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| # Licensed to the Apache Software Foundation (ASF) under one | |
| # or more contributor license agreements. See the NOTICE file | |
| # distributed with this work for additional information | |
| # regarding copyright ownership. The ASF licenses this file | |
| # to you under the Apache License, Version 2.0 (the | |
| # "License"); you may not use this file except in compliance | |
| # with the License. You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, | |
| # software distributed under the License is distributed on an | |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | |
| # KIND, either express or implied. See the License for the | |
| # specific language governing permissions and limitations | |
| # under the License. | |
| import datetime | |
| from unittest.mock import MagicMock | |
| import pandas as pd | |
| from superset.common.utils import dataframe_utils | |
| def test_is_datetime_series(): | |
| assert not dataframe_utils.is_datetime_series(None) | |
| assert not dataframe_utils.is_datetime_series(pd.DataFrame({"foo": [1]})) | |
| assert not dataframe_utils.is_datetime_series(pd.Series([1, 2, 3])) | |
| assert not dataframe_utils.is_datetime_series(pd.Series(["1", "2", "3"])) | |
| assert not dataframe_utils.is_datetime_series(pd.Series()) | |
| assert not dataframe_utils.is_datetime_series(pd.Series([None, None])) | |
| assert dataframe_utils.is_datetime_series( | |
| pd.Series([datetime.date(2018, 1, 1), datetime.date(2018, 1, 2), None]) | |
| ) | |
| assert dataframe_utils.is_datetime_series( | |
| pd.Series([datetime.date(2018, 1, 1), datetime.date(2018, 1, 2)]) | |
| ) | |
| assert dataframe_utils.is_datetime_series( | |
| pd.Series([datetime.datetime(2018, 1, 1), datetime.datetime(2018, 1, 2), None]) | |
| ) | |
| assert dataframe_utils.is_datetime_series( | |
| pd.Series([datetime.datetime(2018, 1, 1), datetime.datetime(2018, 1, 2)]) | |
| ) | |
| assert dataframe_utils.is_datetime_series( | |
| pd.date_range(datetime.date(2018, 1, 1), datetime.date(2018, 2, 1)).to_series() | |
| ) | |
| assert dataframe_utils.is_datetime_series( | |
| pd.date_range( | |
| datetime.datetime(2018, 1, 1), datetime.datetime(2018, 2, 1) | |
| ).to_series() | |
| ) | |
| def test_df_metrics_to_num_converts_string_numerics(): | |
| """Test that string-encoded numeric columns (e.g. from ClickHouse) are converted.""" | |
| query_object = MagicMock() | |
| query_object.metric_names = ["sum_col", "mixed_col", "text_col"] | |
| df = pd.DataFrame( | |
| { | |
| "sum_col": pd.Series(["100", "200", "300"], dtype=object), | |
| "mixed_col": pd.Series(["1", "not_a_number", "3"], dtype=object), | |
| "text_col": pd.Series(["foo", "bar", "baz"], dtype=object), | |
| "dim_col": pd.Series(["a", "b", "c"], dtype=object), | |
| } | |
| ) | |
| dataframe_utils.df_metrics_to_num(df, query_object) | |
| # sum_col: all numeric strings -> should be converted to numeric | |
| assert pd.api.types.is_numeric_dtype(df["sum_col"]), "sum_col should be numeric" | |
| assert df["sum_col"].tolist() == [100.0, 200.0, 300.0] | |
| # mixed_col: has non-numeric value -> should NOT be converted | |
| assert df["mixed_col"].dtype == object, "mixed_col should remain object" | |
| # text_col: all non-numeric -> should NOT be converted | |
| assert df["text_col"].dtype == object, "text_col should remain object" | |
| # dim_col: not in metric_names -> should NOT be touched | |
| assert df["dim_col"].dtype == object, "dim_col should not be touched" | |