In [ ]:

!sudo apt-get install -y font-nanum

Reading package lists... Done
Building dependency tree       
Reading state information... Done
E: Unable to locate package font-nanum

In [ ]:

!sudo fc-cache -fv

/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs
/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 2 dirs
/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs
/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs
/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs
/root/.local/share/fonts: skipping, no such directory
/root/.fonts: skipping, no such directory
/var/cache/fontconfig: cleaning cache directory
/root/.cache/fontconfig: not cleaning non-existent cache directory
/root/.fontconfig: not cleaning non-existent cache directory
fc-cache: succeeded

In [ ]:

!rm ~/.cache/matplotlib -rf

In [ ]:

from google.colab import drive
drive.mount('/drive')

Mounted at /drive

In [ ]:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

In [ ]:

df = pd.read_excel('ingu.xlsx',thousands=',')
df.dtypes

Out[ ]:

Unnamed: 0     object
2012          float64
2013          float64
2014          float64
2015          float64
2016          float64
2017          float64
2018          float64
2019          float64
2020          float64
2021           object
dtype: object

In [ ]:

df= df.set_index('Unnamed: 0')
df

Out[ ]:

	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Unnamed: 0
출생아수(명)	484550.000	436455.000	435435.000	438420.000	406243.000	357771.000	326822.000	302676.000	272337.000	260562
조출생률(인구1천명당 명)	9.600	8.600	8.600	8.600	7.900	7.000	6.400	5.900	5.300	5.1
합계출산율(가임여성1명당 명)	1.297	1.187	1.205	1.239	1.172	1.052	0.977	0.918	0.837	0.808
사망자수(명)	267221.000	266257.000	267692.000	275895.000	280827.000	285534.000	298820.000	295110.000	304948.000	-
조사망률(인구1천명당 명)	5.300	5.300	5.300	5.400	5.500	5.600	5.800	5.700	5.900	-
전체(년)	80.900	81.400	81.800	82.100	82.400	82.700	82.700	83.300	83.500	-
남자(년)	77.600	78.100	78.600	79.000	79.300	79.700	79.700	80.300	80.500	-
여자(년)	84.200	84.600	85.000	85.200	85.400	85.700	85.700	86.300	86.500	-

In [ ]:

df.index.name= 'object'
df

Out[ ]:

	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
object
출생아수(명)	484550.000	436455.000	435435.000	438420.000	406243.000	357771.000	326822.000	302676.000	272337.000	260562
조출생률(인구1천명당 명)	9.600	8.600	8.600	8.600	7.900	7.000	6.400	5.900	5.300	5.1
합계출산율(가임여성1명당 명)	1.297	1.187	1.205	1.239	1.172	1.052	0.977	0.918	0.837	0.808
사망자수(명)	267221.000	266257.000	267692.000	275895.000	280827.000	285534.000	298820.000	295110.000	304948.000	-
조사망률(인구1천명당 명)	5.300	5.300	5.300	5.400	5.500	5.600	5.800	5.700	5.900	-
전체(년)	80.900	81.400	81.800	82.100	82.400	82.700	82.700	83.300	83.500	-
남자(년)	77.600	78.100	78.600	79.000	79.300	79.700	79.700	80.300	80.500	-
여자(년)	84.200	84.600	85.000	85.200	85.400	85.700	85.700	86.300	86.500	-

In [ ]:

df.index=['출생아수','조출생률','합계출산율','사망자수','조사망률','전체','남자','여자']
df

Out[ ]:

	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
출생아수	484550.000	436455.000	435435.000	438420.000	406243.000	357771.000	326822.000	302676.000	272337.000	260562
조출생률	9.600	8.600	8.600	8.600	7.900	7.000	6.400	5.900	5.300	5.1
합계출산율	1.297	1.187	1.205	1.239	1.172	1.052	0.977	0.918	0.837	0.808
사망자수	267221.000	266257.000	267692.000	275895.000	280827.000	285534.000	298820.000	295110.000	304948.000	-
조사망률	5.300	5.300	5.300	5.400	5.500	5.600	5.800	5.700	5.900	-
전체	80.900	81.400	81.800	82.100	82.400	82.700	82.700	83.300	83.500	-
남자	77.600	78.100	78.600	79.000	79.300	79.700	79.700	80.300	80.500	-
여자	84.200	84.600	85.000	85.200	85.400	85.700	85.700	86.300	86.500	-

In [ ]:

df.loc['출생아수'].values

Out[ ]:

array(['484,550', '436,455', '435,435', '438,420', '406,243', '357,771',
       '326,822', '302,676', '272,337', '260,562'], dtype=object)

In [ ]:

year = df.columns
y1 = list(map(int, df.loc['출생아수'].values))
plt.bar(year,y1)
plt.show()

In [ ]:

year = df.columns
y1 = list(map(int, df.loc['출생아수'].values))
y2 = df.loc['조출생률'].values
fig, baseAxis = plt.subplots()
baseAxis.bar(year, y1)

subAxis = baseAxis.twinx()
subAxis.plot(year, y2, 'y')
plt.show()

In [93]:

year = df.columns
y1 = list(map(int, df.loc['출생아수'].values))
y2 = df.loc['조출생률'].values
fig, baseAxis = plt.subplots()
plt.figure(figsize=(6,8))
plt.rcParams['figsize'](6,8)
baseAxis.bar(year, y1, color= 'red')
baseAxis.set_xlabel('year')

subAxis = baseAxis.twinx()
subAxis.plot(year, y2, 'y-o')

plt.show()

<Figure size 432x576 with 0 Axes>

In [156]:

df.columns[:-1]
y3 = df.iloc[6,:-1].values
y4 = df.iloc[7,:-1].values
print(len(y3))
print(len(y5))
for i in range(2012,2020):
  print(i)

In [166]:

from pandas.core.internals.managers import BaseBlockManager
import matplotlib.pyplot as plt

#plt.rcParams['font.family']= 'NanumGothic'
plt.rc('font',family='NanumGothic')
year = np.array([y for y in  range(2012,2021)])

y3 = df.iloc[6,:-1].values
y4 = df.iloc[7,:-1].values
y5 = df.iloc[4,:-1].values


fig, baseAxis = plt.subplots()

baseAxis.bar(year-0.2, y3, width=0.4, label='men')
baseAxis.bar(year+0.2, y4, width=0.4,label='women')
baseAxis.set_xlabel('가나')
subAx = baseAxis.twinx()
baseAxis.legend()
subAx.plot(year, y5,'-o', color='darkorange')
plt.show()

/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 44032 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45208 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 44032 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45208 missing from current font.
  font.set_text(s, 0, flags=flags)

In [168]:

import matplotlib.pyplot as plt
import matplotlib
matplotlib.matplotlib_fname()

Out[168]:

'/usr/local/lib/python3.7/dist-packages/matplotlib/mpl-data/matplotlibrc'

티스토리

인구통계분석_학교 수업 때 배운 부분