craftbeerpi4-pione/venv/lib/python3.8/site-packages/pandas/tests/plotting/test_groupby.py
2021-01-30 22:29:33 +01:00

114 lines
4.4 KiB
Python

""" Test cases for GroupBy.plot """
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import DataFrame, Index, Series
import pandas._testing as tm
from pandas.tests.plotting.common import TestPlotBase
@td.skip_if_no_mpl
class TestDataFrameGroupByPlots(TestPlotBase):
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_plotting_with_float_index_works(self):
# GH 7025
df = DataFrame(
{"def": [1, 1, 1, 2, 2, 2, 3, 3, 3], "val": np.random.randn(9)},
index=[1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0],
)
df.groupby("def")["val"].plot()
tm.close()
df.groupby("def")["val"].apply(lambda x: x.plot())
tm.close()
def test_hist_single_row(self):
# GH10214
bins = np.arange(80, 100 + 2, 1)
df = DataFrame({"Name": ["AAA", "BBB"], "ByCol": [1, 2], "Mark": [85, 89]})
df["Mark"].hist(by=df["ByCol"], bins=bins)
df = DataFrame({"Name": ["AAA"], "ByCol": [1], "Mark": [85]})
df["Mark"].hist(by=df["ByCol"], bins=bins)
def test_plot_submethod_works(self):
df = DataFrame({"x": [1, 2, 3, 4, 5], "y": [1, 2, 3, 2, 1], "z": list("ababa")})
df.groupby("z").plot.scatter("x", "y")
tm.close()
df.groupby("z")["x"].plot.line()
tm.close()
def test_plot_kwargs(self):
df = DataFrame({"x": [1, 2, 3, 4, 5], "y": [1, 2, 3, 2, 1], "z": list("ababa")})
res = df.groupby("z").plot(kind="scatter", x="x", y="y")
# check that a scatter plot is effectively plotted: the axes should
# contain a PathCollection from the scatter plot (GH11805)
assert len(res["a"].collections) == 1
res = df.groupby("z").plot.scatter(x="x", y="y")
assert len(res["a"].collections) == 1
@pytest.mark.parametrize("column, expected_axes_num", [(None, 2), ("b", 1)])
def test_groupby_hist_frame_with_legend(self, column, expected_axes_num):
# GH 6279 - DataFrameGroupBy histogram can have a legend
expected_layout = (1, expected_axes_num)
expected_labels = column or [["a"], ["b"]]
index = Index(15 * ["1"] + 15 * ["2"], name="c")
df = DataFrame(np.random.randn(30, 2), index=index, columns=["a", "b"])
g = df.groupby("c")
for axes in g.hist(legend=True, column=column):
self._check_axes_shape(
axes, axes_num=expected_axes_num, layout=expected_layout
)
for ax, expected_label in zip(axes[0], expected_labels):
self._check_legend_labels(ax, expected_label)
@pytest.mark.parametrize("column", [None, "b"])
def test_groupby_hist_frame_with_legend_raises(self, column):
# GH 6279 - DataFrameGroupBy histogram with legend and label raises
index = Index(15 * ["1"] + 15 * ["2"], name="c")
df = DataFrame(np.random.randn(30, 2), index=index, columns=["a", "b"])
g = df.groupby("c")
with pytest.raises(ValueError, match="Cannot use both legend and label"):
g.hist(legend=True, column=column, label="d")
def test_groupby_hist_series_with_legend(self):
# GH 6279 - SeriesGroupBy histogram can have a legend
index = Index(15 * ["1"] + 15 * ["2"], name="c")
df = DataFrame(np.random.randn(30, 2), index=index, columns=["a", "b"])
g = df.groupby("c")
for ax in g["a"].hist(legend=True):
self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
self._check_legend_labels(ax, ["1", "2"])
def test_groupby_hist_series_with_legend_raises(self):
# GH 6279 - SeriesGroupBy histogram with legend and label raises
index = Index(15 * ["1"] + 15 * ["2"], name="c")
df = DataFrame(np.random.randn(30, 2), index=index, columns=["a", "b"])
g = df.groupby("c")
with pytest.raises(ValueError, match="Cannot use both legend and label"):
g.hist(legend=True, label="d")