44_geojson-1

import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

#df = pd.read_csv('C:\python\project\초혼연령&출산율\평균+초혼연령(구별)_20220927162356.xls', encoding='utf_8')
df = pd.read_excel('C:\python\project\초혼연령&출산율\평균+초혼연령(구별)_20220927162356.xls')
df=df.drop(0,axis=0)
df.set_index('자치구별(1)', inplace=True)
df

	2000	2000.1	2001	2001.1	2002	2002.1	2003	2003.1	2004	2004.1	...	2017	2017.1	2018	2018.1	2019	2019.1	2020	2020.1	2021	2021.1
자치구별(1)
서울시	29.7	27.3	29.9	27.5	30.1	27.8	30.5	28	30.9	28.3	...	33.4	31.2	33.5	31.3	33.7	31.6	33.6	31.6	33.9	31.9
종로구	29.7	27.8	30.5	27.9	30.7	28.5	31.1	28.7	31.4	28.6	...	33.1	31.5	33.3	31.3	34.1	31.6	33.7	32.1	33.7	32.1
중구	30.2	27.5	30.3	27.8	30.7	28.1	31.1	28.4	31.8	28.4	...	33.6	31.5	33.8	31.7	33.9	31.9	33.7	32.1	34.2	32.2
용산구	29.9	27.7	30.3	28.1	30.6	28.2	31	28.4	31.5	28.8	...	33.7	31.6	33.8	31.9	34.1	32.1	33.9	32.2	34.7	32.8
성동구	29.8	27.2	30	27.4	30.2	27.8	30.7	28	31	28.1	...	33.5	31.2	33.7	31.6	33.8	31.5	33.8	31.6	34.1	32.2
광진구	29.6	27.2	29.8	27.4	29.9	27.7	30.4	28	30.8	28.3	...	33.1	31.1	33.4	31.1	33.7	31.5	33.4	31.3	33.4	31.7
동대문구	29.6	27.2	29.8	27.6	30.4	27.7	30.7	28	31.3	28.4	...	33.7	31.5	33.4	31.3	34	31.7	33.3	31.4	33.6	31.5
중랑구	29.4	26.9	29.6	27.1	30.1	27.4	30.5	27.7	30.6	28	...	33.6	31.1	33.3	30.9	33.7	31.5	33.6	31.5	33.8	31.6
성북구	29.7	27.2	29.8	27.5	30.1	27.8	30.5	28	31	28.3	...	33.7	31.2	33.8	31.5	33.9	31.6	33.9	31.8	34	32
강북구	29.3	27	29.8	27.3	30	27.6	30.4	27.8	30.9	28.3	...	33.5	31	33.7	31	34.2	31.5	34	31.3	34.3	32.1
도봉구	29.6	27.2	29.9	27.6	30.1	27.9	30.5	27.9	30.9	28.3	...	33.2	30.9	33.5	31	33.6	31.3	33.6	31.6	33.7	31.7
노원구	29.8	27.3	30	27.5	30.2	27.7	30.5	27.9	30.9	28.4	...	33.1	30.9	33.7	31.2	33.7	31.5	33.4	31.6	33.4	31.5
은평구	29.6	27.3	29.7	27.5	29.9	27.9	30.5	28	30.9	28.4	...	33.8	31.3	33.7	31.5	33.9	31.4	34	31.6	33.9	32.1
서대문구	29.6	27.6	29.9	27.8	30.3	28	30.6	28.3	31	28.6	...	33.2	31.2	33.4	31.4	33.6	31.8	33.4	31.8	34	32.1
마포구	29.9	27.5	30	27.8	30.3	28	30.7	28.3	30.9	28.5	...	33.6	31.5	33.6	31.6	33.8	31.7	34	31.7	34.2	32.1
양천구	29.7	27.1	29.9	27.3	30.2	27.6	30.5	27.7	30.7	28	...	33.2	31	33.4	31.3	33.7	31.4	33.6	31.4	33.5	31.7
강서구	29.6	27.1	29.8	27.3	30	27.6	30.5	27.8	30.7	28.1	...	33.2	31.1	33.3	31	33.6	31.4	33.4	31.4	33.7	31.6
구로구	29.5	26.9	29.7	27.3	30	27.4	30.6	27.8	30.8	28.2	...	33.7	31.3	33.9	31.3	34	31.7	33.7	31.7	33.9	32
금천구	29.5	26.7	29.7	27	30.1	27.5	30.7	27.7	30.9	28	...	33	30.9	33.3	30.8	33.5	31.4	33.4	31.1	33.9	31.5
영등포구	29.8	27.2	29.8	27.4	30.3	27.8	30.7	28	31.1	28.5	...	33.2	31	33.1	31.1	33.5	31.4	33.4	31.3	33.7	31.9
동작구	29.6	27.3	30	27.7	30	27.8	30.4	28.2	30.7	28.3	...	33.2	31.2	33.5	31.4	33.6	31.4	33.6	31.8	33.7	31.7
관악구	29.5	27.2	29.8	27.5	29.9	27.7	30.3	28	30.8	28.2	...	33	30.8	33.1	31.1	33.3	31.2	33	31.1	33.4	31.5
서초구	29.7	27.7	30.1	28	30.3	28.2	30.5	28.4	30.8	28.6	...	33.5	31.5	33.7	31.9	33.9	31.8	34	32	34.3	32.5
강남구	29.9	27.7	30.1	27.9	30.3	28.3	30.5	28.4	30.9	28.7	...	33.9	31.7	33.9	31.9	34.2	32.1	34.1	32.3	34.3	32.6
송파구	29.7	27.3	29.9	27.5	30	27.8	30.2	27.9	30.7	28.2	...	33.1	31.3	33.4	31.4	33.5	31.6	33.4	31.6	33.9	32.1
강동구	29.6	26.9	29.6	27.2	29.9	27.5	30.3	27.9	30.9	28.2	...	33.3	31.3	33.5	31.2	33.5	31.4	33.6	31.6	33.8	31.8

26 rows × 44 columns

new_df = pd.DataFrame()
col_li = list(df.columns)
length=int(len(df.columns) / 2)

for i in range(0, length):
    #남녀의 초혼연령의 평균을 계산한다.
    new_df[col_li[i*2]] = (df[col_li[i*2]] + df[col_li[(2*i)+1]])/2
    
new_df = new_df.reset_index()    
new_df=new_df.drop(0,axis=0)
new_df.index = new_df.index-1
new_df

	자치구별(1)	2000	2001	2002	2003	2004	2005	2006	2007	2008	...	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
0	종로구	28.75	29.2	29.6	29.9	30	30.75	30.65	30.65	30.8	...	31.75	31.9	31.8	32.2	32.4	32.3	32.3	32.85	32.9	32.9
1	중구	28.85	29.05	29.4	29.75	30.1	30.5	30.5	30.35	30.95	...	31.7	31.7	32	32.55	32.4	32.55	32.75	32.9	32.9	33.2
2	용산구	28.8	29.2	29.4	29.7	30.15	30.6	30.4	30.85	31.15	...	31.6	32	32.5	32.25	32.4	32.65	32.85	33.1	33.05	33.75
3	성동구	28.5	28.7	29	29.35	29.55	29.95	30.1	30.35	30.7	...	31.45	31.6	31.9	32.05	32.35	32.35	32.65	32.65	32.7	33.15
4	광진구	28.4	28.6	28.8	29.2	29.55	29.8	29.85	30.15	30.4	...	31.25	31.35	31.65	31.9	32	32.1	32.25	32.6	32.35	32.55
5	동대문구	28.4	28.7	29.05	29.35	29.85	30	30	30.4	30.55	...	31.3	31.6	31.75	31.8	32.15	32.6	32.35	32.85	32.35	32.55
6	중랑구	28.15	28.35	28.75	29.1	29.3	29.75	29.9	29.95	30.2	...	31.3	31.4	31.6	31.65	31.75	32.35	32.1	32.6	32.55	32.7
7	성북구	28.45	28.65	28.95	29.25	29.65	29.95	30.05	30.4	30.55	...	31.4	31.6	31.7	31.9	32.25	32.45	32.65	32.75	32.85	33
8	강북구	28.15	28.55	28.8	29.1	29.6	29.85	29.95	30.15	30.45	...	31.2	31.35	31.75	31.8	32.25	32.25	32.35	32.85	32.65	33.2
9	도봉구	28.4	28.75	29	29.2	29.6	29.8	30.05	29.95	30.35	...	31.15	31.4	31.65	31.7	32.05	32.05	32.25	32.45	32.6	32.7
10	노원구	28.55	28.75	28.95	29.2	29.65	29.75	29.9	30.05	30.5	...	31.35	31.4	31.85	31.75	32	32	32.45	32.6	32.5	32.45
11	은평구	28.45	28.6	28.9	29.25	29.65	29.95	30.3	30.45	30.6	...	31.35	31.5	32	32	32.05	32.55	32.6	32.65	32.8	33
12	서대문구	28.6	28.85	29.15	29.45	29.8	30.3	30.2	30.45	30.7	...	31.55	31.5	31.8	31.85	32.3	32.2	32.4	32.7	32.6	33.05
13	마포구	28.7	28.9	29.15	29.5	29.7	30.05	30.25	30.45	30.75	...	31.5	31.6	31.95	32.05	32.3	32.55	32.6	32.75	32.85	33.15
14	양천구	28.4	28.6	28.9	29.1	29.35	29.65	29.85	30.05	30.4	...	31.2	31.25	31.45	31.65	31.9	32.1	32.35	32.55	32.5	32.6
15	강서구	28.35	28.55	28.8	29.15	29.4	29.7	29.85	30.05	30.35	...	31.25	31.4	31.6	31.8	32.1	32.15	32.15	32.5	32.4	32.65
16	구로구	28.2	28.5	28.7	29.2	29.5	29.75	29.9	30.1	30.45	...	31.3	31.45	31.65	31.85	32.2	32.5	32.6	32.85	32.7	32.95
17	금천구	28.1	28.35	28.8	29.2	29.45	29.9	29.85	30.25	30.45	...	31.2	31.25	31.5	31.45	31.85	31.95	32.05	32.45	32.25	32.7
18	영등포구	28.5	28.6	29.05	29.35	29.8	29.95	30	30.25	30.7	...	31.35	31.45	31.7	31.8	31.95	32.1	32.1	32.45	32.35	32.8
19	동작구	28.45	28.85	28.9	29.3	29.5	29.85	30	30.35	30.55	...	31.3	31.45	31.7	31.95	32	32.2	32.45	32.5	32.7	32.7
20	관악구	28.35	28.65	28.8	29.15	29.5	29.8	29.9	30.1	30.35	...	31.1	31.35	31.6	31.7	31.7	31.9	32.1	32.25	32.05	32.45
21	서초구	28.7	29.05	29.25	29.45	29.7	30.05	30.15	30.45	30.75	...	31.5	31.7	31.95	32	32.35	32.5	32.8	32.85	33	33.4
22	강남구	28.8	29	29.3	29.45	29.8	30.05	30.15	30.5	30.65	...	31.6	31.7	32.05	32.2	32.65	32.8	32.9	33.15	33.2	33.45
23	송파구	28.5	28.7	28.9	29.05	29.45	29.85	29.8	30.2	30.4	...	31.25	31.45	31.7	31.85	32.1	32.2	32.4	32.55	32.5	33
24	강동구	28.25	28.4	28.7	29.1	29.55	29.6	29.75	29.95	30.2	...	31.2	31.35	31.6	31.85	32.15	32.3	32.35	32.45	32.6	32.8

25 rows × 23 columns

df2 = pd.read_excel('C:\python\project\초혼연령&출산율\합계출산율+및+모의+연령별+출산율_20220927204055.xls')
df2=df2.drop([0,1],axis=0)

#1997~1999까지의 자료는 비어있으므로, 열에서 제거해준다.
df2=df2.drop(['1997','1998','1999'],axis=1)
df2=df2.rename(columns={'자치구별(1)':'자치구'})
df2.index = df2.index-2
df2

	자치구	2000	2001	2002	2003	2004	2005	2006	2007	2008	...	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
0	종로구	1.097	0.972	0.869	0.88	0.846	0.778	0.811	0.879	0.857	...	0.89	0.729	0.79	0.813	0.784	0.646	0.606	0.648	0.522	0.531
1	중구	1.225	1.102	1.018	0.913	0.997	0.903	0.942	1.084	0.978	...	1.121	0.955	1.004	0.946	0.974	0.824	0.737	0.78	0.688	0.634
2	용산구	1.233	1.105	0.937	0.951	0.989	0.906	0.965	1.081	1.017	...	1.098	1.008	0.978	1.005	0.907	0.793	0.747	0.713	0.634	0.664
3	성동구	1.342	1.194	1.097	1.1	1.066	0.985	1.035	1.154	1.091	...	1.077	1.079	1.027	1.095	1.013	0.972	0.909	0.855	0.783	0.764
4	광진구	1.32	1.115	1.012	1.035	0.982	0.881	0.945	0.991	0.939	...	0.957	0.877	0.895	0.903	0.867	0.749	0.713	0.652	0.527	0.525
5	동대문구	1.181	1.048	0.982	0.923	0.956	0.879	0.939	1.009	0.993	...	0.998	0.909	0.914	0.96	0.889	0.822	0.753	0.765	0.699	0.66
6	중랑구	1.22	1.045	0.992	0.947	1.008	0.915	0.978	1.046	1.01	...	1.082	0.991	1.006	1.051	0.995	0.906	0.825	0.777	0.66	0.65
7	성북구	1.186	1.042	0.899	1	1.025	0.946	1.012	1.12	1.042	...	1.044	0.983	0.949	0.991	0.909	0.818	0.737	0.707	0.676	0.656
8	강북구	1.077	0.992	0.922	0.988	0.921	0.858	0.886	1.032	0.971	...	1.051	0.963	0.955	0.922	0.878	0.825	0.647	0.617	0.55	0.541
9	도봉구	1.353	1.178	1.061	1.057	1.086	0.964	0.986	1.079	1.015	...	1.137	0.956	1	1.01	0.947	0.833	0.774	0.697	0.601	0.579
10	노원구	1.524	1.349	1.177	1.216	1.197	1.061	1.114	1.21	1.134	...	1.162	1.057	1.109	1.096	1.068	0.938	0.809	0.776	0.695	0.701
11	은평구	1.258	1.075	1.012	1.074	1.044	0.949	0.994	1.07	0.966	...	1.139	1.024	1.028	1.003	0.943	0.814	0.772	0.697	0.623	0.604
12	서대문구	1.206	1.092	1.009	0.999	0.99	0.902	0.92	1.012	0.958	...	0.967	0.938	0.954	0.913	0.934	0.823	0.727	0.685	0.685	0.635
13	마포구	1.239	1.082	0.986	0.931	1.022	0.901	0.967	1.077	0.997	...	0.991	0.892	0.915	0.986	0.945	0.806	0.733	0.681	0.594	0.587
14	양천구	1.259	1.149	1.059	1.049	1.022	0.961	1.019	1.124	1.043	...	1.055	0.99	1.005	1.034	1.008	0.898	0.771	0.742	0.644	0.645
15	강서구	1.404	1.185	1.075	1.102	1.095	1.026	1.064	1.165	1.123	...	1.151	1.032	1.064	1.129	1.059	0.925	0.826	0.76	0.675	0.617
16	구로구	1.431	1.269	1.142	1.131	1.147	1.082	1.156	1.232	1.202	...	1.258	1.162	1.142	1.169	1.07	0.943	0.838	0.794	0.737	0.737
17	금천구	1.301	1.129	0.997	1.028	1.063	0.951	1.052	1.08	1.01	...	1.104	0.944	1.019	1.086	0.944	0.901	0.796	0.709	0.668	0.612
18	영등포구	1.387	1.194	1.085	1.106	1.101	1.05	1.101	1.208	1.116	...	1.142	0.999	1.025	1.085	1.011	0.884	0.851	0.829	0.705	0.713
19	동작구	1.253	1.156	0.996	1.003	1.057	0.97	1.005	1.061	1.008	...	1.017	0.974	1.007	1.012	0.912	0.831	0.737	0.697	0.655	0.602
20	관악구	1.273	1.091	0.979	0.993	0.976	0.91	0.922	1.005	0.942	...	0.891	0.825	0.834	0.833	0.777	0.662	0.597	0.536	0.474	0.437
21	서초구	1.135	0.958	0.901	0.896	0.917	0.843	0.904	0.973	0.972	...	1.095	1.007	1.014	1.012	0.928	0.799	0.8	0.715	0.656	0.666
22	강남구	1.129	0.926	0.81	0.827	0.792	0.73	0.797	0.877	0.823	...	0.886	0.842	0.863	0.857	0.802	0.706	0.631	0.612	0.537	0.523
23	송파구	1.341	1.135	1.036	1.034	0.991	0.931	0.958	1.046	0.996	...	1.099	0.973	0.996	1.008	0.958	0.871	0.8	0.77	0.665	0.623
24	강동구	1.347	1.14	1.06	1.039	1.038	0.958	0.993	1.073	1.008	...	1.101	1.006	1.039	1.036	0.938	0.884	0.82	0.758	0.748	0.797

25 rows × 23 columns