Archivo:Comparison gender life expectancy CIA factbook.svg
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DescripciónComparison gender life expectancy CIA factbook.svg |
English: Comparison of male and female life expectancy at birth for countries and territories as defined in the 2011 CIA Factbook, with selected bubbles labelled. Hover over a bubble to highlight it and show its data. The green line corresponds to equal female and male life expectancy. The apparent 3D volumes of the bubbles are linearly proportional to their population, i.e. their radii are linearly proportional to the cube root of the population. Data is from https://www.cia.gov/library/publications/the-world-factbook/fields/2102.html and https://www.cia.gov/library/publications/the-world-factbook/fields/2119.html . |
Fuente | Trabajo propio |
Autor | Cmglee |
Otras versiones | Derivative chart based on data of WHO: File:Comparison gender life expectancy WHO.svg |
SVG desarrollo InfoField | El código fuente de esta imagen SVG es válido. This flag uses embedded text that can be easily translated using a text editor. |
Python script to fetch data and update data table
import re, os, urllib2, time, datetime, collections
data_oldss = [line.split('|') for line in '''\
-1|WORLD|69|67|71.1|7323187457|-
-20|EUROPEAN UNION|80.2|77.4|83.2|515052778|-
20|China|75.5|73.5|77.9|1373541278|ea
10|India|68.5|67.3|69.8|1266883598|as
25|USA|79.8|77.5|82.1|323995528|na
|Indonesia|72.7|70.1|75.5|258316051|ea
|Brazil|73.8|70.2|77.5|205823665|sa
|Pakistan|67.7|65.8|69.8|201995540|as
-20|Nigeria|53.4|52.4|54.5|186053386|af
|Bangladesh|73.2|71|75.4|156186882|as
-10|Russia|70.8|65|76.8|142355415|eu
1|Japan|85|81.7|88.5|126702133|ea
|Mexico|75.9|73.1|78.8|123166749|na
|Philippines|69.2|65.7|72.9|102624209|ea
|Ethiopia|62.2|59.8|64.7|102374044|af
|Vietnam|73.4|70.9|76.2|95261021|ea
|Egypt|72.7|71.4|74.2|94666993|af
|Iran|71.4|69.8|73.1|82801633|me
-15|DR Congo|57.3|55.8|58.9|81331050|af
|Germany|80.7|78.4|83.1|80722792|eu
|Turkey|74.8|72.5|77.3|80274604|me
|Thailand|74.7|71.5|78|68200824|ea
|France|81.8|78.7|85.1|66836154|eu
12|UK|80.7|78.5|83|64430428|eu
|Italy|82.2|79.6|85|62007540|eu
|Burma|66.6|64.2|69.2|56890418|ea
|South Africa|63.1|61.6|64.6|54300704|af
|Tanzania|62.2|60.8|63.6|52482726|af
|Korea, South|82.4|79.3|85.8|50924172|ea
|Spain|81.7|78.7|84.9|48563476|eu
|Colombia|75.7|72.6|79|47220856|sa
|Kenya|64|62.6|65.5|46790758|af
|Ukraine|71.8|67.1|76.9|44209733|eu
|Argentina|77.1|74|80.4|43886748|sa
|Algeria|76.8|75.5|78.2|40263711|af
|Poland|77.6|73.7|81.7|38523261|eu
|Uganda|55.4|54|56.9|38319241|af
|Iraq|74.9|72.6|77.2|38146025|me
|Sudan|64.1|62|66.3|36729501|af
|Canada|81.9|79.2|84.6|35362905|na
|Morocco|76.9|73.8|80.1|33655786|af
-15|Afghanistan|51.3|49.9|52.7|33332025|as
|Malaysia|75|72.2|78|30949962|ea
|Venezuela|75.8|72.7|78.9|30912302|sa
|Peru|73.7|71.7|75.9|30741062|sa
|Uzbekistan|73.8|70.7|77|29473614|ca
|Nepal|70.7|70.1|71.3|29033914|as
|Saudi Arabia|75.3|73.2|77.4|28160273|me
|Yemen|65.5|63.4|67.8|27392779|me
|Ghana|66.6|64.1|69.1|26908262|af
|Mozambique|53.3|52.6|54.1|25930150|af
|Korea, North|70.4|66.6|74.5|25115311|ea
|Madagascar|65.9|64.4|67.4|24430325|af
|Cameroon|58.5|57.1|59.9|24360803|af
|Cote d'Ivoire|58.7|57.5|59.9|23740424|af
|Taiwan|80.1|77|83.5|23464787|ea
|Australia|82.2|79.8|84.8|22992654|oc
|Sri Lanka|76.8|73.3|80.4|22235000|as
|Romania|75.1|71.7|78.8|21599736|eu
|Angola|56|54.8|57.2|20172332|af
|Burkina Faso|55.5|53.4|57.6|19512533|af
|Niger|55.5|54.3|56.8|18638600|af
|Malawi|61.2|59.2|63.2|18570321|af
|Kazakhstan|70.8|65.6|75.7|18360353|ca
|Chile|78.8|75.7|81.9|17650114|sa
|Mali|55.8|53.9|57.7|17467108|af
|Syria|74.9|72.5|77.4|17185170|me
|Netherlands|81.3|79.2|83.6|17016967|eu
|Ecuador|76.8|73.8|79.9|16080778|sa
|Cambodia|64.5|62|67.1|15957223|ea
|Zambia|52.5|50.8|54.1|15510711|af
|Guatemala|72.3|70.3|74.4|15189958|la
|Zimbabwe|58|57.3|58.7|14546961|af
|Senegal|61.7|59.7|63.8|14320055|af
|Rwanda|60.1|58.5|61.7|12988423|af
|Guinea|60.6|59|62.2|12093349|af
-1|Chad|50.2|49|51.5|11852462|af
|Belgium|81|78.4|83.7|11409077|eu
|Cuba|78.7|76.4|81.1|11179995|la
|Tunisia|76.1|74|78.4|11134588|af
|Burundi|60.5|58.8|62.3|11099298|af
|Bolivia|69.2|66.4|72.1|10969649|sa
|Portugal|79.3|76.1|82.8|10833816|eu
|Somalia|52.4|50.3|54.5|10817354|af
|Greece|80.5|77.9|83.3|10773253|eu
|Benin|61.9|60.5|63.3|10741458|af
|Czechia|78.6|75.7|81.8|10644842|eu
|Dominican Republic|78.1|75.9|80.5|10606865|la
|Haiti|63.8|61.2|66.4|10485800|la
|Sweden|82.1|80.2|84.1|9880604|eu
|Hungary|75.9|72.2|79.8|9874784|eu
|Azerbaijan|72.5|69.5|75.8|9872765|me
-17|Belarus|72.7|67.2|78.6|9570376|eu
|Honduras|71.1|69.5|72.8|8893259|la
|Austria|81.5|78.9|84.3|8711770|eu
|Tajikistan|67.7|64.6|71|8330946|ca
|Jordan|74.6|73.2|76.1|8185384|me
|Switzerland|82.6|80.3|85|8179294|eu
|Israel|82.4|80.6|84.4|8174527|me
|Togo|65|62.3|67.7|7756937|af
|Hong Kong|82.9|80.3|85.8|7167403|ea
|Bulgaria|74.5|71.2|78|7144653|eu
|Serbia|75.5|72.6|78.5|7143921|eu
|Laos|64.3|62.2|66.4|7019073|ea
|Paraguay|77.2|74.5|80|6862812|sa
|Papua New Guinea|67.2|65|69.5|6791317|ea
|Libya|76.5|74.7|78.3|6541948|af
|Lebanon|77.6|76.3|78.9|6237738|me
|El Salvador|74.7|71.4|78.1|6156670|la
|Sierra Leone|58.2|55.6|60.9|6018888|af
|Nicaragua|73.2|71.1|75.5|5966798|la
|United Arab Emirates|77.5|74.8|80.2|5927482|me
|Eritrea|64.9|62.4|67.5|5869869|af
10|Singapore|85|82.3|87.8|5781728|ea
|Kyrgyzstan|70.7|66.5|75.1|5727553|ca
|Denmark|79.4|77|82|5593785|eu
|Central African Republic|52.3|51|53.7|5507257|af
|Finland|80.9|77.9|84|5498211|eu
|Slovakia|77.1|73.5|80.9|5445802|eu
|Turkmenistan|70.1|67.1|73.3|5291317|ca
|Norway|81.8|79.8|83.9|5265158|eu
|Ireland|80.8|78.5|83.2|4952473|eu
|Georgia|76.2|72.1|80.6|4928052|me
|Costa Rica|78.6|75.9|81.4|4872543|la
|Congo, Republic of the|59.3|58.1|60.6|4852412|af
|New Zealand|81.2|79.1|83.3|4474549|oc
|Croatia|75.9|72.7|79.2|4313707|eu
|Liberia|59|57.3|60.8|4299944|af
|Bosnia and Herzegovina|76.7|73.7|80|3861912|eu
|Panama|78.6|75.8|81.6|3705246|la
|Mauritania|63|60.7|65.4|3677293|af
|Puerto Rico|79.4|75.8|83.1|3578056|la
|Moldova|70.7|66.9|74.8|3510485|eu
|Oman|75.5|73.5|77.5|3355262|me
|Uruguay|77.2|74.1|80.5|3351016|sa
|Armenia|74.6|71.4|78.3|3051250|me
|Albania|78.3|75.7|81.2|3038594|eu
|Mongolia|69.6|65.4|74.1|3031330|ea
|Jamaica|73.6|72|75.3|2970340|la
|Lithuania|74.9|69.5|80.6|2854235|eu
|Kuwait|78|76.6|79.4|2832776|me
|West Bank|75|73|77.1|2697687|me
|Namibia|63.6|62.1|65.1|2436469|af
|Qatar|78.7|76.7|80.8|2258283|me
1|Botswana|54.5|56.3|52.6|2209208|af
|Macedonia|76.2|73.6|79|2100025|eu
|Gambia, The|64.9|62.5|67.3|2009648|af
|Slovenia|78.2|74.6|82|1978029|eu
|Latvia|74.5|69.9|79.3|1965686|eu
|Lesotho|53|52.9|53.1|1953070|af
-2|Guinea-Bissau|50.6|48.6|52.7|1759159|af
|Gaza Strip|73.9|72.3|75.7|1753327|me
|Gabon|52.1|51.6|52.5|1738541|af
1|Swaziland|51.6|52.2|51|1451428|af
|Bahrain|78.9|76.7|81.1|1378904|me
|Mauritius|75.6|72.2|79.2|1348242|af
|Timor-Leste|68.1|66.5|69.7|1261072|ea
|Estonia|76.7|71.9|81.7|1258545|eu
|Trinidad and Tobago|72.9|69.9|75.9|1220479|la
|Cyprus|78.7|75.8|81.6|1205575|eu
|Fiji|72.7|70|75.5|915303|oc
|Djibouti|63.2|60.7|65.8|846687|af
|Comoros|64.2|61.9|66.6|794678|af
|Equatorial Guinea|64.2|63.1|65.4|759451|af
|Bhutan|70.1|69.1|71.1|750125|as
|Guyana|68.4|65.4|71.5|735909|sa
|Solomon Islands|75.3|72.7|78.1|635027|oc
-10|Macau|84.5|81.6|87.6|597425|ea
|Western Sahara|63|60.7|65.4|587020|af
|Suriname|72.2|69.8|74.8|585824|sa
|Luxembourg|82.3|79.8|84.9|582291|eu
|Cabo Verde|72.1|69.8|74.5|553432|af
|Brunei|77.2|74.8|79.6|436620|ea
|Malta|80.4|78|82.8|415196|eu
|Maldives|75.6|73.3|78|392960|as
|Belize|68.7|67.2|70.4|353858|la
|Iceland|83|80.9|85.3|335878|eu
|Bahamas, The|72.4|70|74.8|327316|la
|Barbados|75.3|73|77.7|291495|la
|French Polynesia|77.2|74.9|79.6|285321|oc
|Vanuatu|73.4|71.8|75.1|277554|oc
|New Caledonia|77.7|73.7|81.9|275355|oc
|Samoa|73.7|70.8|76.8|198926|oc
|Sao Tome and Principe|64.9|63.6|66.3|197541|af
|Saint Lucia|77.8|75|80.7|164464|la
|Guam|79.1|76.1|82.4|162742|oc
|Curacao|78.3|76|80.7|149035|la
|Aruba|76.8|73.7|79.9|113648|la
|Grenada|74.3|71.7|77.1|111219|la
|Kiribati|66.2|63.7|68.8|106925|oc
|Tonga|76.2|74.7|77.8|106513|oc
|Micronesia, Federated States of|72.9|70.8|75|104719|oc
|Virgin Islands|80|77|83.2|102951|la
|Saint Vincent and the Grenadines|75.3|73.3|77.4|102350|la
|Jersey|81.9|79.4|84.5|98069|eu
|Antigua and Barbuda|76.5|74.4|78.8|93581|la
|Seychelles|74.7|70.2|79.4|93186|af
|Isle of Man|81.2|79.5|83|88195|eu
|Andorra|82.8|80.6|85.1|85660|eu
|Dominica|77|74|80.1|73757|la
|Marshall Islands|73.1|70.9|75.4|73376|oc
|Bermuda|81.3|78.1|84.5|70537|na
|Guernsey|82.5|79.9|85.4|66297|eu
|Greenland|72.4|69.7|75.2|57728|na
|Cayman Islands|81.2|78.5|84|57268|la
|American Samoa|75.4|72.4|78.5|54194|oc
|Northern Mariana Islands|78|75.3|80.8|53467|oc
|Saint Kitts and Nevis|75.7|73.3|78.2|52329|la
|Turks and Caicos Islands|79.8|77.1|82.7|51430|la
|Faroe Islands|80.4|77.8|83.1|50456|eu
|Sint Maarten|78.1|75.8|80.6|41486|la
|Liechtenstein|81.9|79.7|84.6|37937|eu
|British Virgin Islands|78.6|77.2|80.1|34232|la
|San Marino|83.3|80.7|86.1|33285|eu
-1|Monaco|89.5|85.6|93.5|30581|eu
|Gibraltar|79.4|76.6|82.5|29328|eu
|Palau|73.1|69.9|76.5|21347|oc
|Anguilla|81.4|78.8|84.1|16752|la
|Wallis and Futuna|79.7|76.7|82.8|15664|oc
|Tuvalu|66.5|64.3|68.8|10959|oc
|Nauru|67.1|63|70.5|9591|oc
|Cook Islands|75.8|73|78.8|9556|oc
|Saint Helena, Ascension, and Tristan da Cunha|79.5|76.6|82.6|7795|af
|Saint Pierre and Miquelon|80.5|78.2|83|5595|na
1|Montserrat|74.4|75.8|72.9|5267|la
|Falkland Islands (Islas Malvinas)|77.9|75.6|79.6|2931|sa
|Svalbard|NA|NA|NA|2667|eu
|Norfolk Island|NA|NA|NA|2210|oc
|Christmas Island|NA|NA|NA|2205|oc
|Tokelau|NA|NA|NA|1285|oc
|Niue|NA|NA|NA|1190|oc
|Cocos (Keeling) Islands|NA|NA|NA|596|oc
|Pitcairn Islands|NA|NA|NA|54|oc
'''.strip().split('\n')]
# do_refresh_cache = True
def read_url(url, headers={}, path_cache=None, is_verbose=True):
if (path_cache is None):
file_cache = os.path.basename(url)
path_cache = os.path.join('%s.cache' % (os.path.splitext(__file__)[0]),
file_cache if (len(file_cache) > 0) else
'%s.htm' % (os.path.basename(url.rstrip('/'))))
if (('do_refresh_cache' in globals() and do_refresh_cache) or
(not os.path.isfile(path_cache))):
request = urllib2.Request(url, headers=headers)
try: html = urllib2.urlopen(request).read()
except urllib2.HTTPError as e: html = ''; print(e)
try: os.makedirs(os.path.dirname(path_cache))
except OSError: pass
with open(path_cache, 'wb') as f_html: f_html.write(html)
if (is_verbose): print('%s > %s' % (url, path_cache))
time.sleep(1) ## avoid rate-limit-exceeded error
else:
with open(path_cache) as f_html: html = f_html.read()
if (is_verbose): print('< %s' % (path_cache))
try: html = html.decode('utf-8')
except UnicodeDecodeError: pass
return html
def fmt(string): ## string.format(**vars()) using tags {expression!format} by CMG Lee
def f(tag): i_sep = tag.rfind('!'); return (re.sub('\.0+$', '', str(eval(tag[1:-1])))
if (i_sep < 0) else ('{:%s}' % tag[i_sep + 1:-1]).format(eval(tag[1:i_sep])))
return (re.sub(r'(?<!{){[^{}]+}', lambda m:f(m.group()), string)
.replace('{{', '{').replace('}}', '}'))
def append(obj, string): return obj.append(fmt(string))
def format_tab(*arg): return '\t'.join([str(el) for el in (arg if len(arg) > 1 else arg[0])])
def tabbify(cellss, separator='|'):
cellpadss = [list(rows) + [''] * (len(max(cellss, key=len)) - len(rows)) for rows in cellss]
fmts = ['%%%ds' % (max([len(str(cell)) for cell in cols])) for cols in zip(*cellpadss)]
return '\n'.join([separator.join(fmts) % tuple(rows) for rows in cellpadss])
def hex_rgb(colour): ## convert [#]RGB to #RRGGBB and [#]RRGGBB to #RRGGBB
return '#%s' % (colour if len(colour) > 4 else ''.join([c * 2 for c in colour])).lstrip('#')
def try_int_float(field):
try: return int(field)
except:
try: return float(field)
except: return field
def roundm(x, multiple=1):
try: x[0]; return [roundm(element, multiple) for element in x] ## x[0] checks if x is iterable
except: return int(math.floor(float(x) / multiple + 0.5)) * multiple
def findall(regex, string):
return re.findall(regex, string, flags=re.I|re.DOTALL)
def sub(regex_replace, regex_with, string):
return str(re.sub(regex_replace, regex_with, string, flags=re.DOTALL).strip())
def make_serial(name): return sub(r'[^a-z]', '', name.lower())
def make_table(datass):
return '\n'.join(['|'.join([str(data) for data in datas]) for datas in datass])
data_newss = {}
html_expectancy = read_url('http://cia.gov/library/publications/resources/the-world-factbook/fields/355.html')
html_expectancyss = findall(r'(<td.+?</td>)\s*(<td.+?</td>)', html_expectancy)
for html_expectancys in html_expectancyss:
html_divs = findall(r'<div.+?</div>', html_expectancys[1])
name = sub(r'<.*?>', '', html_expectancys[0])
serial = make_serial(name)
# expectancy_male = None
# expectancy_female = None
# try: expectancy_male = float(findall(r'[\d.]+(?= years)', html_divs[1])[0])
# except Exception: pass
# try: expectancy_female = float(findall(r'[\d.]+(?= years)', html_divs[2])[0])
# except Exception: pass
# if (not serial in data_newss): data_newss[serial] = {}
# data_newss[serial]['male' ] = expectancy_male
# data_newss[serial]['female'] = expectancy_female
try:
expectancy_overall = float(findall(r'(?:[\d.]+(?= years)|\d+\.\d+)', html_divs[0])[0])
expectancy_male = float(findall(r'(?:[\d.]+(?= years)|\d+\.\d+)', html_divs[1])[0])
expectancy_female = float(findall(r'(?:[\d.]+(?= years)|\d+\.\d+)', html_divs[2])[0])
if (not serial in data_newss): data_newss[serial] = {}
data_newss[serial]['overall'] = expectancy_overall
data_newss[serial]['male' ] = expectancy_male
data_newss[serial]['female' ] = expectancy_female
except Exception: pass
html_population = read_url('http://cia.gov/library/publications/resources/the-world-factbook/fields/335.html')
html_populationss = findall(r'(<td.+?</td>)\s*(<td.+?</td>)', html_population)
for html_populations in html_populationss:
name = sub(r'<.*?>', '', html_populations[0])
serial = make_serial(name)
# population = None
# if (not 'no indigenous' in html_populations[1]):
# try: population = int(sub(',','',findall(r'[\d,]+', html_populations[1])[0]))
# except Exception: pass
# if (not serial in data_newss): data_newss[serial] = {}
# data_newss[serial]['population'] = population
if (not 'no indigenous' in html_populations[1]):
try:
population = int(sub(',','',findall(r'[\d,]+', html_populations[1])[0]))
if (not serial in data_newss): data_newss[serial] = {}
data_newss[serial]['name'] = name
data_newss[serial]['population'] = population
except Exception: pass
outss = []
for serial in sorted(data_newss):
data_news = data_newss[serial]
try: outss.append([serial, data_news['name'], data_news['population'],
data_news['overall'], data_news['male'], data_news['female']])
# data_news['population'] if ('population' in data_news) else None,
# data_news['male'] if ('male' in data_news) else None,
# data_news['female'] if ('female' in data_news) else None])
except Exception: pass
# print(data_newss.pop(serial))
# print(tabbify(outss))
outss = []
# print(tabbify(data_oldss))
map_keeps = {'usa':'unitedstates', 'uk':'unitedkingdom', 'drcongo':'congodemocraticrepublicofthe'}
map_changes = {'swaziland':'eswatini'}
for data_olds in data_oldss:
name = data_olds[1]
serial = make_serial(name)
data_news = None
try:
if (serial in map_keeps): serial = map_keeps[serial]
if (serial in map_changes):
serial = map_changes[serial]
data_news = data_newss[serial]
name = data_news['name']
else:
data_news = data_newss[serial]
except Exception: pass
outss.append([data_olds[0],
name,
# data_news['name' ] if ('name' in data_news) else 'NA',
data_news['overall' ] if ('overall' in data_news) else 'NA',
data_news['male' ] if ('male' in data_news) else 'NA',
data_news['female' ] if ('female' in data_news) else 'NA',
data_news['population'] if ('population' in data_news) else 'NA',
data_olds[6]])
# outss.append(data_olds)
if (name != data_news['name']): print(name, data_news['name'])
# print(tabbify(outss))
outss = outss[:2] + sorted(outss[2:], key=lambda lines:lines[5], reverse=True)
dir_cache = '%s.cache' % (os.path.splitext(__file__)[0])
with open(os.path.join(dir_cache, 'old.txt'), 'w') as f: f.write(make_table(data_oldss))
with open(os.path.join(dir_cache, 'new.txt'), 'w') as f: f.write(make_table(outss))
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- compartir igual – En caso de mezclar, transformar o modificar este trabajo, deberás distribuir el trabajo resultante bajo la misma licencia o una compatible como el original.
Se autoriza la copia, distribución y modificación de este documento bajo los términos de la licencia de documentación libre GNU, versión 1.2 o cualquier otra que posteriormente publique la Fundación para el Software Libre; sin secciones invariables, textos de portada, ni textos de contraportada. Se incluye una copia de la dicha licencia en la sección titulada Licencia de Documentación Libre GNU.http://www.gnu.org/copyleft/fdl.htmlGFDLGNU Free Documentation Licensetruetrue |
Puedes usar la licencia que prefieras.
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image/svg+xml
70ad5dbacbff2843897fff5dd186c06900a6682e
130 119 byte
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Fecha y hora | Miniatura | Dimensiones | Usuario | Comentario | |
---|---|---|---|---|---|
actual | 22:21 27 feb 2019 | 512 × 448 (127 kB) | Cmglee | Update with 2018 data. | |
19:59 19 jun 2017 | 512 × 448 (127 kB) | Cmglee | Update with 2016 data. | ||
01:42 7 feb 2016 | 512 × 512 (128 kB) | Cmglee | Add interactivity using CSS and title tag. | ||
04:34 28 jun 2015 | 512 × 512 (95 kB) | Leftcry | Fix | ||
03:49 25 jun 2015 | 512 × 512 (95 kB) | Leftcry | Europe classification | ||
21:26 20 nov 2011 | 512 × 512 (59 kB) | Cmglee | Update colours. | ||
20:21 20 nov 2011 | 512 × 512 (59 kB) | Cmglee | {{Information |Description ={{en|1=Comparison of male and female life expectancy at birth for countries and territories as defined in the 2011 CIA Factbook, with selected bubbles labelled. The dotted line corresponds to equal female and male life expec |
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Título breve | comparison gender life expectancy CIA factbook |
---|---|
Título de la imagen | Comparison of male and female life expectancy at birth (2018 estimate) for countries and territories as defined in the CIA Factbook, with selected bubbles labelled, by CMG Lee. Hover over a bubble to highlight it and show its data. The dotted line corresponds to equal female and male life expectancy. The apparent 3D volumes of the bubbles are linearly proportional to their populations, i.e. their radii are linearly proportional to the cube root of the populations. Data is from https://www.cia.gov/library/publications/resources/the-world-factbook/fields/355.html and https://www.cia.gov/library/publications/resources/the-world-factbook/fields/335.html . |
Anchura | 100% |
Altura | 100% |