In [1]:
import pandas as pd
In [2]:
from pandas.io.parsers import ExcelFile
In [3]:
files = ["bankCapitalToAssetsRatioWorldBank.xls", "giniWorldBank.xls",
"governmentDebtWorldBank.xls", "privateSectorDebtWorldBank.xls",
"currentAccountBalanceWorldBank.xls", "incomeShareOfTopTenWorldBank.xls", "surplusDeficitWorldBank.xls",
"gdpWorldBank.xls", "inflationConsumerPricesWorldBank.xls"]
In [4]:
data_names = map(lambda x: x[:-4], files)
In [5]:
data_names
Out[5]:
['bankCapitalToAssetsRatioWorldBank',
'giniWorldBank',
'governmentDebtWorldBank',
'privateSectorDebtWorldBank',
'currentAccountBalanceWorldBank',
'incomeShareOfTopTenWorldBank',
'surplusDeficitWorldBank',
'gdpWorldBank',
'inflationConsumerPricesWorldBank']
In [6]:
xls = ExcelFile("data/" + files[0])
df = xls.parse('Sheet1', index_col=None, na_values=['NA'])
In [7]:
del df['Country Code']
In [8]:
df.index = df['Country Name']
In [9]:
del df['Country Name']
In [9]:
In [9]:
In [10]:
datasets = {}
for name, fn in zip(data_names, files):
xls = ExcelFile("data/" + fn)
df = xls.parse('Sheet1', index_col=None, na_values=['NA'])
df.index = df['Country Name']
del df['Country Code']
del df['Country Name']
datasets[name] = df
In [11]:
datasets
Out[11]:
{'bankCapitalToAssetsRatioWorldBank': <class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 0 non-null values
1961 0 non-null values
1962 0 non-null values
1963 0 non-null values
1964 0 non-null values
1965 0 non-null values
1966 0 non-null values
1967 0 non-null values
1968 0 non-null values
1969 0 non-null values
1970 0 non-null values
1971 0 non-null values
1972 0 non-null values
1973 0 non-null values
1974 0 non-null values
1975 0 non-null values
1976 0 non-null values
1977 0 non-null values
1978 0 non-null values
1979 0 non-null values
1980 0 non-null values
1981 0 non-null values
1982 0 non-null values
1983 0 non-null values
1984 0 non-null values
1985 0 non-null values
1986 0 non-null values
1987 0 non-null values
1988 0 non-null values
1989 0 non-null values
1990 0 non-null values
1991 0 non-null values
1992 0 non-null values
1993 0 non-null values
1994 0 non-null values
1995 0 non-null values
1996 0 non-null values
1997 0 non-null values
1998 0 non-null values
1999 0 non-null values
2000 92 non-null values
2001 105 non-null values
2002 108 non-null values
2003 110 non-null values
2004 114 non-null values
2005 115 non-null values
2006 111 non-null values
2007 119 non-null values
2008 121 non-null values
2009 122 non-null values
2010 122 non-null values
2011 120 non-null values
2012 84 non-null values
dtypes: float64(53),
'currentAccountBalanceWorldBank': <class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 0 non-null values
1961 0 non-null values
1962 0 non-null values
1963 0 non-null values
1964 0 non-null values
1965 0 non-null values
1966 0 non-null values
1967 0 non-null values
1968 0 non-null values
1969 0 non-null values
1970 0 non-null values
1971 0 non-null values
1972 0 non-null values
1973 0 non-null values
1974 0 non-null values
1975 0 non-null values
1976 0 non-null values
1977 0 non-null values
1978 0 non-null values
1979 0 non-null values
1980 0 non-null values
1981 0 non-null values
1982 0 non-null values
1983 0 non-null values
1984 0 non-null values
1985 0 non-null values
1986 0 non-null values
1987 0 non-null values
1988 0 non-null values
1989 0 non-null values
1990 0 non-null values
1991 0 non-null values
1992 0 non-null values
1993 0 non-null values
1994 0 non-null values
1995 0 non-null values
1996 0 non-null values
1997 0 non-null values
1998 0 non-null values
1999 0 non-null values
2000 0 non-null values
2001 0 non-null values
2002 0 non-null values
2003 0 non-null values
2004 0 non-null values
2005 169 non-null values
2006 170 non-null values
2007 172 non-null values
2008 172 non-null values
2009 172 non-null values
2010 170 non-null values
2011 152 non-null values
2012 64 non-null values
dtypes: float64(53),
'gdpWorldBank': <class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 125 non-null values
1961 126 non-null values
1962 130 non-null values
1963 130 non-null values
1964 130 non-null values
1965 135 non-null values
1966 136 non-null values
1967 139 non-null values
1968 143 non-null values
1969 143 non-null values
1970 153 non-null values
1971 156 non-null values
1972 156 non-null values
1973 156 non-null values
1974 156 non-null values
1975 158 non-null values
1976 159 non-null values
1977 162 non-null values
1978 161 non-null values
1979 163 non-null values
1980 173 non-null values
1981 177 non-null values
1982 179 non-null values
1983 180 non-null values
1984 181 non-null values
1985 186 non-null values
1986 187 non-null values
1987 193 non-null values
1988 195 non-null values
1989 197 non-null values
1990 215 non-null values
1991 214 non-null values
1992 215 non-null values
1993 218 non-null values
1994 220 non-null values
1995 222 non-null values
1996 223 non-null values
1997 224 non-null values
1998 226 non-null values
1999 227 non-null values
2000 231 non-null values
2001 230 non-null values
2002 230 non-null values
2003 229 non-null values
2004 230 non-null values
2005 230 non-null values
2006 229 non-null values
2007 229 non-null values
2008 228 non-null values
2009 225 non-null values
2010 221 non-null values
2011 218 non-null values
2012 202 non-null values
dtypes: float64(53),
'giniWorldBank': <class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 0 non-null values
1961 0 non-null values
1962 0 non-null values
1963 0 non-null values
1964 0 non-null values
1965 0 non-null values
1966 0 non-null values
1967 0 non-null values
1968 0 non-null values
1969 0 non-null values
1970 0 non-null values
1971 0 non-null values
1972 0 non-null values
1973 0 non-null values
1974 0 non-null values
1975 0 non-null values
1976 0 non-null values
1977 0 non-null values
1978 1 non-null values
1979 1 non-null values
1980 2 non-null values
1981 6 non-null values
1982 2 non-null values
1983 2 non-null values
1984 6 non-null values
1985 9 non-null values
1986 11 non-null values
1987 18 non-null values
1988 24 non-null values
1989 17 non-null values
1990 11 non-null values
1991 14 non-null values
1992 30 non-null values
1993 33 non-null values
1994 25 non-null values
1995 28 non-null values
1996 37 non-null values
1997 25 non-null values
1998 47 non-null values
1999 32 non-null values
2000 47 non-null values
2001 39 non-null values
2002 49 non-null values
2003 46 non-null values
2004 48 non-null values
2005 47 non-null values
2006 46 non-null values
2007 44 non-null values
2008 49 non-null values
2009 42 non-null values
2010 35 non-null values
2011 11 non-null values
2012 0 non-null values
dtypes: float64(53),
'governmentDebtWorldBank': <class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 0 non-null values
1961 0 non-null values
1962 0 non-null values
1963 0 non-null values
1964 0 non-null values
1965 0 non-null values
1966 0 non-null values
1967 0 non-null values
1968 0 non-null values
1969 0 non-null values
1970 0 non-null values
1971 0 non-null values
1972 0 non-null values
1973 0 non-null values
1974 0 non-null values
1975 0 non-null values
1976 0 non-null values
1977 0 non-null values
1978 0 non-null values
1979 0 non-null values
1980 0 non-null values
1981 0 non-null values
1982 0 non-null values
1983 0 non-null values
1984 0 non-null values
1985 0 non-null values
1986 0 non-null values
1987 0 non-null values
1988 0 non-null values
1989 0 non-null values
1990 35 non-null values
1991 39 non-null values
1992 42 non-null values
1993 45 non-null values
1994 44 non-null values
1995 64 non-null values
1996 60 non-null values
1997 64 non-null values
1998 68 non-null values
1999 69 non-null values
2000 60 non-null values
2001 60 non-null values
2002 60 non-null values
2003 65 non-null values
2004 64 non-null values
2005 65 non-null values
2006 74 non-null values
2007 76 non-null values
2008 75 non-null values
2009 72 non-null values
2010 69 non-null values
2011 61 non-null values
2012 0 non-null values
dtypes: float64(53),
'incomeShareOfTopTenWorldBank': <class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 0 non-null values
1961 0 non-null values
1962 0 non-null values
1963 0 non-null values
1964 0 non-null values
1965 0 non-null values
1966 0 non-null values
1967 0 non-null values
1968 0 non-null values
1969 0 non-null values
1970 0 non-null values
1971 0 non-null values
1972 0 non-null values
1973 0 non-null values
1974 0 non-null values
1975 0 non-null values
1976 0 non-null values
1977 0 non-null values
1978 1 non-null values
1979 1 non-null values
1980 2 non-null values
1981 6 non-null values
1982 2 non-null values
1983 2 non-null values
1984 6 non-null values
1985 9 non-null values
1986 11 non-null values
1987 18 non-null values
1988 24 non-null values
1989 19 non-null values
1990 11 non-null values
1991 15 non-null values
1992 30 non-null values
1993 33 non-null values
1994 25 non-null values
1995 28 non-null values
1996 37 non-null values
1997 25 non-null values
1998 47 non-null values
1999 33 non-null values
2000 47 non-null values
2001 39 non-null values
2002 49 non-null values
2003 46 non-null values
2004 48 non-null values
2005 47 non-null values
2006 46 non-null values
2007 44 non-null values
2008 49 non-null values
2009 41 non-null values
2010 35 non-null values
2011 11 non-null values
2012 0 non-null values
dtypes: float64(53),
'inflationConsumerPricesWorldBank': <class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 0 non-null values
1961 69 non-null values
1962 71 non-null values
1963 72 non-null values
1964 76 non-null values
1965 82 non-null values
1966 88 non-null values
1967 96 non-null values
1968 97 non-null values
1969 98 non-null values
1970 103 non-null values
1971 105 non-null values
1972 107 non-null values
1973 110 non-null values
1974 114 non-null values
1975 118 non-null values
1976 119 non-null values
1977 124 non-null values
1978 124 non-null values
1979 119 non-null values
1980 125 non-null values
1981 138 non-null values
1982 140 non-null values
1983 140 non-null values
1984 144 non-null values
1985 145 non-null values
1986 151 non-null values
1987 154 non-null values
1988 155 non-null values
1989 159 non-null values
1990 159 non-null values
1991 163 non-null values
1992 167 non-null values
1993 176 non-null values
1994 181 non-null values
1995 186 non-null values
1996 189 non-null values
1997 189 non-null values
1998 189 non-null values
1999 192 non-null values
2000 194 non-null values
2001 198 non-null values
2002 199 non-null values
2003 202 non-null values
2004 203 non-null values
2005 205 non-null values
2006 207 non-null values
2007 209 non-null values
2008 209 non-null values
2009 211 non-null values
2010 211 non-null values
2011 208 non-null values
2012 191 non-null values
dtypes: float64(53),
'privateSectorDebtWorldBank': <class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 77 non-null values
1961 80 non-null values
1962 87 non-null values
1963 92 non-null values
1964 94 non-null values
1965 113 non-null values
1966 115 non-null values
1967 118 non-null values
1968 119 non-null values
1969 120 non-null values
1970 122 non-null values
1971 124 non-null values
1972 127 non-null values
1973 131 non-null values
1974 135 non-null values
1975 139 non-null values
1976 141 non-null values
1977 146 non-null values
1978 145 non-null values
1979 151 non-null values
1980 155 non-null values
1981 156 non-null values
1982 159 non-null values
1983 160 non-null values
1984 160 non-null values
1985 163 non-null values
1986 158 non-null values
1987 158 non-null values
1988 163 non-null values
1989 164 non-null values
1990 169 non-null values
1991 174 non-null values
1992 180 non-null values
1993 191 non-null values
1994 194 non-null values
1995 201 non-null values
1996 201 non-null values
1997 203 non-null values
1998 199 non-null values
1999 204 non-null values
2000 208 non-null values
2001 209 non-null values
2002 210 non-null values
2003 210 non-null values
2004 211 non-null values
2005 211 non-null values
2006 211 non-null values
2007 209 non-null values
2008 207 non-null values
2009 204 non-null values
2010 200 non-null values
2011 196 non-null values
2012 178 non-null values
dtypes: float64(53),
'surplusDeficitWorldBank': <class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 0 non-null values
1961 0 non-null values
1962 0 non-null values
1963 0 non-null values
1964 0 non-null values
1965 0 non-null values
1966 0 non-null values
1967 0 non-null values
1968 0 non-null values
1969 0 non-null values
1970 0 non-null values
1971 0 non-null values
1972 0 non-null values
1973 0 non-null values
1974 0 non-null values
1975 0 non-null values
1976 0 non-null values
1977 0 non-null values
1978 0 non-null values
1979 0 non-null values
1980 0 non-null values
1981 0 non-null values
1982 0 non-null values
1983 0 non-null values
1984 0 non-null values
1985 0 non-null values
1986 0 non-null values
1987 0 non-null values
1988 0 non-null values
1989 0 non-null values
1990 44 non-null values
1991 50 non-null values
1992 54 non-null values
1993 54 non-null values
1994 55 non-null values
1995 72 non-null values
1996 72 non-null values
1997 75 non-null values
1998 79 non-null values
1999 86 non-null values
2000 85 non-null values
2001 98 non-null values
2002 114 non-null values
2003 125 non-null values
2004 133 non-null values
2005 128 non-null values
2006 134 non-null values
2007 135 non-null values
2008 134 non-null values
2009 126 non-null values
2010 121 non-null values
2011 113 non-null values
2012 2 non-null values
dtypes: float64(53)}
In [12]:
xls = ExcelFile("data/betterLifeData.xlsx")
better = xls.parse('average_value', index_col=None, na_values=['NA'])
In [13]:
better
Out[13]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 37 entries, 0 to 36
Data columns (total 26 columns):
COUNTRY 37 non-null values
Households’ income 33 non-null values
Household financial wealth 32 non-null values
Employment 37 non-null values
Personal earnings 35 non-null values
Job security 37 non-null values
Long-term unemployment 35 non-null values
Rooms per person 31 non-null values
Housing expenditure 37 non-null values
Dwellings with basic facilities 33 non-null values
Employees working very long 34 non-null values
Time devoted to leisure and personal care 24 non-null values
Life expectancy 37 non-null values
Self-reported health 35 non-null values
Educational attainment 36 non-null values
Years in education 36 non-null values
Students’ skills 37 non-null values
Social network 37 non-null values
Consultation on rule-making 36 non-null values
Voter turn-out 37 non-null values
Water quality 37 non-null values
Air pollution 37 non-null values
Homicide 37 non-null values
Assault 37 non-null values
Life Satisfaction 37 non-null values
0 non-null values
dtypes: float64(24), object(2)
In [14]:
better.index
Out[14]:
Int64Index([0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36], dtype=int64)
In [15]:
better.index = better.COUNTRY
In [24]:
better.index
Out[24]:
Index([Australia, Austria, Belgium, Brazil, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Russian Federation, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States, OECD average], dtype=object)
In [31]:
cs = list(better.index)
cs[35] = u'United States'
better.index = cs
In [32]:
better.index
Out[32]:
Index([Australia, Austria, Belgium, Brazil, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Russian Federation, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States, OECD average], dtype=object)
In [21]:
df = datasets["privateSectorDebtWorldBank"]
In [22]:
df
Out[22]:
<class 'pandas.core.frame.DataFrame'>
Index: 246 entries, Arab World to Zimbabwe
Data columns (total 53 columns):
1960 77 non-null values
1961 80 non-null values
1962 87 non-null values
1963 92 non-null values
1964 94 non-null values
1965 113 non-null values
1966 115 non-null values
1967 118 non-null values
1968 119 non-null values
1969 120 non-null values
1970 122 non-null values
1971 124 non-null values
1972 127 non-null values
1973 131 non-null values
1974 135 non-null values
1975 139 non-null values
1976 141 non-null values
1977 146 non-null values
1978 145 non-null values
1979 151 non-null values
1980 155 non-null values
1981 156 non-null values
1982 159 non-null values
1983 160 non-null values
1984 160 non-null values
1985 163 non-null values
1986 158 non-null values
1987 158 non-null values
1988 163 non-null values
1989 164 non-null values
1990 169 non-null values
1991 174 non-null values
1992 180 non-null values
1993 191 non-null values
1994 194 non-null values
1995 201 non-null values
1996 201 non-null values
1997 203 non-null values
1998 199 non-null values
1999 204 non-null values
2000 208 non-null values
2001 209 non-null values
2002 210 non-null values
2003 210 non-null values
2004 211 non-null values
2005 211 non-null values
2006 211 non-null values
2007 209 non-null values
2008 207 non-null values
2009 204 non-null values
2010 200 non-null values
2011 196 non-null values
2012 178 non-null values
dtypes: float64(53)
In [50]:
cs.remove(u'Slovenia')
cs.remove(u'Korea')
cs.remove(u'OECD average')
In [46]:
for i in xrange(2000,2012):
figure()
df[str(i)][cs].plot(kind='bar')
title(i)
In [47]:
import json
In [70]:
def df_to_yearly_json(df, countries=cs, start=2000, end=2013):
data = {}
for year in xrange(start, end):
d = dict(df[str(year)][countries])
data[year] = d
return json.dumps(data)
In [71]:
df_to_yearly_json(df)
Out[71]:
'{"2000": {"Brazil": 31.658735890797228, "Canada": 95.674810069321552, "Italy": 75.514657334512961, "Czech Republic": 47.24533472559088, "Luxembourg": 102.25743690450216, "France": 85.131472622434543, "Slovak Republic": 51.060933661086402, "Ireland": 104.61112475842694, "Norway": 65.499830547608013, "Israel": 76.769717321079654, "Australia": 87.61940344723935, "Iceland": 97.060861813283466, "Germany": 119.44644688644688, "Chile": 69.802603397251403, "Belgium": 77.831102030149324, "Spain": 97.773163339985118, "Netherlands": 134.20159823906596, "Denmark": 135.3288308862, "Poland": 26.576188377410404, "Finland": 53.150270433828815, "Turkey": 17.752138674441596, "United States": 168.40549307356446, "Russian Federation": 13.645696452036793, "Sweden": 42.317961973950396, "Japan": 219.28225395206528, "Switzerland": 154.7030090084339, "New Zealand": 110.00535989949915, "Portugal": 126.27310278525474, "Estonia": 36.144866222339047, "Mexico": 18.311665862292006, "United Kingdom": 129.46619173295437, "Austria": 102.62067149492968, "Greece": 47.403554194936298, "Hungary": 32.457192930307073}, "2001": {"Brazil": 30.384001067795918, "Canada": 178.30623158924524, "Italy": 77.491099904760674, "Czech Republic": 39.089651578460291, "Luxembourg": 129.05626938413823, "France": 87.90170915996714, "Slovak Republic": 37.229089604766131, "Ireland": 109.7211192726372, "Norway": 70.015036889504572, "Israel": 84.94868011085947, "Australia": 88.422451707820827, "Iceland": 98.844061353961351, "Germany": 118.80060897283411, "Chile": 72.253362724994531, "Belgium": 75.949084498639351, "Spain": 101.18372068072023, "Netherlands": 135.29708686689105, "Denmark": 142.56321638560928, "Poland": 27.271402472407651, "Finland": 55.92010797771524, "Turkey": 15.351427589179007, "United States": 176.65959094798757, "Russian Federation": 16.837738718189541, "Sweden": 97.868438298276416, "Japan": 189.56407681875652, "Switzerland": 149.11514874914326, "New Zealand": 108.69314241797159, "Portugal": 133.40561652280675, "Estonia": 39.029597653452392, "Mexico": 15.664309980539908, "United Kingdom": 134.63128457657001, "Austria": 105.06677415204229, "Greece": 57.410125868581432, "Hungary": 33.280083006573932}, "2002": {"Brazil": 30.653703288737145, "Canada": 173.17717175309329, "Italy": 79.577268316090368, "Czech Republic": 29.56647049888414, "Luxembourg": 103.74203556059646, "France": 85.954011834905515, "Slovak Republic": 39.267158314148162, "Ireland": 108.7903978450972, "Norway": 74.740902516987788, "Israel": 89.764969640354977, "Australia": 91.398701408641386, "Iceland": 105.19766653050307, "Germany": 117.52166776099804, "Chile": 73.623762101947989, "Belgium": 74.043630407266775, "Spain": 105.71114749512518, "Netherlands": 141.16385147480514, "Denmark": 145.47484332395791, "Poland": 27.434744947302548, "Finland": 58.342731437004858, "Turkey": 14.521289914704338, "United States": 167.60161474005969, "Russian Federation": 17.991143481379058, "Sweden": 99.113040844972431, "Japan": 186.87550961941071, "Switzerland": 148.37006001701081, "New Zealand": 103.38059593266959, "Portugal": 135.90193416937711, "Estonia": 44.711617416074148, "Mexico": 17.707933109576615, "United Kingdom": 139.22285160289314, "Austria": 104.58026746157405, "Greece": 61.002750520300765, "Hungary": 34.989387955085903}, "2003": {"Brazil": 28.653790972218502, "Canada": 167.03114900982959, "Italy": 83.208547090801829, "Czech Republic": 30.48100391837081, "Luxembourg": 103.12515958810475, "France": 88.664236037770095, "Slovak Republic": 31.84525597332048, "Ireland": 113.77540883244717, "Norway": 77.44425783959376, "Israel": 85.659913035433419, "Australia": 99.403546728301635, "Iceland": 130.38636132987199, "Germany": 116.29471478463329, "Chile": 74.533831692025771, "Belgium": 73.808377118812857, "Spain": 113.17282225871621, "Netherlands": 147.99253582698216, "Denmark": 151.62080947305219, "Poland": 28.070051568155836, "Finland": 64.176017480811652, "Turkey": 14.546437011815867, "United States": 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{"Brazil": 54.38481799448315, "Canada": NaN, "Italy": 122.89462509041486, "Czech Republic": 53.09527075507534, "Luxembourg": 187.12463736327072, "France": 114.20986556480219, "Slovak Republic": NaN, "Ireland": 214.36651064749478, "Norway": NaN, "Israel": 95.652788661055496, "Australia": 126.35954158619067, "Iceland": 108.74474812957156, "Germany": 106.92893197660445, "Chile": 66.496203349898934, "Belgium": 94.420182605056894, "Spain": 214.39245368644549, "Netherlands": 199.35132656180997, "Denmark": 215.83104122613159, "Poland": 51.913005168132131, "Finland": 95.674959171346117, "Turkey": 44.206969515137537, "United States": 201.40521493067604, "Russian Federation": 43.801422974680129, "Sweden": 135.40284719452794, "Japan": 175.04776273859602, "Switzerland": 167.40145901315029, "New Zealand": 149.03339046824894, "Portugal": 190.6854989167503, "Estonia": 98.429936504795606, "Mexico": 24.706684127246817, "United Kingdom": 202.46652565204911, "Austria": 122.29114266913801, "Greece": 118.64606018981763, "Hungary": 68.795533979754936}, "2011": {"Brazil": 61.349925853161039, "Canada": NaN, "Italy": 122.48720570630287, "Czech Republic": 55.634106911621352, "Luxembourg": 170.6940130229961, "France": 115.89503705120048, "Slovak Republic": NaN, "Ireland": 204.26530540442567, "Norway": NaN, "Israel": 95.093590554061848, "Australia": 123.44448256669691, "Iceland": 97.001391850620848, "Germany": 104.50578569775514, "Chile": 70.438231337213637, "Belgium": 92.572861849662544, "Spain": 205.888155883971, "Netherlands": 198.36055769943169, "Denmark": 208.40633473958957, "Poland": 54.817622512052665, "Finland": 96.6536316092227, "Turkey": 49.973310662482298, "United States": 192.29134032061262, "Russian Federation": 45.814867048329745, "Sweden": 135.88239596744378, "Japan": 174.8172635773162, "Switzerland": 169.36245381329363, "New Zealand": NaN, "Portugal": 192.1800891068329, "Estonia": 84.720055099115299, "Mexico": 25.98424361388804, "United Kingdom": 186.86103227754072, "Austria": 119.75760084708436, "Greece": 121.87691293737093, "Hungary": 65.647675366928851}, "2012": {"Brazil": 68.372192263552591, "Canada": NaN, "Italy": 124.42659360204604, "Czech Republic": 56.908872184219881, "Luxembourg": 165.21130742340583, "France": 115.96347484302747, "Slovak Republic": NaN, "Ireland": 186.1237686019951, "Norway": NaN, "Israel": NaN, "Australia": 123.32488799996879, "Iceland": 96.778097016275538, "Germany": 101.93850692080781, "Chile": 73.245924371071752, "Belgium": 92.16886523899008, "Spain": 188.82694552297468, "Netherlands": 200.24207592593211, "Denmark": 205.79443339283077, "Poland": 53.781007643939041, "Finland": 98.241364947626622, "Turkey": 54.39521741379788, "United States": 193.59052794381057, "Russian Federation": 47.775320066396667, "Sweden": 138.47319817524507, "Japan": 176.73178223237326, "Switzerland": 176.1465627837517, "New Zealand": NaN, "Portugal": 184.15727263704954, "Estonia": 79.255476197999883, "Mexico": 27.687025839377583, "United Kingdom": 178.72070515425261, "Austria": 116.98291756055208, "Greece": 120.70826293295272, "Hungary": 56.361005776319516}}'
WTF? Dataframe . notnull() ['something actually null'] gives the item. Damn it
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Content source: jamesporter/CrashMap
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