[3rd_week-day3]Python으로 데이터 다루기 Ⅱ - seaborn

Seaborn

Matplotlib를 기반으로 더 다양한 시각화 방법을 제공하는 라이브러리

커널밀도그림
카운트그림
캣그림
스트립그림
히트맵

Seaborn Import 하기

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

커널밀도그림 (Kernel Density Plot)

히스토그램과 같은 연속적인 분포를 곡선화해서 그린 그림
sns.kdeplot()

# in Histogram

x = np.arange(0, 22, 2)
y = np.random.randint(0, 20, 20)

plt.hist(y, bins=x)

(array([1., 2., 5., 0., 2., 2., 3., 3., 1., 1.]),
 array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20]),
 <BarContainer object of 10 artists>)

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# kdeplot

sns.kdeplot(y, shade=True)

plt.show()

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카운트그림 (Count plot)

범주형 column의 빈도수를 시각화 -> Groupby 후의 도수를 하는 것과 동일한 효과
sns.countplot()

vote_df = pd.DataFrame({"name":['Andy', 'Bob', 'Cat'], "vote":[True, True, False]})
vote_df

	name	vote
0	Andy	True
1	Bob	True
2	Cat	False

# in matplotlib barplot

vote_count = vote_df.groupby('vote').count()
vote_count

	name
vote
False	1
True	2

plt.bar(x=[False, True], height=vote_count['name'])
plt.show()

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# sns의 countplot

sns.countplot(x=vote_df['vote'])

plt.show()

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캣그림 (Cat Plot)

숫자형 변수와 하나 이상의 범주형 변수의 관계를 보여주는 함수
sns.catplot()

covid = pd.read_csv("./country_wise_latest.csv")

covid.head(5)

	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	1 week change	1 week % increase	WHO Region
0	Afghanistan	36263	1269	25198	9796	106	10	18	3.50	69.49	5.04	35526	737	2.07	Eastern Mediterranean
1	Albania	4880	144	2745	1991	117	6	63	2.95	56.25	5.25	4171	709	17.00	Europe
2	Algeria	27973	1163	18837	7973	616	8	749	4.16	67.34	6.17	23691	4282	18.07	Africa
3	Andorra	907	52	803	52	10	0	0	5.73	88.53	6.48	884	23	2.60	Europe
4	Angola	950	41	242	667	18	1	0	4.32	25.47	16.94	749	201	26.84	Africa

s = sns.catplot(x='WHO Region', y='Confirmed' , data=covid) # hue 속성은 점들 간의 분포를 나눠서 다룰 수 있다(?) - EDA에서 다뤄볼 것..
s.fig.set_size_inches(10, 6)
plt.show() # strip plot 형태.

s = sns.catplot(x='WHO Region', y='Confirmed' , data=covid, kind="violin")
s.fig.set_size_inches(10, 6)
plt.show() # violin plot 형태.

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스트립그림 (Strip Plot)

scatter plot과 유사하게 데이터의 수치를 표현하는 그래프
sns.stripplot()

sns.stripplot(x="WHO Region", y="Recovered", data=covid)

plt.show()

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# cf) swarmplot
# 값이 겹치면 양옆으로 분산 시켜준 형태

s = sns.swarmplot(x="WHO Region", y="Recovered", data=covid)

plt.show()

C:\Users\Taeho Kim\AppData\Local\Programs\Python\Python38\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 22.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
C:\Users\Taeho Kim\AppData\Local\Programs\Python\Python38\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 69.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
C:\Users\Taeho Kim\AppData\Local\Programs\Python\Python38\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 79.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
C:\Users\Taeho Kim\AppData\Local\Programs\Python\Python38\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 54.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
C:\Users\Taeho Kim\AppData\Local\Programs\Python\Python38\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 31.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

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히트맵 (Heatmap)

데이터의 행렬을 색상으로 표현해주는 그래프
sns.heatmap()

# 히트맵 예제

covid.corr()

	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	1 week change	1 week % increase
Confirmed	1.000000	0.934698	0.906377	0.927018	0.909720	0.871683	0.859252	0.063550	-0.064815	0.025175	0.999127	0.954710	-0.010161
Deaths	0.934698	1.000000	0.832098	0.871586	0.806975	0.814161	0.765114	0.251565	-0.114529	0.169006	0.939082	0.855330	-0.034708
Recovered	0.906377	0.832098	1.000000	0.682103	0.818942	0.820338	0.919203	0.048438	0.026610	-0.027277	0.899312	0.910013	-0.013697
Active	0.927018	0.871586	0.682103	1.000000	0.851190	0.781123	0.673887	0.054380	-0.132618	0.058386	0.931459	0.847642	-0.003752
New cases	0.909720	0.806975	0.818942	0.851190	1.000000	0.935947	0.914765	0.020104	-0.078666	-0.011637	0.896084	0.959993	0.030791
New deaths	0.871683	0.814161	0.820338	0.781123	0.935947	1.000000	0.889234	0.060399	-0.062792	-0.020750	0.862118	0.894915	0.025293
New recovered	0.859252	0.765114	0.919203	0.673887	0.914765	0.889234	1.000000	0.017090	-0.024293	-0.023340	0.839692	0.954321	0.032662
Deaths / 100 Cases	0.063550	0.251565	0.048438	0.054380	0.020104	0.060399	0.017090	1.000000	-0.168920	0.334594	0.069894	0.015095	-0.134534
Recovered / 100 Cases	-0.064815	-0.114529	0.026610	-0.132618	-0.078666	-0.062792	-0.024293	-0.168920	1.000000	-0.295381	-0.064600	-0.063013	-0.394254
Deaths / 100 Recovered	0.025175	0.169006	-0.027277	0.058386	-0.011637	-0.020750	-0.023340	0.334594	-0.295381	1.000000	0.030460	-0.013763	-0.049083
Confirmed last week	0.999127	0.939082	0.899312	0.931459	0.896084	0.862118	0.839692	0.069894	-0.064600	0.030460	1.000000	0.941448	-0.015247
1 week change	0.954710	0.855330	0.910013	0.847642	0.959993	0.894915	0.954321	0.015095	-0.063013	-0.013763	0.941448	1.000000	0.026594
1 week % increase	-0.010161	-0.034708	-0.013697	-0.003752	0.030791	0.025293	0.032662	-0.134534	-0.394254	-0.049083	-0.015247	0.026594	1.000000

sns.heatmap(covid.corr())

<AxesSubplot:>

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