知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
485 lines
13 KiB
Python
485 lines
13 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/2/20 14:00
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Desc: 电影票房数据
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https://ys.endata.cn/BoxOffice/Movie
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"""
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import datetime
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import json
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import os
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import pandas as pd
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import requests
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import py_mini_racer
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def _get_js_path(name: str = "", module_file: str = "") -> str:
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"""
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get JS file path
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:param name: file name
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:type name: str
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:param module_file: filename
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:type module_file: str
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:return: 路径
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:rtype: str
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"""
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module_folder = os.path.abspath(os.path.dirname(os.path.dirname(module_file)))
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module_json_path = os.path.join(module_folder, "movie", name)
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return module_json_path
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def _get_file_content(file_name: str = "jm.js"):
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"""
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read the file content
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:param file_name: filename
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:type file_name: str
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:return: file content
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:rtype: str
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"""
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setting_file_name = file_name
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setting_file_path = _get_js_path(setting_file_name, __file__)
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with open(setting_file_path) as f:
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file_data = f.read()
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return file_data
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def get_current_week(date: str = "20201019") -> datetime.date:
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"""
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当前周的周一
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:param date: 具体的日期
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:type date: str
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:return: 当前周的周一
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:rtype: datetime.date
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"""
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monday = datetime.datetime.strptime(date, "%Y%m%d").date()
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one_day = datetime.timedelta(days=1)
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while monday.weekday() != 0:
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monday -= one_day
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return monday
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def decrypt(origin_data: str = "") -> str:
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"""
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解密
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:param origin_data: 解密前的字符串
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:type origin_data: str
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:return: 解密后的字符串
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:rtype: str
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"""
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file_data = _get_file_content(file_name="jm.js")
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ctx = py_mini_racer.MiniRacer()
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ctx.eval(file_data)
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data = ctx.call("webInstace.shell", origin_data)
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return data
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def movie_boxoffice_realtime() -> pd.DataFrame:
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"""
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电影票房-实时票房
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https://ys.endata.cn/BoxOffice/Movie
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:return: 实时票房数据
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:rtype: pandas.DataFrame
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"""
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today = datetime.datetime.today().date().strftime("%Y%m%d")
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url = "https://www.endata.com.cn/API/GetData.ashx"
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payload = {
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"showDate": "",
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"tdate": f"{today[:4]}-{today[4:6]}-{today[6:]}",
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"MethodName": "BoxOffice_GetHourBoxOffice",
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}
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r = requests.post(url, data=payload)
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r.encoding = "utf8"
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data_json = json.loads(decrypt(r.text))
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temp_df = pd.DataFrame(data_json["Data"]["Table1"])
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temp_df = temp_df.iloc[:, :7]
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temp_df.columns = [
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"排序",
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"_",
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"影片名称",
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"实时票房",
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"累计票房",
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"上映天数",
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"票房占比",
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]
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temp_df = temp_df[
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["排序", "影片名称", "实时票房", "票房占比", "上映天数", "累计票房"]
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]
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return temp_df
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def movie_boxoffice_daily(date: str = "20240219") -> pd.DataFrame:
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"""
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电影票房-单日票房
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https://www.endata.com.cn/BoxOffice/BO/Day/index.html
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:param date: 只能设置当前日期的前一天的票房数据
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:type date: str
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:return: 每日票房
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:rtype: pandas.DataFrame
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"""
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last_date = datetime.datetime.strptime(date, "%Y%m%d") - datetime.timedelta(days=1)
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last_date = last_date.strftime("%Y%m%d")
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url = "https://www.endata.com.cn/API/GetData.ashx"
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payload = {
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"sdate": f"{date[:4]}-{date[4:6]}-{date[6:]}",
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"edate": f"{last_date[:4]}-{last_date[4:6]}-{last_date[6:]}",
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"MethodName": "BoxOffice_GetDayBoxOffice",
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}
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r = requests.post(url, data=payload)
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r.encoding = "utf8"
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data_json = json.loads(decrypt(r.text))
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temp_df = pd.DataFrame(data_json["Data"]["Table"])
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temp_df.columns = [
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"排序",
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"_",
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"影片名称",
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"_",
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"累计票房",
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"平均票价",
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"上映天数",
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"场均人次",
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"_",
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"_",
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"_",
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"_",
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"_",
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"单日票房",
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"环比变化",
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"_",
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"口碑指数",
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]
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temp_df = temp_df[
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[
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"排序",
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"影片名称",
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"单日票房",
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"环比变化",
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"累计票房",
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"平均票价",
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"场均人次",
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"口碑指数",
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"上映天数",
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]
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]
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return temp_df
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def movie_boxoffice_weekly(date: str = "20240218") -> pd.DataFrame:
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"""
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电影票房-单周票房
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https://www.endata.com.cn/BoxOffice/BO/Week/oneWeek.html
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:param date: 只能获取指定日期所在完整周的票房数据
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:type date: str
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:return: 单周票房
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:rtype: pandas.DataFrame
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"""
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url = "https://www.endata.com.cn/API/GetData.ashx"
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payload = {
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"sdate": get_current_week(date=date).strftime("%Y-%m-%d"),
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"MethodName": "BoxOffice_GetWeekInfoData",
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}
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r = requests.post(url, data=payload)
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data_json = json.loads(decrypt(r.text))
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temp_df = pd.DataFrame(data_json["Data"]["Table"])
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temp_df.columns = [
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"排序",
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"_",
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"影片名称",
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"单周票房",
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"累计票房",
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"_",
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"上映天数",
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"平均票价",
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"场均人次",
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"环比变化",
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"_",
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"_",
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"_",
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"排名变化",
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"口碑指数",
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]
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temp_df = temp_df[
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[
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"排序",
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"影片名称",
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"排名变化",
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"单周票房",
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"环比变化",
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"累计票房",
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"平均票价",
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"场均人次",
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"口碑指数",
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"上映天数",
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]
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]
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temp_df["单周票房"] = pd.to_numeric(temp_df["单周票房"], errors="coerce")
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temp_df["环比变化"] = pd.to_numeric(temp_df["环比变化"], errors="coerce")
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temp_df["累计票房"] = pd.to_numeric(temp_df["累计票房"], errors="coerce")
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return temp_df
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def movie_boxoffice_monthly(date: str = "20240218") -> pd.DataFrame:
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"""
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电影票房-单月票房
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https://www.endata.com.cn/BoxOffice/BO/Month/oneMonth.html
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:param date: 指定日期所在月份的月度票房
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:type date: str
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:return: 单月票房
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:rtype: pandas.DataFrame
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"""
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url = "https://www.endata.com.cn/API/GetData.ashx"
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payload = {
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"startTime": f"{date[:4]}-{date[4:6]}-01",
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"MethodName": "BoxOffice_GetMonthBox",
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}
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r = requests.post(url, data=payload)
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r.encoding = "utf8"
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data_json = json.loads(decrypt(r.text))
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temp_df = pd.DataFrame(data_json["Data"]["Table"])
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temp_df.columns = [
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"排序",
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"_",
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"影片名称",
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"月内天数",
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"单月票房",
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"平均票价",
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"场均人次",
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"月度占比",
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"上映日期",
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"_",
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"口碑指数",
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]
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temp_df = temp_df[
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[
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"排序",
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"影片名称",
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"单月票房",
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"月度占比",
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"平均票价",
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"场均人次",
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"上映日期",
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"口碑指数",
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"月内天数",
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]
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]
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temp_df["上映日期"] = pd.to_datetime(temp_df["上映日期"], errors="coerce").dt.date
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return temp_df
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def movie_boxoffice_yearly(date: str = "20240218") -> pd.DataFrame:
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"""
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电影票房-年度票房
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https://www.endata.com.cn/BoxOffice/BO/Year/index.html
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:param date: 当前日期所在年度的票房数据
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:type date: str
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:return: 年度票房
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:rtype: pandas.DataFrame
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"""
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url = "https://www.endata.com.cn/API/GetData.ashx"
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payload = {
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"year": f"{date[:4]}",
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"MethodName": "BoxOffice_GetYearInfoData",
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}
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r = requests.post(url, data=payload)
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r.encoding = "utf8"
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data_json = json.loads(decrypt(r.text))
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temp_df = pd.DataFrame(data_json["Data"]["Table"])
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temp_df.reset_index(inplace=True)
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temp_df.columns = [
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"排序",
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"_",
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"影片名称",
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"类型",
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"总票房",
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"平均票价",
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"场均人次",
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"国家及地区",
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"上映日期",
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"_",
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]
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temp_df["排序"] = range(1, len(temp_df) + 1)
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temp_df = temp_df[
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[
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"排序",
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"影片名称",
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"类型",
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"总票房",
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"平均票价",
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"场均人次",
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"国家及地区",
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"上映日期",
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]
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]
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temp_df["上映日期"] = pd.to_datetime(temp_df["上映日期"], errors="coerce").dt.date
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return temp_df
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def movie_boxoffice_yearly_first_week(date: str = "20201018") -> pd.DataFrame:
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"""
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电影票房-年度票房-年度首周票房
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https://www.endata.com.cn/BoxOffice/BO/Year/firstWeek.html
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:param date: 当前日期所在年度的年度首周票房票房数据
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:type date: str
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:return: 年度首周票房
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:rtype: pandas.DataFrame
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"""
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url = "https://www.endata.com.cn/API/GetData.ashx"
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payload = {
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"year": f"{date[:4]}",
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"MethodName": "BoxOffice_getYearInfo_fData",
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}
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r = requests.post(url, data=payload)
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r.encoding = "utf8"
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data_json = json.loads(decrypt(r.text))
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temp_df = pd.DataFrame(data_json["Data"]["Table"])
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temp_df.reset_index(inplace=True)
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temp_df.columns = [
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"排序",
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"_",
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"_",
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"影片名称",
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"首周票房",
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"场均人次",
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"上映日期",
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"首周天数",
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"类型",
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"国家及地区",
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"_",
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"占总票房比重",
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]
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temp_df["排序"] = range(1, len(temp_df) + 1)
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temp_df = temp_df[
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[
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"排序",
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"影片名称",
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"类型",
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"首周票房",
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"占总票房比重",
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"场均人次",
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"国家及地区",
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"上映日期",
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"首周天数",
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]
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]
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temp_df["上映日期"] = pd.to_datetime(temp_df["上映日期"], errors="coerce").dt.date
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return temp_df
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def movie_boxoffice_cinema_daily(date: str = "20240219") -> pd.DataFrame:
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"""
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电影票房-影院票房-日票房排行
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https://www.endata.com.cn/BoxOffice/BO/Cinema/day.html
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:param date: 当前日期前一日的票房数据
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:type date: str
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:return: 影票房-影院票房-日票房排行
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:rtype: pandas.DataFrame
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"""
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url = "https://www.endata.com.cn/API/GetData.ashx"
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payload = {
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"rowNum1": "1",
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"rowNum2": "100",
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"date": date,
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"MethodName": "BoxOffice_GetCinemaDayBoxOffice",
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}
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r = requests.post(url, data=payload)
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r.encoding = "utf8"
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data_json = json.loads(decrypt(r.text))
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temp_df = pd.DataFrame(data_json["Data"]["Table"])
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temp_df.columns = [
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"排序",
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"_",
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"影院名称",
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"单日票房",
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"单日场次",
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"_",
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"_",
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"场均票价",
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"场均人次",
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"上座率",
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]
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temp_df = temp_df[
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["排序", "影院名称", "单日票房", "单日场次", "场均人次", "场均票价", "上座率"]
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]
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return temp_df
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def movie_boxoffice_cinema_weekly(date: str = "20240219") -> pd.DataFrame:
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"""
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电影票房-影院票房-周票房排行
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https://www.endata.com.cn/BoxOffice/BO/Cinema/week.html
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:param date: 当前日期前完整一周的票房数据
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:type date: str
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:return: 影票房-影院票房-轴票房排行
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:rtype: pandas.DataFrame
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"""
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url = "https://www.endata.com.cn/API/GetData.ashx"
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payload = {
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"dateID": str(
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datetime.date.fromisoformat(
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f"{date[:4]}-{date[4:6]}-{date[6:]}"
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).isocalendar()[1]
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- 1
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- 41
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+ 1128
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),
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"rowNum1": "1",
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"rowNum2": "100",
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"MethodName": "BoxOffice_GetCinemaWeekBoxOffice",
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}
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r = requests.post(url, data=payload)
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r.encoding = "utf8"
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data_json = json.loads(decrypt(r.text))
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temp_df = pd.DataFrame(data_json["Data"]["Table"])
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temp_df.columns = [
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"排序",
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"_",
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"影院名称",
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"当周票房",
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"_",
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"单银幕票房",
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"场均人次",
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"单日单厅票房",
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"单日单厅场次",
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]
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temp_df = temp_df[
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[
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"排序",
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"影院名称",
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"当周票房",
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"单银幕票房",
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"场均人次",
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"单日单厅票房",
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"单日单厅场次",
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]
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]
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return temp_df
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if __name__ == "__main__":
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movie_boxoffice_realtime_df = movie_boxoffice_realtime()
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print(movie_boxoffice_realtime_df)
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movie_boxoffice_daily_df = movie_boxoffice_daily(date="20240219")
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print(movie_boxoffice_daily_df)
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movie_boxoffice_weekly_df = movie_boxoffice_weekly(date="20240218")
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print(movie_boxoffice_weekly_df)
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movie_boxoffice_monthly_df = movie_boxoffice_monthly(date="20240218")
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print(movie_boxoffice_monthly_df)
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movie_boxoffice_yearly_df = movie_boxoffice_yearly(date="20240218")
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print(movie_boxoffice_yearly_df)
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movie_boxoffice_yearly_first_week_df = movie_boxoffice_yearly_first_week(
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date="20201018"
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)
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print(movie_boxoffice_yearly_first_week_df)
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movie_boxoffice_cinema_daily_df = movie_boxoffice_cinema_daily(date="20240219")
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print(movie_boxoffice_cinema_daily_df)
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movie_boxoffice_cinema_weekly_df = movie_boxoffice_cinema_weekly(date="20240219")
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print(movie_boxoffice_cinema_weekly_df)
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