# 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 __future__ import annotations

from typing import List, TYPE_CHECKING

import numpy as np
import pandas as pd

if TYPE_CHECKING:
    from superset.common.query_object import QueryObject


def left_join_df(
    left_df: pd.DataFrame, right_df: pd.DataFrame, join_keys: List[str],
) -> pd.DataFrame:
    df = left_df.set_index(join_keys).join(right_df.set_index(join_keys))
    df.reset_index(inplace=True)
    return df


def df_metrics_to_num(df: pd.DataFrame, query_object: QueryObject) -> None:
    """Converting metrics to numeric when pandas.read_sql cannot"""
    for col, dtype in df.dtypes.items():
        if dtype.type == np.object_ and col in query_object.metric_names:
            # soft-convert a metric column to numeric
            # will stay as strings if conversion fails
            df[col] = df[col].infer_objects()