def get_china_yearly_gdp():
"""
获取中国年度GDP数据, 数据区间从20110120-至今
:return: pandas.Series
2011-01-20 9.8
2011-04-15 9.7
2011-07-13 9.5
2011-10-18 9.1
2012-01-17 8.9
2012-04-13 8.1
2012-07-13 7.6
2012-10-18 7.4
2013-01-18 7.9
2013-04-15 7.7
2013-07-15 7.5
2013-10-18 7.8
2014-01-20 7.7
2014-04-16 7.4
2014-07-16 7.5
2014-10-21 7.3
2015-01-20 7.3
2015-04-15 7
2015-07-15 7
2015-10-19 6.9
2016-01-19 6.8
2016-04-15 6.7
2016-07-15 6.7
2016-10-19 6.7
2017-01-20 6.8
2017-04-17 6.9
2017-07-17 6.9
2017-10-19 6.8
2018-01-18 6.8
2018-04-17 6.8
2018-07-16 6.7
2018-10-19 6.5
2019-01-21 6.4
2019-04-17 6.4
2019-07-15 6.2
2019-10-18 6
"""
t = time.time()
res = requests.get(
JS_CHINA_GDP_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GDP年率报告"] 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 = "gdp"
return temp_df
def macro_china_gdp_yearly():
"""
中国年度GDP数据, 数据区间从20110120-至今
https://datacenter.jin10.com/reportType/dc_chinese_gdp_yoy
:return: pandas.Series
"""
t = time.time()
res = requests.get(
JS_CHINA_GDP_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GDP年率报告"] 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 = "gdp"
return temp_df
