4d2b559d92
### What changes were proposed in this pull request? Consolidate PySpark testing utils by removing `python/pyspark/pandas/testing`, and then creating a file `pandasutils` under `python/pyspark/testing` for test utilities used in `pyspark/pandas`. ### Why are the changes needed? `python/pyspark/pandas/testing` hold test utilites for pandas-on-spark, and `python/pyspark/testing` contain test utilities for pyspark. Consolidating them makes code cleaner and easier to maintain. Updated import statements are as shown below: - from pyspark.testing.sqlutils import SQLTestUtils - from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils (PandasOnSparkTestCase is the original ReusedSQLTestCase in `python/pyspark/pandas/testing/utils.py`) Minor improvements include: - Usage of missing library's requirement_message - `except ImportError` rather than `except` - import pyspark.pandas alias as `ps` rather than `pp` ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Unit tests under python/pyspark/pandas/tests. Closes #32177 from xinrong-databricks/port.merge_utils. Authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
475 lines
18 KiB
Python
475 lines
18 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 base64
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from distutils.version import LooseVersion
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from io import BytesIO
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import unittest
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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_matplotlib,
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matplotlib_requirement_message,
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PandasOnSparkTestCase,
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TestUtils,
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)
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if have_matplotlib:
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import matplotlib
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from matplotlib import pyplot as plt
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matplotlib.use("agg")
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@unittest.skipIf(not have_matplotlib, matplotlib_requirement_message)
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class DataFramePlotMatplotlibTest(PandasOnSparkTestCase, TestUtils):
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sample_ratio_default = None
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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if LooseVersion(pd.__version__) >= LooseVersion("0.25"):
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pd.set_option("plotting.backend", "matplotlib")
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set_option("plotting.backend", "matplotlib")
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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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if LooseVersion(pd.__version__) >= LooseVersion("0.25"):
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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 kdf1(self):
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return ps.from_pandas(self.pdf1)
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@staticmethod
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def plot_to_base64(ax):
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bytes_data = BytesIO()
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ax.figure.savefig(bytes_data, format="png")
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bytes_data.seek(0)
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b64_data = base64.b64encode(bytes_data.read())
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plt.close(ax.figure)
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return b64_data
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def test_line_plot(self):
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def check_line_plot(pdf, kdf):
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ax1 = pdf.plot(kind="line", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot(kind="line", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax3 = pdf.plot.line(colormap="Paired")
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bin3 = self.plot_to_base64(ax3)
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ax4 = kdf.plot.line(colormap="Paired")
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bin4 = self.plot_to_base64(ax4)
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self.assertEqual(bin3, bin4)
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pdf1 = self.pdf1
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kdf1 = self.kdf1
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check_line_plot(pdf1, kdf1)
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")])
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pdf1.columns = columns
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kdf1.columns = columns
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check_line_plot(pdf1, kdf1)
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def test_area_plot(self):
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def check_area_plot(pdf, kdf):
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ax1 = pdf.plot(kind="area", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot(kind="area", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax3 = pdf.plot.area(colormap="Paired")
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bin3 = self.plot_to_base64(ax3)
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ax4 = kdf.plot.area(colormap="Paired")
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bin4 = self.plot_to_base64(ax4)
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self.assertEqual(bin3, bin4)
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pdf = self.pdf1
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kdf = self.kdf1
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check_area_plot(pdf, kdf)
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")])
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pdf.columns = columns
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kdf.columns = columns
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check_area_plot(pdf, kdf)
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def test_area_plot_stacked_false(self):
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def check_area_plot_stacked_false(pdf, kdf):
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ax1 = pdf.plot.area(stacked=False)
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot.area(stacked=False)
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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# test if frame area plot is correct when stacked=False because default is True
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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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kdf = ps.from_pandas(pdf)
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check_area_plot_stacked_false(pdf, kdf)
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "sales"), ("x", "signups"), ("y", "visits")])
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pdf.columns = columns
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kdf.columns = columns
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check_area_plot_stacked_false(pdf, kdf)
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def test_area_plot_y(self):
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def check_area_plot_y(pdf, kdf, y):
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ax1 = pdf.plot.area(y=y)
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot.area(y=y)
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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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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kdf = ps.from_pandas(pdf)
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check_area_plot_y(pdf, kdf, y="sales")
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "sales"), ("x", "signups"), ("y", "visits")])
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pdf.columns = columns
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kdf.columns = columns
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check_area_plot_y(pdf, kdf, y=("x", "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, kdf, x, y):
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ax1 = pdf.plot(kind="barh", x=x, y=y, colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot(kind="barh", x=x, y=y, colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax3 = pdf.plot.barh(x=x, y=y, colormap="Paired")
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bin3 = self.plot_to_base64(ax3)
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ax4 = kdf.plot.barh(x=x, y=y, colormap="Paired")
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bin4 = self.plot_to_base64(ax4)
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self.assertEqual(bin3, bin4)
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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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kdf1 = ps.from_pandas(pdf1)
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check_barh_plot_with_x_y(pdf1, kdf1, x="lab", y="val")
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "lab"), ("y", "val")])
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pdf1.columns = columns
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kdf1.columns = columns
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check_barh_plot_with_x_y(pdf1, kdf1, x=("x", "lab"), y=("y", "val"))
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def test_barh_plot(self):
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def check_barh_plot(pdf, kdf):
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ax1 = pdf.plot(kind="barh", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot(kind="barh", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax3 = pdf.plot.barh(colormap="Paired")
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bin3 = self.plot_to_base64(ax3)
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ax4 = kdf.plot.barh(colormap="Paired")
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bin4 = self.plot_to_base64(ax4)
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self.assertEqual(bin3, bin4)
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# this is testing when x or y is not assigned
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pdf1 = pd.DataFrame({"lab": ["A", "B", "C"], "val": [10, 30, 20]})
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kdf1 = ps.from_pandas(pdf1)
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check_barh_plot(pdf1, kdf1)
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "lab"), ("y", "val")])
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pdf1.columns = columns
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kdf1.columns = columns
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check_barh_plot(pdf1, kdf1)
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def test_bar_plot(self):
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def check_bar_plot(pdf, kdf):
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ax1 = pdf.plot(kind="bar", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot(kind="bar", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax3 = pdf.plot.bar(colormap="Paired")
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bin3 = self.plot_to_base64(ax3)
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ax4 = kdf.plot.bar(colormap="Paired")
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bin4 = self.plot_to_base64(ax4)
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self.assertEqual(bin3, bin4)
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pdf1 = self.pdf1
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kdf1 = self.kdf1
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check_bar_plot(pdf1, kdf1)
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "lab"), ("y", "val")])
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pdf1.columns = columns
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kdf1.columns = columns
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check_bar_plot(pdf1, kdf1)
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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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kdf = ps.from_pandas(pdf)
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ax1 = pdf.plot(kind="bar", x="lab", y="val", colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot(kind="bar", x="lab", y="val", colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax3 = pdf.plot.bar(x="lab", y="val", colormap="Paired")
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bin3 = self.plot_to_base64(ax3)
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ax4 = kdf.plot.bar(x="lab", y="val", colormap="Paired")
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bin4 = self.plot_to_base64(ax4)
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self.assertEqual(bin3, bin4)
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "lab"), ("y", "val")])
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pdf.columns = columns
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kdf.columns = columns
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ax5 = pdf.plot(kind="bar", x=("x", "lab"), y=("y", "val"), colormap="Paired")
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bin5 = self.plot_to_base64(ax5)
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ax6 = kdf.plot(kind="bar", x=("x", "lab"), y=("y", "val"), colormap="Paired")
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bin6 = self.plot_to_base64(ax6)
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self.assertEqual(bin5, bin6)
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ax7 = pdf.plot.bar(x=("x", "lab"), y=("y", "val"), colormap="Paired")
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bin7 = self.plot_to_base64(ax7)
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ax8 = kdf.plot.bar(x=("x", "lab"), y=("y", "val"), colormap="Paired")
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bin8 = self.plot_to_base64(ax8)
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self.assertEqual(bin7, bin8)
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def test_pie_plot(self):
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def check_pie_plot(pdf, kdf, y):
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ax1 = pdf.plot.pie(y=y, figsize=(5, 5), colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot.pie(y=y, figsize=(5, 5), colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf.plot(kind="pie", y=y, figsize=(5, 5), colormap="Paired")
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot(kind="pie", y=y, figsize=(5, 5), colormap="Paired")
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax11, ax12 = pdf.plot.pie(figsize=(5, 5), subplots=True, colormap="Paired")
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bin11 = self.plot_to_base64(ax11)
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bin12 = self.plot_to_base64(ax12)
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self.assertEqual(bin11, bin12)
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ax21, ax22 = kdf.plot.pie(figsize=(5, 5), subplots=True, colormap="Paired")
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bin21 = self.plot_to_base64(ax21)
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bin22 = self.plot_to_base64(ax22)
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self.assertEqual(bin21, bin22)
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ax11, ax12 = pdf.plot(kind="pie", figsize=(5, 5), subplots=True, colormap="Paired")
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bin11 = self.plot_to_base64(ax11)
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bin12 = self.plot_to_base64(ax12)
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self.assertEqual(bin11, bin12)
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ax21, ax22 = kdf.plot(kind="pie", figsize=(5, 5), subplots=True, colormap="Paired")
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bin21 = self.plot_to_base64(ax21)
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bin22 = self.plot_to_base64(ax22)
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self.assertEqual(bin21, bin22)
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pdf1 = pd.DataFrame(
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{"mass": [0.330, 4.87, 5.97], "radius": [2439.7, 6051.8, 6378.1]},
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index=["Mercury", "Venus", "Earth"],
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)
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kdf1 = ps.from_pandas(pdf1)
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check_pie_plot(pdf1, kdf1, y="mass")
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "mass"), ("y", "radius")])
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pdf1.columns = columns
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kdf1.columns = columns
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check_pie_plot(pdf1, kdf1, y=("x", "mass"))
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def test_pie_plot_error_message(self):
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# this is to test if error is correctly raising when y is not specified
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# and subplots is not set to True
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pdf = pd.DataFrame(
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{"mass": [0.330, 4.87, 5.97], "radius": [2439.7, 6051.8, 6378.1]},
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index=["Mercury", "Venus", "Earth"],
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)
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kdf = ps.from_pandas(pdf)
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with self.assertRaises(ValueError) as context:
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kdf.plot.pie(figsize=(5, 5), colormap="Paired")
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error_message = "pie requires either y column or 'subplots=True'"
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self.assertTrue(error_message in str(context.exception))
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def test_scatter_plot(self):
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def check_scatter_plot(pdf, kdf, x, y, c):
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ax1 = pdf.plot.scatter(x=x, y=y)
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot.scatter(x=x, y=y)
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf.plot(kind="scatter", x=x, y=y)
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot(kind="scatter", x=x, y=y)
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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# check when keyword c is given as name of a column
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ax1 = pdf.plot.scatter(x=x, y=y, c=c, s=50)
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot.scatter(x=x, y=y, c=c, s=50)
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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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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kdf1 = ps.from_pandas(pdf1)
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check_scatter_plot(pdf1, kdf1, x="a", y="b", c="c")
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c"), ("z", "d")])
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pdf1.columns = columns
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kdf1.columns = columns
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check_scatter_plot(pdf1, kdf1, x=("x", "a"), y=("x", "b"), c=("y", "c"))
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def test_hist_plot(self):
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def check_hist_plot(pdf, kdf):
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_, ax1 = plt.subplots(1, 1)
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ax1 = pdf.plot.hist()
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bin1 = self.plot_to_base64(ax1)
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_, ax2 = plt.subplots(1, 1)
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ax2 = kdf.plot.hist()
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf.plot.hist(bins=15)
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bin1 = self.plot_to_base64(ax1)
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ax2 = kdf.plot.hist(bins=15)
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bin2 = self.plot_to_base64(ax2)
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self.assertEqual(bin1, bin2)
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ax1 = pdf.plot(kind="hist", bins=15)
|
|
bin1 = self.plot_to_base64(ax1)
|
|
ax2 = kdf.plot(kind="hist", bins=15)
|
|
bin2 = self.plot_to_base64(ax2)
|
|
self.assertEqual(bin1, bin2)
|
|
|
|
ax1 = pdf.plot.hist(bins=3, bottom=[2, 1, 3])
|
|
bin1 = self.plot_to_base64(ax1)
|
|
ax2 = kdf.plot.hist(bins=3, bottom=[2, 1, 3])
|
|
bin2 = self.plot_to_base64(ax2)
|
|
self.assertEqual(bin1, bin2)
|
|
|
|
pdf1 = self.pdf1
|
|
kdf1 = self.kdf1
|
|
check_hist_plot(pdf1, kdf1)
|
|
|
|
# multi-index columns
|
|
columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")])
|
|
pdf1.columns = columns
|
|
kdf1.columns = columns
|
|
check_hist_plot(pdf1, kdf1)
|
|
|
|
def test_kde_plot(self):
|
|
def moving_average(a, n=10):
|
|
ret = np.cumsum(a, dtype=float)
|
|
ret[n:] = ret[n:] - ret[:-n]
|
|
return ret[n - 1:] / n
|
|
|
|
def check_kde_plot(pdf, kdf, *args, **kwargs):
|
|
_, ax1 = plt.subplots(1, 1)
|
|
ax1 = pdf.plot.kde(*args, **kwargs)
|
|
_, ax2 = plt.subplots(1, 1)
|
|
ax2 = kdf.plot.kde(*args, **kwargs)
|
|
|
|
try:
|
|
for i, (line1, line2) in enumerate(zip(ax1.get_lines(), ax2.get_lines())):
|
|
expected = line1.get_xydata().ravel()
|
|
actual = line2.get_xydata().ravel()
|
|
# TODO: Due to implementation difference, the output is different comparing
|
|
# to pandas'. We should identify the root cause of difference, and reduce
|
|
# the diff.
|
|
|
|
# Note: Data is from 1 to 50. So, it smooths them by moving average and compares
|
|
# both.
|
|
self.assertTrue(
|
|
np.allclose(moving_average(actual), moving_average(expected), rtol=3.0)
|
|
)
|
|
finally:
|
|
ax1.cla()
|
|
ax2.cla()
|
|
|
|
pdf1 = self.pdf1
|
|
kdf1 = self.kdf1
|
|
check_kde_plot(pdf1, kdf1, bw_method=0.3)
|
|
check_kde_plot(pdf1, kdf1, ind=[1, 2, 3], bw_method=3.0)
|
|
|
|
# multi-index columns
|
|
columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")])
|
|
pdf1.columns = columns
|
|
pdf1.columns = columns
|
|
check_kde_plot(pdf1, kdf1, bw_method=0.3)
|
|
check_kde_plot(pdf1, kdf1, ind=[1, 2, 3], bw_method=3.0)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.pandas.tests.plot.test_frame_plot_matplotlib import * # noqa: F401
|
|
|
|
try:
|
|
import xmlrunner # type: ignore[import]
|
|
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
|
|
except ImportError:
|
|
testRunner = None
|
|
unittest.main(testRunner=testRunner, verbosity=2)
|