def get_china_yearly_pmi():
"""
获取中国年度PMI数据, 数据区间从20050201-至今
:return: pandas.Series
2005-02-01 54.7
2005-03-01 54.5
2005-04-01 57.9
2005-05-01 56.7
2005-06-01 52.9
...
2019-06-30 49.4
2019-07-31 49.7
2019-08-31 49.5
2019-09-30 49.5
2019-10-31 0
"""
t = time.time()
res = requests.get(
JS_CHINA_PMI_YEARLY_URL.format(
str(int(round(t * 1000))), str(int(round(t * 1000)) + 90)
)
)
json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["中国官方制造业PMI报告"] for item in json_data["list"]]
value_df = pd.DataFrame(value_list)
value_df.columns = json_data["kinds"]
value_df.index = pd.to_datetime(date_list)
temp_df = value_df["今值"]
temp_df.name = "pmi"
return temp_df
def macro_china_pmi_yearly():
"""
中国年度PMI数据, 数据区间从20050201-至今
https://datacenter.jin10.com/reportType/dc_chinese_manufacturing_pmi
https://cdn.jin10.com/dc/reports/dc_chinese_manufacturing_pmi_all.js?v=1578817858
:return: pandas.Series
"""
t = time.time()
res = requests.get(
JS_CHINA_PMI_YEARLY_URL.format(str(int(round(t * 1000))),
str(int(round(t * 1000)) + 90)))
json_data = json.loads(res.text[res.text.find("{"):res.text.rfind("}") +
1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["中国官方制造业PMI报告"] for item in json_data["list"]]
value_df = pd.DataFrame(value_list)
value_df.columns = json_data["kinds"]
value_df.index = pd.to_datetime(date_list)
temp_df = value_df["今值"]
temp_df.name = "pmi"
return temp_df