compete/app/common/data_cleaning.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# time: 2023/10/11 11:00
# file: data_cleaning.py
# author: David
# company: shenyang JY
import numpy as np
import pandas as pd
from app.common.config import logger, parser
np.random.seed(42)

def clean_power(power, env, plant_id):
    env_power = pd.merge(env, power, on=params['col_time'])
    if 'HubSpeed' in env.columns.tolist():
        from app.common.limited_power_wind import LimitPower
        lp = LimitPower(logger, params, env_power)
        power = lp.clean_limited_power(plant_id, True)
    elif 'Irradiance' in env.columns.tolist():
        from app.common.limited_power_solar import LimitPower
        lp = LimitPower(logger, params, env_power)
        power = lp.clean_limited_power(plant_id, True)
    return power


def cleaning(df, name, logger, cols=None, dup=True, col_time='Datetime'):
    logger.info("开始清洗：{}……".format(name))
    data = df.copy()
    data = data_column_cleaning(data, logger)
    if dup:
        data = rm_duplicated(data, logger, col_time)
    if cols is not None:
        data = key_field_row_cleaning(data, cols, logger)
    return data


def data_column_cleaning(data, logger, clean_value='null'):
    """
    列的清洗
    :param data:
    :param logger:
    :param clean_value:
    :return:
    """
    data1 = data.copy()
    cols_pre = data.columns.to_list()
    for val in clean_value:
        data1 = data1.replace(val, np.nan)
    # nan 列超过80% 删除
    data1 = data1.dropna(axis=1, thresh=len(data) * 0.8)
    # 删除取值全部相同的列
    data1 = data1.loc[:, (data1 != data1.iloc[0]).any()]
    data = data[data1.columns.tolist()]
    cols_late = data.columns.tolist()
    if len(cols_pre) > len(cols_late):
        logger.info("列清洗：清洗的列有：{}".format(set(cols_pre) - set(cols_late)))
    return data


def key_field_row_cleaning(data, cols, logger):
    """
    行的重要字段清洗: 过滤含有- 99的数字，过滤空值
    :param data:
    :param cols: 指定的字段列表
    :param logger:
    :return:
    """
    rows_pre = len(data)
    nan_cols = []
    for col in cols:
        begin = len(data)
        if col in data.columns.tolist():
            # data = data[~((data.loc[:, col] < 0) & (data.loc[:, col].astype(str).str.contains('99')))]
            data = data[~(data[col] == -99)]
            data = data[~data.loc[:, col].isnull()]
        end = len(data)
        if end - begin > 0:
            nan_cols.append(col)
    rows_late = len(data)
    if rows_pre - rows_late > 0:
        logger.info("行清洗：清洗的行数有：{}，缺失的列有：{}".format(rows_pre-rows_late, ', '.join(nan_cols)))
    return data

def rm_duplicated(data, logger, col_time='Datetime'):
    """
    按照时间去重
    :param data:
    :param logger:
    :return:
    """
    # 按照时间去重
    rows_pre = len(data)
    data = data.drop_duplicates(subset=col_time)
    rows_late = len(data)
    if rows_pre - rows_late > 0:
        logger.info("时间去重的行数有：{}".format(rows_pre - rows_late))
    return data