b8740a1d1e
### What changes were proposed in this pull request? This PR proposes applying `black` to pandas API on Spark codes, for improving static analysis. By executing the `./dev/reformat-python` in the spark home directory, all the code of the pandas API on Spark is fixed according to the static analysis rules. ### Why are the changes needed? This can be reduces the cost of static analysis during development. It has been used continuously for about a year in the Koalas project and its convenience has been proven. ### Does this PR introduce _any_ user-facing change? No, it's dev-only. ### How was this patch tested? Manually reformat the pandas API on Spark codes by running the `./dev/reformat-python`, and checked the `./dev/lint-python` is passed. Closes #32779 from itholic/SPARK-35499. Authored-by: itholic <haejoon.lee@databricks.com> Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
280 lines
9.8 KiB
Python
280 lines
9.8 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import unittest
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from distutils.version import LooseVersion
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import pprint
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import pandas as pd
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import numpy as np
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from pyspark import pandas as ps
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from pyspark.pandas.config import set_option, reset_option
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from pyspark.testing.pandasutils import (
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have_plotly,
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plotly_requirement_message,
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PandasOnSparkTestCase,
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TestUtils,
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)
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from pyspark.pandas.utils import name_like_string
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if have_plotly:
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from plotly import express
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import plotly.graph_objs as go
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@unittest.skipIf(not have_plotly, plotly_requirement_message)
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@unittest.skipIf(
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LooseVersion(pd.__version__) < "1.0.0",
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"pandas<1.0; pandas<1.0 does not support latest plotly and/or 'plotting.backend' option.",
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)
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class DataFramePlotPlotlyTest(PandasOnSparkTestCase, TestUtils):
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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pd.set_option("plotting.backend", "plotly")
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set_option("plotting.backend", "plotly")
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set_option("plotting.max_rows", 2000)
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set_option("plotting.sample_ratio", None)
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@classmethod
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def tearDownClass(cls):
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pd.reset_option("plotting.backend")
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reset_option("plotting.backend")
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reset_option("plotting.max_rows")
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reset_option("plotting.sample_ratio")
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super().tearDownClass()
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@property
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def pdf1(self):
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return pd.DataFrame(
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{"a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 50], "b": [2, 3, 4, 5, 7, 9, 10, 15, 34, 45, 49]},
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index=[0, 1, 3, 5, 6, 8, 9, 9, 9, 10, 10],
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)
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@property
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def psdf1(self):
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return ps.from_pandas(self.pdf1)
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def test_line_plot(self):
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def check_line_plot(pdf, psdf):
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self.assertEqual(pdf.plot(kind="line"), psdf.plot(kind="line"))
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self.assertEqual(pdf.plot.line(), psdf.plot.line())
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pdf1 = self.pdf1
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psdf1 = self.psdf1
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check_line_plot(pdf1, psdf1)
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def test_area_plot(self):
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def check_area_plot(pdf, psdf):
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self.assertEqual(pdf.plot(kind="area"), psdf.plot(kind="area"))
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self.assertEqual(pdf.plot.area(), psdf.plot.area())
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pdf = self.pdf1
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psdf = self.psdf1
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check_area_plot(pdf, psdf)
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def test_area_plot_y(self):
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def check_area_plot_y(pdf, psdf, y):
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self.assertEqual(pdf.plot.area(y=y), psdf.plot.area(y=y))
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# test if frame area plot is correct when y is specified
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pdf = pd.DataFrame(
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{
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"sales": [3, 2, 3, 9, 10, 6],
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"signups": [5, 5, 6, 12, 14, 13],
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"visits": [20, 42, 28, 62, 81, 50],
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},
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index=pd.date_range(start="2018/01/01", end="2018/07/01", freq="M"),
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)
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psdf = ps.from_pandas(pdf)
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check_area_plot_y(pdf, psdf, y="sales")
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def test_barh_plot_with_x_y(self):
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def check_barh_plot_with_x_y(pdf, psdf, x, y):
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self.assertEqual(pdf.plot(kind="barh", x=x, y=y), psdf.plot(kind="barh", x=x, y=y))
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self.assertEqual(pdf.plot.barh(x=x, y=y), psdf.plot.barh(x=x, y=y))
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# this is testing plot with specified x and y
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pdf1 = pd.DataFrame({"lab": ["A", "B", "C"], "val": [10, 30, 20]})
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psdf1 = ps.from_pandas(pdf1)
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check_barh_plot_with_x_y(pdf1, psdf1, x="lab", y="val")
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def test_barh_plot(self):
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def check_barh_plot(pdf, psdf):
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self.assertEqual(pdf.plot(kind="barh"), psdf.plot(kind="barh"))
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self.assertEqual(pdf.plot.barh(), psdf.plot.barh())
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# this is testing when x or y is not assigned
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pdf1 = pd.DataFrame({"lab": [20.1, 40.5, 60.6], "val": [10, 30, 20]})
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psdf1 = ps.from_pandas(pdf1)
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check_barh_plot(pdf1, psdf1)
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def test_bar_plot(self):
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def check_bar_plot(pdf, psdf):
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self.assertEqual(pdf.plot(kind="bar"), psdf.plot(kind="bar"))
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self.assertEqual(pdf.plot.bar(), psdf.plot.bar())
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pdf1 = self.pdf1
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psdf1 = self.psdf1
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check_bar_plot(pdf1, psdf1)
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def test_bar_with_x_y(self):
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# this is testing plot with specified x and y
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pdf = pd.DataFrame({"lab": ["A", "B", "C"], "val": [10, 30, 20]})
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psdf = ps.from_pandas(pdf)
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self.assertEqual(
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pdf.plot(kind="bar", x="lab", y="val"), psdf.plot(kind="bar", x="lab", y="val")
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)
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self.assertEqual(pdf.plot.bar(x="lab", y="val"), psdf.plot.bar(x="lab", y="val"))
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def test_scatter_plot(self):
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def check_scatter_plot(pdf, psdf, x, y, c):
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self.assertEqual(pdf.plot.scatter(x=x, y=y), psdf.plot.scatter(x=x, y=y))
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self.assertEqual(
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pdf.plot(kind="scatter", x=x, y=y), psdf.plot(kind="scatter", x=x, y=y)
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)
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# check when keyword c is given as name of a column
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self.assertEqual(
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pdf.plot.scatter(x=x, y=y, c=c, s=50), psdf.plot.scatter(x=x, y=y, c=c, s=50)
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)
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# Use pandas scatter plot example
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pdf1 = pd.DataFrame(np.random.rand(50, 4), columns=["a", "b", "c", "d"])
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psdf1 = ps.from_pandas(pdf1)
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check_scatter_plot(pdf1, psdf1, x="a", y="b", c="c")
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def test_pie_plot(self):
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def check_pie_plot(psdf):
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pdf = psdf.to_pandas()
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self.assertEqual(
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psdf.plot(kind="pie", y=psdf.columns[0]),
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express.pie(pdf, values="a", names=pdf.index),
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)
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self.assertEqual(
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psdf.plot(kind="pie", values="a"),
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express.pie(pdf, values="a"),
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)
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psdf1 = self.psdf1
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check_pie_plot(psdf1)
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# TODO: support multi-index columns
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# columns = pd.MultiIndex.from_tuples([("x", "y"), ("y", "z")])
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# psdf1.columns = columns
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# check_pie_plot(psdf1)
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# TODO: support multi-index
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# psdf1 = ps.DataFrame(
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# {
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# "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 50],
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# "b": [2, 3, 4, 5, 7, 9, 10, 15, 34, 45, 49]
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# },
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# index=pd.MultiIndex.from_tuples([("x", "y")] * 11),
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# )
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# check_pie_plot(psdf1)
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def test_hist_plot(self):
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def check_hist_plot(psdf):
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bins = np.array([1.0, 5.9, 10.8, 15.7, 20.6, 25.5, 30.4, 35.3, 40.2, 45.1, 50.0])
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data = [
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np.array([5.0, 4.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]),
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np.array([4.0, 3.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0]),
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]
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prev = bins[0]
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text_bins = []
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for b in bins[1:]:
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text_bins.append("[%s, %s)" % (prev, b))
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prev = b
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text_bins[-1] = text_bins[-1][:-1] + "]"
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bins = 0.5 * (bins[:-1] + bins[1:])
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name_a = name_like_string(psdf.columns[0])
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name_b = name_like_string(psdf.columns[1])
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bars = [
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go.Bar(
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x=bins,
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y=data[0],
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name=name_a,
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text=text_bins,
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hovertemplate=("variable=" + name_a + "<br>value=%{text}<br>count=%{y}"),
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),
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go.Bar(
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x=bins,
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y=data[1],
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name=name_b,
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text=text_bins,
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hovertemplate=("variable=" + name_b + "<br>value=%{text}<br>count=%{y}"),
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),
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]
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fig = go.Figure(data=bars, layout=go.Layout(barmode="stack"))
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fig["layout"]["xaxis"]["title"] = "value"
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fig["layout"]["yaxis"]["title"] = "count"
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self.assertEqual(
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pprint.pformat(psdf.plot(kind="hist").to_dict()), pprint.pformat(fig.to_dict())
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)
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psdf1 = self.psdf1
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check_hist_plot(psdf1)
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columns = pd.MultiIndex.from_tuples([("x", "y"), ("y", "z")])
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psdf1.columns = columns
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check_hist_plot(psdf1)
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def test_kde_plot(self):
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psdf = ps.DataFrame({"a": [1, 2, 3, 4, 5], "b": [1, 3, 5, 7, 9], "c": [2, 4, 6, 8, 10]})
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pdf = pd.DataFrame(
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{
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"Density": [
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0.03515491,
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0.06834979,
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0.00663503,
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0.02372059,
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0.06834979,
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0.01806934,
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0.01806934,
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0.06834979,
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0.02372059,
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],
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"names": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
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"index": [-3.5, 5.5, 14.5, -3.5, 5.5, 14.5, -3.5, 5.5, 14.5],
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}
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)
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actual = psdf.plot.kde(bw_method=5, ind=3)
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expected = express.line(pdf, x="index", y="Density", color="names")
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expected["layout"]["xaxis"]["title"] = None
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self.assertEqual(pprint.pformat(actual.to_dict()), pprint.pformat(expected.to_dict()))
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if __name__ == "__main__":
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from pyspark.pandas.tests.plot.test_frame_plot_plotly import * # noqa: F401
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try:
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import xmlrunner # type: ignore[import]
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testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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