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4b7d06c14b
* chore(common.utils): modified utils from module based to package based * refactor(common.utils): move QueryCacheManager to ad-hoc module * refactor(QueryContext): move df method utils to utils module
43 lines
1.6 KiB
Python
43 lines
1.6 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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from typing import List, 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, right_df: pd.DataFrame, join_keys: List[str],
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) -> pd.DataFrame:
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df = left_df.set_index(join_keys).join(right_df.set_index(join_keys))
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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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