stock-tracker

2026-07-04 00:17:11 +08:00
commit 6087341a48
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+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2024/5/12 22:30
+Desc: 百度地图慧眼-百度迁徙数据
+"""
+
+import json
+
+import pandas as pd
+import requests
+
+from akshare.event.cons import province_dict, city_dict
+
+
+def migration_area_baidu(
+    area: str = "重庆市", indicator: str = "move_in", date: str = "20230922"
+) -> pd.DataFrame:
+    """
+    百度地图慧眼-百度迁徙-XXX迁入地详情
+    百度地图慧眼-百度迁徙-XXX迁出地详情
+    以上展示 top100 结果，如不够 100 则展示全部
+    迁入来源地比例: 从 xx 地迁入到当前区域的人数与当前区域迁入总人口的比值
+    迁出目的地比例: 从当前区域迁出到 xx 的人口与从当前区域迁出总人口的比值
+    https://qianxi.baidu.com/?from=shoubai#city=0
+    :param area: 可以输入 省份 或者 具体城市 但是需要用全称
+    :type area: str
+    :param indicator: move_in 迁入 move_out 迁出
+    :type indicator: str
+    :param date: 查询的日期 20200101 以后的时间
+    :type date: str
+    :return: 迁入地详情/迁出地详情的前 50 个
+    :rtype: pandas.DataFrame
+    """
+    city_dict.update(province_dict)
+    inner_dict = dict(zip(city_dict.values(), city_dict.keys()))
+    if inner_dict[area] in province_dict.keys():
+        dt_flag = "province"
+    else:
+        dt_flag = "city"
+    url = "https://huiyan.baidu.com/migration/cityrank.jsonp"
+    params = {
+        "dt": dt_flag,
+        "id": inner_dict[area],
+        "type": indicator,
+        "date": date,
+    }
+    r = requests.get(url, params=params)
+    data_text = r.text[r.text.find("({") + 1 : r.text.rfind(");")]
+    data_json = json.loads(data_text)
+    temp_df = pd.DataFrame(data_json["data"]["list"])
+    temp_df["value"] = pd.to_numeric(temp_df["value"], errors="coerce")
+    return temp_df
+
+
+def migration_scale_baidu(
+    area: str = "广州市",
+    indicator: str = "move_in",
+) -> pd.DataFrame:
+    """
+    百度地图慧眼-百度迁徙-迁徙规模
+    迁徙规模指数：反映迁入或迁出人口规模，城市间可横向对比城市迁徙边界采用该城市行政区划，包含该城市管辖的区、县、乡、村
+    https://qianxi.baidu.com/?from=shoubai#city=0
+    :param area: 可以输入 省份 或者 具体城市 但是需要用全称
+    :type area: str
+    :param indicator: move_in 迁入 move_out 迁出
+    :type indicator: str
+    :return: 时间序列的迁徙规模指数
+    :rtype: pandas.DataFrame
+    """
+    city_dict.update(province_dict)
+    inner_dict = dict(zip(city_dict.values(), city_dict.keys()))
+    if inner_dict[area] in province_dict.keys():
+        dt_flag = "province"
+    else:
+        dt_flag = "city"
+    url = "https://huiyan.baidu.com/migration/historycurve.jsonp"
+    params = {
+        "dt": dt_flag,
+        "id": inner_dict[area],
+        "type": indicator,
+    }
+    r = requests.get(url, params=params)
+    json_data = json.loads(r.text[r.text.find("({") + 1 : r.text.rfind(");")])
+    temp_df = pd.DataFrame.from_dict(json_data["data"]["list"], orient="index")
+    temp_df.index = pd.to_datetime(temp_df.index)
+    temp_df.reset_index(inplace=True)
+    temp_df.columns = ["日期", "迁徙规模指数"]
+    temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
+    temp_df["迁徙规模指数"] = pd.to_numeric(temp_df["迁徙规模指数"], errors="coerce")
+    return temp_df
+
+
+if __name__ == "__main__":
+    migration_area_baidu_df = migration_area_baidu(
+        area="杭州市", indicator="move_out", date="20240401"
+    )
+    print(migration_area_baidu_df)
+
+    migration_scale_baidu_df = migration_scale_baidu(
+        area="广州市",
+        indicator="move_in",
+    )
+    print(migration_scale_baidu_df)