suicide rate

데이터 시각화 연습 with 캐글 (자살률)¶

개발 도상국과 선진국을 포함하여 많은 국가에서 자살률이 수년 동안 상당히 높다. 처음에는 성별 자살률, 그 다음에는 연령과 세대, 나라별, 소득별 자살률 살펴보고자한다. 다음으로 우리는 자살률의 top 10 순위를 해볼 것이다.

reference
- https://philosopher-chan.tistory.com/853

In [3]:

import pandas as pd

df = pd.read_csv('./data/master.csv')

In [4]:

df

Out[4]:

	country	year	sex	age	suicides_no	population	suicides/100k pop	country-year	HDI for year	gdp_for_year ($)	gdp_per_capita ($)	generation
0	Albania	1987	male	15-24 years	21	312900	6.71	Albania1987	NaN	2,156,624,900	796	Generation X
1	Albania	1987	male	35-54 years	16	308000	5.19	Albania1987	NaN	2,156,624,900	796	Silent
2	Albania	1987	female	15-24 years	14	289700	4.83	Albania1987	NaN	2,156,624,900	796	Generation X
3	Albania	1987	male	75+ years	1	21800	4.59	Albania1987	NaN	2,156,624,900	796	G.I. Generation
4	Albania	1987	male	25-34 years	9	274300	3.28	Albania1987	NaN	2,156,624,900	796	Boomers
...	...	...	...	...	...	...	...	...	...	...	...	...
27815	Uzbekistan	2014	female	35-54 years	107	3620833	2.96	Uzbekistan2014	0.675	63,067,077,179	2309	Generation X
27816	Uzbekistan	2014	female	75+ years	9	348465	2.58	Uzbekistan2014	0.675	63,067,077,179	2309	Silent
27817	Uzbekistan	2014	male	5-14 years	60	2762158	2.17	Uzbekistan2014	0.675	63,067,077,179	2309	Generation Z
27818	Uzbekistan	2014	female	5-14 years	44	2631600	1.67	Uzbekistan2014	0.675	63,067,077,179	2309	Generation Z
27819	Uzbekistan	2014	female	55-74 years	21	1438935	1.46	Uzbekistan2014	0.675	63,067,077,179	2309	Boomers

27820 rows × 12 columns

In [6]:

df.head(30)

Out[6]:

	country	year	sex	age	suicides_no	population	suicides/100k pop	country-year	HDI for year	gdp_for_year ($)	gdp_per_capita ($)	generation
0	Albania	1987	male	15-24 years	21	312900	6.71	Albania1987	NaN	2,156,624,900	796	Generation X
1	Albania	1987	male	35-54 years	16	308000	5.19	Albania1987	NaN	2,156,624,900	796	Silent
2	Albania	1987	female	15-24 years	14	289700	4.83	Albania1987	NaN	2,156,624,900	796	Generation X
3	Albania	1987	male	75+ years	1	21800	4.59	Albania1987	NaN	2,156,624,900	796	G.I. Generation
4	Albania	1987	male	25-34 years	9	274300	3.28	Albania1987	NaN	2,156,624,900	796	Boomers
5	Albania	1987	female	75+ years	1	35600	2.81	Albania1987	NaN	2,156,624,900	796	G.I. Generation
6	Albania	1987	female	35-54 years	6	278800	2.15	Albania1987	NaN	2,156,624,900	796	Silent
7	Albania	1987	female	25-34 years	4	257200	1.56	Albania1987	NaN	2,156,624,900	796	Boomers
8	Albania	1987	male	55-74 years	1	137500	0.73	Albania1987	NaN	2,156,624,900	796	G.I. Generation
9	Albania	1987	female	5-14 years	0	311000	0.00	Albania1987	NaN	2,156,624,900	796	Generation X
10	Albania	1987	female	55-74 years	0	144600	0.00	Albania1987	NaN	2,156,624,900	796	G.I. Generation
11	Albania	1987	male	5-14 years	0	338200	0.00	Albania1987	NaN	2,156,624,900	796	Generation X
12	Albania	1988	female	75+ years	2	36400	5.49	Albania1988	NaN	2,126,000,000	769	G.I. Generation
13	Albania	1988	male	15-24 years	17	319200	5.33	Albania1988	NaN	2,126,000,000	769	Generation X
14	Albania	1988	male	75+ years	1	22300	4.48	Albania1988	NaN	2,126,000,000	769	G.I. Generation
15	Albania	1988	male	35-54 years	14	314100	4.46	Albania1988	NaN	2,126,000,000	769	Silent
16	Albania	1988	male	55-74 years	4	140200	2.85	Albania1988	NaN	2,126,000,000	769	G.I. Generation
17	Albania	1988	female	15-24 years	8	295600	2.71	Albania1988	NaN	2,126,000,000	769	Generation X
18	Albania	1988	female	55-74 years	3	147500	2.03	Albania1988	NaN	2,126,000,000	769	G.I. Generation
19	Albania	1988	female	25-34 years	5	262400	1.91	Albania1988	NaN	2,126,000,000	769	Boomers
20	Albania	1988	male	25-34 years	5	279900	1.79	Albania1988	NaN	2,126,000,000	769	Boomers
21	Albania	1988	female	35-54 years	4	284500	1.41	Albania1988	NaN	2,126,000,000	769	Silent
22	Albania	1988	female	5-14 years	0	317200	0.00	Albania1988	NaN	2,126,000,000	769	Generation X
23	Albania	1988	male	5-14 years	0	345000	0.00	Albania1988	NaN	2,126,000,000	769	Generation X
24	Albania	1989	male	75+ years	2	22500	8.89	Albania1989	NaN	2,335,124,988	833	G.I. Generation
25	Albania	1989	male	25-34 years	18	283600	6.35	Albania1989	NaN	2,335,124,988	833	Boomers
26	Albania	1989	male	35-54 years	15	318400	4.71	Albania1989	NaN	2,335,124,988	833	Silent
27	Albania	1989	male	55-74 years	6	142100	4.22	Albania1989	NaN	2,335,124,988	833	G.I. Generation
28	Albania	1989	male	15-24 years	12	323500	3.71	Albania1989	NaN	2,335,124,988	833	Generation X
29	Albania	1989	female	35-54 years	7	288600	2.43	Albania1989	NaN	2,335,124,988	833	Silent

In [7]:

df.sample(30)

Out[7]:

	country	year	sex	age	suicides_no	population	suicides/100k pop	country-year	HDI for year	gdp_for_year ($)	gdp_per_capita ($)	generation
6864	Cuba	2011	male	15-24 years	75	808772	9.27	Cuba2011	0.776	68,990,000,000	6419	Millenials
4116	Belize	2013	female	15-24 years	1	35551	2.81	Belize2013	0.715	1,612,573,850	5272	Millenials
14272	Kuwait	1997	female	55-74 years	0	25000	0.00	Kuwait1997	NaN	30,354,434,553	18766	Silent
5069	Canada	1997	female	75+ years	42	970900	4.33	Canada1997	NaN	652,825,364,726	23245	G.I. Generation
13465	Japan	1993	female	55-74 years	2418	13512000	17.90	Japan1993	NaN	4,454,143,876,947	37832	Silent
14718	Kyrgyzstan	2009	male	5-14 years	8	531076	1.51	Kyrgyzstan2009	NaN	4,690,062,255	977	Generation Z
5563	Chile	2009	female	5-14 years	11	1277663	0.86	Chile2009	NaN	172,389,498,445	11062	Generation Z
17729	New Zealand	2008	female	15-24 years	38	304640	12.47	New Zealand2008	NaN	133,279,679,483	33585	Millenials
22471	Singapore	1990	female	5-14 years	1	194700	0.51	Singapore1990	0.718	36,152,027,893	14393	Generation X
4679	Bulgaria	1996	male	35-54 years	376	1129300	33.29	Bulgaria1996	NaN	10,109,612,142	1270	Boomers
23771	Spain	2005	male	25-34 years	402	3844965	10.46	Spain2005	0.845	1,157,276,458,152	28092	Generation X
7125	Czech Republic	1992	female	55-74 years	164	1035800	15.83	Czech Republic1992	NaN	34,590,052,812	3573	Silent
26610	United Kingdom	1996	male	75+ years	218	1460275	14.93	United Kingdom1996	NaN	1,408,781,591,264	25609	G.I. Generation
3692	Belgium	2006	male	75+ years	142	319905	44.39	Belgium2006	NaN	409,813,197,842	41135	Silent
6701	Cuba	1997	male	5-14 years	5	868234	0.58	Cuba1997	NaN	25,366,200,000	2472	Millenials
6672	Cuba	1995	female	35-54 years	241	1330000	18.12	Cuba1995	0.653	30,429,803,651	2993	Boomers
17146	Netherlands	1991	female	25-34 years	107	1245900	8.59	Netherlands1991	NaN	323,320,449,906	22906	Boomers
23804	Spain	2008	male	75+ years	484	1470360	32.92	Spain2008	NaN	1,635,015,380,108	37848	Silent
1020	Armenia	1995	male	55-74 years	29	242300	11.97	Armenia1995	0.605	1,468,317,350	426	Silent
18008	Norway	1997	female	75+ years	12	214100	5.60	Norway1997	NaN	161,354,369,893	39335	G.I. Generation
1668	Australia	2006	male	25-34 years	277	1442942	19.20	Australia2006	NaN	745,521,862,833	39014	Generation X
22320	Seychelles	2009	female	35-54 years	0	12916	0.00	Seychelles2009	NaN	847,397,850	10157	Boomers
6677	Cuba	1995	male	5-14 years	1	846300	0.12	Cuba1995	0.653	30,429,803,651	2993	Millenials
8565	Estonia	2012	female	35-54 years	7	184354	3.80	Estonia2012	0.855	23,043,864,510	18411	Generation X
25131	Thailand	2000	female	55-74 years	153	3820685	4.00	Thailand2000	0.648	126,392,308,498	2169	Silent
6601	Croatia	2014	male	25-34 years	42	287190	14.62	Croatia2014	0.818	57,629,518,806	14299	Millenials
444	Antigua and Barbuda	2002	female	15-24 years	0	7196	0.00	Antigua and Barbuda2002	NaN	814,615,333	10499	Millenials
4606	Bulgaria	1990	male	55-74 years	294	873400	33.66	Bulgaria1990	0.695	20,632,090,909	2451	G.I. Generation
1128	Armenia	2006	male	75+ years	6	48123	12.47	Armenia2006	NaN	6,384,451,606	2310	Silent
16675	Mexico	1994	male	35-54 years	554	7691700	7.20	Mexico1994	NaN	527,813,238,126	6735	Boomers

In [9]:

df.isnull().sum()

Out[9]:

country                   0
year                      0
sex                       0
age                       0
suicides_no               0
population                0
suicides/100k pop         0
country-year              0
HDI for year          19456
 gdp_for_year ($)         0
gdp_per_capita ($)        0
generation                0
dtype: int64

In [12]:

df.groupby("age")

Out[12]:

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f2810e74790>

In [24]:

df.groupby("age").head(1)

Out[24]:

	country	year	sex	age	suicides_no	population	suicides/100k pop	country-year	HDI for year	gdp_for_year ($)	gdp_per_capita ($)	generation
0	Albania	1987	male	15-24 years	21	312900	6.71	Albania1987	NaN	2,156,624,900	796	Generation X
1	Albania	1987	male	35-54 years	16	308000	5.19	Albania1987	NaN	2,156,624,900	796	Silent
3	Albania	1987	male	75+ years	1	21800	4.59	Albania1987	NaN	2,156,624,900	796	G.I. Generation
4	Albania	1987	male	25-34 years	9	274300	3.28	Albania1987	NaN	2,156,624,900	796	Boomers
8	Albania	1987	male	55-74 years	1	137500	0.73	Albania1987	NaN	2,156,624,900	796	G.I. Generation
9	Albania	1987	female	5-14 years	0	311000	0.00	Albania1987	NaN	2,156,624,900	796	Generation X

In [21]:

df.groupby("age").describe()

Out[21]:

	year								suicides_no		...	HDI for year		gdp_per_capita ($)
	count	mean	std	min	25%	50%	75%	max	count	mean	...	75%	max	count	mean	std	min	25%	50%	75%	max
age
15-24 years	4642.0	2001.275312	8.479669	1985.0	1995.0	2002.0	2008.0	2016.0	4642.0	174.179664	...	0.855	0.944	4642.0	16876.57346	18894.414357	251.0	3450.0	9378.0	24874.0	126352.0
25-34 years	4642.0	2001.275312	8.479669	1985.0	1995.0	2002.0	2008.0	2016.0	4642.0	242.118053	...	0.855	0.944	4642.0	16876.57346	18894.414357	251.0	3450.0	9378.0	24874.0	126352.0
35-54 years	4642.0	2001.275312	8.479669	1985.0	1995.0	2002.0	2008.0	2016.0	4642.0	528.250969	...	0.855	0.944	4642.0	16876.57346	18894.414357	251.0	3450.0	9378.0	24874.0	126352.0
5-14 years	4610.0	2001.173102	8.419515	1985.0	1994.0	2002.0	2008.0	2015.0	4610.0	11.337093	...	0.855	0.944	4610.0	16815.56833	18863.290560	251.0	3436.0	9283.0	24796.0	126352.0
55-74 years	4642.0	2001.275312	8.479669	1985.0	1995.0	2002.0	2008.0	2016.0	4642.0	357.269065	...	0.855	0.944	4642.0	16876.57346	18894.414357	251.0	3450.0	9378.0	24874.0	126352.0
75+ years	4642.0	2001.275312	8.479669	1985.0	1995.0	2002.0	2008.0	2016.0	4642.0	140.697544	...	0.855	0.944	4642.0	16876.57346	18894.414357	251.0	3450.0	9378.0	24874.0	126352.0

6 rows × 48 columns

In [25]:

df.groupby(["age", "sex"]).head(1)

Out[25]:

	country	year	sex	age	suicides_no	population	suicides/100k pop	country-year	HDI for year	gdp_for_year ($)	gdp_per_capita ($)	generation
0	Albania	1987	male	15-24 years	21	312900	6.71	Albania1987	NaN	2,156,624,900	796	Generation X
1	Albania	1987	male	35-54 years	16	308000	5.19	Albania1987	NaN	2,156,624,900	796	Silent
2	Albania	1987	female	15-24 years	14	289700	4.83	Albania1987	NaN	2,156,624,900	796	Generation X
3	Albania	1987	male	75+ years	1	21800	4.59	Albania1987	NaN	2,156,624,900	796	G.I. Generation
4	Albania	1987	male	25-34 years	9	274300	3.28	Albania1987	NaN	2,156,624,900	796	Boomers
5	Albania	1987	female	75+ years	1	35600	2.81	Albania1987	NaN	2,156,624,900	796	G.I. Generation
6	Albania	1987	female	35-54 years	6	278800	2.15	Albania1987	NaN	2,156,624,900	796	Silent
7	Albania	1987	female	25-34 years	4	257200	1.56	Albania1987	NaN	2,156,624,900	796	Boomers
8	Albania	1987	male	55-74 years	1	137500	0.73	Albania1987	NaN	2,156,624,900	796	G.I. Generation
9	Albania	1987	female	5-14 years	0	311000	0.00	Albania1987	NaN	2,156,624,900	796	Generation X
10	Albania	1987	female	55-74 years	0	144600	0.00	Albania1987	NaN	2,156,624,900	796	G.I. Generation
11	Albania	1987	male	5-14 years	0	338200	0.00	Albania1987	NaN	2,156,624,900	796	Generation X

1. 어떤 나이 연령대에 자살을 많이 하는지 살펴보자. (나이와 성별)¶

In [26]:

df["age"].value_counts()

Out[26]:

25-34 years    4642
35-54 years    4642
15-24 years    4642
75+ years      4642
55-74 years    4642
5-14 years     4610
Name: age, dtype: int64

In [27]:

# 시각화 

import matplotlib.pyplot as plt
import seaborn as sns

In [37]:

plt.figure(figsize=(10,5))
sns.barplot(x = "age", y = 'suicides/100k pop', hue = "sex", data = df.groupby(["age", "sex"]).sum().reset_index())
plt.show()

성별은 female 보다 male이 높다.
5-14세 자살률이 낮다.
75세 이상이 자살률이 가장 높다.

In [34]:

df.groupby(["age", "sex"]).sum()

Out[34]:

		year	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
age	sex
15-24 years	female	4644960	175437	4245159089	10045.33	541.291	39170527
15-24 years	male	4644960	633105	4397787807	31487.36	541.291	39170527
25-34 years	female	4644960	208823	4190523226	10614.42	541.291	39170527
25-34 years	male	4644960	915089	4247580361	45957.10	541.291	39170527
35-54 years	female	4644960	506233	7266872023	13732.15	541.291	39170527
35-54 years	male	4644960	1945908	7109016100	55653.87	541.291	39170527
5-14 years	female	4612704	16997	4107939076	1065.49	541.291	38759885
5-14 years	male	4612704	35267	4290754161	1792.90	541.291	38759885
55-74 years	female	4644960	430036	4756740046	16533.52	541.291	39170527
55-74 years	male	4644960	1228407	4046505294	58460.68	541.291	39170527
75+ years	female	4644960	221984	1705548397	23023.86	541.291	39170527
75+ years	male	4644960	431134	957732856	88177.15	541.291	39170527

그룹으로 묶여져 있는것을 .reset_index()로 풀어줄 수 있다. 행으로..

In [35]:

df.groupby(["age", "sex"]).sum().reset_index()

Out[35]:

	age	sex	year	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
0	15-24 years	female	4644960	175437	4245159089	10045.33	541.291	39170527
1	15-24 years	male	4644960	633105	4397787807	31487.36	541.291	39170527
2	25-34 years	female	4644960	208823	4190523226	10614.42	541.291	39170527
3	25-34 years	male	4644960	915089	4247580361	45957.10	541.291	39170527
4	35-54 years	female	4644960	506233	7266872023	13732.15	541.291	39170527
5	35-54 years	male	4644960	1945908	7109016100	55653.87	541.291	39170527
6	5-14 years	female	4612704	16997	4107939076	1065.49	541.291	38759885
7	5-14 years	male	4612704	35267	4290754161	1792.90	541.291	38759885
8	55-74 years	female	4644960	430036	4756740046	16533.52	541.291	39170527
9	55-74 years	male	4644960	1228407	4046505294	58460.68	541.291	39170527
10	75+ years	female	4644960	221984	1705548397	23023.86	541.291	39170527
11	75+ years	male	4644960	431134	957732856	88177.15	541.291	39170527

In [38]:

df.head()

Out[38]:

	country	year	sex	age	suicides_no	population	suicides/100k pop	country-year	HDI for year	gdp_for_year ($)	gdp_per_capita ($)	generation
0	Albania	1987	male	15-24 years	21	312900	6.71	Albania1987	NaN	2,156,624,900	796	Generation X
1	Albania	1987	male	35-54 years	16	308000	5.19	Albania1987	NaN	2,156,624,900	796	Silent
2	Albania	1987	female	15-24 years	14	289700	4.83	Albania1987	NaN	2,156,624,900	796	Generation X
3	Albania	1987	male	75+ years	1	21800	4.59	Albania1987	NaN	2,156,624,900	796	G.I. Generation
4	Albania	1987	male	25-34 years	9	274300	3.28	Albania1987	NaN	2,156,624,900	796	Boomers

2. 나라별 자살률¶

In [39]:

plt.figure(figsize=(10,5))
sns.barplot(x = "country", y = 'suicides/100k pop', hue = "sex", data = df.groupby(["country", "sex"]).sum().reset_index())
plt.show()

x축 글자가 겹친다. 이를 해결하자.

In [41]:

plt.figure(figsize=(20,5))
sns.barplot(x = "country", y = 'suicides/100k pop', hue = "sex", data = df.groupby(["country", "sex"]).sum().reset_index())
plt.xticks(rotation = 90) # 글자 회전 90도
plt.show()

In [94]:

df.groupby(["country"]).sum()

Out[94]:

	year	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
country
Albania	527796	1970	62325467	924.76	32.304	490788
Antigua and Barbuda	647832	11	1990228	179.14	28.140	3385212
Argentina	744000	82219	1035985431	3894.59	93.552	2944044
Armenia	596832	1905	77348173	976.21	66.252	558428
Aruba	336720	101	1259677	1596.52	0.000	4069236
...	...	...	...	...	...	...
United Arab Emirates	144540	622	36502275	94.89	19.800	3035664
United Kingdom	744000	136805	1738767780	2790.92	103.620	11869908
United States	744000	1034013	8054027201	5140.97	106.992	14608296
Uruguay	672072	13138	84068943	6538.96	80.628	2561016
Uzbekistan	528348	34803	486422532	2138.17	54.600	257712

101 rows × 6 columns

시각화는 되었지만, 눈에 확 들어오지 않는다. 이것을 자살률이 높은 순서대로 나타내 보자.

In [47]:

country_sucides = df.groupby("country").sum().reset_index()

나라별로 정렬

In [50]:

country_sucides

Out[50]:

	country	year	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
0	Albania	527796	1970	62325467	924.76	32.304	490788
1	Antigua and Barbuda	647832	11	1990228	179.14	28.140	3385212
2	Argentina	744000	82219	1035985431	3894.59	93.552	2944044
3	Armenia	596832	1905	77348173	976.21	66.252	558428
4	Aruba	336720	101	1259677	1596.52	0.000	4069236
...	...	...	...	...	...	...	...
96	United Arab Emirates	144540	622	36502275	94.89	19.800	3035664
97	United Kingdom	744000	136805	1738767780	2790.92	103.620	11869908
98	United States	744000	1034013	8054027201	5140.97	106.992	14608296
99	Uruguay	672072	13138	84068943	6538.96	80.628	2561016
100	Uzbekistan	528348	34803	486422532	2138.17	54.600	257712

101 rows × 7 columns

오름차순으로 정렬

In [52]:

country_sucides.sort_values(by = "suicides/100k pop", ascending = False)

Out[52]:

	country	year	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
75	Russian Federation	648648	1209742	3690802620	11305.13	0.000	2112096
52	Lithuania	525420	28039	68085210	10588.88	77.136	2431504
40	Hungary	621060	73891	248644256	10156.07	77.172	2904716
47	Kazakhstan	624780	101546	377513869	9519.52	80.016	1662684
73	Republic of Korea	744000	261730	1354944936	9350.45	0.000	5506068
...	...	...	...	...	...	...	...
45	Jamaica	407604	184	39481817	106.44	50.448	673452
96	United Arab Emirates	144540	622	36502275	94.89	19.800	3035664
65	Oman	72396	33	8987087	26.50	19.056	746664
76	Saint Kitts and Nevis	71676	0	117300	0.00	0.000	198900
27	Dominica	23820	0	66400	0.00	0.000	17820

101 rows × 7 columns

In [53]:

plt.figure(figsize=(20,5))
sns.barplot(x = "country", y = 'suicides/100k pop', data = country_sucides.sort_values(by = "suicides/100k pop", ascending = False))
plt.xticks(rotation = 90) # 글자 회전 90도
plt.show()

자살률이 가장 높은 나라는 러시아이고, 그 반대는 도미니카 라는 나라이다.
우리나라도 상위권으로 자살률이 5위 이다.

3.자살률이 높은 순서대로 top 10¶

In [54]:

country_sucides.sort_values(by = "suicides/100k pop", ascending = False)[:10]

Out[54]:

	country	year	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
75	Russian Federation	648648	1209742	3690802620	11305.13	0.000	2112096
52	Lithuania	525420	28039	68085210	10588.88	77.136	2431504
40	Hungary	621060	73891	248644256	10156.07	77.172	2904716
47	Kazakhstan	624780	101546	377513869	9519.52	80.016	1662684
73	Republic of Korea	744000	261730	1354944936	9350.45	0.000	5506068
6	Austria	764160	50073	243853094	9076.23	101.700	13088000
95	Ukraine	672192	319950	1286469184	8931.66	68.496	627492
46	Japan	744000	806902	3681024844	8025.23	103.356	13539888
32	Finland	696348	33677	141925658	7924.11	92.760	12342960
12	Belgium	744000	62761	303302621	7900.50	103.284	11928828

In [55]:

plt.figure(figsize=(20,5))
sns.barplot(x = "country", y = 'suicides/100k pop', data = country_sucides.sort_values(by = "suicides/100k pop", ascending = False)[:10])
plt.xticks(rotation = 90) # 글자 회전 90도
plt.show()

4.연도별 자살률¶

In [56]:

sns.distplot(df['year'])

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Out[56]:

<AxesSubplot:xlabel='year', ylabel='Density'>

In [57]:

df['year'].value_counts()

Out[57]:

2009    1068
2010    1056
2001    1056
2000    1032
2011    1032
2007    1032
2002    1032
2003    1032
2006    1020
2008    1020
2005    1008
2004    1008
1999     996
2012     972
2013     960
1998     948
1995     936
2014     936
1997     924
1996     924
1994     816
1992     780
1993     780
1991     768
1990     768
2015     744
1987     648
1989     624
1988     588
1986     576
1985     576
2016     160
Name: year, dtype: int64

In [65]:

plt.figure(figsize=(20,5))
sns.countplot('year', data = df)
plt.show()

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

In [66]:

plt.figure(figsize=(20,5))
sns.countplot('year', data = df)
plt.xticks(rotation = 90)
plt.show()

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

80~90년대 보다 2000대 들어서 자살률이 늘어 난 것을 확인 할 수 있다.

In [67]:

plt.figure(figsize=(20,5))
sns.stripplot( x = 'year', y = 'suicides/100k pop', data = df)
plt.xticks(rotation = 90)
plt.show()

In [68]:

df

Out[68]:

	country	year	sex	age	suicides_no	population	suicides/100k pop	country-year	HDI for year	gdp_for_year ($)	gdp_per_capita ($)	generation
0	Albania	1987	male	15-24 years	21	312900	6.71	Albania1987	NaN	2,156,624,900	796	Generation X
1	Albania	1987	male	35-54 years	16	308000	5.19	Albania1987	NaN	2,156,624,900	796	Silent
2	Albania	1987	female	15-24 years	14	289700	4.83	Albania1987	NaN	2,156,624,900	796	Generation X
3	Albania	1987	male	75+ years	1	21800	4.59	Albania1987	NaN	2,156,624,900	796	G.I. Generation
4	Albania	1987	male	25-34 years	9	274300	3.28	Albania1987	NaN	2,156,624,900	796	Boomers
...	...	...	...	...	...	...	...	...	...	...	...	...
27815	Uzbekistan	2014	female	35-54 years	107	3620833	2.96	Uzbekistan2014	0.675	63,067,077,179	2309	Generation X
27816	Uzbekistan	2014	female	75+ years	9	348465	2.58	Uzbekistan2014	0.675	63,067,077,179	2309	Silent
27817	Uzbekistan	2014	male	5-14 years	60	2762158	2.17	Uzbekistan2014	0.675	63,067,077,179	2309	Generation Z
27818	Uzbekistan	2014	female	5-14 years	44	2631600	1.67	Uzbekistan2014	0.675	63,067,077,179	2309	Generation Z
27819	Uzbekistan	2014	female	55-74 years	21	1438935	1.46	Uzbekistan2014	0.675	63,067,077,179	2309	Boomers

27820 rows × 12 columns

In [70]:

df["age"].unique()

Out[70]:

array(['15-24 years', '35-54 years', '75+ years', '25-34 years',
       '55-74 years', '5-14 years'], dtype=object)

In [71]:

df["age"].value_counts()

Out[71]:

25-34 years    4642
35-54 years    4642
15-24 years    4642
75+ years      4642
55-74 years    4642
5-14 years     4610
Name: age, dtype: int64

In [72]:

df["generation"].unique()

Out[72]:

array(['Generation X', 'Silent', 'G.I. Generation', 'Boomers',
       'Millenials', 'Generation Z'], dtype=object)

In [73]:

df["generation"].value_counts()

Out[73]:

Generation X       6408
Silent             6364
Millenials         5844
Boomers            4990
G.I. Generation    2744
Generation Z       1470
Name: generation, dtype: int64

In [74]:

sns.countplot('generation', data = df)

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

Out[74]:

<AxesSubplot:xlabel='generation', ylabel='count'>

파이 형태로 그려보자.

In [75]:

df['generation'].value_counts().plot.pie()

Out[75]:

<AxesSubplot:ylabel='generation'>

In [76]:

sns.boxplot(df['year'])

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

Out[76]:

<AxesSubplot:xlabel='year'>

In [77]:

sns.boxplot(df['suicides/100k pop'])

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

Out[77]:

<AxesSubplot:xlabel='suicides/100k pop'>

In [79]:

sns.jointplot('suicides/100k pop', 'year', data = df)

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

Out[79]:

<seaborn.axisgrid.JointGrid at 0x7f280c692e20>

조금 변경해서 시각화를 해보자.

In [81]:

sns.jointplot('suicides/100k pop', 'year', data = df, kind = 'kde')

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

Out[81]:

<seaborn.axisgrid.JointGrid at 0x7f27fa8af6a0>

5.소득과 자살률¶

In [82]:

sns.boxplot(df['gdp_per_capita ($)'])

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

Out[82]:

<AxesSubplot:xlabel='gdp_per_capita ($)'>

In [83]:

sns.distplot(df['gdp_per_capita ($)'])

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Out[83]:

<AxesSubplot:xlabel='gdp_per_capita ($)', ylabel='Density'>

In [84]:

sns.jointplot('suicides/100k pop', 'gdp_per_capita ($)', data = df)

/home/goldang/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

Out[84]:

<seaborn.axisgrid.JointGrid at 0x7f27facb1fd0>

소득률이 낮을 수록 자살률이 높다.

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데이터 시각화 연습 with 캐글 (자살률)¶

1. 어떤 나이 연령대에 자살을 많이 하는지 살펴보자. (나이와 성별)¶

2. 나라별 자살률¶

3.자살률이 높은 순서대로 top 10¶

4.연도별 자살률¶

5.소득과 자살률¶

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데이터 시각화 연습 with 캐글 (자살률)¶

1. 어떤 나이 연령대에 자살을 많이 하는지 살펴보자. (나이와 성별)¶

2. 나라별 자살률¶

3.자살률이 높은 순서대로 top 10¶

4.연도별 자살률¶

5.소득과 자살률¶

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