316 lines
12 KiB
Python
316 lines
12 KiB
Python
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# coding: utf-8
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""" Test cases for misc plot functions """
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import pytest
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from pandas import DataFrame
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from pandas.compat import lmap
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import pandas.util.testing as tm
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import pandas.util._test_decorators as td
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import numpy as np
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from numpy import random
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from numpy.random import randn
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import pandas.plotting as plotting
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from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
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@td.skip_if_mpl
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def test_import_error_message():
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# GH-19810
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df = DataFrame({"A": [1, 2]})
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with tm.assert_raises_regex(ImportError, 'matplotlib is required'):
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df.plot()
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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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@pytest.mark.slow
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def test_autocorrelation_plot(self):
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from pandas.plotting import autocorrelation_plot
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_check_plot_works(autocorrelation_plot, series=self.ts)
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_check_plot_works(autocorrelation_plot, series=self.ts.values)
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ax = autocorrelation_plot(self.ts, label='Test')
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self._check_legend_labels(ax, labels=['Test'])
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@pytest.mark.slow
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def test_lag_plot(self):
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from pandas.plotting import lag_plot
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_check_plot_works(lag_plot, series=self.ts)
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_check_plot_works(lag_plot, series=self.ts, lag=5)
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@pytest.mark.slow
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def test_bootstrap_plot(self):
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from pandas.plotting import bootstrap_plot
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_check_plot_works(bootstrap_plot, series=self.ts, size=10)
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@td.skip_if_no_mpl
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class TestDataFramePlots(TestPlotBase):
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@td.xfail_if_mpl_2_2
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@td.skip_if_no_scipy
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def test_scatter_matrix_axis(self):
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scatter_matrix = plotting.scatter_matrix
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with tm.RNGContext(42):
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df = DataFrame(randn(100, 3))
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# we are plotting multiples on a sub-plot
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with tm.assert_produces_warning(UserWarning):
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axes = _check_plot_works(scatter_matrix, filterwarnings='always',
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frame=df, range_padding=.1)
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axes0_labels = axes[0][0].yaxis.get_majorticklabels()
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# GH 5662
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if self.mpl_ge_2_0_0:
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expected = ['-2', '0', '2']
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else:
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expected = ['-2', '-1', '0', '1', '2']
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self._check_text_labels(axes0_labels, expected)
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self._check_ticks_props(
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axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
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df[0] = ((df[0] - 2) / 3)
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# we are plotting multiples on a sub-plot
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with tm.assert_produces_warning(UserWarning):
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axes = _check_plot_works(scatter_matrix, filterwarnings='always',
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frame=df, range_padding=.1)
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axes0_labels = axes[0][0].yaxis.get_majorticklabels()
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if self.mpl_ge_2_0_0:
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expected = ['-1.0', '-0.5', '0.0']
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else:
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expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0']
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self._check_text_labels(axes0_labels, expected)
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self._check_ticks_props(
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axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
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@pytest.mark.slow
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def test_andrews_curves(self, iris):
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from pandas.plotting import andrews_curves
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from matplotlib import cm
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df = iris
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_check_plot_works(andrews_curves, frame=df, class_column='Name')
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rgba = ('#556270', '#4ECDC4', '#C7F464')
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ax = _check_plot_works(andrews_curves, frame=df,
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class_column='Name', color=rgba)
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self._check_colors(
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ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10])
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cnames = ['dodgerblue', 'aquamarine', 'seagreen']
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ax = _check_plot_works(andrews_curves, frame=df,
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class_column='Name', color=cnames)
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self._check_colors(
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ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10])
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ax = _check_plot_works(andrews_curves, frame=df,
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class_column='Name', colormap=cm.jet)
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cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
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self._check_colors(
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ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])
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length = 10
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df = DataFrame({"A": random.rand(length),
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"B": random.rand(length),
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"C": random.rand(length),
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"Name": ["A"] * length})
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_check_plot_works(andrews_curves, frame=df, class_column='Name')
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rgba = ('#556270', '#4ECDC4', '#C7F464')
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ax = _check_plot_works(andrews_curves, frame=df,
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class_column='Name', color=rgba)
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self._check_colors(
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ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10])
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cnames = ['dodgerblue', 'aquamarine', 'seagreen']
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ax = _check_plot_works(andrews_curves, frame=df,
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class_column='Name', color=cnames)
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self._check_colors(
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ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10])
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ax = _check_plot_works(andrews_curves, frame=df,
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class_column='Name', colormap=cm.jet)
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cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
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self._check_colors(
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ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])
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colors = ['b', 'g', 'r']
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df = DataFrame({"A": [1, 2, 3],
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"B": [1, 2, 3],
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"C": [1, 2, 3],
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"Name": colors})
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ax = andrews_curves(df, 'Name', color=colors)
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handles, labels = ax.get_legend_handles_labels()
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self._check_colors(handles, linecolors=colors)
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with tm.assert_produces_warning(FutureWarning):
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andrews_curves(data=df, class_column='Name')
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@pytest.mark.slow
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def test_parallel_coordinates(self, iris):
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from pandas.plotting import parallel_coordinates
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from matplotlib import cm
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df = iris
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ax = _check_plot_works(parallel_coordinates,
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frame=df, class_column='Name')
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nlines = len(ax.get_lines())
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nxticks = len(ax.xaxis.get_ticklabels())
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rgba = ('#556270', '#4ECDC4', '#C7F464')
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ax = _check_plot_works(parallel_coordinates,
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frame=df, class_column='Name', color=rgba)
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self._check_colors(
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ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10])
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cnames = ['dodgerblue', 'aquamarine', 'seagreen']
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ax = _check_plot_works(parallel_coordinates,
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frame=df, class_column='Name', color=cnames)
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self._check_colors(
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ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10])
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ax = _check_plot_works(parallel_coordinates,
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frame=df, class_column='Name', colormap=cm.jet)
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cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
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self._check_colors(
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ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])
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ax = _check_plot_works(parallel_coordinates,
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frame=df, class_column='Name', axvlines=False)
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assert len(ax.get_lines()) == (nlines - nxticks)
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colors = ['b', 'g', 'r']
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df = DataFrame({"A": [1, 2, 3],
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"B": [1, 2, 3],
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"C": [1, 2, 3],
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"Name": colors})
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ax = parallel_coordinates(df, 'Name', color=colors)
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handles, labels = ax.get_legend_handles_labels()
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self._check_colors(handles, linecolors=colors)
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with tm.assert_produces_warning(FutureWarning):
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parallel_coordinates(data=df, class_column='Name')
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with tm.assert_produces_warning(FutureWarning):
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parallel_coordinates(df, 'Name', colors=colors)
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@pytest.mark.xfail(reason="unreliable test")
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def test_parallel_coordinates_with_sorted_labels(self):
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""" For #15908 """
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from pandas.plotting import parallel_coordinates
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df = DataFrame({"feat": [i for i in range(30)],
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"class": [2 for _ in range(10)] +
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[3 for _ in range(10)] +
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[1 for _ in range(10)]})
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ax = parallel_coordinates(df, 'class', sort_labels=True)
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polylines, labels = ax.get_legend_handles_labels()
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color_label_tuples = \
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zip([polyline.get_color() for polyline in polylines], labels)
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ordered_color_label_tuples = sorted(color_label_tuples,
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key=lambda x: x[1])
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prev_next_tupels = zip([i for i in ordered_color_label_tuples[0:-1]],
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[i for i in ordered_color_label_tuples[1:]])
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for prev, nxt in prev_next_tupels:
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# labels and colors are ordered strictly increasing
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assert prev[1] < nxt[1] and prev[0] < nxt[0]
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@pytest.mark.slow
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def test_radviz(self, iris):
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from pandas.plotting import radviz
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from matplotlib import cm
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df = iris
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_check_plot_works(radviz, frame=df, class_column='Name')
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rgba = ('#556270', '#4ECDC4', '#C7F464')
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ax = _check_plot_works(
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radviz, frame=df, class_column='Name', color=rgba)
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# skip Circle drawn as ticks
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patches = [p for p in ax.patches[:20] if p.get_label() != '']
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self._check_colors(
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patches[:10], facecolors=rgba, mapping=df['Name'][:10])
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cnames = ['dodgerblue', 'aquamarine', 'seagreen']
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_check_plot_works(radviz, frame=df, class_column='Name', color=cnames)
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patches = [p for p in ax.patches[:20] if p.get_label() != '']
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self._check_colors(patches, facecolors=cnames, mapping=df['Name'][:10])
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_check_plot_works(radviz, frame=df,
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class_column='Name', colormap=cm.jet)
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cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
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patches = [p for p in ax.patches[:20] if p.get_label() != '']
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self._check_colors(patches, facecolors=cmaps, mapping=df['Name'][:10])
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colors = [[0., 0., 1., 1.],
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[0., 0.5, 1., 1.],
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[1., 0., 0., 1.]]
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df = DataFrame({"A": [1, 2, 3],
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"B": [2, 1, 3],
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"C": [3, 2, 1],
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"Name": ['b', 'g', 'r']})
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ax = radviz(df, 'Name', color=colors)
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handles, labels = ax.get_legend_handles_labels()
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self._check_colors(handles, facecolors=colors)
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@pytest.mark.slow
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def test_subplot_titles(self, iris):
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df = iris.drop('Name', axis=1).head()
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# Use the column names as the subplot titles
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title = list(df.columns)
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# Case len(title) == len(df)
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plot = df.plot(subplots=True, title=title)
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assert [p.get_title() for p in plot] == title
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# Case len(title) > len(df)
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pytest.raises(ValueError, df.plot, subplots=True,
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title=title + ["kittens > puppies"])
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# Case len(title) < len(df)
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pytest.raises(ValueError, df.plot, subplots=True, title=title[:2])
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# Case subplots=False and title is of type list
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pytest.raises(ValueError, df.plot, subplots=False, title=title)
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# Case df with 3 numeric columns but layout of (2,2)
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plot = df.drop('SepalWidth', axis=1).plot(subplots=True, layout=(2, 2),
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title=title[:-1])
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title_list = [ax.get_title() for sublist in plot for ax in sublist]
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assert title_list == title[:3] + ['']
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def test_get_standard_colors_random_seed(self):
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# GH17525
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df = DataFrame(np.zeros((10, 10)))
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# Make sure that the random seed isn't reset by _get_standard_colors
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plotting.parallel_coordinates(df, 0)
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rand1 = random.random()
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plotting.parallel_coordinates(df, 0)
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rand2 = random.random()
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assert rand1 != rand2
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# Make sure it produces the same colors every time it's called
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from pandas.plotting._style import _get_standard_colors
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color1 = _get_standard_colors(1, color_type='random')
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color2 = _get_standard_colors(1, color_type='random')
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assert color1 == color2
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