一些机器学习相关学习笔记.zip

# Life satisfaction and GDP per capita ## Life satisfaction ### Source This dataset was obtained from the OECD's website at: http://stats.oecd.org/index.aspx?DataSetCode=BLI ### Data description Int64Index: 3292 entries, 0 to 3291 Data columns (total 17 columns): "LOCATION" 3292 non-null object Country 3292 non-null object INDICATOR 3292 non-null object Indicator 3292 non-null object MEASURE 3292 non-null object Measure 3292 non-null object INEQUALITY 3292 non-null object Inequality 3292 non-null object Unit Code 3292 non-null object Unit 3292 non-null object PowerCode Code 3292 non-null int64 PowerCode 3292 non-null object Reference Period Code 0 non-null float64 Reference Period 0 non-null float64 Value 3292 non-null float64 Flag Codes 1120 non-null object Flags 1120 non-null object dtypes: float64(3), int64(1), object(13) memory usage: 462.9+ KB ### Example usage using python Pandas >>> life_sat = pd.read_csv("oecd_bli_2015.csv", thousands=',') >>> life_sat_total = life_sat[life_sat["INEQUALITY"]=="TOT"] >>> life_sat_total = life_sat_total.pivot(index="Country", columns="Indicator", values="Value") >>> life_sat_total.info() <class 'pandas.core.frame.DataFrame'> Index: 37 entries, Australia to United States Data columns (total 24 columns): Air pollution 37 non-null float64 Assault rate 37 non-null float64 Consultation on rule-making 37 non-null float64 Dwellings without basic facilities 37 non-null float64 Educational attainment 37 non-null float64 Employees working very long hours 37 non-null float64 Employment rate 37 non-null float64 Homicide rate 37 non-null float64 Household net adjusted disposable income 37 non-null float64 Household net financial wealth 37 non-null float64 Housing expenditure 37 non-null float64 Job security 37 non-null float64 Life expectancy 37 non-null float64 Life satisfaction 37 non-null float64 Long-term unemployment rate 37 non-null float64 Personal earnings 37 non-null float64 Quality of support network 37 non-null float64 Rooms per person 37 non-null float64 Self-reported health 37 non-null float64 Student skills 37 non-null float64 Time devoted to leisure and personal care 37 non-null float64 Voter turnout 37 non-null float64 Water quality 37 non-null float64 Years in education 37 non-null float64 dtypes: float64(24) memory usage: 7.2+ KB ## GDP per capita ### Source Dataset obtained from the IMF's website at: http://goo.gl/j1MSKe ### Data description Int64Index: 190 entries, 0 to 189 Data columns (total 7 columns): Country 190 non-null object Subject Descriptor 189 non-null object Units 189 non-null object Scale 189 non-null object Country/Series-specific Notes 188 non-null object 2015 187 non-null float64 Estimates Start After 188 non-null float64 dtypes: float64(2), object(5) memory usage: 11.9+ KB ### Example usage using python Pandas >>> gdp_per_capita = pd.read_csv( ... datapath+"gdp_per_capita.csv", thousands=',', delimiter='\t', ... encoding='latin1', na_values="n/a", index_col="Country") ... >>> gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True)