px.scatter_geo, animation

강의영상

https://youtube.com/playlist?list=PLQqh36zP38-zGS9nTgAujTBUgZCr23-u7

강의노트 저장하는 방법

https://youtube.com/playlist?list=PLQqh36zP38-xNZZK-6-kzMGSHvxGdzlz0

import

import pandas as pd
import plotly.express as px

`px.scatter_geo`

세계지도 그리기

- 기본그리기1

px.scatter_geo()

- 기본그리기2

px.scatter_geo(projection='natural earth')

세계지도 + 버블

- 좌표에 점을 찍어보기1

df=pd.DataFrame({'lat':[0,38],'lon':[0,127],'size':[5,20]})
df

	lat	lon	size
0	0	0	5
1	38	127	20

px.scatter_geo(df,lat='lat',lon='lon',size='size')

- 좌표에 점을 찍어보기2

df=pd.DataFrame({'iso_alpha':['KOR','JPN'],'size':[10,2]})
df

	iso_alpha	size
0	KOR	10
1	JPN	2

px.scatter_geo(df,locations='iso_alpha',size='size')

Gapminder data 시각화

- Gapminder

스웨덴 스톡홀름에 등록된 비영리 벤처 기업
Trendalyzer 소프트웨어를 개발하여 애니메이션 거품 그래프를 생성 \(\to\) 소프트웨어는 2006년, Gapminder의 개발자는 2007년에 글고..

https://www.youtube.com/watch?v=N91kkT-vC6w

- Gapminder data: 국가별 기대수명, 1인당 GDP, 인구에 대한 데이터 - 특징: 연도별로 정리가 되어있다.

df = px.data.gapminder()
df

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4
...	...	...	...	...	...	...	...	...
1699	Zimbabwe	Africa	1987	62.351	9216418	706.157306	ZWE	716
1700	Zimbabwe	Africa	1992	60.377	10704340	693.420786	ZWE	716
1701	Zimbabwe	Africa	1997	46.809	11404948	792.449960	ZWE	716
1702	Zimbabwe	Africa	2002	39.989	11926563	672.038623	ZWE	716
1703	Zimbabwe	Africa	2007	43.487	12311143	469.709298	ZWE	716

1704 rows × 8 columns

- 2007년만 추출

df.query('year==2007')

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
11	Afghanistan	Asia	2007	43.828	31889923	974.580338	AFG	4
23	Albania	Europe	2007	76.423	3600523	5937.029526	ALB	8
35	Algeria	Africa	2007	72.301	33333216	6223.367465	DZA	12
47	Angola	Africa	2007	42.731	12420476	4797.231267	AGO	24
59	Argentina	Americas	2007	75.320	40301927	12779.379640	ARG	32
...	...	...	...	...	...	...	...	...
1655	Vietnam	Asia	2007	74.249	85262356	2441.576404	VNM	704
1667	West Bank and Gaza	Asia	2007	73.422	4018332	3025.349798	PSE	275
1679	Yemen, Rep.	Asia	2007	62.698	22211743	2280.769906	YEM	887
1691	Zambia	Africa	2007	42.384	11746035	1271.211593	ZMB	894
1703	Zimbabwe	Africa	2007	43.487	12311143	469.709298	ZWE	716

142 rows × 8 columns

- 시각화예시1: (x,y) = (lon,lat) = locations

px.scatter_geo(
    data_frame = df.query('year==2007'),
    locations= 'iso_alpha'
)

- 시각화예시2: (x,y) = (lon,lat) = locations, size= pop

px.scatter_geo(
    data_frame = df.query('year==2007'),
    locations= 'iso_alpha',
    size='pop'
)

- 시각화예시3: (x,y) = (lon,lat) = locations, size= pop, color= continent

px.scatter_geo(
    data_frame = df.query('year==2007'),
    locations= 'iso_alpha',
    size='pop',
    color='continent'
)

`px.scatter_geo` + animation

- 시각화예시1

px.scatter_geo(
    data_frame = df,
    locations= 'iso_alpha',
    size='pop',
    color='continent',
    animation_frame='year'
)

px.scatter_geo(
    data_frame = df,
    projection = 'natural earth',
    locations= 'iso_alpha',
    size='pop',
    color='continent',
    animation_frame='year'
)

숙제

없음