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Jun 29, 2023
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TST: Clean plotting tests #53917

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1de287c
TST: Clean plotting tests
mroeschke Jun 28, 2023
fdab1d6
Fix random state
mroeschke Jun 28, 2023
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108 changes: 80 additions & 28 deletions pandas/tests/plotting/test_boxplot_method.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,12 @@
mpl = pytest.importorskip("matplotlib")


def _check_ax_limits(col, ax):
y_min, y_max = ax.get_ylim()
assert y_min <= col.min()
assert y_max >= col.max()


class TestDataFramePlots:
def test_stacked_boxplot_set_axis(self):
# GH2980
Expand Down Expand Up @@ -88,18 +94,30 @@ def test_boxplot_legacy2(self):
with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
_check_plot_works(df.boxplot, by="X")

def test_boxplot_legacy2_with_ax(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)
# When ax is supplied and required number of axes is 1,
# passed ax should be used:
fig, ax = mpl.pyplot.subplots()
_, ax = mpl.pyplot.subplots()
axes = df.boxplot("Col1", by="X", ax=ax)
ax_axes = ax.axes
assert ax_axes is axes

def test_boxplot_legacy2_with_ax_return_type(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)
fig, ax = mpl.pyplot.subplots()
axes = df.groupby("Y").boxplot(ax=ax, return_type="axes")
ax_axes = ax.axes
assert ax_axes is axes["A"]

def test_boxplot_legacy2_with_multi_col(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)
# Multiple columns with an ax argument should use same figure
fig, ax = mpl.pyplot.subplots()
with tm.assert_produces_warning(UserWarning):
Expand All @@ -108,9 +126,13 @@ def test_boxplot_legacy2(self):
)
assert axes["Col1"].get_figure() is fig

def test_boxplot_legacy2_by_none(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)
# When by is None, check that all relevant lines are present in the
# dict
fig, ax = mpl.pyplot.subplots()
_, ax = mpl.pyplot.subplots()
d = df.boxplot(ax=ax, return_type="dict")
lines = list(itertools.chain.from_iterable(d.values()))
assert len(ax.get_lines()) == len(lines)
Expand All @@ -135,24 +157,20 @@ def test_boxplot_return_type_legacy(self):
result = df.boxplot()
_check_box_return_type(result, "axes")

with tm.assert_produces_warning(False):
result = df.boxplot(return_type="dict")
_check_box_return_type(result, "dict")

with tm.assert_produces_warning(False):
result = df.boxplot(return_type="axes")
_check_box_return_type(result, "axes")
@pytest.mark.parametrize("return_type", ["dict", "axes", "both"])
def test_boxplot_return_type_legacy_return_type(self, return_type):
# API change in https://github.com/pandas-dev/pandas/pull/7096

df = DataFrame(
np.random.randn(6, 4),
index=list(string.ascii_letters[:6]),
columns=["one", "two", "three", "four"],
)
with tm.assert_produces_warning(False):
result = df.boxplot(return_type="both")
_check_box_return_type(result, "both")
result = df.boxplot(return_type=return_type)
_check_box_return_type(result, return_type)

def test_boxplot_axis_limits(self, hist_df):
def _check_ax_limits(col, ax):
y_min, y_max = ax.get_ylim()
assert y_min <= col.min()
assert y_max >= col.max()

df = hist_df.copy()
df["age"] = np.random.randint(1, 20, df.shape[0])
# One full row
Expand All @@ -161,6 +179,9 @@ def _check_ax_limits(col, ax):
_check_ax_limits(df["weight"], weight_ax)
assert weight_ax._sharey == height_ax

def test_boxplot_axis_limits_two_rows(self, hist_df):
df = hist_df.copy()
df["age"] = np.random.randint(1, 20, df.shape[0])
# Two rows, one partial
p = df.boxplot(["height", "weight", "age"], by="category")
height_ax, weight_ax, age_ax = p[0, 0], p[0, 1], p[1, 0]
Expand Down Expand Up @@ -275,7 +296,7 @@ def test_color_kwd_errors(self, dict_colors, msg):
)
def test_specified_props_kwd(self, props, expected):
# GH 30346
df = DataFrame({k: np.random.random(100) for k in "ABC"})
df = DataFrame({k: np.random.random(10) for k in "ABC"})
kwd = {props: {"color": "C1"}}
result = df.boxplot(return_type="dict", **kwd)

Expand All @@ -285,9 +306,9 @@ def test_specified_props_kwd(self, props, expected):
def test_plot_xlabel_ylabel(self, vert):
df = DataFrame(
{
"a": np.random.randn(100),
"b": np.random.randn(100),
"group": np.random.choice(["group1", "group2"], 100),
"a": np.random.randn(10),
"b": np.random.randn(10),
"group": np.random.choice(["group1", "group2"], 10),
}
)
xlabel, ylabel = "x", "y"
Expand All @@ -299,9 +320,9 @@ def test_plot_xlabel_ylabel(self, vert):
def test_boxplot_xlabel_ylabel(self, vert):
df = DataFrame(
{
"a": np.random.randn(100),
"b": np.random.randn(100),
"group": np.random.choice(["group1", "group2"], 100),
"a": np.random.randn(10),
"b": np.random.randn(10),
"group": np.random.choice(["group1", "group2"], 10),
}
)
xlabel, ylabel = "x", "y"
Expand All @@ -313,9 +334,9 @@ def test_boxplot_xlabel_ylabel(self, vert):
def test_boxplot_group_xlabel_ylabel(self, vert):
df = DataFrame(
{
"a": np.random.randn(100),
"b": np.random.randn(100),
"group": np.random.choice(["group1", "group2"], 100),
"a": np.random.randn(10),
"b": np.random.randn(10),
"group": np.random.choice(["group1", "group2"], 10),
}
)
xlabel, ylabel = "x", "y"
Expand All @@ -325,6 +346,15 @@ def test_boxplot_group_xlabel_ylabel(self, vert):
assert subplot.get_ylabel() == ylabel
mpl.pyplot.close()

@pytest.mark.parametrize("vert", [True, False])
def test_boxplot_group_no_xlabel_ylabel(self, vert):
df = DataFrame(
{
"a": np.random.randn(10),
"b": np.random.randn(10),
"group": np.random.choice(["group1", "group2"], 10),
}
)
ax = df.boxplot(by="group", vert=vert)
for subplot in ax:
target_label = subplot.get_xlabel() if vert else subplot.get_ylabel()
Expand All @@ -338,6 +368,9 @@ def test_boxplot_legacy1(self, hist_df):
with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
axes = _check_plot_works(grouped.boxplot, return_type="axes")
_check_axes_shape(list(axes.values), axes_num=2, layout=(1, 2))

def test_boxplot_legacy1_return_type(self, hist_df):
grouped = hist_df.groupby(by="gender")
axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
_check_axes_shape(axes, axes_num=1, layout=(1, 1))

Expand All @@ -350,6 +383,11 @@ def test_boxplot_legacy2(self):
axes = _check_plot_works(grouped.boxplot, return_type="axes")
_check_axes_shape(list(axes.values), axes_num=10, layout=(4, 3))

@pytest.mark.slow
def test_boxplot_legacy2_return_type(self):
tuples = zip(string.ascii_letters[:10], range(10))
df = DataFrame(np.random.rand(10, 3), index=MultiIndex.from_tuples(tuples))
grouped = df.groupby(level=1)
axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
_check_axes_shape(axes, axes_num=1, layout=(1, 1))

Expand Down Expand Up @@ -387,8 +425,14 @@ def test_grouped_plot_fignums(self):
assert len(res) == 2
tm.close()

def test_grouped_plot_fignums_excluded_col(self):
n = 10
weight = Series(np.random.normal(166, 20, size=n))
height = Series(np.random.normal(60, 10, size=n))
gender = np.random.RandomState(42).choice(["male", "female"], size=n)
df = DataFrame({"height": height, "weight": weight, "gender": gender})
# now works with GH 5610 as gender is excluded
res = df.groupby("gender").hist()
df.groupby("gender").hist()
tm.close()

@pytest.mark.slow
Expand Down Expand Up @@ -545,10 +589,14 @@ def test_grouped_box_multiple_axes(self, hist_df):
# location should be changed if other test is added
# which has earlier alphabetical order
with tm.assert_produces_warning(UserWarning):
fig, axes = mpl.pyplot.subplots(2, 2)
_, axes = mpl.pyplot.subplots(2, 2)
df.groupby("category").boxplot(column="height", return_type="axes", ax=axes)
_check_axes_shape(mpl.pyplot.gcf().axes, axes_num=4, layout=(2, 2))

@pytest.mark.slow
def test_grouped_box_multiple_axes_on_fig(self, hist_df):
# GH 6970, GH 7069
df = hist_df
fig, axes = mpl.pyplot.subplots(2, 3)
with tm.assert_produces_warning(UserWarning):
returned = df.boxplot(
Expand All @@ -572,6 +620,10 @@ def test_grouped_box_multiple_axes(self, hist_df):
tm.assert_numpy_array_equal(returned, axes[1])
assert returned[0].figure is fig

@pytest.mark.slow
def test_grouped_box_multiple_axes_ax_error(self, hist_df):
# GH 6970, GH 7069
df = hist_df
msg = "The number of passed axes must be 3, the same as the output plot"
with pytest.raises(ValueError, match=msg):
fig, axes = mpl.pyplot.subplots(2, 3)
Expand Down
18 changes: 8 additions & 10 deletions pandas/tests/plotting/test_converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -222,17 +222,15 @@ def test_conversion_float(self, dtc):
rs = dtc.convert(datetime(2012, 1, 1, 1, 2, 3), None, None)
tm.assert_almost_equal(rs, xp, rtol=rtol)

def test_conversion_outofbounds_datetime(self, dtc):
@pytest.mark.parametrize(
"values",
[
[date(1677, 1, 1), date(1677, 1, 2)],
[datetime(1677, 1, 1, 12), datetime(1677, 1, 2, 12)],
],
)
def test_conversion_outofbounds_datetime(self, dtc, values):
# 2579
values = [date(1677, 1, 1), date(1677, 1, 2)]
rs = dtc.convert(values, None, None)
xp = converter.mdates.date2num(values)
tm.assert_numpy_array_equal(rs, xp)
rs = dtc.convert(values[0], None, None)
xp = converter.mdates.date2num(values[0])
assert rs == xp

values = [datetime(1677, 1, 1, 12), datetime(1677, 1, 2, 12)]
rs = dtc.convert(values, None, None)
xp = converter.mdates.date2num(values)
tm.assert_numpy_array_equal(rs, xp)
Expand Down
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