Pandas dataframe日期和时间过滤
嘿，团队！脚本的整个想法是在指定的时间访问链接[“20：00”，“20：30”，“22：15”，“22：45”，“23：30”，“00：00”，“01：45”，“02：30”，“03：00”，“03：30”]，并按照脚本运行的时间+ 1小时过滤列Expected Ship Date。
示例：脚本在04/06/2023 23：30运行，它将在05/06/2023 00：30之前过滤预期发货日期
目前，它似乎不能正常工作，无论什么原因，无论我运行它的时间，它返回当前一天21：00。
你知道我错过了什么吗

__author__ = "studorie"
__version__ = "1.0.1"

from datetime import datetime, timedelta
from io import StringIO

import pandas as pd
from apscheduler.schedulers.blocking import BlockingScheduler

from function.requests_retryer import requests_retry_session

def roster(fc):
    url = (
        f"https://link.com/{fc}"
        )

    with requests_retry_session() as request:
        response = request.get(url)

    if response.status_code != 200:
        print(response.raise_for_status())

    df = pd.read_csv(StringIO(response.text), sep=",")
    df.drop(['FN SKU', 'Scannable ID', 'Condition', 'Ship Method', 'Ship Option', 'Pick Priority'], axis=1,
            inplace=True)

    df["Expected Ship Date"] = pd.to_datetime(df["Expected Ship Date"])

    # Get today's date
    today = datetime.now().date()

    # Get the current time
    current_time = datetime.now().time()

    # Find the index of the next job time
    job_index = next(
        (i for i, job_time in enumerate(job_times) if datetime.strptime(job_time, "%H:%M").time() > current_time), 0)

    # Get the adjusted job time
    adjusted_job_time = job_times[job_index]

    # Calculate the adjusted expected ship date based on the scheduled job time + 1 hour
    if adjusted_job_time == "00:00":
        # If the adjusted job time is midnight, add 1 day instead of 1 hour
        adjusted_date = today + timedelta(days=1)
    else:
        adjusted_date = today

    adjusted_datetime = datetime.combine(adjusted_date,
                                         datetime.strptime(adjusted_job_time, "%H:%M").time()) + timedelta(hours=1)
    adjusted_expected_ship_date = adjusted_datetime.strftime("%d/%m/%Y %H:%M")

    # Filter the DataFrame based on the adjusted expected ship date
    filter_condition = df["Expected Ship Date"].dt.strftime("%d/%m/%Y %H:%M") == adjusted_expected_ship_date
    filtered_df = df[filter_condition]

    timestamp = datetime.now().strftime("%d-%m-%Y-%H-%M")
    filename = f"Y:/Public/L&D/Reports for ops/Flow risk/Pick_SLA_{timestamp}.csv"

    # Save the filtered data to the specified filename
    filtered_df.to_csv(filename, index=False)
    print(f"Filtered data saved to {filename}")

if __name__ == "__main__":
    schedule = BlockingScheduler()
    job_times = ["20:00", "20:30", "22:15", "22:45", "23:30", "00:00", "01:45", "02:30", "03:00", "03:30"]
    for job_time in job_times:
        schedule.add_job(roster, 'cron', timezone="Europe/London", hour=int(job_time.split(':')[0]),
                         minute=int(job_time.split(':')[1]), args=["EMA2"])
    schedule.start()