def _plot_colorbar(self, ax, **kwds):
# Addresses issues #10611 and #10678:
# When plotting scatterplots and hexbinplots in IPython
# inline backend the colorbar axis height tends not to
# exactly match the parent axis height.
# The difference is due to small fractional differences
# in floating points with similar representation.
# To deal with this, this method forces the colorbar
# height to take the height of the parent axes.
# For a more detailed description of the issue
# see the following link:
# https://github.com/ipython/ipython/issues/11215
img = ax.collections[0]
cbar = self.fig.colorbar(img, ax=ax, **kwds)
if _mpl_ge_3_0_0():
# The workaround below is no longer necessary.
return
points = ax.get_position().get_points()
cbar_points = cbar.ax.get_position().get_points()
cbar.ax.set_position([
cbar_points[0, 0],
points[0, 1],
cbar_points[1, 0] - cbar_points[0, 0],
points[1, 1] - points[0, 1],
])
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 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 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.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": 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 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)
})
