""" Test cases for misc plot functions """ import numpy as np import pytest import pandas.util._test_decorators as td from pandas import DataFrame, Series import pandas._testing as tm from pandas.tests.plotting.common import TestPlotBase, _check_plot_works import pandas.plotting as plotting pytestmark = pytest.mark.slow @td.skip_if_mpl def test_import_error_message(): # GH-19810 df = DataFrame({"A": [1, 2]}) with pytest.raises(ImportError, match="matplotlib is required for plotting"): df.plot() def test_get_accessor_args(): func = plotting._core.PlotAccessor._get_call_args msg = "Called plot accessor for type list, expected Series or DataFrame" with pytest.raises(TypeError, match=msg): func(backend_name="", data=[], args=[], kwargs={}) msg = "should not be called with positional arguments" with pytest.raises(TypeError, match=msg): func(backend_name="", data=Series(dtype=object), args=["line", None], kwargs={}) x, y, kind, kwargs = func( backend_name="", data=DataFrame(), args=["x"], kwargs={"y": "y", "kind": "bar", "grid": False}, ) assert x == "x" assert y == "y" assert kind == "bar" assert kwargs == {"grid": False} x, y, kind, kwargs = func( backend_name="pandas.plotting._matplotlib", data=Series(dtype=object), args=[], kwargs={}, ) assert x is None assert y is None assert kind == "line" assert len(kwargs) == 24 @td.skip_if_no_mpl class TestSeriesPlots(TestPlotBase): def setup_method(self, method): TestPlotBase.setup_method(self, method) import matplotlib as mpl mpl.rcdefaults() self.ts = tm.makeTimeSeries() self.ts.name = "ts" def test_autocorrelation_plot(self): from pandas.plotting import autocorrelation_plot _check_plot_works(autocorrelation_plot, series=self.ts) _check_plot_works(autocorrelation_plot, series=self.ts.values) ax = autocorrelation_plot(self.ts, label="Test") self._check_legend_labels(ax, labels=["Test"]) def test_lag_plot(self): from pandas.plotting import lag_plot _check_plot_works(lag_plot, series=self.ts) _check_plot_works(lag_plot, series=self.ts, lag=5) def test_bootstrap_plot(self): from pandas.plotting import bootstrap_plot _check_plot_works(bootstrap_plot, series=self.ts, size=10) @td.skip_if_no_mpl class TestDataFramePlots(TestPlotBase): @td.skip_if_no_scipy def test_scatter_matrix_axis(self): from pandas.plotting._matplotlib.compat import mpl_ge_3_0_0 scatter_matrix = plotting.scatter_matrix with tm.RNGContext(42): df = DataFrame(np.random.randn(100, 3)) # we are plotting multiples on a sub-plot with tm.assert_produces_warning( UserWarning, raise_on_extra_warnings=mpl_ge_3_0_0() ): axes = _check_plot_works( scatter_matrix, filterwarnings="always", frame=df, range_padding=0.1 ) axes0_labels = axes[0][0].yaxis.get_majorticklabels() # GH 5662 expected = ["-2", "0", "2"] self._check_text_labels(axes0_labels, expected) self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) df[0] = (df[0] - 2) / 3 # we are plotting multiples on a sub-plot with tm.assert_produces_warning(UserWarning): axes = _check_plot_works( scatter_matrix, filterwarnings="always", frame=df, range_padding=0.1 ) axes0_labels = axes[0][0].yaxis.get_majorticklabels() expected = ["-1.0", "-0.5", "0.0"] self._check_text_labels(axes0_labels, expected) self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) def test_andrews_curves(self, iris): from matplotlib import cm from pandas.plotting import andrews_curves df = iris _check_plot_works(andrews_curves, frame=df, class_column="Name") rgba = ("#556270", "#4ECDC4", "#C7F464") ax = _check_plot_works( andrews_curves, frame=df, class_column="Name", color=rgba ) self._check_colors( ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10] ) cnames = ["dodgerblue", "aquamarine", "seagreen"] ax = _check_plot_works( andrews_curves, frame=df, class_column="Name", color=cnames ) self._check_colors( ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10] ) ax = _check_plot_works( andrews_curves, frame=df, class_column="Name", colormap=cm.jet ) cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())] self._check_colors( ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10] ) length = 10 df = DataFrame( { "A": np.random.rand(length), "B": np.random.rand(length), "C": np.random.rand(length), "Name": ["A"] * length, } ) _check_plot_works(andrews_curves, frame=df, class_column="Name") rgba = ("#556270", "#4ECDC4", "#C7F464") ax = _check_plot_works( andrews_curves, frame=df, class_column="Name", color=rgba ) self._check_colors( ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10] ) cnames = ["dodgerblue", "aquamarine", "seagreen"] ax = _check_plot_works( andrews_curves, frame=df, class_column="Name", color=cnames ) self._check_colors( ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10] ) ax = _check_plot_works( andrews_curves, frame=df, class_column="Name", colormap=cm.jet ) cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())] self._check_colors( ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10] ) colors = ["b", "g", "r"] df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors}) ax = andrews_curves(df, "Name", color=colors) handles, labels = ax.get_legend_handles_labels() self._check_colors(handles, linecolors=colors) def test_parallel_coordinates(self, iris): from matplotlib import cm from pandas.plotting import parallel_coordinates df = iris ax = _check_plot_works(parallel_coordinates, frame=df, class_column="Name") nlines = len(ax.get_lines()) nxticks = len(ax.xaxis.get_ticklabels()) rgba = ("#556270", "#4ECDC4", "#C7F464") ax = _check_plot_works( parallel_coordinates, frame=df, class_column="Name", color=rgba ) self._check_colors( ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10] ) cnames = ["dodgerblue", "aquamarine", "seagreen"] ax = _check_plot_works( parallel_coordinates, frame=df, class_column="Name", color=cnames ) self._check_colors( ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10] ) ax = _check_plot_works( parallel_coordinates, frame=df, class_column="Name", colormap=cm.jet ) cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())] self._check_colors( ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10] ) ax = _check_plot_works( parallel_coordinates, frame=df, class_column="Name", axvlines=False ) assert len(ax.get_lines()) == (nlines - nxticks) colors = ["b", "g", "r"] df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors}) ax = parallel_coordinates(df, "Name", color=colors) handles, labels = ax.get_legend_handles_labels() self._check_colors(handles, linecolors=colors) # not sure if this is indicative of a problem @pytest.mark.filterwarnings("ignore:Attempting to set:UserWarning") def test_parallel_coordinates_with_sorted_labels(self): """ For #15908 """ from pandas.plotting import parallel_coordinates df = DataFrame( { "feat": list(range(30)), "class": [2 for _ in range(10)] + [3 for _ in range(10)] + [1 for _ in range(10)], } ) ax = parallel_coordinates(df, "class", sort_labels=True) polylines, labels = ax.get_legend_handles_labels() color_label_tuples = zip( [polyline.get_color() for polyline in polylines], labels ) ordered_color_label_tuples = sorted(color_label_tuples, key=lambda x: x[1]) prev_next_tupels = zip( list(ordered_color_label_tuples[0:-1]), list(ordered_color_label_tuples[1:]) ) for prev, nxt in prev_next_tupels: # labels and colors are ordered strictly increasing assert prev[1] < nxt[1] and prev[0] < nxt[0] def test_radviz(self, iris): from matplotlib import cm from pandas.plotting import radviz df = iris _check_plot_works(radviz, frame=df, class_column="Name") rgba = ("#556270", "#4ECDC4", "#C7F464") ax = _check_plot_works(radviz, frame=df, class_column="Name", color=rgba) # skip Circle drawn as ticks patches = [p for p in ax.patches[:20] if p.get_label() != ""] self._check_colors(patches[:10], facecolors=rgba, mapping=df["Name"][:10]) cnames = ["dodgerblue", "aquamarine", "seagreen"] _check_plot_works(radviz, frame=df, class_column="Name", color=cnames) patches = [p for p in ax.patches[:20] if p.get_label() != ""] self._check_colors(patches, facecolors=cnames, mapping=df["Name"][:10]) _check_plot_works(radviz, frame=df, class_column="Name", colormap=cm.jet) cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())] patches = [p for p in ax.patches[:20] if p.get_label() != ""] self._check_colors(patches, facecolors=cmaps, mapping=df["Name"][:10]) colors = [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 1.0, 1.0], [1.0, 0.0, 0.0, 1.0]] df = DataFrame( {"A": [1, 2, 3], "B": [2, 1, 3], "C": [3, 2, 1], "Name": ["b", "g", "r"]} ) ax = radviz(df, "Name", color=colors) handles, labels = ax.get_legend_handles_labels() self._check_colors(handles, facecolors=colors) def test_subplot_titles(self, iris): df = iris.drop("Name", axis=1).head() # Use the column names as the subplot titles title = list(df.columns) # Case len(title) == len(df) plot = df.plot(subplots=True, title=title) assert [p.get_title() for p in plot] == title # Case len(title) > len(df) msg = ( "The length of `title` must equal the number of columns if " "using `title` of type `list` and `subplots=True`" ) with pytest.raises(ValueError, match=msg): df.plot(subplots=True, title=title + ["kittens > puppies"]) # Case len(title) < len(df) with pytest.raises(ValueError, match=msg): df.plot(subplots=True, title=title[:2]) # Case subplots=False and title is of type list msg = ( "Using `title` of type `list` is not supported unless " "`subplots=True` is passed" ) with pytest.raises(ValueError, match=msg): df.plot(subplots=False, title=title) # Case df with 3 numeric columns but layout of (2,2) plot = df.drop("SepalWidth", axis=1).plot( subplots=True, layout=(2, 2), title=title[:-1] ) title_list = [ax.get_title() for sublist in plot for ax in sublist] assert title_list == title[:3] + [""] def test_get_standard_colors_random_seed(self): # GH17525 df = DataFrame(np.zeros((10, 10))) # Make sure that the np.random.seed isn't reset by get_standard_colors plotting.parallel_coordinates(df, 0) rand1 = np.random.random() plotting.parallel_coordinates(df, 0) rand2 = np.random.random() assert rand1 != rand2 # Make sure it produces the same colors every time it's called from pandas.plotting._matplotlib.style import get_standard_colors color1 = get_standard_colors(1, color_type="random") color2 = get_standard_colors(1, color_type="random") assert color1 == color2 def test_get_standard_colors_default_num_colors(self): from pandas.plotting._matplotlib.style import get_standard_colors # Make sure the default color_types returns the specified amount color1 = get_standard_colors(1, color_type="default") color2 = get_standard_colors(9, color_type="default") color3 = get_standard_colors(20, color_type="default") assert len(color1) == 1 assert len(color2) == 9 assert len(color3) == 20 def test_plot_single_color(self): # Example from #20585. All 3 bars should have the same color df = DataFrame( { "account-start": ["2017-02-03", "2017-03-03", "2017-01-01"], "client": ["Alice Anders", "Bob Baker", "Charlie Chaplin"], "balance": [-1432.32, 10.43, 30000.00], "db-id": [1234, 2424, 251], "proxy-id": [525, 1525, 2542], "rank": [52, 525, 32], } ) ax = df.client.value_counts().plot.bar() colors = [rect.get_facecolor() for rect in ax.get_children()[0:3]] assert all(color == colors[0] for color in colors) def test_get_standard_colors_no_appending(self): # GH20726 # Make sure not to add more colors so that matplotlib can cycle # correctly. from matplotlib import cm from pandas.plotting._matplotlib.style import get_standard_colors color_before = cm.gnuplot(range(5)) color_after = get_standard_colors(1, color=color_before) assert len(color_after) == len(color_before) df = DataFrame(np.random.randn(48, 4), columns=list("ABCD")) color_list = cm.gnuplot(np.linspace(0, 1, 16)) p = df.A.plot.bar(figsize=(16, 7), color=color_list) assert p.patches[1].get_facecolor() == p.patches[17].get_facecolor() def test_dictionary_color(self): # issue-8193 # Test plot color dictionary format data_files = ["a", "b"] expected = [(0.5, 0.24, 0.6), (0.3, 0.7, 0.7)] df1 = DataFrame(np.random.rand(2, 2), columns=data_files) dic_color = {"b": (0.3, 0.7, 0.7), "a": (0.5, 0.24, 0.6)} # Bar color test ax = df1.plot(kind="bar", color=dic_color) colors = [rect.get_facecolor()[0:-1] for rect in ax.get_children()[0:3:2]] assert all(color == expected[index] for index, color in enumerate(colors)) # Line color test ax = df1.plot(kind="line", color=dic_color) colors = [rect.get_color() for rect in ax.get_lines()[0:2]] assert all(color == expected[index] for index, color in enumerate(colors)) def test_has_externally_shared_axis_x_axis(self): # GH33819 # Test _has_externally_shared_axis() works for x-axis func = plotting._matplotlib.tools._has_externally_shared_axis fig = self.plt.figure() plots = fig.subplots(2, 4) # Create *externally* shared axes for first and third columns plots[0][0] = fig.add_subplot(231, sharex=plots[1][0]) plots[0][2] = fig.add_subplot(233, sharex=plots[1][2]) # Create *internally* shared axes for second and third columns plots[0][1].twinx() plots[0][2].twinx() # First column is only externally shared # Second column is only internally shared # Third column is both # Fourth column is neither assert func(plots[0][0], "x") assert not func(plots[0][1], "x") assert func(plots[0][2], "x") assert not func(plots[0][3], "x") def test_has_externally_shared_axis_y_axis(self): # GH33819 # Test _has_externally_shared_axis() works for y-axis func = plotting._matplotlib.tools._has_externally_shared_axis fig = self.plt.figure() plots = fig.subplots(4, 2) # Create *externally* shared axes for first and third rows plots[0][0] = fig.add_subplot(321, sharey=plots[0][1]) plots[2][0] = fig.add_subplot(325, sharey=plots[2][1]) # Create *internally* shared axes for second and third rows plots[1][0].twiny() plots[2][0].twiny() # First row is only externally shared # Second row is only internally shared # Third row is both # Fourth row is neither assert func(plots[0][0], "y") assert not func(plots[1][0], "y") assert func(plots[2][0], "y") assert not func(plots[3][0], "y") def test_has_externally_shared_axis_invalid_compare_axis(self): # GH33819 # Test _has_externally_shared_axis() raises an exception when # passed an invalid value as compare_axis parameter func = plotting._matplotlib.tools._has_externally_shared_axis fig = self.plt.figure() plots = fig.subplots(4, 2) # Create arbitrary axes plots[0][0] = fig.add_subplot(321, sharey=plots[0][1]) # Check that an invalid compare_axis value triggers the expected exception msg = "needs 'x' or 'y' as a second parameter" with pytest.raises(ValueError, match=msg): func(plots[0][0], "z") def test_externally_shared_axes(self): # Example from GH33819 # Create data df = DataFrame({"a": np.random.randn(1000), "b": np.random.randn(1000)}) # Create figure fig = self.plt.figure() plots = fig.subplots(2, 3) # Create *externally* shared axes plots[0][0] = fig.add_subplot(231, sharex=plots[1][0]) # note: no plots[0][1] that's the twin only case plots[0][2] = fig.add_subplot(233, sharex=plots[1][2]) # Create *internally* shared axes # note: no plots[0][0] that's the external only case twin_ax1 = plots[0][1].twinx() twin_ax2 = plots[0][2].twinx() # Plot data to primary axes df["a"].plot(ax=plots[0][0], title="External share only").set_xlabel( "this label should never be visible" ) df["a"].plot(ax=plots[1][0]) df["a"].plot(ax=plots[0][1], title="Internal share (twin) only").set_xlabel( "this label should always be visible" ) df["a"].plot(ax=plots[1][1]) df["a"].plot(ax=plots[0][2], title="Both").set_xlabel( "this label should never be visible" ) df["a"].plot(ax=plots[1][2]) # Plot data to twinned axes df["b"].plot(ax=twin_ax1, color="green") df["b"].plot(ax=twin_ax2, color="yellow") assert not plots[0][0].xaxis.get_label().get_visible() assert plots[0][1].xaxis.get_label().get_visible() assert not plots[0][2].xaxis.get_label().get_visible()