[2:5] = np.nan _, ax = self.plt.subplots() ser.plot(ax=ax) lines = ax.get_lines() assert len(lines) == 1 line = lines[0] data = line.get_xydata() data = np.ma.MaskedArray(data, mask=isna(data), fill_value=np.nan) assert isinstance(data, np.ma.core.MaskedArray) mask = data.mask assert mask[2:5, 1].all() def test_gap_upsample(self): low = tm.makeTimeSeries() low.iloc[5:25] = np.nan _, ax = self.plt.subplots() low.plot(ax=ax) idxh = date_range(low.index[0], low.index[-1], freq="12h") s = Series(np.random.randn(len(idxh)), idxh) s.plot(secondary_y=True) lines = ax.get_lines() assert len(lines) == 1 assert len(ax.right_ax.get_lines()) == 1 line = lines[0] data = line.get_xydata() data = np.ma.MaskedArray(data, mask=isna(data), fill_value=np.nan) assert isinstance(data, np.ma.core.MaskedArray) mask = data.mask assert mask[5:25, 1].all() def test_secondary_y(self): ser = Series(np.random.randn(10)) ser2 = Series(np.random.randn(10)) fig, _ = self.plt.subplots() ax = ser.plot(secondary_y=True) assert hasattr(ax, "left_ax") assert not hasattr(ax, "right_ax") axes = fig.get_axes() line = ax.get_lines()[0] xp = Series(line.get_ydata(), line.get_xdata()) tm.assert_series_equal(ser, xp) assert ax.get_yaxis().get_ticks_position() == "right" assert not axes[0].get_yaxis().get_visible() self.plt.close(fig) _, ax2 = self.plt.subplots() ser2.plot(ax=ax2) assert ax2.get_yaxis().get_ticks_position() == "left" self.plt.close(ax2.get_figure()) ax = ser2.plot() ax2 = ser.plot(secondary_y=True) assert ax.get_yaxis().get_visible() assert not hasattr(ax, "left_ax") assert hasattr(ax, "right_ax") assert hasattr(ax2, "left_ax") assert not hasattr(ax2, "right_ax") def test_secondary_y_ts(self): idx = date_range("1/1/2000", periods=10) ser = Series(np.random.randn(10), idx) ser2 = Series(np.random.randn(10), idx) fig, _ = self.plt.subplots() ax = ser.plot(secondary_y=True) assert hasattr(ax, "left_ax") assert not hasattr(ax, "right_ax") axes = fig.get_axes() line = ax.get_lines()[0] xp = Series(line.get_ydata(), line.get_xdata()).to_timestamp() tm.assert_series_equal(ser, xp) assert ax.get_yaxis().get_ticks_position() == "right" assert not axes[0].get_yaxis().get_visible() self.plt.close(fig) _, ax2 = self.plt.subplots() ser2.plot(ax=ax2) assert ax2.get_yaxis().get_ticks_position() == "left" self.plt.close(ax2.get_figure()) ax = ser2.plot() ax2 = ser.plot(secondary_y=True) assert ax.get_yaxis().get_visible() @td.skip_if_no_scipy def test_secondary_kde(self): ser = Series(np.random.randn(10)) fig, ax = self.plt.subplots() ax = ser.plot(secondary_y=True, kind="density", ax=ax) assert hasattr(ax, "left_ax") assert not hasattr(ax, "right_ax") axes = fig.get_axes() assert axes[1].get_yaxis().get_ticks_position() == "right" def test_secondary_bar(self): ser = Series(np.random.randn(10)) fig, ax = self.plt.subplots() ser.plot(secondary_y=True, kind="bar", ax=ax) axes = fig.get_axes() assert axes[1].get_yaxis().get_ticks_position() == "right" def test_secondary_frame(self): df = DataFrame(np.random.randn(5, 3), columns=["a", "b", "c"]) axes = df.plot(secondary_y=["a", "c"], subplots=True) assert axes[0].get_yaxis().get_ticks_position() == "right" assert axes[1].get_yaxis().get_ticks_position() == "left" assert axes[2].get_yaxis().get_ticks_position() == "right" def test_secondary_bar_frame(self): df = DataFrame(np.random.randn(5, 3), columns=["a", "b", "c"]) axes = df.plot(kind="bar", secondary_y=["a", "c"], subplots=True) assert axes[0].get_yaxis().get_ticks_position() == "right" assert axes[1].get_yaxis().get_ticks_position() == "left" assert axes[2].get_yaxis().get_ticks_position() == "right" def test_mixed_freq_regular_first(self): # TODO s1 = tm.makeTimeSeries() s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]] # it works! _, ax = self.plt.subplots() s1.plot(ax=ax) ax2 = s2.plot(style="g", ax=ax) lines = ax2.get_lines() idx1 = PeriodIndex(lines[0].get_xdata()) idx2 = PeriodIndex(lines[1].get_xdata()) tm.assert_index_equal(idx1, s1.index.to_period("B")) tm.assert_index_equal(idx2, s2.index.to_period("B")) left, right = ax2.get_xlim() pidx = s1.index.to_period() assert left <= pidx[0].ordinal assert right >= pidx[-1].ordinal def test_mixed_freq_irregular_first(self): s1 = tm.makeTimeSeries() s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]] _, ax = self.plt.subplots() s2.plot(style="g", ax=ax) s1.plot(ax=ax) assert not hasattr(ax, "freq") lines = ax.get_lines() x1 = lines[0].get_xdata() tm.assert_numpy_array_equal(x1, s2.index.astype(object).values) x2 = lines[1].get_xdata() tm.assert_numpy_array_equal(x2, s1.index.astype(object).values) def test_mixed_freq_regular_first_df(self): # GH 9852 s1 = tm.makeTimeSeries().to_frame() s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15], :] _, ax = self.plt.subplots() s1.plot(ax=ax) ax2 = s2.plot(style="g", ax=ax) lines = ax2.get_lines() idx1 = PeriodIndex(lines[0].get_xdata()) idx2 = PeriodIndex(lines[1].get_xdata()) assert idx1.equals(s1.index.to_period("B")) assert idx2.equals(s2.index.to_period("B")) left, right = ax2.get_xlim() pidx = s1.index.to_period() assert left <= pidx[0].ordinal assert right >= pidx[-1].ordinal def test_mixed_freq_irregular_first_df(self): # GH 9852 s1 = tm.makeTimeSeries().to_frame() s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15], :] _, ax = self.plt.subplots() s2.plot(style="g", ax=ax) s1.plot(ax=ax) assert not hasattr(ax, "freq") lines = ax.get_lines() x1 = lines[0].get_xdata() tm.assert_numpy_array_equal(x1, s2.index.astype(object).values) x2 = lines[1].get_xdata() tm.assert_numpy_array_equal(x2, s1.index.astype(object).values) def test_mixed_freq_hf_first(self): idxh = date_range("1/1/1999", periods=365, freq="D") idxl = date_range("1/1/1999", periods=12, freq="M") high = Series(np.random.randn(len(idxh)), idxh) low = Series(np.random.randn(len(idxl)), idxl) _, ax = self.plt.subplots() high.plot(ax=ax) low.plot(ax=ax) for line in ax.get_lines(): assert PeriodIndex(data=line.get_xdata()).freq == "D" def test_mixed_freq_alignment(self): ts_ind = date_range("2012-01-01 13:00", "2012-01-02", freq="H") ts_data = np.random.randn(12) ts = Series(ts_data, index=ts_ind) ts2 = ts.asfreq("T").interpolate() _, ax = self.plt.subplots() ax = ts.plot(ax=ax) ts2.plot(style="r", ax=ax) assert ax.lines[0].get_xdata()[0] == ax.lines[1].get_xdata()[0] def test_mixed_freq_lf_first(self): idxh = date_range("1/1/1999", periods=365, freq="D") idxl = date_range("1/1/1999", periods=12, freq="M") high = Series(np.random.randn(len(idxh)), idxh) low = Series(np.random.randn(len(idxl)), idxl) _, ax = self.plt.subplots() low.plot(legend=True, ax=ax) high.plot(legend=True, ax=ax) for line in ax.get_lines(): assert PeriodIndex(data=line.get_xdata()).freq == "D" leg = ax.get_legend() assert len(leg.texts) == 2 self.plt.close(ax.get_figure()) idxh = date_range("1/1/1999", periods=240, freq="T") idxl = date_range("1/1/1999", periods=4, freq="H") high = Series(np.random.randn(len(idxh)), idxh) low = Series(np.random.randn(len(idxl)), idxl) _, ax = self.plt.subplots() low.plot(ax=ax) high.plot(ax=ax) for line in ax.get_lines(): assert PeriodIndex(data=line.get_xdata()).freq == "T" def test_mixed_freq_irreg_period(self): ts = tm.makeTimeSeries() irreg = ts[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 16, 17, 18, 29]] rng = period_range("1/3/2000", periods=30, freq="B") ps = Series(np.random.randn(len(rng)), rng) _, ax = self.plt.subplots() irreg.plot(ax=ax) ps.plot(ax=ax) def test_mixed_freq_shared_ax(self): # GH13341, using sharex=True idx1 = date_range("2015-01-01", periods=3, freq="M") idx2 = idx1[:1].union(idx1[2:]) s1 = Series(range(len(idx1)), idx1) s2 = Series(range(len(idx2)), idx2) fig, (ax1, ax2) = self.plt.subplots(nrows=2, sharex=True) s1.plot(ax=ax1) s2.plot(ax=ax2) assert ax1.freq == "M" assert ax2.freq == "M" assert ax1.lines[0].get_xydata()[0, 0] == ax2.lines[0].get_xydata()[0, 0] # using twinx fig, ax1 = self.plt.subplots() ax2 = ax1.twinx() s1.plot(ax=ax1) s2.plot(ax=ax2) assert ax1.lines[0].get_xydata()[0, 0] == ax2.lines[0].get_xydata()[0, 0] # TODO (GH14330, GH14322) # plotting the irregular first does not yet work # fig, ax1 = plt.subplots() # ax2 = ax1.twinx() # s2.plot(ax=ax1) # s1.plot(ax=ax2) # assert (ax1.lines[0].get_xydata()[0, 0] == # ax2.lines[0].get_xydata()[0, 0]) def test_nat_handling(self): _, ax = self.plt.subplots() dti = DatetimeIndex(["2015-01-01", NaT, "2015-01-03"]) s = Series(range(len(dti)), dti) s.plot(ax=ax) xdata = ax.get_lines()[0].get_xdata() # plot x data is bounded by index values assert s.index.min() <= Series(xdata).min() assert Series(xdata).max() <= s.index.max() def test_to_weekly_resampling(self): idxh = date_range("1/1/1999", periods=52, freq="W") idxl = date_range("1/1/1999", periods=12, freq="M") high = Series(np.random.randn(len(idxh)), idxh) low = Series(np.random.randn(len(idxl)), idxl) _, ax = self.plt.subplots() high.plot(ax=ax) low.plot(ax=ax) for line in ax.get_lines(): assert PeriodIndex(data=line.get_xdata()).freq == idxh.freq def test_from_weekly_resampling(self): idxh = date_range("1/1/1999", periods=52, freq="W") idxl = date_range("1/1/1999", periods=12, freq="M") high = Series(np.random.randn(len(idxh)), idxh) low = Series(np.random.randn(len(idxl)), idxl) _, ax = self.plt.subplots() low.plot(ax=ax) high.plot(ax=ax) expected_h = idxh.to_period().asi8.astype(np.float64) expected_l = np.array( [1514, 1519, 1523, 1527, 1531, 1536, 1540, 1544, 1549, 1553, 1558, 1562], dtype=np.float64, ) for line in ax.get_lines(): assert PeriodIndex(data=line.get_xdata()).freq == idxh.freq xdata = line.get_xdata(orig=False) if len(xdata) == 12: # idxl lines tm.assert_numpy_array_equal(xdata, expected_l) else: tm.assert_numpy_array_equal(xdata, expected_h) tm.close() def test_from_resampling_area_line_mixed(self): idxh = date_range("1/1/1999", periods=52, freq="W") idxl = date_range("1/1/1999", periods=12, freq="M") high = DataFrame(np.random.rand(len(idxh), 3), index=idxh, columns=[0, 1, 2]) low = DataFrame(np.random.rand(len(idxl), 3), index=idxl, columns=[0, 1, 2]) # low to high for kind1, kind2 in [("line", "area"), ("area", "line")]: _, ax = self.plt.subplots() low.plot(kind=kind1, stacked=True, ax=ax) high.plot(kind=kind2, stacked=True, ax=ax) # check low dataframe result expected_x = np.array( [ 1514, 1519, 1523, 1527, 1531, 1536, 1540, 1544, 1549, 1553, 1558, 1562, ], dtype=np.float64, ) expected_y = np.zeros(len(expected_x), dtype=np.float64) for i in range(3): line = ax.lines[i] assert PeriodIndex(line.get_xdata()).freq == idxh.freq tm.assert_numpy_array_equal(line.get_xdata(orig=False), expected_x) # check stacked values are correct expected_y += low[i].values tm.assert_numpy_array_equal(line.get_ydata(orig=False), expected_y) # check high dataframe result expected_x = idxh.to_period().asi8.astype(np.float64) expected_y = np.zeros(len(expected_x), dtype=np.float64) for i in range(3): line = ax.lines[3 + i] assert PeriodIndex(data=line.get_xdata()).freq == idxh.freq tm.assert_numpy_array_equal(line.get_xdata(orig=False), expected_x) expected_y += high[i].values tm.assert_numpy_array_equal(line.get_ydata(orig=False), expected_y) # high to low for kind1, kind2 in [("line", "area"), ("area", "line")]: _, ax = self.plt.subplots() high.plot(kind=kind1, stacked=True, ax=ax) low.plot(kind=kind2, stacked=True, ax=ax) # check high dataframe result expected_x = idxh.to_period().asi8.astype(np.float64) expected_y = np.zeros(len(expected_x), dtype=np.float64) for i in range(3): line = ax.lines[i] assert PeriodIndex(data=line.get_xdata()).freq == idxh.freq tm.assert_numpy_array_equal(line.get_xdata(orig=False), expected_x) expected_y += high[i].values tm.assert_numpy_array_equal(line.get_ydata(orig=False), expected_y) # check low dataframe result expected_x = np.array( [ 1514, 1519, 1523, 1527, 1531, 1536, 1540, 1544, 1549, 1553, 1558, 1562, ], dtype=np.float64, ) expected_y = np.zeros(len(expected_x), dtype=np.float64) for i in range(3): lines = ax.lines[3 + i] assert PeriodIndex(data=lines.get_xdata()).freq == idxh.freq tm.assert_numpy_array_equal(lines.get_xdata(orig=False), expected_x) expected_y += low[i].values tm.assert_numpy_array_equal(lines.get_ydata(orig=False), expected_y) def test_mixed_freq_second_millisecond(self): # GH 7772, GH 7760 idxh = date_range("2014-07-01 09:00", freq="S", periods=50) idxl = date_range("2014-07-01 09:00", freq="100L", periods=500) high = Series(np.random.randn(len(idxh)), idxh) low = Series(np.random.randn(len(idxl)), idxl) # high to low _, ax = self.plt.subplots() high.plot(ax=ax) low.plot(ax=ax) assert len(ax.get_lines()) == 2 for line in ax.get_lines(): assert PeriodIndex(data=line.get_xdata()).freq == "L" tm.close() # low to high _, ax = self.plt.subplots() low.plot(ax=ax) high.plot(ax=ax) assert len(ax.get_lines()) == 2 for line in ax.get_lines(): assert PeriodIndex(data=line.get_xdata()).freq == "L" def test_irreg_dtypes(self): # date idx = [date(2000, 1, 1), date(2000, 1, 5), date(2000, 1, 20)] df = DataFrame(np.random.randn(len(idx), 3), Index(idx, dtype=object)) _check_plot_works(df.plot) # np.datetime64 idx = date_range("1/1/2000", periods=10) idx = idx[[0, 2, 5, 9]].astype(object) df = DataFrame(np.random.randn(len(idx), 3), idx) _, ax = self.plt.subplots() _check_plot_works(df.plot, ax=ax) def test_time(self): t = datetime(1, 1, 1, 3, 30, 0) deltas = np.random.randint(1, 20, 3).cumsum() ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas]) df = DataFrame( {"a": np.random.randn(len(ts)), "b": np.random.randn(len(ts))}, index=ts ) fig, ax = self.plt.subplots() df.plot(ax=ax) # verify tick labels ticks = ax.get_xticks() labels = ax.get_xticklabels() for t, l in zip(ticks, labels): m, s = divmod(int(t), 60) h, m = divmod(m, 60) rs = l.get_text() if len(rs) > 0: if s != 0: xp = time(h, m, s).strftime("%H:%M:%S") else: xp = time(h, m, s).strftime("%H:%M") assert xp == rs def test_time_change_xlim(self): t = datetime(1, 1, 1, 3, 30, 0) deltas = np.random.randint(1, 20, 3).cumsum() ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas]) df = DataFrame( {"a": np.random.randn(len(ts)), "b": np.random.randn(len(ts))}, index=ts ) fig, ax = self.plt.subplots() df.plot(ax=ax) # verify tick labels ticks = ax.get_xticks() labels = ax.get_xticklabels() for t, l in zip(ticks, labels): m, s = divmod(int(t), 60) h, m = divmod(m, 60) rs = l.get_text() if len(rs) > 0: if s != 0: xp = time(h, m, s).strftime("%H:%M:%S") else: xp = time(h, m, s).strftime("%H:%M") assert xp == rs # change xlim ax.set_xlim("1:30", "5:00") # check tick labels again ticks = ax.get_xticks() labels = ax.get_xticklabels() for t, l in zip(ticks, labels): m, s = divmod(int(t), 60) h, m = divmod(m, 60) rs = l.get_text() if len(rs) > 0: if s != 0: xp = time(h, m, s).strftime("%H:%M:%S") else: xp = time(h, m, s).strftime("%H:%M") assert xp == rs def test_time_musec(self): t = datetime(1, 1, 1, 3, 30, 0) deltas = np.random.randint(1, 20, 3).cumsum() ts = np.array([(t + timedelta(microseconds=int(x))).time() for x in deltas]) df = DataFrame( {"a": np.random.randn(len(ts)), "b": np.random.randn(len(ts))}, index=ts ) fig, ax = self.plt.subplots() ax = df.plot(ax=ax) # verify tick labels ticks = ax.get_xticks() labels = ax.get_xticklabels() for t, l in zip(ticks, labels): m, s = divmod(int(t), 60) us = round((t - int(t)) * 1e6) h, m = divmod(m, 60) rs = l.get_text() if len(rs) > 0: if (us % 1000) != 0: xp = time(h, m, s, us).strftime("%H:%M:%S.%f") elif (us // 1000) != 0: xp = time(h, m, s, us).strftime("%H:%M:%S.%f")[:-3] elif s != 0: xp = time(h, m, s, us).strftime("%H:%M:%S") else: xp = time(h, m, s, us).strftime("%H:%M") assert xp == rs def test_secondary_upsample(self): idxh = date_range("1/1/1999", periods=365, freq="D") idxl = date_range("1/1/1999", periods=12, freq="M") high = Series(np.random.randn(len(idxh)), idxh) low = Series(np.random.randn(len(idxl)), idxl) _, ax = self.plt.subplots() low.plot(ax=ax) ax = high.plot(secondary_y=True, ax=ax) for line in ax.get_lines(): assert PeriodIndex(line.get_xdata()).freq == "D" assert hasattr(ax, "left_ax") assert not hasattr(ax, "right_ax") for line in ax.left_ax.get_lines(): assert PeriodIndex(line.get_xdata()).freq == "D" def test_secondary_legend(self): fig = self.plt.figure() ax = fig.add_subplot(211) # ts df = tm.makeTimeDataFrame() df.plot(secondary_y=["A", "B"], ax=ax) leg = ax.get_legend() assert len(leg.get_lines()) == 4 assert leg.get_texts()[0].get_text() == "A (right)" assert leg.get_texts()[1].get_text() == "B (right)" assert leg.get_texts()[2].get_text() == "C" assert leg.get_texts()[3].get_text() == "D" assert ax.right_ax.get_legend() is None colors = set() for line in leg.get_lines(): colors.add(line.get_color()) # TODO: color cycle problems assert len(colors) == 4 self.plt.close(fig) fig = self.plt.figure() ax = fig.add_subplot(211) df.plot(secondary_y=["A", "C"], mark_right=False, ax=ax) leg = ax.get_legend() assert len(leg.get_lines()) == 4 assert leg.get_texts()[0].get_text() == "A" assert leg.get_texts()[1].get_text() == "B" assert leg.get_texts()[2].get_text() == "C" assert leg.get_texts()[3].get_text() == "D" self.plt.close(fig) fig, ax = self.plt.subplots() df.plot(kind="bar", secondary_y=["A"], ax=ax) leg = ax.get_legend() assert leg.get_texts()[0].get_text() == "A (right)" assert leg.get_texts()[1].get_text() == "B" self.plt.close(fig) fig, ax = self.plt.subplots() df.plot(kind="bar", secondary_y=["A"], mark_right=False, ax=ax) leg = ax.get_legend() assert leg.get_texts()[0].get_text() == "A" assert leg.get_texts()[1].get_text() == "B" self.plt.close(fig) fig = self.plt.figure() ax = fig.add_subplot(211) df = tm.makeTimeDataFrame() ax = df.plot(secondary_y=["C", "D"], ax=ax) leg = ax.get_legend() assert len(leg.get_lines()) == 4 assert ax.right_ax.get_legend() is None colors = set() for line in leg.get_lines(): colors.add(line.get_color()) # TODO: color cycle problems assert len(colors) == 4 self.plt.close(fig) # non-ts df = tm.makeDataFrame() fig = self.plt.figure() ax = fig.add_subplot(211) ax = df.plot(secondary_y=["A", "B"], ax=ax) leg = ax.get_legend() assert len(leg.get_lines()) == 4 assert ax.right_ax.get_legend() is None colors = set() for line in leg.get_lines(): colors.add(line.get_color()) # TODO: color cycle problems assert len(colors) == 4 self.plt.close() fig = self.plt.figure() ax = fig.add_subplot(211) ax = df.plot(secondary_y=["C", "D"], ax=ax) leg = ax.get_legend() assert len(leg.get_lines()) == 4 assert ax.right_ax.get_legend() is None colors = set() for line in leg.get_lines(): colors.add(line.get_color()) # TODO: color cycle problems assert len(colors) == 4 @pytest.mark.xfail(mpl_ge_3_6_0(), reason="Api changed") def test_format_date_axis(self): rng = date_range("1/1/2012", periods=12, freq="M") df = DataFrame(np.random.randn(len(rng), 3), rng) _, ax = self.plt.subplots() ax = df.plot(ax=ax) xaxis = ax.get_xaxis() for line in xaxis.get_ticklabels(): if len(line.get_text()) > 0: assert line.get_rotation() == 30 def test_ax_plot(self): x = date_range(start="2012-01-02", periods=10, freq="D") y = list(range(len(x))) _, ax = self.plt.subplots() lines = ax.plot(x, y, label="Y") tm.assert_index_equal(DatetimeIndex(lines[0].get_xdata()), x) def test_mpl_nopandas(self): dates = [date(2008, 12, 31), date(2009, 1, 31)] values1 = np.arange(10.0, 11.0, 0.5) values2 = np.arange(11.0, 12.0, 0.5) kw = {"fmt": "-", "lw": 4} _, ax = self.plt.subplots() ax.plot_date([x.toordinal() for x in dates], values1, **kw) ax.plot_date([x.toordinal() for x in dates], values2, **kw) line1, line2 = ax.get_lines() exp = np.array([x.toordinal() for x in dates], dtype=np.float64) tm.assert_numpy_array_equal(line1.get_xydata()[:, 0], exp) exp = np.array([x.toordinal() for x in dates], dtype=np.float64) tm.assert_numpy_array_equal(line2.get_xydata()[:, 0], exp) def test_irregular_ts_shared_ax_xlim(self): # GH 2960 from pandas.plotting._matplotlib.converter import DatetimeConverter ts = tm.makeTimeSeries()[:20] ts_irregular = ts[[1, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 17, 18]] # plot the left section of the irregular series, then the right section _, ax = self.plt.subplots() ts_irregular[:5].plot(ax=ax) ts_irregular[5:].plot(ax=ax) # check that axis limits are correct left, right = ax.get_xlim() assert left <= DatetimeConverter.convert(ts_irregular.index.min(), "", ax) assert right >= DatetimeConverter.convert(ts_irregular.index.max(), "", ax) def test_secondary_y_non_ts_xlim(self): # GH 3490 - non-timeseries with secondary y index_1 = [1, 2, 3, 4] index_2 = [5, 6, 7, 8] s1 = Series(1, index=index_1) s2 = Series(2, index=index_2) _, ax = self.plt.subplots() s1.plot(ax=ax) left_before, right_before = ax.get_xlim() s2.plot(secondary_y=True, ax=ax) left_after, right_after = ax.get_xlim() assert left_before >= left_after assert right_before < right_after def test_secondary_y_regular_ts_xlim(self): # GH 3490 - regular-timeseries with secondary y index_1 = date_range(start="2000-01-01", periods=4, freq="D") index_2 = date_range(start="2000-01-05", periods=4, freq="D") s1 = Series(1, index=index_1) s2 = Series(2, index=index_2) _, ax = self.plt.subplots() s1.plot(ax=ax) left_before, right_before = ax.get_xlim() s2.plot(secondary_y=True, ax=ax) left_after, right_after = ax.get_xlim() assert left_before >= left_after assert right_before < right_after def test_secondary_y_mixed_freq_ts_xlim(self): # GH 3490 - mixed frequency timeseries with secondary y rng = date_range("2000-01-01", periods=10000, freq="min") ts = Series(1, index=rng) _, ax = self.plt.subplots() ts.plot(ax=ax) left_before, right_before = ax.get_xlim() ts.resample("D").mean().plot(secondary_y=True, ax=ax) left_after, right_after = ax.get_xlim() # a downsample should not have changed either limit assert left_before == left_after assert right_before == right_after def test_secondary_y_irregular_ts_xlim(self): # GH 3490 - irregular-timeseries with secondary y from pandas.plotting._matplotlib.converter import DatetimeConverter ts = tm.makeTimeSeries()[:20] ts_irregular = ts[[1, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 17, 18]] _, ax = self.plt.subplots() ts_irregular[:5].plot(ax=ax) # plot higher-x values on secondary axis ts_irregular[5:].plot(secondary_y=True, ax=ax) # ensure secondary limits aren't overwritten by plot on primary ts_irregular[:5].plot(ax=ax) left, right = ax.get_xlim() assert left <= DatetimeConverter.convert(ts_irregular.index.min(), "", ax) assert right >= DatetimeConverter.convert(ts_irregular.index.max(), "", ax) def test_plot_outofbounds_datetime(self): # 2579 - checking this does not raise values = [date(1677, 1, 1), date(1677, 1, 2)] _, ax = self.plt.subplots() ax.plot(values) values = [datetime(1677, 1, 1, 12), datetime(1677, 1, 2, 12)] ax.plot(values) def test_format_timedelta_ticks_narrow(self): expected_labels = [f"00:00:00.0000000{i:0>2d}" for i in np.arange(10)] rng = timedelta_range("0", periods=10, freq="ns") df = DataFrame(np.random.randn(len(rng), 3), rng) fig, ax = self.plt.subplots() df.plot(fontsize=2, ax=ax) self.plt.draw() labels = ax.get_xticklabels() result_labels = [x.get_text() for x in labels] assert len(result_labels) == len(expected_labels) assert result_labels == expected_labels def test_format_timedelta_ticks_wide(self): expected_labels = [ "00:00:00", "1 days 03:46:40", "2 days 07:33:20", "3 days 11:20:00", "4 days 15:06:40", "5 days 18:53:20", "6 days 22:40:00", "8 days 02:26:40", "9 days 06:13:20", ] rng = timedelta_range("0", periods=10, freq="1 d") df = DataFrame(np.random.randn(len(rng), 3), rng) fig, ax = self.plt.subplots() ax = df.plot(fontsize=2, ax=ax) self.plt.draw() labels = ax.get_xticklabels() result_labels = [x.get_text() for x in labels] assert len(result_labels) == len(expected_labels) assert result_labels == expected_labels def test_timedelta_plot(self): # test issue #8711 s = Series(range(5), timedelta_range("1day", periods=5)) _, ax = self.plt.subplots() _check_plot_works(s.plot, ax=ax) # test long period index = timedelta_range("1 day 2 hr 30 min 10 s", periods=10, freq="1 d") s = Series(np.random.randn(len(index)), index) _, ax = self.plt.subplots() _check_plot_works(s.plot, ax=ax) # test short period index = timedelta_range("1 day 2 hr 30 min 10 s", periods=10, freq="1 ns") s = Series(np.random.randn(len(index)), index) _, ax = self.plt.subplots() _check_plot_works(s.plot, ax=ax) def test_hist(self): # https://github.com/matplotlib/matplotlib/issues/8459 rng = date_range("1/1/2011", periods=10, freq="H") x = rng w1 = np.arange(0, 1, 0.1) w2 = np.arange(0, 1, 0.1)[::-1] _, ax = self.plt.subplots() ax.hist([x, x], weights=[w1, w2]) def test_overlapping_datetime(self): # GB 6608 s1 = Series( [1, 2, 3], index=[ datetime(1995, 12, 31), datetime(2000, 12, 31), datetime(2005, 12, 31), ], ) s2 = Series( [1, 2, 3], index=[ datetime(1997, 12, 31), datetime(2003, 12, 31), datetime(2008, 12, 31), ], ) # plot first series, then add the second series to those axes, # then try adding the first series again _, ax = self.plt.subplots() s1.plot(ax=ax) s2.plot(ax=ax) s1.plot(ax=ax) @pytest.mark.xfail(reason="GH9053 matplotlib does not use ax.xaxis.converter") def test_add_matplotlib_datetime64(self): # GH9053 - ensure that a plot with PeriodConverter still understands # datetime64 data. This still fails because matplotlib overrides the # ax.xaxis.converter with a DatetimeConverter s = Series(np.random.randn(10), index=date_range("1970-01-02", periods=10)) ax = s.plot() with tm.assert_produces_warning(DeprecationWarning): # multi-dimensional indexing ax.plot(s.index, s.values, color="g") l1, l2 = ax.lines tm.assert_numpy_array_equal(l1.get_xydata(), l2.get_xydata()) def test_matplotlib_scatter_datetime64(self): # https://github.com/matplotlib/matplotlib/issues/11391 df = DataFrame(np.random.RandomState(0).rand(10, 2), columns=["x", "y"]) df["time"] = date_range("2018-01-01", periods=10, freq="D") fig, ax = self.plt.subplots() ax.scatter(x="time", y="y", data=df) self.plt.draw() label = ax.get_xticklabels()[0] expected = "2018-01-01" assert label.get_text() == expected def test_check_xticks_rot(self): # https://github.com/pandas-dev/pandas/issues/29460 # regular time series x = to_datetime(["2020-05-01", "2020-05-02", "2020-05-03"]) df = DataFrame({"x": x, "y": [1, 2, 3]}) axes = df.plot(x="x", y="y") self._check_ticks_props(axes, xrot=0) # irregular time series x = to_datetime(["2020-05-01", "2020-05-02", "2020-05-04"]) df = DataFrame({"x": x, "y": [1, 2, 3]}) axes = df.plot(x="x", y="y") self._check_ticks_props(axes, xrot=30) # use timeseries index or not axes = df.set_index("x").plot(y="y", use_index=True) self._check_ticks_props(axes, xrot=30) axes = df.set_index("x").plot(y="y", use_index=False) self._check_ticks_props(axes, xrot=0) # separate subplots axes = df.plot(x="x", y="y", subplots=True, sharex=True) self._check_ticks_props(axes, xrot=30) axes = df.plot(x="x", y="y", subplots=True, sharex=False) self._check_ticks_props(axes, xrot=0) def _check_plot_works(f, freq=None, series=None, *args, **kwargs): import matplotlib.pyplot as plt fig = plt.gcf() try: plt.clf() ax = fig.add_subplot(211) orig_ax = kwargs.pop("ax", plt.gca()) orig_axfreq = getattr(orig_ax, "freq", None) ret = f(*args, **kwargs) assert ret is not None # do something more intelligent ax = kwargs.pop("ax", plt.gca()) if series is not None: dfreq = series.index.freq if isinstance(dfreq, BaseOffset): dfreq = dfreq.rule_code if orig_axfreq is None: assert ax.freq == dfreq if freq is not None and orig_axfreq is None: assert ax.freq == freq ax = fig.add_subplot(212) kwargs["ax"] = ax ret = f(*args, **kwargs) assert ret is not None # TODO: do something more intelligent with tm.ensure_clean(return_filelike=True) as path: plt.savefig(path) # GH18439, GH#24088, statsmodels#4772 with tm.ensure_clean(return_filelike=True) as path: pickle.dump(fig, path) finally: plt.close(fig)