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b1f85dce71
Co-authored-by: lilykuang <jialikuang@gmail.com> Co-authored-by: Kamil Gabryjelski <kamil.gabryjelski@gmail.com>
73 lines
2.3 KiB
Python
73 lines
2.3 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 __future__ import annotations
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import datetime
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from typing import Any, TYPE_CHECKING
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import numpy as np
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import pandas as pd
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if TYPE_CHECKING:
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from superset.common.query_object import QueryObject
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def left_join_df(
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left_df: pd.DataFrame,
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right_df: pd.DataFrame,
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join_keys: list[str],
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lsuffix: str = "",
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rsuffix: str = "",
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) -> pd.DataFrame:
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df = left_df.set_index(join_keys).join(
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right_df.set_index(join_keys), lsuffix=lsuffix, rsuffix=rsuffix
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)
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df.reset_index(inplace=True)
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return df
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def full_outer_join_df(
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left_df: pd.DataFrame,
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right_df: pd.DataFrame,
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lsuffix: str = "",
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rsuffix: str = "",
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) -> pd.DataFrame:
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df = left_df.join(right_df, lsuffix=lsuffix, rsuffix=rsuffix, how="outer")
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df.reset_index(inplace=True)
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return df
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def df_metrics_to_num(df: pd.DataFrame, query_object: QueryObject) -> None:
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"""Converting metrics to numeric when pandas.read_sql cannot"""
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for col, dtype in df.dtypes.items():
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if dtype.type == np.object_ and col in query_object.metric_names:
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# soft-convert a metric column to numeric
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# will stay as strings if conversion fails
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df[col] = df[col].infer_objects()
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def is_datetime_series(series: Any) -> bool:
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if series is None or not isinstance(series, pd.Series):
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return False
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if series.isnull().all():
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return False
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return pd.api.types.is_datetime64_any_dtype(series) or (
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series.apply(lambda x: isinstance(x, datetime.date) or x is None).all()
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)
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