def test_rng_context():
import numpy as np
expected0 = 1.764052345967664
expected1 = 1.6243453636632417
with tm.RNGContext(0):
with tm.RNGContext(1):
assert np.random.randn() == expected1
assert np.random.randn() == expected0
def test_scatter_matrix_axis(self):
from pandas.plotting._matplotlib.compat import _mpl_ge_3_0_0
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, raise_on_extra_warnings=_mpl_ge_3_0_0()
):
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 hist_df():
n = 100
with tm.RNGContext(42):
gender = np.random.choice(["Male", "Female"], size=n)
classroom = np.random.choice(["A", "B", "C"], size=n)
hist_df = DataFrame({
"gender":
gender,
"classroom":
classroom,
"height":
np.random.normal(66, 4, size=n),
"weight":
np.random.normal(161, 32, size=n),
"category":
np.random.randint(4, size=n),
"datetime":
to_datetime(
np.random.randint(
812419200000000000,
819331200000000000,
size=n,
dtype=np.int64,
)),
})
return hist_df
def setup_method(self, method):
import matplotlib as mpl
from pandas.plotting._matplotlib import compat
self.compat = compat
mpl.rcdefaults()
self.start_date_to_int64 = 812419200000000000
self.end_date_to_int64 = 819331200000000000
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.mpl_ge_3_2_0 = compat.mpl_ge_3_2_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":
np.random.normal(66, 4, size=n),
"weight":
np.random.normal(161, 32, size=n),
"category":
np.random.randint(4, size=n),
"datetime":
to_datetime(
np.random.randint(
self.start_date_to_int64,
self.end_date_to_int64,
size=n,
dtype=np.int64,
)),
})
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_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()
assert len(axes) == 2
assert len(self.plt.get_fignums()) == 2
tm.close()
def test_hist_df_with_nonnumerics(self):
# GH 9853
with tm.RNGContext(1):
df = DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])
df["E"] = ["x", "y"] * 5
_, ax = self.plt.subplots()
ax = df.plot.hist(bins=5, ax=ax)
assert len(ax.patches) == 20
_, ax = self.plt.subplots()
ax = df.plot.hist(ax=ax) # bins=10
assert len(ax.patches) == 40
def test_series_groupby_plotting_nominally_works(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)
weight.groupby(gender).plot()
tm.close()
height.groupby(gender).hist()
tm.close()
# Regression test for GH8733
height.groupby(gender).plot(alpha=0.5)
tm.close()
def test_scatter_matrix_axis(self, pass_axis):
scatter_matrix = plotting.scatter_matrix
ax = None
if pass_axis:
_, ax = self.plt.subplots(3, 3)
with tm.RNGContext(42):
df = DataFrame(np.random.randn(100, 3))
# we are plotting multiples on a sub-plot
with tm.assert_produces_warning(UserWarning,
raise_on_extra_warnings=True):
axes = _check_plot_works(
scatter_matrix,
filterwarnings="always",
frame=df,
range_padding=0.1,
ax=ax,
)
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,
ax=ax,
)
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_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()
