def test_boxplot_legacy2(self):
df = DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2'])
df['X'] = Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])
df['Y'] = Series(['A'] * 10)
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.boxplot, by='X')
# When ax is supplied and required number of axes is 1,
# passed ax should be used:
fig, ax = self.plt.subplots()
axes = df.boxplot('Col1', by='X', ax=ax)
ax_axes = ax.axes
assert ax_axes is axes
fig, ax = self.plt.subplots()
axes = df.groupby('Y').boxplot(ax=ax, return_type='axes')
ax_axes = ax.axes
assert ax_axes is axes['A']
# Multiple columns with an ax argument should use same figure
fig, ax = self.plt.subplots()
with tm.assert_produces_warning(UserWarning):
axes = df.boxplot(column=['Col1', 'Col2'],
by='X', ax=ax, return_type='axes')
assert axes['Col1'].get_figure() is fig
# When by is None, check that all relevant lines are present in the
# dict
fig, ax = self.plt.subplots()
d = df.boxplot(ax=ax, return_type='dict')
lines = list(itertools.chain.from_iterable(d.values()))
assert len(ax.get_lines()) == len(lines)
def test_dup_datetime_index_plot(self):
dr1 = date_range('1/1/2009', periods=4)
dr2 = date_range('1/2/2009', periods=4)
index = dr1.append(dr2)
values = randn(index.size)
s = Series(values, index=index)
_check_plot_works(s.plot)
def test_tight_layout(self):
if self.mpl_ge_2_0_1:
df = DataFrame(randn(100, 3))
_check_plot_works(df.hist)
self.plt.tight_layout()
tm.close()
def test_autocorrelation_plot(self):
from pandas.plotting import autocorrelation_plot
_check_plot_works(autocorrelation_plot, series=self.ts)
_check_plot_works(autocorrelation_plot, series=self.ts.values)
ax = autocorrelation_plot(self.ts, label='Test')
self._check_legend_labels(ax, labels=['Test'])
def test_scatter_matrix_axis(self):
scatter_matrix = plotting.scatter_matrix
with tm.RNGContext(42):
df = DataFrame(randn(100, 3))
# we are plotting multiples on a sub-plot
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(scatter_matrix, filterwarnings='always',
frame=df, range_padding=.1)
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
# GH 5662
if self.mpl_ge_2_0_0:
expected = ['-2', '0', '2']
else:
expected = ['-2', '-1', '0', '1', '2']
self._check_text_labels(axes0_labels, expected)
self._check_ticks_props(
axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
df[0] = ((df[0] - 2) / 3)
# we are plotting multiples on a sub-plot
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(scatter_matrix, filterwarnings='always',
frame=df, range_padding=.1)
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
if self.mpl_ge_2_0_0:
expected = ['-1.0', '-0.5', '0.0']
else:
expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0']
self._check_text_labels(axes0_labels, expected)
self._check_ticks_props(
axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
def test_custom_business_day_freq(self):
# GH7222
from pandas.tseries.offsets import CustomBusinessDay
s = Series(range(100, 121), index=pd.bdate_range(
start='2014-05-01', end='2014-06-01',
freq=CustomBusinessDay(holidays=['2014-05-26'])))
_check_plot_works(s.plot)
def test_boxplot_legacy1(self):
grouped = self.hist_df.groupby(by='gender')
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(grouped.boxplot, return_type='axes')
self._check_axes_shape(list(axes.values), axes_num=2, layout=(1, 2))
axes = _check_plot_works(grouped.boxplot, subplots=False,
return_type='axes')
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
def test_kde_kwargs(self):
tm._skip_if_no_scipy()
_skip_if_no_scipy_gaussian_kde()
from numpy import linspace
_check_plot_works(self.ts.plot.kde, bw_method=0.5, ind=linspace(-100, 100, 20))
_check_plot_works(self.ts.plot.density, bw_method=0.5, ind=linspace(-100, 100, 20))
ax = self.ts.plot.kde(logy=True, bw_method=0.5, ind=linspace(-100, 100, 20))
self._check_ax_scales(ax, yaxis="log")
self._check_text_labels(ax.yaxis.get_label(), "Density")
def test_boxplot_legacy3(self):
tuples = zip(string.ascii_letters[:10], range(10))
df = DataFrame(np.random.rand(10, 3),
index=MultiIndex.from_tuples(tuples))
grouped = df.unstack(level=1).groupby(level=0, axis=1)
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(grouped.boxplot, return_type='axes')
self._check_axes_shape(list(axes.values), axes_num=3, layout=(2, 2))
axes = _check_plot_works(grouped.boxplot, subplots=False,
return_type='axes')
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
def test_grouped_hist_layout(self):
df = self.hist_df
msg = "Layout of 1x1 must be larger than required size 2"
with pytest.raises(ValueError, match=msg):
df.hist(column='weight', by=df.gender, layout=(1, 1))
msg = "Layout of 1x3 must be larger than required size 4"
with pytest.raises(ValueError, match=msg):
df.hist(column='height', by=df.category, layout=(1, 3))
msg = "At least one dimension of layout must be positive"
with pytest.raises(ValueError, match=msg):
df.hist(column='height', by=df.category, layout=(-1, -1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.hist, column='height', by=df.gender,
layout=(2, 1))
self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.hist, column='height', by=df.gender,
layout=(2, -1))
self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
axes = df.hist(column='height', by=df.category, layout=(4, 1))
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
axes = df.hist(column='height', by=df.category, layout=(-1, 1))
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
axes = df.hist(column='height', by=df.category,
layout=(4, 2), figsize=(12, 8))
self._check_axes_shape(
axes, axes_num=4, layout=(4, 2), figsize=(12, 8))
tm.close()
# GH 6769
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(
df.hist, column='height', by='classroom', layout=(2, 2))
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
# without column
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.hist, by='classroom')
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
axes = df.hist(by='gender', layout=(3, 5))
self._check_axes_shape(axes, axes_num=2, layout=(3, 5))
axes = df.hist(column=['height', 'weight', 'category'])
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
def test_kde_kwargs(self):
sample_points = np.linspace(-100, 100, 20)
_check_plot_works(self.ts.plot.kde, bw_method='scott', ind=20)
_check_plot_works(self.ts.plot.kde, bw_method=None, ind=20)
_check_plot_works(self.ts.plot.kde, bw_method=None, ind=np.int(20))
_check_plot_works(self.ts.plot.kde, bw_method=.5, ind=sample_points)
_check_plot_works(self.ts.plot.density, bw_method=.5,
ind=sample_points)
_, ax = self.plt.subplots()
ax = self.ts.plot.kde(logy=True, bw_method=.5, ind=sample_points,
ax=ax)
self._check_ax_scales(ax, yaxis='log')
self._check_text_labels(ax.yaxis.get_label(), 'Density')
def test_kde_kwargs(self):
_skip_if_no_scipy_gaussian_kde()
if not self.mpl_ge_1_5_0:
pytest.skip("mpl is not supported")
from numpy import linspace
_check_plot_works(self.ts.plot.kde, bw_method=.5,
ind=linspace(-100, 100, 20))
_check_plot_works(self.ts.plot.density, bw_method=.5,
ind=linspace(-100, 100, 20))
_, ax = self.plt.subplots()
ax = self.ts.plot.kde(logy=True, bw_method=.5,
ind=linspace(-100, 100, 20), ax=ax)
self._check_ax_scales(ax, yaxis='log')
self._check_text_labels(ax.yaxis.get_label(), 'Density')
def test_kde_missing_vals(self):
s = Series(np.random.uniform(size=50))
s[0] = np.nan
axes = _check_plot_works(s.plot.kde)
# gh-14821: check if the values have any missing values
assert any(~np.isnan(axes.lines[0].get_xdata()))
def test_grouped_hist_layout(self):
df = self.hist_df
pytest.raises(ValueError, df.hist, column='weight', by=df.gender,
layout=(1, 1))
pytest.raises(ValueError, df.hist, column='height', by=df.category,
layout=(1, 3))
pytest.raises(ValueError, df.hist, column='height', by=df.category,
layout=(-1, -1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.hist, column='height', by=df.gender,
layout=(2, 1))
self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.hist, column='height', by=df.gender,
layout=(2, -1))
self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
axes = df.hist(column='height', by=df.category, layout=(4, 1))
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
axes = df.hist(column='height', by=df.category, layout=(-1, 1))
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
axes = df.hist(column='height', by=df.category,
layout=(4, 2), figsize=(12, 8))
self._check_axes_shape(
axes, axes_num=4, layout=(4, 2), figsize=(12, 8))
tm.close()
# GH 6769
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(
df.hist, column='height', by='classroom', layout=(2, 2))
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
# without column
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.hist, by='classroom')
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
axes = df.hist(by='gender', layout=(3, 5))
self._check_axes_shape(axes, axes_num=2, layout=(3, 5))
axes = df.hist(column=['height', 'weight', 'category'])
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
def test_kde_kwargs(self):
_skip_if_no_scipy_gaussian_kde()
if not self.mpl_ge_1_5_0:
pytest.skip("mpl is not supported")
sample_points = np.linspace(-100, 100, 20)
_check_plot_works(self.ts.plot.kde, bw_method='scott', ind=20)
_check_plot_works(self.ts.plot.kde, bw_method=None, ind=20)
_check_plot_works(self.ts.plot.kde, bw_method=None, ind=np.int(20))
_check_plot_works(self.ts.plot.kde, bw_method=.5, ind=sample_points)
_check_plot_works(self.ts.plot.density, bw_method=.5,
ind=sample_points)
_, ax = self.plt.subplots()
ax = self.ts.plot.kde(logy=True, bw_method=.5, ind=sample_points,
ax=ax)
self._check_ax_scales(ax, yaxis='log')
self._check_text_labels(ax.yaxis.get_label(), 'Density')
def test_parallel_coordinates(self):
from pandas.plotting import parallel_coordinates
from matplotlib import cm
df = self.iris
ax = _check_plot_works(parallel_coordinates,
frame=df, class_column='Name')
nlines = len(ax.get_lines())
nxticks = len(ax.xaxis.get_ticklabels())
rgba = ('#556270', '#4ECDC4', '#C7F464')
ax = _check_plot_works(parallel_coordinates,
frame=df, class_column='Name', color=rgba)
self._check_colors(
ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10])
cnames = ['dodgerblue', 'aquamarine', 'seagreen']
ax = _check_plot_works(parallel_coordinates,
frame=df, class_column='Name', color=cnames)
self._check_colors(
ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10])
ax = _check_plot_works(parallel_coordinates,
frame=df, class_column='Name', colormap=cm.jet)
cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
self._check_colors(
ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])
ax = _check_plot_works(parallel_coordinates,
frame=df, class_column='Name', axvlines=False)
assert len(ax.get_lines()) == (nlines - nxticks)
colors = ['b', 'g', 'r']
df = DataFrame({"A": [1, 2, 3],
"B": [1, 2, 3],
"C": [1, 2, 3],
"Name": colors})
ax = parallel_coordinates(df, 'Name', color=colors)
handles, labels = ax.get_legend_handles_labels()
self._check_colors(handles, linecolors=colors)
with tm.assert_produces_warning(FutureWarning):
parallel_coordinates(data=df, class_column='Name')
with tm.assert_produces_warning(FutureWarning):
parallel_coordinates(df, 'Name', colors=colors)
def test_hist_layout_with_by(self):
df = self.hist_df
# _check_plot_works adds an ax so catch warning. see GH #13188
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.gender, layout=(2, 1))
self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.gender, layout=(3, -1))
self._check_axes_shape(axes, axes_num=2, layout=(3, 1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.category, layout=(4, 1))
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.category, layout=(2, -1))
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.category, layout=(3, -1))
self._check_axes_shape(axes, axes_num=4, layout=(3, 2))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.category, layout=(-1, 4))
self._check_axes_shape(axes, axes_num=4, layout=(1, 4))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.classroom, layout=(2, 2))
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7))
self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 7))
def test_kde_missing_vals(self):
tm._skip_if_no_scipy()
_skip_if_no_scipy_gaussian_kde()
s = Series(np.random.uniform(size=50))
s[0] = np.nan
axes = _check_plot_works(s.plot.kde)
# check if the values have any missing values
# GH14821
self.assertTrue(any(~np.isnan(axes.lines[0].get_xdata())), msg="Missing Values not dropped")
def test_hist_kde(self):
ax = self.ts.plot.hist(logy=True)
self._check_ax_scales(ax, yaxis='log')
xlabels = ax.get_xticklabels()
# ticks are values, thus ticklabels are blank
self._check_text_labels(xlabels, [''] * len(xlabels))
ylabels = ax.get_yticklabels()
self._check_text_labels(ylabels, [''] * len(ylabels))
tm._skip_if_no_scipy()
_skip_if_no_scipy_gaussian_kde()
_check_plot_works(self.ts.plot.kde)
_check_plot_works(self.ts.plot.density)
ax = self.ts.plot.kde(logy=True)
self._check_ax_scales(ax, yaxis='log')
xlabels = ax.get_xticklabels()
self._check_text_labels(xlabels, [''] * len(xlabels))
ylabels = ax.get_yticklabels()
self._check_text_labels(ylabels, [''] * len(ylabels))
def test_hist_kde(self):
_, ax = self.plt.subplots()
ax = self.ts.plot.hist(logy=True, ax=ax)
self._check_ax_scales(ax, yaxis='log')
xlabels = ax.get_xticklabels()
# ticks are values, thus ticklabels are blank
self._check_text_labels(xlabels, [''] * len(xlabels))
ylabels = ax.get_yticklabels()
self._check_text_labels(ylabels, [''] * len(ylabels))
_check_plot_works(self.ts.plot.kde)
_check_plot_works(self.ts.plot.density)
_, ax = self.plt.subplots()
ax = self.ts.plot.kde(logy=True, ax=ax)
self._check_ax_scales(ax, yaxis='log')
xlabels = ax.get_xticklabels()
self._check_text_labels(xlabels, [''] * len(xlabels))
ylabels = ax.get_yticklabels()
self._check_text_labels(ylabels, [''] * len(ylabels))
def test_line_area_nan_series(self):
values = [1, 2, np.nan, 3]
s = Series(values)
ts = Series(values, index=tm.makeDateIndex(k=4))
for d in [s, ts]:
ax = _check_plot_works(d.plot)
masked = ax.lines[0].get_ydata()
# remove nan for comparison purpose
exp = np.array([1, 2, 3], dtype=np.float64)
self.assert_numpy_array_equal(np.delete(masked.data, 2), exp)
self.assert_numpy_array_equal(masked.mask, np.array([False, False, True, False]))
expected = np.array([1, 2, 0, 3], dtype=np.float64)
ax = _check_plot_works(d.plot, stacked=True)
self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area)
self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot.area, stacked=False)
self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
def test_kde_missing_vals(self):
_skip_if_no_scipy_gaussian_kde()
if not self.mpl_ge_1_5_0:
pytest.skip("mpl is not supported")
s = Series(np.random.uniform(size=50))
s[0] = np.nan
axes = _check_plot_works(s.plot.kde)
# gh-14821: check if the values have any missing values
assert any(~np.isnan(axes.lines[0].get_xdata()))
def test_hist_kde(self):
if not self.mpl_ge_1_5_0:
pytest.skip("mpl is not supported")
_, ax = self.plt.subplots()
ax = self.ts.plot.hist(logy=True, ax=ax)
self._check_ax_scales(ax, yaxis='log')
xlabels = ax.get_xticklabels()
# ticks are values, thus ticklabels are blank
self._check_text_labels(xlabels, [''] * len(xlabels))
ylabels = ax.get_yticklabels()
self._check_text_labels(ylabels, [''] * len(ylabels))
_skip_if_no_scipy_gaussian_kde()
_check_plot_works(self.ts.plot.kde)
_check_plot_works(self.ts.plot.density)
_, ax = self.plt.subplots()
ax = self.ts.plot.kde(logy=True, ax=ax)
self._check_ax_scales(ax, yaxis='log')
xlabels = ax.get_xticklabels()
self._check_text_labels(xlabels, [''] * len(xlabels))
ylabels = ax.get_yticklabels()
self._check_text_labels(ylabels, [''] * len(ylabels))
def test_errorbar_plot(self):
s = Series(np.arange(10), name='x')
s_err = np.random.randn(10)
d_err = DataFrame(randn(10, 2), index=s.index, columns=['x', 'y'])
# test line and bar plots
kinds = ['line', 'bar']
for kind in kinds:
ax = _check_plot_works(s.plot, yerr=Series(s_err), kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, yerr=s_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, yerr=s_err.tolist(), kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, yerr=d_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, xerr=0.2, yerr=0.2, kind=kind)
self._check_has_errorbars(ax, xerr=1, yerr=1)
ax = _check_plot_works(s.plot, xerr=s_err)
self._check_has_errorbars(ax, xerr=1, yerr=0)
# test time series plotting
ix = date_range('1/1/2000', '1/1/2001', freq='M')
ts = Series(np.arange(12), index=ix, name='x')
ts_err = Series(np.random.randn(12), index=ix)
td_err = DataFrame(randn(12, 2), index=ix, columns=['x', 'y'])
ax = _check_plot_works(ts.plot, yerr=ts_err)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(ts.plot, yerr=td_err)
self._check_has_errorbars(ax, xerr=0, yerr=1)
# check incorrect lengths and types
with pytest.raises(ValueError):
s.plot(yerr=np.arange(11))
s_err = ['zzz'] * 10
# in mpl 1.5+ this is a TypeError
with pytest.raises((ValueError, TypeError)):
s.plot(yerr=s_err)
def test_errorbar_plot(self):
s = Series(np.arange(10), name="x")
s_err = np.random.randn(10)
d_err = DataFrame(randn(10, 2), index=s.index, columns=["x", "y"])
# test line and bar plots
kinds = ["line", "bar"]
for kind in kinds:
ax = _check_plot_works(s.plot, yerr=Series(s_err), kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, yerr=s_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, yerr=s_err.tolist(), kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, yerr=d_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, xerr=0.2, yerr=0.2, kind=kind)
self._check_has_errorbars(ax, xerr=1, yerr=1)
ax = _check_plot_works(s.plot, xerr=s_err)
self._check_has_errorbars(ax, xerr=1, yerr=0)
# test time series plotting
ix = date_range("1/1/2000", "1/1/2001", freq="M")
ts = Series(np.arange(12), index=ix, name="x")
ts_err = Series(np.random.randn(12), index=ix)
td_err = DataFrame(randn(12, 2), index=ix, columns=["x", "y"])
ax = _check_plot_works(ts.plot, yerr=ts_err)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(ts.plot, yerr=td_err)
self._check_has_errorbars(ax, xerr=0, yerr=1)
# check incorrect lengths and types
with tm.assertRaises(ValueError):
s.plot(yerr=np.arange(11))
s_err = ["zzz"] * 10
# in mpl 1.5+ this is a TypeError
with tm.assertRaises((ValueError, TypeError)):
s.plot(yerr=s_err)
def test_pie_series(self):
# if sum of values is less than 1.0, pie handle them as rate and draw
# semicircle.
series = Series(np.random.randint(1, 5),
index=['a', 'b', 'c', 'd', 'e'], name='YLABEL')
ax = _check_plot_works(series.plot.pie)
self._check_text_labels(ax.texts, series.index)
assert ax.get_ylabel() == 'YLABEL'
# without wedge labels
ax = _check_plot_works(series.plot.pie, labels=None)
self._check_text_labels(ax.texts, [''] * 5)
# with less colors than elements
color_args = ['r', 'g', 'b']
ax = _check_plot_works(series.plot.pie, colors=color_args)
color_expected = ['r', 'g', 'b', 'r', 'g']
self._check_colors(ax.patches, facecolors=color_expected)
# with labels and colors
labels = ['A', 'B', 'C', 'D', 'E']
color_args = ['r', 'g', 'b', 'c', 'm']
ax = _check_plot_works(series.plot.pie, labels=labels,
colors=color_args)
self._check_text_labels(ax.texts, labels)
self._check_colors(ax.patches, facecolors=color_args)
# with autopct and fontsize
ax = _check_plot_works(series.plot.pie, colors=color_args,
autopct='%.2f', fontsize=7)
pcts = ['{0:.2f}'.format(s * 100)
for s in series.values / float(series.sum())]
iters = [iter(series.index), iter(pcts)]
expected_texts = list(next(it) for it in itertools.cycle(iters))
self._check_text_labels(ax.texts, expected_texts)
for t in ax.texts:
assert t.get_fontsize() == 7
# includes negative value
with pytest.raises(ValueError):
series = Series([1, 2, 0, 4, -1], index=['a', 'b', 'c', 'd', 'e'])
series.plot.pie()
# includes nan
series = Series([1, 2, np.nan, 4], index=['a', 'b', 'c', 'd'],
name='YLABEL')
ax = _check_plot_works(series.plot.pie)
self._check_text_labels(ax.texts, ['a', 'b', '', 'd'])
def test_hist_layout_with_by(self):
df = self.hist_df
# _check_plot_works adds an `ax` kwarg to the method call
# so we get a warning about an axis being cleared, even
# though we don't explicing pass one, see GH #13188
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.gender,
layout=(2, 1))
self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.gender,
layout=(3, -1))
self._check_axes_shape(axes, axes_num=2, layout=(3, 1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.height.hist, by=df.category,
layout=(4, 1))
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(
df.height.hist, by=df.category, layout=(2, -1))
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(
df.height.hist, by=df.category, layout=(3, -1))
self._check_axes_shape(axes, axes_num=4, layout=(3, 2))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(
df.height.hist, by=df.category, layout=(-1, 4))
self._check_axes_shape(axes, axes_num=4, layout=(1, 4))
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(
df.height.hist, by=df.classroom, layout=(2, 2))
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7))
self._check_axes_shape(
axes, axes_num=4, layout=(4, 2), figsize=(12, 7))
def test_pie_series(self):
# if sum of values is less than 1.0, pie handle them as rate and draw
# semicircle.
series = Series(np.random.randint(1, 5), index=["a", "b", "c", "d", "e"], name="YLABEL")
ax = _check_plot_works(series.plot.pie)
self._check_text_labels(ax.texts, series.index)
self.assertEqual(ax.get_ylabel(), "YLABEL")
# without wedge labels
ax = _check_plot_works(series.plot.pie, labels=None)
self._check_text_labels(ax.texts, [""] * 5)
# with less colors than elements
color_args = ["r", "g", "b"]
ax = _check_plot_works(series.plot.pie, colors=color_args)
color_expected = ["r", "g", "b", "r", "g"]
self._check_colors(ax.patches, facecolors=color_expected)
# with labels and colors
labels = ["A", "B", "C", "D", "E"]
color_args = ["r", "g", "b", "c", "m"]
ax = _check_plot_works(series.plot.pie, labels=labels, colors=color_args)
self._check_text_labels(ax.texts, labels)
self._check_colors(ax.patches, facecolors=color_args)
# with autopct and fontsize
ax = _check_plot_works(series.plot.pie, colors=color_args, autopct="%.2f", fontsize=7)
pcts = ["{0:.2f}".format(s * 100) for s in series.values / float(series.sum())]
iters = [iter(series.index), iter(pcts)]
expected_texts = list(next(it) for it in itertools.cycle(iters))
self._check_text_labels(ax.texts, expected_texts)
for t in ax.texts:
self.assertEqual(t.get_fontsize(), 7)
# includes negative value
with tm.assertRaises(ValueError):
series = Series([1, 2, 0, 4, -1], index=["a", "b", "c", "d", "e"])
series.plot.pie()
# includes nan
series = Series([1, 2, np.nan, 4], index=["a", "b", "c", "d"], name="YLABEL")
ax = _check_plot_works(series.plot.pie)
self._check_text_labels(ax.texts, ["a", "b", "", "d"])
def test_radviz(self):
from pandas.plotting import radviz
from matplotlib import cm
df = self.iris
_check_plot_works(radviz, frame=df, class_column='Name')
rgba = ('#556270', '#4ECDC4', '#C7F464')
ax = _check_plot_works(
radviz, frame=df, class_column='Name', color=rgba)
# skip Circle drawn as ticks
patches = [p for p in ax.patches[:20] if p.get_label() != '']
self._check_colors(
patches[:10], facecolors=rgba, mapping=df['Name'][:10])
cnames = ['dodgerblue', 'aquamarine', 'seagreen']
_check_plot_works(radviz, frame=df, class_column='Name', color=cnames)
patches = [p for p in ax.patches[:20] if p.get_label() != '']
self._check_colors(patches, facecolors=cnames, mapping=df['Name'][:10])
_check_plot_works(radviz, frame=df,
class_column='Name', colormap=cm.jet)
cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
patches = [p for p in ax.patches[:20] if p.get_label() != '']
self._check_colors(patches, facecolors=cmaps, mapping=df['Name'][:10])
colors = [[0., 0., 1., 1.],
[0., 0.5, 1., 1.],
[1., 0., 0., 1.]]
df = DataFrame({"A": [1, 2, 3],
"B": [2, 1, 3],
"C": [3, 2, 1],
"Name": ['b', 'g', 'r']})
ax = radviz(df, 'Name', color=colors)
handles, labels = ax.get_legend_handles_labels()
self._check_colors(handles, facecolors=colors)
def test_bootstrap_plot(self):
from pandas.plotting import bootstrap_plot
_check_plot_works(bootstrap_plot, series=self.ts, size=10)
def test_table(self, series):
_check_plot_works(series.plot, table=True)
_check_plot_works(series.plot, table=series)
def test_plot_series_kinds(self, series, kind):
_check_plot_works(series[:5].plot, kind=kind)
def test_plot_series_barh(self, series):
_check_plot_works(series[:10].plot.barh)
def test_plot_series_bar_ax(self):
ax = _check_plot_works(Series(np.random.randn(10)).plot.bar,
color="black")
self._check_colors([ax.patches[0]], facecolors=["black"])
def test_grouped_box_layout(self):
df = self.hist_df
pytest.raises(ValueError,
df.boxplot,
column=['weight', 'height'],
by=df.gender,
layout=(1, 1))
pytest.raises(ValueError,
df.boxplot,
column=['height', 'weight', 'category'],
layout=(2, 1),
return_type='dict')
pytest.raises(ValueError,
df.boxplot,
column=['weight', 'height'],
by=df.gender,
layout=(-1, -1))
# _check_plot_works adds an ax so catch warning. see GH #13188
with tm.assert_produces_warning(UserWarning):
box = _check_plot_works(df.groupby('gender').boxplot,
column='height',
return_type='dict')
self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2))
with tm.assert_produces_warning(UserWarning):
box = _check_plot_works(df.groupby('category').boxplot,
column='height',
return_type='dict')
self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))
# GH 6769
with tm.assert_produces_warning(UserWarning):
box = _check_plot_works(df.groupby('classroom').boxplot,
column='height',
return_type='dict')
self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
# GH 5897
axes = df.boxplot(column=['height', 'weight', 'category'],
by='gender',
return_type='axes')
self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
for ax in [axes['height']]:
self._check_visible(ax.get_xticklabels(), visible=False)
self._check_visible([ax.xaxis.get_label()], visible=False)
for ax in [axes['weight'], axes['category']]:
self._check_visible(ax.get_xticklabels())
self._check_visible([ax.xaxis.get_label()])
box = df.groupby('classroom').boxplot(
column=['height', 'weight', 'category'], return_type='dict')
self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
with tm.assert_produces_warning(UserWarning):
box = _check_plot_works(df.groupby('category').boxplot,
column='height',
layout=(3, 2),
return_type='dict')
self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2))
with tm.assert_produces_warning(UserWarning):
box = _check_plot_works(df.groupby('category').boxplot,
column='height',
layout=(3, -1),
return_type='dict')
self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2))
box = df.boxplot(column=['height', 'weight', 'category'],
by='gender',
layout=(4, 1))
self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(4, 1))
box = df.boxplot(column=['height', 'weight', 'category'],
by='gender',
layout=(-1, 1))
self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(3, 1))
box = df.groupby('classroom').boxplot(
column=['height', 'weight', 'category'],
layout=(1, 4),
return_type='dict')
self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 4))
box = df.groupby('classroom').boxplot( # noqa
column=['height', 'weight', 'category'],
layout=(1, -1),
return_type='dict')
self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 3))
def test_scatter_plot_legacy(self):
tm._skip_if_no_scipy()
df = DataFrame(randn(100, 2))
def scat(**kwds):
return plotting.scatter_matrix(df, **kwds)
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat)
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat, marker='+')
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat, vmin=0)
if _ok_for_gaussian_kde('kde'):
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat, diagonal='kde')
if _ok_for_gaussian_kde('density'):
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat, diagonal='density')
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat, diagonal='hist')
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat, range_padding=.1)
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat, color='rgb')
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat, c='rgb')
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat, facecolor='rgb')
def scat2(x, y, by=None, ax=None, figsize=None):
return plotting._core.scatter_plot(df, x, y, by, ax, figsize=None)
_check_plot_works(scat2, x=0, y=1)
grouper = Series(np.repeat([1, 2, 3, 4, 5], 20), df.index)
with tm.assert_produces_warning(UserWarning):
_check_plot_works(scat2, x=0, y=1, by=grouper)
def test_andrews_curves(self):
from pandas.plotting import andrews_curves
from matplotlib import cm
df = self.iris
_check_plot_works(andrews_curves, frame=df, class_column='Name')
rgba = ('#556270', '#4ECDC4', '#C7F464')
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
color=rgba)
self._check_colors(ax.get_lines()[:10],
linecolors=rgba,
mapping=df['Name'][:10])
cnames = ['dodgerblue', 'aquamarine', 'seagreen']
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
color=cnames)
self._check_colors(ax.get_lines()[:10],
linecolors=cnames,
mapping=df['Name'][:10])
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
colormap=cm.jet)
cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
self._check_colors(ax.get_lines()[:10],
linecolors=cmaps,
mapping=df['Name'][:10])
length = 10
df = DataFrame({
"A": random.rand(length),
"B": random.rand(length),
"C": random.rand(length),
"Name": ["A"] * length
})
_check_plot_works(andrews_curves, frame=df, class_column='Name')
rgba = ('#556270', '#4ECDC4', '#C7F464')
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
color=rgba)
self._check_colors(ax.get_lines()[:10],
linecolors=rgba,
mapping=df['Name'][:10])
cnames = ['dodgerblue', 'aquamarine', 'seagreen']
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
color=cnames)
self._check_colors(ax.get_lines()[:10],
linecolors=cnames,
mapping=df['Name'][:10])
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
colormap=cm.jet)
cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
self._check_colors(ax.get_lines()[:10],
linecolors=cmaps,
mapping=df['Name'][:10])
colors = ['b', 'g', 'r']
df = DataFrame({
"A": [1, 2, 3],
"B": [1, 2, 3],
"C": [1, 2, 3],
"Name": colors
})
ax = andrews_curves(df, 'Name', color=colors)
handles, labels = ax.get_legend_handles_labels()
self._check_colors(handles, linecolors=colors)
with tm.assert_produces_warning(FutureWarning):
andrews_curves(data=df, class_column='Name')
def test_bootstrap_plot(self):
from pandas.plotting import bootstrap_plot
_check_plot_works(bootstrap_plot, series=self.ts, size=10)
def test_boxplot_legacy(self):
df = DataFrame(randn(6, 4),
index=list(string.ascii_letters[:6]),
columns=['one', 'two', 'three', 'four'])
df['indic'] = ['foo', 'bar'] * 3
df['indic2'] = ['foo', 'bar', 'foo'] * 2
_check_plot_works(df.boxplot, return_type='dict')
_check_plot_works(df.boxplot,
column=['one', 'two'],
return_type='dict')
# _check_plot_works adds an ax so catch warning. see GH #13188
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.boxplot, column=['one', 'two'], by='indic')
_check_plot_works(df.boxplot, column='one', by=['indic', 'indic2'])
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.boxplot, by='indic')
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.boxplot, by=['indic', 'indic2'])
_check_plot_works(plotting._core.boxplot,
data=df['one'],
return_type='dict')
_check_plot_works(df.boxplot, notch=1, return_type='dict')
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.boxplot, by='indic', notch=1)
df = DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2'])
df['X'] = Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])
df['Y'] = Series(['A'] * 10)
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.boxplot, by='X')
# When ax is supplied and required number of axes is 1,
# passed ax should be used:
fig, ax = self.plt.subplots()
axes = df.boxplot('Col1', by='X', ax=ax)
ax_axes = ax.axes if self.mpl_ge_1_5_0 else ax.get_axes()
assert ax_axes is axes
fig, ax = self.plt.subplots()
axes = df.groupby('Y').boxplot(ax=ax, return_type='axes')
ax_axes = ax.axes if self.mpl_ge_1_5_0 else ax.get_axes()
assert ax_axes is axes['A']
# Multiple columns with an ax argument should use same figure
fig, ax = self.plt.subplots()
with tm.assert_produces_warning(UserWarning):
axes = df.boxplot(column=['Col1', 'Col2'],
by='X',
ax=ax,
return_type='axes')
assert axes['Col1'].get_figure() is fig
# When by is None, check that all relevant lines are present in the
# dict
fig, ax = self.plt.subplots()
d = df.boxplot(ax=ax, return_type='dict')
lines = list(itertools.chain.from_iterable(d.values()))
assert len(ax.get_lines()) == len(lines)
def test_lag_plot(self):
from pandas.plotting import lag_plot
_check_plot_works(lag_plot, series=self.ts)
_check_plot_works(lag_plot, series=self.ts, lag=5)
def test_boxplot_empty_column(self):
_skip_if_mpl_14_or_dev_boxplot()
df = DataFrame(np.random.randn(20, 4))
df.loc[:, 0] = np.nan
_check_plot_works(df.boxplot, return_type='axes')
def test_plot_iseries(self, iseries):
_check_plot_works(iseries.plot)
def test_valid_object_plot(self, kind):
s = Series(range(10), dtype=object)
_check_plot_works(s.plot, kind=kind)
def test_plot(self, ts):
_check_plot_works(ts.plot, label="foo")
_check_plot_works(ts.plot, use_index=False)
axes = _check_plot_works(ts.plot, rot=0)
self._check_ticks_props(axes, xrot=0)
ax = _check_plot_works(ts.plot, style=".", logy=True)
self._check_ax_scales(ax, yaxis="log")
ax = _check_plot_works(ts.plot, style=".", logx=True)
self._check_ax_scales(ax, xaxis="log")
ax = _check_plot_works(ts.plot, style=".", loglog=True)
self._check_ax_scales(ax, xaxis="log", yaxis="log")
_check_plot_works(ts[:10].plot.bar)
_check_plot_works(ts.plot.area, stacked=False)
def test_andrews_curves(self, iris):
from matplotlib import cm
from pandas.plotting import andrews_curves
df = iris
# Ensure no UserWarning when making plot
with tm.assert_produces_warning(None):
_check_plot_works(andrews_curves, frame=df, class_column="Name")
rgba = ("#556270", "#4ECDC4", "#C7F464")
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
color=rgba)
self._check_colors(ax.get_lines()[:10],
linecolors=rgba,
mapping=df["Name"][:10])
cnames = ["dodgerblue", "aquamarine", "seagreen"]
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
color=cnames)
self._check_colors(ax.get_lines()[:10],
linecolors=cnames,
mapping=df["Name"][:10])
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
colormap=cm.jet)
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
self._check_colors(ax.get_lines()[:10],
linecolors=cmaps,
mapping=df["Name"][:10])
length = 10
df = DataFrame({
"A": np.random.rand(length),
"B": np.random.rand(length),
"C": np.random.rand(length),
"Name": ["A"] * length,
})
_check_plot_works(andrews_curves, frame=df, class_column="Name")
rgba = ("#556270", "#4ECDC4", "#C7F464")
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
color=rgba)
self._check_colors(ax.get_lines()[:10],
linecolors=rgba,
mapping=df["Name"][:10])
cnames = ["dodgerblue", "aquamarine", "seagreen"]
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
color=cnames)
self._check_colors(ax.get_lines()[:10],
linecolors=cnames,
mapping=df["Name"][:10])
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
colormap=cm.jet)
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
self._check_colors(ax.get_lines()[:10],
linecolors=cmaps,
mapping=df["Name"][:10])
colors = ["b", "g", "r"]
df = DataFrame({
"A": [1, 2, 3],
"B": [1, 2, 3],
"C": [1, 2, 3],
"Name": colors
})
ax = andrews_curves(df, "Name", color=colors)
handles, labels = ax.get_legend_handles_labels()
self._check_colors(handles, linecolors=colors)