refactor: postprocessing move to unit test (#18779)

tests/integration_tests/fixtures/dataframes.py

@@ -1,183 +0,0 @@
-# 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.
-from datetime import date, datetime
-
-from pandas import DataFrame, to_datetime
-
-names_df = DataFrame(
-    [
-        {
-            "dt": date(2020, 1, 2),
-            "name": "John",
-            "region": "EU",
-            "country": "United Kingdom",
-            "cars": 3,
-            "bikes": 1,
-            "seconds": 30,
-        },
-        {
-            "dt": date(2020, 1, 2),
-            "name": "Peter",
-            "region": "EU",
-            "country": "Sweden",
-            "cars": 4,
-            "bikes": 2,
-            "seconds": 1,
-        },
-        {
-            "dt": date(2020, 1, 3),
-            "name": "Mary",
-            "region": "EU",
-            "country": "Finland",
-            "cars": 5,
-            "bikes": 3,
-            "seconds": None,
-        },
-        {
-            "dt": date(2020, 1, 3),
-            "name": "Peter",
-            "region": "Asia",
-            "country": "India",
-            "cars": 6,
-            "bikes": 4,
-            "seconds": 12,
-        },
-        {
-            "dt": date(2020, 1, 4),
-            "name": "John",
-            "region": "EU",
-            "country": "Portugal",
-            "cars": 7,
-            "bikes": None,
-            "seconds": 75,
-        },
-        {
-            "dt": date(2020, 1, 4),
-            "name": "Peter",
-            "region": "EU",
-            "country": "Italy",
-            "cars": None,
-            "bikes": 5,
-            "seconds": 600,
-        },
-        {
-            "dt": date(2020, 1, 4),
-            "name": "Mary",
-            "region": None,
-            "country": None,
-            "cars": 9,
-            "bikes": 6,
-            "seconds": 2,
-        },
-        {
-            "dt": date(2020, 1, 4),
-            "name": None,
-            "region": "Oceania",
-            "country": "Australia",
-            "cars": 10,
-            "bikes": 7,
-            "seconds": 99,
-        },
-        {
-            "dt": date(2020, 1, 1),
-            "name": "John",
-            "region": "North America",
-            "country": "USA",
-            "cars": 1,
-            "bikes": 8,
-            "seconds": None,
-        },
-        {
-            "dt": date(2020, 1, 1),
-            "name": "Mary",
-            "region": "Oceania",
-            "country": "Fiji",
-            "cars": 2,
-            "bikes": 9,
-            "seconds": 50,
-        },
-    ]
-)
-
-categories_df = DataFrame(
-    {
-        "constant": ["dummy" for _ in range(0, 101)],
-        "category": [f"cat{i%3}" for i in range(0, 101)],
-        "dept": [f"dept{i%5}" for i in range(0, 101)],
-        "name": [f"person{i}" for i in range(0, 101)],
-        "asc_idx": [i for i in range(0, 101)],
-        "desc_idx": [i for i in range(100, -1, -1)],
-        "idx_nulls": [i if i % 5 == 0 else None for i in range(0, 101)],
-    }
-)
-
-timeseries_df = DataFrame(
-    index=to_datetime(["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"]),
-    data={"label": ["x", "y", "z", "q"], "y": [1.0, 2.0, 3.0, 4.0]},
-)
-
-timeseries_df2 = DataFrame(
-    index=to_datetime(["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"]),
-    data={
-        "label": ["x", "y", "z", "q"],
-        "y": [2.0, 2.0, 2.0, 2.0],
-        "z": [2.0, 4.0, 10.0, 8.0],
-    },
-)
-
-lonlat_df = DataFrame(
-    {
-        "city": ["New York City", "Sydney"],
-        "geohash": ["dr5regw3pg6f", "r3gx2u9qdevk"],
-        "latitude": [40.71277496, -33.85598011],
-        "longitude": [-74.00597306, 151.20666526],
-        "altitude": [5.5, 0.012],
-        "geodetic": [
-            "40.71277496, -74.00597306, 5.5km",
-            "-33.85598011, 151.20666526, 12m",
-        ],
-    }
-)
-
-prophet_df = DataFrame(
-    {
-        "__timestamp": [
-            datetime(2018, 12, 31),
-            datetime(2019, 12, 31),
-            datetime(2020, 12, 31),
-            datetime(2021, 12, 31),
-        ],
-        "a": [1.1, 1, 1.9, 3.15],
-        "b": [4, 3, 4.1, 3.95],
-    }
-)
-
-single_metric_df = DataFrame(
-    {
-        "dttm": to_datetime(["2019-01-01", "2019-01-01", "2019-01-02", "2019-01-02",]),
-        "country": ["UK", "US", "UK", "US"],
-        "sum_metric": [5, 6, 7, 8],
-    }
-)
-multiple_metrics_df = DataFrame(
-    {
-        "dttm": to_datetime(["2019-01-01", "2019-01-01", "2019-01-02", "2019-01-02",]),
-        "country": ["UK", "US", "UK", "US"],
-        "sum_metric": [5, 6, 7, 8],
-        "count_metric": [1, 2, 3, 4],
-    }
-)