Test your AI Agents with the all-new Agent to Agent Testing Platform.Learn More

How to use mock_dataframe method in pandera

Best Python code snippet using pandera_python

plot_workflow_result_tests.py

Source:plot_workflow_result_tests.py

1from unittest import mock2import pandas as pd3from datetime import datetime as dt4from tethysext.atcore.models.resource_workflow_results import PlotWorkflowResult5from tethysext.atcore.tests.utilities.sqlalchemy_helpers import SqlAlchemyTestCase6from tethysext.atcore.tests.utilities.sqlalchemy_helpers import setup_module_for_sqlalchemy_tests, \7    tear_down_module_for_sqlalchemy_tests8import plotly.graph_objs as go9from bokeh.plotting import figure10def setUpModule():11    setup_module_for_sqlalchemy_tests()12def tearDownModule():13    tear_down_module_for_sqlalchemy_tests()14class PlotWorkflowResultTests(SqlAlchemyTestCase):15    def setUp(self):16        super().setUp()17        self.instance = PlotWorkflowResult(name='test')18    def bind_instance_to_session(self):19        self.session.add(self.instance)20        self.session.commit()21    def test_default_options(self):22        baseline = {23            'renderer': 'plotly',24            'axes': [],25            'plot_type': 'lines',26            'axis_labels': ['x', 'y'],27            'line_shape': 'linear',28            'x_axis_type': 'datetime',29            'no_dataset_message': 'No dataset found.'30        }31        self.assertDictEqual(baseline, self.instance.default_options)32    def test_datasets(self):33        self.assertDictEqual({}, self.instance.data)34        ret = self.instance.datasets35        self.assertListEqual([], ret)36    def test_reset(self):37        self.instance.data['datasets'] = 'Bad data to reset'38        self.instance.reset()39        ret = self.instance.data['datasets']40        self.assertEqual([], ret)41    def test__add_dataset(self):42        test_data = 'Test data'43        self.instance._add_dataset(test_data)44        self.assertListEqual([test_data], self.instance.datasets)45    def test__add_plot(self):46        test_data = 'Test plot'47        self.instance._add_plot(test_data)48        self.assertEqual(test_data, self.instance.plot)49    def test_datasets_bound(self):50        self.bind_instance_to_session()51        self.test_datasets()52    def test_reset_datasets_bound(self):53        self.bind_instance_to_session()54        self.test_reset()55    def test__add_dataset_bound(self):56        self.bind_instance_to_session()57        self.test__add_dataset()58    def test_add_series_no_title(self):59        self.assertRaises(ValueError, self.instance.add_series, '', ['test'])60    def test_add_series_no_data(self):61        self.assertRaises(ValueError, self.instance.add_series, 'title', '')62    def test_add_series_empty_data_frame(self):63        mock_dataframe = mock.MagicMock(spec=pd.DataFrame, empty=True)64        self.assertRaises(ValueError, self.instance.add_series, 'title', mock_dataframe)65    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.PlotWorkflowResult._add_dataset')  # noqa: E50166    def test_add_series(self, mock_add_dataset):67        mock_dataframe = mock.MagicMock(spec=pd.DataFrame, empty=False)68        self.instance.add_series('series title', mock_dataframe)69        baseline = {70            'title': 'series title',71            'dataset': mock_dataframe,72        }73        mock_add_dataset.assert_called_with(baseline)74    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.PlotWorkflowResult._add_dataset')  # noqa: E50175    def test_plot_from_dataframe(self, mock_add_dataset):76        mock_dataframe = mock.MagicMock(spec=pd.DataFrame, empty=False)77        self.instance.plot_from_dataframe(mock_dataframe)78        baseline = {79            'dataset': mock_dataframe,80            'series_axes': [],81            'series_labels': []82        }83        mock_add_dataset.assert_called_with(baseline)84    def test_plot_from_dataframe_not_dataframe(self):85        self.assertRaises(ValueError, self.instance.plot_from_dataframe, ['test'])86    def test_plot_from_dataframe_empty_dataframe(self):87        mock_dataframe = mock.MagicMock(spec=pd.DataFrame, empty=True)88        self.assertRaises(ValueError, self.instance.plot_from_dataframe, mock_dataframe)89    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.PlotWorkflowResult._add_plot')  # noqa: E50190    def test_add_plot(self, mock_add_dataset):91        mock_plot = mock.MagicMock(spec=go.Figure, empty=False)92        self.instance.add_plot(mock_plot)93        baseline = {94            'plot_object': mock_plot,95        }96        mock_add_dataset.assert_called_with(baseline)97    def test_add_plot_not_plolty(self):98        self.assertRaises(ValueError, self.instance.add_plot, 'foo')99    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.PlotWorkflowResult.options')100    def test_get_plot_object_dict(self, mock_options):101        mock_plotly_plot = mock.MagicMock(spec=go.Figure, empty=False)102        mock_options.get = mock.MagicMock(side_effect=['plotly', 'lines', ['x', 'y'], 'linear', 'linear'])103        self.instance.add_plot(mock_plotly_plot)104        ret_plot = self.instance.get_plot_object()105        self.assertEqual(ret_plot, mock_plotly_plot)106    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.PlotWorkflowResult.options')107    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.figure')108    def test_get_plot_object_bokeh_line(self, mock_bokeh_figure, mock_options):109        mock_bokeh_plot = mock.MagicMock(110            spec=figure, xaxis=mock.MagicMock(axis_label='x'), yaxis=mock.MagicMock(axis_label='y'),111            line=mock.MagicMock(return_value='test line type')112        )113        mock_bokeh_figure.return_value = mock_bokeh_plot114        mock_dataframe = pd.DataFrame({115            'x': [dt(2020, 1, 2), dt(2020, 1, 3), dt(2020, 1, 4), dt(2020, 1, 5), dt(2020, 1, 6), dt(2020, 1, 7)],116            'y': [2, 3, 4, 5, 6, 7]117        })118        mock_datasets = [{119            'title': 'Test Title',120            'series_axes': [('x', 'y')],121            'series_labels': ['test_label'],122            'dataset': mock_dataframe123        }]124        mock_options.get = mock.MagicMock(side_effect=['bokeh', 'lines', ['x', 'y'], 'linear', 'linear'])125        self.instance.plot = mock_bokeh_plot126        self.instance.datasets = mock_datasets127        ret_plot = self.instance.get_plot_object()128        self.assertEqual(ret_plot, mock_bokeh_plot)129    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.PlotWorkflowResult.options')130    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.figure')131    def test_get_plot_object_bokeh_scatter(self, mock_bokeh_figure, mock_options):132        mock_bokeh_plot = mock.MagicMock(133            spec=figure, xaxis=mock.MagicMock(axis_label='x'), yaxis=mock.MagicMock(axis_label='y'),134            scatter=mock.MagicMock(return_value='test scatter type')135        )136        mock_bokeh_figure.return_value = mock_bokeh_plot137        mock_dataframe = pd.DataFrame(138            {'x': [dt(2020, 1, 2), dt(2020, 1, 3), dt(2020, 1, 4)], 'y': [2, 3, 4]},139            columns=['x', 'y']140        )141        mock_datasets = [{142            'title': 'Test Title',143            'series_axes': [],144            'series_labels': [],145            'dataset': mock_dataframe146        }]147        mock_options.get = mock.MagicMock(side_effect=['bokeh', 'scatter', ['x', 'y'], 'linear', 'linear'])148        self.instance.plot = mock_bokeh_plot149        self.instance.datasets = mock_datasets150        ret_plot = self.instance.get_plot_object()151        self.assertEqual(ret_plot, mock_bokeh_plot)152    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.PlotWorkflowResult.options')153    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.go.Figure')154    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.go.Scatter')155    def test_get_plot_object_plotly_line(self, mock_scatter_plot, mock_plotly_figure, mock_options):156        mock_plotly_plot = mock.MagicMock(157            spec=go.Figure, xaxis=mock.MagicMock(axis_label='x'), yaxis=mock.MagicMock(axis_label='y'),158            add_trace=mock.MagicMock(return_value=mock_scatter_plot)159        )160        mock_plotly_figure.return_value = mock_plotly_plot161        mock_dataframe = pd.DataFrame({162            'x': [dt(2020, 1, 2), dt(2020, 1, 3), dt(2020, 1, 4), dt(2020, 1, 5), dt(2020, 1, 6), dt(2020, 1, 7)],163            'y': [2, 3, 4, 5, 6, 7]164        })165        mock_datasets = [{166            'title': 'Test Title',167            'series_axes': [('x', 'y')],168            'series_labels': ['test_label'],169            'dataset': mock_dataframe170        }]171        mock_options.get = mock.MagicMock(side_effect=['plotly', 'lines', ['x', 'y'], 'linear', 'linear'])172        self.instance.plot = mock_plotly_plot173        self.instance.datasets = mock_datasets174        ret_plot = self.instance.get_plot_object()175        self.assertEqual(ret_plot, mock_plotly_plot)176    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.PlotWorkflowResult.options')177    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.go.Figure')178    @mock.patch('tethysext.atcore.models.resource_workflow_results.plot_workflow_result.go.Scatter')179    def test_get_plot_object_plotly_list_dataset(self, mock_scatter_plot, mock_plotly_figure,180                                                 mock_options):181        mock_plotly_plot = mock.MagicMock(182            spec=go.Figure, xaxis=mock.MagicMock(axis_label='x'), yaxis=mock.MagicMock(axis_label='y'),183            add_trace=mock.MagicMock(return_value=mock_scatter_plot)184        )185        mock_plotly_figure.return_value = mock_plotly_plot186        mock_dataset = [187            [dt(2020, 1, 2), dt(2020, 1, 3), dt(2020, 1, 4), dt(2020, 1, 5), dt(2020, 1, 6), dt(2020, 1, 7)],188            [2, 3, 4, 5, 6, 7]189        ]190        mock_datasets = [{191            'title': 'Test Title',192            'series_axes': [('x', 'y')],193            'series_labels': ['test_label'],194            'dataset': mock_dataset195        }]196        mock_options.get = mock.MagicMock(side_effect=['plotly', 'scatter', ['x', 'y'], 'linear', 'linear'])197        self.instance.plot = mock_plotly_plot198        self.instance.datasets = mock_datasets199        ret_plot = self.instance.get_plot_object()...

test_stock_data.py

Source:test_stock_data.py

1import os2import unittest3import datetime as dt4import requests_mock5from unittest.mock import Mock, patch, call6import logging7import pandas as pd8import pandas_datareader.data as web9import requests10import scan_data as scan_data11from alpaca import Alpaca12from stock_data import Stock13logging.disable(logging.CRITICAL)14class TestStockData(unittest.TestCase):15    def setUp(self):16        self.mock_stock = Stock('FAKE')17        self.mock_dict = {18            'date': ['2019-11-12', '2019-11-13'],19            'open': [261.55, 261.13],20            'high': [262.79, 264.78],21            'low': [260.92, 261.07],22            'close': [261.96, 264.47],23            'adjusted close': [260.6421, 263.1395],24            'volume': [21847226, 25817593],25            'dividend amount': [0., 0.],26            'split coefficient': [1., 1.],27            }28        self.mock_dataframe = pd.DataFrame.from_dict(data=self.mock_dict)29        self.mock_dataframe.set_index('date')30    ### ------------------- DATA GETTERS TESTS ------------------- ###31    @patch('stock_data.web.DataReader')32    def test_get_trend_data(self, mock_datareader):33        mock_datareader.return_value = self.mock_dataframe34        mock_result_dataframe = self.mock_dataframe.copy()35        mock_result_dataframe['sma50'] = [float('nan'), float('nan')]36        actual_result = self.mock_stock.get_trend_data()37        pd.testing.assert_frame_equal(actual_result, mock_result_dataframe)38    39    @patch.object(Stock, 'get_stochastic')40    @patch('stock_data.TimeSeries')41    def test_get_tactical_data(self, mock_timeseries, mock_get_stochastic):42        mock_timeseries.return_value.get_intraday.return_value = (self.mock_dataframe, '')43        mock_result_dataframe = self.mock_dataframe.copy()44        mock_result_dataframe.rename(lambda s: '   ' + s, axis=1, inplace=True)45        mock_result_dataframe['fast k'] = [float('nan'), float('nan')]46        mock_result_dataframe['k'] = [float('nan'), float('nan')]47        mock_result_dataframe['d'] = [float('nan'), float('nan')]48        mock_get_stochastic.return_value = mock_result_dataframe49        actual_result = self.mock_stock.get_tactical_data()50        mock_get_stochastic.assert_called_once_with(self.mock_dataframe)51        pd.testing.assert_frame_equal(actual_result, mock_result_dataframe)52    53    @patch('stock_data.json.loads')54    #@patch('stock_data.pd.DataFrame.from_dict')55    @requests_mock.Mocker()56    def test_get_execution_data(self, mock_json_loads, mock_request):57        58        mock_bars = {59            "FAKE": [60                {61                "t": 1544129220,62                "o": 172.26,63                "h": 172.3,64                "l": 172.16,65                "c": 172.18,66                "v": 3892,67                }68            ]69            }70        url = 'https://data.alpaca.markets/v1/bars/1Min?symbols=FAKE&limit=10'71        mock_request.get(url, text='request ok')72        mock_json_loads.return_value = mock_bars73        actual_result = self.mock_stock.get_execution_data()74        self.assertIsInstance(actual_result, pd.DataFrame)75        76    ### ------------------- POTENTIAL GETTERS TESTS ------------------- ###77    78    def test_get_trend_potential(self):79        pass80 81    82    def test_get_tactical_potential(self):83        pass84 85    def test_get_execution_potential(self):86        pass87    ### ------------------- STATIC METHODS TESTS ------------------- ###88    def test_is_in_range_true_default_range(self):89        x = 9590        y = 10591        actual_result = self.mock_stock.is_in_range(x, y)92        self.assertEqual(actual_result, True)93    94    95    def test_is_in_range_false_default_range(self):96        x = 8997        y = 11198        actual_result = self.mock_stock.is_in_range(x, y)99        self.assertEqual(actual_result, False)100 101    def test_is_in_range_true_custom_range(self):102        x = 89103        y = 111104        range_percent = 20105        actual_result = self.mock_stock.is_in_range(x, y, range_percent)106        self.assertEqual(actual_result, True)107 108    def test_is_trending_up_true_default_step(self):109        array = [1, 2, 3]110        actual_result = self.mock_stock.is_trending_up(array)111        self.assertEqual(actual_result, True)112        113    def test_is_trending_up_false_default_step(self):114        array = [3, 1, 2]115        actual_result = self.mock_stock.is_trending_up(array)116        self.assertEqual(actual_result, False)117 118    def test_is_trending_up_true_custom_step(self):119        array = [1, 2, 3, 4, 5, 6, 7, 6, 9, 10]120        step = 2121        actual_result = self.mock_stock.is_trending_up(array, step)122        self.assertEqual(actual_result, True)123 124    def test_is_trending_up_false_custom_step(self):125        array = [1, 2, 3, 8, 5, 6, 7, 8, 9, 10]126        step = 2127        actual_result = self.mock_stock.is_trending_up(array, step)128        self.assertEqual(actual_result, False)...

dataset_workflow_result_tests.py

Source:dataset_workflow_result_tests.py

1from unittest import mock2import pandas as pd3from tethysext.atcore.models.resource_workflow_results import DatasetWorkflowResult4from tethysext.atcore.tests.utilities.sqlalchemy_helpers import SqlAlchemyTestCase5from tethysext.atcore.tests.utilities.sqlalchemy_helpers import setup_module_for_sqlalchemy_tests, \6    tear_down_module_for_sqlalchemy_tests7def setUpModule():8    setup_module_for_sqlalchemy_tests()9def tearDownModule():10    tear_down_module_for_sqlalchemy_tests()11class DatasetWorkflowResultTests(SqlAlchemyTestCase):12    def setUp(self):13        super().setUp()14        self.instance = DatasetWorkflowResult(name='test')15    def bind_instance_to_session(self):16        self.session.add(self.instance)17        self.session.commit()18    def test_default_options(self):19        baseline = {20            'data_table_kwargs': {21                'searching': False,22                'paging': False,23                'info': False24            },25            'no_dataset_message': 'No dataset found.'26        }27        self.assertDictEqual(baseline, self.instance.default_options)28    def test_datasets(self):29        self.assertDictEqual({}, self.instance.data)30        ret = self.instance.datasets31        self.assertListEqual([], ret)32    def test_reset(self):33        self.instance.data['datasets'] = 'Bad data to reset'34        self.instance.reset()35        ret = self.instance.data['datasets']36        self.assertEqual([], ret)37    def test__add_dataset(self):38        test_data = 'Test data'39        self.instance._add_dataset(test_data)40        self.assertListEqual([test_data], self.instance.datasets)41    def test_datasets_bound(self):42        self.bind_instance_to_session()43        self.test_datasets()44    def test_reset_datasets_bound(self):45        self.bind_instance_to_session()46        self.test_reset()47    def test__add_dataset_bound(self):48        self.bind_instance_to_session()49        self.test__add_dataset()50    @mock.patch('tethysext.atcore.models.resource_workflow_results.dataset_workflow_result.DatasetWorkflowResult._add_dataset')  # noqa: E50151    def test_add_pandas_dataframe(self, mock_add_dataset):52        mock_dataframe = mock.MagicMock(spec=pd.DataFrame, empty=False)53        self.instance.add_pandas_dataframe('foo', mock_dataframe)54        baseline = {55            'title': 'foo',56            'dataset': mock_dataframe,57            'show_export_button': False58        }59        mock_add_dataset.assert_called_with(baseline)60    def test_add_pandas_dataframe_no_title(self):61        mock_dataframe = mock.MagicMock(spec=pd.DataFrame, empty=False)62        self.assertRaises(ValueError, self.instance.add_pandas_dataframe, '', mock_dataframe)63        self.assertRaises(ValueError, self.instance.add_pandas_dataframe, None, mock_dataframe)64    def test_add_pandas_dataframe_not_dataframe(self):65        self.assertRaises(ValueError, self.instance.add_pandas_dataframe, 'foo', ['test'])66    def test_add_pandas_dataframe_empty_dataframe(self):67        mock_dataframe = mock.MagicMock(spec=pd.DataFrame, empty=True)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.