In [1]:

import pandas as pd

In [2]:

wh_data_file = 'WHR20_DataForTable2.1.xls'
wh_all_df = pd.read_excel(wh_data_file)

In [3]:

wh_all_df

Out[3]:

	Country name	year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Positive affect	...	GINI index (World Bank estimate)	GINI index (World Bank estimate), average 2000-2017, unbalanced panel	gini of household income reported in Gallup, by wp5-year	Most people can be trusted, Gallup	Most people can be trusted, WVS round 1981-1984	Most people can be trusted, WVS round 1989-1993	Most people can be trusted, WVS round 1994-1998	Most people can be trusted, WVS round 1999-2004	Most people can be trusted, WVS round 2005-2009	Most people can be trusted, WVS round 2010-2014
0	Afghanistan	2008	3.723590	7.144916	0.450662	50.799999	0.718114	0.178993	0.881686	0.517637	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Afghanistan	2009	4.401778	7.314788	0.552308	51.200001	0.678896	0.201228	0.850035	0.583926	...	NaN	NaN	0.441906	0.286315	NaN	NaN	NaN	NaN	NaN	NaN
2	Afghanistan	2010	4.758381	7.421525	0.539075	51.599998	0.600127	0.131578	0.706766	0.618265	...	NaN	NaN	0.327318	0.275833	NaN	NaN	NaN	NaN	NaN	NaN
3	Afghanistan	2011	3.831719	7.394349	0.521104	51.919998	0.495901	0.173452	0.731109	0.611387	...	NaN	NaN	0.336764	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	Afghanistan	2012	3.782938	7.480296	0.520637	52.240002	0.530935	0.246943	0.775620	0.710385	...	NaN	NaN	0.344540	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1843	Zimbabwe	2015	3.703191	7.827643	0.735800	53.799999	0.667193	-0.117035	0.810457	0.715079	...	NaN	0.432	0.655137	NaN	NaN	NaN	NaN	0.116683	NaN	0.082942
1844	Zimbabwe	2016	3.735400	7.819675	0.768425	54.400002	0.732971	-0.088488	0.723612	0.737636	...	NaN	0.432	0.596690	NaN	NaN	NaN	NaN	0.116683	NaN	0.082942
1845	Zimbabwe	2017	3.638300	7.851042	0.754147	55.000000	0.752826	-0.091540	0.751208	0.806428	...	NaN	0.432	0.581484	NaN	NaN	NaN	NaN	0.116683	NaN	0.082942
1846	Zimbabwe	2018	3.616480	7.896704	0.775388	55.599998	0.762675	-0.063282	0.844209	0.710119	...	NaN	0.432	0.541772	NaN	NaN	NaN	NaN	0.116683	NaN	0.082942
1847	Zimbabwe	2019	2.693523	7.850442	0.759162	56.200001	0.631908	-0.062325	0.830652	0.716004	...	NaN	0.432	0.699588	NaN	NaN	NaN	NaN	0.116683	NaN	0.082942

1848 rows × 26 columns

In [4]:

wh_all_df.columns

Out[4]:

Index(['Country name', 'year', 'Life Ladder', 'Log GDP per capita',
       'Social support', 'Healthy life expectancy at birth',
       'Freedom to make life choices', 'Generosity',
       'Perceptions of corruption', 'Positive affect', 'Negative affect',
       'Confidence in national government', 'Democratic Quality',
       'Delivery Quality', 'Standard deviation of ladder by country-year',
       'Standard deviation/Mean of ladder by country-year',
       'GINI index (World Bank estimate)',
       'GINI index (World Bank estimate), average 2000-2017, unbalanced panel',
       'gini of household income reported in Gallup, by wp5-year',
       'Most people can be trusted, Gallup',
       'Most people can be trusted, WVS round 1981-1984',
       'Most people can be trusted, WVS round 1989-1993',
       'Most people can be trusted, WVS round 1994-1998',
       'Most people can be trusted, WVS round 1999-2004',
       'Most people can be trusted, WVS round 2005-2009',
       'Most people can be trusted, WVS round 2010-2014'],
      dtype='object')

In [5]:

cols_of_interest = wh_all_df.columns[1:9]
cols_of_interest

Out[5]:

Index(['year', 'Life Ladder', 'Log GDP per capita', 'Social support',
       'Healthy life expectancy at birth', 'Freedom to make life choices',
       'Generosity', 'Perceptions of corruption'],
      dtype='object')

In [6]:

wh_withna_df = wh_all_df[cols_of_interest]
wh_withna_df

Out[6]:

	year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption
0	2008	3.723590	7.144916	0.450662	50.799999	0.718114	0.178993	0.881686
1	2009	4.401778	7.314788	0.552308	51.200001	0.678896	0.201228	0.850035
2	2010	4.758381	7.421525	0.539075	51.599998	0.600127	0.131578	0.706766
3	2011	3.831719	7.394349	0.521104	51.919998	0.495901	0.173452	0.731109
4	2012	3.782938	7.480296	0.520637	52.240002	0.530935	0.246943	0.775620
...	...	...	...	...	...	...	...	...
1843	2015	3.703191	7.827643	0.735800	53.799999	0.667193	-0.117035	0.810457
1844	2016	3.735400	7.819675	0.768425	54.400002	0.732971	-0.088488	0.723612
1845	2017	3.638300	7.851042	0.754147	55.000000	0.752826	-0.091540	0.751208
1846	2018	3.616480	7.896704	0.775388	55.599998	0.762675	-0.063282	0.844209
1847	2019	2.693523	7.850442	0.759162	56.200001	0.631908	-0.062325	0.830652

1848 rows × 8 columns

Plotting data¶

In [7]:

import seaborn as sns

In [8]:

sns.pairplot(wh_withna_df)

Out[8]:

<seaborn.axisgrid.PairGrid at 0x7fa74cab4760>

In [9]:

wh_withna_df.isna().sum()

Out[9]:

year                                  0
Life Ladder                           0
Log GDP per capita                   29
Social support                       13
Healthy life expectancy at birth     52
Freedom to make life choices         31
Generosity                           83
Perceptions of corruption           103
dtype: int64

In [11]:

wh_df = wh_withna_df.dropna()

In [13]:

wh_withna_df.shape[0] - wh_df.shape[0]

Out[13]:

In [14]:

%load_ext autoreload
%autoreload 2

In [15]:

from happypred import whdata_to_csv

In [16]:

X_train_path, y_train_path, X_test_path, y_test_path = whdata_to_csv('WHR20_DataForTable2.1.xls')

In [17]:

X_test_path

Out[17]:

'X_test.csv'

In [ ]: