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184 lines
5.9 KiB
Python
184 lines
5.9 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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import pandas as pd
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from superset.utils import pandas_postprocessing as pp
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from superset.utils.pandas_postprocessing.utils import FLAT_COLUMN_SEPARATOR
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from tests.unit_tests.fixtures.dataframes import timeseries_df
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def test_flat_should_not_change():
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df = pd.DataFrame(
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data={
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"foo": [1, 2, 3],
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"bar": [4, 5, 6],
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}
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)
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assert pp.flatten(df).equals(df)
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def test_flat_should_not_reset_index():
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index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
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index.name = "__timestamp"
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df = pd.DataFrame(index=index, data={"foo": [1, 2, 3], "bar": [4, 5, 6]})
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assert pp.flatten(df, reset_index=False).equals(df)
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def test_flat_should_flat_datetime_index():
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index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
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index.name = "__timestamp"
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df = pd.DataFrame(index=index, data={"foo": [1, 2, 3], "bar": [4, 5, 6]})
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assert pp.flatten(df).equals(
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pd.DataFrame(
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{
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"__timestamp": index,
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"foo": [1, 2, 3],
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"bar": [4, 5, 6],
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}
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)
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)
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def test_flat_should_flat_multiple_index():
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index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
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index.name = "__timestamp"
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iterables = [["foo", "bar"], [1, "two"]]
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columns = pd.MultiIndex.from_product(iterables, names=["level1", "level2"])
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df = pd.DataFrame(index=index, columns=columns, data=1)
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assert pp.flatten(df).equals(
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pd.DataFrame(
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{
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"__timestamp": index,
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FLAT_COLUMN_SEPARATOR.join(["foo", "1"]): [1, 1, 1],
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FLAT_COLUMN_SEPARATOR.join(["foo", "two"]): [1, 1, 1],
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FLAT_COLUMN_SEPARATOR.join(["bar", "1"]): [1, 1, 1],
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FLAT_COLUMN_SEPARATOR.join(["bar", "two"]): [1, 1, 1],
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}
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)
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)
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def test_flat_should_drop_index_level():
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index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
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index.name = "__timestamp"
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columns = pd.MultiIndex.from_arrays(
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[["a"] * 3, ["b"] * 3, ["c", "d", "e"], ["ff", "ii", "gg"]],
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names=["level1", "level2", "level3", "level4"],
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)
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df = pd.DataFrame(index=index, columns=columns, data=1)
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# drop level by index
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assert pp.flatten(
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df.copy(),
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drop_levels=(
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0,
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1,
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),
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).equals(
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pd.DataFrame(
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{
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"__timestamp": index,
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FLAT_COLUMN_SEPARATOR.join(["c", "ff"]): [1, 1, 1],
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FLAT_COLUMN_SEPARATOR.join(["d", "ii"]): [1, 1, 1],
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FLAT_COLUMN_SEPARATOR.join(["e", "gg"]): [1, 1, 1],
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}
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)
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)
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# drop level by name
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assert pp.flatten(df.copy(), drop_levels=("level1", "level2")).equals(
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pd.DataFrame(
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{
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"__timestamp": index,
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FLAT_COLUMN_SEPARATOR.join(["c", "ff"]): [1, 1, 1],
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FLAT_COLUMN_SEPARATOR.join(["d", "ii"]): [1, 1, 1],
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FLAT_COLUMN_SEPARATOR.join(["e", "gg"]): [1, 1, 1],
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}
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)
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)
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# only leave 1 level
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assert pp.flatten(df.copy(), drop_levels=(0, 1, 2)).equals(
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pd.DataFrame(
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{
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"__timestamp": index,
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FLAT_COLUMN_SEPARATOR.join(["ff"]): [1, 1, 1],
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FLAT_COLUMN_SEPARATOR.join(["ii"]): [1, 1, 1],
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FLAT_COLUMN_SEPARATOR.join(["gg"]): [1, 1, 1],
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}
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)
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)
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def test_flat_should_not_droplevel():
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assert pp.flatten(timeseries_df, drop_levels=(0,)).equals(
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pd.DataFrame(
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{
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"index": pd.to_datetime(
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["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"]
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),
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"label": ["x", "y", "z", "q"],
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"y": [1.0, 2.0, 3.0, 4.0],
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}
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)
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)
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def test_flat_integer_column_name():
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index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
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index.name = "__timestamp"
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columns = pd.MultiIndex.from_arrays(
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[["a"] * 3, [100, 200, 300]],
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names=["level1", "level2"],
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)
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df = pd.DataFrame(index=index, columns=columns, data=1)
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assert pp.flatten(df, drop_levels=(0,)).equals(
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pd.DataFrame(
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{
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"__timestamp": pd.to_datetime(
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["2021-01-01", "2021-01-02", "2021-01-03"]
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),
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"100": [1, 1, 1],
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"200": [1, 1, 1],
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"300": [1, 1, 1],
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}
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)
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)
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def test_escape_column_name():
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index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
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index.name = "__timestamp"
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columns = pd.MultiIndex.from_arrays(
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[
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["level1,value1", "level1,value2", "level1,value3"],
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["level2, value1", "level2, value2", "level2, value3"],
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],
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names=["level1", "level2"],
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)
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df = pd.DataFrame(index=index, columns=columns, data=1)
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assert list(pp.flatten(df).columns.values) == [
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"__timestamp",
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"level1\\,value1" + FLAT_COLUMN_SEPARATOR + "level2\\, value1",
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"level1\\,value2" + FLAT_COLUMN_SEPARATOR + "level2\\, value2",
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"level1\\,value3" + FLAT_COLUMN_SEPARATOR + "level2\\, value3",
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]
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