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1651 lines
61 KiB
1651 lines
61 KiB
1 year ago
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""" Test cases for time series specific (freq conversion, etc) """
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from datetime import (
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date,
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datetime,
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time,
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timedelta,
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)
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import pickle
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import numpy as np
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import pytest
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from pandas._libs.tslibs import (
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BaseOffset,
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to_offset,
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)
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from pandas import (
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DataFrame,
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Index,
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NaT,
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Series,
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concat,
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isna,
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to_datetime,
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)
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import pandas._testing as tm
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from pandas.core.indexes.datetimes import (
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DatetimeIndex,
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bdate_range,
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date_range,
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)
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from pandas.core.indexes.period import (
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Period,
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PeriodIndex,
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period_range,
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)
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from pandas.core.indexes.timedeltas import timedelta_range
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from pandas.tests.plotting.common import _check_ticks_props
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from pandas.tseries.offsets import WeekOfMonth
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mpl = pytest.importorskip("matplotlib")
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class TestTSPlot:
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@pytest.mark.filterwarnings("ignore::UserWarning")
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def test_ts_plot_with_tz(self, tz_aware_fixture):
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# GH2877, GH17173, GH31205, GH31580
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tz = tz_aware_fixture
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index = date_range("1/1/2011", periods=2, freq="H", tz=tz)
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ts = Series([188.5, 328.25], index=index)
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_check_plot_works(ts.plot)
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ax = ts.plot()
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xdata = next(iter(ax.get_lines())).get_xdata()
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# Check first and last points' labels are correct
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assert (xdata[0].hour, xdata[0].minute) == (0, 0)
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assert (xdata[-1].hour, xdata[-1].minute) == (1, 0)
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def test_fontsize_set_correctly(self):
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# For issue #8765
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df = DataFrame(
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np.random.default_rng(2).standard_normal((10, 9)), index=range(10)
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)
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_, ax = mpl.pyplot.subplots()
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df.plot(fontsize=2, ax=ax)
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for label in ax.get_xticklabels() + ax.get_yticklabels():
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assert label.get_fontsize() == 2
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def test_frame_inferred(self):
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# inferred freq
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idx = date_range("1/1/1987", freq="MS", periods=100)
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idx = DatetimeIndex(idx.values, freq=None)
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df = DataFrame(
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np.random.default_rng(2).standard_normal((len(idx), 3)), index=idx
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)
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_check_plot_works(df.plot)
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# axes freq
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idx = idx[0:40].union(idx[45:99])
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df2 = DataFrame(
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np.random.default_rng(2).standard_normal((len(idx), 3)), index=idx
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)
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_check_plot_works(df2.plot)
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def test_frame_inferred_n_gt_1(self):
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# N > 1
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idx = date_range("2008-1-1 00:15:00", freq="15T", periods=10)
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idx = DatetimeIndex(idx.values, freq=None)
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df = DataFrame(
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np.random.default_rng(2).standard_normal((len(idx), 3)), index=idx
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)
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_check_plot_works(df.plot)
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def test_is_error_nozeroindex(self):
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# GH11858
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i = np.array([1, 2, 3])
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a = DataFrame(i, index=i)
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_check_plot_works(a.plot, xerr=a)
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_check_plot_works(a.plot, yerr=a)
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def test_nonnumeric_exclude(self):
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idx = date_range("1/1/1987", freq="A", periods=3)
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df = DataFrame({"A": ["x", "y", "z"], "B": [1, 2, 3]}, idx)
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fig, ax = mpl.pyplot.subplots()
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df.plot(ax=ax) # it works
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assert len(ax.get_lines()) == 1 # B was plotted
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mpl.pyplot.close(fig)
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def test_nonnumeric_exclude_error(self):
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idx = date_range("1/1/1987", freq="A", periods=3)
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df = DataFrame({"A": ["x", "y", "z"], "B": [1, 2, 3]}, idx)
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msg = "no numeric data to plot"
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with pytest.raises(TypeError, match=msg):
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df["A"].plot()
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@pytest.mark.parametrize("freq", ["S", "T", "H", "D", "W", "M", "Q", "A"])
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def test_tsplot_period(self, freq):
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idx = period_range("12/31/1999", freq=freq, periods=100)
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ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
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_, ax = mpl.pyplot.subplots()
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_check_plot_works(ser.plot, ax=ax)
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@pytest.mark.parametrize(
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"freq", ["S", "T", "H", "D", "W", "M", "Q-DEC", "A", "1B30Min"]
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)
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def test_tsplot_datetime(self, freq):
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idx = date_range("12/31/1999", freq=freq, periods=100)
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ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
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_, ax = mpl.pyplot.subplots()
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_check_plot_works(ser.plot, ax=ax)
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def test_tsplot(self):
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ts = tm.makeTimeSeries()
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_, ax = mpl.pyplot.subplots()
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ts.plot(style="k", ax=ax)
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color = (0.0, 0.0, 0.0, 1)
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assert color == ax.get_lines()[0].get_color()
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def test_both_style_and_color(self):
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ts = tm.makeTimeSeries()
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msg = (
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"Cannot pass 'style' string with a color symbol and 'color' "
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"keyword argument. Please use one or the other or pass 'style' "
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"without a color symbol"
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)
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with pytest.raises(ValueError, match=msg):
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ts.plot(style="b-", color="#000099")
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s = ts.reset_index(drop=True)
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with pytest.raises(ValueError, match=msg):
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s.plot(style="b-", color="#000099")
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@pytest.mark.parametrize("freq", ["ms", "us"])
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def test_high_freq(self, freq):
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_, ax = mpl.pyplot.subplots()
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rng = date_range("1/1/2012", periods=100, freq=freq)
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ser = Series(np.random.default_rng(2).standard_normal(len(rng)), rng)
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_check_plot_works(ser.plot, ax=ax)
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def test_get_datevalue(self):
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from pandas.plotting._matplotlib.converter import get_datevalue
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assert get_datevalue(None, "D") is None
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assert get_datevalue(1987, "A") == 1987
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assert get_datevalue(Period(1987, "A"), "M") == Period("1987-12", "M").ordinal
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assert get_datevalue("1/1/1987", "D") == Period("1987-1-1", "D").ordinal
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def test_ts_plot_format_coord(self):
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def check_format_of_first_point(ax, expected_string):
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first_line = ax.get_lines()[0]
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first_x = first_line.get_xdata()[0].ordinal
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first_y = first_line.get_ydata()[0]
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assert expected_string == ax.format_coord(first_x, first_y)
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annual = Series(1, index=date_range("2014-01-01", periods=3, freq="A-DEC"))
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_, ax = mpl.pyplot.subplots()
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annual.plot(ax=ax)
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check_format_of_first_point(ax, "t = 2014 y = 1.000000")
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# note this is added to the annual plot already in existence, and
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# changes its freq field
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daily = Series(1, index=date_range("2014-01-01", periods=3, freq="D"))
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daily.plot(ax=ax)
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check_format_of_first_point(ax, "t = 2014-01-01 y = 1.000000")
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@pytest.mark.parametrize("freq", ["S", "T", "H", "D", "W", "M", "Q", "A"])
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def test_line_plot_period_series(self, freq):
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idx = period_range("12/31/1999", freq=freq, periods=100)
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ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
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_check_plot_works(ser.plot, ser.index.freq)
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@pytest.mark.parametrize(
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"frqncy", ["1S", "3S", "5T", "7H", "4D", "8W", "11M", "3A"]
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)
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def test_line_plot_period_mlt_series(self, frqncy):
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# test period index line plot for series with multiples (`mlt`) of the
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# frequency (`frqncy`) rule code. tests resolution of issue #14763
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idx = period_range("12/31/1999", freq=frqncy, periods=100)
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s = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
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_check_plot_works(s.plot, s.index.freq.rule_code)
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@pytest.mark.parametrize(
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"freq", ["S", "T", "H", "D", "W", "M", "Q-DEC", "A", "1B30Min"]
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)
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def test_line_plot_datetime_series(self, freq):
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idx = date_range("12/31/1999", freq=freq, periods=100)
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ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
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_check_plot_works(ser.plot, ser.index.freq.rule_code)
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@pytest.mark.parametrize("freq", ["S", "T", "H", "D", "W", "M", "Q", "A"])
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def test_line_plot_period_frame(self, freq):
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idx = date_range("12/31/1999", freq=freq, periods=100)
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df = DataFrame(
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np.random.default_rng(2).standard_normal((len(idx), 3)),
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index=idx,
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columns=["A", "B", "C"],
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)
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_check_plot_works(df.plot, df.index.freq)
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@pytest.mark.parametrize(
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"frqncy", ["1S", "3S", "5T", "7H", "4D", "8W", "11M", "3A"]
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)
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def test_line_plot_period_mlt_frame(self, frqncy):
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# test period index line plot for DataFrames with multiples (`mlt`)
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# of the frequency (`frqncy`) rule code. tests resolution of issue
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# #14763
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idx = period_range("12/31/1999", freq=frqncy, periods=100)
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df = DataFrame(
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np.random.default_rng(2).standard_normal((len(idx), 3)),
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index=idx,
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columns=["A", "B", "C"],
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)
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freq = df.index.asfreq(df.index.freq.rule_code).freq
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_check_plot_works(df.plot, freq)
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@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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@pytest.mark.parametrize(
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"freq", ["S", "T", "H", "D", "W", "M", "Q-DEC", "A", "1B30Min"]
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)
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def test_line_plot_datetime_frame(self, freq):
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idx = date_range("12/31/1999", freq=freq, periods=100)
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df = DataFrame(
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np.random.default_rng(2).standard_normal((len(idx), 3)),
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index=idx,
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columns=["A", "B", "C"],
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)
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freq = df.index.to_period(df.index.freq.rule_code).freq
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_check_plot_works(df.plot, freq)
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@pytest.mark.parametrize(
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"freq", ["S", "T", "H", "D", "W", "M", "Q-DEC", "A", "1B30Min"]
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)
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def test_line_plot_inferred_freq(self, freq):
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idx = date_range("12/31/1999", freq=freq, periods=100)
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ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
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ser = Series(ser.values, Index(np.asarray(ser.index)))
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_check_plot_works(ser.plot, ser.index.inferred_freq)
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ser = ser.iloc[[0, 3, 5, 6]]
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_check_plot_works(ser.plot)
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def test_fake_inferred_business(self):
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_, ax = mpl.pyplot.subplots()
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rng = date_range("2001-1-1", "2001-1-10")
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ts = Series(range(len(rng)), index=rng)
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ts = concat([ts[:3], ts[5:]])
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ts.plot(ax=ax)
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assert not hasattr(ax, "freq")
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def test_plot_offset_freq(self):
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ser = tm.makeTimeSeries()
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_check_plot_works(ser.plot)
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def test_plot_offset_freq_business(self):
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dr = date_range("2023-01-01", freq="BQS", periods=10)
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ser = Series(np.random.default_rng(2).standard_normal(len(dr)), index=dr)
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_check_plot_works(ser.plot)
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def test_plot_multiple_inferred_freq(self):
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dr = Index([datetime(2000, 1, 1), datetime(2000, 1, 6), datetime(2000, 1, 11)])
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ser = Series(np.random.default_rng(2).standard_normal(len(dr)), index=dr)
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_check_plot_works(ser.plot)
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@pytest.mark.xfail(reason="Api changed in 3.6.0")
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def test_uhf(self):
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import pandas.plotting._matplotlib.converter as conv
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idx = date_range("2012-6-22 21:59:51.960928", freq="L", periods=500)
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df = DataFrame(
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np.random.default_rng(2).standard_normal((len(idx), 2)), index=idx
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)
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_, ax = mpl.pyplot.subplots()
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df.plot(ax=ax)
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axis = ax.get_xaxis()
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tlocs = axis.get_ticklocs()
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tlabels = axis.get_ticklabels()
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for loc, label in zip(tlocs, tlabels):
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xp = conv._from_ordinal(loc).strftime("%H:%M:%S.%f")
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rs = str(label.get_text())
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if len(rs):
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assert xp == rs
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def test_irreg_hf(self):
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idx = date_range("2012-6-22 21:59:51", freq="S", periods=10)
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df = DataFrame(
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np.random.default_rng(2).standard_normal((len(idx), 2)), index=idx
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)
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irreg = df.iloc[[0, 1, 3, 4]]
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_, ax = mpl.pyplot.subplots()
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irreg.plot(ax=ax)
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diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()
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sec = 1.0 / 24 / 60 / 60
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assert (np.fabs(diffs[1:] - [sec, sec * 2, sec]) < 1e-8).all()
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||
|
|
|
||
|
|
def test_irreg_hf_object(self):
|
||
|
|
idx = date_range("2012-6-22 21:59:51", freq="S", periods=10)
|
||
|
|
df2 = DataFrame(
|
||
|
|
np.random.default_rng(2).standard_normal((len(idx), 2)), index=idx
|
||
|
|
)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
df2.index = df2.index.astype(object)
|
||
|
|
df2.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) < 1e-8).all()
|
||
|
|
|
||
|
|
def test_irregular_datetime64_repr_bug(self):
|
||
|
|
ser = tm.makeTimeSeries()
|
||
|
|
ser = ser.iloc[[0, 1, 2, 7]]
|
||
|
|
|
||
|
|
_, ax = mpl.pyplot.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()
|
||
|
|
msg = r"PeriodDtype\[B\] is deprecated"
|
||
|
|
dt = bts.index[0].to_timestamp()
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
bts.index = period_range(start=dt, periods=len(bts), freq="B")
|
||
|
|
_, ax = mpl.pyplot.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()
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
assert PeriodIndex(data=idx).freqstr == "B"
|
||
|
|
|
||
|
|
def test_business_freq_convert(self):
|
||
|
|
bts = tm.makeTimeSeries(300).asfreq("BM")
|
||
|
|
ts = bts.to_period("M")
|
||
|
|
_, ax = mpl.pyplot.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 = mpl.pyplot.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 = mpl.pyplot.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 = mpl.pyplot.subplots()
|
||
|
|
bts.plot(ax=ax)
|
||
|
|
idx = ax.get_lines()[0].get_xdata()
|
||
|
|
msg = r"PeriodDtype\[B\] is deprecated"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
tm.assert_index_equal(bts.index.to_period(), PeriodIndex(idx))
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings(
|
||
|
|
"ignore:Period with BDay freq is deprecated:FutureWarning"
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"obj",
|
||
|
|
[
|
||
|
|
tm.makeTimeSeries(),
|
||
|
|
DataFrame({"a": tm.makeTimeSeries(), "b": tm.makeTimeSeries() + 1}),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_axis_limits(self, obj):
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
obj.plot(ax=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()
|
||
|
|
mpl.pyplot.close(fig)
|
||
|
|
|
||
|
|
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]
|
||
|
|
|
||
|
|
msg = "Period with BDay freq is deprecated"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
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.default_rng(2).standard_normal(len(rng)), rng)
|
||
|
|
_, ax = mpl.pyplot.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])
|
||
|
|
mpl.pyplot.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.default_rng(2).standard_normal(len(rng)), rng)
|
||
|
|
_, ax = mpl.pyplot.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])
|
||
|
|
mpl.pyplot.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.default_rng(2).standard_normal(len(rng)), rng)
|
||
|
|
_, ax = mpl.pyplot.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])
|
||
|
|
mpl.pyplot.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.default_rng(2).standard_normal(len(rng)), rng)
|
||
|
|
_, ax = mpl.pyplot.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.default_rng(2).standard_normal(len(rng)), rng)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
ser.plot(ax=ax)
|
||
|
|
xaxis = ax.get_xaxis()
|
||
|
|
rs.append(xaxis.get_majorticklocs()[0])
|
||
|
|
mpl.pyplot.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.default_rng(2).standard_normal(len(rng)), rng)
|
||
|
|
_, ax = mpl.pyplot.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.default_rng(2).standard_normal(len(rng)), rng)
|
||
|
|
_, ax = mpl.pyplot.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 = mpl.pyplot.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()
|
||
|
|
mpl.pyplot.close(ax.get_figure())
|
||
|
|
|
||
|
|
def test_gaps_irregular(self):
|
||
|
|
# irregular
|
||
|
|
ts = tm.makeTimeSeries()
|
||
|
|
ts = ts.iloc[[0, 1, 2, 5, 7, 9, 12, 15, 20]]
|
||
|
|
ts.iloc[2:5] = np.nan
|
||
|
|
_, ax = mpl.pyplot.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()
|
||
|
|
mpl.pyplot.close(ax.get_figure())
|
||
|
|
|
||
|
|
def test_gaps_non_ts(self):
|
||
|
|
# non-ts
|
||
|
|
idx = [0, 1, 2, 5, 7, 9, 12, 15, 20]
|
||
|
|
ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)
|
||
|
|
ser.iloc[2:5] = np.nan
|
||
|
|
_, ax = mpl.pyplot.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 = mpl.pyplot.subplots()
|
||
|
|
low.plot(ax=ax)
|
||
|
|
|
||
|
|
idxh = date_range(low.index[0], low.index[-1], freq="12h")
|
||
|
|
s = Series(np.random.default_rng(2).standard_normal(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.default_rng(2).standard_normal(10))
|
||
|
|
fig, _ = mpl.pyplot.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()
|
||
|
|
mpl.pyplot.close(fig)
|
||
|
|
|
||
|
|
def test_secondary_y_yaxis(self):
|
||
|
|
Series(np.random.default_rng(2).standard_normal(10))
|
||
|
|
ser2 = Series(np.random.default_rng(2).standard_normal(10))
|
||
|
|
_, ax2 = mpl.pyplot.subplots()
|
||
|
|
ser2.plot(ax=ax2)
|
||
|
|
assert ax2.get_yaxis().get_ticks_position() == "left"
|
||
|
|
mpl.pyplot.close(ax2.get_figure())
|
||
|
|
|
||
|
|
def test_secondary_both(self):
|
||
|
|
ser = Series(np.random.default_rng(2).standard_normal(10))
|
||
|
|
ser2 = Series(np.random.default_rng(2).standard_normal(10))
|
||
|
|
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.default_rng(2).standard_normal(10), idx)
|
||
|
|
fig, _ = mpl.pyplot.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()
|
||
|
|
mpl.pyplot.close(fig)
|
||
|
|
|
||
|
|
def test_secondary_y_ts_yaxis(self):
|
||
|
|
idx = date_range("1/1/2000", periods=10)
|
||
|
|
ser2 = Series(np.random.default_rng(2).standard_normal(10), idx)
|
||
|
|
_, ax2 = mpl.pyplot.subplots()
|
||
|
|
ser2.plot(ax=ax2)
|
||
|
|
assert ax2.get_yaxis().get_ticks_position() == "left"
|
||
|
|
mpl.pyplot.close(ax2.get_figure())
|
||
|
|
|
||
|
|
def test_secondary_y_ts_visible(self):
|
||
|
|
idx = date_range("1/1/2000", periods=10)
|
||
|
|
ser2 = Series(np.random.default_rng(2).standard_normal(10), idx)
|
||
|
|
ax = ser2.plot()
|
||
|
|
assert ax.get_yaxis().get_visible()
|
||
|
|
|
||
|
|
def test_secondary_kde(self):
|
||
|
|
pytest.importorskip("scipy")
|
||
|
|
ser = Series(np.random.default_rng(2).standard_normal(10))
|
||
|
|
fig, ax = mpl.pyplot.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.default_rng(2).standard_normal(10))
|
||
|
|
fig, ax = mpl.pyplot.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.default_rng(2).standard_normal((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.default_rng(2).standard_normal((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.iloc[[0, 5, 10, 11, 12, 13, 14, 15]]
|
||
|
|
|
||
|
|
# it works!
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
s1.plot(ax=ax)
|
||
|
|
|
||
|
|
ax2 = s2.plot(style="g", ax=ax)
|
||
|
|
lines = ax2.get_lines()
|
||
|
|
msg = r"PeriodDtype\[B\] is deprecated"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
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.iloc[[0, 5, 10, 11, 12, 13, 14, 15]]
|
||
|
|
_, ax = mpl.pyplot.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 = mpl.pyplot.subplots()
|
||
|
|
s1.plot(ax=ax)
|
||
|
|
ax2 = s2.plot(style="g", ax=ax)
|
||
|
|
lines = ax2.get_lines()
|
||
|
|
msg = r"PeriodDtype\[B\] is deprecated"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
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 = mpl.pyplot.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.default_rng(2).standard_normal(len(idxh)), idxh)
|
||
|
|
low = Series(np.random.default_rng(2).standard_normal(len(idxl)), idxl)
|
||
|
|
_, ax = mpl.pyplot.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.default_rng(2).standard_normal(12)
|
||
|
|
|
||
|
|
ts = Series(ts_data, index=ts_ind)
|
||
|
|
ts2 = ts.asfreq("T").interpolate()
|
||
|
|
|
||
|
|
_, ax = mpl.pyplot.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.default_rng(2).standard_normal(len(idxh)), idxh)
|
||
|
|
low = Series(np.random.default_rng(2).standard_normal(len(idxl)), idxl)
|
||
|
|
_, ax = mpl.pyplot.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
|
||
|
|
mpl.pyplot.close(ax.get_figure())
|
||
|
|
|
||
|
|
def test_mixed_freq_lf_first_hourly(self):
|
||
|
|
idxh = date_range("1/1/1999", periods=240, freq="T")
|
||
|
|
idxl = date_range("1/1/1999", periods=4, freq="H")
|
||
|
|
high = Series(np.random.default_rng(2).standard_normal(len(idxh)), idxh)
|
||
|
|
low = Series(np.random.default_rng(2).standard_normal(len(idxl)), idxl)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
low.plot(ax=ax)
|
||
|
|
high.plot(ax=ax)
|
||
|
|
for line in ax.get_lines():
|
||
|
|
assert PeriodIndex(data=line.get_xdata()).freq == "T"
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
|
||
|
|
def test_mixed_freq_irreg_period(self):
|
||
|
|
ts = tm.makeTimeSeries()
|
||
|
|
irreg = ts.iloc[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 16, 17, 18, 29]]
|
||
|
|
msg = r"PeriodDtype\[B\] is deprecated"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
rng = period_range("1/3/2000", periods=30, freq="B")
|
||
|
|
ps = Series(np.random.default_rng(2).standard_normal(len(rng)), rng)
|
||
|
|
_, ax = mpl.pyplot.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)
|
||
|
|
|
||
|
|
_, (ax1, ax2) = mpl.pyplot.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]
|
||
|
|
|
||
|
|
def test_mixed_freq_shared_ax_twin_x(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)
|
||
|
|
# using twinx
|
||
|
|
_, ax1 = mpl.pyplot.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]
|
||
|
|
|
||
|
|
@pytest.mark.xfail(reason="TODO (GH14330, GH14322)")
|
||
|
|
def test_mixed_freq_shared_ax_twin_x_irregular_first(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)
|
||
|
|
_, ax1 = mpl.pyplot.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 = mpl.pyplot.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.default_rng(2).standard_normal(len(idxh)), idxh)
|
||
|
|
low = Series(np.random.default_rng(2).standard_normal(len(idxl)), idxl)
|
||
|
|
_, ax = mpl.pyplot.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.default_rng(2).standard_normal(len(idxh)), idxh)
|
||
|
|
low = Series(np.random.default_rng(2).standard_normal(len(idxl)), idxl)
|
||
|
|
_, ax = mpl.pyplot.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)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("kind1, kind2", [("line", "area"), ("area", "line")])
|
||
|
|
def test_from_resampling_area_line_mixed(self, kind1, kind2):
|
||
|
|
idxh = date_range("1/1/1999", periods=52, freq="W")
|
||
|
|
idxl = date_range("1/1/1999", periods=12, freq="M")
|
||
|
|
high = DataFrame(
|
||
|
|
np.random.default_rng(2).random((len(idxh), 3)),
|
||
|
|
index=idxh,
|
||
|
|
columns=[0, 1, 2],
|
||
|
|
)
|
||
|
|
low = DataFrame(
|
||
|
|
np.random.default_rng(2).random((len(idxl), 3)),
|
||
|
|
index=idxl,
|
||
|
|
columns=[0, 1, 2],
|
||
|
|
)
|
||
|
|
|
||
|
|
_, ax = mpl.pyplot.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)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("kind1, kind2", [("line", "area"), ("area", "line")])
|
||
|
|
def test_from_resampling_area_line_mixed_high_to_low(self, kind1, kind2):
|
||
|
|
idxh = date_range("1/1/1999", periods=52, freq="W")
|
||
|
|
idxl = date_range("1/1/1999", periods=12, freq="M")
|
||
|
|
high = DataFrame(
|
||
|
|
np.random.default_rng(2).random((len(idxh), 3)),
|
||
|
|
index=idxh,
|
||
|
|
columns=[0, 1, 2],
|
||
|
|
)
|
||
|
|
low = DataFrame(
|
||
|
|
np.random.default_rng(2).random((len(idxl), 3)),
|
||
|
|
index=idxl,
|
||
|
|
columns=[0, 1, 2],
|
||
|
|
)
|
||
|
|
_, ax = mpl.pyplot.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.default_rng(2).standard_normal(len(idxh)), idxh)
|
||
|
|
low = Series(np.random.default_rng(2).standard_normal(len(idxl)), idxl)
|
||
|
|
# high to low
|
||
|
|
_, ax = mpl.pyplot.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"
|
||
|
|
|
||
|
|
def test_mixed_freq_second_millisecond_low_to_high(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.default_rng(2).standard_normal(len(idxh)), idxh)
|
||
|
|
low = Series(np.random.default_rng(2).standard_normal(len(idxl)), idxl)
|
||
|
|
# low to high
|
||
|
|
_, ax = mpl.pyplot.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.default_rng(2).standard_normal((len(idx), 3)),
|
||
|
|
Index(idx, dtype=object),
|
||
|
|
)
|
||
|
|
_check_plot_works(df.plot)
|
||
|
|
|
||
|
|
def test_irreg_dtypes_dt64(self):
|
||
|
|
# np.datetime64
|
||
|
|
idx = date_range("1/1/2000", periods=10)
|
||
|
|
idx = idx[[0, 2, 5, 9]].astype(object)
|
||
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((len(idx), 3)), idx)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
_check_plot_works(df.plot, ax=ax)
|
||
|
|
|
||
|
|
def test_time(self):
|
||
|
|
t = datetime(1, 1, 1, 3, 30, 0)
|
||
|
|
deltas = np.random.default_rng(2).integers(1, 20, 3).cumsum()
|
||
|
|
ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas])
|
||
|
|
df = DataFrame(
|
||
|
|
{
|
||
|
|
"a": np.random.default_rng(2).standard_normal(len(ts)),
|
||
|
|
"b": np.random.default_rng(2).standard_normal(len(ts)),
|
||
|
|
},
|
||
|
|
index=ts,
|
||
|
|
)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
df.plot(ax=ax)
|
||
|
|
|
||
|
|
# verify tick labels
|
||
|
|
ticks = ax.get_xticks()
|
||
|
|
labels = ax.get_xticklabels()
|
||
|
|
for _tick, _label in zip(ticks, labels):
|
||
|
|
m, s = divmod(int(_tick), 60)
|
||
|
|
h, m = divmod(m, 60)
|
||
|
|
rs = _label.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.default_rng(2).integers(1, 20, 3).cumsum()
|
||
|
|
ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas])
|
||
|
|
df = DataFrame(
|
||
|
|
{
|
||
|
|
"a": np.random.default_rng(2).standard_normal(len(ts)),
|
||
|
|
"b": np.random.default_rng(2).standard_normal(len(ts)),
|
||
|
|
},
|
||
|
|
index=ts,
|
||
|
|
)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
df.plot(ax=ax)
|
||
|
|
|
||
|
|
# verify tick labels
|
||
|
|
ticks = ax.get_xticks()
|
||
|
|
labels = ax.get_xticklabels()
|
||
|
|
for _tick, _label in zip(ticks, labels):
|
||
|
|
m, s = divmod(int(_tick), 60)
|
||
|
|
h, m = divmod(m, 60)
|
||
|
|
rs = _label.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 _tick, _label in zip(ticks, labels):
|
||
|
|
m, s = divmod(int(_tick), 60)
|
||
|
|
h, m = divmod(m, 60)
|
||
|
|
rs = _label.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.default_rng(2).integers(1, 20, 3).cumsum()
|
||
|
|
ts = np.array([(t + timedelta(microseconds=int(x))).time() for x in deltas])
|
||
|
|
df = DataFrame(
|
||
|
|
{
|
||
|
|
"a": np.random.default_rng(2).standard_normal(len(ts)),
|
||
|
|
"b": np.random.default_rng(2).standard_normal(len(ts)),
|
||
|
|
},
|
||
|
|
index=ts,
|
||
|
|
)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
ax = df.plot(ax=ax)
|
||
|
|
|
||
|
|
# verify tick labels
|
||
|
|
ticks = ax.get_xticks()
|
||
|
|
labels = ax.get_xticklabels()
|
||
|
|
for _tick, _label in zip(ticks, labels):
|
||
|
|
m, s = divmod(int(_tick), 60)
|
||
|
|
|
||
|
|
us = round((_tick - int(_tick)) * 1e6)
|
||
|
|
|
||
|
|
h, m = divmod(m, 60)
|
||
|
|
rs = _label.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.default_rng(2).standard_normal(len(idxh)), idxh)
|
||
|
|
low = Series(np.random.default_rng(2).standard_normal(len(idxl)), idxl)
|
||
|
|
_, ax = mpl.pyplot.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 = mpl.pyplot.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
|
||
|
|
mpl.pyplot.close(fig)
|
||
|
|
|
||
|
|
def test_secondary_legend_right(self):
|
||
|
|
df = tm.makeTimeDataFrame()
|
||
|
|
fig = mpl.pyplot.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"
|
||
|
|
mpl.pyplot.close(fig)
|
||
|
|
|
||
|
|
def test_secondary_legend_bar(self):
|
||
|
|
df = tm.makeTimeDataFrame()
|
||
|
|
fig, ax = mpl.pyplot.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"
|
||
|
|
mpl.pyplot.close(fig)
|
||
|
|
|
||
|
|
def test_secondary_legend_bar_right(self):
|
||
|
|
df = tm.makeTimeDataFrame()
|
||
|
|
fig, ax = mpl.pyplot.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"
|
||
|
|
mpl.pyplot.close(fig)
|
||
|
|
|
||
|
|
def test_secondary_legend_multi_col(self):
|
||
|
|
df = tm.makeTimeDataFrame()
|
||
|
|
fig = mpl.pyplot.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
|
||
|
|
mpl.pyplot.close(fig)
|
||
|
|
|
||
|
|
def test_secondary_legend_nonts(self):
|
||
|
|
# non-ts
|
||
|
|
df = tm.makeDataFrame()
|
||
|
|
fig = mpl.pyplot.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
|
||
|
|
mpl.pyplot.close()
|
||
|
|
|
||
|
|
def test_secondary_legend_nonts_multi_col(self):
|
||
|
|
# non-ts
|
||
|
|
df = tm.makeDataFrame()
|
||
|
|
fig = mpl.pyplot.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(reason="Api changed in 3.6.0")
|
||
|
|
def test_format_date_axis(self):
|
||
|
|
rng = date_range("1/1/2012", periods=12, freq="M")
|
||
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((len(rng), 3)), rng)
|
||
|
|
_, ax = mpl.pyplot.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 = mpl.pyplot.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 = mpl.pyplot.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.iloc[[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 = mpl.pyplot.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 = mpl.pyplot.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 = mpl.pyplot.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 = mpl.pyplot.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.iloc[[1, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 17, 18]]
|
||
|
|
|
||
|
|
_, ax = mpl.pyplot.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 = mpl.pyplot.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.default_rng(2).standard_normal((len(rng), 3)), rng)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
df.plot(fontsize=2, ax=ax)
|
||
|
|
mpl.pyplot.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.default_rng(2).standard_normal((len(rng), 3)), rng)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
ax = df.plot(fontsize=2, ax=ax)
|
||
|
|
mpl.pyplot.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 = mpl.pyplot.subplots()
|
||
|
|
_check_plot_works(s.plot, ax=ax)
|
||
|
|
|
||
|
|
def test_timedelta_long_period(self):
|
||
|
|
# test long period
|
||
|
|
index = timedelta_range("1 day 2 hr 30 min 10 s", periods=10, freq="1 d")
|
||
|
|
s = Series(np.random.default_rng(2).standard_normal(len(index)), index)
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
_check_plot_works(s.plot, ax=ax)
|
||
|
|
|
||
|
|
def test_timedelta_short_period(self):
|
||
|
|
# test short period
|
||
|
|
index = timedelta_range("1 day 2 hr 30 min 10 s", periods=10, freq="1 ns")
|
||
|
|
s = Series(np.random.default_rng(2).standard_normal(len(index)), index)
|
||
|
|
_, ax = mpl.pyplot.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 = mpl.pyplot.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 = mpl.pyplot.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.default_rng(2).standard_normal(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.default_rng(2).random((10, 2)), columns=["x", "y"])
|
||
|
|
df["time"] = date_range("2018-01-01", periods=10, freq="D")
|
||
|
|
_, ax = mpl.pyplot.subplots()
|
||
|
|
ax.scatter(x="time", y="y", data=df)
|
||
|
|
mpl.pyplot.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")
|
||
|
|
_check_ticks_props(axes, xrot=0)
|
||
|
|
|
||
|
|
def test_check_xticks_rot_irregular(self):
|
||
|
|
# 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")
|
||
|
|
_check_ticks_props(axes, xrot=30)
|
||
|
|
|
||
|
|
def test_check_xticks_rot_use_idx(self):
|
||
|
|
# irregular time series
|
||
|
|
x = to_datetime(["2020-05-01", "2020-05-02", "2020-05-04"])
|
||
|
|
df = DataFrame({"x": x, "y": [1, 2, 3]})
|
||
|
|
# use timeseries index or not
|
||
|
|
axes = df.set_index("x").plot(y="y", use_index=True)
|
||
|
|
_check_ticks_props(axes, xrot=30)
|
||
|
|
axes = df.set_index("x").plot(y="y", use_index=False)
|
||
|
|
_check_ticks_props(axes, xrot=0)
|
||
|
|
|
||
|
|
def test_check_xticks_rot_sharex(self):
|
||
|
|
# irregular time series
|
||
|
|
x = to_datetime(["2020-05-01", "2020-05-02", "2020-05-04"])
|
||
|
|
df = DataFrame({"x": x, "y": [1, 2, 3]})
|
||
|
|
# separate subplots
|
||
|
|
axes = df.plot(x="x", y="y", subplots=True, sharex=True)
|
||
|
|
_check_ticks_props(axes, xrot=30)
|
||
|
|
axes = df.plot(x="x", y="y", subplots=True, sharex=False)
|
||
|
|
_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)
|