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6311e2c315
Co-authored-by: Emmanuela Opurum <youremail@example.com> Co-authored-by: Đỗ Trọng Hải <41283691+hainenber@users.noreply.github.com>
76 lines
3.4 KiB
Python
76 lines
3.4 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with 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,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import datetime
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from unittest.mock import MagicMock
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import pandas as pd
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from superset.common.utils import dataframe_utils
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def test_is_datetime_series():
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assert not dataframe_utils.is_datetime_series(None)
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assert not dataframe_utils.is_datetime_series(pd.DataFrame({"foo": [1]}))
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assert not dataframe_utils.is_datetime_series(pd.Series([1, 2, 3]))
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assert not dataframe_utils.is_datetime_series(pd.Series(["1", "2", "3"]))
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assert not dataframe_utils.is_datetime_series(pd.Series())
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assert not dataframe_utils.is_datetime_series(pd.Series([None, None]))
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assert dataframe_utils.is_datetime_series(
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pd.Series([datetime.date(2018, 1, 1), datetime.date(2018, 1, 2), None])
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)
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assert dataframe_utils.is_datetime_series(
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pd.Series([datetime.date(2018, 1, 1), datetime.date(2018, 1, 2)])
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)
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assert dataframe_utils.is_datetime_series(
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pd.Series([datetime.datetime(2018, 1, 1), datetime.datetime(2018, 1, 2), None])
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)
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assert dataframe_utils.is_datetime_series(
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pd.Series([datetime.datetime(2018, 1, 1), datetime.datetime(2018, 1, 2)])
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)
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assert dataframe_utils.is_datetime_series(
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pd.date_range(datetime.date(2018, 1, 1), datetime.date(2018, 2, 1)).to_series()
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)
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assert dataframe_utils.is_datetime_series(
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pd.date_range(
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datetime.datetime(2018, 1, 1), datetime.datetime(2018, 2, 1)
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).to_series()
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)
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def test_df_metrics_to_num_converts_string_numerics():
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"""Test that string-encoded numeric columns (e.g. from ClickHouse) are converted."""
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query_object = MagicMock()
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query_object.metric_names = ["sum_col", "mixed_col", "text_col"]
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df = pd.DataFrame(
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{
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"sum_col": pd.Series(["100", "200", "300"], dtype=object),
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"mixed_col": pd.Series(["1", "not_a_number", "3"], dtype=object),
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"text_col": pd.Series(["foo", "bar", "baz"], dtype=object),
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"dim_col": pd.Series(["a", "b", "c"], dtype=object),
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}
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)
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dataframe_utils.df_metrics_to_num(df, query_object)
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# sum_col: all numeric strings -> should be converted to numeric
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assert pd.api.types.is_numeric_dtype(df["sum_col"]), "sum_col should be numeric"
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assert df["sum_col"].tolist() == [100.0, 200.0, 300.0]
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# mixed_col: has non-numeric value -> should NOT be converted
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assert df["mixed_col"].dtype == object, "mixed_col should remain object"
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# text_col: all non-numeric -> should NOT be converted
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assert df["text_col"].dtype == object, "text_col should remain object"
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# dim_col: not in metric_names -> should NOT be touched
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assert df["dim_col"].dtype == object, "dim_col should not be touched"
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