In [2]:
# -*- coding: utf-8 -*-
"""
Realiza cálculos demográficos

@author: Patrick Alves
patrickalves@ufpa.br

"""

import pandas as pd

In [3]:
# Carrega as bases populacionais
pop_homens = pd.read_csv("../datasets/IBGE/pop_homens_IBGE_2013.csv", index_col = 0)    
pop_mulheres = pd.read_csv("../datasets/IBGE/pop_homens_IBGE_2013.csv", index_col = 0) 

# Cria um intervalo entre 80 e 90 para somar as idades
interval = [str(i) for i in range(80,90)] + ["90+"]
            
# Soma as idades e cria uma nova linha no dataframe
pop_homens.loc["80+"] = pop_homens.loc[interval].sum(axis=0)
pop_mulheres.loc["80+"] = pop_mulheres.loc[interval].sum(axis=0)

# Remove as idades entre 80 e 90
pop_homens.drop(interval, inplace=True)
pop_mulheres.drop(interval, inplace=True)

In [7]:
pop_homens.head()


Out[7]:
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ... 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060
0 1774654 1790542 1767804 1744989 1721442 1697264 1672682 1647974 1623412 1599362 ... 988361 977183 966313 955717 945363 935224 925279 915515 905910 896440
1 1768376 1762944 1779556 1757644 1735613 1712808 1689342 1665430 1641346 1617369 ... 999045 987575 976411 965554 954970 944628 934500 924566 914812 905218
2 1763299 1763920 1758749 1775577 1753971 1732251 1709769 1686636 1663060 1639305 ... 1010401 998640 987179 976024 965175 954599 944264 934143 924216 914469
3 1759424 1760566 1761316 1756286 1773236 1751808 1730282 1707999 1685075 1661706 ... 1022190 1010116 998362 986907 975758 964914 954343 944013 933897 923975
4 1756766 1757342 1758574 1759415 1754495 1771538 1750257 1728882 1706762 1683996 ... 1034360 1021959 1009890 998141 986691 975546 964706 954140 943813 933701

5 rows × 61 columns


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