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69 lines
2.9 KiB
Python
69 lines
2.9 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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from superset.utils.pandas_postprocessing import aggregate
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from tests.unit_tests.fixtures.dataframes import categories_df
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from tests.unit_tests.pandas_postprocessing.utils import series_to_list
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def test_aggregate():
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aggregates = {
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"asc sum": {"column": "asc_idx", "operator": "sum"},
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"asc q2": {
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"column": "asc_idx",
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"operator": "percentile",
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"options": {"q": 75},
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},
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"desc q1": {
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"column": "desc_idx",
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"operator": "percentile",
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"options": {"q": 25},
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},
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}
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df = aggregate(df=categories_df, groupby=["constant"], aggregates=aggregates)
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assert df.columns.tolist() == ["constant", "asc sum", "asc q2", "desc q1"]
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assert series_to_list(df["asc sum"])[0] == 5050
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assert series_to_list(df["asc q2"])[0] == 75
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assert series_to_list(df["desc q1"])[0] == 25
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def test_aggregate_string_operators():
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"""mean, median, and other operators in _PANDAS_STRING_AGGREGATORS use the
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pandas string path; verify results match expected values on asc_idx [0..100]."""
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aggregates = {
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"asc mean": {"column": "asc_idx", "operator": "mean"},
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"asc median": {"column": "asc_idx", "operator": "median"},
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"asc max": {"column": "asc_idx", "operator": "max"},
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"asc min": {"column": "asc_idx", "operator": "min"},
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}
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df = aggregate(df=categories_df, groupby=["constant"], aggregates=aggregates)
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assert series_to_list(df["asc mean"])[0] == 50.0
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assert series_to_list(df["asc median"])[0] == 50.0
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assert series_to_list(df["asc max"])[0] == 100
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assert series_to_list(df["asc min"])[0] == 0
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def test_aggregate_count_includes_nulls():
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"""'count' operator uses np.ma.count, which counts all rows including NaN.
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It is intentionally excluded from _PANDAS_STRING_AGGREGATORS to preserve this
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behavior (pandas SeriesGroupBy.count excludes NaN)."""
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aggregates = {
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"null_count": {"column": "idx_nulls", "operator": "count"},
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}
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df = aggregate(df=categories_df, groupby=["constant"], aggregates=aggregates)
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# idx_nulls has 101 rows total; np.ma.count returns all 101 (NaN included)
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assert series_to_list(df["null_count"])[0] == 101
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