In [1]:
from copy import deepcopy
from lxml import etree
from os import listdir
from os.path import isfile, join
from sklearn.linear_model import LinearRegression
import bz2
import cPickle
import csv
import pandas as pd
"For this study, functions in the life table can be generated from the qx, where qx is the probability of death within a year of a person aged x. Although a life table does not give mortality at non-integral ages or for non-integral durations, as can be obtained from a mathematical formula, acceptable methods for estimating such values are well known." 1
In [2]:
# Parse the "Life Tables" using lxml & XPath (Using Chrome Developer Tools).
data = open('Table 7_1900 Life Tables.html')
htmlparser = etree.HTMLParser()
tree = etree.parse(data, htmlparser)
float(tree.xpath('//*[@id="wp1004907table999968"]/tr[4]/td[2]/div')[0].text.strip())
Out[2]:
0.14596
In [4]:
# Generate row numbers for the life tables excluding blank lines (every 5 lines)
a = 4
i = 0
rows = []
row = []
while a <= 146:
row.append(a)
a += 1
if i%5 == 4:
a += 1
rows.append(row)
row = []
i += 1
rows
Out[4]:
[[4, 5, 6, 7, 8],
[10, 11, 12, 13, 14],
[16, 17, 18, 19, 20],
[22, 23, 24, 25, 26],
[28, 29, 30, 31, 32],
[34, 35, 36, 37, 38],
[40, 41, 42, 43, 44],
[46, 47, 48, 49, 50],
[52, 53, 54, 55, 56],
[58, 59, 60, 61, 62],
[64, 65, 66, 67, 68],
[70, 71, 72, 73, 74],
[76, 77, 78, 79, 80],
[82, 83, 84, 85, 86],
[88, 89, 90, 91, 92],
[94, 95, 96, 97, 98],
[100, 101, 102, 103, 104],
[106, 107, 108, 109, 110],
[112, 113, 114, 115, 116],
[118, 119, 120, 121, 122],
[124, 125, 126, 127, 128],
[130, 131, 132, 133, 134],
[136, 137, 138, 139, 140],
[142, 143, 144, 145, 146]]
In [5]:
files = [ f for f in listdir('.') if isfile(join('.',f)) and f.startswith('Table 7_')]
files
Out[5]:
['Table 7_1900 Life Tables.html',
'Table 7_1910 Life Tables.html',
'Table 7_1920 Life Tables.html',
'Table 7_1930 Life Tables.html',
'Table 7_1940 Life Tables.html',
'Table 7_1950 Life Tables.html',
'Table 7_1960 Life Tables.html',
'Table 7_1970 Life Tables.html',
'Table 7_1980 Life Tables.html',
'Table 7_1990 Life Tables.html',
'Table 7_2000 Life Tables.html',
'Table 7_2010 Life Tables.html',
'Table 7_2020 Life Tables.html']
In [6]:
death_probabilities = {}
for f in files:
year = f[8:12]
death_probabilities[year] = {}
data = open(f)
htmlparser = etree.HTMLParser()
tree = etree.parse(data, htmlparser)
for chunk in rows:
for row in chunk:
age = int(tree.xpath('//*[@id="wp1004907table999968"]/tr[{0}]/td[1]/div'.format(row))[0].text.strip())
male = float(tree.xpath('//*[@id="wp1004907table999968"]/tr[{0}]/td[2]/div'.format(row))[0].text.strip())
female = float(tree.xpath('//*[@id="wp1004907table999968"]/tr[{0}]/td[10]/div'.format(row))[0].text.strip())
death_probabilities[year][age] = {'male': male, 'female': female}
death_probabilities
Out[6]:
{'1900': {0: {'female': 0.11969, 'male': 0.14596},
1: {'female': 0.03061, 'male': 0.03282},
2: {'female': 0.01489, 'male': 0.01634},
3: {'female': 0.01011, 'male': 0.01052},
4: {'female': 0.00823, 'male': 0.00875},
5: {'female': 0.00585, 'male': 0.00628},
6: {'female': 0.00428, 'male': 0.00462},
7: {'female': 0.00306, 'male': 0.00326},
8: {'female': 0.00232, 'male': 0.00256},
9: {'female': 0.00186, 'male': 0.00203},
10: {'female': 0.00203, 'male': 0.00211},
11: {'female': 0.00198, 'male': 0.00217},
12: {'female': 0.00195, 'male': 0.00212},
13: {'female': 0.00227, 'male': 0.00239},
14: {'female': 0.00233, 'male': 0.00254},
15: {'female': 0.00256, 'male': 0.00267},
16: {'female': 0.00304, 'male': 0.0032},
17: {'female': 0.00364, 'male': 0.00378},
18: {'female': 0.00785, 'male': 0.00913},
19: {'female': 0.00518, 'male': 0.00531},
20: {'female': 0.00535, 'male': 0.00507},
21: {'female': 0.00458, 'male': 0.00437},
22: {'female': 0.00468, 'male': 0.00449},
23: {'female': 0.00479, 'male': 0.00472},
24: {'female': 0.00471, 'male': 0.00456},
25: {'female': 0.00477, 'male': 0.00462},
26: {'female': 0.00487, 'male': 0.00467},
27: {'female': 0.00458, 'male': 0.00457},
28: {'female': 0.00472, 'male': 0.00494},
29: {'female': 0.00476, 'male': 0.00504},
30: {'female': 0.00445, 'male': 0.00491},
31: {'female': 0.00447, 'male': 0.00496},
32: {'female': 0.00432, 'male': 0.00472},
33: {'female': 0.00426, 'male': 0.00486},
34: {'female': 0.00437, 'male': 0.00521},
35: {'female': 0.00458, 'male': 0.00553},
36: {'female': 0.00494, 'male': 0.00605},
37: {'female': 0.00482, 'male': 0.00601},
38: {'female': 0.00451, 'male': 0.00555},
39: {'female': 0.00448, 'male': 0.00563},
40: {'female': 0.0046, 'male': 0.00595},
41: {'female': 0.00469, 'male': 0.00627},
42: {'female': 0.00476, 'male': 0.00654},
43: {'female': 0.00503, 'male': 0.00701},
44: {'female': 0.00505, 'male': 0.00721},
45: {'female': 0.00526, 'male': 0.00775},
46: {'female': 0.00539, 'male': 0.00795},
47: {'female': 0.00575, 'male': 0.00869},
48: {'female': 0.00592, 'male': 0.00947},
49: {'female': 0.00628, 'male': 0.0102},
50: {'female': 0.00656, 'male': 0.01094},
51: {'female': 0.00727, 'male': 0.01211},
52: {'female': 0.00765, 'male': 0.01313},
53: {'female': 0.00805, 'male': 0.0141},
54: {'female': 0.00807, 'male': 0.01446},
55: {'female': 0.00837, 'male': 0.01556},
56: {'female': 0.00914, 'male': 0.01698},
57: {'female': 0.0099, 'male': 0.01859},
58: {'female': 0.01038, 'male': 0.01984},
59: {'female': 0.01108, 'male': 0.02158},
60: {'female': 0.01237, 'male': 0.02392},
61: {'female': 0.01331, 'male': 0.02562},
62: {'female': 0.01474, 'male': 0.02813},
63: {'female': 0.01606, 'male': 0.03112},
64: {'female': 0.01674, 'male': 0.03302},
65: {'female': 0.01794, 'male': 0.03554},
66: {'female': 0.01949, 'male': 0.03827},
67: {'female': 0.02081, 'male': 0.03986},
68: {'female': 0.02165, 'male': 0.04376},
69: {'female': 0.02296, 'male': 0.04579},
70: {'female': 0.02513, 'male': 0.04887},
71: {'female': 0.02737, 'male': 0.05267},
72: {'female': 0.03027, 'male': 0.0573},
73: {'female': 0.03229, 'male': 0.06049},
74: {'female': 0.03419, 'male': 0.06249},
75: {'female': 0.03628, 'male': 0.06575},
76: {'female': 0.03935, 'male': 0.07065},
77: {'female': 0.04218, 'male': 0.07488},
78: {'female': 0.04627, 'male': 0.07999},
79: {'female': 0.04954, 'male': 0.08371},
80: {'female': 0.0562, 'male': 0.09204},
81: {'female': 0.06011, 'male': 0.09753},
82: {'female': 0.06453, 'male': 0.1024},
83: {'female': 0.07291, 'male': 0.11353},
84: {'female': 0.07979, 'male': 0.12095},
85: {'female': 0.08888, 'male': 0.13187},
86: {'female': 0.0966, 'male': 0.14038},
87: {'female': 0.10639, 'male': 0.15028},
88: {'female': 0.11968, 'male': 0.16689},
89: {'female': 0.1269, 'male': 0.17336},
90: {'female': 0.13718, 'male': 0.18509},
91: {'female': 0.14864, 'male': 0.19691},
92: {'female': 0.16007, 'male': 0.21241},
93: {'female': 0.18423, 'male': 0.23458},
94: {'female': 0.20021, 'male': 0.25351},
95: {'female': 0.22006, 'male': 0.27339},
96: {'female': 0.23851, 'male': 0.29246},
97: {'female': 0.25991, 'male': 0.31316},
98: {'female': 0.27881, 'male': 0.33011},
99: {'female': 0.30675, 'male': 0.35368},
100: {'female': 0.32915, 'male': 0.37561},
101: {'female': 0.34475, 'male': 0.39247},
102: {'female': 0.3726, 'male': 0.41986},
103: {'female': 0.39781, 'male': 0.44411},
104: {'female': 0.42451, 'male': 0.46967},
105: {'female': 0.45317, 'male': 0.49678},
106: {'female': 0.48253, 'male': 0.52404},
107: {'female': 0.5127, 'male': 0.55161},
108: {'female': 0.54382, 'male': 0.57962},
109: {'female': 0.57602, 'male': 0.60819},
110: {'female': 0.60944, 'male': 0.63746},
111: {'female': 0.64414, 'male': 0.66752},
112: {'female': 0.68038, 'male': 0.69852},
113: {'female': 0.71823, 'male': 0.73053},
114: {'female': 0.75783, 'male': 0.76365},
115: {'female': 0.79799, 'male': 0.79799},
116: {'female': 0.83363, 'male': 0.83363},
117: {'female': 0.87066, 'male': 0.87066},
118: {'female': 0.90919, 'male': 0.90919},
119: {'female': 0.94927, 'male': 0.94927}},
'1910': {0: {'female': 0.09826, 'male': 0.12006},
1: {'female': 0.02159, 'male': 0.02284},
2: {'female': 0.00987, 'male': 0.01104},
3: {'female': 0.00782, 'male': 0.00833},
4: {'female': 0.00536, 'male': 0.0058},
5: {'female': 0.00372, 'male': 0.0041},
6: {'female': 0.00311, 'male': 0.00353},
7: {'female': 0.00243, 'male': 0.00283},
8: {'female': 0.00332, 'male': 0.00355},
9: {'female': 0.00205, 'male': 0.00235},
10: {'female': 0.00184, 'male': 0.00216},
11: {'female': 0.00178, 'male': 0.00214},
12: {'female': 0.00164, 'male': 0.00194},
13: {'female': 0.00186, 'male': 0.00218},
14: {'female': 0.00203, 'male': 0.00233},
15: {'female': 0.00238, 'male': 0.00261},
16: {'female': 0.00267, 'male': 0.00284},
17: {'female': 0.00283, 'male': 0.003},
18: {'female': 0.0033, 'male': 0.00352},
19: {'female': 0.00346, 'male': 0.0037},
20: {'female': 0.00341, 'male': 0.00374},
21: {'female': 0.00351, 'male': 0.00381},
22: {'female': 0.00335, 'male': 0.00356},
23: {'female': 0.00326, 'male': 0.00359},
24: {'female': 0.00331, 'male': 0.00374},
25: {'female': 0.00336, 'male': 0.00379},
26: {'female': 0.00357, 'male': 0.00396},
27: {'female': 0.00333, 'male': 0.00385},
28: {'female': 0.00298, 'male': 0.00341},
29: {'female': 0.00285, 'male': 0.00331},
30: {'female': 0.00277, 'male': 0.0034},
31: {'female': 0.00273, 'male': 0.00345},
32: {'female': 0.00264, 'male': 0.00353},
33: {'female': 0.0028, 'male': 0.00358},
34: {'female': 0.00282, 'male': 0.00375},
35: {'female': 0.00288, 'male': 0.00411},
36: {'female': 0.0028, 'male': 0.00372},
37: {'female': 0.00279, 'male': 0.00381},
38: {'female': 0.00284, 'male': 0.00399},
39: {'female': 0.00283, 'male': 0.00415},
40: {'female': 0.00297, 'male': 0.00436},
41: {'female': 0.00318, 'male': 0.00486},
42: {'female': 0.00332, 'male': 0.00519},
43: {'female': 0.00346, 'male': 0.00559},
44: {'female': 0.00351, 'male': 0.00567},
45: {'female': 0.00365, 'male': 0.00617},
46: {'female': 0.00399, 'male': 0.00681},
47: {'female': 0.00436, 'male': 0.0075},
48: {'female': 0.00466, 'male': 0.00817},
49: {'female': 0.00494, 'male': 0.00913},
50: {'female': 0.00543, 'male': 0.01033},
51: {'female': 0.00588, 'male': 0.01104},
52: {'female': 0.00638, 'male': 0.01228},
53: {'female': 0.00685, 'male': 0.01335},
54: {'female': 0.00722, 'male': 0.01412},
55: {'female': 0.00751, 'male': 0.0152},
56: {'female': 0.00808, 'male': 0.01676},
57: {'female': 0.00876, 'male': 0.01804},
58: {'female': 0.00982, 'male': 0.02036},
59: {'female': 0.01041, 'male': 0.02185},
60: {'female': 0.01123, 'male': 0.02348},
61: {'female': 0.01197, 'male': 0.02473},
62: {'female': 0.01272, 'male': 0.02704},
63: {'female': 0.0135, 'male': 0.02852},
64: {'female': 0.01414, 'male': 0.0297},
65: {'female': 0.01465, 'male': 0.03122},
66: {'female': 0.01575, 'male': 0.03341},
67: {'female': 0.01694, 'male': 0.03514},
68: {'female': 0.01831, 'male': 0.03771},
69: {'female': 0.01951, 'male': 0.03964},
70: {'female': 0.02194, 'male': 0.04312},
71: {'female': 0.02346, 'male': 0.04553},
72: {'female': 0.02529, 'male': 0.04792},
73: {'female': 0.02808, 'male': 0.05286},
74: {'female': 0.03057, 'male': 0.05641},
75: {'female': 0.03347, 'male': 0.06095},
76: {'female': 0.03654, 'male': 0.06523},
77: {'female': 0.03991, 'male': 0.06982},
78: {'female': 0.04363, 'male': 0.0754},
79: {'female': 0.04707, 'male': 0.07903},
80: {'female': 0.05072, 'male': 0.08448},
81: {'female': 0.05507, 'male': 0.09042},
82: {'female': 0.0604, 'male': 0.09652},
83: {'female': 0.06893, 'male': 0.10764},
84: {'female': 0.07585, 'male': 0.11561},
85: {'female': 0.08466, 'male': 0.12587},
86: {'female': 0.09392, 'male': 0.13612},
87: {'female': 0.10383, 'male': 0.14854},
88: {'female': 0.11634, 'male': 0.16026},
89: {'female': 0.13286, 'male': 0.1782},
90: {'female': 0.14762, 'male': 0.19257},
91: {'female': 0.16227, 'male': 0.20698},
92: {'female': 0.18119, 'male': 0.22877},
93: {'female': 0.20041, 'male': 0.24908},
94: {'female': 0.22104, 'male': 0.27105},
95: {'female': 0.24247, 'male': 0.29359},
96: {'female': 0.26366, 'male': 0.31553},
97: {'female': 0.2842, 'male': 0.33642},
98: {'female': 0.30367, 'male': 0.35583},
99: {'female': 0.32165, 'male': 0.37338},
100: {'female': 0.34031, 'male': 0.39134},
101: {'female': 0.35969, 'male': 0.4098},
102: {'female': 0.37992, 'male': 0.42883},
103: {'female': 0.40106, 'male': 0.44849},
104: {'female': 0.42317, 'male': 0.46882},
105: {'female': 0.44633, 'male': 0.4899},
106: {'female': 0.47062, 'male': 0.51178},
107: {'female': 0.49612, 'male': 0.53451},
108: {'female': 0.5229, 'male': 0.55816},
109: {'female': 0.55105, 'male': 0.58277},
110: {'female': 0.58064, 'male': 0.6084},
111: {'female': 0.61177, 'male': 0.63511},
112: {'female': 0.64453, 'male': 0.66295},
113: {'female': 0.67903, 'male': 0.69197},
114: {'female': 0.71533, 'male': 0.72225},
115: {'female': 0.75357, 'male': 0.75383},
116: {'female': 0.78679, 'male': 0.78679},
117: {'female': 0.82118, 'male': 0.82118},
118: {'female': 0.85707, 'male': 0.85707},
119: {'female': 0.89447, 'male': 0.89447}},
'1920': {0: {'female': 0.06773, 'male': 0.08594},
1: {'female': 0.01418, 'male': 0.01538},
2: {'female': 0.0065, 'male': 0.00752},
3: {'female': 0.00521, 'male': 0.00564},
4: {'female': 0.0035, 'male': 0.00388},
5: {'female': 0.00267, 'male': 0.00301},
6: {'female': 0.00225, 'male': 0.00263},
7: {'female': 0.00178, 'male': 0.00222},
8: {'female': 0.00156, 'male': 0.00196},
9: {'female': 0.00134, 'male': 0.00176},
10: {'female': 0.00116, 'male': 0.00153},
11: {'female': 0.00117, 'male': 0.00149},
12: {'female': 0.00116, 'male': 0.00151},
13: {'female': 0.00124, 'male': 0.00162},
14: {'female': 0.00143, 'male': 0.00182},
15: {'female': 0.00161, 'male': 0.00205},
16: {'female': 0.00187, 'male': 0.00237},
17: {'female': 0.00195, 'male': 0.00251},
18: {'female': 0.00191, 'male': 0.00233},
19: {'female': 0.00187, 'male': 0.00234},
20: {'female': 0.00191, 'male': 0.00244},
21: {'female': 0.00192, 'male': 0.00265},
22: {'female': 0.0019, 'male': 0.00287},
23: {'female': 0.0019, 'male': 0.00353},
24: {'female': 0.00182, 'male': 0.00369},
25: {'female': 0.00171, 'male': 0.00359},
26: {'female': 0.00163, 'male': 0.00231},
27: {'female': 0.00156, 'male': 0.00217},
28: {'female': 0.00145, 'male': 0.0021},
29: {'female': 0.00142, 'male': 0.00206},
30: {'female': 0.00143, 'male': 0.00213},
31: {'female': 0.00146, 'male': 0.00222},
32: {'female': 0.00148, 'male': 0.00227},
33: {'female': 0.00149, 'male': 0.00232},
34: {'female': 0.00149, 'male': 0.0023},
35: {'female': 0.00158, 'male': 0.00243},
36: {'female': 0.00166, 'male': 0.00256},
37: {'female': 0.00188, 'male': 0.00288},
38: {'female': 0.00196, 'male': 0.00311},
39: {'female': 0.00208, 'male': 0.00337},
40: {'female': 0.00235, 'male': 0.00375},
41: {'female': 0.00246, 'male': 0.00411},
42: {'female': 0.00273, 'male': 0.00451},
43: {'female': 0.00301, 'male': 0.00501},
44: {'female': 0.00324, 'male': 0.00557},
45: {'female': 0.00359, 'male': 0.00608},
46: {'female': 0.00393, 'male': 0.00681},
47: {'female': 0.00422, 'male': 0.00746},
48: {'female': 0.00467, 'male': 0.00842},
49: {'female': 0.00487, 'male': 0.00904},
50: {'female': 0.00528, 'male': 0.00972},
51: {'female': 0.00556, 'male': 0.01033},
52: {'female': 0.00577, 'male': 0.01125},
53: {'female': 0.00624, 'male': 0.01196},
54: {'female': 0.00652, 'male': 0.01272},
55: {'female': 0.00686, 'male': 0.0134},
56: {'female': 0.00726, 'male': 0.01422},
57: {'female': 0.0076, 'male': 0.01495},
58: {'female': 0.00813, 'male': 0.01598},
59: {'female': 0.00862, 'male': 0.01702},
60: {'female': 0.00954, 'male': 0.01844},
61: {'female': 0.0103, 'male': 0.01955},
62: {'female': 0.01112, 'male': 0.0209},
63: {'female': 0.01218, 'male': 0.02277},
64: {'female': 0.01314, 'male': 0.02459},
65: {'female': 0.01423, 'male': 0.02656},
66: {'female': 0.01551, 'male': 0.02839},
67: {'female': 0.01666, 'male': 0.03025},
68: {'female': 0.0176, 'male': 0.03234},
69: {'female': 0.01886, 'male': 0.03397},
70: {'female': 0.02032, 'male': 0.03617},
71: {'female': 0.02207, 'male': 0.03867},
72: {'female': 0.02413, 'male': 0.04202},
73: {'female': 0.02659, 'male': 0.0459},
74: {'female': 0.02868, 'male': 0.04795},
75: {'female': 0.03149, 'male': 0.05184},
76: {'female': 0.0345, 'male': 0.05578},
77: {'female': 0.03748, 'male': 0.05989},
78: {'female': 0.04127, 'male': 0.06478},
79: {'female': 0.04622, 'male': 0.07083},
80: {'female': 0.05102, 'male': 0.07576},
81: {'female': 0.05613, 'male': 0.08171},
82: {'female': 0.06343, 'male': 0.09118},
83: {'female': 0.07024, 'male': 0.09965},
84: {'female': 0.07814, 'male': 0.10968},
85: {'female': 0.08701, 'male': 0.12086},
86: {'female': 0.09691, 'male': 0.13313},
87: {'female': 0.10801, 'male': 0.1465},
88: {'female': 0.12038, 'male': 0.16098},
89: {'female': 0.13401, 'male': 0.17651},
90: {'female': 0.14886, 'male': 0.19307},
91: {'female': 0.16486, 'male': 0.21059},
92: {'female': 0.18192, 'male': 0.22904},
93: {'female': 0.2, 'male': 0.24838},
94: {'female': 0.21903, 'male': 0.26858},
95: {'female': 0.23808, 'male': 0.28845},
96: {'female': 0.25681, 'male': 0.30765},
97: {'female': 0.2749, 'male': 0.32584},
98: {'female': 0.29199, 'male': 0.34266},
99: {'female': 0.3077, 'male': 0.35777},
100: {'female': 0.32423, 'male': 0.37351},
101: {'female': 0.34161, 'male': 0.3899},
102: {'female': 0.3599, 'male': 0.40699},
103: {'female': 0.37917, 'male': 0.42481},
104: {'female': 0.39944, 'male': 0.4434},
105: {'female': 0.42079, 'male': 0.46279},
106: {'female': 0.44327, 'male': 0.48302},
107: {'female': 0.467, 'male': 0.50413},
108: {'female': 0.49196, 'male': 0.52617},
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88: {'female': 0.08075, 'male': 0.1079},
89: {'female': 0.08974, 'male': 0.11844},
90: {'female': 0.09933, 'male': 0.1294},
91: {'female': 0.10965, 'male': 0.14098},
92: {'female': 0.12081, 'male': 0.15332},
93: {'female': 0.13293, 'male': 0.16659},
94: {'female': 0.14611, 'male': 0.18091},
95: {'female': 0.1593, 'male': 0.19499},
96: {'female': 0.17227, 'male': 0.20858},
97: {'female': 0.18478, 'male': 0.2214},
98: {'female': 0.19656, 'male': 0.23318},
99: {'female': 0.20734, 'male': 0.24367},
100: {'female': 0.21871, 'male': 0.25463},
101: {'female': 0.23072, 'male': 0.2661},
102: {'female': 0.24339, 'male': 0.27808},
103: {'female': 0.25676, 'male': 0.29061},
104: {'female': 0.27087, 'male': 0.30371},
105: {'female': 0.28577, 'male': 0.3174},
106: {'female': 0.30148, 'male': 0.33173},
107: {'female': 0.31807, 'male': 0.3467},
108: {'female': 0.33557, 'male': 0.36235},
109: {'female': 0.35406, 'male': 0.37872},
110: {'female': 0.37355, 'male': 0.39583},
111: {'female': 0.39414, 'male': 0.41373},
112: {'female': 0.41587, 'male': 0.43244},
113: {'female': 0.43881, 'male': 0.45201},
114: {'female': 0.46301, 'male': 0.47247},
115: {'female': 0.48858, 'male': 0.49386},
116: {'female': 0.51555, 'male': 0.51625},
117: {'female': 0.53965, 'male': 0.53965},
118: {'female': 0.56411, 'male': 0.56411},
119: {'female': 0.58969, 'male': 0.58969}},
'2000': {0: {'female': 0.00623, 'male': 0.00759},
1: {'female': 0.00046, 'male': 0.00055},
2: {'female': 0.00028, 'male': 0.00034},
3: {'female': 0.0002, 'male': 0.00027},
4: {'female': 0.00015, 'male': 0.00021},
5: {'female': 0.00014, 'male': 0.00018},
6: {'female': 0.00013, 'male': 0.00016},
7: {'female': 0.00012, 'male': 0.00015},
8: {'female': 0.00012, 'male': 0.00013},
9: {'female': 0.00011, 'male': 0.00011},
10: {'female': 0.0001, 'male': 9e-05},
11: {'female': 0.0001, 'male': 9e-05},
12: {'female': 0.00012, 'male': 0.00014},
13: {'female': 0.00015, 'male': 0.00024},
14: {'female': 0.00021, 'male': 0.00038},
15: {'female': 0.00027, 'male': 0.00053},
16: {'female': 0.00032, 'male': 0.00067},
17: {'female': 0.00036, 'male': 0.00079},
18: {'female': 0.00038, 'male': 0.00089},
19: {'female': 0.00038, 'male': 0.00096},
20: {'female': 0.00037, 'male': 0.00104},
21: {'female': 0.00038, 'male': 0.0011},
22: {'female': 0.00038, 'male': 0.00113},
23: {'female': 0.00038, 'male': 0.00111},
24: {'female': 0.00039, 'male': 0.00106},
25: {'female': 0.0004, 'male': 0.00099},
26: {'female': 0.00041, 'male': 0.00094},
27: {'female': 0.00042, 'male': 0.00091},
28: {'female': 0.00045, 'male': 0.00091},
29: {'female': 0.00048, 'male': 0.00093},
30: {'female': 0.00051, 'male': 0.00097},
31: {'female': 0.00055, 'male': 0.001},
32: {'female': 0.00059, 'male': 0.00105},
33: {'female': 0.00065, 'male': 0.00112},
34: {'female': 0.00071, 'male': 0.00119},
35: {'female': 0.00078, 'male': 0.00128},
36: {'female': 0.00085, 'male': 0.00138},
37: {'female': 0.00093, 'male': 0.00149},
38: {'female': 0.001, 'male': 0.0016},
39: {'female': 0.00108, 'male': 0.00173},
40: {'female': 0.00116, 'male': 0.00186},
41: {'female': 0.00125, 'male': 0.002},
42: {'female': 0.00132, 'male': 0.00214},
43: {'female': 0.00139, 'male': 0.00229},
44: {'female': 0.00146, 'male': 0.00244},
45: {'female': 0.00153, 'male': 0.00261},
46: {'female': 0.00162, 'male': 0.00278},
47: {'female': 0.00171, 'male': 0.00293},
48: {'female': 0.00181, 'male': 0.00304},
49: {'female': 0.00191, 'male': 0.00314},
50: {'female': 0.00204, 'male': 0.00326},
51: {'female': 0.00219, 'male': 0.00341},
52: {'female': 0.00237, 'male': 0.00361},
53: {'female': 0.00259, 'male': 0.00386},
54: {'female': 0.00284, 'male': 0.00418},
55: {'female': 0.00313, 'male': 0.00454},
56: {'female': 0.00345, 'male': 0.00495},
57: {'female': 0.00379, 'male': 0.00538},
58: {'female': 0.00414, 'male': 0.00584},
59: {'female': 0.00451, 'male': 0.00633},
60: {'female': 0.00491, 'male': 0.00687},
61: {'female': 0.00537, 'male': 0.00749},
62: {'female': 0.00592, 'male': 0.00824},
63: {'female': 0.00656, 'male': 0.00912},
64: {'female': 0.00729, 'male': 0.01014},
65: {'female': 0.0081, 'male': 0.01126},
66: {'female': 0.00897, 'male': 0.01246},
67: {'female': 0.00985, 'male': 0.0137},
68: {'female': 0.01074, 'male': 0.01494},
69: {'female': 0.01166, 'male': 0.01622},
70: {'female': 0.0127, 'male': 0.01768},
71: {'female': 0.01385, 'male': 0.01929},
72: {'female': 0.01503, 'male': 0.02092},
73: {'female': 0.01623, 'male': 0.02255},
74: {'female': 0.01751, 'male': 0.02426},
75: {'female': 0.0191, 'male': 0.02636},
76: {'female': 0.02094, 'male': 0.02883},
77: {'female': 0.0228, 'male': 0.0314},
78: {'female': 0.0246, 'male': 0.03402},
79: {'female': 0.02654, 'male': 0.03691},
80: {'female': 0.02877, 'male': 0.04011},
81: {'female': 0.03165, 'male': 0.04409},
82: {'female': 0.03551, 'male': 0.04939},
83: {'female': 0.04057, 'male': 0.05629},
84: {'female': 0.04667, 'male': 0.0645},
85: {'female': 0.05353, 'male': 0.07355},
86: {'female': 0.06091, 'male': 0.08304},
87: {'female': 0.06867, 'male': 0.09271},
88: {'female': 0.07679, 'male': 0.10253},
89: {'female': 0.08535, 'male': 0.11258},
90: {'female': 0.09447, 'male': 0.12304},
91: {'female': 0.10429, 'male': 0.13408},
92: {'female': 0.11492, 'male': 0.14586},
93: {'female': 0.12649, 'male': 0.15856},
94: {'female': 0.13909, 'male': 0.1723},
95: {'female': 0.15172, 'male': 0.18581},
96: {'female': 0.16414, 'male': 0.19885},
97: {'female': 0.17611, 'male': 0.21115},
98: {'female': 0.18738, 'male': 0.22245},
99: {'female': 0.19771, 'male': 0.2325},
100: {'female': 0.20859, 'male': 0.243},
101: {'female': 0.22008, 'male': 0.25399},
102: {'female': 0.23222, 'male': 0.26548},
103: {'female': 0.24503, 'male': 0.2775},
104: {'female': 0.25854, 'male': 0.29006},
105: {'female': 0.27282, 'male': 0.30319},
106: {'female': 0.28788, 'male': 0.31693},
107: {'female': 0.30378, 'male': 0.3313},
108: {'female': 0.32057, 'male': 0.34631},
109: {'female': 0.33828, 'male': 0.36202},
110: {'female': 0.357, 'male': 0.37845},
111: {'female': 0.37674, 'male': 0.39563},
112: {'female': 0.3976, 'male': 0.41361},
113: {'female': 0.4196, 'male': 0.43238},
114: {'female': 0.44283, 'male': 0.45204},
115: {'female': 0.46736, 'male': 0.4726},
116: {'female': 0.49327, 'male': 0.49409},
117: {'female': 0.51656, 'male': 0.51656},
118: {'female': 0.54008, 'male': 0.54008},
119: {'female': 0.56469, 'male': 0.56469}},
'2010': {0: {'female': 0.00495, 'male': 0.00587},
1: {'female': 0.00035, 'male': 0.0004},
2: {'female': 0.00022, 'male': 0.00026},
3: {'female': 0.00016, 'male': 0.00022},
4: {'female': 0.00012, 'male': 0.00017},
5: {'female': 0.00011, 'male': 0.00014},
6: {'female': 0.00011, 'male': 0.00013},
7: {'female': 0.0001, 'male': 0.00012},
8: {'female': 0.0001, 'male': 0.00011},
9: {'female': 9e-05, 'male': 9e-05},
10: {'female': 8e-05, 'male': 7e-05},
11: {'female': 8e-05, 'male': 8e-05},
12: {'female': 0.0001, 'male': 0.00012},
13: {'female': 0.00014, 'male': 0.00022},
14: {'female': 0.00019, 'male': 0.00035},
15: {'female': 0.00024, 'male': 0.00049},
16: {'female': 0.00029, 'male': 0.00062},
17: {'female': 0.00033, 'male': 0.00073},
18: {'female': 0.00035, 'male': 0.00082},
19: {'female': 0.00035, 'male': 0.00089},
20: {'female': 0.00035, 'male': 0.00096},
21: {'female': 0.00035, 'male': 0.00102},
22: {'female': 0.00035, 'male': 0.00104},
23: {'female': 0.00035, 'male': 0.00102},
24: {'female': 0.00036, 'male': 0.00097},
25: {'female': 0.00037, 'male': 0.00091},
26: {'female': 0.00038, 'male': 0.00086},
27: {'female': 0.00039, 'male': 0.00083},
28: {'female': 0.00041, 'male': 0.00083},
29: {'female': 0.00044, 'male': 0.00086},
30: {'female': 0.00047, 'male': 0.00089},
31: {'female': 0.00051, 'male': 0.00092},
32: {'female': 0.00055, 'male': 0.00096},
33: {'female': 0.0006, 'male': 0.00102},
34: {'female': 0.00066, 'male': 0.00109},
35: {'female': 0.00073, 'male': 0.00118},
36: {'female': 0.00079, 'male': 0.00126},
37: {'female': 0.00086, 'male': 0.00136},
38: {'female': 0.00093, 'male': 0.00147},
39: {'female': 0.001, 'male': 0.00158},
40: {'female': 0.00108, 'male': 0.0017},
41: {'female': 0.00116, 'male': 0.00183},
42: {'female': 0.00123, 'male': 0.00196},
43: {'female': 0.00129, 'male': 0.00209},
44: {'female': 0.00135, 'male': 0.00223},
45: {'female': 0.00142, 'male': 0.00238},
46: {'female': 0.0015, 'male': 0.00254},
47: {'female': 0.00159, 'male': 0.00268},
48: {'female': 0.00168, 'male': 0.00278},
49: {'female': 0.00178, 'male': 0.00288},
50: {'female': 0.0019, 'male': 0.00299},
51: {'female': 0.00204, 'male': 0.00313},
52: {'female': 0.00221, 'male': 0.00331},
53: {'female': 0.00241, 'male': 0.00355},
54: {'female': 0.00265, 'male': 0.00385},
55: {'female': 0.00292, 'male': 0.00419},
56: {'female': 0.00322, 'male': 0.00457},
57: {'female': 0.00354, 'male': 0.00497},
58: {'female': 0.00386, 'male': 0.00539},
59: {'female': 0.0042, 'male': 0.00585},
60: {'female': 0.00457, 'male': 0.00635},
61: {'female': 0.005, 'male': 0.00693},
62: {'female': 0.00551, 'male': 0.00763},
63: {'female': 0.00613, 'male': 0.00847},
64: {'female': 0.00683, 'male': 0.00944},
65: {'female': 0.00762, 'male': 0.01053},
66: {'female': 0.00845, 'male': 0.01167},
67: {'female': 0.0093, 'male': 0.01284},
68: {'female': 0.01014, 'male': 0.01402},
69: {'female': 0.01101, 'male': 0.01523},
70: {'female': 0.01199, 'male': 0.0166},
71: {'female': 0.01308, 'male': 0.01811},
72: {'female': 0.01419, 'male': 0.01965},
73: {'female': 0.01533, 'male': 0.02119},
74: {'female': 0.01653, 'male': 0.0228},
75: {'female': 0.01804, 'male': 0.02479},
76: {'female': 0.01979, 'male': 0.02713},
77: {'female': 0.02154, 'male': 0.02954},
78: {'female': 0.02321, 'male': 0.032},
79: {'female': 0.02502, 'male': 0.03469},
80: {'female': 0.02709, 'male': 0.03768},
81: {'female': 0.02979, 'male': 0.04142},
82: {'female': 0.03347, 'male': 0.04647},
83: {'female': 0.03833, 'male': 0.0531},
84: {'female': 0.04423, 'male': 0.06103},
85: {'female': 0.05085, 'male': 0.06977},
86: {'female': 0.05796, 'male': 0.0789},
87: {'female': 0.06541, 'male': 0.0882},
88: {'female': 0.07317, 'male': 0.09761},
89: {'female': 0.08135, 'male': 0.10722},
90: {'female': 0.09005, 'male': 0.11721},
91: {'female': 0.0994, 'male': 0.12776},
92: {'female': 0.10956, 'male': 0.13904},
93: {'female': 0.12062, 'male': 0.15121},
94: {'female': 0.1327, 'male': 0.16442},
95: {'female': 0.1448, 'male': 0.1774},
96: {'female': 0.15671, 'male': 0.18993},
97: {'female': 0.16819, 'male': 0.20175},
98: {'female': 0.17901, 'male': 0.21261},
99: {'female': 0.1889, 'male': 0.22225},
100: {'female': 0.19934, 'male': 0.23234},
101: {'female': 0.21037, 'male': 0.24289},
102: {'female': 0.22202, 'male': 0.25392},
103: {'female': 0.2343, 'male': 0.26545},
104: {'female': 0.24727, 'male': 0.27752},
105: {'female': 0.26097, 'male': 0.29013},
106: {'female': 0.27544, 'male': 0.30333},
107: {'female': 0.29071, 'male': 0.31713},
108: {'female': 0.30683, 'male': 0.33157},
109: {'female': 0.32385, 'male': 0.34667},
110: {'female': 0.34182, 'male': 0.36246},
111: {'female': 0.3608, 'male': 0.37897},
112: {'female': 0.38082, 'male': 0.39625},
113: {'female': 0.40198, 'male': 0.41432},
114: {'female': 0.42433, 'male': 0.43323},
115: {'female': 0.44791, 'male': 0.453},
116: {'female': 0.47283, 'male': 0.47368},
117: {'female': 0.49531, 'male': 0.49531},
118: {'female': 0.51795, 'male': 0.51795},
119: {'female': 0.54163, 'male': 0.54163}},
'2020': {0: {'female': 0.00411, 'male': 0.00486},
1: {'female': 0.00029, 'male': 0.00034},
2: {'female': 0.00019, 'male': 0.00023},
3: {'female': 0.00014, 'male': 0.00019},
4: {'female': 0.0001, 'male': 0.00014},
5: {'female': 0.0001, 'male': 0.00013},
6: {'female': 9e-05, 'male': 0.00012},
7: {'female': 9e-05, 'male': 0.00011},
8: {'female': 9e-05, 'male': 9e-05},
9: {'female': 8e-05, 'male': 8e-05},
10: {'female': 7e-05, 'male': 6e-05},
11: {'female': 7e-05, 'male': 6e-05},
12: {'female': 8e-05, 'male': 0.0001},
13: {'female': 0.00012, 'male': 0.00019},
14: {'female': 0.00017, 'male': 0.00032},
15: {'female': 0.00022, 'male': 0.00045},
16: {'female': 0.00027, 'male': 0.00057},
17: {'female': 0.00031, 'male': 0.00068},
18: {'female': 0.00032, 'male': 0.00076},
19: {'female': 0.00032, 'male': 0.00082},
20: {'female': 0.00032, 'male': 0.00088},
21: {'female': 0.00032, 'male': 0.00094},
22: {'female': 0.00032, 'male': 0.00096},
23: {'female': 0.00033, 'male': 0.00094},
24: {'female': 0.00033, 'male': 0.0009},
25: {'female': 0.00034, 'male': 0.00084},
26: {'female': 0.00035, 'male': 0.00079},
27: {'female': 0.00036, 'male': 0.00077},
28: {'female': 0.00038, 'male': 0.00077},
29: {'female': 0.00041, 'male': 0.00079},
30: {'female': 0.00044, 'male': 0.00081},
31: {'female': 0.00047, 'male': 0.00084},
32: {'female': 0.00051, 'male': 0.00089},
33: {'female': 0.00056, 'male': 0.00094},
34: {'female': 0.00061, 'male': 0.00101},
35: {'female': 0.00067, 'male': 0.00108},
36: {'female': 0.00073, 'male': 0.00116},
37: {'female': 0.0008, 'male': 0.00125},
38: {'female': 0.00086, 'male': 0.00134},
39: {'female': 0.00093, 'male': 0.00145},
40: {'female': 0.001, 'male': 0.00156},
41: {'female': 0.00107, 'male': 0.00168},
42: {'female': 0.00114, 'male': 0.00179},
43: {'female': 0.0012, 'male': 0.00192},
44: {'female': 0.00126, 'male': 0.00204},
45: {'female': 0.00132, 'male': 0.00218},
46: {'female': 0.0014, 'male': 0.00233},
47: {'female': 0.00148, 'male': 0.00246},
48: {'female': 0.00157, 'male': 0.00256},
49: {'female': 0.00166, 'male': 0.00264},
50: {'female': 0.00177, 'male': 0.00274},
51: {'female': 0.0019, 'male': 0.00288},
52: {'female': 0.00206, 'male': 0.00305},
53: {'female': 0.00225, 'male': 0.00328},
54: {'female': 0.00247, 'male': 0.00355},
55: {'female': 0.00273, 'male': 0.00387},
56: {'female': 0.00302, 'male': 0.00423},
57: {'female': 0.00331, 'male': 0.00461},
58: {'female': 0.00361, 'male': 0.005},
59: {'female': 0.00393, 'male': 0.00542},
60: {'female': 0.00427, 'male': 0.00588},
61: {'female': 0.00467, 'male': 0.00642},
62: {'female': 0.00515, 'male': 0.00708},
63: {'female': 0.00574, 'male': 0.00789},
64: {'female': 0.00642, 'male': 0.00882},
65: {'female': 0.00718, 'male': 0.00986},
66: {'female': 0.00798, 'male': 0.01096},
67: {'female': 0.00879, 'male': 0.01208},
68: {'female': 0.0096, 'male': 0.01319},
69: {'female': 0.01042, 'male': 0.01433},
70: {'female': 0.01135, 'male': 0.01563},
71: {'female': 0.01238, 'male': 0.01706},
72: {'female': 0.01344, 'male': 0.01851},
73: {'female': 0.01451, 'male': 0.01996},
74: {'female': 0.01565, 'male': 0.02149},
75: {'female': 0.01709, 'male': 0.02338},
76: {'female': 0.01875, 'male': 0.0256},
77: {'female': 0.0204, 'male': 0.02788},
78: {'female': 0.02197, 'male': 0.03018},
79: {'female': 0.02366, 'male': 0.03271},
80: {'female': 0.02559, 'male': 0.0355},
81: {'female': 0.02814, 'male': 0.03904},
82: {'female': 0.03165, 'male': 0.04385},
83: {'female': 0.03633, 'male': 0.05023},
84: {'female': 0.04202, 'male': 0.05789},
85: {'female': 0.04842, 'male': 0.06632},
86: {'female': 0.05527, 'male': 0.07512},
87: {'female': 0.06243, 'male': 0.08406},
88: {'female': 0.06987, 'male': 0.09309},
89: {'female': 0.07769, 'male': 0.1023},
90: {'female': 0.086, 'male': 0.11187},
91: {'female': 0.09495, 'male': 0.12197},
92: {'female': 0.10466, 'male': 0.13278},
93: {'female': 0.11526, 'male': 0.14447},
94: {'female': 0.12685, 'male': 0.15718},
95: {'female': 0.13848, 'male': 0.16968},
96: {'female': 0.14992, 'male': 0.18174},
97: {'female': 0.16095, 'male': 0.19311},
98: {'female': 0.17133, 'male': 0.20355},
99: {'female': 0.18084, 'male': 0.21282},
100: {'female': 0.19087, 'male': 0.22252},
101: {'female': 0.20147, 'male': 0.23266},
102: {'female': 0.21265, 'male': 0.24326},
103: {'female': 0.22446, 'male': 0.25436},
104: {'female': 0.23694, 'male': 0.26597},
105: {'female': 0.25011, 'male': 0.27811},
106: {'female': 0.26402, 'male': 0.2908},
107: {'female': 0.27871, 'male': 0.30408},
108: {'female': 0.29421, 'male': 0.31798},
109: {'female': 0.31059, 'male': 0.33251},
110: {'female': 0.32788, 'male': 0.34771},
111: {'female': 0.34615, 'male': 0.36362},
112: {'female': 0.36543, 'male': 0.38025},
113: {'female': 0.3858, 'male': 0.39766},
114: {'female': 0.40731, 'male': 0.41586},
115: {'female': 0.43002, 'male': 0.43491},
116: {'female': 0.45401, 'male': 0.45483},
117: {'female': 0.47568, 'male': 0.47568},
118: {'female': 0.49749, 'male': 0.49749},
119: {'female': 0.52031, 'male': 0.52031}}}
In [7]:
sorted(death_probabilities.keys())
Out[7]:
['1900',
'1910',
'1920',
'1930',
'1940',
'1950',
'1960',
'1970',
'1980',
'1990',
'2000',
'2010',
'2020']
In [9]:
death_probabilities2 = deepcopy(death_probabilities)
In [10]:
sorted(death_probabilities2.items())
Out[10]:
[('1900',
{0: {'female': 0.11969, 'male': 0.14596},
1: {'female': 0.03061, 'male': 0.03282},
2: {'female': 0.01489, 'male': 0.01634},
3: {'female': 0.01011, 'male': 0.01052},
4: {'female': 0.00823, 'male': 0.00875},
5: {'female': 0.00585, 'male': 0.00628},
6: {'female': 0.00428, 'male': 0.00462},
7: {'female': 0.00306, 'male': 0.00326},
8: {'female': 0.00232, 'male': 0.00256},
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63: {'female': 0.01131, 'male': 0.01947},
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65: {'female': 0.01314, 'male': 0.02239},
66: {'female': 0.01428, 'male': 0.02406},
67: {'female': 0.01533, 'male': 0.02588},
68: {'female': 0.01675, 'male': 0.02748},
69: {'female': 0.01825, 'male': 0.02931},
70: {'female': 0.01981, 'male': 0.03125},
71: {'female': 0.02142, 'male': 0.03341},
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73: {'female': 0.02578, 'male': 0.03918},
74: {'female': 0.02803, 'male': 0.04201},
75: {'female': 0.03066, 'male': 0.04523},
76: {'female': 0.0337, 'male': 0.04902},
77: {'female': 0.03707, 'male': 0.05332},
78: {'female': 0.04078, 'male': 0.05816},
79: {'female': 0.04491, 'male': 0.06363},
80: {'female': 0.04965, 'male': 0.06978},
81: {'female': 0.05502, 'male': 0.07671},
82: {'female': 0.06094, 'male': 0.08453},
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84: {'female': 0.07476, 'male': 0.10327},
85: {'female': 0.08303, 'male': 0.11424},
86: {'female': 0.09239, 'male': 0.1262},
87: {'female': 0.10288, 'male': 0.13909},
88: {'female': 0.1145, 'male': 0.15284},
89: {'female': 0.12722, 'male': 0.16744},
90: {'female': 0.141, 'male': 0.18289},
91: {'female': 0.15582, 'male': 0.19919},
92: {'female': 0.17166, 'male': 0.21638},
93: {'female': 0.1885, 'male': 0.23447},
94: {'female': 0.20635, 'male': 0.25348},
95: {'female': 0.2242, 'male': 0.27216},
96: {'female': 0.24176, 'male': 0.2902},
97: {'female': 0.25872, 'male': 0.30727},
98: {'female': 0.27471, 'male': 0.32302},
99: {'female': 0.28936, 'male': 0.33712},
100: {'female': 0.30481, 'male': 0.35183},
101: {'female': 0.32109, 'male': 0.3672},
102: {'female': 0.33823, 'male': 0.38326},
103: {'female': 0.35631, 'male': 0.40001},
104: {'female': 0.37537, 'male': 0.41752},
105: {'female': 0.39545, 'male': 0.4358},
106: {'female': 0.41662, 'male': 0.45488},
107: {'female': 0.43894, 'male': 0.47481},
108: {'female': 0.46245, 'male': 0.49564},
109: {'female': 0.48726, 'male': 0.51738},
110: {'female': 0.51339, 'male': 0.54009},
111: {'female': 0.54093, 'male': 0.56381},
112: {'female': 0.56997, 'male': 0.58858},
113: {'female': 0.60059, 'male': 0.61446},
114: {'female': 0.63288, 'male': 0.64148},
115: {'female': 0.6669, 'male': 0.66972},
116: {'female': 0.6992, 'male': 0.6992},
117: {'female': 0.73001, 'male': 0.73001},
118: {'female': 0.7622, 'male': 0.7622},
119: {'female': 0.7958, 'male': 0.7958}}),
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2: {'female': 0.00208, 'male': 0.00248},
3: {'female': 0.00164, 'male': 0.00199},
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20: {'female': 0.00063, 'male': 0.00167},
21: {'female': 0.00064, 'male': 0.00175},
22: {'female': 0.00071, 'male': 0.00185},
23: {'female': 0.00074, 'male': 0.00187},
24: {'female': 0.00076, 'male': 0.00186},
25: {'female': 0.00077, 'male': 0.00185},
26: {'female': 0.00082, 'male': 0.00187},
27: {'female': 0.00083, 'male': 0.00189},
28: {'female': 0.00091, 'male': 0.00198},
29: {'female': 0.00096, 'male': 0.00206},
30: {'female': 0.00101, 'male': 0.00209},
31: {'female': 0.00108, 'male': 0.00212},
32: {'female': 0.00115, 'male': 0.00222},
33: {'female': 0.00119, 'male': 0.0023},
34: {'female': 0.00119, 'male': 0.00232},
35: {'female': 0.00121, 'male': 0.00237},
36: {'female': 0.00126, 'male': 0.00239},
37: {'female': 0.00134, 'male': 0.00258},
38: {'female': 0.00142, 'male': 0.00272},
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53: {'female': 0.00453, 'male': 0.00791},
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63: {'female': 0.01037, 'male': 0.01589},
64: {'female': 0.01132, 'male': 0.01718},
65: {'female': 0.0124, 'male': 0.01862},
66: {'female': 0.01358, 'male': 0.02021},
67: {'female': 0.01483, 'male': 0.02191},
68: {'female': 0.01614, 'male': 0.02371},
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70: {'female': 0.01912, 'male': 0.02785},
71: {'female': 0.0209, 'male': 0.0303},
72: {'female': 0.02273, 'male': 0.03281},
73: {'female': 0.02461, 'male': 0.03533},
74: {'female': 0.02661, 'male': 0.03799},
75: {'female': 0.02895, 'male': 0.04108},
76: {'female': 0.03168, 'male': 0.04469},
77: {'female': 0.03467, 'male': 0.04872},
78: {'female': 0.03791, 'male': 0.05317},
79: {'female': 0.04148, 'male': 0.05813},
80: {'female': 0.04561, 'male': 0.06369},
81: {'female': 0.05039, 'male': 0.07003},
82: {'female': 0.05585, 'male': 0.07739},
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84: {'female': 0.06929, 'male': 0.09574},
85: {'female': 0.07742, 'male': 0.10653},
86: {'female': 0.08651, 'male': 0.11818},
87: {'female': 0.09656, 'male': 0.13057},
88: {'female': 0.10754, 'male': 0.14363},
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90: {'female': 0.13232, 'male': 0.17185},
91: {'female': 0.14614, 'male': 0.18711},
92: {'female': 0.16096, 'male': 0.20325},
93: {'female': 0.17679, 'male': 0.22031},
94: {'female': 0.19367, 'male': 0.23835},
95: {'female': 0.21054, 'male': 0.25608},
96: {'female': 0.22714, 'male': 0.27319},
97: {'female': 0.24315, 'male': 0.28936},
98: {'female': 0.25823, 'male': 0.30428},
99: {'female': 0.27208, 'male': 0.31763},
100: {'female': 0.28667, 'male': 0.33157},
101: {'female': 0.30205, 'male': 0.34613},
102: {'female': 0.31827, 'male': 0.36134},
103: {'female': 0.33537, 'male': 0.37722},
104: {'female': 0.35339, 'male': 0.39382},
105: {'female': 0.37239, 'male': 0.41115},
106: {'female': 0.39242, 'male': 0.42925},
107: {'female': 0.41354, 'male': 0.44816},
108: {'female': 0.4358, 'male': 0.46792},
109: {'female': 0.45928, 'male': 0.48855},
110: {'female': 0.48404, 'male': 0.51011},
111: {'female': 0.51015, 'male': 0.53264},
112: {'female': 0.53766, 'male': 0.55617},
113: {'female': 0.56667, 'male': 0.58075},
114: {'female': 0.59728, 'male': 0.60642},
115: {'female': 0.62954, 'male': 0.63324},
116: {'female': 0.66128, 'male': 0.66128},
117: {'female': 0.69056, 'male': 0.69056},
118: {'female': 0.72124, 'male': 0.72124},
119: {'female': 0.75314, 'male': 0.75314}}),
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27: {'female': 0.00069, 'male': 0.00187},
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78: {'female': 0.03489, 'male': 0.04882},
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81: {'female': 0.04599, 'male': 0.06406},
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84: {'female': 0.06423, 'male': 0.08889},
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88: {'female': 0.10106, 'male': 0.13506},
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90: {'female': 0.12431, 'male': 0.16165},
91: {'female': 0.13727, 'male': 0.17601},
92: {'female': 0.15119, 'male': 0.19122},
93: {'female': 0.16613, 'male': 0.20737},
94: {'female': 0.18213, 'male': 0.22454},
95: {'female': 0.19814, 'male': 0.24142},
96: {'female': 0.21388, 'male': 0.2577},
97: {'female': 0.22906, 'male': 0.27308},
98: {'female': 0.24335, 'male': 0.28726},
99: {'female': 0.25646, 'male': 0.29993},
100: {'female': 0.27029, 'male': 0.31317},
101: {'female': 0.28486, 'male': 0.32699},
102: {'female': 0.30023, 'male': 0.34144},
103: {'female': 0.31643, 'male': 0.35653},
104: {'female': 0.33352, 'male': 0.37229},
105: {'female': 0.35153, 'male': 0.38876},
106: {'female': 0.37053, 'male': 0.40597},
107: {'female': 0.39058, 'male': 0.42395},
108: {'female': 0.4117, 'male': 0.44278},
109: {'female': 0.43398, 'male': 0.46236},
110: {'female': 0.45748, 'male': 0.48291},
111: {'female': 0.48227, 'male': 0.50434},
112: {'female': 0.50841, 'male': 0.52673},
113: {'female': 0.53598, 'male': 0.55012},
114: {'female': 0.56505, 'male': 0.57458},
115: {'female': 0.59572, 'male': 0.60012},
116: {'female': 0.62683, 'male': 0.62683},
117: {'female': 0.65471, 'male': 0.65471},
118: {'female': 0.68387, 'male': 0.68387},
119: {'female': 0.71433, 'male': 0.71433}}),
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101: {'female': 0.2693, 'male': 0.30962},
102: {'female': 0.28389, 'male': 0.32337},
103: {'female': 0.29929, 'male': 0.33773},
104: {'female': 0.31552, 'male': 0.35274},
105: {'female': 0.33265, 'male': 0.36842},
106: {'female': 0.35071, 'male': 0.38482},
107: {'female': 0.36976, 'male': 0.40194},
108: {'female': 0.38986, 'male': 0.41984},
109: {'female': 0.41105, 'male': 0.43854},
110: {'female': 0.43341, 'male': 0.45809},
111: {'female': 0.457, 'male': 0.47852},
112: {'female': 0.48189, 'male': 0.49987},
113: {'female': 0.50813, 'male': 0.52218},
114: {'female': 0.53583, 'male': 0.54551},
115: {'female': 0.56503, 'male': 0.56987},
116: {'female': 0.59535, 'male': 0.59535},
117: {'female': 0.62197, 'male': 0.62197},
118: {'female': 0.64981, 'male': 0.64981},
119: {'female': 0.67891, 'male': 0.67891}}),
('1970',
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1: {'female': 0.00109, 'male': 0.00128},
2: {'female': 0.00071, 'male': 0.0009},
3: {'female': 0.0005, 'male': 0.00071},
4: {'female': 0.00041, 'male': 0.00056},
5: {'female': 0.00036, 'male': 0.00049},
6: {'female': 0.0003, 'male': 0.00044},
7: {'female': 0.00026, 'male': 0.00041},
8: {'female': 0.00025, 'male': 0.00035},
9: {'female': 0.0002, 'male': 0.00028},
10: {'female': 0.00019, 'male': 0.00023},
11: {'female': 0.00018, 'male': 0.00023},
12: {'female': 0.00019, 'male': 0.00027},
13: {'female': 0.00022, 'male': 0.00041},
14: {'female': 0.0003, 'male': 0.00059},
15: {'female': 0.00037, 'male': 0.0008},
16: {'female': 0.00045, 'male': 0.00109},
17: {'female': 0.0005, 'male': 0.00122},
18: {'female': 0.00053, 'male': 0.0014},
19: {'female': 0.00053, 'male': 0.00144},
20: {'female': 0.0005, 'male': 0.00156},
21: {'female': 0.00051, 'male': 0.00163},
22: {'female': 0.00049, 'male': 0.0016},
23: {'female': 0.00052, 'male': 0.00167},
24: {'female': 0.00054, 'male': 0.00165},
25: {'female': 0.00056, 'male': 0.0016},
26: {'female': 0.00056, 'male': 0.00143},
27: {'female': 0.00055, 'male': 0.00134},
28: {'female': 0.00056, 'male': 0.00128},
29: {'female': 0.00059, 'male': 0.00131},
30: {'female': 0.00063, 'male': 0.00135},
31: {'female': 0.00069, 'male': 0.00142},
32: {'female': 0.00073, 'male': 0.0014},
33: {'female': 0.00079, 'male': 0.00147},
34: {'female': 0.00087, 'male': 0.00156},
35: {'female': 0.00096, 'male': 0.00167},
36: {'female': 0.00105, 'male': 0.00179},
37: {'female': 0.00115, 'male': 0.00193},
38: {'female': 0.00125, 'male': 0.00209},
39: {'female': 0.00135, 'male': 0.00226},
40: {'female': 0.00146, 'male': 0.00244},
41: {'female': 0.00157, 'male': 0.00262},
42: {'female': 0.00167, 'male': 0.00282},
43: {'female': 0.00176, 'male': 0.00302},
44: {'female': 0.00184, 'male': 0.00324},
45: {'female': 0.00193, 'male': 0.00347},
46: {'female': 0.00204, 'male': 0.0037},
47: {'female': 0.00215, 'male': 0.0039},
48: {'female': 0.00228, 'male': 0.00405},
49: {'female': 0.00242, 'male': 0.00417},
50: {'female': 0.00257, 'male': 0.00431},
51: {'female': 0.00276, 'male': 0.0045},
52: {'female': 0.00299, 'male': 0.00475},
53: {'female': 0.00326, 'male': 0.00507},
54: {'female': 0.00357, 'male': 0.00546},
55: {'female': 0.00393, 'male': 0.00592},
56: {'female': 0.00432, 'male': 0.00643},
57: {'female': 0.00474, 'male': 0.00697},
58: {'female': 0.00519, 'male': 0.00755},
59: {'female': 0.00567, 'male': 0.00819},
60: {'female': 0.00619, 'male': 0.00889},
61: {'female': 0.00678, 'male': 0.00969},
62: {'female': 0.00743, 'male': 0.01059},
63: {'female': 0.00818, 'male': 0.01163},
64: {'female': 0.009, 'male': 0.01279},
65: {'female': 0.00993, 'male': 0.01409},
66: {'female': 0.01092, 'male': 0.01548},
67: {'female': 0.01195, 'male': 0.01693},
68: {'female': 0.01301, 'male': 0.01842},
69: {'female': 0.01411, 'male': 0.01998},
70: {'female': 0.01537, 'male': 0.02176},
71: {'female': 0.01677, 'male': 0.02373},
72: {'female': 0.01821, 'male': 0.02572},
73: {'female': 0.01967, 'male': 0.02771},
74: {'female': 0.02122, 'male': 0.02981},
75: {'female': 0.02311, 'male': 0.03232},
76: {'female': 0.02531, 'male': 0.03527},
77: {'female': 0.0276, 'male': 0.03842},
78: {'female': 0.02992, 'male': 0.04174},
79: {'female': 0.03245, 'male': 0.04542},
80: {'female': 0.03537, 'male': 0.04951},
81: {'female': 0.03897, 'male': 0.05442},
82: {'female': 0.0435, 'male': 0.06061},
83: {'female': 0.04917, 'male': 0.06836},
84: {'female': 0.05589, 'male': 0.07743},
85: {'female': 0.06346, 'male': 0.08743},
86: {'female': 0.07171, 'male': 0.09801},
87: {'female': 0.08053, 'male': 0.10896},
88: {'female': 0.08991, 'male': 0.12021},
89: {'female': 0.09989, 'male': 0.13185},
90: {'female': 0.11058, 'male': 0.14401},
91: {'female': 0.12207, 'male': 0.15683},
92: {'female': 0.13447, 'male': 0.17046},
93: {'female': 0.14786, 'male': 0.18504},
94: {'female': 0.16233, 'male': 0.20068},
95: {'female': 0.17681, 'male': 0.21604},
96: {'female': 0.19105, 'male': 0.23087},
97: {'female': 0.20477, 'male': 0.24486},
98: {'female': 0.21769, 'male': 0.25774},
99: {'female': 0.22953, 'male': 0.26923},
100: {'female': 0.24201, 'male': 0.28123},
101: {'female': 0.25519, 'male': 0.29377},
102: {'female': 0.26908, 'male': 0.30687},
103: {'female': 0.28374, 'male': 0.32058},
104: {'female': 0.2992, 'male': 0.3349},
105: {'female': 0.31551, 'male': 0.34985},
106: {'female': 0.33272, 'male': 0.36549},
107: {'female': 0.35088, 'male': 0.38184},
108: {'female': 0.37003, 'male': 0.39893},
109: {'female': 0.39024, 'male': 0.41679},
110: {'female': 0.41157, 'male': 0.43546},
111: {'female': 0.43405, 'male': 0.45497},
112: {'female': 0.45779, 'male': 0.47536},
113: {'female': 0.48284, 'male': 0.49668},
114: {'female': 0.50927, 'male': 0.51897},
115: {'female': 0.53715, 'male': 0.54227},
116: {'female': 0.56657, 'male': 0.56662},
117: {'female': 0.59208, 'male': 0.59208},
118: {'female': 0.6187, 'male': 0.6187},
119: {'female': 0.64653, 'male': 0.64653}}),
('1980',
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1: {'female': 0.00083, 'male': 0.00097},
2: {'female': 0.00054, 'male': 0.00064},
3: {'female': 0.00035, 'male': 0.00052},
4: {'female': 0.00028, 'male': 0.00036},
5: {'female': 0.00024, 'male': 0.00034},
6: {'female': 0.00021, 'male': 0.0003},
7: {'female': 0.00019, 'male': 0.0003},
8: {'female': 0.00019, 'male': 0.00026},
9: {'female': 0.00017, 'male': 0.00022},
10: {'female': 0.00015, 'male': 0.00016},
11: {'female': 0.00014, 'male': 0.00018},
12: {'female': 0.00016, 'male': 0.00023},
13: {'female': 0.00021, 'male': 0.0004},
14: {'female': 0.00028, 'male': 0.00063},
15: {'female': 0.00036, 'male': 0.00083},
16: {'female': 0.00041, 'male': 0.00098},
17: {'female': 0.00045, 'male': 0.00107},
18: {'female': 0.00044, 'male': 0.00112},
19: {'female': 0.00046, 'male': 0.00117},
20: {'female': 0.00045, 'male': 0.00127},
21: {'female': 0.00045, 'male': 0.00139},
22: {'female': 0.00045, 'male': 0.00137},
23: {'female': 0.00045, 'male': 0.00135},
24: {'female': 0.00046, 'male': 0.00128},
25: {'female': 0.00047, 'male': 0.00121},
26: {'female': 0.00048, 'male': 0.00114},
27: {'female': 0.0005, 'male': 0.0011},
28: {'female': 0.00052, 'male': 0.0011},
29: {'female': 0.00056, 'male': 0.00112},
30: {'female': 0.0006, 'male': 0.00116},
31: {'female': 0.00064, 'male': 0.0012},
32: {'female': 0.00069, 'male': 0.00126},
33: {'female': 0.00076, 'male': 0.00134},
34: {'female': 0.00083, 'male': 0.00143},
35: {'female': 0.00091, 'male': 0.00153},
36: {'female': 0.001, 'male': 0.00165},
37: {'female': 0.00108, 'male': 0.00178},
38: {'female': 0.00117, 'male': 0.00192},
39: {'female': 0.00126, 'male': 0.00208},
40: {'female': 0.00136, 'male': 0.00224},
41: {'female': 0.00146, 'male': 0.0024},
42: {'female': 0.00155, 'male': 0.00258},
43: {'female': 0.00163, 'male': 0.00276},
44: {'female': 0.0017, 'male': 0.00294},
45: {'female': 0.00178, 'male': 0.00315},
46: {'female': 0.00188, 'male': 0.00336},
47: {'female': 0.00199, 'male': 0.00353},
48: {'female': 0.0021, 'male': 0.00367},
49: {'female': 0.00223, 'male': 0.00378},
50: {'female': 0.00237, 'male': 0.00391},
51: {'female': 0.00255, 'male': 0.00409},
52: {'female': 0.00276, 'male': 0.00432},
53: {'female': 0.00301, 'male': 0.00461},
54: {'female': 0.0033, 'male': 0.00498},
55: {'female': 0.00363, 'male': 0.0054},
56: {'female': 0.004, 'male': 0.00587},
57: {'female': 0.00439, 'male': 0.00637},
58: {'female': 0.00479, 'male': 0.00691},
59: {'female': 0.00523, 'male': 0.00749},
60: {'female': 0.00571, 'male': 0.00813},
61: {'female': 0.00625, 'male': 0.00886},
62: {'female': 0.00687, 'male': 0.00971},
63: {'female': 0.00757, 'male': 0.01069},
64: {'female': 0.00836, 'male': 0.01179},
65: {'female': 0.00925, 'male': 0.01303},
66: {'female': 0.01019, 'male': 0.01435},
67: {'female': 0.01117, 'male': 0.01572},
68: {'female': 0.01216, 'male': 0.01712},
69: {'female': 0.0132, 'male': 0.01858},
70: {'female': 0.01437, 'male': 0.02023},
71: {'female': 0.01568, 'male': 0.02207},
72: {'female': 0.01702, 'male': 0.02392},
73: {'female': 0.01838, 'male': 0.02578},
74: {'female': 0.01983, 'male': 0.02773},
75: {'female': 0.02161, 'male': 0.03009},
76: {'female': 0.02368, 'male': 0.03286},
77: {'female': 0.0258, 'male': 0.03579},
78: {'female': 0.02791, 'male': 0.03885},
79: {'female': 0.03021, 'male': 0.04222},
80: {'female': 0.03286, 'male': 0.04597},
81: {'female': 0.03618, 'male': 0.05052},
82: {'female': 0.04047, 'male': 0.05639},
83: {'female': 0.04593, 'male': 0.06385},
84: {'female': 0.05245, 'male': 0.07264},
85: {'female': 0.05979, 'male': 0.08233},
86: {'female': 0.06774, 'male': 0.09254},
87: {'female': 0.07619, 'male': 0.10304},
88: {'female': 0.08511, 'male': 0.11377},
89: {'female': 0.09457, 'male': 0.12484},
90: {'female': 0.10468, 'male': 0.13637},
91: {'female': 0.11555, 'male': 0.14853},
92: {'female': 0.1273, 'male': 0.16149},
93: {'female': 0.14003, 'male': 0.17538},
94: {'female': 0.15382, 'male': 0.19034},
95: {'female': 0.16763, 'male': 0.20503},
96: {'female': 0.18121, 'male': 0.21921},
97: {'female': 0.1943, 'male': 0.23259},
98: {'female': 0.20662, 'male': 0.24491},
99: {'female': 0.21791, 'male': 0.25587},
100: {'female': 0.22982, 'male': 0.26733},
101: {'female': 0.24237, 'male': 0.27931},
102: {'female': 0.25563, 'male': 0.29183},
103: {'female': 0.26962, 'male': 0.30492},
104: {'female': 0.28437, 'male': 0.31861},
105: {'female': 0.29994, 'male': 0.33291},
106: {'female': 0.31637, 'male': 0.34786},
107: {'female': 0.3337, 'male': 0.36349},
108: {'female': 0.352, 'male': 0.37983},
109: {'female': 0.37131, 'male': 0.39691},
110: {'female': 0.39168, 'male': 0.41477},
111: {'female': 0.41319, 'male': 0.43345},
112: {'female': 0.43587, 'male': 0.45297},
113: {'female': 0.45982, 'male': 0.47338},
114: {'female': 0.48508, 'male': 0.49471},
115: {'female': 0.51176, 'male': 0.51702},
116: {'female': 0.5399, 'male': 0.54035},
117: {'female': 0.56473, 'male': 0.56473},
118: {'female': 0.59023, 'male': 0.59023},
119: {'female': 0.6169, 'male': 0.6169}}),
('1990',
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1: {'female': 0.00067, 'male': 0.00075},
2: {'female': 0.0004, 'male': 0.00049},
3: {'female': 0.00027, 'male': 0.00039},
4: {'female': 0.00025, 'male': 0.00031},
5: {'female': 0.0002, 'male': 0.00026},
6: {'female': 0.00017, 'male': 0.00024},
7: {'female': 0.00016, 'male': 0.0002},
8: {'female': 0.00014, 'male': 0.00018},
9: {'female': 0.00013, 'male': 0.00015},
10: {'female': 0.00012, 'male': 0.00013},
11: {'female': 0.00011, 'male': 0.00012},
12: {'female': 0.00014, 'male': 0.00018},
13: {'female': 0.00017, 'male': 0.00028},
14: {'female': 0.00023, 'male': 0.00043},
15: {'female': 0.00029, 'male': 0.00059},
16: {'female': 0.00035, 'male': 0.00074},
17: {'female': 0.00039, 'male': 0.00087},
18: {'female': 0.00041, 'male': 0.00097},
19: {'female': 0.00041, 'male': 0.00105},
20: {'female': 0.00041, 'male': 0.00113},
21: {'female': 0.00041, 'male': 0.0012},
22: {'female': 0.00041, 'male': 0.00123},
23: {'female': 0.00041, 'male': 0.00121},
24: {'female': 0.00042, 'male': 0.00115},
25: {'female': 0.00043, 'male': 0.00108},
26: {'female': 0.00044, 'male': 0.00102},
27: {'female': 0.00046, 'male': 0.00099},
28: {'female': 0.00048, 'male': 0.00099},
29: {'female': 0.00052, 'male': 0.00102},
30: {'female': 0.00055, 'male': 0.00105},
31: {'female': 0.00059, 'male': 0.00109},
32: {'female': 0.00064, 'male': 0.00115},
33: {'female': 0.0007, 'male': 0.00122},
34: {'female': 0.00077, 'male': 0.00131},
35: {'female': 0.00085, 'male': 0.0014},
36: {'female': 0.00092, 'male': 0.00151},
37: {'female': 0.001, 'male': 0.00162},
38: {'female': 0.00108, 'male': 0.00175},
39: {'female': 0.00116, 'male': 0.00189},
40: {'female': 0.00125, 'male': 0.00204},
41: {'female': 0.00135, 'male': 0.00219},
42: {'female': 0.00143, 'male': 0.00234},
43: {'female': 0.0015, 'male': 0.00251},
44: {'female': 0.00157, 'male': 0.00267},
45: {'female': 0.00165, 'male': 0.00286},
46: {'female': 0.00174, 'male': 0.00305},
47: {'female': 0.00184, 'male': 0.00321},
48: {'female': 0.00194, 'male': 0.00333},
49: {'female': 0.00206, 'male': 0.00344},
50: {'female': 0.0022, 'male': 0.00356},
51: {'female': 0.00236, 'male': 0.00373},
52: {'female': 0.00255, 'male': 0.00394},
53: {'female': 0.00278, 'male': 0.00422},
54: {'female': 0.00306, 'male': 0.00455},
55: {'female': 0.00337, 'male': 0.00494},
56: {'female': 0.00371, 'male': 0.00538},
57: {'female': 0.00407, 'male': 0.00585},
58: {'female': 0.00445, 'male': 0.00634},
59: {'female': 0.00485, 'male': 0.00687},
60: {'female': 0.00529, 'male': 0.00746},
61: {'female': 0.00579, 'male': 0.00813},
62: {'female': 0.00636, 'male': 0.00893},
63: {'female': 0.00704, 'male': 0.00986},
64: {'female': 0.00779, 'male': 0.01092},
65: {'female': 0.00864, 'male': 0.01209},
66: {'female': 0.00954, 'male': 0.01335},
67: {'female': 0.01047, 'male': 0.01465},
68: {'female': 0.01141, 'male': 0.01597},
69: {'female': 0.01239, 'male': 0.01733},
70: {'female': 0.01349, 'male': 0.01888},
71: {'female': 0.01471, 'male': 0.0206},
72: {'female': 0.01597, 'male': 0.02233},
73: {'female': 0.01725, 'male': 0.02407},
74: {'female': 0.0186, 'male': 0.0259},
75: {'female': 0.02028, 'male': 0.02812},
76: {'female': 0.02223, 'male': 0.03073},
77: {'female': 0.02421, 'male': 0.03347},
78: {'female': 0.02615, 'male': 0.03629},
79: {'female': 0.02826, 'male': 0.0394},
80: {'female': 0.03068, 'male': 0.04286},
81: {'female': 0.03377, 'male': 0.04711},
82: {'female': 0.03783, 'male': 0.05267},
83: {'female': 0.04308, 'male': 0.05985},
84: {'female': 0.04939, 'male': 0.06835},
85: {'female': 0.05649, 'male': 0.07772},
86: {'female': 0.06415, 'male': 0.08756},
87: {'female': 0.07225, 'male': 0.09764},
88: {'female': 0.08075, 'male': 0.1079},
89: {'female': 0.08974, 'male': 0.11844},
90: {'female': 0.09933, 'male': 0.1294},
91: {'female': 0.10965, 'male': 0.14098},
92: {'female': 0.12081, 'male': 0.15332},
93: {'female': 0.13293, 'male': 0.16659},
94: {'female': 0.14611, 'male': 0.18091},
95: {'female': 0.1593, 'male': 0.19499},
96: {'female': 0.17227, 'male': 0.20858},
97: {'female': 0.18478, 'male': 0.2214},
98: {'female': 0.19656, 'male': 0.23318},
99: {'female': 0.20734, 'male': 0.24367},
100: {'female': 0.21871, 'male': 0.25463},
101: {'female': 0.23072, 'male': 0.2661},
102: {'female': 0.24339, 'male': 0.27808},
103: {'female': 0.25676, 'male': 0.29061},
104: {'female': 0.27087, 'male': 0.30371},
105: {'female': 0.28577, 'male': 0.3174},
106: {'female': 0.30148, 'male': 0.33173},
107: {'female': 0.31807, 'male': 0.3467},
108: {'female': 0.33557, 'male': 0.36235},
109: {'female': 0.35406, 'male': 0.37872},
110: {'female': 0.37355, 'male': 0.39583},
111: {'female': 0.39414, 'male': 0.41373},
112: {'female': 0.41587, 'male': 0.43244},
113: {'female': 0.43881, 'male': 0.45201},
114: {'female': 0.46301, 'male': 0.47247},
115: {'female': 0.48858, 'male': 0.49386},
116: {'female': 0.51555, 'male': 0.51625},
117: {'female': 0.53965, 'male': 0.53965},
118: {'female': 0.56411, 'male': 0.56411},
119: {'female': 0.58969, 'male': 0.58969}}),
('2000',
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1: {'female': 0.00046, 'male': 0.00055},
2: {'female': 0.00028, 'male': 0.00034},
3: {'female': 0.0002, 'male': 0.00027},
4: {'female': 0.00015, 'male': 0.00021},
5: {'female': 0.00014, 'male': 0.00018},
6: {'female': 0.00013, 'male': 0.00016},
7: {'female': 0.00012, 'male': 0.00015},
8: {'female': 0.00012, 'male': 0.00013},
9: {'female': 0.00011, 'male': 0.00011},
10: {'female': 0.0001, 'male': 9e-05},
11: {'female': 0.0001, 'male': 9e-05},
12: {'female': 0.00012, 'male': 0.00014},
13: {'female': 0.00015, 'male': 0.00024},
14: {'female': 0.00021, 'male': 0.00038},
15: {'female': 0.00027, 'male': 0.00053},
16: {'female': 0.00032, 'male': 0.00067},
17: {'female': 0.00036, 'male': 0.00079},
18: {'female': 0.00038, 'male': 0.00089},
19: {'female': 0.00038, 'male': 0.00096},
20: {'female': 0.00037, 'male': 0.00104},
21: {'female': 0.00038, 'male': 0.0011},
22: {'female': 0.00038, 'male': 0.00113},
23: {'female': 0.00038, 'male': 0.00111},
24: {'female': 0.00039, 'male': 0.00106},
25: {'female': 0.0004, 'male': 0.00099},
26: {'female': 0.00041, 'male': 0.00094},
27: {'female': 0.00042, 'male': 0.00091},
28: {'female': 0.00045, 'male': 0.00091},
29: {'female': 0.00048, 'male': 0.00093},
30: {'female': 0.00051, 'male': 0.00097},
31: {'female': 0.00055, 'male': 0.001},
32: {'female': 0.00059, 'male': 0.00105},
33: {'female': 0.00065, 'male': 0.00112},
34: {'female': 0.00071, 'male': 0.00119},
35: {'female': 0.00078, 'male': 0.00128},
36: {'female': 0.00085, 'male': 0.00138},
37: {'female': 0.00093, 'male': 0.00149},
38: {'female': 0.001, 'male': 0.0016},
39: {'female': 0.00108, 'male': 0.00173},
40: {'female': 0.00116, 'male': 0.00186},
41: {'female': 0.00125, 'male': 0.002},
42: {'female': 0.00132, 'male': 0.00214},
43: {'female': 0.00139, 'male': 0.00229},
44: {'female': 0.00146, 'male': 0.00244},
45: {'female': 0.00153, 'male': 0.00261},
46: {'female': 0.00162, 'male': 0.00278},
47: {'female': 0.00171, 'male': 0.00293},
48: {'female': 0.00181, 'male': 0.00304},
49: {'female': 0.00191, 'male': 0.00314},
50: {'female': 0.00204, 'male': 0.00326},
51: {'female': 0.00219, 'male': 0.00341},
52: {'female': 0.00237, 'male': 0.00361},
53: {'female': 0.00259, 'male': 0.00386},
54: {'female': 0.00284, 'male': 0.00418},
55: {'female': 0.00313, 'male': 0.00454},
56: {'female': 0.00345, 'male': 0.00495},
57: {'female': 0.00379, 'male': 0.00538},
58: {'female': 0.00414, 'male': 0.00584},
59: {'female': 0.00451, 'male': 0.00633},
60: {'female': 0.00491, 'male': 0.00687},
61: {'female': 0.00537, 'male': 0.00749},
62: {'female': 0.00592, 'male': 0.00824},
63: {'female': 0.00656, 'male': 0.00912},
64: {'female': 0.00729, 'male': 0.01014},
65: {'female': 0.0081, 'male': 0.01126},
66: {'female': 0.00897, 'male': 0.01246},
67: {'female': 0.00985, 'male': 0.0137},
68: {'female': 0.01074, 'male': 0.01494},
69: {'female': 0.01166, 'male': 0.01622},
70: {'female': 0.0127, 'male': 0.01768},
71: {'female': 0.01385, 'male': 0.01929},
72: {'female': 0.01503, 'male': 0.02092},
73: {'female': 0.01623, 'male': 0.02255},
74: {'female': 0.01751, 'male': 0.02426},
75: {'female': 0.0191, 'male': 0.02636},
76: {'female': 0.02094, 'male': 0.02883},
77: {'female': 0.0228, 'male': 0.0314},
78: {'female': 0.0246, 'male': 0.03402},
79: {'female': 0.02654, 'male': 0.03691},
80: {'female': 0.02877, 'male': 0.04011},
81: {'female': 0.03165, 'male': 0.04409},
82: {'female': 0.03551, 'male': 0.04939},
83: {'female': 0.04057, 'male': 0.05629},
84: {'female': 0.04667, 'male': 0.0645},
85: {'female': 0.05353, 'male': 0.07355},
86: {'female': 0.06091, 'male': 0.08304},
87: {'female': 0.06867, 'male': 0.09271},
88: {'female': 0.07679, 'male': 0.10253},
89: {'female': 0.08535, 'male': 0.11258},
90: {'female': 0.09447, 'male': 0.12304},
91: {'female': 0.10429, 'male': 0.13408},
92: {'female': 0.11492, 'male': 0.14586},
93: {'female': 0.12649, 'male': 0.15856},
94: {'female': 0.13909, 'male': 0.1723},
95: {'female': 0.15172, 'male': 0.18581},
96: {'female': 0.16414, 'male': 0.19885},
97: {'female': 0.17611, 'male': 0.21115},
98: {'female': 0.18738, 'male': 0.22245},
99: {'female': 0.19771, 'male': 0.2325},
100: {'female': 0.20859, 'male': 0.243},
101: {'female': 0.22008, 'male': 0.25399},
102: {'female': 0.23222, 'male': 0.26548},
103: {'female': 0.24503, 'male': 0.2775},
104: {'female': 0.25854, 'male': 0.29006},
105: {'female': 0.27282, 'male': 0.30319},
106: {'female': 0.28788, 'male': 0.31693},
107: {'female': 0.30378, 'male': 0.3313},
108: {'female': 0.32057, 'male': 0.34631},
109: {'female': 0.33828, 'male': 0.36202},
110: {'female': 0.357, 'male': 0.37845},
111: {'female': 0.37674, 'male': 0.39563},
112: {'female': 0.3976, 'male': 0.41361},
113: {'female': 0.4196, 'male': 0.43238},
114: {'female': 0.44283, 'male': 0.45204},
115: {'female': 0.46736, 'male': 0.4726},
116: {'female': 0.49327, 'male': 0.49409},
117: {'female': 0.51656, 'male': 0.51656},
118: {'female': 0.54008, 'male': 0.54008},
119: {'female': 0.56469, 'male': 0.56469}}),
('2010',
{0: {'female': 0.00495, 'male': 0.00587},
1: {'female': 0.00035, 'male': 0.0004},
2: {'female': 0.00022, 'male': 0.00026},
3: {'female': 0.00016, 'male': 0.00022},
4: {'female': 0.00012, 'male': 0.00017},
5: {'female': 0.00011, 'male': 0.00014},
6: {'female': 0.00011, 'male': 0.00013},
7: {'female': 0.0001, 'male': 0.00012},
8: {'female': 0.0001, 'male': 0.00011},
9: {'female': 9e-05, 'male': 9e-05},
10: {'female': 8e-05, 'male': 7e-05},
11: {'female': 8e-05, 'male': 8e-05},
12: {'female': 0.0001, 'male': 0.00012},
13: {'female': 0.00014, 'male': 0.00022},
14: {'female': 0.00019, 'male': 0.00035},
15: {'female': 0.00024, 'male': 0.00049},
16: {'female': 0.00029, 'male': 0.00062},
17: {'female': 0.00033, 'male': 0.00073},
18: {'female': 0.00035, 'male': 0.00082},
19: {'female': 0.00035, 'male': 0.00089},
20: {'female': 0.00035, 'male': 0.00096},
21: {'female': 0.00035, 'male': 0.00102},
22: {'female': 0.00035, 'male': 0.00104},
23: {'female': 0.00035, 'male': 0.00102},
24: {'female': 0.00036, 'male': 0.00097},
25: {'female': 0.00037, 'male': 0.00091},
26: {'female': 0.00038, 'male': 0.00086},
27: {'female': 0.00039, 'male': 0.00083},
28: {'female': 0.00041, 'male': 0.00083},
29: {'female': 0.00044, 'male': 0.00086},
30: {'female': 0.00047, 'male': 0.00089},
31: {'female': 0.00051, 'male': 0.00092},
32: {'female': 0.00055, 'male': 0.00096},
33: {'female': 0.0006, 'male': 0.00102},
34: {'female': 0.00066, 'male': 0.00109},
35: {'female': 0.00073, 'male': 0.00118},
36: {'female': 0.00079, 'male': 0.00126},
37: {'female': 0.00086, 'male': 0.00136},
38: {'female': 0.00093, 'male': 0.00147},
39: {'female': 0.001, 'male': 0.00158},
40: {'female': 0.00108, 'male': 0.0017},
41: {'female': 0.00116, 'male': 0.00183},
42: {'female': 0.00123, 'male': 0.00196},
43: {'female': 0.00129, 'male': 0.00209},
44: {'female': 0.00135, 'male': 0.00223},
45: {'female': 0.00142, 'male': 0.00238},
46: {'female': 0.0015, 'male': 0.00254},
47: {'female': 0.00159, 'male': 0.00268},
48: {'female': 0.00168, 'male': 0.00278},
49: {'female': 0.00178, 'male': 0.00288},
50: {'female': 0.0019, 'male': 0.00299},
51: {'female': 0.00204, 'male': 0.00313},
52: {'female': 0.00221, 'male': 0.00331},
53: {'female': 0.00241, 'male': 0.00355},
54: {'female': 0.00265, 'male': 0.00385},
55: {'female': 0.00292, 'male': 0.00419},
56: {'female': 0.00322, 'male': 0.00457},
57: {'female': 0.00354, 'male': 0.00497},
58: {'female': 0.00386, 'male': 0.00539},
59: {'female': 0.0042, 'male': 0.00585},
60: {'female': 0.00457, 'male': 0.00635},
61: {'female': 0.005, 'male': 0.00693},
62: {'female': 0.00551, 'male': 0.00763},
63: {'female': 0.00613, 'male': 0.00847},
64: {'female': 0.00683, 'male': 0.00944},
65: {'female': 0.00762, 'male': 0.01053},
66: {'female': 0.00845, 'male': 0.01167},
67: {'female': 0.0093, 'male': 0.01284},
68: {'female': 0.01014, 'male': 0.01402},
69: {'female': 0.01101, 'male': 0.01523},
70: {'female': 0.01199, 'male': 0.0166},
71: {'female': 0.01308, 'male': 0.01811},
72: {'female': 0.01419, 'male': 0.01965},
73: {'female': 0.01533, 'male': 0.02119},
74: {'female': 0.01653, 'male': 0.0228},
75: {'female': 0.01804, 'male': 0.02479},
76: {'female': 0.01979, 'male': 0.02713},
77: {'female': 0.02154, 'male': 0.02954},
78: {'female': 0.02321, 'male': 0.032},
79: {'female': 0.02502, 'male': 0.03469},
80: {'female': 0.02709, 'male': 0.03768},
81: {'female': 0.02979, 'male': 0.04142},
82: {'female': 0.03347, 'male': 0.04647},
83: {'female': 0.03833, 'male': 0.0531},
84: {'female': 0.04423, 'male': 0.06103},
85: {'female': 0.05085, 'male': 0.06977},
86: {'female': 0.05796, 'male': 0.0789},
87: {'female': 0.06541, 'male': 0.0882},
88: {'female': 0.07317, 'male': 0.09761},
89: {'female': 0.08135, 'male': 0.10722},
90: {'female': 0.09005, 'male': 0.11721},
91: {'female': 0.0994, 'male': 0.12776},
92: {'female': 0.10956, 'male': 0.13904},
93: {'female': 0.12062, 'male': 0.15121},
94: {'female': 0.1327, 'male': 0.16442},
95: {'female': 0.1448, 'male': 0.1774},
96: {'female': 0.15671, 'male': 0.18993},
97: {'female': 0.16819, 'male': 0.20175},
98: {'female': 0.17901, 'male': 0.21261},
99: {'female': 0.1889, 'male': 0.22225},
100: {'female': 0.19934, 'male': 0.23234},
101: {'female': 0.21037, 'male': 0.24289},
102: {'female': 0.22202, 'male': 0.25392},
103: {'female': 0.2343, 'male': 0.26545},
104: {'female': 0.24727, 'male': 0.27752},
105: {'female': 0.26097, 'male': 0.29013},
106: {'female': 0.27544, 'male': 0.30333},
107: {'female': 0.29071, 'male': 0.31713},
108: {'female': 0.30683, 'male': 0.33157},
109: {'female': 0.32385, 'male': 0.34667},
110: {'female': 0.34182, 'male': 0.36246},
111: {'female': 0.3608, 'male': 0.37897},
112: {'female': 0.38082, 'male': 0.39625},
113: {'female': 0.40198, 'male': 0.41432},
114: {'female': 0.42433, 'male': 0.43323},
115: {'female': 0.44791, 'male': 0.453},
116: {'female': 0.47283, 'male': 0.47368},
117: {'female': 0.49531, 'male': 0.49531},
118: {'female': 0.51795, 'male': 0.51795},
119: {'female': 0.54163, 'male': 0.54163}}),
('2020',
{0: {'female': 0.00411, 'male': 0.00486},
1: {'female': 0.00029, 'male': 0.00034},
2: {'female': 0.00019, 'male': 0.00023},
3: {'female': 0.00014, 'male': 0.00019},
4: {'female': 0.0001, 'male': 0.00014},
5: {'female': 0.0001, 'male': 0.00013},
6: {'female': 9e-05, 'male': 0.00012},
7: {'female': 9e-05, 'male': 0.00011},
8: {'female': 9e-05, 'male': 9e-05},
9: {'female': 8e-05, 'male': 8e-05},
10: {'female': 7e-05, 'male': 6e-05},
11: {'female': 7e-05, 'male': 6e-05},
12: {'female': 8e-05, 'male': 0.0001},
13: {'female': 0.00012, 'male': 0.00019},
14: {'female': 0.00017, 'male': 0.00032},
15: {'female': 0.00022, 'male': 0.00045},
16: {'female': 0.00027, 'male': 0.00057},
17: {'female': 0.00031, 'male': 0.00068},
18: {'female': 0.00032, 'male': 0.00076},
19: {'female': 0.00032, 'male': 0.00082},
20: {'female': 0.00032, 'male': 0.00088},
21: {'female': 0.00032, 'male': 0.00094},
22: {'female': 0.00032, 'male': 0.00096},
23: {'female': 0.00033, 'male': 0.00094},
24: {'female': 0.00033, 'male': 0.0009},
25: {'female': 0.00034, 'male': 0.00084},
26: {'female': 0.00035, 'male': 0.00079},
27: {'female': 0.00036, 'male': 0.00077},
28: {'female': 0.00038, 'male': 0.00077},
29: {'female': 0.00041, 'male': 0.00079},
30: {'female': 0.00044, 'male': 0.00081},
31: {'female': 0.00047, 'male': 0.00084},
32: {'female': 0.00051, 'male': 0.00089},
33: {'female': 0.00056, 'male': 0.00094},
34: {'female': 0.00061, 'male': 0.00101},
35: {'female': 0.00067, 'male': 0.00108},
36: {'female': 0.00073, 'male': 0.00116},
37: {'female': 0.0008, 'male': 0.00125},
38: {'female': 0.00086, 'male': 0.00134},
39: {'female': 0.00093, 'male': 0.00145},
40: {'female': 0.001, 'male': 0.00156},
41: {'female': 0.00107, 'male': 0.00168},
42: {'female': 0.00114, 'male': 0.00179},
43: {'female': 0.0012, 'male': 0.00192},
44: {'female': 0.00126, 'male': 0.00204},
45: {'female': 0.00132, 'male': 0.00218},
46: {'female': 0.0014, 'male': 0.00233},
47: {'female': 0.00148, 'male': 0.00246},
48: {'female': 0.00157, 'male': 0.00256},
49: {'female': 0.00166, 'male': 0.00264},
50: {'female': 0.00177, 'male': 0.00274},
51: {'female': 0.0019, 'male': 0.00288},
52: {'female': 0.00206, 'male': 0.00305},
53: {'female': 0.00225, 'male': 0.00328},
54: {'female': 0.00247, 'male': 0.00355},
55: {'female': 0.00273, 'male': 0.00387},
56: {'female': 0.00302, 'male': 0.00423},
57: {'female': 0.00331, 'male': 0.00461},
58: {'female': 0.00361, 'male': 0.005},
59: {'female': 0.00393, 'male': 0.00542},
60: {'female': 0.00427, 'male': 0.00588},
61: {'female': 0.00467, 'male': 0.00642},
62: {'female': 0.00515, 'male': 0.00708},
63: {'female': 0.00574, 'male': 0.00789},
64: {'female': 0.00642, 'male': 0.00882},
65: {'female': 0.00718, 'male': 0.00986},
66: {'female': 0.00798, 'male': 0.01096},
67: {'female': 0.00879, 'male': 0.01208},
68: {'female': 0.0096, 'male': 0.01319},
69: {'female': 0.01042, 'male': 0.01433},
70: {'female': 0.01135, 'male': 0.01563},
71: {'female': 0.01238, 'male': 0.01706},
72: {'female': 0.01344, 'male': 0.01851},
73: {'female': 0.01451, 'male': 0.01996},
74: {'female': 0.01565, 'male': 0.02149},
75: {'female': 0.01709, 'male': 0.02338},
76: {'female': 0.01875, 'male': 0.0256},
77: {'female': 0.0204, 'male': 0.02788},
78: {'female': 0.02197, 'male': 0.03018},
79: {'female': 0.02366, 'male': 0.03271},
80: {'female': 0.02559, 'male': 0.0355},
81: {'female': 0.02814, 'male': 0.03904},
82: {'female': 0.03165, 'male': 0.04385},
83: {'female': 0.03633, 'male': 0.05023},
84: {'female': 0.04202, 'male': 0.05789},
85: {'female': 0.04842, 'male': 0.06632},
86: {'female': 0.05527, 'male': 0.07512},
87: {'female': 0.06243, 'male': 0.08406},
88: {'female': 0.06987, 'male': 0.09309},
89: {'female': 0.07769, 'male': 0.1023},
90: {'female': 0.086, 'male': 0.11187},
91: {'female': 0.09495, 'male': 0.12197},
92: {'female': 0.10466, 'male': 0.13278},
93: {'female': 0.11526, 'male': 0.14447},
94: {'female': 0.12685, 'male': 0.15718},
95: {'female': 0.13848, 'male': 0.16968},
96: {'female': 0.14992, 'male': 0.18174},
97: {'female': 0.16095, 'male': 0.19311},
98: {'female': 0.17133, 'male': 0.20355},
99: {'female': 0.18084, 'male': 0.21282},
100: {'female': 0.19087, 'male': 0.22252},
101: {'female': 0.20147, 'male': 0.23266},
102: {'female': 0.21265, 'male': 0.24326},
103: {'female': 0.22446, 'male': 0.25436},
104: {'female': 0.23694, 'male': 0.26597},
105: {'female': 0.25011, 'male': 0.27811},
106: {'female': 0.26402, 'male': 0.2908},
107: {'female': 0.27871, 'male': 0.30408},
108: {'female': 0.29421, 'male': 0.31798},
109: {'female': 0.31059, 'male': 0.33251},
110: {'female': 0.32788, 'male': 0.34771},
111: {'female': 0.34615, 'male': 0.36362},
112: {'female': 0.36543, 'male': 0.38025},
113: {'female': 0.3858, 'male': 0.39766},
114: {'female': 0.40731, 'male': 0.41586},
115: {'female': 0.43002, 'male': 0.43491},
116: {'female': 0.45401, 'male': 0.45483},
117: {'female': 0.47568, 'male': 0.47568},
118: {'female': 0.49749, 'male': 0.49749},
119: {'female': 0.52031, 'male': 0.52031}})]
In [11]:
reg1 = LinearRegression()
reg1.fit([[69], [74], [79], [84]], [0.74, 0.68, 0.47, 0.37])
reg1.predict([[24], [29], [34], [39], [44], [49], [54], [59], [64]])
Out[11]:
array([ 1.951, 1.819, 1.687, 1.555, 1.423, 1.291, 1.159, 1.027,
0.895])
In [12]:
for age in range(120):
print 'age', age
for gender in ['male', 'female']:
print gender
X = []
y = []
for k, v in sorted(death_probabilities.items()):
X.append([int(k)])
y.append(v[age][gender])
for i in range(0, len(X)):
try:
reg = LinearRegression()
X_t = np.array([X[i], X[i+1]])
y_t = np.array([y[i], y[i+1]])
reg.fit(X_t, y_t)
for date in range(X[i][0]+1, X[i+1][0]):
if not str(date) in death_probabilities2:
death_probabilities2[str(date)] = {i: {'female': None, 'male': None} for i in range(120)}
death_probabilities2[str(date)][age][gender] = reg.predict(date)[0]
except:
pass
age 0
male
female
age 1
male
female
age 2
male
female
age 3
male
female
age 4
male
female
age 5
male
female
age 6
male
female
age 7
male
female
age 8
male
female
age 9
male
female
age 10
male
female
age 11
male
female
age 12
male
female
age 13
male
female
age 14
male
female
age 15
male
female
age 16
male
female
age 17
male
female
age 18
male
female
age 19
male
female
age 20
male
female
age 21
male
female
age 22
male
female
age 23
male
female
age 24
male
female
age 25
male
female
age 26
male
female
age 27
male
female
age 28
male
female
age 29
male
female
age 30
male
female
age 31
male
female
age 32
male
female
age 33
male
female
age 34
male
female
age 35
male
female
age 36
male
female
age 37
male
female
age 38
male
female
age 39
male
female
age 40
male
female
age 41
male
female
age 42
male
female
age 43
male
female
age 44
male
female
age 45
male
female
age 46
male
female
age 47
male
female
age 48
male
female
age 49
male
female
age 50
male
female
age 51
male
female
age 52
male
female
age 53
male
female
age 54
male
female
age 55
male
female
age 56
male
female
age 57
male
female
age 58
male
female
age 59
male
female
age 60
male
female
age 61
male
female
age 62
male
female
age 63
male
female
age 64
male
female
age 65
male
female
age 66
male
female
age 67
male
female
age 68
male
female
age 69
male
female
age 70
male
female
age 71
male
female
age 72
male
female
age 73
male
female
age 74
male
female
age 75
male
female
age 76
male
female
age 77
male
female
age 78
male
female
age 79
male
female
age 80
male
female
age 81
male
female
age 82
male
female
age 83
male
female
age 84
male
female
age 85
male
female
age 86
male
female
age 87
male
female
age 88
male
female
age 89
male
female
age 90
male
female
age 91
male
female
age 92
male
female
age 93
male
female
age 94
male
female
age 95
male
female
age 96
male
female
age 97
male
female
age 98
male
female
age 99
male
female
age 100
male
female
age 101
male
female
age 102
male
female
age 103
male
female
age 104
male
female
age 105
male
female
age 106
male
female
age 107
male
female
age 108
male
female
age 109
male
female
age 110
male
female
age 111
male
female
age 112
male
female
age 113
male
female
age 114
male
female
age 115
male
female
age 116
male
female
age 117
male
female
age 118
male
female
age 119
male
female
In [13]:
death_probabilities3 = {}
for k, v in death_probabilities2.iteritems():
death_probabilities3[int(k)] = v
In [14]:
age = 32
gender = 'female'
X = []
y = []
df_dicts = []
for k, v in sorted(death_probabilities3.items()):
X.append([int(k)])
y.append(v[age][gender])
df_dicts.append({'year': X[-1][0], 'proba': y[-1]})
df = pd.DataFrame(df_dicts)
In [15]:
import seaborn as sns
sns.set(style="darkgrid")
tips = sns.load_dataset("tips")
color = sns.color_palette()[2]
xlim=(1900, 2020)
ylim=(0.0005, 0.005)
g = sns.jointplot("year", "proba", data=df, kind="scatter", xlim=xlim, ylim=ylim, size=7)
In [16]:
names = open('names/yob1880.txt').readlines()
names[1].strip()
Out[16]:
'Anna,F,2604'
In [17]:
born = {}
for year in range(1880, 2014):
with open('names/yob{0}.txt'.format(year)) as f:
born[year] = [row for row in csv.DictReader(f, ['name', 'sex', 'num'])]
born[1880][0], born[2013][0]
Out[17]:
({'name': 'Mary', 'num': '7065', 'sex': 'F'},
{'name': 'Sophia', 'num': '21075', 'sex': 'F'})
In [18]:
born[1880][2]
Out[18]:
{'name': 'Emma', 'num': '2003', 'sex': 'F'}
In [19]:
death_probabilities3.keys()[0]
Out[19]:
1900
In [20]:
death_probabilities4 = deepcopy(death_probabilities3)
for age in range(120):
print 'age', age
for gender in ['male', 'female']:
print gender
X = [[1900], [1910], [1920]]
y = [death_probabilities4[1900][age][gender], death_probabilities4[1910][age][gender], death_probabilities4[1920][age][gender]]
reg = LinearRegression()
X_t = np.array(X)
y_t = np.array(y)
reg.fit(X_t, y_t)
for date in range(1880, 1900):
if not date in death_probabilities4:
death_probabilities4[date] = {i: {'female': None, 'male': None} for i in range(120)}
death_probabilities4[date][age][gender] = reg.predict(date)[0]
age 0
male
female
age 1
male
female
age 2
male
female
age 3
male
female
age 4
male
female
age 5
male
female
age 6
male
female
age 7
male
female
age 8
male
female
age 9
male
female
age 10
male
female
age 11
male
female
age 12
male
female
age 13
male
female
age 14
male
female
age 15
male
female
age 16
male
female
age 17
male
female
age 18
male
female
age 19
male
female
age 20
male
female
age 21
male
female
age 22
male
female
age 23
male
female
age 24
male
female
age 25
male
female
age 26
male
female
age 27
male
female
age 28
male
female
age 29
male
female
age 30
male
female
age 31
male
female
age 32
male
female
age 33
male
female
age 34
male
female
age 35
male
female
age 36
male
female
age 37
male
female
age 38
male
female
age 39
male
female
age 40
male
female
age 41
male
female
age 42
male
female
age 43
male
female
age 44
male
female
age 45
male
female
age 46
male
female
age 47
male
female
age 48
male
female
age 49
male
female
age 50
male
female
age 51
male
female
age 52
male
female
age 53
male
female
age 54
male
female
age 55
male
female
age 56
male
female
age 57
male
female
age 58
male
female
age 59
male
female
age 60
male
female
age 61
male
female
age 62
male
female
age 63
male
female
age 64
male
female
age 65
male
female
age 66
male
female
age 67
male
female
age 68
male
female
age 69
male
female
age 70
male
female
age 71
male
female
age 72
male
female
age 73
male
female
age 74
male
female
age 75
male
female
age 76
male
female
age 77
male
female
age 78
male
female
age 79
male
female
age 80
male
female
age 81
male
female
age 82
male
female
age 83
male
female
age 84
male
female
age 85
male
female
age 86
male
female
age 87
male
female
age 88
male
female
age 89
male
female
age 90
male
female
age 91
male
female
age 92
male
female
age 93
male
female
age 94
male
female
age 95
male
female
age 96
male
female
age 97
male
female
age 98
male
female
age 99
male
female
age 100
male
female
age 101
male
female
age 102
male
female
age 103
male
female
age 104
male
female
age 105
male
female
age 106
male
female
age 107
male
female
age 108
male
female
age 109
male
female
age 110
male
female
age 111
male
female
age 112
male
female
age 113
male
female
age 114
male
female
age 115
male
female
age 116
male
female
age 117
male
female
age 118
male
female
age 119
male
female
In [21]:
death_probabilities4[1880][32]
Out[21]:
{'female': 0.0070733333333333204, 'male': 0.0071816666666666418}
In [22]:
age = 0
gender = 'male'
X = []
y = []
df_dicts = []
for k, v in sorted(death_probabilities4.items()):
X.append([int(k)])
y.append(v[age][gender])
df_dicts.append({'year': X[-1][0], 'proba': y[-1]})
df = pd.DataFrame(df_dicts)
In [23]:
sns.set(style="darkgrid")
tips = sns.load_dataset("tips")
color = sns.color_palette()[2]
xlim=(1880, 2020)
ylim=(df['proba'].min(1)*0.99, df['proba'].max(1)*1.01)
g = sns.jointplot("year", "proba", data=df, kind="scatter", xlim=xlim, ylim=ylim, size=7)
In [24]:
with bz2.BZ2File('death_probabilities.pkl.bz2', 'wb') as f:
cPickle.dump(death_probabilities4, f, -1)
In [25]:
alive = {}
# structure:
# year:
# age:
# gender:
# name: num
for current_year in range(1880, 2014):
print current_year
for persons in born[current_year]:
# Add births
if not 0 in alive:
alive[0] = {'female': {}, 'male': {}}
sex = 'male'
if persons['sex'] == 'F':
sex = 'female'
if not persons['name'] in alive[0][sex]:
alive[0][sex][persons['name']] = 0
alive[0][sex][persons['name']] += int(persons['num'])
#handle deaths
still_alive = {}
for age, sex_data in alive.iteritems():
born_year = current_year - age
for sex, name_data in sex_data.iteritems():
for name, num in name_data.iteritems():
# death_probabilities4 structure:
# year (born)
# age
# gender: probability of dying
try:
death_prob = death_probabilities4[born_year][age][sex]
except KeyError:
# We could project probabilities past 119, but let's be real!
death_prob = 1.0
num_dead = num * death_prob
num_left = num - num_dead
new_age = age + 1
if num_left:
if not new_age in still_alive:
still_alive[new_age] = {'female': {}, 'male': {}}
still_alive[age+1][sex][name] = num_left
alive = still_alive
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
In [26]:
len(alive[1]['female'].keys()), len(alive[1]['male'].keys())
Out[26]:
(19114, 13958)
In [27]:
with bz2.BZ2File('alive_2014.pkl.bz2', 'wb') as f:
cPickle.dump(alive, f, -1)
In [28]:
def name_stats(name, gender, round_it=True):
figsize(20,10)
names = []
for age in alive:
num = alive[age][gender].get(name, 0.0)
if round_it:
num = int(round(num, 0))
names.append({'age': age, 'num': num})
name_df = pd.DataFrame(names)
ages = []
for age in names:
ages.extend([age['age']] * age['num'])
ages_series = pd.Series(ages)
print ages_series.describe()
plt = name_df['num'].plot()
plt.set_ylabel('Number of {0}s'.format(name), fontsize=20)
plt.set_xlabel('Age of {0}s'.format(name), fontsize=20)
plt.set_title('{0}s'.format(name), fontsize=30)
plt.set_xticks(range(10,111,10))
plt.set_xticklabels([str(x) for x in range(10,111,10)], fontsize=15)
y_ticks = plt.get_yticks()
plt.set_yticklabels([str(int(y)) for y in y_ticks], fontsize=15)
In [29]:
name_stats('Joseph', 'male')
count 1774469.000000
mean 39.871436
std 21.483059
min 1.000000
25% 23.000000
50% 38.000000
75% 57.000000
max 105.000000
dtype: float64
In [856]:
name_stats('Brittany', 'female')
count 350633.000000
mean 22.844555
std 5.313526
min 1.000000
25% 20.000000
50% 24.000000
75% 26.000000
max 51.000000
dtype: float64
In [857]:
name_stats('Andrew', 'male')
count 1096101.000000
mean 27.634421
std 16.771533
min 1.000000
25% 15.000000
50% 25.000000
75% 36.000000
max 103.000000
dtype: float64
In [858]:
name_stats('Matthew', 'male')
count 1460635.000000
mean 27.044503
std 13.320720
min 1.000000
25% 17.000000
50% 27.000000
75% 35.000000
max 101.000000
dtype: float64
In [864]:
name_stats('George', 'male')
count 672484.000000
mean 55.020725
std 20.613719
min 1.000000
25% 43.000000
50% 59.000000
75% 70.000000
max 105.000000
dtype: float64
In [865]:
name_stats('Donald', 'male')
count 850154.000000
mean 57.514215
std 17.058564
min 1.000000
25% 48.000000
50% 59.000000
75% 69.000000
max 103.000000
dtype: float64
In [866]:
name_stats('Emily', 'female')
count 730278.000000
mean 20.843131
std 15.147081
min 1.000000
25% 11.000000
50% 18.000000
75% 27.000000
max 105.000000
dtype: float64
In [867]:
name_stats('Dorothy', 'female')
count 425422.000000
mean 70.984493
std 16.172318
min 1.000000
25% 62.000000
50% 73.000000
75% 83.000000
max 108.000000
dtype: float64
In [868]:
name_stats('Anna', 'female')
count 455305.000000
mean 38.126287
std 26.927475
min 1.000000
25% 15.000000
50% 31.000000
75% 60.000000
max 108.000000
dtype: float64
In [869]:
name_stats('Liam', 'male')
count 117652.000000
mean 6.927711
std 6.885766
min 1.000000
25% 2.000000
50% 5.000000
75% 10.000000
max 67.000000
dtype: float64
In [870]:
name_stats('Aiden', 'male')
count 143461.000000
mean 5.690808
std 3.415430
min 1.000000
25% 3.000000
50% 5.000000
75% 8.000000
max 44.000000
dtype: float64
In [871]:
name_stats('Jayden', 'male')
count 155291.000000
mean 5.790819
std 3.621317
min 1.000000
25% 3.000000
50% 5.000000
75% 8.000000
max 37.000000
dtype: float64
In [873]:
name_stats('Mason', 'male')
count 202550.000000
mean 9.761106
std 9.563262
min 1.000000
25% 3.000000
50% 7.000000
75% 13.000000
max 98.000000
dtype: float64
In [874]:
name_stats('Noah', 'male')
count 287479.000000
mean 11.379690
std 10.266295
min 1.000000
25% 5.000000
50% 9.000000
75% 15.000000
max 99.000000
dtype: float64
In [875]:
name_stats('Elijah', 'male')
count 214836.000000
mean 11.471886
std 11.802690
min 1.000000
25% 4.000000
50% 9.000000
75% 14.000000
max 99.000000
dtype: float64
In [876]:
name_stats('Isaiah', 'male')
count 171502.000000
mean 11.749746
std 9.987560
min 1.000000
25% 6.000000
50% 10.000000
75% 15.000000
max 98.000000
dtype: float64
In [881]:
name_stats('Herman', 'male')
count 52167.000000
mean 58.953150
std 19.168889
min 1.000000
25% 48.000000
50% 62.000000
75% 73.000000
max 102.000000
dtype: float64
In [882]:
name_stats('Howard', 'male')
count 155132.000000
mean 59.423008
std 17.369065
min 1.000000
25% 50.000000
50% 61.000000
75% 71.000000
max 103.000000
dtype: float64
In [883]:
name_stats('Harry', 'male')
count 153151.000000
mean 59.197772
std 19.280925
min 1.000000
25% 50.000000
50% 63.000000
75% 72.000000
max 103.000000
dtype: float64
In [884]:
name_stats('Harold', 'male')
count 226744.000000
mean 61.268501
std 17.726715
min 1.000000
25% 52.000000
50% 64.000000
75% 74.000000
max 103.000000
dtype: float64
In [885]:
name_stats('Harvey', 'male')
count 53104.000000
mean 58.833007
std 19.753041
min 1.000000
25% 50.000000
50% 63.000000
75% 72.000000
max 102.000000
dtype: float64
In [886]:
name_stats('Herbert', 'male')
count 88042.000000
mean 61.093728
std 18.207442
min 1.000000
25% 51.000000
50% 63.000000
75% 74.000000
max 103.000000
dtype: float64
In [887]:
name_stats('Elmer', 'male')
count 35263.000000
mean 58.844313
std 24.045038
min 1.000000
25% 46.000000
50% 65.000000
75% 77.000000
max 102.000000
dtype: float64
In [888]:
name_stats('Ava', 'female')
count 179797.000000
mean 9.055118
std 12.516808
min 1.000000
25% 3.000000
50% 6.000000
75% 9.000000
max 102.000000
dtype: float64
In [889]:
name_stats('Mia', 'female')
count 158470.000000
mean 10.300069
std 10.420978
min 1.000000
25% 4.000000
50% 8.000000
75% 12.000000
max 81.000000
dtype: float64
In [890]:
name_stats('Sophia', 'female')
count 251048.000000
mean 10.505489
std 12.134404
min 1.000000
25% 3.000000
50% 7.000000
75% 12.000000
max 104.000000
dtype: float64
In [891]:
name_stats('Ella', 'female')
count 156064.000000
mean 25.823713
std 29.944199
min 1.000000
25% 5.000000
50% 9.000000
75% 56.000000
max 106.000000
dtype: float64
In [892]:
name_stats('Isabella', 'female')
count 249178.000000
mean 8.091047
std 8.120167
min 1.000000
25% 4.000000
50% 7.000000
75% 11.000000
max 102.000000
dtype: float64
In [893]:
name_stats('Gertrude', 'female')
count 30280.000000
mean 76.428269
std 14.537838
min 1.000000
25% 68.000000
50% 79.000000
75% 87.000000
max 107.000000
dtype: float64
In [894]:
name_stats('Betty', 'female')
count 531967.000000
mean 70.729970
std 13.517087
min 1.000000
25% 63.000000
50% 72.000000
75% 81.000000
max 106.000000
dtype: float64
In [895]:
name_stats('Wilma', 'female')
count 61510.000000
mean 70.929605
std 14.282908
min 1.000000
25% 63.000000
50% 73.000000
75% 81.000000
max 105.000000
dtype: float64
In [896]:
name_stats('Violet', 'female')
count 51814.000000
mean 41.011406
std 35.269257
min 1.000000
25% 5.000000
50% 40.000000
75% 77.000000
max 106.000000
dtype: float64
In [897]:
name_stats('Lola', 'female')
count 38896.000000
mean 45.573118
std 32.126198
min 1.000000
25% 8.000000
50% 56.000000
75% 74.000000
max 105.000000
dtype: float64
In [898]:
name_stats('Stella', 'female')
count 65988.000000
mean 40.146481
std 32.659193
min 1.000000
25% 5.000000
50% 45.000000
75% 69.000000
max 106.000000
dtype: float64
In [899]:
name_stats('Clara', 'female')
count 95689.000000
mean 51.597509
std 30.581637
min 1.000000
25% 18.000000
50% 62.000000
75% 77.000000
max 107.000000
dtype: float64
In [900]:
name_stats('Leo', 'male')
count 82318.000000
mean 43.658131
std 28.737156
min 1.000000
25% 12.000000
50% 50.000000
75% 68.000000
max 102.000000
dtype: float64
In [901]:
name_stats('Jack', 'male')
count 381188.000000
mean 39.516289
std 28.674683
min 1.000000
25% 11.000000
50% 41.000000
75% 66.000000
max 103.000000
dtype: float64
In [902]:
name_stats('Mabel', 'female')
count 20351.000000
mean 65.520368
std 27.444831
min 1.000000
25% 54.000000
50% 75.000000
75% 86.000000
max 107.000000
dtype: float64
In [903]:
name_stats('Gertrude', 'female')
count 30280.000000
mean 76.428269
std 14.537838
min 1.000000
25% 68.000000
50% 79.000000
75% 87.000000
max 107.000000
dtype: float64
In [904]:
name_stats('Elmer', 'male')
count 35263.000000
mean 58.844313
std 24.045038
min 1.000000
25% 46.000000
50% 65.000000
75% 77.000000
max 102.000000
dtype: float64
In [905]:
name_stats('Hyman', 'male')
count 763.000000
mean 67.462647
std 17.818408
min 13.000000
25% 56.000000
50% 67.000000
75% 83.000000
max 100.000000
dtype: float64
In [906]:
name_stats('Eino', 'male')
count 54.000000
mean 80.037037
std 10.299227
min 64.000000
25% 68.250000
50% 82.000000
75% 88.750000
max 97.000000
dtype: float64
In [907]:
name_stats('Isidore', 'male')
count 595.000000
mean 53.573109
std 27.501502
min 1.000000
25% 36.000000
50% 58.000000
75% 75.000000
max 99.000000
dtype: float64
In [859]:
name_stats('Michael', 'male')
count 3769486.000000
mean 40.546689
std 17.407317
min 1.000000
25% 27.000000
50% 42.000000
75% 55.000000
max 103.000000
dtype: float64
In [860]:
name_stats('Gayle', 'female')
count 50664.000000
mean 59.386132
std 11.053215
min 2.000000
25% 54.000000
50% 60.000000
75% 66.000000
max 100.000000
dtype: float64
In [861]:
name_stats('Lawrence', 'male')
count 270119.000000
mean 54.435978
std 18.329846
min 1.000000
25% 45.000000
50% 58.000000
75% 67.000000
max 103.000000
dtype: float64
In [862]:
name_stats('Laurence', 'male')
count 21541.000000
mean 55.116104
std 18.734767
min 1.000000
25% 46.000000
50% 58.000000
75% 67.000000
max 100.000000
dtype: float64
In [863]:
name_stats('Lori', 'female')
count 320030.000000
mean 46.906721
std 8.780546
min 1.000000
25% 43.000000
50% 49.000000
75% 53.000000
max 90.000000
dtype: float64
In [878]:
name_stats('Ariadne', 'female')
count 1677.000000
mean 12.126416
std 12.155005
min 1.000000
25% 3.000000
50% 8.000000
75% 16.000000
max 54.000000
dtype: float64
In [951]:
name_stats('Angelo', 'male')
count 51174.000000
mean 32.121018
std 23.564946
min 1.000000
25% 11.000000
50% 27.000000
75% 51.000000
max 101.000000
dtype: float64
In [879]:
name_stats('Robert', 'male')
count 3098709.000000
mean 51.244933
std 19.216628
min 1.000000
25% 38.000000
50% 54.000000
75% 65.000000
max 105.000000
dtype: float64
In [880]:
name_stats('Genevieve', 'female')
count 46237.000000
mean 41.951208
std 31.362843
min 1.000000
25% 11.000000
50% 35.000000
75% 73.000000
max 105.000000
dtype: float64
In [908]:
name_stats('Neal', 'male')
count 39176.000000
mean 47.288723
std 18.125062
min 1.000000
25% 34.000000
50% 50.000000
75% 60.000000
max 99.000000
dtype: float64
In [917]:
name_stats('Suzan', 'female')
count 7951.000000
mean 53.480065
std 14.154034
min 1.000000
25% 49.000000
50% 57.000000
75% 62.000000
max 93.000000
dtype: float64
In [909]:
name_stats('Jeannette', 'female')
count 41217.000000
mean 56.546352
std 19.101847
min 1.000000
25% 44.000000
50% 58.000000
75% 71.000000
max 104.000000
dtype: float64
In [910]:
name_stats('Mildred', 'female')
count 121757.000000
mean 73.754651
std 16.048478
min 1.000000
25% 65.000000
50% 77.000000
75% 86.000000
max 108.000000
dtype: float64
In [913]:
name_stats('Frederick', 'male')
count 152889.000000
mean 51.735645
std 19.219405
min 1.000000
25% 40.000000
50% 55.000000
75% 65.000000
max 102.000000
dtype: float64
In [912]:
name_stats('Stanley', 'male')
count 157874.000000
mean 56.226624
std 18.092260
min 1.000000
25% 48.000000
50% 59.000000
75% 68.000000
max 102.000000
dtype: float64
In [914]:
name_stats('Ian', 'male')
count 195938.000000
mean 18.985337
std 12.602534
min 1.000000
25% 9.000000
50% 17.000000
75% 27.000000
max 93.000000
dtype: float64
In [915]:
name_stats('John', 'male')
count 3160874.000000
mean 49.254889
std 19.892265
min 1.000000
25% 36.000000
50% 52.000000
75% 64.000000
max 106.000000
dtype: float64
In [916]:
name_stats('Andrew', 'male')
count 1096101.000000
mean 27.634421
std 16.771533
min 1.000000
25% 15.000000
50% 25.000000
75% 36.000000
max 103.000000
dtype: float64
In [929]:
name_stats('Marilyn', 'female')
count 263750.000000
mean 63.048224
std 16.839789
min 1.000000
25% 58.000000
50% 66.000000
75% 74.000000
max 100.000000
dtype: float64
In [ ]:
Content source: mdbecker/name_ages
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