0.3.2

README.md

@@ -56,6 +56,11 @@ print(df)
         - 货币供应量数据
         - 外汇储备数据
         - 货币汇率数据
+        - 工业增加值增长
+        - 财政收入
+        - 社会消费品零售总额
+        - 信贷数据
+        - 外商直接投资数据(FDI)
     - 利率数据
         - Shibor数据
         - Shibor报价数据
@@ -102,7 +107,11 @@ print(df)
 
 
 #### 版本更新
-
+
+    0.3.2 
+    中国宏观数据： 工业增加值增长、财政收入、社会消费品零售总额、信贷数据、外商直接投资数据(FDI)
+    迭代百度迁入与迁出地详情
+
     0.3.1
     迭代微博数据采集
 

gopup/__init__.py

@@ -76,7 +76,12 @@
     get_pmi,
     get_rrr,
     get_money_supply,
-    get_gold_and_foreign_reserves
+    get_gold_and_foreign_reserves,
+    get_industrial_growth,
+    get_fiscal_revenue,
+    get_consumer_total,
+    get_credit_data,
+    get_fdi_data
 )
 
 

gopup/event/area_baidu.py

@@ -55,7 +55,7 @@ def migration_area_baidu(area="武汉市", indicator="move_in", date="20200201")
     return pd.DataFrame(json_data["data"]["list"])
 
 
-def migration_scale_baidu(area="武汉市", indicator="move_out", start_date="20190112", end_date="20200201"):
+def migration_scale_baidu(area="武汉市", indicator="move_out", date="20210112"):
     """
     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
@@ -65,9 +65,7 @@ def migration_scale_baidu(area="武汉市", indicator="move_out", start_date="20
     :type area: str
     :param indicator: move_in 迁入 move_out 迁出
     :type indicator: str
-    :param start_date: 开始查询的日期 默认就可以
-    :type start_date: str
-    :param end_date: 结束查询的日期 20200101 以后的时间
+    :param date: 结束查询的日期 20200101 以后的时间
     :type end_date: str
     :return: 时间序列的迁徙规模指数
     :rtype: pandas.DataFrame
@@ -77,24 +75,25 @@ def migration_scale_baidu(area="武汉市", indicator="move_out", start_date="20
             "dt": "country",
             "id": 0,
             "type": indicator,
-            "startDate": start_date,
-            "endDate": end_date,
+            "date": date
         }
     else:
         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"
+        try:
+            if inner_dict[area] in province_dict.keys():
+                dt_flag = "province"
+            else:
+                dt_flag = "city"
 
-        payload = {
-            "dt": dt_flag,
-            "id": inner_dict[area],
-            "type": indicator,
-            "startDate": start_date,
-            "endDate": end_date,
-        }
+            payload = {
+                "dt": dt_flag,
+                "id": inner_dict[area],
+                "type": indicator,
+                "date": date
+            }
+        except Exception as e:
+            return "省份 或者 具体城市名 错误"
     url = "https://huiyan.baidu.com/migration/historycurve.jsonp"
     r = requests.get(url, params=payload)
     json_data = json.loads(r.text[r.text.find("({") + 1 : r.text.rfind(");")])
@@ -105,7 +104,7 @@ def migration_scale_baidu(area="武汉市", indicator="move_out", start_date="20
 
 
 if __name__ == "__main__":
-    tmp = migration_scale_baidu(area="全国", indicator="move_in", start_date="20190112", end_date="20200201")
+    tmp = migration_scale_baidu(area="武汉", indicator="move_out")
     print(tmp)
 
 

gopup/life/train.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time    : 2021/2/5 0005
+# @Author  : justin.郑 [email protected]
+# @File    : train.py
+# @Desc    : 12306车站、车次数据
+
+import json
+import pandas as pd
+import requests
+
+
+def station_name():
+    """
+    获取12306车站信息
+    Returns
+    -------
+    DataFrame
+        "拼音码、站名、电报码、拼音、首字母、ID""
+    """
+    url = "https://kyfw.12306.cn/otn/resources/js/framework/station_name.js"
+    r = requests.get(url=url)
+    data_text = r.text
+    tmp_str = data_text[data_text.find("='")+3: -2]
+    tmp_list = tmp_str.split('@')
+    res_list = []
+    for li in tmp_list:
+        res_list.append(li.split('|'))
+    columns = ["拼音码", "站名", "电报码", "拼音", "首字母", "ID"]
+    data_df = pd.DataFrame(res_list, columns=columns)
+    data_df.set_index("ID", inplace=True)
+    return data_df
+
+
+def train_list():
+    """
+    获取12306车次信息
+    Returns
+    -------
+    DataFrame
+        "拼音码、站名、电报码、拼音、首字母、ID""
+    """
+    url = "https://kyfw.12306.cn/otn/resources/js/query/train_list.js"
+    r = requests.get(url=url)
+    data_text = r.text
+    tmp_str = data_text[data_text.find("={")+1:]
+    tmp_dict = json.loads(tmp_str)['2019-07-16']
+    tmp_list = tmp_str.split('@')
+    res_list = []
+    for li in tmp_list:
+        res_list.append(li.split('|'))
+    columns = ["拼音码", "站名", "电报码", "拼音", "首字母", "ID"]
+    data_df = pd.DataFrame(res_list, columns=columns)
+    data_df.set_index("ID", inplace=True)
+    return data_df
+
+
+if __name__ == '__main__':
+    tmp = train_list()
+    print(tmp)