| Current File : //usr/local/lib64/python3.6/site-packages/pandas/tests/plotting/test_frame.py |
""" Test cases for DataFrame.plot """
from datetime import date, datetime
import itertools
import string
import warnings
import numpy as np
from numpy.random import rand, randn
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.api import is_list_like
import pandas as pd
from pandas import DataFrame, MultiIndex, PeriodIndex, Series, bdate_range, date_range
import pandas._testing as tm
from pandas.core.arrays import integer_array
from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
from pandas.io.formats.printing import pprint_thing
import pandas.plotting as plotting
@td.skip_if_no_mpl
class TestDataFramePlots(TestPlotBase):
def setup_method(self, method):
TestPlotBase.setup_method(self, method)
import matplotlib as mpl
mpl.rcdefaults()
self.tdf = tm.makeTimeDataFrame()
self.hexbin_df = DataFrame(
{
"A": np.random.uniform(size=20),
"B": np.random.uniform(size=20),
"C": np.arange(20) + np.random.uniform(size=20),
}
)
def _assert_ytickslabels_visibility(self, axes, expected):
for ax, exp in zip(axes, expected):
self._check_visible(ax.get_yticklabels(), visible=exp)
def _assert_xtickslabels_visibility(self, axes, expected):
for ax, exp in zip(axes, expected):
self._check_visible(ax.get_xticklabels(), visible=exp)
@pytest.mark.xfail(reason="Waiting for PR 34334", strict=True)
@pytest.mark.slow
def test_plot(self):
from pandas.plotting._matplotlib.compat import _mpl_ge_3_1_0
df = self.tdf
_check_plot_works(df.plot, grid=False)
# _check_plot_works adds an ax so catch warning. see GH #13188
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.plot, subplots=True)
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.plot, subplots=True, layout=(-1, 2))
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.plot, subplots=True, use_index=False)
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
df = DataFrame({"x": [1, 2], "y": [3, 4]})
if _mpl_ge_3_1_0():
msg = "'Line2D' object has no property 'blarg'"
else:
msg = "Unknown property blarg"
with pytest.raises(AttributeError, match=msg):
df.plot.line(blarg=True)
df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))
_check_plot_works(df.plot, use_index=True)
_check_plot_works(df.plot, sort_columns=False)
_check_plot_works(df.plot, yticks=[1, 5, 10])
_check_plot_works(df.plot, xticks=[1, 5, 10])
_check_plot_works(df.plot, ylim=(-100, 100), xlim=(-100, 100))
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.plot, subplots=True, title="blah")
# We have to redo it here because _check_plot_works does two plots,
# once without an ax kwarg and once with an ax kwarg and the new sharex
# behaviour does not remove the visibility of the latter axis (as ax is
# present). see: https://github.com/pandas-dev/pandas/issues/9737
axes = df.plot(subplots=True, title="blah")
self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
# axes[0].figure.savefig("test.png")
for ax in axes[:2]:
self._check_visible(ax.xaxis) # xaxis must be visible for grid
self._check_visible(ax.get_xticklabels(), visible=False)
self._check_visible(ax.get_xticklabels(minor=True), visible=False)
self._check_visible([ax.xaxis.get_label()], visible=False)
for ax in [axes[2]]:
self._check_visible(ax.xaxis)
self._check_visible(ax.get_xticklabels())
self._check_visible([ax.xaxis.get_label()])
self._check_ticks_props(ax, xrot=0)
_check_plot_works(df.plot, title="blah")
tuples = zip(string.ascii_letters[:10], range(10))
df = DataFrame(np.random.rand(10, 3), index=MultiIndex.from_tuples(tuples))
_check_plot_works(df.plot, use_index=True)
# unicode
index = MultiIndex.from_tuples(
[
("\u03b1", 0),
("\u03b1", 1),
("\u03b2", 2),
("\u03b2", 3),
("\u03b3", 4),
("\u03b3", 5),
("\u03b4", 6),
("\u03b4", 7),
],
names=["i0", "i1"],
)
columns = MultiIndex.from_tuples(
[("bar", "\u0394"), ("bar", "\u0395")], names=["c0", "c1"]
)
df = DataFrame(np.random.randint(0, 10, (8, 2)), columns=columns, index=index)
_check_plot_works(df.plot, title="\u03A3")
# GH 6951
# Test with single column
df = DataFrame({"x": np.random.rand(10)})
axes = _check_plot_works(df.plot.bar, subplots=True)
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
axes = _check_plot_works(df.plot.bar, subplots=True, layout=(-1, 1))
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
# When ax is supplied and required number of axes is 1,
# passed ax should be used:
fig, ax = self.plt.subplots()
axes = df.plot.bar(subplots=True, ax=ax)
assert len(axes) == 1
result = ax.axes
assert result is axes[0]
def test_integer_array_plot(self):
# GH 25587
arr = integer_array([1, 2, 3, 4], dtype="UInt32")
s = Series(arr)
_check_plot_works(s.plot.line)
_check_plot_works(s.plot.bar)
_check_plot_works(s.plot.hist)
_check_plot_works(s.plot.pie)
df = DataFrame({"x": arr, "y": arr})
_check_plot_works(df.plot.line)
_check_plot_works(df.plot.bar)
_check_plot_works(df.plot.hist)
_check_plot_works(df.plot.pie, y="y")
_check_plot_works(df.plot.scatter, x="x", y="y")
_check_plot_works(df.plot.hexbin, x="x", y="y")
def test_mpl2_color_cycle_str(self):
# GH 15516
colors = ["C" + str(x) for x in range(10)]
df = DataFrame(randn(10, 3), columns=["a", "b", "c"])
for c in colors:
_check_plot_works(df.plot, color=c)
def test_color_single_series_list(self):
# GH 3486
df = DataFrame({"A": [1, 2, 3]})
_check_plot_works(df.plot, color=["red"])
def test_rgb_tuple_color(self):
# GH 16695
df = DataFrame({"x": [1, 2], "y": [3, 4]})
_check_plot_works(df.plot, x="x", y="y", color=(1, 0, 0))
_check_plot_works(df.plot, x="x", y="y", color=(1, 0, 0, 0.5))
def test_color_empty_string(self):
df = DataFrame(randn(10, 2))
with pytest.raises(ValueError):
df.plot(color="")
def test_color_and_style_arguments(self):
df = DataFrame({"x": [1, 2], "y": [3, 4]})
# passing both 'color' and 'style' arguments should be allowed
# if there is no color symbol in the style strings:
ax = df.plot(color=["red", "black"], style=["-", "--"])
# check that the linestyles are correctly set:
linestyle = [line.get_linestyle() for line in ax.lines]
assert linestyle == ["-", "--"]
# check that the colors are correctly set:
color = [line.get_color() for line in ax.lines]
assert color == ["red", "black"]
# passing both 'color' and 'style' arguments should not be allowed
# if there is a color symbol in the style strings:
with pytest.raises(ValueError):
df.plot(color=["red", "black"], style=["k-", "r--"])
def test_nonnumeric_exclude(self):
df = DataFrame({"A": ["x", "y", "z"], "B": [1, 2, 3]})
ax = df.plot()
assert len(ax.get_lines()) == 1 # B was plotted
@pytest.mark.slow
def test_implicit_label(self):
df = DataFrame(randn(10, 3), columns=["a", "b", "c"])
ax = df.plot(x="a", y="b")
self._check_text_labels(ax.xaxis.get_label(), "a")
@pytest.mark.slow
def test_donot_overwrite_index_name(self):
# GH 8494
df = DataFrame(randn(2, 2), columns=["a", "b"])
df.index.name = "NAME"
df.plot(y="b", label="LABEL")
assert df.index.name == "NAME"
@pytest.mark.slow
def test_plot_xy(self):
# columns.inferred_type == 'string'
df = self.tdf
self._check_data(df.plot(x=0, y=1), df.set_index("A")["B"].plot())
self._check_data(df.plot(x=0), df.set_index("A").plot())
self._check_data(df.plot(y=0), df.B.plot())
self._check_data(df.plot(x="A", y="B"), df.set_index("A").B.plot())
self._check_data(df.plot(x="A"), df.set_index("A").plot())
self._check_data(df.plot(y="B"), df.B.plot())
# columns.inferred_type == 'integer'
df.columns = np.arange(1, len(df.columns) + 1)
self._check_data(df.plot(x=1, y=2), df.set_index(1)[2].plot())
self._check_data(df.plot(x=1), df.set_index(1).plot())
self._check_data(df.plot(y=1), df[1].plot())
# figsize and title
ax = df.plot(x=1, y=2, title="Test", figsize=(16, 8))
self._check_text_labels(ax.title, "Test")
self._check_axes_shape(ax, axes_num=1, layout=(1, 1), figsize=(16.0, 8.0))
# columns.inferred_type == 'mixed'
# TODO add MultiIndex test
@pytest.mark.slow
@pytest.mark.parametrize(
"input_log, expected_log", [(True, "log"), ("sym", "symlog")]
)
def test_logscales(self, input_log, expected_log):
df = DataFrame({"a": np.arange(100)}, index=np.arange(100))
ax = df.plot(logy=input_log)
self._check_ax_scales(ax, yaxis=expected_log)
assert ax.get_yscale() == expected_log
ax = df.plot(logx=input_log)
self._check_ax_scales(ax, xaxis=expected_log)
assert ax.get_xscale() == expected_log
ax = df.plot(loglog=input_log)
self._check_ax_scales(ax, xaxis=expected_log, yaxis=expected_log)
assert ax.get_xscale() == expected_log
assert ax.get_yscale() == expected_log
@pytest.mark.parametrize("input_param", ["logx", "logy", "loglog"])
def test_invalid_logscale(self, input_param):
# GH: 24867
df = DataFrame({"a": np.arange(100)}, index=np.arange(100))
msg = "Boolean, None and 'sym' are valid options, 'sm' is given."
with pytest.raises(ValueError, match=msg):
df.plot(**{input_param: "sm"})
@pytest.mark.slow
def test_xcompat(self):
import pandas as pd
df = self.tdf
ax = df.plot(x_compat=True)
lines = ax.get_lines()
assert not isinstance(lines[0].get_xdata(), PeriodIndex)
tm.close()
pd.plotting.plot_params["xaxis.compat"] = True
ax = df.plot()
lines = ax.get_lines()
assert not isinstance(lines[0].get_xdata(), PeriodIndex)
tm.close()
pd.plotting.plot_params["x_compat"] = False
ax = df.plot()
lines = ax.get_lines()
assert not isinstance(lines[0].get_xdata(), PeriodIndex)
assert isinstance(PeriodIndex(lines[0].get_xdata()), PeriodIndex)
tm.close()
# useful if you're plotting a bunch together
with pd.plotting.plot_params.use("x_compat", True):
ax = df.plot()
lines = ax.get_lines()
assert not isinstance(lines[0].get_xdata(), PeriodIndex)
tm.close()
ax = df.plot()
lines = ax.get_lines()
assert not isinstance(lines[0].get_xdata(), PeriodIndex)
assert isinstance(PeriodIndex(lines[0].get_xdata()), PeriodIndex)
def test_period_compat(self):
# GH 9012
# period-array conversions
df = DataFrame(
np.random.rand(21, 2),
index=bdate_range(datetime(2000, 1, 1), datetime(2000, 1, 31)),
columns=["a", "b"],
)
df.plot()
self.plt.axhline(y=0)
tm.close()
def test_unsorted_index(self):
df = DataFrame(
{"y": np.arange(100)}, index=np.arange(99, -1, -1), dtype=np.int64
)
ax = df.plot()
lines = ax.get_lines()[0]
rs = lines.get_xydata()
rs = Series(rs[:, 1], rs[:, 0], dtype=np.int64, name="y")
tm.assert_series_equal(rs, df.y, check_index_type=False)
tm.close()
df.index = pd.Index(np.arange(99, -1, -1), dtype=np.float64)
ax = df.plot()
lines = ax.get_lines()[0]
rs = lines.get_xydata()
rs = Series(rs[:, 1], rs[:, 0], dtype=np.int64, name="y")
tm.assert_series_equal(rs, df.y)
def test_unsorted_index_lims(self):
df = DataFrame({"y": [0.0, 1.0, 2.0, 3.0]}, index=[1.0, 0.0, 3.0, 2.0])
ax = df.plot()
xmin, xmax = ax.get_xlim()
lines = ax.get_lines()
assert xmin <= np.nanmin(lines[0].get_data()[0])
assert xmax >= np.nanmax(lines[0].get_data()[0])
df = DataFrame(
{"y": [0.0, 1.0, np.nan, 3.0, 4.0, 5.0, 6.0]},
index=[1.0, 0.0, 3.0, 2.0, np.nan, 3.0, 2.0],
)
ax = df.plot()
xmin, xmax = ax.get_xlim()
lines = ax.get_lines()
assert xmin <= np.nanmin(lines[0].get_data()[0])
assert xmax >= np.nanmax(lines[0].get_data()[0])
df = DataFrame({"y": [0.0, 1.0, 2.0, 3.0], "z": [91.0, 90.0, 93.0, 92.0]})
ax = df.plot(x="z", y="y")
xmin, xmax = ax.get_xlim()
lines = ax.get_lines()
assert xmin <= np.nanmin(lines[0].get_data()[0])
assert xmax >= np.nanmax(lines[0].get_data()[0])
@pytest.mark.slow
def test_subplots(self):
df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))
for kind in ["bar", "barh", "line", "area"]:
axes = df.plot(kind=kind, subplots=True, sharex=True, legend=True)
self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
assert axes.shape == (3,)
for ax, column in zip(axes, df.columns):
self._check_legend_labels(ax, labels=[pprint_thing(column)])
for ax in axes[:-2]:
self._check_visible(ax.xaxis) # xaxis must be visible for grid
self._check_visible(ax.get_xticklabels(), visible=False)
if not (kind == "bar" and self.mpl_ge_3_1_0):
# change https://github.com/pandas-dev/pandas/issues/26714
self._check_visible(ax.get_xticklabels(minor=True), visible=False)
self._check_visible(ax.xaxis.get_label(), visible=False)
self._check_visible(ax.get_yticklabels())
self._check_visible(axes[-1].xaxis)
self._check_visible(axes[-1].get_xticklabels())
self._check_visible(axes[-1].get_xticklabels(minor=True))
self._check_visible(axes[-1].xaxis.get_label())
self._check_visible(axes[-1].get_yticklabels())
axes = df.plot(kind=kind, subplots=True, sharex=False)
for ax in axes:
self._check_visible(ax.xaxis)
self._check_visible(ax.get_xticklabels())
self._check_visible(ax.get_xticklabels(minor=True))
self._check_visible(ax.xaxis.get_label())
self._check_visible(ax.get_yticklabels())
axes = df.plot(kind=kind, subplots=True, legend=False)
for ax in axes:
assert ax.get_legend() is None
def test_groupby_boxplot_sharey(self):
# https://github.com/pandas-dev/pandas/issues/20968
# sharey can now be switched check whether the right
# pair of axes is turned on or off
df = DataFrame(
{
"a": [-1.43, -0.15, -3.70, -1.43, -0.14],
"b": [0.56, 0.84, 0.29, 0.56, 0.85],
"c": [0, 1, 2, 3, 1],
},
index=[0, 1, 2, 3, 4],
)
# behavior without keyword
axes = df.groupby("c").boxplot()
expected = [True, False, True, False]
self._assert_ytickslabels_visibility(axes, expected)
# set sharey=True should be identical
axes = df.groupby("c").boxplot(sharey=True)
expected = [True, False, True, False]
self._assert_ytickslabels_visibility(axes, expected)
# sharey=False, all yticklabels should be visible
axes = df.groupby("c").boxplot(sharey=False)
expected = [True, True, True, True]
self._assert_ytickslabels_visibility(axes, expected)
def test_groupby_boxplot_sharex(self):
# https://github.com/pandas-dev/pandas/issues/20968
# sharex can now be switched check whether the right
# pair of axes is turned on or off
df = DataFrame(
{
"a": [-1.43, -0.15, -3.70, -1.43, -0.14],
"b": [0.56, 0.84, 0.29, 0.56, 0.85],
"c": [0, 1, 2, 3, 1],
},
index=[0, 1, 2, 3, 4],
)
# behavior without keyword
axes = df.groupby("c").boxplot()
expected = [True, True, True, True]
self._assert_xtickslabels_visibility(axes, expected)
# set sharex=False should be identical
axes = df.groupby("c").boxplot(sharex=False)
expected = [True, True, True, True]
self._assert_xtickslabels_visibility(axes, expected)
# sharex=True, yticklabels should be visible
# only for bottom plots
axes = df.groupby("c").boxplot(sharex=True)
expected = [False, False, True, True]
self._assert_xtickslabels_visibility(axes, expected)
@pytest.mark.xfail(reason="Waiting for PR 34334", strict=True)
@pytest.mark.slow
def test_subplots_timeseries(self):
idx = date_range(start="2014-07-01", freq="M", periods=10)
df = DataFrame(np.random.rand(10, 3), index=idx)
for kind in ["line", "area"]:
axes = df.plot(kind=kind, subplots=True, sharex=True)
self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
for ax in axes[:-2]:
# GH 7801
self._check_visible(ax.xaxis) # xaxis must be visible for grid
self._check_visible(ax.get_xticklabels(), visible=False)
self._check_visible(ax.get_xticklabels(minor=True), visible=False)
self._check_visible(ax.xaxis.get_label(), visible=False)
self._check_visible(ax.get_yticklabels())
self._check_visible(axes[-1].xaxis)
self._check_visible(axes[-1].get_xticklabels())
self._check_visible(axes[-1].get_xticklabels(minor=True))
self._check_visible(axes[-1].xaxis.get_label())
self._check_visible(axes[-1].get_yticklabels())
self._check_ticks_props(axes, xrot=0)
axes = df.plot(kind=kind, subplots=True, sharex=False, rot=45, fontsize=7)
for ax in axes:
self._check_visible(ax.xaxis)
self._check_visible(ax.get_xticklabels())
self._check_visible(ax.get_xticklabels(minor=True))
self._check_visible(ax.xaxis.get_label())
self._check_visible(ax.get_yticklabels())
self._check_ticks_props(ax, xlabelsize=7, xrot=45, ylabelsize=7)
def test_subplots_timeseries_y_axis(self):
# GH16953
data = {
"numeric": np.array([1, 2, 5]),
"timedelta": [
pd.Timedelta(-10, unit="s"),
pd.Timedelta(10, unit="m"),
pd.Timedelta(10, unit="h"),
],
"datetime_no_tz": [
pd.to_datetime("2017-08-01 00:00:00"),
pd.to_datetime("2017-08-01 02:00:00"),
pd.to_datetime("2017-08-02 00:00:00"),
],
"datetime_all_tz": [
pd.to_datetime("2017-08-01 00:00:00", utc=True),
pd.to_datetime("2017-08-01 02:00:00", utc=True),
pd.to_datetime("2017-08-02 00:00:00", utc=True),
],
"text": ["This", "should", "fail"],
}
testdata = DataFrame(data)
ax_numeric = testdata.plot(y="numeric")
assert (
ax_numeric.get_lines()[0].get_data()[1] == testdata["numeric"].values
).all()
ax_timedelta = testdata.plot(y="timedelta")
assert (
ax_timedelta.get_lines()[0].get_data()[1] == testdata["timedelta"].values
).all()
ax_datetime_no_tz = testdata.plot(y="datetime_no_tz")
assert (
ax_datetime_no_tz.get_lines()[0].get_data()[1]
== testdata["datetime_no_tz"].values
).all()
ax_datetime_all_tz = testdata.plot(y="datetime_all_tz")
assert (
ax_datetime_all_tz.get_lines()[0].get_data()[1]
== testdata["datetime_all_tz"].values
).all()
msg = "no numeric data to plot"
with pytest.raises(TypeError, match=msg):
testdata.plot(y="text")
@pytest.mark.xfail(reason="not support for period, categorical, datetime_mixed_tz")
def test_subplots_timeseries_y_axis_not_supported(self):
"""
This test will fail for:
period:
since period isn't yet implemented in ``select_dtypes``
and because it will need a custom value converter +
tick formatter (as was done for x-axis plots)
categorical:
because it will need a custom value converter +
tick formatter (also doesn't work for x-axis, as of now)
datetime_mixed_tz:
because of the way how pandas handles ``Series`` of
``datetime`` objects with different timezone,
generally converting ``datetime`` objects in a tz-aware
form could help with this problem
"""
data = {
"numeric": np.array([1, 2, 5]),
"period": [
pd.Period("2017-08-01 00:00:00", freq="H"),
pd.Period("2017-08-01 02:00", freq="H"),
pd.Period("2017-08-02 00:00:00", freq="H"),
],
"categorical": pd.Categorical(
["c", "b", "a"], categories=["a", "b", "c"], ordered=False
),
"datetime_mixed_tz": [
pd.to_datetime("2017-08-01 00:00:00", utc=True),
pd.to_datetime("2017-08-01 02:00:00"),
pd.to_datetime("2017-08-02 00:00:00"),
],
}
testdata = pd.DataFrame(data)
ax_period = testdata.plot(x="numeric", y="period")
assert (
ax_period.get_lines()[0].get_data()[1] == testdata["period"].values
).all()
ax_categorical = testdata.plot(x="numeric", y="categorical")
assert (
ax_categorical.get_lines()[0].get_data()[1]
== testdata["categorical"].values
).all()
ax_datetime_mixed_tz = testdata.plot(x="numeric", y="datetime_mixed_tz")
assert (
ax_datetime_mixed_tz.get_lines()[0].get_data()[1]
== testdata["datetime_mixed_tz"].values
).all()
@pytest.mark.slow
def test_subplots_layout(self):
# GH 6667
df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))
axes = df.plot(subplots=True, layout=(2, 2))
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
assert axes.shape == (2, 2)
axes = df.plot(subplots=True, layout=(-1, 2))
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
assert axes.shape == (2, 2)
axes = df.plot(subplots=True, layout=(2, -1))
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
assert axes.shape == (2, 2)
axes = df.plot(subplots=True, layout=(1, 4))
self._check_axes_shape(axes, axes_num=3, layout=(1, 4))
assert axes.shape == (1, 4)
axes = df.plot(subplots=True, layout=(-1, 4))
self._check_axes_shape(axes, axes_num=3, layout=(1, 4))
assert axes.shape == (1, 4)
axes = df.plot(subplots=True, layout=(4, -1))
self._check_axes_shape(axes, axes_num=3, layout=(4, 1))
assert axes.shape == (4, 1)
with pytest.raises(ValueError):
df.plot(subplots=True, layout=(1, 1))
with pytest.raises(ValueError):
df.plot(subplots=True, layout=(-1, -1))
# single column
df = DataFrame(np.random.rand(10, 1), index=list(string.ascii_letters[:10]))
axes = df.plot(subplots=True)
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
assert axes.shape == (1,)
axes = df.plot(subplots=True, layout=(3, 3))
self._check_axes_shape(axes, axes_num=1, layout=(3, 3))
assert axes.shape == (3, 3)
@pytest.mark.slow
def test_subplots_warnings(self):
# GH 9464
with tm.assert_produces_warning(None):
df = DataFrame(np.random.randn(100, 4))
df.plot(subplots=True, layout=(3, 2))
df = DataFrame(
np.random.randn(100, 4), index=date_range("1/1/2000", periods=100)
)
df.plot(subplots=True, layout=(3, 2))
@pytest.mark.slow
def test_subplots_multiple_axes(self):
# GH 5353, 6970, GH 7069
fig, axes = self.plt.subplots(2, 3)
df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))
returned = df.plot(subplots=True, ax=axes[0], sharex=False, sharey=False)
self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
assert returned.shape == (3,)
assert returned[0].figure is fig
# draw on second row
returned = df.plot(subplots=True, ax=axes[1], sharex=False, sharey=False)
self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
assert returned.shape == (3,)
assert returned[0].figure is fig
self._check_axes_shape(axes, axes_num=6, layout=(2, 3))
tm.close()
with pytest.raises(ValueError):
fig, axes = self.plt.subplots(2, 3)
# pass different number of axes from required
df.plot(subplots=True, ax=axes)
# pass 2-dim axes and invalid layout
# invalid lauout should not affect to input and return value
# (show warning is tested in
# TestDataFrameGroupByPlots.test_grouped_box_multiple_axes
fig, axes = self.plt.subplots(2, 2)
with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning)
df = DataFrame(np.random.rand(10, 4), index=list(string.ascii_letters[:10]))
returned = df.plot(
subplots=True, ax=axes, layout=(2, 1), sharex=False, sharey=False
)
self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
assert returned.shape == (4,)
returned = df.plot(
subplots=True, ax=axes, layout=(2, -1), sharex=False, sharey=False
)
self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
assert returned.shape == (4,)
returned = df.plot(
subplots=True, ax=axes, layout=(-1, 2), sharex=False, sharey=False
)
self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
assert returned.shape == (4,)
# single column
fig, axes = self.plt.subplots(1, 1)
df = DataFrame(np.random.rand(10, 1), index=list(string.ascii_letters[:10]))
axes = df.plot(subplots=True, ax=[axes], sharex=False, sharey=False)
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
assert axes.shape == (1,)
def test_subplots_ts_share_axes(self):
# GH 3964
fig, axes = self.plt.subplots(3, 3, sharex=True, sharey=True)
self.plt.subplots_adjust(left=0.05, right=0.95, hspace=0.3, wspace=0.3)
df = DataFrame(
np.random.randn(10, 9),
index=date_range(start="2014-07-01", freq="M", periods=10),
)
for i, ax in enumerate(axes.ravel()):
df[i].plot(ax=ax, fontsize=5)
# Rows other than bottom should not be visible
for ax in axes[0:-1].ravel():
self._check_visible(ax.get_xticklabels(), visible=False)
# Bottom row should be visible
for ax in axes[-1].ravel():
self._check_visible(ax.get_xticklabels(), visible=True)
# First column should be visible
for ax in axes[[0, 1, 2], [0]].ravel():
self._check_visible(ax.get_yticklabels(), visible=True)
# Other columns should not be visible
for ax in axes[[0, 1, 2], [1]].ravel():
self._check_visible(ax.get_yticklabels(), visible=False)
for ax in axes[[0, 1, 2], [2]].ravel():
self._check_visible(ax.get_yticklabels(), visible=False)
def test_subplots_sharex_axes_existing_axes(self):
# GH 9158
d = {"A": [1.0, 2.0, 3.0, 4.0], "B": [4.0, 3.0, 2.0, 1.0], "C": [5, 1, 3, 4]}
df = DataFrame(d, index=date_range("2014 10 11", "2014 10 14"))
axes = df[["A", "B"]].plot(subplots=True)
df["C"].plot(ax=axes[0], secondary_y=True)
self._check_visible(axes[0].get_xticklabels(), visible=False)
self._check_visible(axes[1].get_xticklabels(), visible=True)
for ax in axes.ravel():
self._check_visible(ax.get_yticklabels(), visible=True)
@pytest.mark.slow
def test_subplots_dup_columns(self):
# GH 10962
df = DataFrame(np.random.rand(5, 5), columns=list("aaaaa"))
axes = df.plot(subplots=True)
for ax in axes:
self._check_legend_labels(ax, labels=["a"])
assert len(ax.lines) == 1
tm.close()
axes = df.plot(subplots=True, secondary_y="a")
for ax in axes:
# (right) is only attached when subplots=False
self._check_legend_labels(ax, labels=["a"])
assert len(ax.lines) == 1
tm.close()
ax = df.plot(secondary_y="a")
self._check_legend_labels(ax, labels=["a (right)"] * 5)
assert len(ax.lines) == 0
assert len(ax.right_ax.lines) == 5
def test_negative_log(self):
df = -DataFrame(
rand(6, 4),
index=list(string.ascii_letters[:6]),
columns=["x", "y", "z", "four"],
)
with pytest.raises(ValueError):
df.plot.area(logy=True)
with pytest.raises(ValueError):
df.plot.area(loglog=True)
def _compare_stacked_y_cood(self, normal_lines, stacked_lines):
base = np.zeros(len(normal_lines[0].get_data()[1]))
for nl, sl in zip(normal_lines, stacked_lines):
base += nl.get_data()[1] # get y coordinates
sy = sl.get_data()[1]
tm.assert_numpy_array_equal(base, sy)
def test_line_area_stacked(self):
with tm.RNGContext(42):
df = DataFrame(rand(6, 4), columns=["w", "x", "y", "z"])
neg_df = -df
# each column has either positive or negative value
sep_df = DataFrame(
{"w": rand(6), "x": rand(6), "y": -rand(6), "z": -rand(6)}
)
# each column has positive-negative mixed value
mixed_df = DataFrame(
randn(6, 4),
index=list(string.ascii_letters[:6]),
columns=["w", "x", "y", "z"],
)
for kind in ["line", "area"]:
ax1 = _check_plot_works(df.plot, kind=kind, stacked=False)
ax2 = _check_plot_works(df.plot, kind=kind, stacked=True)
self._compare_stacked_y_cood(ax1.lines, ax2.lines)
ax1 = _check_plot_works(neg_df.plot, kind=kind, stacked=False)
ax2 = _check_plot_works(neg_df.plot, kind=kind, stacked=True)
self._compare_stacked_y_cood(ax1.lines, ax2.lines)
ax1 = _check_plot_works(sep_df.plot, kind=kind, stacked=False)
ax2 = _check_plot_works(sep_df.plot, kind=kind, stacked=True)
self._compare_stacked_y_cood(ax1.lines[:2], ax2.lines[:2])
self._compare_stacked_y_cood(ax1.lines[2:], ax2.lines[2:])
_check_plot_works(mixed_df.plot, stacked=False)
with pytest.raises(ValueError):
mixed_df.plot(stacked=True)
# Use an index with strictly positive values, preventing
# matplotlib from warning about ignoring xlim
df2 = df.set_index(df.index + 1)
_check_plot_works(df2.plot, kind=kind, logx=True, stacked=True)
def test_line_area_nan_df(self):
values1 = [1, 2, np.nan, 3]
values2 = [3, np.nan, 2, 1]
df = DataFrame({"a": values1, "b": values2})
tdf = DataFrame({"a": values1, "b": values2}, index=tm.makeDateIndex(k=4))
for d in [df, tdf]:
ax = _check_plot_works(d.plot)
masked1 = ax.lines[0].get_ydata()
masked2 = ax.lines[1].get_ydata()
# remove nan for comparison purpose
exp = np.array([1, 2, 3], dtype=np.float64)
tm.assert_numpy_array_equal(np.delete(masked1.data, 2), exp)
exp = np.array([3, 2, 1], dtype=np.float64)
tm.assert_numpy_array_equal(np.delete(masked2.data, 1), exp)
tm.assert_numpy_array_equal(
masked1.mask, np.array([False, False, True, False])
)
tm.assert_numpy_array_equal(
masked2.mask, np.array([False, True, False, False])
)
expected1 = np.array([1, 2, 0, 3], dtype=np.float64)
expected2 = np.array([3, 0, 2, 1], dtype=np.float64)
ax = _check_plot_works(d.plot, stacked=True)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
tm.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected1 + expected2)
ax = _check_plot_works(d.plot.area)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
tm.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected1 + expected2)
ax = _check_plot_works(d.plot.area, stacked=False)
tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
tm.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected2)
def test_line_lim(self):
df = DataFrame(rand(6, 3), columns=["x", "y", "z"])
ax = df.plot()
xmin, xmax = ax.get_xlim()
lines = ax.get_lines()
assert xmin <= lines[0].get_data()[0][0]
assert xmax >= lines[0].get_data()[0][-1]
ax = df.plot(secondary_y=True)
xmin, xmax = ax.get_xlim()
lines = ax.get_lines()
assert xmin <= lines[0].get_data()[0][0]
assert xmax >= lines[0].get_data()[0][-1]
axes = df.plot(secondary_y=True, subplots=True)
self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
for ax in axes:
assert hasattr(ax, "left_ax")
assert not hasattr(ax, "right_ax")
xmin, xmax = ax.get_xlim()
lines = ax.get_lines()
assert xmin <= lines[0].get_data()[0][0]
assert xmax >= lines[0].get_data()[0][-1]
def test_area_lim(self):
df = DataFrame(rand(6, 4), columns=["x", "y", "z", "four"])
neg_df = -df
for stacked in [True, False]:
ax = _check_plot_works(df.plot.area, stacked=stacked)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
lines = ax.get_lines()
assert xmin <= lines[0].get_data()[0][0]
assert xmax >= lines[0].get_data()[0][-1]
assert ymin == 0
ax = _check_plot_works(neg_df.plot.area, stacked=stacked)
ymin, ymax = ax.get_ylim()
assert ymax == 0
@pytest.mark.slow
def test_bar_colors(self):
import matplotlib.pyplot as plt
default_colors = self._unpack_cycler(plt.rcParams)
df = DataFrame(randn(5, 5))
ax = df.plot.bar()
self._check_colors(ax.patches[::5], facecolors=default_colors[:5])
tm.close()
custom_colors = "rgcby"
ax = df.plot.bar(color=custom_colors)
self._check_colors(ax.patches[::5], facecolors=custom_colors)
tm.close()
from matplotlib import cm
# Test str -> colormap functionality
ax = df.plot.bar(colormap="jet")
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, 5)]
self._check_colors(ax.patches[::5], facecolors=rgba_colors)
tm.close()
# Test colormap functionality
ax = df.plot.bar(colormap=cm.jet)
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, 5)]
self._check_colors(ax.patches[::5], facecolors=rgba_colors)
tm.close()
ax = df.loc[:, [0]].plot.bar(color="DodgerBlue")
self._check_colors([ax.patches[0]], facecolors=["DodgerBlue"])
tm.close()
ax = df.plot(kind="bar", color="green")
self._check_colors(ax.patches[::5], facecolors=["green"] * 5)
tm.close()
def test_bar_user_colors(self):
df = pd.DataFrame(
{"A": range(4), "B": range(1, 5), "color": ["red", "blue", "blue", "red"]}
)
# This should *only* work when `y` is specified, else
# we use one color per column
ax = df.plot.bar(y="A", color=df["color"])
result = [p.get_facecolor() for p in ax.patches]
expected = [
(1.0, 0.0, 0.0, 1.0),
(0.0, 0.0, 1.0, 1.0),
(0.0, 0.0, 1.0, 1.0),
(1.0, 0.0, 0.0, 1.0),
]
assert result == expected
@pytest.mark.slow
def test_bar_linewidth(self):
df = DataFrame(randn(5, 5))
# regular
ax = df.plot.bar(linewidth=2)
for r in ax.patches:
assert r.get_linewidth() == 2
# stacked
ax = df.plot.bar(stacked=True, linewidth=2)
for r in ax.patches:
assert r.get_linewidth() == 2
# subplots
axes = df.plot.bar(linewidth=2, subplots=True)
self._check_axes_shape(axes, axes_num=5, layout=(5, 1))
for ax in axes:
for r in ax.patches:
assert r.get_linewidth() == 2
@pytest.mark.slow
def test_bar_barwidth(self):
df = DataFrame(randn(5, 5))
width = 0.9
# regular
ax = df.plot.bar(width=width)
for r in ax.patches:
assert r.get_width() == width / len(df.columns)
# stacked
ax = df.plot.bar(stacked=True, width=width)
for r in ax.patches:
assert r.get_width() == width
# horizontal regular
ax = df.plot.barh(width=width)
for r in ax.patches:
assert r.get_height() == width / len(df.columns)
# horizontal stacked
ax = df.plot.barh(stacked=True, width=width)
for r in ax.patches:
assert r.get_height() == width
# subplots
axes = df.plot.bar(width=width, subplots=True)
for ax in axes:
for r in ax.patches:
assert r.get_width() == width
# horizontal subplots
axes = df.plot.barh(width=width, subplots=True)
for ax in axes:
for r in ax.patches:
assert r.get_height() == width
@pytest.mark.slow
def test_bar_barwidth_position(self):
df = DataFrame(randn(5, 5))
self._check_bar_alignment(
df, kind="bar", stacked=False, width=0.9, position=0.2
)
self._check_bar_alignment(df, kind="bar", stacked=True, width=0.9, position=0.2)
self._check_bar_alignment(
df, kind="barh", stacked=False, width=0.9, position=0.2
)
self._check_bar_alignment(
df, kind="barh", stacked=True, width=0.9, position=0.2
)
self._check_bar_alignment(
df, kind="bar", subplots=True, width=0.9, position=0.2
)
self._check_bar_alignment(
df, kind="barh", subplots=True, width=0.9, position=0.2
)
@pytest.mark.slow
def test_bar_barwidth_position_int(self):
# GH 12979
df = DataFrame(randn(5, 5))
for w in [1, 1.0]:
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
self._check_bar_alignment(df, kind="bar", stacked=True, width=1)
self._check_bar_alignment(df, kind="barh", stacked=False, width=1)
self._check_bar_alignment(df, kind="barh", stacked=True, width=1)
self._check_bar_alignment(df, kind="bar", subplots=True, width=1)
self._check_bar_alignment(df, kind="barh", subplots=True, width=1)
@pytest.mark.slow
def test_bar_bottom_left(self):
df = DataFrame(rand(5, 5))
ax = df.plot.bar(stacked=False, bottom=1)
result = [p.get_y() for p in ax.patches]
assert result == [1] * 25
ax = df.plot.bar(stacked=True, bottom=[-1, -2, -3, -4, -5])
result = [p.get_y() for p in ax.patches[:5]]
assert result == [-1, -2, -3, -4, -5]
ax = df.plot.barh(stacked=False, left=np.array([1, 1, 1, 1, 1]))
result = [p.get_x() for p in ax.patches]
assert result == [1] * 25
ax = df.plot.barh(stacked=True, left=[1, 2, 3, 4, 5])
result = [p.get_x() for p in ax.patches[:5]]
assert result == [1, 2, 3, 4, 5]
axes = df.plot.bar(subplots=True, bottom=-1)
for ax in axes:
result = [p.get_y() for p in ax.patches]
assert result == [-1] * 5
axes = df.plot.barh(subplots=True, left=np.array([1, 1, 1, 1, 1]))
for ax in axes:
result = [p.get_x() for p in ax.patches]
assert result == [1] * 5
@pytest.mark.slow
def test_bar_nan(self):
df = DataFrame({"A": [10, np.nan, 20], "B": [5, 10, 20], "C": [1, 2, 3]})
ax = df.plot.bar()
expected = [10, 0, 20, 5, 10, 20, 1, 2, 3]
result = [p.get_height() for p in ax.patches]
assert result == expected
ax = df.plot.bar(stacked=True)
result = [p.get_height() for p in ax.patches]
assert result == expected
result = [p.get_y() for p in ax.patches]
expected = [0.0, 0.0, 0.0, 10.0, 0.0, 20.0, 15.0, 10.0, 40.0]
assert result == expected
@pytest.mark.slow
def test_bar_categorical(self):
# GH 13019
df1 = pd.DataFrame(
np.random.randn(6, 5),
index=pd.Index(list("ABCDEF")),
columns=pd.Index(list("abcde")),
)
# categorical index must behave the same
df2 = pd.DataFrame(
np.random.randn(6, 5),
index=pd.CategoricalIndex(list("ABCDEF")),
columns=pd.CategoricalIndex(list("abcde")),
)
for df in [df1, df2]:
ax = df.plot.bar()
ticks = ax.xaxis.get_ticklocs()
tm.assert_numpy_array_equal(ticks, np.array([0, 1, 2, 3, 4, 5]))
assert ax.get_xlim() == (-0.5, 5.5)
# check left-edge of bars
assert ax.patches[0].get_x() == -0.25
assert ax.patches[-1].get_x() == 5.15
ax = df.plot.bar(stacked=True)
tm.assert_numpy_array_equal(ticks, np.array([0, 1, 2, 3, 4, 5]))
assert ax.get_xlim() == (-0.5, 5.5)
assert ax.patches[0].get_x() == -0.25
assert ax.patches[-1].get_x() == 4.75
@pytest.mark.slow
def test_plot_scatter(self):
df = DataFrame(
randn(6, 4),
index=list(string.ascii_letters[:6]),
columns=["x", "y", "z", "four"],
)
_check_plot_works(df.plot.scatter, x="x", y="y")
_check_plot_works(df.plot.scatter, x=1, y=2)
with pytest.raises(TypeError):
df.plot.scatter(x="x")
with pytest.raises(TypeError):
df.plot.scatter(y="y")
# GH 6951
axes = df.plot(x="x", y="y", kind="scatter", subplots=True)
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
def test_raise_error_on_datetime_time_data(self):
# GH 8113, datetime.time type is not supported by matplotlib in scatter
df = pd.DataFrame(np.random.randn(10), columns=["a"])
df["dtime"] = pd.date_range(start="2014-01-01", freq="h", periods=10).time
msg = "must be a string or a number, not 'datetime.time'"
with pytest.raises(TypeError, match=msg):
df.plot(kind="scatter", x="dtime", y="a")
def test_scatterplot_datetime_data(self):
# GH 30391
dates = pd.date_range(start=date(2019, 1, 1), periods=12, freq="W")
vals = np.random.normal(0, 1, len(dates))
df = pd.DataFrame({"dates": dates, "vals": vals})
_check_plot_works(df.plot.scatter, x="dates", y="vals")
_check_plot_works(df.plot.scatter, x=0, y=1)
def test_scatterplot_object_data(self):
# GH 18755
df = pd.DataFrame(dict(a=["A", "B", "C"], b=[2, 3, 4]))
_check_plot_works(df.plot.scatter, x="a", y="b")
_check_plot_works(df.plot.scatter, x=0, y=1)
df = pd.DataFrame(dict(a=["A", "B", "C"], b=["a", "b", "c"]))
_check_plot_works(df.plot.scatter, x="a", y="b")
_check_plot_works(df.plot.scatter, x=0, y=1)
@pytest.mark.slow
def test_if_scatterplot_colorbar_affects_xaxis_visibility(self):
# addressing issue #10611, to ensure colobar does not
# interfere with x-axis label and ticklabels with
# ipython inline backend.
random_array = np.random.random((1000, 3))
df = pd.DataFrame(random_array, columns=["A label", "B label", "C label"])
ax1 = df.plot.scatter(x="A label", y="B label")
ax2 = df.plot.scatter(x="A label", y="B label", c="C label")
vis1 = [vis.get_visible() for vis in ax1.xaxis.get_minorticklabels()]
vis2 = [vis.get_visible() for vis in ax2.xaxis.get_minorticklabels()]
assert vis1 == vis2
vis1 = [vis.get_visible() for vis in ax1.xaxis.get_majorticklabels()]
vis2 = [vis.get_visible() for vis in ax2.xaxis.get_majorticklabels()]
assert vis1 == vis2
assert (
ax1.xaxis.get_label().get_visible() == ax2.xaxis.get_label().get_visible()
)
@pytest.mark.slow
def test_if_hexbin_xaxis_label_is_visible(self):
# addressing issue #10678, to ensure colobar does not
# interfere with x-axis label and ticklabels with
# ipython inline backend.
random_array = np.random.random((1000, 3))
df = pd.DataFrame(random_array, columns=["A label", "B label", "C label"])
ax = df.plot.hexbin("A label", "B label", gridsize=12)
assert all(vis.get_visible() for vis in ax.xaxis.get_minorticklabels())
assert all(vis.get_visible() for vis in ax.xaxis.get_majorticklabels())
assert ax.xaxis.get_label().get_visible()
@pytest.mark.slow
def test_if_scatterplot_colorbars_are_next_to_parent_axes(self):
import matplotlib.pyplot as plt
random_array = np.random.random((1000, 3))
df = pd.DataFrame(random_array, columns=["A label", "B label", "C label"])
fig, axes = plt.subplots(1, 2)
df.plot.scatter("A label", "B label", c="C label", ax=axes[0])
df.plot.scatter("A label", "B label", c="C label", ax=axes[1])
plt.tight_layout()
points = np.array([ax.get_position().get_points() for ax in fig.axes])
axes_x_coords = points[:, :, 0]
parent_distance = axes_x_coords[1, :] - axes_x_coords[0, :]
colorbar_distance = axes_x_coords[3, :] - axes_x_coords[2, :]
assert np.isclose(parent_distance, colorbar_distance, atol=1e-7).all()
@pytest.mark.parametrize("x, y", [("x", "y"), ("y", "x"), ("y", "y")])
@pytest.mark.slow
def test_plot_scatter_with_categorical_data(self, x, y):
# after fixing GH 18755, should be able to plot categorical data
df = pd.DataFrame(
{"x": [1, 2, 3, 4], "y": pd.Categorical(["a", "b", "a", "c"])}
)
_check_plot_works(df.plot.scatter, x=x, y=y)
@pytest.mark.slow
def test_plot_scatter_with_c(self):
df = DataFrame(
randn(6, 4),
index=list(string.ascii_letters[:6]),
columns=["x", "y", "z", "four"],
)
axes = [df.plot.scatter(x="x", y="y", c="z"), df.plot.scatter(x=0, y=1, c=2)]
for ax in axes:
# default to Greys
assert ax.collections[0].cmap.name == "Greys"
# n.b. there appears to be no public method
# to get the colorbar label
assert ax.collections[0].colorbar._label == "z"
cm = "cubehelix"
ax = df.plot.scatter(x="x", y="y", c="z", colormap=cm)
assert ax.collections[0].cmap.name == cm
# verify turning off colorbar works
ax = df.plot.scatter(x="x", y="y", c="z", colorbar=False)
assert ax.collections[0].colorbar is None
# verify that we can still plot a solid color
ax = df.plot.scatter(x=0, y=1, c="red")
assert ax.collections[0].colorbar is None
self._check_colors(ax.collections, facecolors=["r"])
# Ensure that we can pass an np.array straight through to matplotlib,
# this functionality was accidentally removed previously.
# See https://github.com/pandas-dev/pandas/issues/8852 for bug report
#
# Exercise colormap path and non-colormap path as they are independent
#
df = DataFrame({"A": [1, 2], "B": [3, 4]})
red_rgba = [1.0, 0.0, 0.0, 1.0]
green_rgba = [0.0, 1.0, 0.0, 1.0]
rgba_array = np.array([red_rgba, green_rgba])
ax = df.plot.scatter(x="A", y="B", c=rgba_array)
# expect the face colors of the points in the non-colormap path to be
# identical to the values we supplied, normally we'd be on shaky ground
# comparing floats for equality but here we expect them to be
# identical.
tm.assert_numpy_array_equal(ax.collections[0].get_facecolor(), rgba_array)
# we don't test the colors of the faces in this next plot because they
# are dependent on the spring colormap, which may change its colors
# later.
float_array = np.array([0.0, 1.0])
df.plot.scatter(x="A", y="B", c=float_array, cmap="spring")
@pytest.mark.parametrize("cmap", [None, "Greys"])
def test_scatter_with_c_column_name_with_colors(self, cmap):
# https://github.com/pandas-dev/pandas/issues/34316
df = pd.DataFrame(
[[5.1, 3.5], [4.9, 3.0], [7.0, 3.2], [6.4, 3.2], [5.9, 3.0]],
columns=["length", "width"],
)
df["species"] = ["r", "r", "g", "g", "b"]
ax = df.plot.scatter(x=0, y=1, c="species", cmap=cmap)
assert ax.collections[0].colorbar is None
def test_plot_scatter_with_s(self):
# this refers to GH 32904
df = DataFrame(np.random.random((10, 3)) * 100, columns=["a", "b", "c"],)
ax = df.plot.scatter(x="a", y="b", s="c")
tm.assert_numpy_array_equal(df["c"].values, right=ax.collections[0].get_sizes())
def test_scatter_colors(self):
df = DataFrame({"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
with pytest.raises(TypeError):
df.plot.scatter(x="a", y="b", c="c", color="green")
default_colors = self._unpack_cycler(self.plt.rcParams)
ax = df.plot.scatter(x="a", y="b", c="c")
tm.assert_numpy_array_equal(
ax.collections[0].get_facecolor()[0],
np.array(self.colorconverter.to_rgba(default_colors[0])),
)
ax = df.plot.scatter(x="a", y="b", color="white")
tm.assert_numpy_array_equal(
ax.collections[0].get_facecolor()[0],
np.array([1, 1, 1, 1], dtype=np.float64),
)
def test_scatter_colorbar_different_cmap(self):
# GH 33389
import matplotlib.pyplot as plt
df = pd.DataFrame({"x": [1, 2, 3], "y": [1, 3, 2], "c": [1, 2, 3]})
df["x2"] = df["x"] + 1
fig, ax = plt.subplots()
df.plot("x", "y", c="c", kind="scatter", cmap="cividis", ax=ax)
df.plot("x2", "y", c="c", kind="scatter", cmap="magma", ax=ax)
assert ax.collections[0].cmap.name == "cividis"
assert ax.collections[1].cmap.name == "magma"
@pytest.mark.slow
def test_plot_bar(self):
df = DataFrame(
randn(6, 4),
index=list(string.ascii_letters[:6]),
columns=["one", "two", "three", "four"],
)
_check_plot_works(df.plot.bar)
_check_plot_works(df.plot.bar, legend=False)
# _check_plot_works adds an ax so catch warning. see GH #13188
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.plot.bar, subplots=True)
_check_plot_works(df.plot.bar, stacked=True)
df = DataFrame(
randn(10, 15), index=list(string.ascii_letters[:10]), columns=range(15)
)
_check_plot_works(df.plot.bar)
df = DataFrame({"a": [0, 1], "b": [1, 0]})
ax = _check_plot_works(df.plot.bar)
self._check_ticks_props(ax, xrot=90)
ax = df.plot.bar(rot=35, fontsize=10)
self._check_ticks_props(ax, xrot=35, xlabelsize=10, ylabelsize=10)
ax = _check_plot_works(df.plot.barh)
self._check_ticks_props(ax, yrot=0)
ax = df.plot.barh(rot=55, fontsize=11)
self._check_ticks_props(ax, yrot=55, ylabelsize=11, xlabelsize=11)
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 = self._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
@pytest.mark.slow
def test_bar_stacked_center(self):
# GH2157
df = DataFrame({"A": [3] * 5, "B": list(range(5))}, index=range(5))
self._check_bar_alignment(df, kind="bar", stacked=True)
self._check_bar_alignment(df, kind="bar", stacked=True, width=0.9)
self._check_bar_alignment(df, kind="barh", stacked=True)
self._check_bar_alignment(df, kind="barh", stacked=True, width=0.9)
@pytest.mark.slow
def test_bar_center(self):
df = DataFrame({"A": [3] * 5, "B": list(range(5))}, index=range(5))
self._check_bar_alignment(df, kind="bar", stacked=False)
self._check_bar_alignment(df, kind="bar", stacked=False, width=0.9)
self._check_bar_alignment(df, kind="barh", stacked=False)
self._check_bar_alignment(df, kind="barh", stacked=False, width=0.9)
@pytest.mark.slow
def test_bar_subplots_center(self):
df = DataFrame({"A": [3] * 5, "B": list(range(5))}, index=range(5))
self._check_bar_alignment(df, kind="bar", subplots=True)
self._check_bar_alignment(df, kind="bar", subplots=True, width=0.9)
self._check_bar_alignment(df, kind="barh", subplots=True)
self._check_bar_alignment(df, kind="barh", subplots=True, width=0.9)
@pytest.mark.slow
def test_bar_align_single_column(self):
df = DataFrame(randn(5))
self._check_bar_alignment(df, kind="bar", stacked=False)
self._check_bar_alignment(df, kind="bar", stacked=True)
self._check_bar_alignment(df, kind="barh", stacked=False)
self._check_bar_alignment(df, kind="barh", stacked=True)
self._check_bar_alignment(df, kind="bar", subplots=True)
self._check_bar_alignment(df, kind="barh", subplots=True)
@pytest.mark.slow
def test_bar_edge(self):
df = DataFrame({"A": [3] * 5, "B": list(range(5))}, index=range(5))
self._check_bar_alignment(df, kind="bar", stacked=True, align="edge")
self._check_bar_alignment(df, kind="bar", stacked=True, width=0.9, align="edge")
self._check_bar_alignment(df, kind="barh", stacked=True, align="edge")
self._check_bar_alignment(
df, kind="barh", stacked=True, width=0.9, align="edge"
)
self._check_bar_alignment(df, kind="bar", stacked=False, align="edge")
self._check_bar_alignment(
df, kind="bar", stacked=False, width=0.9, align="edge"
)
self._check_bar_alignment(df, kind="barh", stacked=False, align="edge")
self._check_bar_alignment(
df, kind="barh", stacked=False, width=0.9, align="edge"
)
self._check_bar_alignment(df, kind="bar", subplots=True, align="edge")
self._check_bar_alignment(
df, kind="bar", subplots=True, width=0.9, align="edge"
)
self._check_bar_alignment(df, kind="barh", subplots=True, align="edge")
self._check_bar_alignment(
df, kind="barh", subplots=True, width=0.9, align="edge"
)
@pytest.mark.slow
def test_bar_log_no_subplots(self):
# GH3254, GH3298 matplotlib/matplotlib#1882, #1892
# regressions in 1.2.1
expected = np.array([0.1, 1.0, 10.0, 100])
# no subplots
df = DataFrame({"A": [3] * 5, "B": list(range(1, 6))}, index=range(5))
ax = df.plot.bar(grid=True, log=True)
tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
@pytest.mark.slow
def test_bar_log_subplots(self):
expected = np.array([0.1, 1.0, 10.0, 100.0, 1000.0, 1e4])
ax = DataFrame([Series([200, 300]), Series([300, 500])]).plot.bar(
log=True, subplots=True
)
tm.assert_numpy_array_equal(ax[0].yaxis.get_ticklocs(), expected)
tm.assert_numpy_array_equal(ax[1].yaxis.get_ticklocs(), expected)
@pytest.mark.slow
def test_boxplot(self):
df = self.hist_df
series = df["height"]
numeric_cols = df._get_numeric_data().columns
labels = [pprint_thing(c) for c in numeric_cols]
ax = _check_plot_works(df.plot.box)
self._check_text_labels(ax.get_xticklabels(), labels)
tm.assert_numpy_array_equal(
ax.xaxis.get_ticklocs(), np.arange(1, len(numeric_cols) + 1)
)
assert len(ax.lines) == self.bp_n_objects * len(numeric_cols)
tm.close()
axes = series.plot.box(rot=40)
self._check_ticks_props(axes, xrot=40, yrot=0)
tm.close()
ax = _check_plot_works(series.plot.box)
positions = np.array([1, 6, 7])
ax = df.plot.box(positions=positions)
numeric_cols = df._get_numeric_data().columns
labels = [pprint_thing(c) for c in numeric_cols]
self._check_text_labels(ax.get_xticklabels(), labels)
tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), positions)
assert len(ax.lines) == self.bp_n_objects * len(numeric_cols)
@pytest.mark.slow
def test_boxplot_vertical(self):
df = self.hist_df
numeric_cols = df._get_numeric_data().columns
labels = [pprint_thing(c) for c in numeric_cols]
# if horizontal, yticklabels are rotated
ax = df.plot.box(rot=50, fontsize=8, vert=False)
self._check_ticks_props(ax, xrot=0, yrot=50, ylabelsize=8)
self._check_text_labels(ax.get_yticklabels(), labels)
assert len(ax.lines) == self.bp_n_objects * len(numeric_cols)
# _check_plot_works adds an ax so catch warning. see GH #13188
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.plot.box, subplots=True, vert=False, logx=True)
self._check_axes_shape(axes, axes_num=3, layout=(1, 3))
self._check_ax_scales(axes, xaxis="log")
for ax, label in zip(axes, labels):
self._check_text_labels(ax.get_yticklabels(), [label])
assert len(ax.lines) == self.bp_n_objects
positions = np.array([3, 2, 8])
ax = df.plot.box(positions=positions, vert=False)
self._check_text_labels(ax.get_yticklabels(), labels)
tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), positions)
assert len(ax.lines) == self.bp_n_objects * len(numeric_cols)
@pytest.mark.slow
def test_boxplot_return_type(self):
df = DataFrame(
randn(6, 4),
index=list(string.ascii_letters[:6]),
columns=["one", "two", "three", "four"],
)
with pytest.raises(ValueError):
df.plot.box(return_type="NOTATYPE")
result = df.plot.box(return_type="dict")
self._check_box_return_type(result, "dict")
result = df.plot.box(return_type="axes")
self._check_box_return_type(result, "axes")
result = df.plot.box() # default axes
self._check_box_return_type(result, "axes")
result = df.plot.box(return_type="both")
self._check_box_return_type(result, "both")
@pytest.mark.slow
def test_boxplot_subplots_return_type(self):
df = self.hist_df
# normal style: return_type=None
result = df.plot.box(subplots=True)
assert isinstance(result, Series)
self._check_box_return_type(
result, None, expected_keys=["height", "weight", "category"]
)
for t in ["dict", "axes", "both"]:
returned = df.plot.box(return_type=t, subplots=True)
self._check_box_return_type(
returned,
t,
expected_keys=["height", "weight", "category"],
check_ax_title=False,
)
@pytest.mark.slow
@td.skip_if_no_scipy
def test_kde_df(self):
df = DataFrame(randn(100, 4))
ax = _check_plot_works(df.plot, kind="kde")
expected = [pprint_thing(c) for c in df.columns]
self._check_legend_labels(ax, labels=expected)
self._check_ticks_props(ax, xrot=0)
ax = df.plot(kind="kde", rot=20, fontsize=5)
self._check_ticks_props(ax, xrot=20, xlabelsize=5, ylabelsize=5)
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.plot, kind="kde", subplots=True)
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
axes = df.plot(kind="kde", logy=True, subplots=True)
self._check_ax_scales(axes, yaxis="log")
@pytest.mark.slow
@td.skip_if_no_scipy
def test_kde_missing_vals(self):
df = DataFrame(np.random.uniform(size=(100, 4)))
df.loc[0, 0] = np.nan
_check_plot_works(df.plot, kind="kde")
@pytest.mark.slow
def test_hist_df(self):
from matplotlib.patches import Rectangle
df = DataFrame(randn(100, 4))
series = df[0]
ax = _check_plot_works(df.plot.hist)
expected = [pprint_thing(c) for c in df.columns]
self._check_legend_labels(ax, labels=expected)
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.plot.hist, subplots=True, logy=True)
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
self._check_ax_scales(axes, yaxis="log")
axes = series.plot.hist(rot=40)
self._check_ticks_props(axes, xrot=40, yrot=0)
tm.close()
ax = series.plot.hist(cumulative=True, bins=4, density=True)
# height of last bin (index 5) must be 1.0
rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
tm.assert_almost_equal(rects[-1].get_height(), 1.0)
tm.close()
ax = series.plot.hist(cumulative=True, bins=4)
rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
tm.assert_almost_equal(rects[-2].get_height(), 100.0)
tm.close()
# if horizontal, yticklabels are rotated
axes = df.plot.hist(rot=50, fontsize=8, orientation="horizontal")
self._check_ticks_props(axes, xrot=0, yrot=50, ylabelsize=8)
@pytest.mark.parametrize(
"weights", [0.1 * np.ones(shape=(100,)), 0.1 * np.ones(shape=(100, 2))]
)
def test_hist_weights(self, weights):
# GH 33173
np.random.seed(0)
df = pd.DataFrame(dict(zip(["A", "B"], np.random.randn(2, 100,))))
ax1 = _check_plot_works(df.plot, kind="hist", weights=weights)
ax2 = _check_plot_works(df.plot, kind="hist")
patch_height_with_weights = [patch.get_height() for patch in ax1.patches]
# original heights with no weights, and we manually multiply with example
# weights, so after multiplication, they should be almost same
expected_patch_height = [0.1 * patch.get_height() for patch in ax2.patches]
tm.assert_almost_equal(patch_height_with_weights, expected_patch_height)
def _check_box_coord(
self,
patches,
expected_y=None,
expected_h=None,
expected_x=None,
expected_w=None,
):
result_y = np.array([p.get_y() for p in patches])
result_height = np.array([p.get_height() for p in patches])
result_x = np.array([p.get_x() for p in patches])
result_width = np.array([p.get_width() for p in patches])
# dtype is depending on above values, no need to check
if expected_y is not None:
tm.assert_numpy_array_equal(result_y, expected_y, check_dtype=False)
if expected_h is not None:
tm.assert_numpy_array_equal(result_height, expected_h, check_dtype=False)
if expected_x is not None:
tm.assert_numpy_array_equal(result_x, expected_x, check_dtype=False)
if expected_w is not None:
tm.assert_numpy_array_equal(result_width, expected_w, check_dtype=False)
@pytest.mark.slow
def test_hist_df_coord(self):
normal_df = DataFrame(
{
"A": np.repeat(np.array([1, 2, 3, 4, 5]), np.array([10, 9, 8, 7, 6])),
"B": np.repeat(np.array([1, 2, 3, 4, 5]), np.array([8, 8, 8, 8, 8])),
"C": np.repeat(np.array([1, 2, 3, 4, 5]), np.array([6, 7, 8, 9, 10])),
},
columns=["A", "B", "C"],
)
nan_df = DataFrame(
{
"A": np.repeat(
np.array([np.nan, 1, 2, 3, 4, 5]), np.array([3, 10, 9, 8, 7, 6])
),
"B": np.repeat(
np.array([1, np.nan, 2, 3, 4, 5]), np.array([8, 3, 8, 8, 8, 8])
),
"C": np.repeat(
np.array([1, 2, 3, np.nan, 4, 5]), np.array([6, 7, 8, 3, 9, 10])
),
},
columns=["A", "B", "C"],
)
for df in [normal_df, nan_df]:
ax = df.plot.hist(bins=5)
self._check_box_coord(
ax.patches[:5],
expected_y=np.array([0, 0, 0, 0, 0]),
expected_h=np.array([10, 9, 8, 7, 6]),
)
self._check_box_coord(
ax.patches[5:10],
expected_y=np.array([0, 0, 0, 0, 0]),
expected_h=np.array([8, 8, 8, 8, 8]),
)
self._check_box_coord(
ax.patches[10:],
expected_y=np.array([0, 0, 0, 0, 0]),
expected_h=np.array([6, 7, 8, 9, 10]),
)
ax = df.plot.hist(bins=5, stacked=True)
self._check_box_coord(
ax.patches[:5],
expected_y=np.array([0, 0, 0, 0, 0]),
expected_h=np.array([10, 9, 8, 7, 6]),
)
self._check_box_coord(
ax.patches[5:10],
expected_y=np.array([10, 9, 8, 7, 6]),
expected_h=np.array([8, 8, 8, 8, 8]),
)
self._check_box_coord(
ax.patches[10:],
expected_y=np.array([18, 17, 16, 15, 14]),
expected_h=np.array([6, 7, 8, 9, 10]),
)
axes = df.plot.hist(bins=5, stacked=True, subplots=True)
self._check_box_coord(
axes[0].patches,
expected_y=np.array([0, 0, 0, 0, 0]),
expected_h=np.array([10, 9, 8, 7, 6]),
)
self._check_box_coord(
axes[1].patches,
expected_y=np.array([0, 0, 0, 0, 0]),
expected_h=np.array([8, 8, 8, 8, 8]),
)
self._check_box_coord(
axes[2].patches,
expected_y=np.array([0, 0, 0, 0, 0]),
expected_h=np.array([6, 7, 8, 9, 10]),
)
# horizontal
ax = df.plot.hist(bins=5, orientation="horizontal")
self._check_box_coord(
ax.patches[:5],
expected_x=np.array([0, 0, 0, 0, 0]),
expected_w=np.array([10, 9, 8, 7, 6]),
)
self._check_box_coord(
ax.patches[5:10],
expected_x=np.array([0, 0, 0, 0, 0]),
expected_w=np.array([8, 8, 8, 8, 8]),
)
self._check_box_coord(
ax.patches[10:],
expected_x=np.array([0, 0, 0, 0, 0]),
expected_w=np.array([6, 7, 8, 9, 10]),
)
ax = df.plot.hist(bins=5, stacked=True, orientation="horizontal")
self._check_box_coord(
ax.patches[:5],
expected_x=np.array([0, 0, 0, 0, 0]),
expected_w=np.array([10, 9, 8, 7, 6]),
)
self._check_box_coord(
ax.patches[5:10],
expected_x=np.array([10, 9, 8, 7, 6]),
expected_w=np.array([8, 8, 8, 8, 8]),
)
self._check_box_coord(
ax.patches[10:],
expected_x=np.array([18, 17, 16, 15, 14]),
expected_w=np.array([6, 7, 8, 9, 10]),
)
axes = df.plot.hist(
bins=5, stacked=True, subplots=True, orientation="horizontal"
)
self._check_box_coord(
axes[0].patches,
expected_x=np.array([0, 0, 0, 0, 0]),
expected_w=np.array([10, 9, 8, 7, 6]),
)
self._check_box_coord(
axes[1].patches,
expected_x=np.array([0, 0, 0, 0, 0]),
expected_w=np.array([8, 8, 8, 8, 8]),
)
self._check_box_coord(
axes[2].patches,
expected_x=np.array([0, 0, 0, 0, 0]),
expected_w=np.array([6, 7, 8, 9, 10]),
)
@pytest.mark.slow
def test_plot_int_columns(self):
df = DataFrame(randn(100, 4)).cumsum()
_check_plot_works(df.plot, legend=True)
@pytest.mark.slow
def test_df_legend_labels(self):
kinds = ["line", "bar", "barh", "kde", "area", "hist"]
df = DataFrame(rand(3, 3), columns=["a", "b", "c"])
df2 = DataFrame(rand(3, 3), columns=["d", "e", "f"])
df3 = DataFrame(rand(3, 3), columns=["g", "h", "i"])
df4 = DataFrame(rand(3, 3), columns=["j", "k", "l"])
for kind in kinds:
ax = df.plot(kind=kind, legend=True)
self._check_legend_labels(ax, labels=df.columns)
ax = df2.plot(kind=kind, legend=False, ax=ax)
self._check_legend_labels(ax, labels=df.columns)
ax = df3.plot(kind=kind, legend=True, ax=ax)
self._check_legend_labels(ax, labels=df.columns.union(df3.columns))
ax = df4.plot(kind=kind, legend="reverse", ax=ax)
expected = list(df.columns.union(df3.columns)) + list(reversed(df4.columns))
self._check_legend_labels(ax, labels=expected)
# Secondary Y
ax = df.plot(legend=True, secondary_y="b")
self._check_legend_labels(ax, labels=["a", "b (right)", "c"])
ax = df2.plot(legend=False, ax=ax)
self._check_legend_labels(ax, labels=["a", "b (right)", "c"])
ax = df3.plot(kind="bar", legend=True, secondary_y="h", ax=ax)
self._check_legend_labels(
ax, labels=["a", "b (right)", "c", "g", "h (right)", "i"]
)
# Time Series
ind = date_range("1/1/2014", periods=3)
df = DataFrame(randn(3, 3), columns=["a", "b", "c"], index=ind)
df2 = DataFrame(randn(3, 3), columns=["d", "e", "f"], index=ind)
df3 = DataFrame(randn(3, 3), columns=["g", "h", "i"], index=ind)
ax = df.plot(legend=True, secondary_y="b")
self._check_legend_labels(ax, labels=["a", "b (right)", "c"])
ax = df2.plot(legend=False, ax=ax)
self._check_legend_labels(ax, labels=["a", "b (right)", "c"])
ax = df3.plot(legend=True, ax=ax)
self._check_legend_labels(ax, labels=["a", "b (right)", "c", "g", "h", "i"])
# scatter
ax = df.plot.scatter(x="a", y="b", label="data1")
self._check_legend_labels(ax, labels=["data1"])
ax = df2.plot.scatter(x="d", y="e", legend=False, label="data2", ax=ax)
self._check_legend_labels(ax, labels=["data1"])
ax = df3.plot.scatter(x="g", y="h", label="data3", ax=ax)
self._check_legend_labels(ax, labels=["data1", "data3"])
# ensure label args pass through and
# index name does not mutate
# column names don't mutate
df5 = df.set_index("a")
ax = df5.plot(y="b")
self._check_legend_labels(ax, labels=["b"])
ax = df5.plot(y="b", label="LABEL_b")
self._check_legend_labels(ax, labels=["LABEL_b"])
self._check_text_labels(ax.xaxis.get_label(), "a")
ax = df5.plot(y="c", label="LABEL_c", ax=ax)
self._check_legend_labels(ax, labels=["LABEL_b", "LABEL_c"])
assert df5.columns.tolist() == ["b", "c"]
def test_missing_marker_multi_plots_on_same_ax(self):
# GH 18222
df = pd.DataFrame(
data=[[1, 1, 1, 1], [2, 2, 4, 8]], columns=["x", "r", "g", "b"]
)
fig, ax = self.plt.subplots(nrows=1, ncols=3)
# Left plot
df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[0])
df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[0])
df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[0])
self._check_legend_labels(ax[0], labels=["r", "g", "b"])
self._check_legend_marker(ax[0], expected_markers=["o", "x", "o"])
# Center plot
df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[1])
df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[1])
df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[1])
self._check_legend_labels(ax[1], labels=["b", "r", "g"])
self._check_legend_marker(ax[1], expected_markers=["o", "o", "x"])
# Right plot
df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[2])
df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[2])
df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[2])
self._check_legend_labels(ax[2], labels=["g", "b", "r"])
self._check_legend_marker(ax[2], expected_markers=["x", "o", "o"])
def test_legend_name(self):
multi = DataFrame(
randn(4, 4),
columns=[np.array(["a", "a", "b", "b"]), np.array(["x", "y", "x", "y"])],
)
multi.columns.names = ["group", "individual"]
ax = multi.plot()
leg_title = ax.legend_.get_title()
self._check_text_labels(leg_title, "group,individual")
df = DataFrame(randn(5, 5))
ax = df.plot(legend=True, ax=ax)
leg_title = ax.legend_.get_title()
self._check_text_labels(leg_title, "group,individual")
df.columns.name = "new"
ax = df.plot(legend=False, ax=ax)
leg_title = ax.legend_.get_title()
self._check_text_labels(leg_title, "group,individual")
ax = df.plot(legend=True, ax=ax)
leg_title = ax.legend_.get_title()
self._check_text_labels(leg_title, "new")
@pytest.mark.slow
def test_no_legend(self):
kinds = ["line", "bar", "barh", "kde", "area", "hist"]
df = DataFrame(rand(3, 3), columns=["a", "b", "c"])
for kind in kinds:
ax = df.plot(kind=kind, legend=False)
self._check_legend_labels(ax, visible=False)
@pytest.mark.slow
def test_style_by_column(self):
import matplotlib.pyplot as plt
fig = plt.gcf()
df = DataFrame(randn(100, 3))
for markers in [
{0: "^", 1: "+", 2: "o"},
{0: "^", 1: "+"},
["^", "+", "o"],
["^", "+"],
]:
fig.clf()
fig.add_subplot(111)
ax = df.plot(style=markers)
for i, l in enumerate(ax.get_lines()[: len(markers)]):
assert l.get_marker() == markers[i]
@pytest.mark.slow
def test_line_label_none(self):
s = Series([1, 2])
ax = s.plot()
assert ax.get_legend() is None
ax = s.plot(legend=True)
assert ax.get_legend().get_texts()[0].get_text() == "None"
@pytest.mark.slow
def test_line_colors(self):
from matplotlib import cm
custom_colors = "rgcby"
df = DataFrame(randn(5, 5))
ax = df.plot(color=custom_colors)
self._check_colors(ax.get_lines(), linecolors=custom_colors)
tm.close()
ax2 = df.plot(color=custom_colors)
lines2 = ax2.get_lines()
for l1, l2 in zip(ax.get_lines(), lines2):
assert l1.get_color() == l2.get_color()
tm.close()
ax = df.plot(colormap="jet")
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
self._check_colors(ax.get_lines(), linecolors=rgba_colors)
tm.close()
ax = df.plot(colormap=cm.jet)
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
self._check_colors(ax.get_lines(), linecolors=rgba_colors)
tm.close()
# make color a list if plotting one column frame
# handles cases like df.plot(color='DodgerBlue')
ax = df.loc[:, [0]].plot(color="DodgerBlue")
self._check_colors(ax.lines, linecolors=["DodgerBlue"])
ax = df.plot(color="red")
self._check_colors(ax.get_lines(), linecolors=["red"] * 5)
tm.close()
# GH 10299
custom_colors = ["#FF0000", "#0000FF", "#FFFF00", "#000000", "#FFFFFF"]
ax = df.plot(color=custom_colors)
self._check_colors(ax.get_lines(), linecolors=custom_colors)
tm.close()
@pytest.mark.slow
def test_dont_modify_colors(self):
colors = ["r", "g", "b"]
pd.DataFrame(np.random.rand(10, 2)).plot(color=colors)
assert len(colors) == 3
@pytest.mark.slow
def test_line_colors_and_styles_subplots(self):
# GH 9894
from matplotlib import cm
default_colors = self._unpack_cycler(self.plt.rcParams)
df = DataFrame(randn(5, 5))
axes = df.plot(subplots=True)
for ax, c in zip(axes, list(default_colors)):
c = [c]
self._check_colors(ax.get_lines(), linecolors=c)
tm.close()
# single color char
axes = df.plot(subplots=True, color="k")
for ax in axes:
self._check_colors(ax.get_lines(), linecolors=["k"])
tm.close()
# single color str
axes = df.plot(subplots=True, color="green")
for ax in axes:
self._check_colors(ax.get_lines(), linecolors=["green"])
tm.close()
custom_colors = "rgcby"
axes = df.plot(color=custom_colors, subplots=True)
for ax, c in zip(axes, list(custom_colors)):
self._check_colors(ax.get_lines(), linecolors=[c])
tm.close()
axes = df.plot(color=list(custom_colors), subplots=True)
for ax, c in zip(axes, list(custom_colors)):
self._check_colors(ax.get_lines(), linecolors=[c])
tm.close()
# GH 10299
custom_colors = ["#FF0000", "#0000FF", "#FFFF00", "#000000", "#FFFFFF"]
axes = df.plot(color=custom_colors, subplots=True)
for ax, c in zip(axes, list(custom_colors)):
self._check_colors(ax.get_lines(), linecolors=[c])
tm.close()
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
for cmap in ["jet", cm.jet]:
axes = df.plot(colormap=cmap, subplots=True)
for ax, c in zip(axes, rgba_colors):
self._check_colors(ax.get_lines(), linecolors=[c])
tm.close()
# make color a list if plotting one column frame
# handles cases like df.plot(color='DodgerBlue')
axes = df.loc[:, [0]].plot(color="DodgerBlue", subplots=True)
self._check_colors(axes[0].lines, linecolors=["DodgerBlue"])
# single character style
axes = df.plot(style="r", subplots=True)
for ax in axes:
self._check_colors(ax.get_lines(), linecolors=["r"])
tm.close()
# list of styles
styles = list("rgcby")
axes = df.plot(style=styles, subplots=True)
for ax, c in zip(axes, styles):
self._check_colors(ax.get_lines(), linecolors=[c])
tm.close()
@pytest.mark.slow
def test_area_colors(self):
from matplotlib import cm
from matplotlib.collections import PolyCollection
custom_colors = "rgcby"
df = DataFrame(rand(5, 5))
ax = df.plot.area(color=custom_colors)
self._check_colors(ax.get_lines(), linecolors=custom_colors)
poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
self._check_colors(poly, facecolors=custom_colors)
handles, labels = ax.get_legend_handles_labels()
self._check_colors(handles, facecolors=custom_colors)
for h in handles:
assert h.get_alpha() is None
tm.close()
ax = df.plot.area(colormap="jet")
jet_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
self._check_colors(ax.get_lines(), linecolors=jet_colors)
poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
self._check_colors(poly, facecolors=jet_colors)
handles, labels = ax.get_legend_handles_labels()
self._check_colors(handles, facecolors=jet_colors)
for h in handles:
assert h.get_alpha() is None
tm.close()
# When stacked=False, alpha is set to 0.5
ax = df.plot.area(colormap=cm.jet, stacked=False)
self._check_colors(ax.get_lines(), linecolors=jet_colors)
poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
jet_with_alpha = [(c[0], c[1], c[2], 0.5) for c in jet_colors]
self._check_colors(poly, facecolors=jet_with_alpha)
handles, labels = ax.get_legend_handles_labels()
linecolors = jet_with_alpha
self._check_colors(handles[: len(jet_colors)], linecolors=linecolors)
for h in handles:
assert h.get_alpha() == 0.5
@pytest.mark.slow
def test_hist_colors(self):
default_colors = self._unpack_cycler(self.plt.rcParams)
df = DataFrame(randn(5, 5))
ax = df.plot.hist()
self._check_colors(ax.patches[::10], facecolors=default_colors[:5])
tm.close()
custom_colors = "rgcby"
ax = df.plot.hist(color=custom_colors)
self._check_colors(ax.patches[::10], facecolors=custom_colors)
tm.close()
from matplotlib import cm
# Test str -> colormap functionality
ax = df.plot.hist(colormap="jet")
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, 5)]
self._check_colors(ax.patches[::10], facecolors=rgba_colors)
tm.close()
# Test colormap functionality
ax = df.plot.hist(colormap=cm.jet)
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, 5)]
self._check_colors(ax.patches[::10], facecolors=rgba_colors)
tm.close()
ax = df.loc[:, [0]].plot.hist(color="DodgerBlue")
self._check_colors([ax.patches[0]], facecolors=["DodgerBlue"])
ax = df.plot(kind="hist", color="green")
self._check_colors(ax.patches[::10], facecolors=["green"] * 5)
tm.close()
@pytest.mark.slow
@td.skip_if_no_scipy
def test_kde_colors(self):
from matplotlib import cm
custom_colors = "rgcby"
df = DataFrame(rand(5, 5))
ax = df.plot.kde(color=custom_colors)
self._check_colors(ax.get_lines(), linecolors=custom_colors)
tm.close()
ax = df.plot.kde(colormap="jet")
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
self._check_colors(ax.get_lines(), linecolors=rgba_colors)
tm.close()
ax = df.plot.kde(colormap=cm.jet)
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
self._check_colors(ax.get_lines(), linecolors=rgba_colors)
@pytest.mark.slow
@td.skip_if_no_scipy
def test_kde_colors_and_styles_subplots(self):
from matplotlib import cm
default_colors = self._unpack_cycler(self.plt.rcParams)
df = DataFrame(randn(5, 5))
axes = df.plot(kind="kde", subplots=True)
for ax, c in zip(axes, list(default_colors)):
self._check_colors(ax.get_lines(), linecolors=[c])
tm.close()
# single color char
axes = df.plot(kind="kde", color="k", subplots=True)
for ax in axes:
self._check_colors(ax.get_lines(), linecolors=["k"])
tm.close()
# single color str
axes = df.plot(kind="kde", color="red", subplots=True)
for ax in axes:
self._check_colors(ax.get_lines(), linecolors=["red"])
tm.close()
custom_colors = "rgcby"
axes = df.plot(kind="kde", color=custom_colors, subplots=True)
for ax, c in zip(axes, list(custom_colors)):
self._check_colors(ax.get_lines(), linecolors=[c])
tm.close()
rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
for cmap in ["jet", cm.jet]:
axes = df.plot(kind="kde", colormap=cmap, subplots=True)
for ax, c in zip(axes, rgba_colors):
self._check_colors(ax.get_lines(), linecolors=[c])
tm.close()
# make color a list if plotting one column frame
# handles cases like df.plot(color='DodgerBlue')
axes = df.loc[:, [0]].plot(kind="kde", color="DodgerBlue", subplots=True)
self._check_colors(axes[0].lines, linecolors=["DodgerBlue"])
# single character style
axes = df.plot(kind="kde", style="r", subplots=True)
for ax in axes:
self._check_colors(ax.get_lines(), linecolors=["r"])
tm.close()
# list of styles
styles = list("rgcby")
axes = df.plot(kind="kde", style=styles, subplots=True)
for ax, c in zip(axes, styles):
self._check_colors(ax.get_lines(), linecolors=[c])
tm.close()
@pytest.mark.slow
def test_boxplot_colors(self):
def _check_colors(bp, box_c, whiskers_c, medians_c, caps_c="k", fliers_c=None):
# TODO: outside this func?
if fliers_c is None:
fliers_c = "k"
self._check_colors(bp["boxes"], linecolors=[box_c] * len(bp["boxes"]))
self._check_colors(
bp["whiskers"], linecolors=[whiskers_c] * len(bp["whiskers"])
)
self._check_colors(
bp["medians"], linecolors=[medians_c] * len(bp["medians"])
)
self._check_colors(bp["fliers"], linecolors=[fliers_c] * len(bp["fliers"]))
self._check_colors(bp["caps"], linecolors=[caps_c] * len(bp["caps"]))
default_colors = self._unpack_cycler(self.plt.rcParams)
df = DataFrame(randn(5, 5))
bp = df.plot.box(return_type="dict")
_check_colors(bp, default_colors[0], default_colors[0], default_colors[2])
tm.close()
dict_colors = dict(
boxes="#572923", whiskers="#982042", medians="#804823", caps="#123456"
)
bp = df.plot.box(color=dict_colors, sym="r+", return_type="dict")
_check_colors(
bp,
dict_colors["boxes"],
dict_colors["whiskers"],
dict_colors["medians"],
dict_colors["caps"],
"r",
)
tm.close()
# partial colors
dict_colors = dict(whiskers="c", medians="m")
bp = df.plot.box(color=dict_colors, return_type="dict")
_check_colors(bp, default_colors[0], "c", "m")
tm.close()
from matplotlib import cm
# Test str -> colormap functionality
bp = df.plot.box(colormap="jet", return_type="dict")
jet_colors = [cm.jet(n) for n in np.linspace(0, 1, 3)]
_check_colors(bp, jet_colors[0], jet_colors[0], jet_colors[2])
tm.close()
# Test colormap functionality
bp = df.plot.box(colormap=cm.jet, return_type="dict")
_check_colors(bp, jet_colors[0], jet_colors[0], jet_colors[2])
tm.close()
# string color is applied to all artists except fliers
bp = df.plot.box(color="DodgerBlue", return_type="dict")
_check_colors(bp, "DodgerBlue", "DodgerBlue", "DodgerBlue", "DodgerBlue")
# tuple is also applied to all artists except fliers
bp = df.plot.box(color=(0, 1, 0), sym="#123456", return_type="dict")
_check_colors(bp, (0, 1, 0), (0, 1, 0), (0, 1, 0), (0, 1, 0), "#123456")
with pytest.raises(ValueError):
# Color contains invalid key results in ValueError
df.plot.box(color=dict(boxes="red", xxxx="blue"))
@pytest.mark.parametrize(
"props, expected",
[
("boxprops", "boxes"),
("whiskerprops", "whiskers"),
("capprops", "caps"),
("medianprops", "medians"),
],
)
def test_specified_props_kwd_plot_box(self, props, expected):
# GH 30346
df = DataFrame({k: np.random.random(100) for k in "ABC"})
kwd = {props: dict(color="C1")}
result = df.plot.box(return_type="dict", **kwd)
assert result[expected][0].get_color() == "C1"
def test_default_color_cycle(self):
import cycler
import matplotlib.pyplot as plt
colors = list("rgbk")
plt.rcParams["axes.prop_cycle"] = cycler.cycler("color", colors)
df = DataFrame(randn(5, 3))
ax = df.plot()
expected = self._unpack_cycler(plt.rcParams)[:3]
self._check_colors(ax.get_lines(), linecolors=expected)
def test_unordered_ts(self):
df = DataFrame(
np.array([3.0, 2.0, 1.0]),
index=[date(2012, 10, 1), date(2012, 9, 1), date(2012, 8, 1)],
columns=["test"],
)
ax = df.plot()
xticks = ax.lines[0].get_xdata()
assert xticks[0] < xticks[1]
ydata = ax.lines[0].get_ydata()
tm.assert_numpy_array_equal(ydata, np.array([1.0, 2.0, 3.0]))
@td.skip_if_no_scipy
def test_kind_both_ways(self):
df = DataFrame({"x": [1, 2, 3]})
for kind in plotting.PlotAccessor._common_kinds:
df.plot(kind=kind)
getattr(df.plot, kind)()
for kind in ["scatter", "hexbin"]:
df.plot("x", "x", kind=kind)
getattr(df.plot, kind)("x", "x")
def test_all_invalid_plot_data(self):
df = DataFrame(list("abcd"))
for kind in plotting.PlotAccessor._common_kinds:
msg = "no numeric data to plot"
with pytest.raises(TypeError, match=msg):
df.plot(kind=kind)
@pytest.mark.slow
def test_partially_invalid_plot_data(self):
with tm.RNGContext(42):
df = DataFrame(randn(10, 2), dtype=object)
df[np.random.rand(df.shape[0]) > 0.5] = "a"
for kind in plotting.PlotAccessor._common_kinds:
msg = "no numeric data to plot"
with pytest.raises(TypeError, match=msg):
df.plot(kind=kind)
with tm.RNGContext(42):
# area plot doesn't support positive/negative mixed data
kinds = ["area"]
df = DataFrame(rand(10, 2), dtype=object)
df[np.random.rand(df.shape[0]) > 0.5] = "a"
for kind in kinds:
with pytest.raises(TypeError):
df.plot(kind=kind)
def test_invalid_kind(self):
df = DataFrame(randn(10, 2))
with pytest.raises(ValueError):
df.plot(kind="aasdf")
@pytest.mark.parametrize(
"x,y,lbl",
[
(["B", "C"], "A", "a"),
(["A"], ["B", "C"], ["b", "c"]),
("A", ["B", "C"], "badlabel"),
],
)
def test_invalid_xy_args(self, x, y, lbl):
# GH 18671, 19699 allows y to be list-like but not x
df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
with pytest.raises(ValueError):
df.plot(x=x, y=y, label=lbl)
@pytest.mark.parametrize("x,y", [("A", "B"), (["A"], "B")])
def test_invalid_xy_args_dup_cols(self, x, y):
# GH 18671, 19699 allows y to be list-like but not x
df = DataFrame([[1, 3, 5], [2, 4, 6]], columns=list("AAB"))
with pytest.raises(ValueError):
df.plot(x=x, y=y)
@pytest.mark.parametrize(
"x,y,lbl,colors",
[
("A", ["B"], ["b"], ["red"]),
("A", ["B", "C"], ["b", "c"], ["red", "blue"]),
(0, [1, 2], ["bokeh", "cython"], ["green", "yellow"]),
],
)
def test_y_listlike(self, x, y, lbl, colors):
# GH 19699: tests list-like y and verifies lbls & colors
df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
_check_plot_works(df.plot, x="A", y=y, label=lbl)
ax = df.plot(x=x, y=y, label=lbl, color=colors)
assert len(ax.lines) == len(y)
self._check_colors(ax.get_lines(), linecolors=colors)
@pytest.mark.parametrize("x,y,colnames", [(0, 1, ["A", "B"]), (1, 0, [0, 1])])
def test_xy_args_integer(self, x, y, colnames):
# GH 20056: tests integer args for xy and checks col names
df = DataFrame({"A": [1, 2], "B": [3, 4]})
df.columns = colnames
_check_plot_works(df.plot, x=x, y=y)
@pytest.mark.slow
def test_hexbin_basic(self):
df = self.hexbin_df
ax = df.plot.hexbin(x="A", y="B", gridsize=10)
# TODO: need better way to test. This just does existence.
assert len(ax.collections) == 1
# GH 6951
axes = df.plot.hexbin(x="A", y="B", subplots=True)
# hexbin should have 2 axes in the figure, 1 for plotting and another
# is colorbar
assert len(axes[0].figure.axes) == 2
# return value is single axes
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
@pytest.mark.slow
def test_hexbin_with_c(self):
df = self.hexbin_df
ax = df.plot.hexbin(x="A", y="B", C="C")
assert len(ax.collections) == 1
ax = df.plot.hexbin(x="A", y="B", C="C", reduce_C_function=np.std)
assert len(ax.collections) == 1
@pytest.mark.slow
def test_hexbin_cmap(self):
df = self.hexbin_df
# Default to BuGn
ax = df.plot.hexbin(x="A", y="B")
assert ax.collections[0].cmap.name == "BuGn"
cm = "cubehelix"
ax = df.plot.hexbin(x="A", y="B", colormap=cm)
assert ax.collections[0].cmap.name == cm
@pytest.mark.slow
def test_no_color_bar(self):
df = self.hexbin_df
ax = df.plot.hexbin(x="A", y="B", colorbar=None)
assert ax.collections[0].colorbar is None
@pytest.mark.slow
def test_allow_cmap(self):
df = self.hexbin_df
ax = df.plot.hexbin(x="A", y="B", cmap="YlGn")
assert ax.collections[0].cmap.name == "YlGn"
with pytest.raises(TypeError):
df.plot.hexbin(x="A", y="B", cmap="YlGn", colormap="BuGn")
@pytest.mark.slow
def test_pie_df(self):
df = DataFrame(
np.random.rand(5, 3),
columns=["X", "Y", "Z"],
index=["a", "b", "c", "d", "e"],
)
with pytest.raises(ValueError):
df.plot.pie()
ax = _check_plot_works(df.plot.pie, y="Y")
self._check_text_labels(ax.texts, df.index)
ax = _check_plot_works(df.plot.pie, y=2)
self._check_text_labels(ax.texts, df.index)
# _check_plot_works adds an ax so catch warning. see GH #13188
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.plot.pie, subplots=True)
assert len(axes) == len(df.columns)
for ax in axes:
self._check_text_labels(ax.texts, df.index)
for ax, ylabel in zip(axes, df.columns):
assert ax.get_ylabel() == ylabel
labels = ["A", "B", "C", "D", "E"]
color_args = ["r", "g", "b", "c", "m"]
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(
df.plot.pie, subplots=True, labels=labels, colors=color_args
)
assert len(axes) == len(df.columns)
for ax in axes:
self._check_text_labels(ax.texts, labels)
self._check_colors(ax.patches, facecolors=color_args)
def test_pie_df_nan(self):
df = DataFrame(np.random.rand(4, 4))
for i in range(4):
df.iloc[i, i] = np.nan
fig, axes = self.plt.subplots(ncols=4)
df.plot.pie(subplots=True, ax=axes, legend=True)
base_expected = ["0", "1", "2", "3"]
for i, ax in enumerate(axes):
expected = list(base_expected) # force copy
expected[i] = ""
result = [x.get_text() for x in ax.texts]
assert result == expected
# legend labels
# NaN's not included in legend with subplots
# see https://github.com/pandas-dev/pandas/issues/8390
assert [x.get_text() for x in ax.get_legend().get_texts()] == base_expected[
:i
] + base_expected[i + 1 :]
@pytest.mark.slow
def test_errorbar_plot(self):
with warnings.catch_warnings():
d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
df = DataFrame(d)
d_err = {"x": np.ones(12) * 0.2, "y": np.ones(12) * 0.4}
df_err = DataFrame(d_err)
# check line plots
ax = _check_plot_works(df.plot, yerr=df_err, logy=True)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(df.plot, yerr=df_err, logx=True, logy=True)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(df.plot, yerr=df_err, loglog=True)
self._check_has_errorbars(ax, xerr=0, yerr=2)
kinds = ["line", "bar", "barh"]
for kind in kinds:
ax = _check_plot_works(df.plot, yerr=df_err["x"], kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(df.plot, yerr=d_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(df.plot, yerr=df_err, xerr=df_err, kind=kind)
self._check_has_errorbars(ax, xerr=2, yerr=2)
ax = _check_plot_works(
df.plot, yerr=df_err["x"], xerr=df_err["x"], kind=kind
)
self._check_has_errorbars(ax, xerr=2, yerr=2)
ax = _check_plot_works(df.plot, xerr=0.2, yerr=0.2, kind=kind)
self._check_has_errorbars(ax, xerr=2, yerr=2)
# _check_plot_works adds an ax so catch warning. see GH #13188
axes = _check_plot_works(
df.plot, yerr=df_err, xerr=df_err, subplots=True, kind=kind
)
self._check_has_errorbars(axes, xerr=1, yerr=1)
ax = _check_plot_works(
(df + 1).plot, yerr=df_err, xerr=df_err, kind="bar", log=True
)
self._check_has_errorbars(ax, xerr=2, yerr=2)
# yerr is raw error values
ax = _check_plot_works(df["y"].plot, yerr=np.ones(12) * 0.4)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(df.plot, yerr=np.ones((2, 12)) * 0.4)
self._check_has_errorbars(ax, xerr=0, yerr=2)
# yerr is column name
for yerr in ["yerr", "誤差"]:
s_df = df.copy()
s_df[yerr] = np.ones(12) * 0.2
ax = _check_plot_works(s_df.plot, yerr=yerr)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(s_df.plot, y="y", x="x", yerr=yerr)
self._check_has_errorbars(ax, xerr=0, yerr=1)
with pytest.raises(ValueError):
df.plot(yerr=np.random.randn(11))
df_err = DataFrame({"x": ["zzz"] * 12, "y": ["zzz"] * 12})
with pytest.raises((ValueError, TypeError)):
df.plot(yerr=df_err)
@pytest.mark.xfail(reason="Iterator is consumed", raises=ValueError)
@pytest.mark.slow
def test_errorbar_plot_iterator(self):
with warnings.catch_warnings():
d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
df = DataFrame(d)
# yerr is iterator
ax = _check_plot_works(df.plot, yerr=itertools.repeat(0.1, len(df)))
self._check_has_errorbars(ax, xerr=0, yerr=2)
@pytest.mark.slow
def test_errorbar_with_integer_column_names(self):
# test with integer column names
df = DataFrame(np.random.randn(10, 2))
df_err = DataFrame(np.random.randn(10, 2))
ax = _check_plot_works(df.plot, yerr=df_err)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(df.plot, y=0, yerr=1)
self._check_has_errorbars(ax, xerr=0, yerr=1)
@pytest.mark.slow
def test_errorbar_with_partial_columns(self):
df = DataFrame(np.random.randn(10, 3))
df_err = DataFrame(np.random.randn(10, 2), columns=[0, 2])
kinds = ["line", "bar"]
for kind in kinds:
ax = _check_plot_works(df.plot, yerr=df_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ix = date_range("1/1/2000", periods=10, freq="M")
df.set_index(ix, inplace=True)
df_err.set_index(ix, inplace=True)
ax = _check_plot_works(df.plot, yerr=df_err, kind="line")
self._check_has_errorbars(ax, xerr=0, yerr=2)
d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
df = DataFrame(d)
d_err = {"x": np.ones(12) * 0.2, "z": np.ones(12) * 0.4}
df_err = DataFrame(d_err)
for err in [d_err, df_err]:
ax = _check_plot_works(df.plot, yerr=err)
self._check_has_errorbars(ax, xerr=0, yerr=1)
@pytest.mark.slow
def test_errorbar_timeseries(self):
with warnings.catch_warnings():
d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
d_err = {"x": np.ones(12) * 0.2, "y": np.ones(12) * 0.4}
# check time-series plots
ix = date_range("1/1/2000", "1/1/2001", freq="M")
tdf = DataFrame(d, index=ix)
tdf_err = DataFrame(d_err, index=ix)
kinds = ["line", "bar", "barh"]
for kind in kinds:
ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(tdf.plot, yerr=d_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(tdf.plot, y="y", yerr=tdf_err["x"], kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(tdf.plot, y="y", yerr="x", kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)
# _check_plot_works adds an ax so catch warning. see GH #13188
axes = _check_plot_works(
tdf.plot, kind=kind, yerr=tdf_err, subplots=True
)
self._check_has_errorbars(axes, xerr=0, yerr=1)
def test_errorbar_asymmetrical(self):
np.random.seed(0)
err = np.random.rand(3, 2, 5)
# each column is [0, 1, 2, 3, 4], [3, 4, 5, 6, 7]...
df = DataFrame(np.arange(15).reshape(3, 5)).T
ax = df.plot(yerr=err, xerr=err / 2)
yerr_0_0 = ax.collections[1].get_paths()[0].vertices[:, 1]
expected_0_0 = err[0, :, 0] * np.array([-1, 1])
tm.assert_almost_equal(yerr_0_0, expected_0_0)
with pytest.raises(ValueError):
df.plot(yerr=err.T)
tm.close()
def test_table(self):
df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))
_check_plot_works(df.plot, table=True)
_check_plot_works(df.plot, table=df)
# GH 35945 UserWarning
with tm.assert_produces_warning(None):
ax = df.plot()
assert len(ax.tables) == 0
plotting.table(ax, df.T)
assert len(ax.tables) == 1
def test_errorbar_scatter(self):
df = DataFrame(np.random.randn(5, 2), index=range(5), columns=["x", "y"])
df_err = DataFrame(
np.random.randn(5, 2) / 5, index=range(5), columns=["x", "y"]
)
ax = _check_plot_works(df.plot.scatter, x="x", y="y")
self._check_has_errorbars(ax, xerr=0, yerr=0)
ax = _check_plot_works(df.plot.scatter, x="x", y="y", xerr=df_err)
self._check_has_errorbars(ax, xerr=1, yerr=0)
ax = _check_plot_works(df.plot.scatter, x="x", y="y", yerr=df_err)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(df.plot.scatter, x="x", y="y", xerr=df_err, yerr=df_err)
self._check_has_errorbars(ax, xerr=1, yerr=1)
def _check_errorbar_color(containers, expected, has_err="has_xerr"):
lines = []
errs = [c.lines for c in ax.containers if getattr(c, has_err, False)][0]
for el in errs:
if is_list_like(el):
lines.extend(el)
else:
lines.append(el)
err_lines = [x for x in lines if x in ax.collections]
self._check_colors(
err_lines, linecolors=np.array([expected] * len(err_lines))
)
# GH 8081
df = DataFrame(np.random.randn(10, 5), columns=["a", "b", "c", "d", "e"])
ax = df.plot.scatter(x="a", y="b", xerr="d", yerr="e", c="red")
self._check_has_errorbars(ax, xerr=1, yerr=1)
_check_errorbar_color(ax.containers, "red", has_err="has_xerr")
_check_errorbar_color(ax.containers, "red", has_err="has_yerr")
ax = df.plot.scatter(x="a", y="b", yerr="e", color="green")
self._check_has_errorbars(ax, xerr=0, yerr=1)
_check_errorbar_color(ax.containers, "green", has_err="has_yerr")
@pytest.mark.slow
def test_sharex_and_ax(self):
# https://github.com/pandas-dev/pandas/issues/9737 using gridspec,
# the axis in fig.get_axis() are sorted differently than pandas
# expected them, so make sure that only the right ones are removed
import matplotlib.pyplot as plt
plt.close("all")
gs, axes = _generate_4_axes_via_gridspec()
df = DataFrame(
{
"a": [1, 2, 3, 4, 5, 6],
"b": [1, 2, 3, 4, 5, 6],
"c": [1, 2, 3, 4, 5, 6],
"d": [1, 2, 3, 4, 5, 6],
}
)
def _check(axes):
for ax in axes:
assert len(ax.lines) == 1
self._check_visible(ax.get_yticklabels(), visible=True)
for ax in [axes[0], axes[2]]:
self._check_visible(ax.get_xticklabels(), visible=False)
self._check_visible(ax.get_xticklabels(minor=True), visible=False)
for ax in [axes[1], axes[3]]:
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
for ax in axes:
df.plot(x="a", y="b", title="title", ax=ax, sharex=True)
gs.tight_layout(plt.gcf())
_check(axes)
tm.close()
gs, axes = _generate_4_axes_via_gridspec()
with tm.assert_produces_warning(UserWarning):
axes = df.plot(subplots=True, ax=axes, sharex=True)
_check(axes)
tm.close()
gs, axes = _generate_4_axes_via_gridspec()
# without sharex, no labels should be touched!
for ax in axes:
df.plot(x="a", y="b", title="title", ax=ax)
gs.tight_layout(plt.gcf())
for ax in axes:
assert len(ax.lines) == 1
self._check_visible(ax.get_yticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
tm.close()
@pytest.mark.slow
def test_sharey_and_ax(self):
# https://github.com/pandas-dev/pandas/issues/9737 using gridspec,
# the axis in fig.get_axis() are sorted differently than pandas
# expected them, so make sure that only the right ones are removed
import matplotlib.pyplot as plt
gs, axes = _generate_4_axes_via_gridspec()
df = DataFrame(
{
"a": [1, 2, 3, 4, 5, 6],
"b": [1, 2, 3, 4, 5, 6],
"c": [1, 2, 3, 4, 5, 6],
"d": [1, 2, 3, 4, 5, 6],
}
)
def _check(axes):
for ax in axes:
assert len(ax.lines) == 1
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
for ax in [axes[0], axes[1]]:
self._check_visible(ax.get_yticklabels(), visible=True)
for ax in [axes[2], axes[3]]:
self._check_visible(ax.get_yticklabels(), visible=False)
for ax in axes:
df.plot(x="a", y="b", title="title", ax=ax, sharey=True)
gs.tight_layout(plt.gcf())
_check(axes)
tm.close()
gs, axes = _generate_4_axes_via_gridspec()
with tm.assert_produces_warning(UserWarning):
axes = df.plot(subplots=True, ax=axes, sharey=True)
gs.tight_layout(plt.gcf())
_check(axes)
tm.close()
gs, axes = _generate_4_axes_via_gridspec()
# without sharex, no labels should be touched!
for ax in axes:
df.plot(x="a", y="b", title="title", ax=ax)
gs.tight_layout(plt.gcf())
for ax in axes:
assert len(ax.lines) == 1
self._check_visible(ax.get_yticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
@td.skip_if_no_scipy
def test_memory_leak(self):
""" Check that every plot type gets properly collected. """
import gc
import weakref
results = {}
for kind in plotting.PlotAccessor._all_kinds:
args = {}
if kind in ["hexbin", "scatter", "pie"]:
df = self.hexbin_df
args = {"x": "A", "y": "B"}
elif kind == "area":
df = self.tdf.abs()
else:
df = self.tdf
# Use a weakref so we can see if the object gets collected without
# also preventing it from being collected
results[kind] = weakref.proxy(df.plot(kind=kind, **args))
# have matplotlib delete all the figures
tm.close()
# force a garbage collection
gc.collect()
for key in results:
# check that every plot was collected
with pytest.raises(ReferenceError):
# need to actually access something to get an error
results[key].lines
@pytest.mark.slow
def test_df_subplots_patterns_minorticks(self):
# GH 10657
import matplotlib.pyplot as plt
df = DataFrame(
np.random.randn(10, 2),
index=date_range("1/1/2000", periods=10),
columns=list("AB"),
)
# shared subplots
fig, axes = plt.subplots(2, 1, sharex=True)
axes = df.plot(subplots=True, ax=axes)
for ax in axes:
assert len(ax.lines) == 1
self._check_visible(ax.get_yticklabels(), visible=True)
# xaxis of 1st ax must be hidden
self._check_visible(axes[0].get_xticklabels(), visible=False)
self._check_visible(axes[0].get_xticklabels(minor=True), visible=False)
self._check_visible(axes[1].get_xticklabels(), visible=True)
self._check_visible(axes[1].get_xticklabels(minor=True), visible=True)
tm.close()
fig, 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
self._check_visible(ax.get_yticklabels(), visible=True)
# xaxis of 1st ax must be hidden
self._check_visible(axes[0].get_xticklabels(), visible=False)
self._check_visible(axes[0].get_xticklabels(minor=True), visible=False)
self._check_visible(axes[1].get_xticklabels(), visible=True)
self._check_visible(axes[1].get_xticklabels(minor=True), visible=True)
tm.close()
# not shared
fig, axes = plt.subplots(2, 1)
axes = df.plot(subplots=True, ax=axes)
for ax in axes:
assert len(ax.lines) == 1
self._check_visible(ax.get_yticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
tm.close()
@pytest.mark.slow
def test_df_gridspec_patterns(self):
# GH 10819
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
ts = Series(np.random.randn(10), index=date_range("1/1/2000", periods=10))
df = DataFrame(np.random.randn(10, 2), index=ts.index, columns=list("AB"))
def _get_vertical_grid():
gs = gridspec.GridSpec(3, 1)
fig = plt.figure()
ax1 = fig.add_subplot(gs[:2, :])
ax2 = fig.add_subplot(gs[2, :])
return ax1, ax2
def _get_horizontal_grid():
gs = gridspec.GridSpec(1, 3)
fig = plt.figure()
ax1 = fig.add_subplot(gs[:, :2])
ax2 = fig.add_subplot(gs[:, 2])
return ax1, ax2
for ax1, ax2 in [_get_vertical_grid(), _get_horizontal_grid()]:
ax1 = ts.plot(ax=ax1)
assert len(ax1.lines) == 1
ax2 = df.plot(ax=ax2)
assert len(ax2.lines) == 2
for ax in [ax1, ax2]:
self._check_visible(ax.get_yticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
tm.close()
# subplots=True
for ax1, ax2 in [_get_vertical_grid(), _get_horizontal_grid()]:
axes = df.plot(subplots=True, ax=[ax1, ax2])
assert len(ax1.lines) == 1
assert len(ax2.lines) == 1
for ax in axes:
self._check_visible(ax.get_yticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
tm.close()
# vertical / subplots / sharex=True / sharey=True
ax1, ax2 = _get_vertical_grid()
with tm.assert_produces_warning(UserWarning):
axes = df.plot(subplots=True, ax=[ax1, ax2], sharex=True, sharey=True)
assert len(axes[0].lines) == 1
assert len(axes[1].lines) == 1
for ax in [ax1, ax2]:
# yaxis are visible because there is only one column
self._check_visible(ax.get_yticklabels(), visible=True)
# xaxis of axes0 (top) are hidden
self._check_visible(axes[0].get_xticklabels(), visible=False)
self._check_visible(axes[0].get_xticklabels(minor=True), visible=False)
self._check_visible(axes[1].get_xticklabels(), visible=True)
self._check_visible(axes[1].get_xticklabels(minor=True), visible=True)
tm.close()
# horizontal / subplots / sharex=True / sharey=True
ax1, ax2 = _get_horizontal_grid()
with tm.assert_produces_warning(UserWarning):
axes = df.plot(subplots=True, ax=[ax1, ax2], sharex=True, sharey=True)
assert len(axes[0].lines) == 1
assert len(axes[1].lines) == 1
self._check_visible(axes[0].get_yticklabels(), visible=True)
# yaxis of axes1 (right) are hidden
self._check_visible(axes[1].get_yticklabels(), visible=False)
for ax in [ax1, ax2]:
# xaxis are visible because there is only one column
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
tm.close()
# boxed
def _get_boxed_grid():
gs = gridspec.GridSpec(3, 3)
fig = plt.figure()
ax1 = fig.add_subplot(gs[:2, :2])
ax2 = fig.add_subplot(gs[:2, 2])
ax3 = fig.add_subplot(gs[2, :2])
ax4 = fig.add_subplot(gs[2, 2])
return ax1, ax2, ax3, ax4
axes = _get_boxed_grid()
df = DataFrame(np.random.randn(10, 4), index=ts.index, columns=list("ABCD"))
axes = df.plot(subplots=True, ax=axes)
for ax in axes:
assert len(ax.lines) == 1
# axis are visible because these are not shared
self._check_visible(ax.get_yticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
tm.close()
# subplots / sharex=True / sharey=True
axes = _get_boxed_grid()
with tm.assert_produces_warning(UserWarning):
axes = df.plot(subplots=True, ax=axes, sharex=True, sharey=True)
for ax in axes:
assert len(ax.lines) == 1
for ax in [axes[0], axes[2]]: # left column
self._check_visible(ax.get_yticklabels(), visible=True)
for ax in [axes[1], axes[3]]: # right column
self._check_visible(ax.get_yticklabels(), visible=False)
for ax in [axes[0], axes[1]]: # top row
self._check_visible(ax.get_xticklabels(), visible=False)
self._check_visible(ax.get_xticklabels(minor=True), visible=False)
for ax in [axes[2], axes[3]]: # bottom row
self._check_visible(ax.get_xticklabels(), visible=True)
self._check_visible(ax.get_xticklabels(minor=True), visible=True)
tm.close()
@pytest.mark.slow
def test_df_grid_settings(self):
# Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
self._check_grid_settings(
DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]}),
plotting.PlotAccessor._dataframe_kinds,
kws={"x": "a", "y": "b"},
)
def test_invalid_colormap(self):
df = DataFrame(randn(3, 2), columns=["A", "B"])
with pytest.raises(ValueError):
df.plot(colormap="invalid_colormap")
def test_plain_axes(self):
# supplied ax itself is a SubplotAxes, but figure contains also
# a plain Axes object (GH11556)
fig, ax = self.plt.subplots()
fig.add_axes([0.2, 0.2, 0.2, 0.2])
Series(rand(10)).plot(ax=ax)
# supplied ax itself is a plain Axes, but because the cmap keyword
# a new ax is created for the colorbar -> also multiples axes (GH11520)
df = DataFrame({"a": randn(8), "b": randn(8)})
fig = self.plt.figure()
ax = fig.add_axes((0, 0, 1, 1))
df.plot(kind="scatter", ax=ax, x="a", y="b", c="a", cmap="hsv")
# other examples
fig, ax = self.plt.subplots()
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
Series(rand(10)).plot(ax=ax)
Series(rand(10)).plot(ax=cax)
fig, ax = self.plt.subplots()
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
iax = inset_axes(ax, width="30%", height=1.0, loc=3)
Series(rand(10)).plot(ax=ax)
Series(rand(10)).plot(ax=iax)
def test_passed_bar_colors(self):
import matplotlib as mpl
color_tuples = [(0.9, 0, 0, 1), (0, 0.9, 0, 1), (0, 0, 0.9, 1)]
colormap = mpl.colors.ListedColormap(color_tuples)
barplot = pd.DataFrame([[1, 2, 3]]).plot(kind="bar", cmap=colormap)
assert color_tuples == [c.get_facecolor() for c in barplot.patches]
def test_rcParams_bar_colors(self):
import matplotlib as mpl
color_tuples = [(0.9, 0, 0, 1), (0, 0.9, 0, 1), (0, 0, 0.9, 1)]
with mpl.rc_context(rc={"axes.prop_cycle": mpl.cycler("color", color_tuples)}):
barplot = pd.DataFrame([[1, 2, 3]]).plot(kind="bar")
assert color_tuples == [c.get_facecolor() for c in barplot.patches]
@pytest.mark.parametrize("method", ["line", "barh", "bar"])
def test_secondary_axis_font_size(self, method):
# GH: 12565
df = (
pd.DataFrame(np.random.randn(15, 2), columns=list("AB"))
.assign(C=lambda df: df.B.cumsum())
.assign(D=lambda df: df.C * 1.1)
)
fontsize = 20
sy = ["C", "D"]
kwargs = dict(secondary_y=sy, fontsize=fontsize, mark_right=True)
ax = getattr(df.plot, method)(**kwargs)
self._check_ticks_props(axes=ax.right_ax, ylabelsize=fontsize)
@pytest.mark.slow
def test_x_string_values_ticks(self):
# Test if string plot index have a fixed xtick position
# GH: 7612, GH: 22334
df = pd.DataFrame(
{
"sales": [3, 2, 3],
"visits": [20, 42, 28],
"day": ["Monday", "Tuesday", "Wednesday"],
}
)
ax = df.plot.area(x="day")
ax.set_xlim(-1, 3)
xticklabels = [t.get_text() for t in ax.get_xticklabels()]
labels_position = dict(zip(xticklabels, ax.get_xticks()))
# Testing if the label stayed at the right position
assert labels_position["Monday"] == 0.0
assert labels_position["Tuesday"] == 1.0
assert labels_position["Wednesday"] == 2.0
@pytest.mark.slow
def test_x_multiindex_values_ticks(self):
# Test if multiindex plot index have a fixed xtick position
# GH: 15912
index = pd.MultiIndex.from_product([[2012, 2013], [1, 2]])
df = pd.DataFrame(np.random.randn(4, 2), columns=["A", "B"], index=index)
ax = df.plot()
ax.set_xlim(-1, 4)
xticklabels = [t.get_text() for t in ax.get_xticklabels()]
labels_position = dict(zip(xticklabels, ax.get_xticks()))
# Testing if the label stayed at the right position
assert labels_position["(2012, 1)"] == 0.0
assert labels_position["(2012, 2)"] == 1.0
assert labels_position["(2013, 1)"] == 2.0
assert labels_position["(2013, 2)"] == 3.0
@pytest.mark.parametrize("kind", ["line", "area"])
def test_xlim_plot_line(self, kind):
# test if xlim is set correctly in plot.line and plot.area
# GH 27686
df = pd.DataFrame([2, 4], index=[1, 2])
ax = df.plot(kind=kind)
xlims = ax.get_xlim()
assert xlims[0] < 1
assert xlims[1] > 2
def test_xlim_plot_line_correctly_in_mixed_plot_type(self):
# test if xlim is set correctly when ax contains multiple different kinds
# of plots, GH 27686
fig, ax = self.plt.subplots()
indexes = ["k1", "k2", "k3", "k4"]
df = pd.DataFrame(
{
"s1": [1000, 2000, 1500, 2000],
"s2": [900, 1400, 2000, 3000],
"s3": [1500, 1500, 1600, 1200],
"secondary_y": [1, 3, 4, 3],
},
index=indexes,
)
df[["s1", "s2", "s3"]].plot.bar(ax=ax, stacked=False)
df[["secondary_y"]].plot(ax=ax, secondary_y=True)
xlims = ax.get_xlim()
assert xlims[0] < 0
assert xlims[1] > 3
# make sure axis labels are plotted correctly as well
xticklabels = [t.get_text() for t in ax.get_xticklabels()]
assert xticklabels == indexes
def test_subplots_sharex_false(self):
# test when sharex is set to False, two plots should have different
# labels, GH 25160
df = pd.DataFrame(np.random.rand(10, 2))
df.iloc[5:, 1] = np.nan
df.iloc[:5, 0] = np.nan
figs, axs = self.plt.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_plot_no_rows(self):
# GH 27758
df = pd.DataFrame(columns=["foo"], 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.DataFrame(["a", "b", "c"])
with pytest.raises(TypeError):
df.plot()
def test_missing_markers_legend(self):
# 14958
df = pd.DataFrame(np.random.randn(8, 3), columns=["A", "B", "C"])
ax = df.plot(y=["A"], marker="x", linestyle="solid")
df.plot(y=["B"], marker="o", linestyle="dotted", ax=ax)
df.plot(y=["C"], marker="<", linestyle="dotted", ax=ax)
self._check_legend_labels(ax, labels=["A", "B", "C"])
self._check_legend_marker(ax, expected_markers=["x", "o", "<"])
def test_missing_markers_legend_using_style(self):
# 14563
df = pd.DataFrame(
{
"A": [1, 2, 3, 4, 5, 6],
"B": [2, 4, 1, 3, 2, 4],
"C": [3, 3, 2, 6, 4, 2],
"X": [1, 2, 3, 4, 5, 6],
}
)
fig, ax = self.plt.subplots()
for kind in "ABC":
df.plot("X", kind, label=kind, ax=ax, style=".")
self._check_legend_labels(ax, labels=["A", "B", "C"])
self._check_legend_marker(ax, expected_markers=[".", ".", "."])
def test_colors_of_columns_with_same_name(self):
# ISSUE 11136 -> https://github.com/pandas-dev/pandas/issues/11136
# Creating a DataFrame with duplicate column labels and testing colors of them.
df = pd.DataFrame({"b": [0, 1, 0], "a": [1, 2, 3]})
df1 = pd.DataFrame({"a": [2, 4, 6]})
df_concat = pd.concat([df, df1], axis=1)
result = df_concat.plot()
for legend, line in zip(result.get_legend().legendHandles, result.lines):
assert legend.get_color() == line.get_color()
@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_single_plot(
self, kind, index_name, old_label, new_label
):
# GH 9093
df = pd.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
ax = df.plot(kind=kind)
assert ax.get_xlabel() == old_label
assert ax.get_ylabel() == ""
# old xlabel will be overriden and assigned ylabel will be used as ylabel
ax = df.plot(kind=kind, ylabel=new_label, xlabel=new_label)
assert ax.get_ylabel() == str(new_label)
assert ax.get_xlabel() == str(new_label)
@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 = pd.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 overriden 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)
def _generate_4_axes_via_gridspec():
import matplotlib as mpl
import matplotlib.gridspec # noqa
import matplotlib.pyplot as plt
gs = mpl.gridspec.GridSpec(2, 2)
ax_tl = plt.subplot(gs[0, 0])
ax_ll = plt.subplot(gs[1, 0])
ax_tr = plt.subplot(gs[0, 1])
ax_lr = plt.subplot(gs[1, 1])
return gs, [ax_tl, ax_ll, ax_tr, ax_lr]