In [1]:
%matplotlib inline
import pickle
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
pd.set_option('display.max_columns', 25)
In [2]:
with open ('un_explore.pkl', 'r') as picklefile:
undata = pickle.load(picklefile)
In [3]:
hiv = 'hiv_incidence_rate_1549_years_old_percentage_midpoint'
yearly = pd.DataFrame(undata.groupby(['countryname', 'iso3code', 'year'])[hiv].mean())
hivdf = yearly.unstack()
In [4]:
hivdf['mean'] = hivdf.mean(axis=1, skipna=True)
In [5]:
hivdf = hivdf.sort(columns='mean', ascending=False)
hivdf.head(20)
Out[5]:
hiv_incidence_rate_1549_years_old_percentage_midpoint
mean
year
1990.0
1991.0
1992.0
1993.0
1994.0
1995.0
1996.0
1997.0
1998.0
1999.0
2000.0
2001.0
2002.0
2003.0
2004.0
2005.0
2006.0
2007.0
2008.0
2009.0
2010.0
2011.0
2012.0
2013.0
countryname
iso3code
Swaziland
SWZ
0.78
1.39
2.18
3.06
3.86
4.50
4.91
5.05
4.96
4.74
4.42
4.12
3.83
3.66
3.59
3.49
3.39
3.27
3.22
3.12
3.11
2.89
2.41
2.18
3.422083
Zimbabwe
ZWE
4.25
5.06
5.65
5.95
5.92
5.59
5.05
4.40
3.73
3.13
2.63
2.24
1.97
1.77
1.65
1.56
1.49
1.44
1.41
1.37
1.30
1.21
1.10
0.97
2.951667
Botswana
BWA
1.92
2.69
3.59
4.45
5.13
5.52
5.53
5.29
4.89
4.37
3.83
3.33
2.89
2.47
2.13
1.86
1.70
1.57
1.51
1.35
1.20
1.15
1.06
0.93
2.931667
Lesotho
LSO
0.44
0.87
1.56
2.49
3.53
4.36
4.73
4.62
4.26
3.84
3.43
3.13
2.91
2.78
2.73
2.73
2.70
2.75
2.82
2.65
2.38
2.28
2.30
2.21
2.854167
South Africa
ZAF
0.16
0.33
0.59
0.97
1.46
1.99
2.48
2.83
3.01
3.04
2.95
2.78
2.59
2.40
2.24
2.11
2.01
1.93
1.88
1.84
1.79
1.69
1.52
1.36
1.914583
Namibia
NAM
0.50
0.79
1.16
1.59
2.06
2.49
2.83
3.01
3.03
2.95
2.72
2.45
2.19
1.92
1.68
1.47
1.22
1.08
0.99
0.95
0.91
0.93
0.96
0.91
1.699583
Malawi
MWI
2.32
2.30
2.29
2.32
2.35
2.40
2.43
2.43
2.41
2.36
2.27
2.14
1.98
1.80
1.61
1.41
1.21
1.03
0.86
0.73
0.63
0.55
0.47
0.40
1.695833
Zambia
ZMB
2.18
2.06
2.01
1.99
1.99
1.99
1.97
1.96
1.92
1.87
1.81
1.80
1.73
1.64
1.52
1.43
1.34
1.24
1.11
1.04
1.02
0.98
0.78
0.70
1.586667
Mozambique
MOZ
NaN
0.37
0.47
0.58
0.71
0.87
1.05
1.25
1.44
1.63
1.75
1.81
1.80
1.72
1.63
1.50
1.38
1.26
1.16
1.11
1.07
1.00
1.00
0.98
1.197391
Kenya
KEN
1.15
1.69
2.24
2.60
2.59
2.24
1.75
1.33
1.02
0.82
0.77
0.70
0.65
0.61
0.58
0.55
0.54
0.55
0.56
0.52
0.51
0.51
0.44
0.43
1.056250
Uganda
UGA
1.95
1.60
1.34
1.09
0.89
0.73
0.59
0.58
0.53
0.53
0.56
0.61
0.62
0.66
0.71
0.75
0.79
0.86
0.89
0.89
0.90
0.93
0.87
0.79
0.860833
United Republic of Tanzania
TZA
1.34
1.45
1.48
1.45
1.37
1.26
1.13
1.02
0.90
0.83
0.75
0.71
0.66
0.62
0.59
0.55
0.52
0.48
0.44
0.41
0.38
0.34
0.33
0.27
0.803333
Central African Republic
CAF
0.97
1.11
1.25
1.36
1.42
1.46
1.39
1.34
1.24
1.07
0.96
0.79
0.65
0.52
0.45
0.36
0.31
0.29
0.26
0.26
0.26
0.28
0.28
0.26
0.772500
Cameroon
CMR
0.40
0.48
0.57
0.66
0.74
0.80
0.84
0.83
0.83
0.81
0.78
0.75
0.71
0.67
0.61
0.57
0.51
0.49
0.46
0.42
0.40
0.39
0.36
0.34
0.600833
Cote d'Ivoire
CIV
0.82
0.90
0.95
1.01
1.03
1.03
1.03
0.99
0.89
0.81
0.71
0.59
0.48
0.40
0.31
0.24
0.17
0.14
0.11
0.12
0.14
0.14
0.14
0.14
0.553750
Gabon
GAB
0.25
0.33
0.42
0.52
0.63
0.73
0.82
0.89
0.92
0.94
0.91
0.84
0.75
0.65
0.52
0.42
0.33
0.27
0.23
0.22
0.21
0.19
0.19
0.17
0.514583
Congo
COG
0.90
0.91
0.91
0.87
0.83
0.78
0.69
0.66
0.60
0.53
0.47
0.43
0.40
0.35
0.31
0.29
0.27
0.24
0.23
0.20
0.17
0.15
0.14
0.13
0.477500
Guinea-Bissau
GNB
0.20
0.26
0.32
0.38
0.44
0.48
0.52
0.55
0.57
0.55
0.54
0.54
0.52
0.51
0.49
0.46
0.45
0.43
0.40
0.38
0.37
0.36
0.32
0.31
0.431250
Nigeria
NGA
0.25
0.30
0.35
0.40
0.44
0.48
0.53
0.53
0.51
0.52
0.54
0.50
0.46
0.46
0.42
0.41
0.37
0.37
0.34
0.31
0.29
0.28
0.23
0.20
0.395417
Rwanda
RWA
0.93
0.88
0.81
0.74
0.67
0.60
0.54
0.48
0.42
0.37
0.33
0.29
0.26
0.24
0.23
0.21
0.19
0.17
0.16
0.15
0.14
0.13
0.12
0.10
0.381667
In [6]:
hiv_change = hivdf.pct_change(axis=1)
In [7]:
highest = hiv_change.head(20)
highest
Out[7]:
hiv_incidence_rate_1549_years_old_percentage_midpoint
mean
year
1990.0
1991.0
1992.0
1993.0
1994.0
1995.0
1996.0
1997.0
1998.0
1999.0
2000.0
2001.0
2002.0
2003.0
2004.0
2005.0
2006.0
2007.0
2008.0
2009.0
2010.0
2011.0
2012.0
2013.0
countryname
iso3code
Swaziland
SWZ
NaN
0.782051
0.568345
0.403670
0.261438
0.165803
0.091111
0.028513
-0.017822
-0.044355
-0.067511
-0.067873
-0.070388
-0.044386
-0.019126
-0.027855
-0.028653
-0.035398
-0.015291
-0.031056
-0.003205
-0.070740
-0.166090
-0.095436
0.569763
Zimbabwe
ZWE
NaN
0.190588
0.116601
0.053097
-0.005042
-0.055743
-0.096601
-0.128713
-0.152273
-0.160858
-0.159744
-0.148289
-0.120536
-0.101523
-0.067797
-0.054545
-0.044872
-0.033557
-0.020833
-0.028369
-0.051095
-0.069231
-0.090909
-0.118182
2.042955
Botswana
BWA
NaN
0.401042
0.334572
0.239554
0.152809
0.076023
0.001812
-0.043400
-0.075614
-0.106339
-0.123570
-0.130548
-0.132132
-0.145329
-0.137652
-0.126761
-0.086022
-0.076471
-0.038217
-0.105960
-0.111111
-0.041667
-0.078261
-0.122642
2.152330
Lesotho
LSO
NaN
0.977273
0.793103
0.596154
0.417671
0.235127
0.084862
-0.023256
-0.077922
-0.098592
-0.106771
-0.087464
-0.070288
-0.044674
-0.017986
0.000000
-0.010989
0.018519
0.025455
-0.060284
-0.101887
-0.042017
0.008772
-0.039130
0.291478
South Africa
ZAF
NaN
1.062500
0.787879
0.644068
0.505155
0.363014
0.246231
0.141129
0.063604
0.009967
-0.029605
-0.057627
-0.068345
-0.073359
-0.066667
-0.058036
-0.047393
-0.039801
-0.025907
-0.021277
-0.027174
-0.055866
-0.100592
-0.105263
0.407782
Namibia
NAM
NaN
0.580000
0.468354
0.370690
0.295597
0.208738
0.136546
0.063604
0.006645
-0.026403
-0.077966
-0.099265
-0.106122
-0.123288
-0.125000
-0.125000
-0.170068
-0.114754
-0.083333
-0.040404
-0.042105
0.021978
0.032258
-0.052083
0.867674
Malawi
MWI
NaN
-0.008621
-0.004348
0.013100
0.012931
0.021277
0.012500
0.000000
-0.008230
-0.020747
-0.038136
-0.057269
-0.074766
-0.090909
-0.105556
-0.124224
-0.141844
-0.148760
-0.165049
-0.151163
-0.136986
-0.126984
-0.145455
-0.148936
3.239583
Zambia
ZMB
NaN
-0.055046
-0.024272
-0.009950
0.000000
0.000000
-0.010050
-0.005076
-0.020408
-0.026042
-0.032086
-0.005525
-0.038889
-0.052023
-0.073171
-0.059211
-0.062937
-0.074627
-0.104839
-0.063063
-0.019231
-0.039216
-0.204082
-0.102564
1.266667
Mozambique
MOZ
NaN
-0.830275
0.270270
0.234043
0.224138
0.225352
0.206897
0.190476
0.152000
0.131944
0.073620
0.034286
-0.005525
-0.044444
-0.052326
-0.079755
-0.080000
-0.086957
-0.079365
-0.043103
-0.036036
-0.065421
0.000000
-0.020000
0.221828
Kenya
KEN
NaN
0.469565
0.325444
0.160714
-0.003846
-0.135135
-0.218750
-0.240000
-0.233083
-0.196078
-0.060976
-0.090909
-0.071429
-0.061538
-0.049180
-0.051724
-0.018182
0.018519
0.018182
-0.071429
-0.019231
0.000000
-0.137255
-0.022727
1.456395
Uganda
UGA
NaN
-0.179487
-0.162500
-0.186567
-0.183486
-0.179775
-0.191781
-0.016949
-0.086207
0.000000
0.056604
0.089286
0.016393
0.064516
0.075758
0.056338
0.053333
0.088608
0.034884
0.000000
0.011236
0.033333
-0.064516
-0.091954
0.089662
United Republic of Tanzania
TZA
NaN
0.082090
0.020690
-0.020270
-0.055172
-0.080292
-0.103175
-0.097345
-0.117647
-0.077778
-0.096386
-0.053333
-0.070423
-0.060606
-0.048387
-0.067797
-0.054545
-0.076923
-0.083333
-0.068182
-0.073171
-0.105263
-0.029412
-0.181818
1.975309
Central African Republic
CAF
NaN
0.144330
0.126126
0.088000
0.044118
0.028169
-0.047945
-0.035971
-0.074627
-0.137097
-0.102804
-0.177083
-0.177215
-0.200000
-0.134615
-0.200000
-0.138889
-0.064516
-0.103448
0.000000
0.000000
0.076923
0.000000
-0.071429
1.971154
Cameroon
CMR
NaN
0.200000
0.187500
0.157895
0.121212
0.081081
0.050000
-0.011905
0.000000
-0.024096
-0.037037
-0.038462
-0.053333
-0.056338
-0.089552
-0.065574
-0.105263
-0.039216
-0.061224
-0.086957
-0.047619
-0.025000
-0.076923
-0.055556
0.767157
Cote d'Ivoire
CIV
NaN
0.097561
0.055556
0.063158
0.019802
0.000000
0.000000
-0.038835
-0.101010
-0.089888
-0.123457
-0.169014
-0.186441
-0.166667
-0.225000
-0.225806
-0.291667
-0.176471
-0.214286
0.090909
0.166667
0.000000
0.000000
0.000000
2.955357
Gabon
GAB
NaN
0.320000
0.272727
0.238095
0.211538
0.158730
0.123288
0.085366
0.033708
0.021739
-0.031915
-0.076923
-0.107143
-0.133333
-0.200000
-0.192308
-0.214286
-0.181818
-0.148148
-0.043478
-0.045455
-0.095238
0.000000
-0.105263
2.026961
Congo
COG
NaN
0.011111
0.000000
-0.043956
-0.045977
-0.060241
-0.115385
-0.043478
-0.090909
-0.116667
-0.113208
-0.085106
-0.069767
-0.125000
-0.114286
-0.064516
-0.068966
-0.111111
-0.041667
-0.130435
-0.150000
-0.117647
-0.066667
-0.071429
2.673077
Guinea-Bissau
GNB
NaN
0.300000
0.230769
0.187500
0.157895
0.090909
0.083333
0.057692
0.036364
-0.035088
-0.018182
0.000000
-0.037037
-0.019231
-0.039216
-0.061224
-0.021739
-0.044444
-0.069767
-0.050000
-0.026316
-0.027027
-0.111111
-0.031250
0.391129
Nigeria
NGA
NaN
0.200000
0.166667
0.142857
0.100000
0.090909
0.104167
0.000000
-0.037736
0.019608
0.038462
-0.074074
-0.080000
0.000000
-0.086957
-0.023810
-0.097561
0.000000
-0.081081
-0.088235
-0.064516
-0.034483
-0.178571
-0.130435
0.977083
Rwanda
RWA
NaN
-0.053763
-0.079545
-0.086420
-0.094595
-0.104478
-0.100000
-0.111111
-0.125000
-0.119048
-0.108108
-0.121212
-0.103448
-0.076923
-0.041667
-0.086957
-0.095238
-0.105263
-0.058824
-0.062500
-0.066667
-0.071429
-0.076923
-0.166667
2.816667
Save to CSV for possible visualization
In [8]:
highest.to_csv('hiv_pctchange.csv')
In [9]:
oda_mill = 'net_oda_mill'
oda_pc = 'net_oda_received_per_capita_current_us'
oda_pct = 'net_oda_received__of_gni'
Create a list of the countries with the highest average HIV incidence from 1990 to 2013.
The subset_unstack function takes a column and dataframe, creates a temporary dataframe for each country, and concatenates the dataframes for each country together. Then unstacks to expand the dataframe by year.
In [10]:
highest_countries = []
for i in range(20):
highest_countries.append(highest.index[i][0])
In [11]:
def subset_unstack(col, df):
temp2_df = df
## find only the countries with highest HIV incidence
for country in highest_countries:
country_df = undata[undata['countryname'] == country]
temp1_df = country_df[['countryname', 'iso3code', 'year', col]]
temp2_df = pd.concat([temp2_df, temp1_df])
## unstack dataframe
yearly = pd.DataFrame(temp2_df.groupby(['countryname', 'iso3code', 'year'])[col].mean())
new_df = yearly.unstack()
return new_df
In [12]:
temp = highest_countries[-1]
oda1_df = undata[undata['countryname'] == temp]
oda1_df = oda1_df[['countryname', 'iso3code', 'year', oda_mill]]
oda1b_df = subset_unstack(oda_mill, oda1_df)
oda1b_df
Out[12]:
net_oda_mill
year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
countryname
iso3code
Botswana
BWA
145.22
131.07
111.26
129.90
85.18
89.54
74.45
121.50
106.25
60.89
30.62
29.17
37.19
27.85
50.18
47.98
68.81
107.67
720.26
279.16
156.14
120.15
73.86
108.38
Cameroon
CMR
444.38
516.26
714.78
543.72
730.08
442.80
410.19
498.77
497.31
434.29
376.73
457.76
605.85
885.98
790.66
413.93
1718.93
1926.29
548.59
648.34
540.50
611.84
596.24
737.49
Central African Republic
CAF
248.89
173.44
177.65
171.74
167.56
167.78
168.63
91.00
119.95
118.09
75.28
76.39
60.24
51.25
109.83
88.90
133.66
176.92
257.28
242.03
261.01
268.76
227.25
189.25
Congo
COG
217.16
133.27
112.89
122.36
361.85
124.86
429.29
268.59
64.91
140.35
32.04
73.78
57.66
69.15
115.48
1425.48
258.30
118.71
485.00
283.28
1311.50
259.79
138.60
150.42
Cote d'Ivoire
CIV
686.40
629.90
755.70
763.03
1593.10
1211.84
964.17
446.32
967.08
447.36
350.57
185.91
1067.72
253.67
160.52
91.21
247.14
171.08
625.67
2401.60
844.96
1436.00
2635.63
1262.00
Gabon
GAB
131.23
142.71
68.31
100.66
180.83
143.54
126.31
38.83
44.56
47.61
11.66
8.53
71.66
-10.57
39.87
60.43
29.05
51.14
62.05
77.21
104.00
72.55
73.20
90.87
Guinea-Bissau
GNB
126.35
112.82
107.30
94.53
174.36
117.60
180.77
124.35
95.83
52.42
81.07
60.70
60.04
150.44
76.39
66.03
87.02
122.32
133.78
146.55
125.39
119.68
78.87
103.62
Kenya
KEN
1181.29
916.46
883.14
914.39
676.63
731.85
595.02
448.60
415.21
310.47
512.72
471.23
392.81
523.00
660.24
759.20
946.70
1326.78
1365.96
1776.20
1628.57
2482.42
2654.08
3236.28
Lesotho
LSO
139.13
123.31
143.16
143.06
115.62
112.65
103.21
91.34
61.17
31.02
36.67
55.12
76.80
78.87
98.04
67.49
70.62
128.79
143.80
122.39
256.23
264.71
282.68
320.00
Malawi
MWI
500.36
549.57
577.04
496.34
470.14
434.14
491.54
343.94
434.52
446.62
446.11
409.43
378.14
518.13
505.61
573.35
722.80
743.95
923.63
771.39
1022.85
799.64
1174.60
1125.88
Mozambique
MOZ
997.51
1065.10
1459.68
1175.53
1198.94
1062.39
885.73
947.14
1039.80
818.52
906.22
960.72
2219.27
1047.97
1242.86
1297.15
1639.30
1776.55
1996.38
2012.40
1951.53
2084.98
2096.92
2314.14
Namibia
NAM
119.62
180.45
142.34
152.55
137.10
190.37
185.92
165.16
180.83
178.55
152.33
112.13
142.42
146.17
173.11
125.13
151.73
217.42
210.16
325.53
256.44
290.60
264.86
261.72
Nigeria
NGA
255.08
258.32
258.82
288.42
189.66
210.96
188.75
199.75
203.15
151.80
173.70
176.17
297.93
308.22
576.94
6408.81
11428.02
1956.26
1290.16
1657.07
2061.96
1768.55
1915.82
2529.48
Rwanda
RWA
287.92
359.16
348.93
353.91
711.75
694.70
465.31
229.67
350.07
373.08
321.46
304.88
362.92
335.24
490.11
577.40
603.07
722.57
933.51
933.59
1032.20
1264.00
878.99
1081.11
South Africa
ZAF
NaN
NaN
NaN
270.45
293.08
386.17
362.34
495.59
512.98
540.44
486.37
425.35
511.24
655.68
629.09
690.20
714.99
807.49
1125.18
1074.54
1030.54
1403.15
1067.15
1292.95
Swaziland
SWZ
53.63
52.92
56.01
54.21
57.78
57.75
33.05
28.28
34.80
28.99
13.13
29.15
22.30
40.09
24.89
46.65
34.78
50.67
69.89
56.03
91.45
124.90
88.15
115.93
Uganda
UGA
663.10
663.28
723.72
609.19
751.17
833.16
673.69
813.05
655.38
605.03
853.28
822.19
725.39
997.65
1216.02
1192.16
1586.43
1737.30
1641.47
1784.70
1723.47
1577.82
1655.19
1692.56
United Republic of Tanzania
TZA
1163.15
1073.00
1334.08
944.43
963.57
871.05
867.21
943.85
1001.42
991.86
1063.92
1274.23
1269.84
1725.39
1772.41
1499.07
1883.29
2821.59
2331.46
2933.14
2958.18
2445.77
2831.89
3430.28
Zambia
ZMB
474.81
877.97
1030.78
866.92
715.13
2030.67
608.06
609.51
347.97
623.03
794.65
570.84
811.33
774.54
1130.47
1172.05
1467.54
1007.84
1116.24
1267.06
914.37
1035.06
957.72
1142.42
Zimbabwe
ZWE
334.26
388.68
788.78
496.05
558.98
489.10
369.36
335.51
261.21
244.50
175.64
160.93
198.83
186.97
187.05
372.72
278.24
478.11
612.42
736.22
732.47
715.68
1001.22
811.05
In [13]:
oda2_df = undata[undata['countryname'] == temp]
oda2_df = oda2_df[['countryname', 'iso3code', 'year', oda_pc]]
oda2b_df = subset_unstack(oda_pc, oda2_df)
oda2b_df
Out[13]:
net_oda_received_per_capita_current_us
year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
countryname
iso3code
Botswana
BWA
104.934418
92.010392
75.941660
86.294919
55.137504
56.547305
45.928751
73.309846
62.790016
35.300106
17.443566
16.356866
20.558592
15.196971
27.055020
25.578352
36.293266
56.219053
372.473974
143.033179
79.285406
60.477143
36.857943
53.623097
Cameroon
CMR
36.815806
41.532254
55.856016
41.287099
53.892582
31.788479
28.650657
33.908189
32.917196
27.994051
23.652485
27.996800
36.101085
51.440849
44.733340
22.821484
92.356319
100.865152
27.996391
32.249392
26.206895
28.920029
27.476965
33.139721
Central African Republic
CAF
85.446289
58.209390
58.230838
54.954268
52.352144
51.219665
50.332821
26.571414
34.289651
33.082852
20.690891
20.623400
15.990452
13.382473
28.207864
22.444411
33.148963
43.078753
61.475145
56.731361
60.003389
60.583150
50.218675
40.994997
Congo
COG
91.118008
54.462162
44.943525
47.455345
136.659747
45.893731
153.450921
93.314429
21.919005
46.100372
10.248851
23.015714
17.559359
20.559443
33.483450
402.352106
70.832075
31.581401
125.113666
70.906044
318.966660
61.483533
31.957199
33.820244
Cote d'Ivoire
CIV
56.653268
50.271036
58.350729
57.047424
115.451550
85.236221
65.892854
29.671292
62.649038
28.309896
21.732241
11.322049
64.031234
15.001359
9.362865
5.243764
13.992422
9.531418
34.264430
129.108964
44.526445
74.058969
132.845928
62.118264
Gabon
GAB
138.617919
146.739617
68.390496
98.151350
171.765586
132.848733
113.926425
34.138905
38.198924
39.810388
9.514274
6.795194
55.752740
-8.033014
29.596363
43.806838
20.560447
35.332613
41.845293
50.824307
66.828512
45.513458
44.837226
54.357482
Guinea-Bissau
GNB
124.190940
108.406032
100.780414
86.785680
156.482079
103.188037
155.105473
104.349183
78.656463
42.086501
63.668606
46.629609
45.114059
110.572199
54.925578
46.450442
59.903942
82.407162
88.191862
94.493215
79.029436
73.684236
47.410430
60.800760
Kenya
KEN
50.382491
37.812348
35.273478
35.387799
25.404117
26.692244
21.110313
15.499083
13.979078
10.186493
16.388658
14.668021
11.903144
15.425448
18.953566
21.215167
25.755289
35.144345
35.229418
44.600423
39.809389
59.066014
61.468140
72.965292
Lesotho
LSO
87.090478
75.747896
86.222265
84.477983
67.021502
64.231074
58.009315
50.697155
33.580203
16.862261
19.755148
29.452311
40.732182
41.537701
51.275561
35.044375
36.394314
65.850830
72.913535
61.506438
127.546081
130.430112
137.788837
154.256640
Malawi
MWI
52.964273
56.905926
59.126380
50.643657
47.721534
43.570571
48.411775
33.057616
40.608663
40.554970
39.403803
35.225342
31.705126
42.335244
40.226457
44.360640
54.315093
54.248442
65.328652
52.931593
68.127804
51.731418
73.844105
68.808274
Mozambique
MOZ
73.519532
76.661494
101.716607
78.930558
77.583900
66.475943
53.799859
55.995320
59.928283
45.989173
49.586285
51.140976
114.869678
52.732136
60.808773
61.738543
75.938108
80.127988
87.704681
86.143480
81.424810
84.819530
83.199902
89.578161
Namibia
NAM
84.510406
123.077280
94.035168
97.821069
85.376516
115.081846
109.024230
93.949324
99.921590
96.141990
80.260154
58.059879
72.726233
73.777140
86.411557
61.730831
73.908962
104.493680
99.564571
151.868581
117.688795
131.041505
117.226175
113.627532
Nigeria
NGA
2.667717
2.633624
2.572962
2.796265
1.793428
1.945680
1.697908
1.752508
1.738295
1.266775
1.413612
1.398119
2.305520
2.325309
4.242229
45.913021
79.740622
13.290952
8.532348
10.664559
12.910830
10.771171
11.347374
14.569449
Rwanda
RWA
39.907433
51.502338
53.311638
58.340647
124.247966
122.655344
78.472740
35.494236
48.826597
47.507868
38.289209
34.803641
40.380425
36.733932
52.959793
61.233643
62.423494
72.779975
91.315031
88.662814
95.250118
113.421058
76.715419
91.802147
South Africa
ZAF
NaN
NaN
NaN
7.217043
7.655573
9.871421
9.058444
12.109398
12.243052
12.590776
11.053864
9.471220
11.224611
14.214659
13.462894
14.576861
14.898201
16.595725
22.802667
21.466885
20.289492
27.217368
20.388144
24.323007
Swaziland
SWZ
62.163277
59.645105
61.688623
58.527959
61.193172
59.942206
33.570136
28.090111
33.829302
27.658229
12.343532
27.122309
20.606496
36.849831
22.735618
42.230877
31.102085
44.644076
60.566985
47.738818
76.645982
103.039288
71.609321
92.780073
Uganda
UGA
37.816144
36.532085
38.519430
31.352318
37.406718
40.170243
31.469528
36.815369
28.769404
25.737415
35.149638
32.772199
27.960439
37.172445
43.793734
41.502713
53.394662
56.536637
51.652990
54.305081
50.709365
44.890666
45.539987
45.040198
United Republic of Tanzania
TZA
45.640906
40.740539
48.982051
33.541505
33.149584
29.089007
28.174049
29.881873
30.928708
29.890523
31.272898
36.515703
35.463955
46.935555
46.932437
38.611559
47.150209
68.618946
55.047258
67.212573
65.776317
52.762177
59.265506
69.645935
Zambia
ZMB
60.527635
109.224213
125.254573
102.922240
82.909038
229.679037
67.016330
65.397444
36.332093
63.321340
78.670577
55.089023
76.357431
71.094465
101.163795
102.183762
124.561902
83.226394
89.610852
98.795863
69.181436
75.918695
68.043571
78.578189
Zimbabwe
ZWE
31.950580
36.213385
71.829599
44.247503
48.910392
42.021196
31.179856
27.852832
21.359009
19.742058
14.047096
12.785654
15.729074
14.753293
14.736414
29.323582
21.866808
37.527786
47.905040
57.120388
56.012177
53.573923
72.952264
57.319447
In [14]:
oda3_df = undata[undata['countryname'] == temp]
oda3_df = oda3_df[['countryname', 'year', oda_pct]]
oda3b_df = subset_unstack(oda_pct, oda3_df)
oda3b_df
Out[14]:
net_oda_received__of_gni
year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
countryname
iso3code
Botswana
BWA
3.941052
3.272047
2.612443
2.916636
2.111078
1.905848
1.620354
2.492079
2.163927
1.166028
0.563267
0.544977
0.784489
0.409810
0.627415
0.527505
0.735623
1.055492
6.983930
2.783507
1.275942
0.771658
0.498536
0.728692
Cameroon
CMR
4.163320
4.427735
6.648723
4.247124
8.417524
5.396513
4.487030
5.402998
5.428941
4.334770
4.333259
5.121921
5.935662
6.765157
5.143284
2.571123
9.710903
9.664587
2.394579
2.786467
2.313934
2.327791
2.290883
2.547626
Central African Republic
CAF
17.548801
12.741169
12.721746
13.629466
20.178761
15.330877
17.090080
9.869815
12.644845
11.912229
8.351119
8.267226
6.132488
4.507607
8.742579
6.633935
9.223720
10.488054
13.109881
12.249073
13.078948
12.186924
10.418660
12.207044
Congo
COG
9.344875
5.847937
4.417661
7.916224
23.892303
10.165336
29.502715
16.168992
4.389937
8.532177
1.408215
3.749541
2.653163
2.682102
3.652486
35.351638
5.059348
2.055837
5.531419
4.059131
14.533132
2.424894
1.314758
1.361280
Cote d'Ivoire
CIV
7.453448
6.905650
7.705520
7.883311
21.130249
12.103072
8.590802
4.056422
8.045046
3.809723
3.608517
1.875651
9.880192
1.948645
1.087305
0.555047
1.446001
0.875745
2.682163
10.288401
3.524749
5.886860
10.068062
4.223325
Gabon
GAB
2.459172
2.942942
1.404292
2.687475
4.896820
3.343249
2.561550
0.842240
1.122898
1.162900
0.271850
0.194318
1.483145
-0.182740
0.607302
0.710833
0.330315
0.474665
0.454260
0.692949
0.814020
0.465039
0.476442
0.608394
Guinea-Bissau
GNB
54.152635
46.978863
50.419734
42.661635
78.707159
49.841815
71.785478
48.985463
49.818696
24.944487
22.659609
16.126969
14.748110
32.326152
14.667058
11.476296
14.843658
17.681558
15.727397
17.967953
14.811451
10.851186
8.234039
10.957691
Kenya
KEN
14.394403
11.782833
11.243549
16.959481
9.971231
8.386840
5.034028
3.465811
2.973412
2.440197
4.077109
3.670442
3.015548
3.548543
4.138463
4.053022
3.675728
4.170426
3.808776
4.803545
4.086542
5.916100
5.280407
5.924814
Lesotho
LSO
15.366048
12.530459
12.999280
13.577585
10.663223
9.416239
9.081276
7.717994
5.648042
2.861925
3.614808
5.126883
7.646998
5.379089
5.459223
3.610244
3.772167
6.360815
7.082784
5.805666
9.877622
8.472925
9.878758
11.177310
Malawi
MWI
27.230924
25.478211
32.776356
24.464040
41.291189
32.157038
21.923793
13.117376
25.423372
25.753882
26.131757
24.322205
14.428460
21.741236
19.602801
21.116487
23.475257
20.512126
21.709995
15.515435
19.335110
14.506415
28.616028
30.253134
Mozambique
MOZ
42.153748
41.078048
81.290337
63.227911
60.897529
51.357774
28.846150
26.105472
25.303936
18.929035
22.235916
25.479034
55.092130
23.452112
23.022378
20.858954
25.329415
20.639316
19.218717
19.242911
19.844165
15.971140
14.038942
14.857198
Namibia
NAM
4.209219
5.803335
4.107771
4.658816
3.717559
4.643885
4.627869
3.961536
4.619977
4.696689
3.864420
3.162453
4.194966
2.832642
2.587485
1.750347
1.914115
2.534520
2.524887
3.738328
2.365566
2.406287
2.119051
2.037311
Nigeria
NGA
0.915264
1.035903
0.982185
2.145556
1.205479
0.801326
0.576211
0.594739
0.696240
NaN
0.431682
0.439954
0.561926
0.513733
0.739785
6.481326
8.117241
1.265345
0.668788
1.069640
0.590164
0.454907
0.436658
0.513599
Rwanda
RWA
11.343267
18.897168
17.284268
18.090559
94.946034
53.480621
34.004276
12.515679
17.769297
20.654569
18.697642
18.460456
21.883783
18.465978
23.843437
22.605440
19.569581
19.226348
19.605382
17.703522
18.249577
19.910454
12.336286
14.634387
South Africa
ZAF
NaN
NaN
NaN
0.205435
0.213412
0.253084
0.250761
0.331799
0.381082
0.405056
0.365175
0.361144
0.453657
0.384231
0.280490
0.272990
0.268286
0.278784
0.404992
0.371416
0.280537
0.345649
0.276015
0.362732
Swaziland
SWZ
4.569225
4.316857
4.094175
3.858014
4.118989
3.245246
1.905078
1.524687
2.130006
1.772435
0.842369
2.008027
1.816716
2.212381
1.027134
1.688770
1.174235
1.637205
2.318400
1.825605
2.769848
3.517205
2.548186
3.658063
Uganda
UGA
15.685768
20.324011
26.122758
19.208025
19.116949
14.622617
11.230459
13.002968
9.965762
10.109844
14.021317
14.526869
12.000708
16.077617
15.740073
13.627281
16.363794
14.409358
11.741081
10.685573
9.329992
8.601690
7.109757
7.039984
United Republic of Tanzania
TZA
28.562533
22.489392
30.221686
23.070880
22.115207
16.993606
13.499059
12.480968
10.837667
10.328813
10.519282
12.414321
11.870763
14.956367
13.944823
10.834362
10.216432
13.604326
8.872094
10.734988
9.879824
7.596304
7.589574
8.010578
Zambia
ZMB
15.784197
29.335056
35.948780
28.720137
20.907739
56.961568
17.930415
15.040382
10.496701
19.227083
23.070010
14.527056
20.065827
16.275943
19.415802
15.221159
12.663973
8.054708
6.760067
8.498264
4.837289
4.584692
3.892247
4.450818
Zimbabwe
ZWE
3.927011
4.661839
12.189280
7.854095
8.476233
7.208261
4.485711
4.129572
4.341223
3.759963
2.775202
2.496073
3.274361
3.394693
3.387329
6.800695
5.422667
9.699197
15.417136
8.543809
7.906713
7.121761
8.696128
6.502967
In [15]:
byregion = undata.groupby(['year', 'mdgregions'])[hiv].mean()
byregion = byregion.unstack()
byregion.to_csv('hiv_byregion.csv')
byregion
Out[15]:
mdgregions
Caribbean
Caucasus and Central Asia
Developed
Eastern Asia
Latin America
Northern Africa
Oceania
South-eastern Asia
Southern Asia
Sub-Saharan Africa
Western Asia
year
1990
0.250000
0.001220
0.005650
NaN
0.050175
0.001825
0.010
0.110600
0.002620
0.619250
NaN
1991
0.256000
0.000660
0.010667
NaN
0.059583
0.002275
0.020
0.130940
0.002840
0.719024
NaN
1992
0.252000
0.002600
0.014300
NaN
0.060000
0.002600
0.030
0.131400
0.005180
0.839024
NaN
1993
0.236000
0.002840
0.024667
NaN
0.065833
0.003025
0.040
0.149980
0.009780
0.958293
NaN
1994
0.238000
0.003520
0.031867
NaN
0.075833
0.003425
0.050
0.150320
0.012700
1.058293
NaN
1995
0.234000
0.006400
0.042533
NaN
0.085000
0.003850
0.060
0.136500
0.016840
1.121220
NaN
1996
0.224000
0.009880
0.053333
NaN
0.093333
0.004150
0.070
0.114720
0.021120
1.139268
NaN
1997
0.222000
0.012540
0.063333
NaN
0.104167
0.004350
0.080
0.089080
0.025520
1.120488
NaN
1998
0.210000
0.013360
0.066667
NaN
0.107500
0.004750
0.090
0.083620
0.027960
1.067805
NaN
1999
0.190000
0.014320
0.060000
NaN
0.100833
0.005025
0.100
0.076000
0.028540
1.004146
NaN
2000
0.180000
0.015060
0.060000
NaN
0.081667
0.005300
0.120
0.068000
0.026580
0.930976
NaN
2001
0.164000
0.015340
0.060000
NaN
0.065000
0.005425
0.150
0.060000
0.024680
0.857561
NaN
2002
0.148000
0.015740
0.060000
NaN
0.055000
0.005450
0.170
0.056000
0.022760
0.784878
NaN
2003
0.138000
0.014000
0.063333
NaN
0.050000
0.005725
0.150
0.050000
0.020800
0.719512
NaN
2004
0.124000
0.014000
0.060000
NaN
0.046667
0.005850
0.120
0.046000
0.016940
0.663171
NaN
2005
0.112000
0.016000
0.063333
NaN
0.045833
0.005975
0.070
0.042000
0.013100
0.613415
NaN
2006
0.104000
0.020000
0.056667
NaN
0.045000
0.006300
0.040
0.036000
0.011280
0.567317
NaN
2007
0.098000
0.020000
0.053333
NaN
0.043333
0.006475
0.020
0.032000
0.009480
0.531861
NaN
2008
0.094000
0.020000
0.050000
NaN
0.041667
0.006525
0.010
0.030000
0.009720
0.506254
NaN
2009
0.090000
0.020000
0.046667
NaN
0.041667
0.006825
0.020
0.028000
0.009880
0.477363
NaN
2010
0.086000
0.022000
0.050000
NaN
0.037500
0.007100
0.030
0.026000
0.009860
0.453883
NaN
2011
0.084000
0.026000
0.050000
NaN
0.037500
0.007375
0.040
0.026000
0.010160
0.432398
NaN
2012
0.072000
0.024000
0.046667
NaN
0.037500
0.007525
0.040
0.024000
0.010340
0.395159
NaN
2013
0.078571
0.021117
0.016181
0.0072
0.033684
0.007825
0.025
0.023943
0.007522
0.341362
0.0043
In [16]:
byregion = byregion.transpose()
byregion
Out[16]:
year
1990.0
1991.0
1992.0
1993.0
1994.0
1995.0
1996.0
1997.0
1998.0
1999.0
2000.0
2001.0
2002.0
2003.0
2004.0
2005.0
2006.0
2007.0
2008.0
2009.0
2010.0
2011.0
2012.0
2013.0
mdgregions
Caribbean
0.250000
0.256000
0.252000
0.236000
0.238000
0.234000
0.224000
0.222000
0.210000
0.190000
0.180000
0.164000
0.148000
0.138000
0.124000
0.112000
0.104000
0.098000
0.094000
0.090000
0.086000
0.084000
0.072000
0.078571
Caucasus and Central Asia
0.001220
0.000660
0.002600
0.002840
0.003520
0.006400
0.009880
0.012540
0.013360
0.014320
0.015060
0.015340
0.015740
0.014000
0.014000
0.016000
0.020000
0.020000
0.020000
0.020000
0.022000
0.026000
0.024000
0.021117
Developed
0.005650
0.010667
0.014300
0.024667
0.031867
0.042533
0.053333
0.063333
0.066667
0.060000
0.060000
0.060000
0.060000
0.063333
0.060000
0.063333
0.056667
0.053333
0.050000
0.046667
0.050000
0.050000
0.046667
0.016181
Eastern Asia
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.007200
Latin America
0.050175
0.059583
0.060000
0.065833
0.075833
0.085000
0.093333
0.104167
0.107500
0.100833
0.081667
0.065000
0.055000
0.050000
0.046667
0.045833
0.045000
0.043333
0.041667
0.041667
0.037500
0.037500
0.037500
0.033684
Northern Africa
0.001825
0.002275
0.002600
0.003025
0.003425
0.003850
0.004150
0.004350
0.004750
0.005025
0.005300
0.005425
0.005450
0.005725
0.005850
0.005975
0.006300
0.006475
0.006525
0.006825
0.007100
0.007375
0.007525
0.007825
Oceania
0.010000
0.020000
0.030000
0.040000
0.050000
0.060000
0.070000
0.080000
0.090000
0.100000
0.120000
0.150000
0.170000
0.150000
0.120000
0.070000
0.040000
0.020000
0.010000
0.020000
0.030000
0.040000
0.040000
0.025000
South-eastern Asia
0.110600
0.130940
0.131400
0.149980
0.150320
0.136500
0.114720
0.089080
0.083620
0.076000
0.068000
0.060000
0.056000
0.050000
0.046000
0.042000
0.036000
0.032000
0.030000
0.028000
0.026000
0.026000
0.024000
0.023943
Southern Asia
0.002620
0.002840
0.005180
0.009780
0.012700
0.016840
0.021120
0.025520
0.027960
0.028540
0.026580
0.024680
0.022760
0.020800
0.016940
0.013100
0.011280
0.009480
0.009720
0.009880
0.009860
0.010160
0.010340
0.007522
Sub-Saharan Africa
0.619250
0.719024
0.839024
0.958293
1.058293
1.121220
1.139268
1.120488
1.067805
1.004146
0.930976
0.857561
0.784878
0.719512
0.663171
0.613415
0.567317
0.531861
0.506254
0.477363
0.453883
0.432398
0.395159
0.341362
Western Asia
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.004300
In [17]:
print 'max: %f \nmin: %f' % (byregion.ix[9].max(), byregion.ix[9].min())
max: 1.139268
min: 0.341362
In [18]:
ssafrica = byregion.ix[9]
change = ssafrica[[1996, 2013]]
change.pct_change()
Out[18]:
year
1996 NaN
2013 -0.700367
Name: Sub-Saharan Africa, dtype: float64
In [19]:
# worldwide yearly mean of HIV incidence rate
world_means = undata.groupby(['year'])[hiv].mean()
print 'max: %f \nmin: %f' % (world_means.max(), world_means.min())
max: 0.616360
min: 0.147564
In [20]:
world_change = world_means[[1996, 2013]]
world_change.pct_change()
Out[20]:
year
1996 NaN
2013 -0.760588
Name: hiv_incidence_rate_1549_years_old_percentage_midpoint, dtype: float64
In [ ]:
Content source: UniteIdeas/UN_challenge_HIV-master
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