Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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982 lines
35 KiB
982 lines
35 KiB
4 years ago
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""" Test cases for Series.plot """
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from datetime import datetime
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from itertools import chain
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import numpy as np
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from numpy.random import randn
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import pytest
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import DataFrame, Series, date_range
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import pandas._testing as tm
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from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
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import pandas.plotting as plotting
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@td.skip_if_no_mpl
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class TestSeriesPlots(TestPlotBase):
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def setup_method(self, method):
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TestPlotBase.setup_method(self, method)
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import matplotlib as mpl
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mpl.rcdefaults()
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self.ts = tm.makeTimeSeries()
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self.ts.name = "ts"
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self.series = tm.makeStringSeries()
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self.series.name = "series"
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self.iseries = tm.makePeriodSeries()
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self.iseries.name = "iseries"
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@pytest.mark.slow
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def test_plot(self):
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_check_plot_works(self.ts.plot, label="foo")
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_check_plot_works(self.ts.plot, use_index=False)
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axes = _check_plot_works(self.ts.plot, rot=0)
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self._check_ticks_props(axes, xrot=0)
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ax = _check_plot_works(self.ts.plot, style=".", logy=True)
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self._check_ax_scales(ax, yaxis="log")
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ax = _check_plot_works(self.ts.plot, style=".", logx=True)
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self._check_ax_scales(ax, xaxis="log")
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ax = _check_plot_works(self.ts.plot, style=".", loglog=True)
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self._check_ax_scales(ax, xaxis="log", yaxis="log")
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_check_plot_works(self.ts[:10].plot.bar)
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_check_plot_works(self.ts.plot.area, stacked=False)
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_check_plot_works(self.iseries.plot)
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for kind in ["line", "bar", "barh", "kde", "hist", "box"]:
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_check_plot_works(self.series[:5].plot, kind=kind)
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_check_plot_works(self.series[:10].plot.barh)
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ax = _check_plot_works(Series(randn(10)).plot.bar, color="black")
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self._check_colors([ax.patches[0]], facecolors=["black"])
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# GH 6951
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ax = _check_plot_works(self.ts.plot, subplots=True)
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self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
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ax = _check_plot_works(self.ts.plot, subplots=True, layout=(-1, 1))
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self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
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ax = _check_plot_works(self.ts.plot, subplots=True, layout=(1, -1))
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self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
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@pytest.mark.slow
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def test_plot_figsize_and_title(self):
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# figsize and title
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_, ax = self.plt.subplots()
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ax = self.series.plot(title="Test", figsize=(16, 8), ax=ax)
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self._check_text_labels(ax.title, "Test")
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self._check_axes_shape(ax, axes_num=1, layout=(1, 1), figsize=(16, 8))
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def test_dont_modify_rcParams(self):
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# GH 8242
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key = "axes.prop_cycle"
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colors = self.plt.rcParams[key]
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_, ax = self.plt.subplots()
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Series([1, 2, 3]).plot(ax=ax)
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assert colors == self.plt.rcParams[key]
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def test_ts_line_lim(self):
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fig, ax = self.plt.subplots()
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ax = self.ts.plot(ax=ax)
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xmin, xmax = ax.get_xlim()
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lines = ax.get_lines()
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assert xmin <= lines[0].get_data(orig=False)[0][0]
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assert xmax >= lines[0].get_data(orig=False)[0][-1]
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tm.close()
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ax = self.ts.plot(secondary_y=True, ax=ax)
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xmin, xmax = ax.get_xlim()
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lines = ax.get_lines()
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assert xmin <= lines[0].get_data(orig=False)[0][0]
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assert xmax >= lines[0].get_data(orig=False)[0][-1]
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def test_ts_area_lim(self):
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_, ax = self.plt.subplots()
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ax = self.ts.plot.area(stacked=False, ax=ax)
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xmin, xmax = ax.get_xlim()
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line = ax.get_lines()[0].get_data(orig=False)[0]
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assert xmin <= line[0]
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assert xmax >= line[-1]
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tm.close()
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# GH 7471
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_, ax = self.plt.subplots()
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ax = self.ts.plot.area(stacked=False, x_compat=True, ax=ax)
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xmin, xmax = ax.get_xlim()
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line = ax.get_lines()[0].get_data(orig=False)[0]
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assert xmin <= line[0]
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assert xmax >= line[-1]
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tm.close()
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tz_ts = self.ts.copy()
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tz_ts.index = tz_ts.tz_localize("GMT").tz_convert("CET")
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_, ax = self.plt.subplots()
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ax = tz_ts.plot.area(stacked=False, x_compat=True, ax=ax)
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xmin, xmax = ax.get_xlim()
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line = ax.get_lines()[0].get_data(orig=False)[0]
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assert xmin <= line[0]
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assert xmax >= line[-1]
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tm.close()
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_, ax = self.plt.subplots()
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ax = tz_ts.plot.area(stacked=False, secondary_y=True, ax=ax)
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xmin, xmax = ax.get_xlim()
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line = ax.get_lines()[0].get_data(orig=False)[0]
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assert xmin <= line[0]
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assert xmax >= line[-1]
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def test_label(self):
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s = Series([1, 2])
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_, ax = self.plt.subplots()
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ax = s.plot(label="LABEL", legend=True, ax=ax)
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self._check_legend_labels(ax, labels=["LABEL"])
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self.plt.close()
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_, ax = self.plt.subplots()
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ax = s.plot(legend=True, ax=ax)
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self._check_legend_labels(ax, labels=["None"])
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self.plt.close()
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# get name from index
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s.name = "NAME"
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_, ax = self.plt.subplots()
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ax = s.plot(legend=True, ax=ax)
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self._check_legend_labels(ax, labels=["NAME"])
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self.plt.close()
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# override the default
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_, ax = self.plt.subplots()
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ax = s.plot(legend=True, label="LABEL", ax=ax)
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self._check_legend_labels(ax, labels=["LABEL"])
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self.plt.close()
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# Add lebel info, but don't draw
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_, ax = self.plt.subplots()
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ax = s.plot(legend=False, label="LABEL", ax=ax)
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assert ax.get_legend() is None # Hasn't been drawn
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ax.legend() # draw it
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self._check_legend_labels(ax, labels=["LABEL"])
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def test_boolean(self):
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# GH 23719
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s = Series([False, False, True])
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_check_plot_works(s.plot, include_bool=True)
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msg = "no numeric data to plot"
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with pytest.raises(TypeError, match=msg):
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_check_plot_works(s.plot)
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def test_line_area_nan_series(self):
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values = [1, 2, np.nan, 3]
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s = Series(values)
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ts = Series(values, index=tm.makeDateIndex(k=4))
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for d in [s, ts]:
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ax = _check_plot_works(d.plot)
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masked = ax.lines[0].get_ydata()
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# remove nan for comparison purpose
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exp = np.array([1, 2, 3], dtype=np.float64)
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tm.assert_numpy_array_equal(np.delete(masked.data, 2), exp)
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tm.assert_numpy_array_equal(
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masked.mask, np.array([False, False, True, False])
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)
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expected = np.array([1, 2, 0, 3], dtype=np.float64)
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ax = _check_plot_works(d.plot, stacked=True)
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tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
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ax = _check_plot_works(d.plot.area)
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tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
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ax = _check_plot_works(d.plot.area, stacked=False)
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tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
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def test_line_use_index_false(self):
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s = Series([1, 2, 3], index=["a", "b", "c"])
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s.index.name = "The Index"
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_, ax = self.plt.subplots()
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ax = s.plot(use_index=False, ax=ax)
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label = ax.get_xlabel()
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assert label == ""
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_, ax = self.plt.subplots()
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ax2 = s.plot.bar(use_index=False, ax=ax)
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label2 = ax2.get_xlabel()
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assert label2 == ""
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@pytest.mark.slow
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def test_bar_log(self):
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expected = np.array([1e-1, 1e0, 1e1, 1e2, 1e3, 1e4])
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_, ax = self.plt.subplots()
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ax = Series([200, 500]).plot.bar(log=True, ax=ax)
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tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
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tm.close()
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_, ax = self.plt.subplots()
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ax = Series([200, 500]).plot.barh(log=True, ax=ax)
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tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), expected)
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tm.close()
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# GH 9905
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expected = np.array([1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1])
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_, ax = self.plt.subplots()
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ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind="bar", ax=ax)
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ymin = 0.0007943282347242822
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ymax = 0.12589254117941673
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res = ax.get_ylim()
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tm.assert_almost_equal(res[0], ymin)
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tm.assert_almost_equal(res[1], ymax)
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tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
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tm.close()
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_, ax = self.plt.subplots()
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ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind="barh", ax=ax)
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res = ax.get_xlim()
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tm.assert_almost_equal(res[0], ymin)
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tm.assert_almost_equal(res[1], ymax)
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tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), expected)
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@pytest.mark.slow
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def test_bar_ignore_index(self):
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df = Series([1, 2, 3, 4], index=["a", "b", "c", "d"])
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_, ax = self.plt.subplots()
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ax = df.plot.bar(use_index=False, ax=ax)
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self._check_text_labels(ax.get_xticklabels(), ["0", "1", "2", "3"])
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def test_bar_user_colors(self):
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s = Series([1, 2, 3, 4])
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ax = s.plot.bar(color=["red", "blue", "blue", "red"])
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result = [p.get_facecolor() for p in ax.patches]
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expected = [
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(1.0, 0.0, 0.0, 1.0),
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(0.0, 0.0, 1.0, 1.0),
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(0.0, 0.0, 1.0, 1.0),
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(1.0, 0.0, 0.0, 1.0),
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]
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assert result == expected
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def test_rotation(self):
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df = DataFrame(randn(5, 5))
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# Default rot 0
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_, ax = self.plt.subplots()
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axes = df.plot(ax=ax)
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self._check_ticks_props(axes, xrot=0)
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_, ax = self.plt.subplots()
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axes = df.plot(rot=30, ax=ax)
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self._check_ticks_props(axes, xrot=30)
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def test_irregular_datetime(self):
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from pandas.plotting._matplotlib.converter import DatetimeConverter
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rng = date_range("1/1/2000", "3/1/2000")
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rng = rng[[0, 1, 2, 3, 5, 9, 10, 11, 12]]
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ser = Series(randn(len(rng)), rng)
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_, ax = self.plt.subplots()
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ax = ser.plot(ax=ax)
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xp = DatetimeConverter.convert(datetime(1999, 1, 1), "", ax)
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ax.set_xlim("1/1/1999", "1/1/2001")
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assert xp == ax.get_xlim()[0]
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def test_unsorted_index_xlim(self):
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ser = Series(
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[0.0, 1.0, np.nan, 3.0, 4.0, 5.0, 6.0],
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index=[1.0, 0.0, 3.0, 2.0, np.nan, 3.0, 2.0],
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)
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_, ax = self.plt.subplots()
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ax = ser.plot(ax=ax)
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xmin, xmax = ax.get_xlim()
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lines = ax.get_lines()
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assert xmin <= np.nanmin(lines[0].get_data(orig=False)[0])
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assert xmax >= np.nanmax(lines[0].get_data(orig=False)[0])
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@pytest.mark.slow
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def test_pie_series(self):
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# if sum of values is less than 1.0, pie handle them as rate and draw
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# semicircle.
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series = Series(
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np.random.randint(1, 5), index=["a", "b", "c", "d", "e"], name="YLABEL"
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)
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ax = _check_plot_works(series.plot.pie)
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self._check_text_labels(ax.texts, series.index)
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assert ax.get_ylabel() == "YLABEL"
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# without wedge labels
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ax = _check_plot_works(series.plot.pie, labels=None)
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self._check_text_labels(ax.texts, [""] * 5)
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# with less colors than elements
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color_args = ["r", "g", "b"]
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ax = _check_plot_works(series.plot.pie, colors=color_args)
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color_expected = ["r", "g", "b", "r", "g"]
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self._check_colors(ax.patches, facecolors=color_expected)
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# with labels and colors
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labels = ["A", "B", "C", "D", "E"]
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color_args = ["r", "g", "b", "c", "m"]
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|
|
ax = _check_plot_works(series.plot.pie, labels=labels, colors=color_args)
|
||
|
|
self._check_text_labels(ax.texts, labels)
|
||
|
|
self._check_colors(ax.patches, facecolors=color_args)
|
||
|
|
|
||
|
|
# with autopct and fontsize
|
||
|
|
ax = _check_plot_works(
|
||
|
|
series.plot.pie, colors=color_args, autopct="%.2f", fontsize=7
|
||
|
|
)
|
||
|
|
pcts = [f"{s*100:.2f}" for s in series.values / float(series.sum())]
|
||
|
|
expected_texts = list(chain.from_iterable(zip(series.index, pcts)))
|
||
|
|
self._check_text_labels(ax.texts, expected_texts)
|
||
|
|
for t in ax.texts:
|
||
|
|
assert t.get_fontsize() == 7
|
||
|
|
|
||
|
|
# includes negative value
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
series = Series([1, 2, 0, 4, -1], index=["a", "b", "c", "d", "e"])
|
||
|
|
series.plot.pie()
|
||
|
|
|
||
|
|
# includes nan
|
||
|
|
series = Series([1, 2, np.nan, 4], index=["a", "b", "c", "d"], name="YLABEL")
|
||
|
|
ax = _check_plot_works(series.plot.pie)
|
||
|
|
self._check_text_labels(ax.texts, ["a", "b", "", "d"])
|
||
|
|
|
||
|
|
def test_pie_nan(self):
|
||
|
|
s = Series([1, np.nan, 1, 1])
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = s.plot.pie(legend=True, ax=ax)
|
||
|
|
expected = ["0", "", "2", "3"]
|
||
|
|
result = [x.get_text() for x in ax.texts]
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_hist_df_kwargs(self):
|
||
|
|
df = DataFrame(np.random.randn(10, 2))
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df.plot.hist(bins=5, ax=ax)
|
||
|
|
assert len(ax.patches) == 10
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_hist_df_with_nonnumerics(self):
|
||
|
|
# GH 9853
|
||
|
|
with tm.RNGContext(1):
|
||
|
|
df = DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])
|
||
|
|
df["E"] = ["x", "y"] * 5
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df.plot.hist(bins=5, ax=ax)
|
||
|
|
assert len(ax.patches) == 20
|
||
|
|
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df.plot.hist(ax=ax) # bins=10
|
||
|
|
assert len(ax.patches) == 40
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_hist_legacy(self):
|
||
|
|
_check_plot_works(self.ts.hist)
|
||
|
|
_check_plot_works(self.ts.hist, grid=False)
|
||
|
|
_check_plot_works(self.ts.hist, figsize=(8, 10))
|
||
|
|
# _check_plot_works adds an ax so catch warning. see GH #13188
|
||
|
|
with tm.assert_produces_warning(UserWarning):
|
||
|
|
_check_plot_works(self.ts.hist, by=self.ts.index.month)
|
||
|
|
with tm.assert_produces_warning(UserWarning):
|
||
|
|
_check_plot_works(self.ts.hist, by=self.ts.index.month, bins=5)
|
||
|
|
|
||
|
|
fig, ax = self.plt.subplots(1, 1)
|
||
|
|
_check_plot_works(self.ts.hist, ax=ax)
|
||
|
|
_check_plot_works(self.ts.hist, ax=ax, figure=fig)
|
||
|
|
_check_plot_works(self.ts.hist, figure=fig)
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
fig, (ax1, ax2) = self.plt.subplots(1, 2)
|
||
|
|
_check_plot_works(self.ts.hist, figure=fig, ax=ax1)
|
||
|
|
_check_plot_works(self.ts.hist, figure=fig, ax=ax2)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
self.ts.hist(by=self.ts.index, figure=fig)
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_hist_bins_legacy(self):
|
||
|
|
df = DataFrame(np.random.randn(10, 2))
|
||
|
|
ax = df.hist(bins=2)[0][0]
|
||
|
|
assert len(ax.patches) == 2
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_hist_layout(self):
|
||
|
|
df = self.hist_df
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.height.hist(layout=(1, 1))
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.height.hist(layout=[1, 1])
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_hist_layout_with_by(self):
|
||
|
|
df = self.hist_df
|
||
|
|
|
||
|
|
# _check_plot_works adds an ax so catch warning. see GH #13188
|
||
|
|
with tm.assert_produces_warning(UserWarning):
|
||
|
|
axes = _check_plot_works(df.height.hist, by=df.gender, layout=(2, 1))
|
||
|
|
self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(UserWarning):
|
||
|
|
axes = _check_plot_works(df.height.hist, by=df.gender, layout=(3, -1))
|
||
|
|
self._check_axes_shape(axes, axes_num=2, layout=(3, 1))
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(UserWarning):
|
||
|
|
axes = _check_plot_works(df.height.hist, by=df.category, layout=(4, 1))
|
||
|
|
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(UserWarning):
|
||
|
|
axes = _check_plot_works(df.height.hist, by=df.category, layout=(2, -1))
|
||
|
|
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(UserWarning):
|
||
|
|
axes = _check_plot_works(df.height.hist, by=df.category, layout=(3, -1))
|
||
|
|
self._check_axes_shape(axes, axes_num=4, layout=(3, 2))
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(UserWarning):
|
||
|
|
axes = _check_plot_works(df.height.hist, by=df.category, layout=(-1, 4))
|
||
|
|
self._check_axes_shape(axes, axes_num=4, layout=(1, 4))
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(UserWarning):
|
||
|
|
axes = _check_plot_works(df.height.hist, by=df.classroom, layout=(2, 2))
|
||
|
|
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
|
||
|
|
|
||
|
|
axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7))
|
||
|
|
self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 7))
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_hist_no_overlap(self):
|
||
|
|
from matplotlib.pyplot import gcf, subplot
|
||
|
|
|
||
|
|
x = Series(randn(2))
|
||
|
|
y = Series(randn(2))
|
||
|
|
subplot(121)
|
||
|
|
x.hist()
|
||
|
|
subplot(122)
|
||
|
|
y.hist()
|
||
|
|
fig = gcf()
|
||
|
|
axes = fig.axes
|
||
|
|
assert len(axes) == 2
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_hist_secondary_legend(self):
|
||
|
|
# GH 9610
|
||
|
|
df = DataFrame(np.random.randn(30, 4), columns=list("abcd"))
|
||
|
|
|
||
|
|
# primary -> secondary
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df["a"].plot.hist(legend=True, ax=ax)
|
||
|
|
df["b"].plot.hist(ax=ax, legend=True, secondary_y=True)
|
||
|
|
# both legends are dran on left ax
|
||
|
|
# left and right axis must be visible
|
||
|
|
self._check_legend_labels(ax, labels=["a", "b (right)"])
|
||
|
|
assert ax.get_yaxis().get_visible()
|
||
|
|
assert ax.right_ax.get_yaxis().get_visible()
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
# secondary -> secondary
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df["a"].plot.hist(legend=True, secondary_y=True, ax=ax)
|
||
|
|
df["b"].plot.hist(ax=ax, legend=True, secondary_y=True)
|
||
|
|
# both legends are draw on left ax
|
||
|
|
# left axis must be invisible, right axis must be visible
|
||
|
|
self._check_legend_labels(ax.left_ax, labels=["a (right)", "b (right)"])
|
||
|
|
assert not ax.left_ax.get_yaxis().get_visible()
|
||
|
|
assert ax.get_yaxis().get_visible()
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
# secondary -> primary
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df["a"].plot.hist(legend=True, secondary_y=True, ax=ax)
|
||
|
|
# right axes is returned
|
||
|
|
df["b"].plot.hist(ax=ax, legend=True)
|
||
|
|
# both legends are draw on left ax
|
||
|
|
# left and right axis must be visible
|
||
|
|
self._check_legend_labels(ax.left_ax, labels=["a (right)", "b"])
|
||
|
|
assert ax.left_ax.get_yaxis().get_visible()
|
||
|
|
assert ax.get_yaxis().get_visible()
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_df_series_secondary_legend(self):
|
||
|
|
# GH 9779
|
||
|
|
df = DataFrame(np.random.randn(30, 3), columns=list("abc"))
|
||
|
|
s = Series(np.random.randn(30), name="x")
|
||
|
|
|
||
|
|
# primary -> secondary (without passing ax)
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df.plot(ax=ax)
|
||
|
|
s.plot(legend=True, secondary_y=True, ax=ax)
|
||
|
|
# both legends are dran on left ax
|
||
|
|
# left and right axis must be visible
|
||
|
|
self._check_legend_labels(ax, labels=["a", "b", "c", "x (right)"])
|
||
|
|
assert ax.get_yaxis().get_visible()
|
||
|
|
assert ax.right_ax.get_yaxis().get_visible()
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
# primary -> secondary (with passing ax)
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df.plot(ax=ax)
|
||
|
|
s.plot(ax=ax, legend=True, secondary_y=True)
|
||
|
|
# both legends are dran on left ax
|
||
|
|
# left and right axis must be visible
|
||
|
|
self._check_legend_labels(ax, labels=["a", "b", "c", "x (right)"])
|
||
|
|
assert ax.get_yaxis().get_visible()
|
||
|
|
assert ax.right_ax.get_yaxis().get_visible()
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
# secondary -> secondary (without passing ax)
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df.plot(secondary_y=True, ax=ax)
|
||
|
|
s.plot(legend=True, secondary_y=True, ax=ax)
|
||
|
|
# both legends are dran on left ax
|
||
|
|
# left axis must be invisible and right axis must be visible
|
||
|
|
expected = ["a (right)", "b (right)", "c (right)", "x (right)"]
|
||
|
|
self._check_legend_labels(ax.left_ax, labels=expected)
|
||
|
|
assert not ax.left_ax.get_yaxis().get_visible()
|
||
|
|
assert ax.get_yaxis().get_visible()
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
# secondary -> secondary (with passing ax)
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df.plot(secondary_y=True, ax=ax)
|
||
|
|
s.plot(ax=ax, legend=True, secondary_y=True)
|
||
|
|
# both legends are dran on left ax
|
||
|
|
# left axis must be invisible and right axis must be visible
|
||
|
|
expected = ["a (right)", "b (right)", "c (right)", "x (right)"]
|
||
|
|
self._check_legend_labels(ax.left_ax, expected)
|
||
|
|
assert not ax.left_ax.get_yaxis().get_visible()
|
||
|
|
assert ax.get_yaxis().get_visible()
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
# secondary -> secondary (with passing ax)
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = df.plot(secondary_y=True, mark_right=False, ax=ax)
|
||
|
|
s.plot(ax=ax, legend=True, secondary_y=True)
|
||
|
|
# both legends are dran on left ax
|
||
|
|
# left axis must be invisible and right axis must be visible
|
||
|
|
expected = ["a", "b", "c", "x (right)"]
|
||
|
|
self._check_legend_labels(ax.left_ax, expected)
|
||
|
|
assert not ax.left_ax.get_yaxis().get_visible()
|
||
|
|
assert ax.get_yaxis().get_visible()
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"input_logy, expected_scale", [(True, "log"), ("sym", "symlog")]
|
||
|
|
)
|
||
|
|
def test_secondary_logy(self, input_logy, expected_scale):
|
||
|
|
# GH 25545
|
||
|
|
s1 = Series(np.random.randn(30))
|
||
|
|
s2 = Series(np.random.randn(30))
|
||
|
|
|
||
|
|
# GH 24980
|
||
|
|
ax1 = s1.plot(logy=input_logy)
|
||
|
|
ax2 = s2.plot(secondary_y=True, logy=input_logy)
|
||
|
|
|
||
|
|
assert ax1.get_yscale() == expected_scale
|
||
|
|
assert ax2.get_yscale() == expected_scale
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_plot_fails_with_dupe_color_and_style(self):
|
||
|
|
x = Series(randn(2))
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
x.plot(style="k--", color="k", ax=ax)
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_hist_kde(self):
|
||
|
|
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = self.ts.plot.hist(logy=True, ax=ax)
|
||
|
|
self._check_ax_scales(ax, yaxis="log")
|
||
|
|
xlabels = ax.get_xticklabels()
|
||
|
|
# ticks are values, thus ticklabels are blank
|
||
|
|
self._check_text_labels(xlabels, [""] * len(xlabels))
|
||
|
|
ylabels = ax.get_yticklabels()
|
||
|
|
self._check_text_labels(ylabels, [""] * len(ylabels))
|
||
|
|
|
||
|
|
_check_plot_works(self.ts.plot.kde)
|
||
|
|
_check_plot_works(self.ts.plot.density)
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = self.ts.plot.kde(logy=True, ax=ax)
|
||
|
|
self._check_ax_scales(ax, yaxis="log")
|
||
|
|
xlabels = ax.get_xticklabels()
|
||
|
|
self._check_text_labels(xlabels, [""] * len(xlabels))
|
||
|
|
ylabels = ax.get_yticklabels()
|
||
|
|
self._check_text_labels(ylabels, [""] * len(ylabels))
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_kde_kwargs(self):
|
||
|
|
sample_points = np.linspace(-100, 100, 20)
|
||
|
|
_check_plot_works(self.ts.plot.kde, bw_method="scott", ind=20)
|
||
|
|
_check_plot_works(self.ts.plot.kde, bw_method=None, ind=20)
|
||
|
|
_check_plot_works(self.ts.plot.kde, bw_method=None, ind=np.int_(20))
|
||
|
|
_check_plot_works(self.ts.plot.kde, bw_method=0.5, ind=sample_points)
|
||
|
|
_check_plot_works(self.ts.plot.density, bw_method=0.5, ind=sample_points)
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = self.ts.plot.kde(logy=True, bw_method=0.5, ind=sample_points, ax=ax)
|
||
|
|
self._check_ax_scales(ax, yaxis="log")
|
||
|
|
self._check_text_labels(ax.yaxis.get_label(), "Density")
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_kde_missing_vals(self):
|
||
|
|
s = Series(np.random.uniform(size=50))
|
||
|
|
s[0] = np.nan
|
||
|
|
axes = _check_plot_works(s.plot.kde)
|
||
|
|
|
||
|
|
# gh-14821: check if the values have any missing values
|
||
|
|
assert any(~np.isnan(axes.lines[0].get_xdata()))
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_hist_kwargs(self):
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = self.ts.plot.hist(bins=5, ax=ax)
|
||
|
|
assert len(ax.patches) == 5
|
||
|
|
self._check_text_labels(ax.yaxis.get_label(), "Frequency")
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = self.ts.plot.hist(orientation="horizontal", ax=ax)
|
||
|
|
self._check_text_labels(ax.xaxis.get_label(), "Frequency")
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = self.ts.plot.hist(align="left", stacked=True, ax=ax)
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_hist_kde_color(self):
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = self.ts.plot.hist(logy=True, bins=10, color="b", ax=ax)
|
||
|
|
self._check_ax_scales(ax, yaxis="log")
|
||
|
|
assert len(ax.patches) == 10
|
||
|
|
self._check_colors(ax.patches, facecolors=["b"] * 10)
|
||
|
|
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = self.ts.plot.kde(logy=True, color="r", ax=ax)
|
||
|
|
self._check_ax_scales(ax, yaxis="log")
|
||
|
|
lines = ax.get_lines()
|
||
|
|
assert len(lines) == 1
|
||
|
|
self._check_colors(lines, ["r"])
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_boxplot_series(self):
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = self.ts.plot.box(logy=True, ax=ax)
|
||
|
|
self._check_ax_scales(ax, yaxis="log")
|
||
|
|
xlabels = ax.get_xticklabels()
|
||
|
|
self._check_text_labels(xlabels, [self.ts.name])
|
||
|
|
ylabels = ax.get_yticklabels()
|
||
|
|
self._check_text_labels(ylabels, [""] * len(ylabels))
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_kind_both_ways(self):
|
||
|
|
s = Series(range(3))
|
||
|
|
kinds = (
|
||
|
|
plotting.PlotAccessor._common_kinds + plotting.PlotAccessor._series_kinds
|
||
|
|
)
|
||
|
|
for kind in kinds:
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
s.plot(kind=kind, ax=ax)
|
||
|
|
self.plt.close()
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
getattr(s.plot, kind)()
|
||
|
|
self.plt.close()
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_invalid_plot_data(self):
|
||
|
|
s = Series(list("abcd"))
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
for kind in plotting.PlotAccessor._common_kinds:
|
||
|
|
|
||
|
|
msg = "no numeric data to plot"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
s.plot(kind=kind, ax=ax)
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_valid_object_plot(self):
|
||
|
|
s = Series(range(10), dtype=object)
|
||
|
|
for kind in plotting.PlotAccessor._common_kinds:
|
||
|
|
_check_plot_works(s.plot, kind=kind)
|
||
|
|
|
||
|
|
def test_partially_invalid_plot_data(self):
|
||
|
|
s = Series(["a", "b", 1.0, 2])
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
for kind in plotting.PlotAccessor._common_kinds:
|
||
|
|
|
||
|
|
msg = "no numeric data to plot"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
s.plot(kind=kind, ax=ax)
|
||
|
|
|
||
|
|
def test_invalid_kind(self):
|
||
|
|
s = Series([1, 2])
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
s.plot(kind="aasdf")
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_dup_datetime_index_plot(self):
|
||
|
|
dr1 = date_range("1/1/2009", periods=4)
|
||
|
|
dr2 = date_range("1/2/2009", periods=4)
|
||
|
|
index = dr1.append(dr2)
|
||
|
|
values = randn(index.size)
|
||
|
|
s = Series(values, index=index)
|
||
|
|
_check_plot_works(s.plot)
|
||
|
|
|
||
|
|
def test_errorbar_asymmetrical(self):
|
||
|
|
# GH9536
|
||
|
|
s = Series(np.arange(10), name="x")
|
||
|
|
err = np.random.rand(2, 10)
|
||
|
|
|
||
|
|
ax = s.plot(yerr=err, xerr=err)
|
||
|
|
|
||
|
|
result = np.vstack([i.vertices[:, 1] for i in ax.collections[1].get_paths()])
|
||
|
|
expected = (err.T * np.array([-1, 1])) + s.to_numpy().reshape(-1, 1)
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
msg = (
|
||
|
|
"Asymmetrical error bars should be provided "
|
||
|
|
f"with the shape \\(2, {len(s)}\\)"
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.plot(yerr=np.random.rand(2, 11))
|
||
|
|
|
||
|
|
tm.close()
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_errorbar_plot(self):
|
||
|
|
|
||
|
|
s = Series(np.arange(10), name="x")
|
||
|
|
s_err = np.random.randn(10)
|
||
|
|
d_err = DataFrame(randn(10, 2), index=s.index, columns=["x", "y"])
|
||
|
|
# test line and bar plots
|
||
|
|
kinds = ["line", "bar"]
|
||
|
|
for kind in kinds:
|
||
|
|
ax = _check_plot_works(s.plot, yerr=Series(s_err), kind=kind)
|
||
|
|
self._check_has_errorbars(ax, xerr=0, yerr=1)
|
||
|
|
ax = _check_plot_works(s.plot, yerr=s_err, kind=kind)
|
||
|
|
self._check_has_errorbars(ax, xerr=0, yerr=1)
|
||
|
|
ax = _check_plot_works(s.plot, yerr=s_err.tolist(), kind=kind)
|
||
|
|
self._check_has_errorbars(ax, xerr=0, yerr=1)
|
||
|
|
ax = _check_plot_works(s.plot, yerr=d_err, kind=kind)
|
||
|
|
self._check_has_errorbars(ax, xerr=0, yerr=1)
|
||
|
|
ax = _check_plot_works(s.plot, xerr=0.2, yerr=0.2, kind=kind)
|
||
|
|
self._check_has_errorbars(ax, xerr=1, yerr=1)
|
||
|
|
|
||
|
|
ax = _check_plot_works(s.plot, xerr=s_err)
|
||
|
|
self._check_has_errorbars(ax, xerr=1, yerr=0)
|
||
|
|
|
||
|
|
# test time series plotting
|
||
|
|
ix = date_range("1/1/2000", "1/1/2001", freq="M")
|
||
|
|
ts = Series(np.arange(12), index=ix, name="x")
|
||
|
|
ts_err = Series(np.random.randn(12), index=ix)
|
||
|
|
td_err = DataFrame(randn(12, 2), index=ix, columns=["x", "y"])
|
||
|
|
|
||
|
|
ax = _check_plot_works(ts.plot, yerr=ts_err)
|
||
|
|
self._check_has_errorbars(ax, xerr=0, yerr=1)
|
||
|
|
ax = _check_plot_works(ts.plot, yerr=td_err)
|
||
|
|
self._check_has_errorbars(ax, xerr=0, yerr=1)
|
||
|
|
|
||
|
|
# check incorrect lengths and types
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
s.plot(yerr=np.arange(11))
|
||
|
|
|
||
|
|
s_err = ["zzz"] * 10
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
s.plot(yerr=s_err)
|
||
|
|
|
||
|
|
def test_table(self):
|
||
|
|
_check_plot_works(self.series.plot, table=True)
|
||
|
|
_check_plot_works(self.series.plot, table=self.series)
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_series_grid_settings(self):
|
||
|
|
# Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
|
||
|
|
self._check_grid_settings(
|
||
|
|
Series([1, 2, 3]),
|
||
|
|
plotting.PlotAccessor._series_kinds + plotting.PlotAccessor._common_kinds,
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_standard_colors(self):
|
||
|
|
from pandas.plotting._matplotlib.style import _get_standard_colors
|
||
|
|
|
||
|
|
for c in ["r", "red", "green", "#FF0000"]:
|
||
|
|
result = _get_standard_colors(1, color=c)
|
||
|
|
assert result == [c]
|
||
|
|
|
||
|
|
result = _get_standard_colors(1, color=[c])
|
||
|
|
assert result == [c]
|
||
|
|
|
||
|
|
result = _get_standard_colors(3, color=c)
|
||
|
|
assert result == [c] * 3
|
||
|
|
|
||
|
|
result = _get_standard_colors(3, color=[c])
|
||
|
|
assert result == [c] * 3
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_standard_colors_all(self):
|
||
|
|
import matplotlib.colors as colors
|
||
|
|
|
||
|
|
from pandas.plotting._matplotlib.style import _get_standard_colors
|
||
|
|
|
||
|
|
# multiple colors like mediumaquamarine
|
||
|
|
for c in colors.cnames:
|
||
|
|
result = _get_standard_colors(num_colors=1, color=c)
|
||
|
|
assert result == [c]
|
||
|
|
|
||
|
|
result = _get_standard_colors(num_colors=1, color=[c])
|
||
|
|
assert result == [c]
|
||
|
|
|
||
|
|
result = _get_standard_colors(num_colors=3, color=c)
|
||
|
|
assert result == [c] * 3
|
||
|
|
|
||
|
|
result = _get_standard_colors(num_colors=3, color=[c])
|
||
|
|
assert result == [c] * 3
|
||
|
|
|
||
|
|
# single letter colors like k
|
||
|
|
for c in colors.ColorConverter.colors:
|
||
|
|
result = _get_standard_colors(num_colors=1, color=c)
|
||
|
|
assert result == [c]
|
||
|
|
|
||
|
|
result = _get_standard_colors(num_colors=1, color=[c])
|
||
|
|
assert result == [c]
|
||
|
|
|
||
|
|
result = _get_standard_colors(num_colors=3, color=c)
|
||
|
|
assert result == [c] * 3
|
||
|
|
|
||
|
|
result = _get_standard_colors(num_colors=3, color=[c])
|
||
|
|
assert result == [c] * 3
|
||
|
|
|
||
|
|
def test_series_plot_color_kwargs(self):
|
||
|
|
# GH1890
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = Series(np.arange(12) + 1).plot(color="green", ax=ax)
|
||
|
|
self._check_colors(ax.get_lines(), linecolors=["green"])
|
||
|
|
|
||
|
|
def test_time_series_plot_color_kwargs(self):
|
||
|
|
# #1890
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = Series(np.arange(12) + 1, index=date_range("1/1/2000", periods=12)).plot(
|
||
|
|
color="green", ax=ax
|
||
|
|
)
|
||
|
|
self._check_colors(ax.get_lines(), linecolors=["green"])
|
||
|
|
|
||
|
|
def test_time_series_plot_color_with_empty_kwargs(self):
|
||
|
|
import matplotlib as mpl
|
||
|
|
|
||
|
|
def_colors = self._unpack_cycler(mpl.rcParams)
|
||
|
|
index = date_range("1/1/2000", periods=12)
|
||
|
|
s = Series(np.arange(1, 13), index=index)
|
||
|
|
|
||
|
|
ncolors = 3
|
||
|
|
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
for i in range(ncolors):
|
||
|
|
ax = s.plot(ax=ax)
|
||
|
|
self._check_colors(ax.get_lines(), linecolors=def_colors[:ncolors])
|
||
|
|
|
||
|
|
def test_xticklabels(self):
|
||
|
|
# GH11529
|
||
|
|
s = Series(np.arange(10), index=[f"P{i:02d}" for i in range(10)])
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = s.plot(xticks=[0, 3, 5, 9], ax=ax)
|
||
|
|
exp = [f"P{i:02d}" for i in [0, 3, 5, 9]]
|
||
|
|
self._check_text_labels(ax.get_xticklabels(), exp)
|
||
|
|
|
||
|
|
def test_xtick_barPlot(self):
|
||
|
|
# GH28172
|
||
|
|
s = pd.Series(range(10), index=[f"P{i:02d}" for i in range(10)])
|
||
|
|
ax = s.plot.bar(xticks=range(0, 11, 2))
|
||
|
|
exp = np.array(list(range(0, 11, 2)))
|
||
|
|
tm.assert_numpy_array_equal(exp, ax.get_xticks())
|
||
|
|
|
||
|
|
def test_custom_business_day_freq(self):
|
||
|
|
# GH7222
|
||
|
|
from pandas.tseries.offsets import CustomBusinessDay
|
||
|
|
|
||
|
|
s = Series(
|
||
|
|
range(100, 121),
|
||
|
|
index=pd.bdate_range(
|
||
|
|
start="2014-05-01",
|
||
|
|
end="2014-06-01",
|
||
|
|
freq=CustomBusinessDay(holidays=["2014-05-26"]),
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
_check_plot_works(s.plot)
|
||
|
|
|
||
|
|
@pytest.mark.xfail
|
||
|
|
def test_plot_accessor_updates_on_inplace(self):
|
||
|
|
s = Series([1, 2, 3, 4])
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
ax = s.plot(ax=ax)
|
||
|
|
before = ax.xaxis.get_ticklocs()
|
||
|
|
|
||
|
|
s.drop([0, 1], inplace=True)
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
after = ax.xaxis.get_ticklocs()
|
||
|
|
tm.assert_numpy_array_equal(before, after)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("kind", ["line", "area"])
|
||
|
|
def test_plot_xlim_for_series(self, kind):
|
||
|
|
# test if xlim is also correctly plotted in Series for line and area
|
||
|
|
# GH 27686
|
||
|
|
s = Series([2, 3])
|
||
|
|
_, ax = self.plt.subplots()
|
||
|
|
s.plot(kind=kind, ax=ax)
|
||
|
|
xlims = ax.get_xlim()
|
||
|
|
|
||
|
|
assert xlims[0] < 0
|
||
|
|
assert xlims[1] > 1
|
||
|
|
|
||
|
|
def test_plot_no_rows(self):
|
||
|
|
# GH 27758
|
||
|
|
df = pd.Series(dtype=int)
|
||
|
|
assert df.empty
|
||
|
|
ax = df.plot()
|
||
|
|
assert len(ax.get_lines()) == 1
|
||
|
|
line = ax.get_lines()[0]
|
||
|
|
assert len(line.get_xdata()) == 0
|
||
|
|
assert len(line.get_ydata()) == 0
|
||
|
|
|
||
|
|
def test_plot_no_numeric_data(self):
|
||
|
|
df = pd.Series(["a", "b", "c"])
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
df.plot()
|
||
|
|
|
||
|
|
def test_style_single_ok(self):
|
||
|
|
s = pd.Series([1, 2])
|
||
|
|
ax = s.plot(style="s", color="C3")
|
||
|
|
assert ax.lines[0].get_color() == ["C3"]
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"index_name, old_label, new_label",
|
||
|
|
[(None, "", "new"), ("old", "old", "new"), (None, "", "")],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("kind", ["line", "area", "bar"])
|
||
|
|
def test_xlabel_ylabel_series(self, kind, index_name, old_label, new_label):
|
||
|
|
# GH 9093
|
||
|
|
ser = pd.Series([1, 2, 3, 4])
|
||
|
|
ser.index.name = index_name
|
||
|
|
|
||
|
|
# default is the ylabel is not shown and xlabel is index name
|
||
|
|
ax = ser.plot(kind=kind)
|
||
|
|
assert ax.get_ylabel() == ""
|
||
|
|
assert ax.get_xlabel() == old_label
|
||
|
|
|
||
|
|
# old xlabel will be overriden and assigned ylabel will be used as ylabel
|
||
|
|
ax = ser.plot(kind=kind, ylabel=new_label, xlabel=new_label)
|
||
|
|
assert ax.get_ylabel() == new_label
|
||
|
|
assert ax.get_xlabel() == new_label
|