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py-data-analysis/.venv/lib/python3.12/site-packages/pandas/tests/plotting/test_misc.py

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""" Test cases for misc plot functions """
import os
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import (
DataFrame,
Index,
Series,
Timestamp,
date_range,
interval_range,
period_range,
plotting,
read_csv,
)
import pandas._testing as tm
from pandas.tests.plotting.common import (
_check_colors,
_check_legend_labels,
_check_plot_works,
_check_text_labels,
_check_ticks_props,
)
mpl = pytest.importorskip("matplotlib")
plt = pytest.importorskip("matplotlib.pyplot")
cm = pytest.importorskip("matplotlib.cm")
@pytest.fixture
def iris(datapath) -> DataFrame:
"""
The iris dataset as a DataFrame.
"""
return read_csv(datapath("io", "data", "csv", "iris.csv"))
@td.skip_if_installed("matplotlib")
def test_import_error_message():
# GH-19810
df = DataFrame({"A": [1, 2]})
with pytest.raises(ImportError, match="matplotlib is required for plotting"):
df.plot()
def test_get_accessor_args():
func = plotting._core.PlotAccessor._get_call_args
msg = "Called plot accessor for type list, expected Series or DataFrame"
with pytest.raises(TypeError, match=msg):
func(backend_name="", data=[], args=[], kwargs={})
msg = "should not be called with positional arguments"
with pytest.raises(TypeError, match=msg):
func(backend_name="", data=Series(dtype=object), args=["line", None], kwargs={})
x, y, kind, kwargs = func(
backend_name="",
data=DataFrame(),
args=["x"],
kwargs={"y": "y", "kind": "bar", "grid": False},
)
assert x == "x"
assert y == "y"
assert kind == "bar"
assert kwargs == {"grid": False}
x, y, kind, kwargs = func(
backend_name="pandas.plotting._matplotlib",
data=Series(dtype=object),
args=[],
kwargs={},
)
assert x is None
assert y is None
assert kind == "line"
assert len(kwargs) == 24
@pytest.mark.parametrize("kind", plotting.PlotAccessor._all_kinds)
@pytest.mark.parametrize(
"data", [DataFrame(np.arange(15).reshape(5, 3)), Series(range(5))]
)
@pytest.mark.parametrize(
"index",
[
Index(range(5)),
date_range("2020-01-01", periods=5),
period_range("2020-01-01", periods=5),
],
)
def test_savefig(kind, data, index):
fig, ax = plt.subplots()
data.index = index
kwargs = {}
if kind in ["hexbin", "scatter", "pie"]:
if isinstance(data, Series):
pytest.skip(f"{kind} not supported with Series")
kwargs = {"x": 0, "y": 1}
data.plot(kind=kind, ax=ax, **kwargs)
fig.savefig(os.devnull)
class TestSeriesPlots:
def test_autocorrelation_plot(self):
from pandas.plotting import autocorrelation_plot
ser = Series(
np.arange(10, dtype=np.float64),
index=date_range("2020-01-01", periods=10),
name="ts",
)
# Ensure no UserWarning when making plot
with tm.assert_produces_warning(None):
_check_plot_works(autocorrelation_plot, series=ser)
_check_plot_works(autocorrelation_plot, series=ser.values)
ax = autocorrelation_plot(ser, label="Test")
_check_legend_labels(ax, labels=["Test"])
@pytest.mark.parametrize("kwargs", [{}, {"lag": 5}])
def test_lag_plot(self, kwargs):
from pandas.plotting import lag_plot
ser = Series(
np.arange(10, dtype=np.float64),
index=date_range("2020-01-01", periods=10),
name="ts",
)
_check_plot_works(lag_plot, series=ser, **kwargs)
def test_bootstrap_plot(self):
from pandas.plotting import bootstrap_plot
ser = Series(
np.arange(10, dtype=np.float64),
index=date_range("2020-01-01", periods=10),
name="ts",
)
_check_plot_works(bootstrap_plot, series=ser, size=10)
class TestDataFramePlots:
@pytest.mark.parametrize("pass_axis", [False, True])
def test_scatter_matrix_axis(self, pass_axis):
pytest.importorskip("scipy")
scatter_matrix = plotting.scatter_matrix
ax = None
if pass_axis:
_, ax = mpl.pyplot.subplots(3, 3)
df = DataFrame(np.random.default_rng(2).standard_normal((100, 3)))
# we are plotting multiples on a sub-plot
with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
axes = _check_plot_works(
scatter_matrix,
frame=df,
range_padding=0.1,
ax=ax,
)
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
# GH 5662
expected = ["-2", "0", "2"]
_check_text_labels(axes0_labels, expected)
_check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
@pytest.mark.parametrize("pass_axis", [False, True])
def test_scatter_matrix_axis_smaller(self, pass_axis):
pytest.importorskip("scipy")
scatter_matrix = plotting.scatter_matrix
ax = None
if pass_axis:
_, ax = mpl.pyplot.subplots(3, 3)
df = DataFrame(np.random.default_rng(11).standard_normal((100, 3)))
df[0] = (df[0] - 2) / 3
# we are plotting multiples on a sub-plot
with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
axes = _check_plot_works(
scatter_matrix,
frame=df,
range_padding=0.1,
ax=ax,
)
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
expected = ["-1.0", "-0.5", "0.0"]
_check_text_labels(axes0_labels, expected)
_check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
@pytest.mark.slow
def test_andrews_curves_no_warning(self, iris):
from pandas.plotting import andrews_curves
df = iris
# Ensure no UserWarning when making plot
with tm.assert_produces_warning(None):
_check_plot_works(andrews_curves, frame=df, class_column="Name")
@pytest.mark.slow
@pytest.mark.parametrize(
"linecolors",
[
("#556270", "#4ECDC4", "#C7F464"),
["dodgerblue", "aquamarine", "seagreen"],
],
)
@pytest.mark.parametrize(
"df",
[
"iris",
DataFrame(
{
"A": np.random.default_rng(2).standard_normal(10),
"B": np.random.default_rng(2).standard_normal(10),
"C": np.random.default_rng(2).standard_normal(10),
"Name": ["A"] * 10,
}
),
],
)
def test_andrews_curves_linecolors(self, request, df, linecolors):
from pandas.plotting import andrews_curves
if isinstance(df, str):
df = request.getfixturevalue(df)
ax = _check_plot_works(
andrews_curves, frame=df, class_column="Name", color=linecolors
)
_check_colors(
ax.get_lines()[:10], linecolors=linecolors, mapping=df["Name"][:10]
)
@pytest.mark.slow
@pytest.mark.parametrize(
"df",
[
"iris",
DataFrame(
{
"A": np.random.default_rng(2).standard_normal(10),
"B": np.random.default_rng(2).standard_normal(10),
"C": np.random.default_rng(2).standard_normal(10),
"Name": ["A"] * 10,
}
),
],
)
def test_andrews_curves_cmap(self, request, df):
from pandas.plotting import andrews_curves
if isinstance(df, str):
df = request.getfixturevalue(df)
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
ax = _check_plot_works(
andrews_curves, frame=df, class_column="Name", color=cmaps
)
_check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10])
@pytest.mark.slow
def test_andrews_curves_handle(self):
from pandas.plotting import andrews_curves
colors = ["b", "g", "r"]
df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors})
ax = andrews_curves(df, "Name", color=colors)
handles, _ = ax.get_legend_handles_labels()
_check_colors(handles, linecolors=colors)
@pytest.mark.slow
@pytest.mark.parametrize(
"color",
[("#556270", "#4ECDC4", "#C7F464"), ["dodgerblue", "aquamarine", "seagreen"]],
)
def test_parallel_coordinates_colors(self, iris, color):
from pandas.plotting import parallel_coordinates
df = iris
ax = _check_plot_works(
parallel_coordinates, frame=df, class_column="Name", color=color
)
_check_colors(ax.get_lines()[:10], linecolors=color, mapping=df["Name"][:10])
@pytest.mark.slow
def test_parallel_coordinates_cmap(self, iris):
from matplotlib import cm
from pandas.plotting import parallel_coordinates
df = iris
ax = _check_plot_works(
parallel_coordinates, frame=df, class_column="Name", colormap=cm.jet
)
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
_check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10])
@pytest.mark.slow
def test_parallel_coordinates_line_diff(self, iris):
from pandas.plotting import parallel_coordinates
df = iris
ax = _check_plot_works(parallel_coordinates, frame=df, class_column="Name")
nlines = len(ax.get_lines())
nxticks = len(ax.xaxis.get_ticklabels())
ax = _check_plot_works(
parallel_coordinates, frame=df, class_column="Name", axvlines=False
)
assert len(ax.get_lines()) == (nlines - nxticks)
@pytest.mark.slow
def test_parallel_coordinates_handles(self, iris):
from pandas.plotting import parallel_coordinates
df = iris
colors = ["b", "g", "r"]
df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors})
ax = parallel_coordinates(df, "Name", color=colors)
handles, _ = ax.get_legend_handles_labels()
_check_colors(handles, linecolors=colors)
# not sure if this is indicative of a problem
@pytest.mark.filterwarnings("ignore:Attempting to set:UserWarning")
def test_parallel_coordinates_with_sorted_labels(self):
"""For #15908"""
from pandas.plotting import parallel_coordinates
df = DataFrame(
{
"feat": list(range(30)),
"class": [2 for _ in range(10)]
+ [3 for _ in range(10)]
+ [1 for _ in range(10)],
}
)
ax = parallel_coordinates(df, "class", sort_labels=True)
polylines, labels = ax.get_legend_handles_labels()
color_label_tuples = zip(
[polyline.get_color() for polyline in polylines], labels
)
ordered_color_label_tuples = sorted(color_label_tuples, key=lambda x: x[1])
prev_next_tupels = zip(
list(ordered_color_label_tuples[0:-1]), list(ordered_color_label_tuples[1:])
)
for prev, nxt in prev_next_tupels:
# labels and colors are ordered strictly increasing
assert prev[1] < nxt[1] and prev[0] < nxt[0]
def test_radviz_no_warning(self, iris):
from pandas.plotting import radviz
df = iris
# Ensure no UserWarning when making plot
with tm.assert_produces_warning(None):
_check_plot_works(radviz, frame=df, class_column="Name")
@pytest.mark.parametrize(
"color",
[("#556270", "#4ECDC4", "#C7F464"), ["dodgerblue", "aquamarine", "seagreen"]],
)
def test_radviz_color(self, iris, color):
from pandas.plotting import radviz
df = iris
ax = _check_plot_works(radviz, frame=df, class_column="Name", color=color)
# skip Circle drawn as ticks
patches = [p for p in ax.patches[:20] if p.get_label() != ""]
_check_colors(patches[:10], facecolors=color, mapping=df["Name"][:10])
def test_radviz_color_cmap(self, iris):
from matplotlib import cm
from pandas.plotting import radviz
df = iris
ax = _check_plot_works(radviz, frame=df, class_column="Name", colormap=cm.jet)
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
patches = [p for p in ax.patches[:20] if p.get_label() != ""]
_check_colors(patches, facecolors=cmaps, mapping=df["Name"][:10])
def test_radviz_colors_handles(self):
from pandas.plotting import radviz
colors = [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 1.0, 1.0], [1.0, 0.0, 0.0, 1.0]]
df = DataFrame(
{"A": [1, 2, 3], "B": [2, 1, 3], "C": [3, 2, 1], "Name": ["b", "g", "r"]}
)
ax = radviz(df, "Name", color=colors)
handles, _ = ax.get_legend_handles_labels()
_check_colors(handles, facecolors=colors)
def test_subplot_titles(self, iris):
df = iris.drop("Name", axis=1).head()
# Use the column names as the subplot titles
title = list(df.columns)
# Case len(title) == len(df)
plot = df.plot(subplots=True, title=title)
assert [p.get_title() for p in plot] == title
def test_subplot_titles_too_much(self, iris):
df = iris.drop("Name", axis=1).head()
# Use the column names as the subplot titles
title = list(df.columns)
# Case len(title) > len(df)
msg = (
"The length of `title` must equal the number of columns if "
"using `title` of type `list` and `subplots=True`"
)
with pytest.raises(ValueError, match=msg):
df.plot(subplots=True, title=title + ["kittens > puppies"])
def test_subplot_titles_too_little(self, iris):
df = iris.drop("Name", axis=1).head()
# Use the column names as the subplot titles
title = list(df.columns)
msg = (
"The length of `title` must equal the number of columns if "
"using `title` of type `list` and `subplots=True`"
)
# Case len(title) < len(df)
with pytest.raises(ValueError, match=msg):
df.plot(subplots=True, title=title[:2])
def test_subplot_titles_subplots_false(self, iris):
df = iris.drop("Name", axis=1).head()
# Use the column names as the subplot titles
title = list(df.columns)
# Case subplots=False and title is of type list
msg = (
"Using `title` of type `list` is not supported unless "
"`subplots=True` is passed"
)
with pytest.raises(ValueError, match=msg):
df.plot(subplots=False, title=title)
def test_subplot_titles_numeric_square_layout(self, iris):
df = iris.drop("Name", axis=1).head()
# Use the column names as the subplot titles
title = list(df.columns)
# Case df with 3 numeric columns but layout of (2,2)
plot = df.drop("SepalWidth", axis=1).plot(
subplots=True, layout=(2, 2), title=title[:-1]
)
title_list = [ax.get_title() for sublist in plot for ax in sublist]
assert title_list == title[:3] + [""]
def test_get_standard_colors_random_seed(self):
# GH17525
df = DataFrame(np.zeros((10, 10)))
# Make sure that the random seed isn't reset by get_standard_colors
plotting.parallel_coordinates(df, 0)
rand1 = np.random.default_rng(None).random()
plotting.parallel_coordinates(df, 0)
rand2 = np.random.default_rng(None).random()
assert rand1 != rand2
def test_get_standard_colors_consistency(self):
# GH17525
# Make sure it produces the same colors every time it's called
from pandas.plotting._matplotlib.style import get_standard_colors
color1 = get_standard_colors(1, color_type="random")
color2 = get_standard_colors(1, color_type="random")
assert color1 == color2
def test_get_standard_colors_default_num_colors(self):
from pandas.plotting._matplotlib.style import get_standard_colors
# Make sure the default color_types returns the specified amount
color1 = get_standard_colors(1, color_type="default")
color2 = get_standard_colors(9, color_type="default")
color3 = get_standard_colors(20, color_type="default")
assert len(color1) == 1
assert len(color2) == 9
assert len(color3) == 20
def test_plot_single_color(self):
# Example from #20585. All 3 bars should have the same color
df = DataFrame(
{
"account-start": ["2017-02-03", "2017-03-03", "2017-01-01"],
"client": ["Alice Anders", "Bob Baker", "Charlie Chaplin"],
"balance": [-1432.32, 10.43, 30000.00],
"db-id": [1234, 2424, 251],
"proxy-id": [525, 1525, 2542],
"rank": [52, 525, 32],
}
)
ax = df.client.value_counts().plot.bar()
colors = [rect.get_facecolor() for rect in ax.get_children()[0:3]]
assert all(color == colors[0] for color in colors)
def test_get_standard_colors_no_appending(self):
# GH20726
# Make sure not to add more colors so that matplotlib can cycle
# correctly.
from matplotlib import cm
from pandas.plotting._matplotlib.style import get_standard_colors
color_before = cm.gnuplot(range(5))
color_after = get_standard_colors(1, color=color_before)
assert len(color_after) == len(color_before)
df = DataFrame(
np.random.default_rng(2).standard_normal((48, 4)), columns=list("ABCD")
)
color_list = cm.gnuplot(np.linspace(0, 1, 16))
p = df.A.plot.bar(figsize=(16, 7), color=color_list)
assert p.patches[1].get_facecolor() == p.patches[17].get_facecolor()
@pytest.mark.parametrize("kind", ["bar", "line"])
def test_dictionary_color(self, kind):
# issue-8193
# Test plot color dictionary format
data_files = ["a", "b"]
expected = [(0.5, 0.24, 0.6), (0.3, 0.7, 0.7)]
df1 = DataFrame(np.random.default_rng(2).random((2, 2)), columns=data_files)
dic_color = {"b": (0.3, 0.7, 0.7), "a": (0.5, 0.24, 0.6)}
ax = df1.plot(kind=kind, color=dic_color)
if kind == "bar":
colors = [rect.get_facecolor()[0:-1] for rect in ax.get_children()[0:3:2]]
else:
colors = [rect.get_color() for rect in ax.get_lines()[0:2]]
assert all(color == expected[index] for index, color in enumerate(colors))
def test_bar_plot(self):
# GH38947
# Test bar plot with string and int index
from matplotlib.text import Text
expected = [Text(0, 0, "0"), Text(1, 0, "Total")]
df = DataFrame(
{
"a": [1, 2],
},
index=Index([0, "Total"]),
)
plot_bar = df.plot.bar()
assert all(
(a.get_text() == b.get_text())
for a, b in zip(plot_bar.get_xticklabels(), expected)
)
def test_barh_plot_labels_mixed_integer_string(self):
# GH39126
# Test barh plot with string and integer at the same column
from matplotlib.text import Text
df = DataFrame([{"word": 1, "value": 0}, {"word": "knowledge", "value": 2}])
plot_barh = df.plot.barh(x="word", legend=None)
expected_yticklabels = [Text(0, 0, "1"), Text(0, 1, "knowledge")]
assert all(
actual.get_text() == expected.get_text()
for actual, expected in zip(
plot_barh.get_yticklabels(), expected_yticklabels
)
)
def test_has_externally_shared_axis_x_axis(self):
# GH33819
# Test _has_externally_shared_axis() works for x-axis
func = plotting._matplotlib.tools._has_externally_shared_axis
fig = mpl.pyplot.figure()
plots = fig.subplots(2, 4)
# Create *externally* shared axes for first and third columns
plots[0][0] = fig.add_subplot(231, sharex=plots[1][0])
plots[0][2] = fig.add_subplot(233, sharex=plots[1][2])
# Create *internally* shared axes for second and third columns
plots[0][1].twinx()
plots[0][2].twinx()
# First column is only externally shared
# Second column is only internally shared
# Third column is both
# Fourth column is neither
assert func(plots[0][0], "x")
assert not func(plots[0][1], "x")
assert func(plots[0][2], "x")
assert not func(plots[0][3], "x")
def test_has_externally_shared_axis_y_axis(self):
# GH33819
# Test _has_externally_shared_axis() works for y-axis
func = plotting._matplotlib.tools._has_externally_shared_axis
fig = mpl.pyplot.figure()
plots = fig.subplots(4, 2)
# Create *externally* shared axes for first and third rows
plots[0][0] = fig.add_subplot(321, sharey=plots[0][1])
plots[2][0] = fig.add_subplot(325, sharey=plots[2][1])
# Create *internally* shared axes for second and third rows
plots[1][0].twiny()
plots[2][0].twiny()
# First row is only externally shared
# Second row is only internally shared
# Third row is both
# Fourth row is neither
assert func(plots[0][0], "y")
assert not func(plots[1][0], "y")
assert func(plots[2][0], "y")
assert not func(plots[3][0], "y")
def test_has_externally_shared_axis_invalid_compare_axis(self):
# GH33819
# Test _has_externally_shared_axis() raises an exception when
# passed an invalid value as compare_axis parameter
func = plotting._matplotlib.tools._has_externally_shared_axis
fig = mpl.pyplot.figure()
plots = fig.subplots(4, 2)
# Create arbitrary axes
plots[0][0] = fig.add_subplot(321, sharey=plots[0][1])
# Check that an invalid compare_axis value triggers the expected exception
msg = "needs 'x' or 'y' as a second parameter"
with pytest.raises(ValueError, match=msg):
func(plots[0][0], "z")
def test_externally_shared_axes(self):
# Example from GH33819
# Create data
df = DataFrame(
{
"a": np.random.default_rng(2).standard_normal(1000),
"b": np.random.default_rng(2).standard_normal(1000),
}
)
# Create figure
fig = mpl.pyplot.figure()
plots = fig.subplots(2, 3)
# Create *externally* shared axes
plots[0][0] = fig.add_subplot(231, sharex=plots[1][0])
# note: no plots[0][1] that's the twin only case
plots[0][2] = fig.add_subplot(233, sharex=plots[1][2])
# Create *internally* shared axes
# note: no plots[0][0] that's the external only case
twin_ax1 = plots[0][1].twinx()
twin_ax2 = plots[0][2].twinx()
# Plot data to primary axes
df["a"].plot(ax=plots[0][0], title="External share only").set_xlabel(
"this label should never be visible"
)
df["a"].plot(ax=plots[1][0])
df["a"].plot(ax=plots[0][1], title="Internal share (twin) only").set_xlabel(
"this label should always be visible"
)
df["a"].plot(ax=plots[1][1])
df["a"].plot(ax=plots[0][2], title="Both").set_xlabel(
"this label should never be visible"
)
df["a"].plot(ax=plots[1][2])
# Plot data to twinned axes
df["b"].plot(ax=twin_ax1, color="green")
df["b"].plot(ax=twin_ax2, color="yellow")
assert not plots[0][0].xaxis.get_label().get_visible()
assert plots[0][1].xaxis.get_label().get_visible()
assert not plots[0][2].xaxis.get_label().get_visible()
def test_plot_bar_axis_units_timestamp_conversion(self):
# GH 38736
# Ensure string x-axis from the second plot will not be converted to datetime
# due to axis data from first plot
df = DataFrame(
[1.0],
index=[Timestamp("2022-02-22 22:22:22")],
)
_check_plot_works(df.plot)
s = Series({"A": 1.0})
_check_plot_works(s.plot.bar)
def test_bar_plt_xaxis_intervalrange(self):
# GH 38969
# Ensure IntervalIndex x-axis produces a bar plot as expected
from matplotlib.text import Text
expected = [Text(0, 0, "([0, 1],)"), Text(1, 0, "([1, 2],)")]
s = Series(
[1, 2],
index=[interval_range(0, 2, closed="both")],
)
_check_plot_works(s.plot.bar)
assert all(
(a.get_text() == b.get_text())
for a, b in zip(s.plot.bar().get_xticklabels(), expected)
)
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