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test_graphics.py
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#!/usr/bin/env python
# coding: utf-8
import nose
import itertools
import os
import string
from distutils.version import LooseVersion
from datetime import datetime, date
from pandas import Series, DataFrame, MultiIndex, PeriodIndex, date_range
from pandas.compat import (range, lrange, StringIO, lmap, lzip, u, zip,
iteritems, OrderedDict)
from pandas.util.decorators import cache_readonly
import pandas.core.common as com
import pandas.util.testing as tm
from pandas.util.testing import ensure_clean
from pandas.core.config import set_option
import numpy as np
from numpy import random
from numpy.random import rand, randn
from numpy.testing import assert_array_equal, assert_allclose
from numpy.testing.decorators import slow
import pandas.tools.plotting as plotting
def _skip_if_mpl_14_or_dev_boxplot():
# GH 8382
# Boxplot failures on 1.4 and 1.4.1
# Don't need try / except since that's done at class level
import matplotlib
if matplotlib.__version__ in ('1.4.0', '1.5.x'):
raise nose.SkipTest("Matplotlib Regression in 1.4 and current dev.")
def _skip_if_no_scipy_gaussian_kde():
try:
import scipy
from scipy.stats import gaussian_kde
except ImportError:
raise nose.SkipTest("scipy version doesn't support gaussian_kde")
def _ok_for_gaussian_kde(kind):
if kind in ['kde','density']:
try:
import scipy
from scipy.stats import gaussian_kde
except ImportError:
return False
return True
@tm.mplskip
class TestPlotBase(tm.TestCase):
def setUp(self):
import matplotlib as mpl
mpl.rcdefaults()
n = 100
with tm.RNGContext(42):
gender = tm.choice(['Male', 'Female'], size=n)
classroom = tm.choice(['A', 'B', 'C'], size=n)
self.hist_df = DataFrame({'gender': gender,
'classroom': classroom,
'height': random.normal(66, 4, size=n),
'weight': random.normal(161, 32, size=n),
'category': random.randint(4, size=n)})
def tearDown(self):
tm.close()
@cache_readonly
def plt(self):
import matplotlib.pyplot as plt
return plt
@cache_readonly
def colorconverter(self):
import matplotlib.colors as colors
return colors.colorConverter
def _check_legend_labels(self, axes, labels=None, visible=True):
"""
Check each axes has expected legend labels
Parameters
----------
axes : matplotlib Axes object, or its list-like
labels : list-like
expected legend labels
visible : bool
expected legend visibility. labels are checked only when visible is True
"""
if visible and (labels is None):
raise ValueError('labels must be specified when visible is True')
axes = self._flatten_visible(axes)
for ax in axes:
if visible:
self.assertTrue(ax.get_legend() is not None)
self._check_text_labels(ax.get_legend().get_texts(), labels)
else:
self.assertTrue(ax.get_legend() is None)
def _check_data(self, xp, rs):
"""
Check each axes has identical lines
Parameters
----------
xp : matplotlib Axes object
rs : matplotlib Axes object
"""
xp_lines = xp.get_lines()
rs_lines = rs.get_lines()
def check_line(xpl, rsl):
xpdata = xpl.get_xydata()
rsdata = rsl.get_xydata()
assert_allclose(xpdata, rsdata)
self.assertEqual(len(xp_lines), len(rs_lines))
[check_line(xpl, rsl) for xpl, rsl in zip(xp_lines, rs_lines)]
tm.close()
def _check_visible(self, collections, visible=True):
"""
Check each artist is visible or not
Parameters
----------
collections : matplotlib Artist or its list-like
target Artist or its list or collection
visible : bool
expected visibility
"""
from matplotlib.collections import Collection
if not isinstance(collections, Collection) and not com.is_list_like(collections):
collections = [collections]
for patch in collections:
self.assertEqual(patch.get_visible(), visible)
def _get_colors_mapped(self, series, colors):
unique = series.unique()
# unique and colors length can be differed
# depending on slice value
mapped = dict(zip(unique, colors))
return [mapped[v] for v in series.values]
def _check_colors(self, collections, linecolors=None, facecolors=None,
mapping=None):
"""
Check each artist has expected line colors and face colors
Parameters
----------
collections : list-like
list or collection of target artist
linecolors : list-like which has the same length as collections
list of expected line colors
facecolors : list-like which has the same length as collections
list of expected face colors
mapping : Series
Series used for color grouping key
used for andrew_curves, parallel_coordinates, radviz test
"""
from matplotlib.lines import Line2D
from matplotlib.collections import Collection
conv = self.colorconverter
if linecolors is not None:
if mapping is not None:
linecolors = self._get_colors_mapped(mapping, linecolors)
linecolors = linecolors[:len(collections)]
self.assertEqual(len(collections), len(linecolors))
for patch, color in zip(collections, linecolors):
if isinstance(patch, Line2D):
result = patch.get_color()
# Line2D may contains string color expression
result = conv.to_rgba(result)
else:
result = patch.get_edgecolor()
expected = conv.to_rgba(color)
self.assertEqual(result, expected)
if facecolors is not None:
if mapping is not None:
facecolors = self._get_colors_mapped(mapping, facecolors)
facecolors = facecolors[:len(collections)]
self.assertEqual(len(collections), len(facecolors))
for patch, color in zip(collections, facecolors):
if isinstance(patch, Collection):
# returned as list of np.array
result = patch.get_facecolor()[0]
else:
result = patch.get_facecolor()
if isinstance(result, np.ndarray):
result = tuple(result)
expected = conv.to_rgba(color)
self.assertEqual(result, expected)
def _check_text_labels(self, texts, expected):
"""
Check each text has expected labels
Parameters
----------
texts : matplotlib Text object, or its list-like
target text, or its list
expected : str or list-like which has the same length as texts
expected text label, or its list
"""
if not com.is_list_like(texts):
self.assertEqual(texts.get_text(), expected)
else:
labels = [t.get_text() for t in texts]
self.assertEqual(len(labels), len(expected))
for l, e in zip(labels, expected):
self.assertEqual(l, e)
def _check_ticks_props(self, axes, xlabelsize=None, xrot=None,
ylabelsize=None, yrot=None):
"""
Check each axes has expected tick properties
Parameters
----------
axes : matplotlib Axes object, or its list-like
xlabelsize : number
expected xticks font size
xrot : number
expected xticks rotation
ylabelsize : number
expected yticks font size
yrot : number
expected yticks rotation
"""
from matplotlib.ticker import NullFormatter
axes = self._flatten_visible(axes)
for ax in axes:
if xlabelsize or xrot:
if isinstance(ax.xaxis.get_minor_formatter(), NullFormatter):
# If minor ticks has NullFormatter, rot / fontsize are not retained
labels = ax.get_xticklabels()
else:
labels = ax.get_xticklabels() + ax.get_xticklabels(minor=True)
for label in labels:
if xlabelsize is not None:
self.assertAlmostEqual(label.get_fontsize(), xlabelsize)
if xrot is not None:
self.assertAlmostEqual(label.get_rotation(), xrot)
if ylabelsize or yrot:
if isinstance(ax.yaxis.get_minor_formatter(), NullFormatter):
labels = ax.get_yticklabels()
else:
labels = ax.get_yticklabels() + ax.get_yticklabels(minor=True)
for label in labels:
if ylabelsize is not None:
self.assertAlmostEqual(label.get_fontsize(), ylabelsize)
if yrot is not None:
self.assertAlmostEqual(label.get_rotation(), yrot)
def _check_ax_scales(self, axes, xaxis='linear', yaxis='linear'):
"""
Check each axes has expected scales
Parameters
----------
axes : matplotlib Axes object, or its list-like
xaxis : {'linear', 'log'}
expected xaxis scale
yaxis : {'linear', 'log'}
expected yaxis scale
"""
axes = self._flatten_visible(axes)
for ax in axes:
self.assertEqual(ax.xaxis.get_scale(), xaxis)
self.assertEqual(ax.yaxis.get_scale(), yaxis)
def _check_axes_shape(self, axes, axes_num=None, layout=None, figsize=(8.0, 6.0)):
"""
Check expected number of axes is drawn in expected layout
Parameters
----------
axes : matplotlib Axes object, or its list-like
axes_num : number
expected number of axes. Unnecessary axes should be set to invisible.
layout : tuple
expected layout, (expected number of rows , columns)
figsize : tuple
expected figsize. default is matplotlib default
"""
visible_axes = self._flatten_visible(axes)
if axes_num is not None:
self.assertEqual(len(visible_axes), axes_num)
for ax in visible_axes:
# check something drawn on visible axes
self.assertTrue(len(ax.get_children()) > 0)
if layout is not None:
result = self._get_axes_layout(plotting._flatten(axes))
self.assertEqual(result, layout)
self.assert_numpy_array_equal(np.round(visible_axes[0].figure.get_size_inches()),
np.array(figsize))
def _get_axes_layout(self, axes):
x_set = set()
y_set = set()
for ax in axes:
# check axes coordinates to estimate layout
points = ax.get_position().get_points()
x_set.add(points[0][0])
y_set.add(points[0][1])
return (len(y_set), len(x_set))
def _flatten_visible(self, axes):
"""
Flatten axes, and filter only visible
Parameters
----------
axes : matplotlib Axes object, or its list-like
"""
axes = plotting._flatten(axes)
axes = [ax for ax in axes if ax.get_visible()]
return axes
def _check_has_errorbars(self, axes, xerr=0, yerr=0):
"""
Check axes has expected number of errorbars
Parameters
----------
axes : matplotlib Axes object, or its list-like
xerr : number
expected number of x errorbar
yerr : number
expected number of y errorbar
"""
axes = self._flatten_visible(axes)
for ax in axes:
containers = ax.containers
xerr_count = 0
yerr_count = 0
for c in containers:
has_xerr = getattr(c, 'has_xerr', False)
has_yerr = getattr(c, 'has_yerr', False)
if has_xerr:
xerr_count += 1
if has_yerr:
yerr_count += 1
self.assertEqual(xerr, xerr_count)
self.assertEqual(yerr, yerr_count)
def _check_box_return_type(self, returned, return_type, expected_keys=None,
check_ax_title=True):
"""
Check box returned type is correct
Parameters
----------
returned : object to be tested, returned from boxplot
return_type : str
return_type passed to boxplot
expected_keys : list-like, optional
group labels in subplot case. If not passed,
the function checks assuming boxplot uses single ax
check_ax_title : bool
Whether to check the ax.title is the same as expected_key
Intended to be checked by calling from ``boxplot``.
Normal ``plot`` doesn't attach ``ax.title``, it must be disabled.
"""
from matplotlib.axes import Axes
types = {'dict': dict, 'axes': Axes, 'both': tuple}
if expected_keys is None:
# should be fixed when the returning default is changed
if return_type is None:
return_type = 'dict'
self.assertTrue(isinstance(returned, types[return_type]))
if return_type == 'both':
self.assertIsInstance(returned.ax, Axes)
self.assertIsInstance(returned.lines, dict)
else:
# should be fixed when the returning default is changed
if return_type is None:
for r in self._flatten_visible(returned):
self.assertIsInstance(r, Axes)
return
self.assertTrue(isinstance(returned, OrderedDict))
self.assertEqual(sorted(returned.keys()), sorted(expected_keys))
for key, value in iteritems(returned):
self.assertTrue(isinstance(value, types[return_type]))
# check returned dict has correct mapping
if return_type == 'axes':
if check_ax_title:
self.assertEqual(value.get_title(), key)
elif return_type == 'both':
if check_ax_title:
self.assertEqual(value.ax.get_title(), key)
self.assertIsInstance(value.ax, Axes)
self.assertIsInstance(value.lines, dict)
elif return_type == 'dict':
line = value['medians'][0]
if check_ax_title:
self.assertEqual(line.get_axes().get_title(), key)
else:
raise AssertionError
@tm.mplskip
class TestSeriesPlots(TestPlotBase):
def setUp(self):
TestPlotBase.setUp(self)
import matplotlib as mpl
mpl.rcdefaults()
self.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1')
self.ts = tm.makeTimeSeries()
self.ts.name = 'ts'
self.series = tm.makeStringSeries()
self.series.name = 'series'
self.iseries = tm.makePeriodSeries()
self.iseries.name = 'iseries'
@slow
def test_plot(self):
_check_plot_works(self.ts.plot, label='foo')
_check_plot_works(self.ts.plot, use_index=False)
axes = _check_plot_works(self.ts.plot, rot=0)
self._check_ticks_props(axes, xrot=0)
ax = _check_plot_works(self.ts.plot, style='.', logy=True)
self._check_ax_scales(ax, yaxis='log')
ax = _check_plot_works(self.ts.plot, style='.', logx=True)
self._check_ax_scales(ax, xaxis='log')
ax = _check_plot_works(self.ts.plot, style='.', loglog=True)
self._check_ax_scales(ax, xaxis='log', yaxis='log')
_check_plot_works(self.ts[:10].plot, kind='bar')
_check_plot_works(self.ts.plot, kind='area', stacked=False)
_check_plot_works(self.iseries.plot)
for kind in ['line', 'bar', 'barh', 'kde', 'hist', 'box']:
if not _ok_for_gaussian_kde(kind):
continue
_check_plot_works(self.series[:5].plot, kind=kind)
_check_plot_works(self.series[:10].plot, kind='barh')
ax = _check_plot_works(Series(randn(10)).plot, kind='bar', color='black')
self._check_colors([ax.patches[0]], facecolors=['black'])
# GH 6951
ax = _check_plot_works(self.ts.plot, subplots=True)
self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
ax = _check_plot_works(self.ts.plot, subplots=True, layout=(-1, 1))
self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
ax = _check_plot_works(self.ts.plot, subplots=True, layout=(1, -1))
self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
@slow
def test_plot_figsize_and_title(self):
# figsize and title
ax = self.series.plot(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, 8))
def test_dont_modify_rcParams(self):
# GH 8242
colors = self.plt.rcParams['axes.color_cycle']
Series([1, 2, 3]).plot()
self.assertEqual(colors, self.plt.rcParams['axes.color_cycle'])
def test_ts_line_lim(self):
ax = self.ts.plot()
xmin, xmax = ax.get_xlim()
lines = ax.get_lines()
self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0])
self.assertEqual(xmax, lines[0].get_data(orig=False)[0][-1])
tm.close()
ax = self.ts.plot(secondary_y=True)
xmin, xmax = ax.get_xlim()
lines = ax.get_lines()
self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0])
self.assertEqual(xmax, lines[0].get_data(orig=False)[0][-1])
def test_ts_area_lim(self):
ax = self.ts.plot(kind='area', stacked=False)
xmin, xmax = ax.get_xlim()
line = ax.get_lines()[0].get_data(orig=False)[0]
self.assertEqual(xmin, line[0])
self.assertEqual(xmax, line[-1])
tm.close()
# GH 7471
ax = self.ts.plot(kind='area', stacked=False, x_compat=True)
xmin, xmax = ax.get_xlim()
line = ax.get_lines()[0].get_data(orig=False)[0]
self.assertEqual(xmin, line[0])
self.assertEqual(xmax, line[-1])
tm.close()
tz_ts = self.ts.copy()
tz_ts.index = tz_ts.tz_localize('GMT').tz_convert('CET')
ax = tz_ts.plot(kind='area', stacked=False, x_compat=True)
xmin, xmax = ax.get_xlim()
line = ax.get_lines()[0].get_data(orig=False)[0]
self.assertEqual(xmin, line[0])
self.assertEqual(xmax, line[-1])
tm.close()
ax = tz_ts.plot(kind='area', stacked=False, secondary_y=True)
xmin, xmax = ax.get_xlim()
line = ax.get_lines()[0].get_data(orig=False)[0]
self.assertEqual(xmin, line[0])
self.assertEqual(xmax, line[-1])
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
self.assert_numpy_array_equal(np.delete(masked.data, 2), np.array([1, 2, 3]))
self.assert_numpy_array_equal(masked.mask, np.array([False, False, True, False]))
expected = np.array([1, 2, 0, 3])
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, kind='area')
self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
ax = _check_plot_works(d.plot, kind='area', stacked=False)
self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
@slow
def test_bar_log(self):
expected = np.array([1., 10., 100., 1000.])
if not self.mpl_le_1_2_1:
expected = np.hstack((.1, expected, 1e4))
ax = Series([200, 500]).plot(log=True, kind='bar')
assert_array_equal(ax.yaxis.get_ticklocs(), expected)
@slow
def test_bar_ignore_index(self):
df = Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
ax = df.plot(kind='bar', use_index=False)
self._check_text_labels(ax.get_xticklabels(), ['0', '1', '2', '3'])
def test_rotation(self):
df = DataFrame(randn(5, 5))
# Default rot 0
axes = df.plot()
self._check_ticks_props(axes, xrot=0)
axes = df.plot(rot=30)
self._check_ticks_props(axes, xrot=30)
def test_irregular_datetime(self):
rng = date_range('1/1/2000', '3/1/2000')
rng = rng[[0, 1, 2, 3, 5, 9, 10, 11, 12]]
ser = Series(randn(len(rng)), rng)
ax = ser.plot()
xp = datetime(1999, 1, 1).toordinal()
ax.set_xlim('1/1/1999', '1/1/2001')
self.assertEqual(xp, ax.get_xlim()[0])
@slow
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, kind='pie')
self._check_text_labels(ax.texts, series.index)
self.assertEqual(ax.get_ylabel(), 'YLABEL')
# without wedge labels
ax = _check_plot_works(series.plot, kind='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, kind='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, kind='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, kind='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(kind='pie')
# includes nan
series = Series([1, 2, np.nan, 4],
index=['a', 'b', 'c', 'd'], name='YLABEL')
ax = _check_plot_works(series.plot, kind='pie')
self._check_text_labels(ax.texts, ['a', 'b', '', 'd'])
def test_pie_nan(self):
s = Series([1, np.nan, 1, 1])
ax = s.plot(kind='pie', legend=True)
expected = ['0', '', '2', '3']
result = [x.get_text() for x in ax.texts]
self.assertEqual(result, expected)
@slow
def test_hist_df_kwargs(self):
df = DataFrame(np.random.randn(10, 2))
ax = df.plot(kind='hist', bins=5)
self.assertEqual(len(ax.patches), 10)
@slow
def test_hist_legacy(self):
_check_plot_works(self.ts.hist)
_check_plot_works(self.ts.hist, grid=False)
_check_plot_works(self.ts.hist, figsize=(8, 10))
_check_plot_works(self.ts.hist, by=self.ts.index.month)
_check_plot_works(self.ts.hist, by=self.ts.index.month, bins=5)
fig, ax = self.plt.subplots(1, 1)
_check_plot_works(self.ts.hist, ax=ax)
_check_plot_works(self.ts.hist, ax=ax, figure=fig)
_check_plot_works(self.ts.hist, figure=fig)
tm.close()
fig, (ax1, ax2) = self.plt.subplots(1, 2)
_check_plot_works(self.ts.hist, figure=fig, ax=ax1)
_check_plot_works(self.ts.hist, figure=fig, ax=ax2)
with tm.assertRaises(ValueError):
self.ts.hist(by=self.ts.index, figure=fig)
@slow
def test_hist_bins_legacy(self):
df = DataFrame(np.random.randn(10, 2))
ax = df.hist(bins=2)[0][0]
self.assertEqual(len(ax.patches), 2)
@slow
def test_hist_layout(self):
df = self.hist_df
with tm.assertRaises(ValueError):
df.height.hist(layout=(1, 1))
with tm.assertRaises(ValueError):
df.height.hist(layout=[1, 1])
@slow
def test_hist_layout_with_by(self):
df = self.hist_df
axes = _check_plot_works(df.height.hist, by=df.gender, layout=(2, 1))
self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
axes = _check_plot_works(df.height.hist, by=df.gender, layout=(3, -1))
self._check_axes_shape(axes, axes_num=2, layout=(3, 1))
axes = _check_plot_works(df.height.hist, by=df.category, layout=(4, 1))
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
axes = _check_plot_works(df.height.hist, by=df.category, layout=(2, -1))
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
axes = _check_plot_works(df.height.hist, by=df.category, layout=(3, -1))
self._check_axes_shape(axes, axes_num=4, layout=(3, 2))
axes = _check_plot_works(df.height.hist, by=df.category, layout=(-1, 4))
self._check_axes_shape(axes, axes_num=4, layout=(1, 4))
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))
@slow
def test_hist_no_overlap(self):
from matplotlib.pyplot import subplot, gcf
x = Series(randn(2))
y = Series(randn(2))
subplot(121)
x.hist()
subplot(122)
y.hist()
fig = gcf()
axes = fig.get_axes()
self.assertEqual(len(axes), 2)
@slow
def test_plot_fails_with_dupe_color_and_style(self):
x = Series(randn(2))
with tm.assertRaises(ValueError):
x.plot(style='k--', color='k')
@slow
def test_hist_by_no_extra_plots(self):
df = self.hist_df
axes = df.height.hist(by=df.gender)
self.assertEqual(len(self.plt.get_fignums()), 1)
def test_plot_fails_when_ax_differs_from_figure(self):
from pylab import figure
fig1 = figure()
fig2 = figure()
ax1 = fig1.add_subplot(111)
with tm.assertRaises(AssertionError):
self.ts.hist(ax=ax1, figure=fig2)
@slow
def test_hist_kde(self):
ax = self.ts.plot(kind='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, kind='kde')
_check_plot_works(self.ts.plot, kind='density')
ax = self.ts.plot(kind='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))
@slow
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, kind='kde', bw_method=.5, ind=linspace(-100,100,20))
_check_plot_works(self.ts.plot, kind='density', bw_method=.5, ind=linspace(-100,100,20))
ax = self.ts.plot(kind='kde', logy=True, bw_method=.5, ind=linspace(-100,100,20))
self._check_ax_scales(ax, yaxis='log')
self._check_text_labels(ax.yaxis.get_label(), 'Density')
@slow
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
ax = _check_plot_works(s.plot, kind='kde')
@slow
def test_hist_kwargs(self):
ax = self.ts.plot(kind='hist', bins=5)
self.assertEqual(len(ax.patches), 5)
self._check_text_labels(ax.yaxis.get_label(), 'Degree')
tm.close()
ax = self.ts.plot(kind='hist', orientation='horizontal')
self._check_text_labels(ax.xaxis.get_label(), 'Degree')
tm.close()
ax = self.ts.plot(kind='hist', align='left', stacked=True)
tm.close()
@slow
def test_hist_kde_color(self):
ax = self.ts.plot(kind='hist', logy=True, bins=10, color='b')
self._check_ax_scales(ax, yaxis='log')
self.assertEqual(len(ax.patches), 10)
self._check_colors(ax.patches, facecolors=['b'] * 10)
tm._skip_if_no_scipy()
_skip_if_no_scipy_gaussian_kde()
ax = self.ts.plot(kind='kde', logy=True, color='r')
self._check_ax_scales(ax, yaxis='log')
lines = ax.get_lines()
self.assertEqual(len(lines), 1)
self._check_colors(lines, ['r'])
@slow
def test_boxplot_series(self):
ax = self.ts.plot(kind='box', logy=True)
self._check_ax_scales(ax, yaxis='log')
xlabels = ax.get_xticklabels()
self._check_text_labels(xlabels, [self.ts.name])
ylabels = ax.get_yticklabels()
self._check_text_labels(ylabels, [''] * len(ylabels))
@slow
def test_autocorrelation_plot(self):
from pandas.tools.plotting import autocorrelation_plot
_check_plot_works(autocorrelation_plot, self.ts)
_check_plot_works(autocorrelation_plot, self.ts.values)
ax = autocorrelation_plot(self.ts, label='Test')
self._check_legend_labels(ax, labels=['Test'])
@slow
def test_lag_plot(self):
from pandas.tools.plotting import lag_plot
_check_plot_works(lag_plot, self.ts)
_check_plot_works(lag_plot, self.ts, lag=5)
@slow
def test_bootstrap_plot(self):
from pandas.tools.plotting import bootstrap_plot
_check_plot_works(bootstrap_plot, self.ts, size=10)
def test_invalid_plot_data(self):
s = Series(list('abcd'))
for kind in plotting._common_kinds:
if not _ok_for_gaussian_kde(kind):
continue
with tm.assertRaises(TypeError):
s.plot(kind=kind)
@slow
def test_valid_object_plot(self):
s = Series(lrange(10), dtype=object)
for kind in plotting._common_kinds:
if not _ok_for_gaussian_kde(kind):
continue
_check_plot_works(s.plot, kind=kind)
def test_partially_invalid_plot_data(self):
s = Series(['a', 'b', 1.0, 2])
for kind in plotting._common_kinds:
if not _ok_for_gaussian_kde(kind):
continue
with tm.assertRaises(TypeError):
s.plot(kind=kind)
def test_invalid_kind(self):
s = Series([1, 2])
with tm.assertRaises(ValueError):
s.plot(kind='aasdf')
@slow
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)
@slow
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
with tm.assertRaises(TypeError):
s.plot(yerr=s_err)
def test_table(self):
_check_plot_works(self.series.plot, table=True)
_check_plot_works(self.series.plot, table=self.series)
@tm.mplskip
class TestDataFramePlots(TestPlotBase):
def setUp(self):
TestPlotBase.setUp(self)
import matplotlib as mpl
mpl.rcdefaults()
self.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1')
self.mpl_ge_1_3_1 = str(mpl.__version__) >= LooseVersion('1.3.1')
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)})
from pandas import read_csv
path = os.path.join(curpath(), 'data', 'iris.csv')
self.iris = read_csv(path)
@slow
def test_plot(self):
df = self.tdf
_check_plot_works(df.plot, grid=False)
axes = _check_plot_works(df.plot, subplots=True)
self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
axes = _check_plot_works(df.plot, subplots=True, layout=(-1, 2))
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
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]})
with tm.assertRaises(TypeError):
df.plot(kind='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))
axes = _check_plot_works(df.plot, subplots=True, title='blah')
self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
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')