import pandas as pd
import pandas_datareader.data as web
import pandas_datareader as pdr
import os
import numpy as np
from bokeh.io import output_notebook
from bokeh.plotting import figure, show
from bokeh.palettes import Spectral8 # 色を考えなくても良い 地味に素敵 https://bokeh.pydata.org/en/latest/docs/reference/palettes.html
from datetime import datetime
from datetime import timedelta
%matplotlib inline
output_notebook()
start = datetime(2008, 9, 11)
end = datetime(2018, 2, 9)
mstar_spx = web.DataReader('SPX', 'morningstar', start, end)
mstar_spx.head()
Close | High | Low | Open | Volume | ||
---|---|---|---|---|---|---|
Symbol | Date | |||||
SPX | 2008-09-11 | 1249.0486 | NaN | NaN | NaN | 0 |
2008-09-12 | 1251.7009 | NaN | NaN | NaN | 0 | |
2008-09-15 | 1192.7021 | NaN | NaN | NaN | 0 | |
2008-09-16 | 1213.5862 | NaN | NaN | NaN | 0 | |
2008-09-17 | 1156.3861 | NaN | NaN | NaN | 0 |
mstar_spx.tail()
Close | High | Low | Open | Volume | ||
---|---|---|---|---|---|---|
Symbol | Date | |||||
SPX | 2018-02-05 | 2648.9391 | 2763.39 | 2638.17 | 2741.06 | 3205516825 |
2018-02-06 | 2695.1442 | 2701.04 | 2593.07 | 2614.78 | 3667048182 | |
2018-02-07 | 2681.6601 | 2727.67 | 2681.33 | 2690.95 | 2559829157 | |
2018-02-08 | 2580.9995 | 2685.27 | 2580.56 | 2685.01 | 2910724979 | |
2018-02-09 | 2619.5456 | 2638.67 | 2532.69 | 2601.78 | 3437391201 |
mstar_spx2 = web.DataReader('SPX', 'morningstar', '1900-01-01')
mstar_spx2.head()
Close | High | Low | Open | Volume | ||
---|---|---|---|---|---|---|
Symbol | Date | |||||
SPX | 1928-01-03 | 17.76 | NaN | NaN | NaN | 0 |
1928-01-04 | 17.72 | NaN | NaN | NaN | 0 | |
1928-01-05 | 17.55 | NaN | NaN | NaN | 0 | |
1928-01-06 | 17.66 | NaN | NaN | NaN | 0 | |
1928-01-09 | 17.50 | NaN | NaN | NaN | 0 |
API KEYがいるみたいなのでパス
セントルイス連銀が出しているデータ
色々とあります。データありすぎ問題・・・
# FRED のデータを取得してみる。注目されている米国金利 FF、2年、10年、30年
start_f = datetime(1980,1,1)
end_f = datetime(2018,2,9)
# FF https://fred.stlouisfed.org/series/FF 2年 https://fred.stlouisfed.org/series/GS2
# 10年 https://fred.stlouisfed.org/series/DGS10 30年 https://fred.stlouisfed.org/series/DGS30
# Dがつくとdailyのよう。つけないとmonthly
f_ff = web.DataReader('FF', 'fred', start_f, end_f)
f_2 = web.DataReader('GS2', 'fred', start_f, end_f)
f_10 = web.DataReader('GS10', 'fred', start_f, end_f)
f_30 = web.DataReader('GS30', 'fred', start_f, end_f)
f_2.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x1124ba748>
f_10.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x111aeda58>
f_30.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x112c4ac88>
f_ff.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x11242d128>
# 負債の状況 自動車ローン https://fred.stlouisfed.org/series/MVLOAS
f_car = web.DataReader('MVLOAS', 'fred', start_f, end_f)
f_car.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x1127e3588>
# 不動産ローン
f_re = web.DataReader('RHEACBW027SBOG', 'fred', start_f, end_f)
f_re.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x1129de6d8>
# 全産業ローン
f_loan = web.DataReader('BUSLOANS', 'fred', start_f, end_f)
f_loan.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x11310e9e8>
# FED TOTAL ASSET
f_fed_asset = web.DataReader('WALCL', 'fred', start_f, end_f)
f_fed_asset.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x1133ca198>
f_fed_asset['2014':].plot()
<matplotlib.axes._subplots.AxesSubplot at 0x1132a3cf8>
# BOJ TOTAL ASSET
f_boj = web.DataReader('JPNASSETS', 'fred', start_f, end_f)
f_boj.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x1139add68>
# World Bank
from pandas_datareader import wb
mat = wb.search('elect')
mat
id | name | source | sourceNote | sourceOrganization | topics | unit | |
---|---|---|---|---|---|---|---|
24 | 1.1_ACCESS.ELECTRICITY.TOT | Access to electricity (% of total population) | Sustainable Energy for All | Access to electricity is the percentage of pop... | b'World Bank Global Electrification Database 2... | ||
39 | 1.2_ACCESS.ELECTRICITY.RURAL | Access to electricity (% of rural population) | Sustainable Energy for All | Access to electricity is the percentage of rur... | b'World Bank Global Electrification Database 2... | ||
40 | 1.3_ACCESS.ELECTRICITY.URBAN | Access to electricity (% of urban population) | Sustainable Energy for All | Access to electricity is the percentage of tot... | b'World Bank Global Electrification Database 2... | ||
42 | 2.0.cov.Ele | Coverage: Electricity | LAC Equity Lab | The coverage rate is the childhood access rate... | b'LAC Equity Lab tabulations of SEDLAC (CEDLAS... | Poverty | |
67 | 2.0.hoi.Ele | HOI: Electricity | LAC Equity Lab | The Human Opportunities Index (HOI) is an econ... | b'LAC Equity Lab tabulations of SEDLAC (CEDLAS... | Poverty | |
158 | 4.1.1_TOTAL.ELECTRICITY.OUTPUT | Total electricity output (GWh) | Sustainable Energy for All | Total electricity output (GWh): Total number o... | b'World Bank and International Energy Agency (... | ||
159 | 4.1.2_REN.ELECTRICITY.OUTPUT | Renewable energy electricity output (GWh) | Sustainable Energy for All | Renewable energy electricity output (GWh): Tot... | b'World Bank and International Energy Agency (... | ||
172 | 4.1_SHARE.RE.IN.ELECTRICITY | Renewable electricity (% in total electricity ... | Sustainable Energy for All | Renewable electricity (% in total electricity ... | b'World Bank and International Energy Agency (... | ||
4811 | EG.ELC.ACCS.RU.ZS | Access to electricity, rural (% of rural popul... | World Development Indicators | World Bank, Sustainable Energy for All (SE4ALL... | b'World Bank, Sustainable Energy for All (SE4A... | Agriculture & Rural Development ; Energy & M... | |
4812 | EG.ELC.ACCS.UR.ZS | Access to electricity, urban (% of urban popul... | World Development Indicators | Access to electricity, rural is the percentage... | b'World Bank, Sustainable Energy for All (SE4A... | Energy & Mining ; Urban Development | |
4813 | EG.ELC.ACCS.ZS | Access to electricity (% of population) | World Development Indicators | Access to electricity is the percentage of pop... | b'World Bank, Sustainable Energy for All (SE4A... | Energy & Mining ; Climate Change ; Environment | |
4814 | EG.ELC.COAL.KH | Electricity production from coal sources (kWh) | WDI Database Archives | b'' | |||
4815 | EG.ELC.COAL.ZS | Electricity production from coal sources (% of... | World Development Indicators | Sources of electricity refer to the inputs use... | b'IEA Statistics OECD/IEA 2014 (http://www.ie... | Energy & Mining ; Climate Change ; Infrastruc... | |
4816 | EG.ELC.FOSL.ZS | Electricity production from oil, gas and coal ... | World Development Indicators | Sources of electricity refer to the inputs use... | b'IEA Statistics OECD/IEA 2014 (http://www.ie... | Energy & Mining ; Environment | |
4817 | EG.ELC.HOUS.ZS | Household electrification rate (% of households) | IDA Results Measurement System | Household electrification rate is defined as t... | b'Compiled by World Bank staff from household ... | ||
4818 | EG.ELC.HYRO.KH | Electricity production from hydroelectric sour... | WDI Database Archives | b'' | |||
4819 | EG.ELC.HYRO.ZS | Electricity production from hydroelectric sour... | World Development Indicators | Sources of electricity refer to the inputs use... | b'IEA Statistics OECD/IEA 2014 (http://www.ie... | Energy & Mining ; Climate Change ; Infrastruc... | |
4820 | EG.ELC.LOSS.KH | Electric power transmission and distribution l... | WDI Database Archives | b'' | |||
4821 | EG.ELC.LOSS.ZS | Electric power transmission and distribution l... | World Development Indicators | Electric power transmission and distribution l... | b'IEA Statistics OECD/IEA 2014 (http://www.ie... | Energy & Mining ; Infrastructure | |
4822 | EG.ELC.NGAS.KH | Electricity production from natural gas source... | WDI Database Archives | b'' | |||
4823 | EG.ELC.NGAS.ZS | Electricity production from natural gas source... | World Development Indicators | Sources of electricity refer to the inputs use... | b'IEA Statistics OECD/IEA 2014 (http://www.ie... | Energy & Mining ; Climate Change ; Infrastruc... | |
4824 | EG.ELC.NUCL.KH | Electricity production from nuclear sources (kWh) | WDI Database Archives | b'' | |||
4825 | EG.ELC.NUCL.ZS | Electricity production from nuclear sources (%... | World Development Indicators | Sources of electricity refer to the inputs use... | b'IEA Statistics OECD/IEA 2014 (http://www.ie... | Energy & Mining ; Climate Change ; Infrastruc... | |
4826 | EG.ELC.PETR.KH | Electricity production from oil sources (kWh) | WDI Database Archives | b'' | |||
4827 | EG.ELC.PETR.ZS | Electricity production from oil sources (% of ... | World Development Indicators | Sources of electricity refer to the inputs use... | b'IEA Statistics OECD/IEA 2014 (http://www.ie... | Energy & Mining ; Climate Change ; Infrastruc... | |
4828 | EG.ELC.PROD.KH | Electricity production (kWh) | WDI Database Archives | b'' | |||
4829 | EG.ELC.RNEW.KH | Electricity production from renewable sources ... | WDI Database Archives | b'' | |||
4830 | EG.ELC.RNEW.ZS | Renewable electricity output (% of total elect... | World Development Indicators | Renewable electricity is the share of electrit... | b'World Bank, Sustainable Energy for All (SE4A... | Energy & Mining ; Climate Change ; Environment | |
4831 | EG.ELC.RNWX.KH | Electricity production from renewable sources,... | World Development Indicators | Electricity production from renewable sources,... | b'IEA Statistics OECD/IEA 2014 (http://www.ie... | Energy & Mining ; Climate Change ; Environment | |
4832 | EG.ELC.RNWX.ZS | Electricity production from renewable sources,... | World Development Indicators | Electricity production from renewable sources,... | b'IEA Statistics OECD/IEA 2014 (http://www.ie... | Energy & Mining ; Climate Change ; Environment | |
... | ... | ... | ... | ... | ... | ... | ... |
6066 | IC.GE.COST | Cost to get electricity(% of income per capita) | Doing Business | Cost to obtain an electricity connection (% of... | b'World Bank, Doing Business Project (http://w... | ||
6067 | IC.GE.NUM | Procedures required to connect to electricity ... | Doing Business | This indicator records the number of procedure... | b'World Bank, Doing Business Project (http://w... | ||
6196 | IN.ENRGY.ELEC.GEN | Total-Electricity Generated Gross (GWh) | Country Partnership Strategy for India (FY2013... | Total-Electricity Generated Gross (GWh). Inclu... | b'Source: Central Electricity Authority, Minis... | ||
6201 | IN.ENRGY.TOWNS.ELECTRFIED.NUM | Number of Towns Electrified (Per 2001 Census) | Country Partnership Strategy for India (FY2013... | Number of Towns Electrified (Per 2001 Census) | b'Source: Central Electricity Authority, Minis... | ||
6202 | IN.ENRGY.TOWNS.ELECTRFIED.PERCENT | Number of Towns Electrified (Percentage) | Country Partnership Strategy for India (FY2013... | Percentage of Towns in the State Electrified | b'Source: Central Electricity Authority, Minis... | ||
6204 | IN.ENRGY.VILLAG.ELECTRFIED | Number of Villages Electrified | Country Partnership Strategy for India (FY2013... | Number of Villages Electrified | b'Source: Central Electricity Authority, Minis... | ||
6205 | IN.ENRGY.VILLAG.ELECTRFIED.PERCENT | Number of Villages Electrified (Percentage) | Country Partnership Strategy for India (FY2013... | Percentage of Villages Electrified in the State | b'Source: Central Electricity Authority, Minis... | ||
6333 | IS.RRS.ELEC.KM | Rail lines, electric (km) | WDI Database Archives | b'' | |||
7928 | NV.IND.GELW.CD | Gas, electricity and water, value added (curre... | Africa Development Indicators | Value added in gas, electricity and water is d... | b'World Bank national accounts data, and OECD ... | ||
7929 | NV.IND.GELW.CN | Gas, electricity and water, value added (curre... | Africa Development Indicators | Value added in gas, electricity and water is d... | b'World Bank national accounts data, and OECD ... | ||
7930 | NV.IND.GELW.KN | Gas, electricity and water, value added (const... | Africa Development Indicators | Value added in gas, electricity and water is d... | b'World Bank national accounts data, and OECD ... | ||
8777 | SABER.GRVT.GOAL7.LVL3 | SABER: (Engaging the Private Sector, Governmen... | Education Statistics | Data Interpretation: 1=Latent; 2=Emerging; 3=E... | b'Systems Approach for Better Education Result... | Education | |
9266 | SG.COK.ELEC.ZS | Main cooking fuel: electricity (% of households) | Gender Statistics | Percentage of households who use electricity a... | b'Demographic and Health Surveys (DHS)' | Gender | |
10175 | SL.EMP.ELC | Number of people employed in electricity and u... | Indonesia Database for Policy and Economic Res... | b'BADAN PUSAT STATISTIK - Statistics Indonesia... | |||
11207 | UIS.AFR.SCHBSP.1.PU.WELEC | Africa Dataset: Percentage of primary schools ... | Education Statistics | Share of public primary schools with access to... | b'UNESCO Institute for Statistics' | ||
11208 | UIS.AFR.SCHBSP.1.PU.WNIELEC | Africa Dataset: Percentage of primary schools ... | Education Statistics | Share of public primary schools where informat... | b'UNESCO Institute for Statistics' | ||
11211 | UIS.AFR.SCHBSP.1.PU.WOELEC | Africa Dataset: Percentage of primary schools ... | Education Statistics | Share of public primary schools with no access... | b'UNESCO Institute for Statistics' | ||
11218 | UIS.AFR.SCHBSP.2.PU.WELEC | Africa Dataset: Percentage of lower secondary ... | Education Statistics | Share of public lower secondary schools with a... | b'UNESCO Institute for Statistics' | ||
11219 | UIS.AFR.SCHBSP.2.PU.WNIELEC | Africa Dataset: Percentage of lower secondary ... | Education Statistics | Share of public lower secondary schools where ... | b'UNESCO Institute for Statistics' | ||
11222 | UIS.AFR.SCHBSP.2.PU.WOELEC | Africa Dataset: Percentage of lower secondary ... | Education Statistics | Share of public lower secondary schools with n... | b'UNESCO Institute for Statistics' | ||
12246 | WP11633.1 | Used electronic payments to make payments (% a... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector | |
12247 | WP11633.10 | Used electronic payments to make payments, rur... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector | |
12248 | WP11633.2 | Used electronic payments to make payments, mal... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector | |
12249 | WP11633.3 | Used electronic payments to make payments, fem... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector | |
12250 | WP11633.4 | Used electronic payments to make payments, you... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector | |
12251 | WP11633.5 | Used electronic payments to make payments, old... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector | |
12252 | WP11633.6 | Used electronic payments to make payments, pri... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector | |
12253 | WP11633.7 | Used electronic payments to make payments, sec... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector | |
12254 | WP11633.8 | Used electronic payments to make payments, inc... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector | |
12255 | WP11633.9 | Used electronic payments to make payments, inc... | Global Financial Inclusion | Denotes the percentage of respondents who used... | b'Demirguc-Kunt and Klapper, 2012' | Financial Sector |
93 rows × 7 columns
dat = wb.download(indicator='EG.ELC.ACCS.UR.ZS')
dat
EG.ELC.ACCS.UR.ZS | ||
---|---|---|
country | year | |
Canada | 2005 | 100.000000 |
2004 | 100.000000 | |
2003 | 100.000000 | |
Mexico | 2005 | 99.605581 |
2004 | 99.390082 | |
2003 | 99.579957 | |
United States | 2005 | 100.000000 |
2004 | 100.000000 | |
2003 | 100.000000 |
eu = web.DataReader('teimf060', 'eurostat', start_f)
eu
MATURITY | Maturity: 1 year | Maturity: 10 years | Maturity: 5 years |
---|---|---|---|
BONDS | AAA rated euro area central government bonds | AAA rated euro area central government bonds | AAA rated euro area central government bonds |
CURV_TYP | Zero-coupon yield curve spot rate | Zero-coupon yield curve spot rate | Zero-coupon yield curve spot rate |
GEO | Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19) | Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19) | Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19) |
FREQ | Monthly | Monthly | Monthly |
TIME_PERIOD | |||
2017-02-01 | -0.81 | 0.38 | -0.43 |
2017-03-01 | -0.84 | 0.43 | -0.35 |
2017-04-01 | -0.80 | 0.32 | -0.42 |
2017-05-01 | -0.73 | 0.44 | -0.31 |
2017-06-01 | -0.71 | 0.37 | -0.34 |
2017-07-01 | -0.69 | 0.58 | -0.18 |
2017-08-01 | -0.73 | 0.45 | -0.29 |
2017-09-01 | -0.76 | 0.45 | -0.31 |
2017-10-01 | -0.77 | 0.48 | -0.28 |
2017-11-01 | -0.78 | 0.43 | -0.32 |
2017-12-01 | -0.80 | 0.42 | -0.27 |
2018-01-01 | -0.66 | 0.61 | -0.08 |
eu2 = web.DataReader('teimf050', 'eurostat', '1900')
eu2
INTRT | EMU convergence criterion bond yields | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GEO | Austria | Belgium | Bulgaria | Cyprus | Czech Republic | Germany (until 1990 former territory of the FRG) | Denmark | Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19) | Estonia | Greece | ... | Latvia | Malta | Netherlands | Poland | Portugal | Romania | Sweden | Slovenia | Slovakia | United Kingdom |
FREQ | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly | ... | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly | Monthly |
TIME_PERIOD | |||||||||||||||||||||
2017-01-01 | 0.57 | 0.70 | 1.77 | 3.45 | 0.47 | 0.25 | 0.37 | 1.10 | NaN | 7.04 | ... | 0.89 | 1.17 | 0.48 | 3.68 | 3.95 | 3.75 | 0.65 | 0.99 | 1.03 | 1.38 |
2017-02-01 | 0.59 | 0.87 | 1.75 | 3.37 | 0.63 | 0.26 | 0.33 | 1.24 | NaN | 7.52 | ... | 0.99 | 1.32 | 0.49 | 3.81 | 4.04 | 3.96 | 0.66 | 1.01 | 1.09 | 1.24 |
2017-03-01 | 0.59 | 0.87 | 1.73 | 3.34 | 0.87 | 0.35 | 0.19 | 1.27 | NaN | 7.17 | ... | 0.94 | 1.55 | 0.49 | 3.66 | 3.99 | 3.99 | 0.69 | 0.99 | 1.09 | 1.13 |
2017-04-01 | 0.49 | 0.78 | 1.78 | 3.23 | 0.96 | 0.22 | 0.55 | 1.14 | NaN | 6.70 | ... | 0.92 | 1.43 | 0.50 | 3.42 | 3.77 | 3.79 | 0.57 | 1.00 | 1.06 | 1.00 |
2017-05-01 | 0.65 | 0.77 | 1.74 | 3.03 | 0.74 | 0.34 | 0.64 | 1.13 | NaN | 5.86 | ... | 0.88 | 1.37 | 0.59 | 3.35 | 3.29 | 3.75 | 0.56 | 0.98 | 1.03 | 1.03 |
2017-06-01 | 0.55 | 0.62 | 1.70 | 2.84 | 0.77 | 0.25 | 0.53 | 1.01 | NaN | 5.76 | ... | 0.85 | 1.25 | 0.50 | 3.19 | 2.97 | 3.67 | 0.46 | 0.86 | 0.86 | 0.98 |
2017-07-01 | 0.73 | 0.83 | 1.65 | 2.57 | 0.90 | 0.46 | 0.67 | 1.18 | NaN | 5.33 | ... | 0.98 | 1.36 | 0.69 | 3.30 | 3.03 | 3.84 | 0.66 | 1.15 | 0.93 | NaN |
2017-08-01 | 0.61 | 0.73 | 1.70 | 2.49 | 0.83 | 0.35 | 0.55 | 1.06 | NaN | 5.55 | ... | 0.85 | 1.23 | 0.54 | 3.33 | 2.83 | 3.86 | 0.63 | 1.09 | 0.83 | NaN |
2017-09-01 | 0.59 | 0.70 | 1.66 | 2.20 | 0.97 | 0.35 | 0.51 | 1.06 | NaN | 5.56 | ... | 0.72 | 1.26 | 0.53 | 3.26 | 2.63 | 3.89 | 0.62 | 0.98 | 0.82 | NaN |
2017-10-01 | 0.61 | 0.69 | 1.40 | 1.84 | 1.45 | 0.37 | 0.53 | 1.08 | NaN | 5.59 | ... | 0.71 | 1.24 | 0.54 | 3.38 | 2.32 | 4.17 | 0.83 | 0.97 | 0.83 | NaN |
2017-11-01 | 0.51 | 0.58 | 1.33 | 1.54 | 1.68 | 0.31 | 0.44 | 0.95 | NaN | 5.22 | ... | 0.69 | 1.13 | 0.47 | 3.39 | 1.98 | 4.43 | 0.76 | 0.81 | 0.76 | NaN |
2017-12-01 | 0.50 | 0.53 | 1.02 | 1.58 | 1.50 | 0.30 | 0.41 | 0.91 | NaN | 4.44 | ... | 0.59 | 1.07 | 0.45 | 3.27 | 1.83 | 4.38 | 0.72 | 0.69 | 0.67 | NaN |
12 rows × 31 columns
eu2.columns
MultiIndex(levels=[['EMU convergence criterion bond yields'], ['Austria', 'Belgium', 'Bulgaria', 'Croatia', 'Cyprus', 'Czech Republic', 'Denmark', 'Estonia', 'Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19)', 'European Union (27 countries)', 'European Union (28 countries)', 'Finland', 'France', 'Germany (until 1990 former territory of the FRG)', 'Greece', 'Hungary', 'Ireland', 'Italy', 'Latvia', 'Lithuania', 'Luxembourg', 'Malta', 'Netherlands', 'Poland', 'Portugal', 'Romania', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'United Kingdom'], ['Monthly']], labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 2, 4, 5, 13, 6, 8, 7, 14, 28, 9, 10, 11, 12, 3, 15, 16, 17, 19, 20, 18, 21, 22, 23, 24, 25, 29, 27, 26, 30], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], names=['INTRT', 'GEO', 'FREQ'])
chair_p = {}
chair_p = pd.DataFrame(chair_p)
chair_p['name'] = ['Charles Hamlin', 'William Harding', 'Daniel Crissinger', 'Roy Young', 'Eugene Meyer', \
'Eugene Black', 'Marriner Eccles', 'Thomas McCabe', 'William Martin', 'Authur Burns', \
'William Miller', 'Paul Volker', 'Alan Greenspan', 'Ben Bernanke', 'Janet Yellen', 'Jerome Powell']
chair_p['start'] = [datetime(1914,8,10), datetime(1916,8,10), datetime(1923,5,1), datetime(1927,10,4), \
datetime(1930,9,16), datetime(1933,5,19), datetime(1934,11,15), datetime(1948,4,15), \
datetime(1951,4,2), datetime(1970,2,2), datetime(1978,3,8), datetime(1979,8,6), \
datetime(1987,8,11), datetime(2006,2,1), datetime(2014,2,3), datetime(2018,2,5)]
chair_p['end'] = [datetime(1916,8,10), datetime(1922,8,9), datetime(1927,9,15), datetime(1930,8,30), \
datetime(1933,5,10),datetime(1934,8,15), datetime(1948,1,31), datetime(1951,3,31), \
datetime(1970,1,31), datetime(1978,1,31), datetime(1979,8,6), datetime(1987,8,11), \
datetime(2006,1,31), datetime(2014,1,31), datetime(2018,2,5), '-']
chair_p
name | start | end | |
---|---|---|---|
0 | Charles Hamlin | 1914-08-10 | 1916-08-10 00:00:00 |
1 | William Harding | 1916-08-10 | 1922-08-09 00:00:00 |
2 | Daniel Crissinger | 1923-05-01 | 1927-09-15 00:00:00 |
3 | Roy Young | 1927-10-04 | 1930-08-30 00:00:00 |
4 | Eugene Meyer | 1930-09-16 | 1933-05-10 00:00:00 |
5 | Eugene Black | 1933-05-19 | 1934-08-15 00:00:00 |
6 | Marriner Eccles | 1934-11-15 | 1948-01-31 00:00:00 |
7 | Thomas McCabe | 1948-04-15 | 1951-03-31 00:00:00 |
8 | William Martin | 1951-04-02 | 1970-01-31 00:00:00 |
9 | Authur Burns | 1970-02-02 | 1978-01-31 00:00:00 |
10 | William Miller | 1978-03-08 | 1979-08-06 00:00:00 |
11 | Paul Volker | 1979-08-06 | 1987-08-11 00:00:00 |
12 | Alan Greenspan | 1987-08-11 | 2006-01-31 00:00:00 |
13 | Ben Bernanke | 2006-02-01 | 2014-01-31 00:00:00 |
14 | Janet Yellen | 2014-02-03 | 2018-02-05 00:00:00 |
15 | Jerome Powell | 2018-02-05 | - |
chair_p['before_365'] = 0
chair_p['after_365'] = 0
for i in range(len(chair_p)):
a = chair_p.iloc[i, 1] - timedelta(days=365)
b = chair_p.iloc[i, 1] + timedelta(days=365)
chair_p.iloc[i, 3] = '{}-{}'.format(a.year, a.month)
chair_p.iloc[i, 4] = '{}-{}'.format(b.year, b.month)
chair_p
name | start | end | before_365 | after_365 | |
---|---|---|---|---|---|
0 | Charles Hamlin | 1914-08-10 | 1916-08-10 00:00:00 | 1913-8 | 1915-8 |
1 | William Harding | 1916-08-10 | 1922-08-09 00:00:00 | 1915-8 | 1917-8 |
2 | Daniel Crissinger | 1923-05-01 | 1927-09-15 00:00:00 | 1922-5 | 1924-4 |
3 | Roy Young | 1927-10-04 | 1930-08-30 00:00:00 | 1926-10 | 1928-10 |
4 | Eugene Meyer | 1930-09-16 | 1933-05-10 00:00:00 | 1929-9 | 1931-9 |
5 | Eugene Black | 1933-05-19 | 1934-08-15 00:00:00 | 1932-5 | 1934-5 |
6 | Marriner Eccles | 1934-11-15 | 1948-01-31 00:00:00 | 1933-11 | 1935-11 |
7 | Thomas McCabe | 1948-04-15 | 1951-03-31 00:00:00 | 1947-4 | 1949-4 |
8 | William Martin | 1951-04-02 | 1970-01-31 00:00:00 | 1950-4 | 1952-4 |
9 | Authur Burns | 1970-02-02 | 1978-01-31 00:00:00 | 1969-2 | 1971-2 |
10 | William Miller | 1978-03-08 | 1979-08-06 00:00:00 | 1977-3 | 1979-3 |
11 | Paul Volker | 1979-08-06 | 1987-08-11 00:00:00 | 1978-8 | 1980-8 |
12 | Alan Greenspan | 1987-08-11 | 2006-01-31 00:00:00 | 1986-8 | 1988-8 |
13 | Ben Bernanke | 2006-02-01 | 2014-01-31 00:00:00 | 2005-2 | 2007-2 |
14 | Janet Yellen | 2014-02-03 | 2018-02-05 00:00:00 | 2013-2 | 2015-2 |
15 | Jerome Powell | 2018-02-05 | - | 2017-2 | 2019-2 |
spx = web.DataReader('^SPX', 'stooq')
spx = spx.sort_index()
spx.loc['2014-02-03']
Open 1.782680e+03 High 1.784830e+03 Low 1.739660e+03 Close 1.741890e+03 Volume 7.444766e+08 Name: 2014-02-03 00:00:00, dtype: float64
spx.head()
Open | High | Low | Close | Volume | |
---|---|---|---|---|---|
Date | |||||
1789-05-01 | 0.51 | 0.51 | 0.51 | 0.51 | NaN |
1789-06-01 | 0.51 | 0.51 | 0.51 | 0.51 | NaN |
1789-07-01 | 0.50 | 0.50 | 0.50 | 0.50 | NaN |
1789-08-01 | 0.50 | 0.51 | 0.50 | 0.51 | NaN |
1789-09-01 | 0.51 | 0.51 | 0.50 | 0.51 | NaN |
spx = spx['1941':]
spx.head()
Open | High | Low | Close | Volume | |
---|---|---|---|---|---|
Date | |||||
1941-01-02 | 10.52 | 10.58 | 10.46 | 10.48 | NaN |
1941-01-03 | 10.61 | 10.74 | 10.57 | 10.72 | NaN |
1941-01-04 | 10.69 | 10.78 | 10.69 | 10.72 | NaN |
1941-01-06 | 10.71 | 10.81 | 10.71 | 10.74 | NaN |
1941-01-07 | 10.73 | 10.79 | 10.69 | 10.75 | NaN |
spx7 = spx[chair_p.iloc[7,3]:chair_p.iloc[7,4]] / spx.loc[chair_p.iloc[7,1], 'Close'] * 100
spx8 = spx[chair_p.iloc[8,3]:chair_p.iloc[8,4]] / spx.loc[chair_p.iloc[8,1], 'Close'] * 100
spx9 = spx[chair_p.iloc[9,3]:chair_p.iloc[9,4]] / spx.loc[chair_p.iloc[9,1], 'Close'] * 100
spx10 = spx[chair_p.iloc[10,3]:chair_p.iloc[10,4]] / spx.loc[chair_p.iloc[10,1], 'Close'] * 100
spx11 = spx[chair_p.iloc[11,3]:chair_p.iloc[11,4]] / spx.loc[chair_p.iloc[11,1], 'Close'] * 100
spx12 = spx[chair_p.iloc[12,3]:chair_p.iloc[12,4]] / spx.loc[chair_p.iloc[12,1], 'Close'] * 100
spx13 = spx[chair_p.iloc[13,3]:chair_p.iloc[13,4]] / spx.loc[chair_p.iloc[13,1], 'Close'] * 100
spx14 = spx[chair_p.iloc[14,3]:chair_p.iloc[14,4]] / spx.loc[chair_p.iloc[14,1], 'Close'] * 100
spx15 = spx[chair_p.iloc[15,3]:chair_p.iloc[15,4]] / spx.loc[chair_p.iloc[15,1], 'Close'] * 100
spx7.index = spx7.index - chair_p.iloc[7,1]
spx8.index = spx8.index - chair_p.iloc[8,1]
spx9.index = spx9.index - chair_p.iloc[9,1]
spx10.index = spx10.index - chair_p.iloc[10,1]
spx11.index = spx11.index - chair_p.iloc[11,1]
spx12.index = spx12.index - chair_p.iloc[12,1]
spx13.index = spx13.index - chair_p.iloc[13,1]
spx14.index = spx14.index - chair_p.iloc[14,1]
spx15.index = spx15.index - chair_p.iloc[15,1]
p = figure(width=700, height = 500, title='FRB議長交代1年前後の株価の動き')
p.line(spx7.index, spx7.Close, legend='McCabe', line_color=Spectral8[0])
p.line(spx8.index, spx8.Close, legend='Martin', line_color=Spectral8[1])
p.line(spx9.index, spx9.Close, legend='Burns', line_color=Spectral8[2])
p.line(spx10.index, spx10.Close, legend='Miller', line_color=Spectral8[3])
p.line(spx11.index, spx11.Close, legend='Volker', line_color=Spectral8[4])
p.line(spx12.index, spx12.Close, legend='Greenspan', line_color=Spectral8[5])
p.line(spx13.index, spx13.Close, legend='Bernanke',line_color=Spectral8[6])
p.line(spx14.index, spx14.Close, legend='Yellen', line_color=Spectral8[7])
p.line(spx15.index, spx15.Close, legend='Powell', line_color='black', line_width=3)
p.legend.location = 'bottom_left'
show(p)