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05. 宏观经济数据

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PKUJohnson edited this page Dec 20, 2018 · 1 revision

OpenDataTools通过economy接口，支持对宏观经济数据的获取，要求版本0.3.5以上。

数据来源：国家统计局

导入economy接口

from opendatatools import economy

数据规范

  1. 获取地区列表
df = economy.get_region_map()
df.head(10)

  1. 获取指标集合
df = economy.get_indicator_map()
df.head(20)

  1. 大中城市列表（房价数据使用）
df = economy.get_city_map()
df.head(20)

详细接口

年度数据

  1. 年度GDP数据
df, msg = economy.get_gdp_y()
df[df.date=='2016']
economy.disp_result(df, date_format="%Y")

  1. 年度人口数据
df,msg = economy.get_population_size_y()
df.head(20)

  1. 年度人口结构数据
df, msg = economy.get_population_structure_y()
df = df[(df['indicator'] == 'A030301') | (df['indicator'] == 'A030302') | (df['indicator'] == 'A030303') | (df['indicator'] == 'A030304')]
df = df[df['date'] >= '1990']
df.head(20)

  1. 人口结构（抚养比）
df, msg = economy.get_population_structure_y()
df = df[(df['indicator'] == 'A030305') | (df['indicator'] == 'A030306') | (df['indicator'] == 'A030307')]
df = df[df['date'] >= '1990']
economy.disp_result(df, date_format="%Y")

季度数据

  1. 季度GDP数据
df, msg = economy.get_gdp()
df = df[ (df['indicator'] == 'A010101') | (df['indicator'] == 'A010102')]
economy.disp_result(df, date_format='%YQ')

  1. 季度GDP环比
df, msg = economy.get_gdp_q2q()
df = df[(df['date'] >= '2011')]
economy.disp_result(df, date_format='%YQ')

  1. 地区GDP数据
# 地区GDP(季度)
df, msg = economy.get_region_gdp('上海')
df_1 = df[df['indicator'] == 'A010101']
economy.disp_result(df_1, date_format='%YQ')
df_2 = df[df['indicator'] == 'A010103']
economy.disp_result(df_2, date_format='%YQ')

月度数据

  1. CPI月度数据（同比）
df, msg = economy.get_cpi()
df = df[(df['date'] >= '201701') & (df['value'] > 0) & (df['indicator'] == 'A01010101')]
df['value'] = df['value'].apply(lambda x : x - 100)
economy.disp_result(df, '%Y%m')

# CPI月度数据（同比，明细项）
df, msg = economy.get_cpi()
df = df[(df['date'] >= '201701') & (df['value'] > 0) & (df['indicator'] != 'A01010101')]
df['value'] = df['value'].apply(lambda x : x - 100)
economy.disp_result(df, '%Y%m')
# 区域CPI同比（上海）
df, msg = economy.get_region_cpi('上海')
df = df[(df['date'] >= '201701') & (df['value'] > 0) & (df['indicator'] == 'A01010101')]
economy.disp_result(df, '%Y%m')
  1. 月度PPI数据（同比）
df, msg = economy.get_ppi()
df = df[(df['value'] > 0)]
economy.disp_result(df, '%Y%m')

# 区域月度PPI数据
df, msg = economy.get_region_ppi('上海')
df = df[(df['value'] > 0)]
economy.disp_result(df, '%Y%m')
  1. 月度PMI数据（制造业）
df, msg = economy.get_manufacturing_pmi()
df = df[(df['value'] > 0) & (df['indicator'] == 'A190101')]
economy.disp_result(df, '%Y%m')

# 月度PMI数据（非制造业）
df, msg = economy.get_non_manufacturing_pmi()
df = df[(df['value'] > 0) & (df['indicator'] == 'A190201')]
economy.disp_result(df, '%Y%m')

# 月度PMI数据（综合PMI）
df, msg = economy.get_pmi()
df = df[(df['value'] > 0)]
economy.disp_result(df, '%Y%m')
  1. 月度进出口数据
df, msg = economy.get_import_export()
df = df[(df['value'] > 0) &
        ( (df['indicator'] == 'A160101') #| (df['indicator'] == 'A160103')
           | (df['indicator'] == 'A160105') #| (df['indicator'] == 'A160107')
           | (df['indicator'] == 'A160109') #| (df['indicator'] == 'A16010B')
           | (df['indicator'] == 'A16010D') #| (df['indicator'] == 'A16010E')
        )]
economy.disp_result(df, '%Y%m')

  1. 月度FDI数据
df, msg = economy.get_fdi()
df_1 = df[ (df['value'] > 0) & (df['indicator'] == 'A16020B')]
economy.disp_result(df_1, '%Y%m')
df_2 = df[ (df['value'] > 0) & (df['indicator'] == 'A16020C')]
economy.disp_result(df_2, '%Y%m')
  1. 月度财政收入
df, msg = economy.get_fiscal_revenue()
df_1 = df[(df['value'] > 0) & ( (df['indicator'] == 'A1A0101') | (df['indicator'] == 'A1A0102'))]
economy.disp_result(df_1, '%Y%m')
df_2 = df[(df['value'] > 0) & (df['indicator'] == 'A1A0103')]
economy.disp_result(df_2, '%Y%m')
  1. 月度财政支出
df, msg = economy.get_fiscal_expend()
df_1 = df[(df['value'] > 0) & ( (df['indicator'] == 'A1A0201') | (df['indicator'] == 'A1A0202'))]
economy.disp_result(df_1, '%Y%m')
df_2 = df[(df['value'] > 0) & (df['indicator'] == 'A1A0203')]
economy.disp_result(df_2, '%Y%m')
  1. 货币发行量
df, msg = economy.get_M0_M1_M2()
df_1 = df[(df['value'] > 0) & ((df['indicator'] == 'A1B0101') | (df['indicator'] == 'A1B0103') | (df['indicator'] == 'A1B0105'))]
economy.disp_result(df_1, '%Y%m')
df_2 = df[(df['value'] > 0) & ((df['indicator'] == 'A1B0102') | (df['indicator'] == 'A1B0104') | (df['indicator'] == 'A1B0106'))]
economy.disp_result(df_2, '%Y%m')

  1. 固定资产投资数据
df, msg = economy.get_fixed_asset_investment()
df_1 = df[(df['value'] > 0) & (df['indicator'] == 'A130101') ]
economy.disp_result(df_1, '%Y%m')
df_2 = df[(df['value'] > 0) & (df['indicator'] == 'A130102') ]
economy.disp_result(df_2, '%Y%m')

  1. 房地产开发投资
df, msg = economy.get_realestate_investment()
df_1 = df[(df['value'] > 0) & ( (df['indicator'] == 'A140101') | (df['indicator'] == 'A140107') | (df['indicator'] == 'A140109'))]
economy.disp_result(df_1, '%Y%m')
df_2 = df[(df['value'] > 0) & ( (df['indicator'] == 'A140102') | (df['indicator'] == 'A140108') | (df['indicator'] == 'A14010A'))]
economy.disp_result(df_2, '%Y%m')

  1. 社会零售总额
import pandas as pd
df1, msg = economy.get_retail_sales()
df2, msg = economy.get_online_retail_sales()
df = pd.concat([df1, df2])
df_1 = df[(df['value'] > 0) & ( (df['indicator'] == 'A150101') | (df['indicator'] == 'A150102') | (df['indicator'] == 'A150801'))]
economy.disp_result(df_1, '%Y%m')
df_2 = df[(df['value'] > 0) & ( (df['indicator'] == 'A150103') | (df['indicator'] == 'A150104') | (df['indicator'] == 'A150802'))]
economy.disp_result(df_2, '%Y%m')
  1. 70个大中城市房价指数(北京)
df, msg = economy.get_house_price_index('北京')
df_1 = df[(df['value'] != 0) & (
                                    (df['indicator'] == 'A010801') | (df['indicator'] == 'A010804') | (df['indicator'] == 'A010807') |
                                    (df['indicator'] == 'A01080A') | (df['indicator'] == 'A01080D') | (df['indicator'] == 'A01080G') |
                                    (df['indicator'] == 'A01080J') | (df['indicator'] == 'A01080M') | (df['indicator'] == 'A01080P')
)]
economy.disp_result(df_1, '%Y%m')
df_2 = df[(df['value'] != 0) & (
                                    (df['indicator'] == 'A010802') | (df['indicator'] == 'A010805') | (df['indicator'] == 'A010808') |
                                    (df['indicator'] == 'A01080B') | (df['indicator'] == 'A01080E') | (df['indicator'] == 'A01080H') |
                                    (df['indicator'] == 'A01080K') | (df['indicator'] == 'A01080N') | (df['indicator'] == 'A01080Q')
)]
economy.disp_result(df_2, '%Y%m')

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