def test_grouped_hist_legacy2(self):
n = 10
weight = Series(np.random.normal(166, 20, size=n))
height = Series(np.random.normal(60, 10, size=n))
with tm.RNGContext(42):
gender_int = np.random.choice([0, 1], size=n)
df_int = DataFrame({
'height': height,
'weight': weight,
'gender': gender_int
})
gb = df_int.groupby('gender')
axes = gb.hist()
self.assertEqual(len(axes), 2)
self.assertEqual(len(self.plt.get_fignums()), 2)
tm.close()
def setup_method(self, method):
import matplotlib as mpl
mpl.rcdefaults()
self.mpl_le_1_2_1 = plotting._compat._mpl_le_1_2_1()
self.mpl_ge_1_3_1 = plotting._compat._mpl_ge_1_3_1()
self.mpl_ge_1_4_0 = plotting._compat._mpl_ge_1_4_0()
self.mpl_ge_1_5_0 = plotting._compat._mpl_ge_1_5_0()
self.mpl_ge_2_0_0 = plotting._compat._mpl_ge_2_0_0()
self.mpl_ge_2_0_1 = plotting._compat._mpl_ge_2_0_1()
self.mpl_ge_2_2_0 = plotting._compat._mpl_ge_2_2_0()
if self.mpl_ge_1_4_0:
self.bp_n_objects = 7
else:
self.bp_n_objects = 8
if self.mpl_ge_1_5_0:
# 1.5 added PolyCollections to legend handler
# so we have twice as many items.
self.polycollection_factor = 2
else:
self.polycollection_factor = 1
if self.mpl_ge_2_0_0:
self.default_figsize = (6.4, 4.8)
else:
self.default_figsize = (8.0, 6.0)
self.default_tick_position = 'left' if self.mpl_ge_2_0_0 else 'default'
n = 100
with tm.RNGContext(42):
gender = np.random.choice(['Male', 'Female'], size=n)
classroom = np.random.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)})
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 test_scatter_matrix_axis(self):
tm._skip_if_no_scipy()
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_grouped_plot_fignums(self):
n = 10
weight = Series(np.random.normal(166, 20, size=n))
height = Series(np.random.normal(60, 10, size=n))
with tm.RNGContext(42):
gender = np.random.choice(['male', 'female'], size=n)
df = DataFrame({'height': height, 'weight': weight, 'gender': gender})
gb = df.groupby('gender')
res = gb.plot()
self.assertEqual(len(self.plt.get_fignums()), 2)
self.assertEqual(len(res), 2)
tm.close()
res = gb.boxplot(return_type='axes')
self.assertEqual(len(self.plt.get_fignums()), 1)
self.assertEqual(len(res), 2)
tm.close()
# now works with GH 5610 as gender is excluded
res = df.groupby('gender').hist()
tm.close()
def setup_method(self, method):
import matplotlib as mpl
from pandas.plotting._matplotlib import compat
mpl.rcdefaults()
self.mpl_ge_2_2_3 = compat._mpl_ge_2_2_3()
self.mpl_ge_3_0_0 = compat._mpl_ge_3_0_0()
self.mpl_ge_3_1_0 = compat._mpl_ge_3_1_0()
self.bp_n_objects = 7
self.polycollection_factor = 2
self.default_figsize = (6.4, 4.8)
self.default_tick_position = "left"
n = 100
with tm.RNGContext(42):
gender = np.random.choice(["Male", "Female"], size=n)
classroom = np.random.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),
}
)
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 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=0.1)
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
# GH 5662
expected = ["-2", "0", "2"]
self._check_text_labels(axes0_labels, expected)
self._check_ticks_props(axes,
xlabelsize=8,
xrot=90,
ylabelsize=8,
yrot=0)
df[0] = (df[0] - 2) / 3
# we are plotting multiples on a sub-plot
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(scatter_matrix,
filterwarnings="always",
frame=df,
range_padding=0.1)
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
expected = ["-1.0", "-0.5", "0.0"]
self._check_text_labels(axes0_labels, expected)
self._check_ticks_props(axes,
xlabelsize=8,
xrot=90,
ylabelsize=8,
yrot=0)
def test_scatter_matrix_axis(self):
tm._skip_if_no_scipy()
scatter_matrix = plotting.scatter_matrix
with tm.RNGContext(42):
df = DataFrame(randn(100, 3))
axes = _check_plot_works(scatter_matrix, filterwarnings='always', frame=df,
range_padding=.1)
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
# GH 5662
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)
axes = _check_plot_works(scatter_matrix, filterwarnings='always', frame=df,
range_padding=.1)
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
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_grouped_plot_fignums(self):
n = 10
weight = Series(np.random.normal(166, 20, size=n))
height = Series(np.random.normal(60, 10, size=n))
with tm.RNGContext(42):
gender = np.random.choice(["male", "female"], size=n)
df = DataFrame({"height": height, "weight": weight, "gender": gender})
gb = df.groupby("gender")
res = gb.plot()
assert len(self.plt.get_fignums()) == 2
assert len(res) == 2
tm.close()
res = gb.boxplot(return_type="axes")
assert len(self.plt.get_fignums()) == 1
assert len(res) == 2
tm.close()
# now works with GH 5610 as gender is excluded
res = df.groupby("gender").hist()
tm.close()
def setup_method(self, method):
import matplotlib as mpl
mpl.rcdefaults()
self.mpl_ge_2_0_1 = plotting._compat._mpl_ge_2_0_1()
self.mpl_ge_2_1_0 = plotting._compat._mpl_ge_2_1_0()
self.mpl_ge_2_2_0 = plotting._compat._mpl_ge_2_2_0()
self.mpl_ge_2_2_2 = plotting._compat._mpl_ge_2_2_2()
self.mpl_ge_3_0_0 = plotting._compat._mpl_ge_3_0_0()
self.bp_n_objects = 7
self.polycollection_factor = 2
self.default_figsize = (6.4, 4.8)
self.default_tick_position = 'left'
n = 100
with tm.RNGContext(42):
gender = np.random.choice(['Male', 'Female'], size=n)
classroom = np.random.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)
})
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)
})