def get_gold_and_foreign_reserves():
"""
获取外汇储备
Returns
-------
DataFrame
month :统计时间
gold:黄金储备(万盎司)
foreign_reserves:外汇储备(亿美元)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL % (vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[2], 5, 200,
rdint))
text = urlopen(request,timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.GOLD_AND_FOREIGN_CURRENCY_RESERVES)
for i in df.columns:
df[i] = df[i].apply(lambda x: np.where(x is None, '--', x))
return df
def get_industry_fixed_investment():
"""
获取行业固投完成情况
Returns
-------
DataFrame
month:统计时间
industry:行业
amount:固定资产投资完成额(亿元)
increment:固定资产投资完成额同比增长(%)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL %
(vs.P_TYPE['http'], vs.DOMAINS['sina'], rdint,
vs.MACRO_TYPE[4], 10, 10000, rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.INDUSTRY_FIXED_INVESTMENT)
for i in df.columns:
df[i] = df[i].apply(lambda x: np.where(x is None, '--', x))
return df
def get_total_import_export():
"""
获取进出口总额
Returns
-------
DataFrame
year:统计时间
total:进出口总额(美元亿元)
export:出口总额(美元亿元)
import:进口总额(美元亿元)
delta:差额(美元亿元)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL % (vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[3], 0, 400, rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split(u'\'美元\':')[1]
datastr = datastr.replace("\"", "")
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.TOTAL_IMPORT_EXPORT)
for i in df.columns:
df[i] = df[i].apply(lambda x: np.where(x is None, '--', x))
return df
def get_gold_and_foreign_reserves():
"""
获取外汇储备
Returns
-------
DataFrame
month :统计时间
gold:黄金储备(万盎司)
foreign_reserves:外汇储备(亿美元)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL % (vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[2], 5, 200,
rdint))
text = urlopen(request,timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.GOLD_AND_FOREIGN_CURRENCY_RESERVES)
for i in df.columns:
df[i] = df[i].apply(lambda x: np.where(x is None, '--', x))
return df
def get_gdp_contrib():
"""
获取三大产业贡献率数据
Return
--------
DataFrame
year :统计年度
gdp_yoy :国内生产总值
pi :第一产业献率(%)
si :第二产业献率(%)
industry:其中工业献率(%)
ti :第三产业献率(%)
"""
rdint = vs.random()
url = vs.MACRO_URL%(vs.P_TYPE['http'],vs.DOMAINS['sina'],rdint,vs.MACRO_TYPE[0],6,60,rdint)
request = Request(url)
text = urlopen(request,timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
datastr = datastr.replace('"','').replace('null','0')
js = json.loads(datastr)
df = pd.DataFrame(js,columns=vs.GDP_CONTRIB_COLS)
df[df==0] = np.NaN
return df
def get_money_supply_bal():
"""
获取货币供应量(年底余额)数据
Return
--------
DataFrame
year :统计年度
m2 :货币和准货币(亿元)
m1:货币(亿元)
m0:流通中现金(亿元)
cd:活期存款(亿元)
qm:准货币(亿元)
ftd:定期存款(亿元)
sd:储蓄存款(亿元)
rests:其他存款(亿元)
"""
rdint = vs.random()
url = vs.MACRO_URL%(vs.P_TYPE['http'],vs.DOMAINS['sina'],rdint,vs.MACRO_TYPE[2],0,200,rdint)
request = Request(url)
text = urlopen(request,timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js,columns=vs.MONEY_SUPPLY_BLA_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None,'--',x))
return df
def get_gdp_for():
"""
获取三大需求对GDP贡献数据
Return
--------
DataFrame
year :统计年度
end_for :最终消费支出贡献率(%)
for_rate :最终消费支出拉动(百分点)
asset_for :资本形成总额贡献率(%)
asset_rate:资本形成总额拉动(百分点)
goods_for :货物和服务净出口贡献率(%)
goods_rate :货物和服务净出口拉动(百分点)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[0], 4, 80, rdint))
text = urlopen(request,timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
datastr = datastr.replace('"','').replace('null','0')
js = json.loads(datastr)
df = pd.DataFrame(js,columns=vs.GDP_FOR_COLS)
df[df==0] = np.NaN
return df
def get_loan_rate():
"""
获取贷款利率数据
Return
--------
DataFrame
date :执行日期
loan_type :存款种类
rate:利率（%）
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[2], 3, 800,
rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.LOAN_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None, '--', x))
return df
def get_money_supply_bal():
"""
获取货币供应量(年底余额)数据
Return
--------
DataFrame
year :统计年度
m2 :货币和准货币(亿元)
m1:货币(亿元)
m0:流通中现金(亿元)
cd:活期存款(亿元)
qm:准货币(亿元)
ftd:定期存款(亿元)
sd:储蓄存款(亿元)
rests:其他存款(亿元)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[2], 0, 200,
rdint))
text = urlopen(request,timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.MONEY_SUPPLY_BLA_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None, '--', x))
return df
def get_gdp_quarter():
"""
获取季度国内生产总值数据
Return
--------
DataFrame
quarter :季度
gdp :国内生产总值(亿元)
gdp_yoy :国内生产总值同比增长(%)
pi :第一产业增加值(亿元)
pi_yoy:第一产业增加值同比增长(%)
si :第二产业增加值(亿元)
si_yoy :第二产业增加值同比增长(%)
ti :第三产业增加值(亿元)
ti_yoy :第三产业增加值同比增长(%)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[0], 1, 250,
rdint))
text = urlopen(request,timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
datastr = datastr.replace('"', '').replace('null', '0')
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.GDP_QUARTER_COLS)
df['quarter'] = df['quarter'].astype(object)
df[df==0] = np.NaN
return df
def get_rrr():
"""
获取存款准备金率数据
Return
--------
DataFrame
date :变动日期
before :调整前存款准备金率(%)
now:调整后存款准备金率(%)
changed:调整幅度(%)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[2], 4, 100,
rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.RRR_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None, '--', x))
return df
def get_gdp_year():
"""
获取年度国内生产总值数据
Return
--------
DataFrame
year :统计年度
gdp :国内生产总值(亿元)
pc_gdp :人均国内生产总值(元)
gnp :国民生产总值(亿元)
pi :第一产业(亿元)
si :第二产业(亿元)
industry :工业(亿元)
cons_industry :建筑业(亿元)
ti :第三产业(亿元)
trans_industry :交通运输仓储邮电通信业(亿元)
lbdy :批发零售贸易及餐饮业(亿元)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[0], 0, 70,
rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
datastr = datastr.replace('"', '').replace('null', '0')
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.GDP_YEAR_COLS)
df[df==0] = np.NaN
return df
def get_loan_rate():
"""
获取贷款利率数据
Return
--------
DataFrame
date :执行日期
loan_type :存款种类
rate:利率（%）
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[2], 3, 800,
rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.LOAN_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None, '--', x))
return df
def get_gdp_contrib():
"""
获取三大产业贡献率数据
Return
--------
DataFrame
year :统计年度
gdp_yoy :国内生产总值
pi :第一产业献率(%)
si :第二产业献率(%)
industry:其中工业献率(%)
ti :第三产业献率(%)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'], rdint,
vs.MACRO_TYPE[0], 6, 60, rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
datastr = datastr.replace('"', '').replace('null', '0')
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.GDP_CONTRIB_COLS)
df[df==0] = np.NaN
return df
def get_ppi():
"""
获取工业品出厂价格指数数据
Return
--------
DataFrame
month :统计月份
ppiip :工业品出厂价格指数
ppi :生产资料价格指数
qm:采掘工业价格指数
rmi:原材料工业价格指数
pi:加工工业价格指数
cg:生活资料价格指数
food:食品类价格指数
clothing:衣着类价格指数
roeu:一般日用品价格指数
dcg:耐用消费品价格指数
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[1], 3, 600,
rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.PPI_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None, np.NaN, x))
if i != 'month':
df[i] = df[i].astype(float)
return df
def get_gdp_quarter():
"""
获取季度国内生产总值数据
Return
--------
DataFrame
quarter :季度
gdp :国内生产总值(亿元)
gdp_yoy :国内生产总值同比增长(%)
pi :第一产业增加值(亿元)
pi_yoy:第一产业增加值同比增长(%)
si :第二产业增加值(亿元)
si_yoy :第二产业增加值同比增长(%)
ti :第三产业增加值(亿元)
ti_yoy :第三产业增加值同比增长(%)
"""
rdint = vs.random()
url = vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'], rdint, vs.MACRO_TYPE[0],
1, 250, rdint)
request = Request(url)
text = urlopen(request,timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
datastr = datastr.replace('"', '').replace('null', '0')
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.GDP_QUARTER_COLS)
df['quarter'] = df['quarter'].astype(object)
df[df==0] = np.NaN
return df
def get_gdp_for():
"""
获取三大需求对GDP贡献数据
Return
--------
DataFrame
year :统计年度
end_for :最终消费支出贡献率(%)
for_rate :最终消费支出拉动(百分点)
asset_for :资本形成总额贡献率(%)
asset_rate:资本形成总额拉动(百分点)
goods_for :货物和服务净出口贡献率(%)
goods_rate :货物和服务净出口拉动(百分点)
"""
rdint = vs.random()
url = vs.MACRO_URL%(vs.P_TYPE['http'],vs.DOMAINS['sina'],rdint,vs.MACRO_TYPE[0],4,80,rdint)
request = Request(url)
text = urlopen(request,timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
datastr = datastr.replace('"','').replace('null','0')
js = json.loads(datastr)
df = pd.DataFrame(js,columns=vs.GDP_FOR_COLS)
df[df==0] = np.NaN
return df
def get_rrr():
"""
获取存款准备金率数据
Return
--------
DataFrame
date :变动日期
before :调整前存款准备金率(%)
now:调整后存款准备金率(%)
changed:调整幅度(%)
"""
rdint = vs.random()
url = vs.MACRO_URL%(vs.P_TYPE['http'],vs.DOMAINS['sina'],rdint,vs.MACRO_TYPE[2],4,100,rdint)
request = Request(url)
text = urlopen(request,timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js,columns=vs.RRR_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None,'--',x))
return df
def get_gdp_year():
"""
获取年度国内生产总值数据
Return
--------
DataFrame
year :统计年度
gdp :国内生产总值(亿元)
pc_gdp :人均国内生产总值(元)
gnp :国民生产总值(亿元)
pi :第一产业(亿元)
si :第二产业(亿元)
industry :工业(亿元)
cons_industry :建筑业(亿元)
ti :第三产业(亿元)
trans_industry :交通运输仓储邮电通信业(亿元)
lbdy :批发零售贸易及餐饮业(亿元)
"""
rdint = vs.random()
url = vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'], rdint, vs.MACRO_TYPE[0],
0, 70, rdint)
request = Request(url)
text = urlopen(request, timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
datastr = datastr.replace('"', '').replace('null','0')
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.GDP_YEAR_COLS)
df[df==0] = np.NaN
return df
def get_ppi():
"""
获取工业品出厂价格指数数据
Return
--------
DataFrame
month :统计月份
ppiip :工业品出厂价格指数
ppi :生产资料价格指数
qm:采掘工业价格指数
rmi:原材料工业价格指数
pi:加工工业价格指数
cg:生活资料价格指数
food:食品类价格指数
clothing:衣着类价格指数
roeu:一般日用品价格指数
dcg:耐用消费品价格指数
"""
rdint = vs.random()
url = vs.MACRO_URL%(vs.P_TYPE['http'],vs.DOMAINS['sina'],rdint,vs.MACRO_TYPE[1],3,600,rdint)
request = Request(url)
text = urlopen(request,timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js,columns=vs.PPI_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None,np.NaN,x))
if i != 'month':
df[i] = df[i].astype(float)
return df
def get_money_supply():
"""
获取货币供应量数据
Return
--------
DataFrame
month :统计时间
m2 :货币和准货币（广义货币M2）(亿元)
m2_yoy:货币和准货币（广义货币M2）同比增长(%)
m1:货币(狭义货币M1)(亿元)
m1_yoy:货币(狭义货币M1)同比增长(%)
m0:流通中现金(M0)(亿元)
m0_yoy:流通中现金(M0)同比增长(%)
cd:活期存款(亿元)
cd_yoy:活期存款同比增长(%)
qm:准货币(亿元)
qm_yoy:准货币同比增长(%)
ftd:定期存款(亿元)
ftd_yoy:定期存款同比增长(%)
sd:储蓄存款(亿元)
sd_yoy:储蓄存款同比增长(%)
rests:其他存款(亿元)
rests_yoy:其他存款同比增长(%)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[2], 1, 600,
rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.MONEY_SUPPLY_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None, '--', x))
return df
def get_money_supply():
"""
获取货币供应量数据
Return
--------
DataFrame
month :统计时间
m2 :货币和准货币（广义货币M2）(亿元)
m2_yoy:货币和准货币（广义货币M2）同比增长(%)
m1:货币(狭义货币M1)(亿元)
m1_yoy:货币(狭义货币M1)同比增长(%)
m0:流通中现金(M0)(亿元)
m0_yoy:流通中现金(M0)同比增长(%)
cd:活期存款(亿元)
cd_yoy:活期存款同比增长(%)
qm:准货币(亿元)
qm_yoy:准货币同比增长(%)
ftd:定期存款(亿元)
ftd_yoy:定期存款同比增长(%)
sd:储蓄存款(亿元)
sd_yoy:储蓄存款同比增长(%)
rests:其他存款(亿元)
rests_yoy:其他存款同比增长(%)
"""
rdint = vs.random()
request = Request(vs.MACRO_URL%(vs.P_TYPE['http'], vs.DOMAINS['sina'],
rdint, vs.MACRO_TYPE[2], 1, 600,
rdint))
text = urlopen(request, timeout=10).read()
text = text.decode('gbk')
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js, columns=vs.MONEY_SUPPLY_COLS)
for i in df.columns:
df[i] = df[i].apply(lambda x:np.where(x is None, '--', x))
return df
def get_cpi():
"""
获取居民消费价格指数数据
Return
--------
DataFrame
month :统计月份
cpi :价格指数
"""
rdint = vs.random()
url = vs.MACRO_URL%(vs.P_TYPE['http'],vs.DOMAINS['sina'],rdint,vs.MACRO_TYPE[1],0,600,rdint)
request = Request(url)
text = urlopen(request,timeout=10).read()
text = text.decode('gbk') if ct.PY3 else text
regSym = re.compile(r'\,count:(.*?)\}')
datastr = regSym.findall(text)
datastr = datastr[0]
datastr = datastr.split('data:')[1]
js = json.loads(datastr)
df = pd.DataFrame(js,columns=vs.CPI_COLS)
df['cpi'] = df['cpi'].astype(float)
return df
