""" Test cases for DataFrame.plot """
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import string
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import numpy as np
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import pytest
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from pandas.compat import is_platform_linux
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from pandas.compat.numpy import np_version_gte1p24
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import pandas as pd
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from pandas import (
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DataFrame,
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Series,
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date_range,
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)
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import pandas._testing as tm
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from pandas.tests.plotting.common import (
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_check_axes_shape,
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_check_box_return_type,
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_check_legend_labels,
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_check_ticks_props,
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_check_visible,
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_flatten_visible,
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)
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from pandas.io.formats.printing import pprint_thing
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mpl = pytest.importorskip("matplotlib")
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plt = pytest.importorskip("matplotlib.pyplot")
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class TestDataFramePlotsSubplots:
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@pytest.mark.slow
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@pytest.mark.parametrize("kind", ["bar", "barh", "line", "area"])
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def test_subplots(self, kind):
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df = DataFrame(
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np.random.default_rng(2).random((10, 3)),
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index=list(string.ascii_letters[:10]),
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)
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axes = df.plot(kind=kind, subplots=True, sharex=True, legend=True)
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_check_axes_shape(axes, axes_num=3, layout=(3, 1))
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assert axes.shape == (3,)
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for ax, column in zip(axes, df.columns):
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_check_legend_labels(ax, labels=[pprint_thing(column)])
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for ax in axes[:-2]:
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_check_visible(ax.xaxis) # xaxis must be visible for grid
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_check_visible(ax.get_xticklabels(), visible=False)
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if kind != "bar":
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# change https://github.com/pandas-dev/pandas/issues/26714
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_check_visible(ax.get_xticklabels(minor=True), visible=False)
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_check_visible(ax.xaxis.get_label(), visible=False)
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_check_visible(ax.get_yticklabels())
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_check_visible(axes[-1].xaxis)
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_check_visible(axes[-1].get_xticklabels())
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_check_visible(axes[-1].get_xticklabels(minor=True))
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_check_visible(axes[-1].xaxis.get_label())
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_check_visible(axes[-1].get_yticklabels())
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@pytest.mark.slow
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@pytest.mark.parametrize("kind", ["bar", "barh", "line", "area"])
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def test_subplots_no_share_x(self, kind):
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df = DataFrame(
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np.random.default_rng(2).random((10, 3)),
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index=list(string.ascii_letters[:10]),
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)
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axes = df.plot(kind=kind, subplots=True, sharex=False)
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for ax in axes:
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_check_visible(ax.xaxis)
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_check_visible(ax.get_xticklabels())
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_check_visible(ax.get_xticklabels(minor=True))
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_check_visible(ax.xaxis.get_label())
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_check_visible(ax.get_yticklabels())
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@pytest.mark.slow
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@pytest.mark.parametrize("kind", ["bar", "barh", "line", "area"])
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def test_subplots_no_legend(self, kind):
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df = DataFrame(
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np.random.default_rng(2).random((10, 3)),
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index=list(string.ascii_letters[:10]),
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)
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axes = df.plot(kind=kind, subplots=True, legend=False)
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for ax in axes:
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assert ax.get_legend() is None
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@pytest.mark.parametrize("kind", ["line", "area"])
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def test_subplots_timeseries(self, kind):
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idx = date_range(start="2014-07-01", freq="ME", periods=10)
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df = DataFrame(np.random.default_rng(2).random((10, 3)), index=idx)
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axes = df.plot(kind=kind, subplots=True, sharex=True)
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_check_axes_shape(axes, axes_num=3, layout=(3, 1))
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for ax in axes[:-2]:
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# GH 7801
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_check_visible(ax.xaxis) # xaxis must be visible for grid
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_check_visible(ax.get_xticklabels(), visible=False)
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_check_visible(ax.get_xticklabels(minor=True), visible=False)
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_check_visible(ax.xaxis.get_label(), visible=False)
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_check_visible(ax.get_yticklabels())
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_check_visible(axes[-1].xaxis)
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_check_visible(axes[-1].get_xticklabels())
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_check_visible(axes[-1].get_xticklabels(minor=True))
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_check_visible(axes[-1].xaxis.get_label())
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_check_visible(axes[-1].get_yticklabels())
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_check_ticks_props(axes, xrot=0)
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@pytest.mark.parametrize("kind", ["line", "area"])
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def test_subplots_timeseries_rot(self, kind):
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idx = date_range(start="2014-07-01", freq="ME", periods=10)
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df = DataFrame(np.random.default_rng(2).random((10, 3)), index=idx)
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axes = df.plot(kind=kind, subplots=True, sharex=False, rot=45, fontsize=7)
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for ax in axes:
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_check_visible(ax.xaxis)
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_check_visible(ax.get_xticklabels())
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_check_visible(ax.get_xticklabels(minor=True))
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_check_visible(ax.xaxis.get_label())
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_check_visible(ax.get_yticklabels())
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_check_ticks_props(ax, xlabelsize=7, xrot=45, ylabelsize=7)
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@pytest.mark.parametrize(
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"col", ["numeric", "timedelta", "datetime_no_tz", "datetime_all_tz"]
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)
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def test_subplots_timeseries_y_axis(self, col):
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# GH16953
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data = {
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"numeric": np.array([1, 2, 5]),
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"timedelta": [
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pd.Timedelta(-10, unit="s"),
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pd.Timedelta(10, unit="m"),
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pd.Timedelta(10, unit="h"),
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],
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"datetime_no_tz": [
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pd.to_datetime("2017-08-01 00:00:00"),
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pd.to_datetime("2017-08-01 02:00:00"),
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pd.to_datetime("2017-08-02 00:00:00"),
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],
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"datetime_all_tz": [
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pd.to_datetime("2017-08-01 00:00:00", utc=True),
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pd.to_datetime("2017-08-01 02:00:00", utc=True),
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pd.to_datetime("2017-08-02 00:00:00", utc=True),
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],
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"text": ["This", "should", "fail"],
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}
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testdata = DataFrame(data)
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ax = testdata.plot(y=col)
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result = ax.get_lines()[0].get_data()[1]
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expected = testdata[col].values
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assert (result == expected).all()
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def test_subplots_timeseries_y_text_error(self):
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# GH16953
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data = {
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"numeric": np.array([1, 2, 5]),
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"text": ["This", "should", "fail"],
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}
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testdata = DataFrame(data)
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msg = "no numeric data to plot"
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with pytest.raises(TypeError, match=msg):
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testdata.plot(y="text")
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@pytest.mark.xfail(reason="not support for period, categorical, datetime_mixed_tz")
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def test_subplots_timeseries_y_axis_not_supported(self):
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"""
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This test will fail for:
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period:
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since period isn't yet implemented in ``select_dtypes``
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and because it will need a custom value converter +
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tick formatter (as was done for x-axis plots)
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categorical:
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because it will need a custom value converter +
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tick formatter (also doesn't work for x-axis, as of now)
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datetime_mixed_tz:
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because of the way how pandas handles ``Series`` of
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``datetime`` objects with different timezone,
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generally converting ``datetime`` objects in a tz-aware
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form could help with this problem
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"""
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data = {
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"numeric": np.array([1, 2, 5]),
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"period": [
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pd.Period("2017-08-01 00:00:00", freq="H"),
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pd.Period("2017-08-01 02:00", freq="H"),
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pd.Period("2017-08-02 00:00:00", freq="H"),
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],
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"categorical": pd.Categorical(
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["c", "b", "a"], categories=["a", "b", "c"], ordered=False
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),
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"datetime_mixed_tz": [
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pd.to_datetime("2017-08-01 00:00:00", utc=True),
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pd.to_datetime("2017-08-01 02:00:00"),
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pd.to_datetime("2017-08-02 00:00:00"),
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],
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}
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testdata = DataFrame(data)
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ax_period = testdata.plot(x="numeric", y="period")
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assert (
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ax_period.get_lines()[0].get_data()[1] == testdata["period"].values
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).all()
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ax_categorical = testdata.plot(x="numeric", y="categorical")
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assert (
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ax_categorical.get_lines()[0].get_data()[1]
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== testdata["categorical"].values
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).all()
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ax_datetime_mixed_tz = testdata.plot(x="numeric", y="datetime_mixed_tz")
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assert (
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ax_datetime_mixed_tz.get_lines()[0].get_data()[1]
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== testdata["datetime_mixed_tz"].values
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).all()
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@pytest.mark.parametrize(
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"layout, exp_layout",
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[
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[(2, 2), (2, 2)],
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[(-1, 2), (2, 2)],
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[(2, -1), (2, 2)],
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[(1, 4), (1, 4)],
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[(-1, 4), (1, 4)],
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[(4, -1), (4, 1)],
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],
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)
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def test_subplots_layout_multi_column(self, layout, exp_layout):
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# GH 6667
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df = DataFrame(
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np.random.default_rng(2).random((10, 3)),
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index=list(string.ascii_letters[:10]),
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)
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axes = df.plot(subplots=True, layout=layout)
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_check_axes_shape(axes, axes_num=3, layout=exp_layout)
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assert axes.shape == exp_layout
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def test_subplots_layout_multi_column_error(self):
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# GH 6667
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df = DataFrame(
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np.random.default_rng(2).random((10, 3)),
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index=list(string.ascii_letters[:10]),
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)
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msg = "Layout of 1x1 must be larger than required size 3"
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with pytest.raises(ValueError, match=msg):
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df.plot(subplots=True, layout=(1, 1))
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msg = "At least one dimension of layout must be positive"
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with pytest.raises(ValueError, match=msg):
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df.plot(subplots=True, layout=(-1, -1))
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@pytest.mark.parametrize(
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"kwargs, expected_axes_num, expected_layout, expected_shape",
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[
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({}, 1, (1, 1), (1,)),
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({"layout": (3, 3)}, 1, (3, 3), (3, 3)),
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],
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)
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def test_subplots_layout_single_column(
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self, kwargs, expected_axes_num, expected_layout, expected_shape
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):
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# GH 6667
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df = DataFrame(
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np.random.default_rng(2).random((10, 1)),
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index=list(string.ascii_letters[:10]),
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)
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axes = df.plot(subplots=True, **kwargs)
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_check_axes_shape(
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axes,
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axes_num=expected_axes_num,
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layout=expected_layout,
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)
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assert axes.shape == expected_shape
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@pytest.mark.slow
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@pytest.mark.parametrize("idx", [range(5), date_range("1/1/2000", periods=5)])
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def test_subplots_warnings(self, idx):
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# GH 9464
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with tm.assert_produces_warning(None):
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df = DataFrame(np.random.default_rng(2).standard_normal((5, 4)), index=idx)
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df.plot(subplots=True, layout=(3, 2))
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def test_subplots_multiple_axes(self):
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# GH 5353, 6970, GH 7069
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fig, axes = mpl.pyplot.subplots(2, 3)
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df = DataFrame(
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np.random.default_rng(2).random((10, 3)),
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index=list(string.ascii_letters[:10]),
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)
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returned = df.plot(subplots=True, ax=axes[0], sharex=False, sharey=False)
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_check_axes_shape(returned, axes_num=3, layout=(1, 3))
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assert returned.shape == (3,)
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assert returned[0].figure is fig
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# draw on second row
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returned = df.plot(subplots=True, ax=axes[1], sharex=False, sharey=False)
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_check_axes_shape(returned, axes_num=3, layout=(1, 3))
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assert returned.shape == (3,)
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assert returned[0].figure is fig
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_check_axes_shape(axes, axes_num=6, layout=(2, 3))
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def test_subplots_multiple_axes_error(self):
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# GH 5353, 6970, GH 7069
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df = DataFrame(
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np.random.default_rng(2).random((10, 3)),
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index=list(string.ascii_letters[:10]),
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)
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msg = "The number of passed axes must be 3, the same as the output plot"
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_, axes = mpl.pyplot.subplots(2, 3)
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with pytest.raises(ValueError, match=msg):
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# pass different number of axes from required
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df.plot(subplots=True, ax=axes)
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@pytest.mark.parametrize(
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"layout, exp_layout",
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[
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[(2, 1), (2, 2)],
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[(2, -1), (2, 2)],
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[(-1, 2), (2, 2)],
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],
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)
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def test_subplots_multiple_axes_2_dim(self, layout, exp_layout):
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# GH 5353, 6970, GH 7069
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# pass 2-dim axes and invalid layout
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# invalid lauout should not affect to input and return value
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# (show warning is tested in
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# TestDataFrameGroupByPlots.test_grouped_box_multiple_axes
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_, axes = mpl.pyplot.subplots(2, 2)
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df = DataFrame(
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np.random.default_rng(2).random((10, 4)),
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index=list(string.ascii_letters[:10]),
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)
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with tm.assert_produces_warning(UserWarning):
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returned = df.plot(
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subplots=True, ax=axes, layout=layout, sharex=False, sharey=False
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)
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_check_axes_shape(returned, axes_num=4, layout=exp_layout)
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assert returned.shape == (4,)
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def test_subplots_multiple_axes_single_col(self):
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# GH 5353, 6970, GH 7069
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# single column
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_, axes = mpl.pyplot.subplots(1, 1)
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df = DataFrame(
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np.random.default_rng(2).random((10, 1)),
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index=list(string.ascii_letters[:10]),
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)
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axes = df.plot(subplots=True, ax=[axes], sharex=False, sharey=False)
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_check_axes_shape(axes, axes_num=1, layout=(1, 1))
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assert axes.shape == (1,)
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def test_subplots_ts_share_axes(self):
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# GH 3964
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_, axes = mpl.pyplot.subplots(3, 3, sharex=True, sharey=True)
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mpl.pyplot.subplots_adjust(left=0.05, right=0.95, hspace=0.3, wspace=0.3)
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df = DataFrame(
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np.random.default_rng(2).standard_normal((10, 9)),
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index=date_range(start="2014-07-01", freq="ME", periods=10),
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)
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for i, ax in enumerate(axes.ravel()):
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df[i].plot(ax=ax, fontsize=5)
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# Rows other than bottom should not be visible
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for ax in axes[0:-1].ravel():
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_check_visible(ax.get_xticklabels(), visible=False)
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# Bottom row should be visible
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for ax in axes[-1].ravel():
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_check_visible(ax.get_xticklabels(), visible=True)
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# First column should be visible
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for ax in axes[[0, 1, 2], [0]].ravel():
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_check_visible(ax.get_yticklabels(), visible=True)
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# Other columns should not be visible
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for ax in axes[[0, 1, 2], [1]].ravel():
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_check_visible(ax.get_yticklabels(), visible=False)
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for ax in axes[[0, 1, 2], [2]].ravel():
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_check_visible(ax.get_yticklabels(), visible=False)
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def test_subplots_sharex_axes_existing_axes(self):
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# GH 9158
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d = {"A": [1.0, 2.0, 3.0, 4.0], "B": [4.0, 3.0, 2.0, 1.0], "C": [5, 1, 3, 4]}
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df = DataFrame(d, index=date_range("2014 10 11", "2014 10 14"))
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axes = df[["A", "B"]].plot(subplots=True)
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df["C"].plot(ax=axes[0], secondary_y=True)
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_check_visible(axes[0].get_xticklabels(), visible=False)
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_check_visible(axes[1].get_xticklabels(), visible=True)
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for ax in axes.ravel():
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_check_visible(ax.get_yticklabels(), visible=True)
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def test_subplots_dup_columns(self):
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# GH 10962
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df = DataFrame(np.random.default_rng(2).random((5, 5)), columns=list("aaaaa"))
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axes = df.plot(subplots=True)
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for ax in axes:
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_check_legend_labels(ax, labels=["a"])
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assert len(ax.lines) == 1
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def test_subplots_dup_columns_secondary_y(self):
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# GH 10962
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df = DataFrame(np.random.default_rng(2).random((5, 5)), columns=list("aaaaa"))
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axes = df.plot(subplots=True, secondary_y="a")
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for ax in axes:
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# (right) is only attached when subplots=False
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_check_legend_labels(ax, labels=["a"])
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assert len(ax.lines) == 1
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def test_subplots_dup_columns_secondary_y_no_subplot(self):
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# GH 10962
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df = DataFrame(np.random.default_rng(2).random((5, 5)), columns=list("aaaaa"))
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ax = df.plot(secondary_y="a")
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_check_legend_labels(ax, labels=["a (right)"] * 5)
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assert len(ax.lines) == 0
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assert len(ax.right_ax.lines) == 5
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@pytest.mark.xfail(
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np_version_gte1p24 and is_platform_linux(),
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reason="Weird rounding problems",
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strict=False,
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)
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def test_bar_log_no_subplots(self):
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# GH3254, GH3298 matplotlib/matplotlib#1882, #1892
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# regressions in 1.2.1
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expected = np.array([0.1, 1.0, 10.0, 100])
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# no subplots
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df = DataFrame({"A": [3] * 5, "B": list(range(1, 6))}, index=range(5))
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ax = df.plot.bar(grid=True, log=True)
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tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
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|
@pytest.mark.xfail(
|
np_version_gte1p24 and is_platform_linux(),
|
reason="Weird rounding problems",
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strict=False,
|
)
|
def test_bar_log_subplots(self):
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expected = np.array([0.1, 1.0, 10.0, 100.0, 1000.0, 1e4])
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ax = DataFrame([Series([200, 300]), Series([300, 500])]).plot.bar(
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log=True, subplots=True
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)
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tm.assert_numpy_array_equal(ax[0].yaxis.get_ticklocs(), expected)
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tm.assert_numpy_array_equal(ax[1].yaxis.get_ticklocs(), expected)
|
|
def test_boxplot_subplots_return_type_default(self, hist_df):
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df = hist_df
|
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# normal style: return_type=None
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result = df.plot.box(subplots=True)
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assert isinstance(result, Series)
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_check_box_return_type(
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result, None, expected_keys=["height", "weight", "category"]
|
)
|
|
@pytest.mark.parametrize("rt", ["dict", "axes", "both"])
|
def test_boxplot_subplots_return_type(self, hist_df, rt):
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df = hist_df
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returned = df.plot.box(return_type=rt, subplots=True)
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_check_box_return_type(
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returned,
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rt,
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expected_keys=["height", "weight", "category"],
|
check_ax_title=False,
|
)
|
|
def test_df_subplots_patterns_minorticks(self):
|
# GH 10657
|
df = DataFrame(
|
np.random.default_rng(2).standard_normal((10, 2)),
|
index=date_range("1/1/2000", periods=10),
|
columns=list("AB"),
|
)
|
|
# shared subplots
|
_, axes = plt.subplots(2, 1, sharex=True)
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axes = df.plot(subplots=True, ax=axes)
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for ax in axes:
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assert len(ax.lines) == 1
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_check_visible(ax.get_yticklabels(), visible=True)
|
# xaxis of 1st ax must be hidden
|
_check_visible(axes[0].get_xticklabels(), visible=False)
|
_check_visible(axes[0].get_xticklabels(minor=True), visible=False)
|
_check_visible(axes[1].get_xticklabels(), visible=True)
|
_check_visible(axes[1].get_xticklabels(minor=True), visible=True)
|
|
def test_df_subplots_patterns_minorticks_1st_ax_hidden(self):
|
# GH 10657
|
df = DataFrame(
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np.random.default_rng(2).standard_normal((10, 2)),
|
index=date_range("1/1/2000", periods=10),
|
columns=list("AB"),
|
)
|
_, axes = plt.subplots(2, 1)
|
with tm.assert_produces_warning(UserWarning):
|
axes = df.plot(subplots=True, ax=axes, sharex=True)
|
for ax in axes:
|
assert len(ax.lines) == 1
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_check_visible(ax.get_yticklabels(), visible=True)
|
# xaxis of 1st ax must be hidden
|
_check_visible(axes[0].get_xticklabels(), visible=False)
|
_check_visible(axes[0].get_xticklabels(minor=True), visible=False)
|
_check_visible(axes[1].get_xticklabels(), visible=True)
|
_check_visible(axes[1].get_xticklabels(minor=True), visible=True)
|
|
def test_df_subplots_patterns_minorticks_not_shared(self):
|
# GH 10657
|
df = DataFrame(
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np.random.default_rng(2).standard_normal((10, 2)),
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index=date_range("1/1/2000", periods=10),
|
columns=list("AB"),
|
)
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# not shared
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_, axes = plt.subplots(2, 1)
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axes = df.plot(subplots=True, ax=axes)
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for ax in axes:
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assert len(ax.lines) == 1
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_check_visible(ax.get_yticklabels(), visible=True)
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_check_visible(ax.get_xticklabels(), visible=True)
|
_check_visible(ax.get_xticklabels(minor=True), visible=True)
|
|
def test_subplots_sharex_false(self):
|
# test when sharex is set to False, two plots should have different
|
# labels, GH 25160
|
df = DataFrame(np.random.default_rng(2).random((10, 2)))
|
df.iloc[5:, 1] = np.nan
|
df.iloc[:5, 0] = np.nan
|
|
_, axs = mpl.pyplot.subplots(2, 1)
|
df.plot.line(ax=axs, subplots=True, sharex=False)
|
|
expected_ax1 = np.arange(4.5, 10, 0.5)
|
expected_ax2 = np.arange(-0.5, 5, 0.5)
|
|
tm.assert_numpy_array_equal(axs[0].get_xticks(), expected_ax1)
|
tm.assert_numpy_array_equal(axs[1].get_xticks(), expected_ax2)
|
|
def test_subplots_constrained_layout(self):
|
# GH 25261
|
idx = date_range(start="now", periods=10)
|
df = DataFrame(np.random.default_rng(2).random((10, 3)), index=idx)
|
kwargs = {}
|
if hasattr(mpl.pyplot.Figure, "get_constrained_layout"):
|
kwargs["constrained_layout"] = True
|
_, axes = mpl.pyplot.subplots(2, **kwargs)
|
with tm.assert_produces_warning(None):
|
df.plot(ax=axes[0])
|
with tm.ensure_clean(return_filelike=True) as path:
|
mpl.pyplot.savefig(path)
|
|
@pytest.mark.parametrize(
|
"index_name, old_label, new_label",
|
[
|
(None, "", "new"),
|
("old", "old", "new"),
|
(None, "", ""),
|
(None, "", 1),
|
(None, "", [1, 2]),
|
],
|
)
|
@pytest.mark.parametrize("kind", ["line", "area", "bar"])
|
def test_xlabel_ylabel_dataframe_subplots(
|
self, kind, index_name, old_label, new_label
|
):
|
# GH 9093
|
df = DataFrame([[1, 2], [2, 5]], columns=["Type A", "Type B"])
|
df.index.name = index_name
|
|
# default is the ylabel is not shown and xlabel is index name
|
axes = df.plot(kind=kind, subplots=True)
|
assert all(ax.get_ylabel() == "" for ax in axes)
|
assert all(ax.get_xlabel() == old_label for ax in axes)
|
|
# old xlabel will be overridden and assigned ylabel will be used as ylabel
|
axes = df.plot(kind=kind, ylabel=new_label, xlabel=new_label, subplots=True)
|
assert all(ax.get_ylabel() == str(new_label) for ax in axes)
|
assert all(ax.get_xlabel() == str(new_label) for ax in axes)
|
|
@pytest.mark.parametrize(
|
"kwargs",
|
[
|
# stacked center
|
{"kind": "bar", "stacked": True},
|
{"kind": "bar", "stacked": True, "width": 0.9},
|
{"kind": "barh", "stacked": True},
|
{"kind": "barh", "stacked": True, "width": 0.9},
|
# center
|
{"kind": "bar", "stacked": False},
|
{"kind": "bar", "stacked": False, "width": 0.9},
|
{"kind": "barh", "stacked": False},
|
{"kind": "barh", "stacked": False, "width": 0.9},
|
# subplots center
|
{"kind": "bar", "subplots": True},
|
{"kind": "bar", "subplots": True, "width": 0.9},
|
{"kind": "barh", "subplots": True},
|
{"kind": "barh", "subplots": True, "width": 0.9},
|
# align edge
|
{"kind": "bar", "stacked": True, "align": "edge"},
|
{"kind": "bar", "stacked": True, "width": 0.9, "align": "edge"},
|
{"kind": "barh", "stacked": True, "align": "edge"},
|
{"kind": "barh", "stacked": True, "width": 0.9, "align": "edge"},
|
{"kind": "bar", "stacked": False, "align": "edge"},
|
{"kind": "bar", "stacked": False, "width": 0.9, "align": "edge"},
|
{"kind": "barh", "stacked": False, "align": "edge"},
|
{"kind": "barh", "stacked": False, "width": 0.9, "align": "edge"},
|
{"kind": "bar", "subplots": True, "align": "edge"},
|
{"kind": "bar", "subplots": True, "width": 0.9, "align": "edge"},
|
{"kind": "barh", "subplots": True, "align": "edge"},
|
{"kind": "barh", "subplots": True, "width": 0.9, "align": "edge"},
|
],
|
)
|
def test_bar_align_multiple_columns(self, kwargs):
|
# GH2157
|
df = DataFrame({"A": [3] * 5, "B": list(range(5))}, index=range(5))
|
self._check_bar_alignment(df, **kwargs)
|
|
@pytest.mark.parametrize(
|
"kwargs",
|
[
|
{"kind": "bar", "stacked": False},
|
{"kind": "bar", "stacked": True},
|
{"kind": "barh", "stacked": False},
|
{"kind": "barh", "stacked": True},
|
{"kind": "bar", "subplots": True},
|
{"kind": "barh", "subplots": True},
|
],
|
)
|
def test_bar_align_single_column(self, kwargs):
|
df = DataFrame(np.random.default_rng(2).standard_normal(5))
|
self._check_bar_alignment(df, **kwargs)
|
|
@pytest.mark.parametrize(
|
"kwargs",
|
[
|
{"kind": "bar", "stacked": False},
|
{"kind": "bar", "stacked": True},
|
{"kind": "barh", "stacked": False},
|
{"kind": "barh", "stacked": True},
|
{"kind": "bar", "subplots": True},
|
{"kind": "barh", "subplots": True},
|
],
|
)
|
def test_bar_barwidth_position(self, kwargs):
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 5)))
|
self._check_bar_alignment(df, width=0.9, position=0.2, **kwargs)
|
|
@pytest.mark.parametrize("w", [1, 1.0])
|
def test_bar_barwidth_position_int(self, w):
|
# GH 12979
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 5)))
|
ax = df.plot.bar(stacked=True, width=w)
|
ticks = ax.xaxis.get_ticklocs()
|
tm.assert_numpy_array_equal(ticks, np.array([0, 1, 2, 3, 4]))
|
assert ax.get_xlim() == (-0.75, 4.75)
|
# check left-edge of bars
|
assert ax.patches[0].get_x() == -0.5
|
assert ax.patches[-1].get_x() == 3.5
|
|
@pytest.mark.parametrize(
|
"kind, kwargs",
|
[
|
["bar", {"stacked": True}],
|
["barh", {"stacked": False}],
|
["barh", {"stacked": True}],
|
["bar", {"subplots": True}],
|
["barh", {"subplots": True}],
|
],
|
)
|
def test_bar_barwidth_position_int_width_1(self, kind, kwargs):
|
# GH 12979
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 5)))
|
self._check_bar_alignment(df, kind=kind, width=1, **kwargs)
|
|
def _check_bar_alignment(
|
self,
|
df,
|
kind="bar",
|
stacked=False,
|
subplots=False,
|
align="center",
|
width=0.5,
|
position=0.5,
|
):
|
axes = df.plot(
|
kind=kind,
|
stacked=stacked,
|
subplots=subplots,
|
align=align,
|
width=width,
|
position=position,
|
grid=True,
|
)
|
|
axes = _flatten_visible(axes)
|
|
for ax in axes:
|
if kind == "bar":
|
axis = ax.xaxis
|
ax_min, ax_max = ax.get_xlim()
|
min_edge = min(p.get_x() for p in ax.patches)
|
max_edge = max(p.get_x() + p.get_width() for p in ax.patches)
|
elif kind == "barh":
|
axis = ax.yaxis
|
ax_min, ax_max = ax.get_ylim()
|
min_edge = min(p.get_y() for p in ax.patches)
|
max_edge = max(p.get_y() + p.get_height() for p in ax.patches)
|
else:
|
raise ValueError
|
|
# GH 7498
|
# compare margins between lim and bar edges
|
tm.assert_almost_equal(ax_min, min_edge - 0.25)
|
tm.assert_almost_equal(ax_max, max_edge + 0.25)
|
|
p = ax.patches[0]
|
if kind == "bar" and (stacked is True or subplots is True):
|
edge = p.get_x()
|
center = edge + p.get_width() * position
|
elif kind == "bar" and stacked is False:
|
center = p.get_x() + p.get_width() * len(df.columns) * position
|
edge = p.get_x()
|
elif kind == "barh" and (stacked is True or subplots is True):
|
center = p.get_y() + p.get_height() * position
|
edge = p.get_y()
|
elif kind == "barh" and stacked is False:
|
center = p.get_y() + p.get_height() * len(df.columns) * position
|
edge = p.get_y()
|
else:
|
raise ValueError
|
|
# Check the ticks locates on integer
|
assert (axis.get_ticklocs() == np.arange(len(df))).all()
|
|
if align == "center":
|
# Check whether the bar locates on center
|
tm.assert_almost_equal(axis.get_ticklocs()[0], center)
|
elif align == "edge":
|
# Check whether the bar's edge starts from the tick
|
tm.assert_almost_equal(axis.get_ticklocs()[0], edge)
|
else:
|
raise ValueError
|
|
return axes
|
