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

sns scatterplot

mean = [2,2]
cov = [[1,0.7],[0.7,1]]
arr=np.random.multivariate_normal(mean,cov,size=1000).T

np.random.multivariate_normal(mean,cov,size=1000).T.shape

(2, 1000)

len(arr)   # len = 2 가 나온다는 것은 첫번째와 두번째 원소에 접근할 수 있다는 말

arr[0].shape

(1000,)

x,y = arr

plt.plot(x,y,'o')

- matplotlib을 이용한 그림

x1,y1 =  np.random.multivariate_normal([0,0],[[1,0],[0,1]],size=1000).T
x2,y2 =  np.random.multivariate_normal([2,2],[[1,0.7],[0.7,1]],size=1000).T

plt.plot(x1,y1,'o')
plt.plot(x2,y2,'o')

sns: wide df

pd.DataFrame({'x':x1,'y':y1})

	x	y
0	0.053500	-1.258803
1	-0.517300	-0.273907
2	-0.938688	0.582421
3	1.017930	-0.223879
4	0.827941	0.519745
...	...	...
995	1.317776	-0.898103
996	-0.054884	0.290131
997	-0.082623	0.971652
998	-0.796771	-0.140001
999	0.030602	0.435023

1000 rows × 2 columns

               
sns.scatterplot(data=pd.DataFrame({'x':x1,'y':y1}),x='x',y='y')
sns.scatterplot(data=pd.DataFrame({'x':x2,'y':y2}),x='x',y='y')
# #sns.scatterplot(data=None,x=x2,y=y2)

<AxesSubplot:xlabel='x', ylabel='y'>

sns: long df

x=np.concatenate([x1,x2])
# np.array(list(x1) + list(x2))
# x1.tolist() + x2.tolist()
# 다 같은 코드이지만 cocaternate쓰는 방식이 제일 편하다

y=np.concatenate([y1,y2])

cat=['x1']*len(x1) + ['x2']*len(x2)

df2=pd.DataFrame({'x':x, 'y':y, 'cat':cat})
df2

	x	y	cat
0	0.082929	1.218634	x1
1	-0.019451	-1.352701	x1
2	-1.253126	1.246169	x1
3	-0.343648	0.078081	x1
4	0.479097	-1.039999	x1
...	...	...	...
1995	2.503038	2.350603	x2
1996	1.940601	2.356207	x2
1997	3.213965	2.769489	x2
1998	1.203818	-0.276493	x2
1999	1.378763	1.914046	x2

2000 rows × 3 columns

sns.scatterplot(data=df2, x='x', y='y', hue='cat')

<AxesSubplot:xlabel='x', ylabel='y'>

sns을 이용하여 matplotlib 액시즈에 그림 그리기 (\(\star\))

예제1

fig, ax = plt.subplots()
ax.plot([1,2,4,3])

ax

<AxesSubplot:>

fig, mypltax = plt.subplots()

sns.scatterplot(data=df2, x='x', y='y', hue='cat', ax=mypltax)
# ax라는 변수를 넣자

<AxesSubplot:xlabel='x', ylabel='y'>

fig  # 오 sns에 ax넣으니 fig에 sns가 들어옴

seaborn에서 배우지 않은 내용을 matplotlib이용해서 사용가능함

fig, mypltax = plt.subplots()
sns.scatterplot(data=df2, x='x', y='y', hue='cat', ax=mypltax)
mypltax.set_title('coco babo')
fig.suptitle('coco ddong')

Text(0.5, 0.98, 'coco ddong')

fig, ax = plt.subplots(1,3, figsize=(12,4))
ax[0].plot([1,2,4,3], '--o')
sns.scatterplot(x=x1, y=y1, ax=ax[1])
sns.scatterplot(x=x1, y=y1, ax=ax[2]) # 겹쳐그리자
sns.scatterplot(x=x2, y=y2, ax=ax[-1]) # 겹쳐그리자
ax[2].plot([1,2,4,3], '--m', lw=5)

예제2

import cv2

!wget https://upload.wikimedia.org/wikipedia/commons/0/08/Unequalized_Hawkes_Bay_NZ.jpg 
img = cv2.imread('Unequalized_Hawkes_Bay_NZ.jpg',0) # 채널이 1개인 흑백이미지
!rm Unequalized_Hawkes_Bay_NZ.jpg

--2023-02-25 16:17:39--  https://upload.wikimedia.org/wikipedia/commons/0/08/Unequalized_Hawkes_Bay_NZ.jpg
Resolving upload.wikimedia.org (upload.wikimedia.org)... 103.102.166.240, 2001:df2:e500:ed1a::2:b
Connecting to upload.wikimedia.org (upload.wikimedia.org)|103.102.166.240|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 110895 (108K) [image/jpeg]
Saving to: ‘Unequalized_Hawkes_Bay_NZ.jpg’

Unequalized_Hawkes_ 100%[===================>] 108.30K   553KB/s    in 0.2s    

2023-02-25 16:17:40 (553 KB/s) - ‘Unequalized_Hawkes_Bay_NZ.jpg’ saved [110895/110895]

plt.imshow(img,vmin=0,vmax=255,cmap='gray')

<matplotlib.image.AxesImage at 0x7f61e88d36d0>

img2 = cv2.equalizeHist(img)

img.shape

(683, 1024)

img.reshape(683*1024,1) # 컬럼벡터
img.reshape(683*1024) #길이가 1

array([127, 145, 149, ..., 146, 145, 144], dtype=uint8)

fig, ax = plt.subplots(2,2, figsize=(10,5))
ax[0,0].imshow(img,vmin=0,vmax=255,cmap='gray')
#ax[0,1].hist(img.reshape(-1))
sns.histplot(img.reshape(-1),ax=ax[0,1], bins=15, lw=0, kde=True, color='C1')
ax[1,0].imshow(img2,vmin=0,vmax=255,cmap='gray')
sns.histplot(img2.reshape(-1),ax=ax[1,1], bins=15, lw=0, kde=True, color='C1')

<AxesSubplot:ylabel='Count'>

ax[0,1]의 x축을 ax[1,1]의 x축과 맞춰서 비교해보고 싶다.

fig, ax = plt.subplots(2,2, figsize=(10,5))
ax[0,0].imshow(img,vmin=0,vmax=255,cmap='gray')
sns.histplot(img.reshape(-1),ax=ax[0,1], bins=15, lw=0, kde=True, color='C1')
ax[0,1].set_xlim(0,255)
ax[1,0].imshow(img2,vmin=0,vmax=255,cmap='gray')
sns.histplot(img2.reshape(-1),ax=ax[1,1], bins=15, lw=0, kde=True, color='C1')

<AxesSubplot:ylabel='Count'>

mpl 미세먼지 팁(2)

축 간격조정

import matplotlib as mpl

fig, ax = plt.subplots()
ax.plot([(xi/30)**2 for xi in range(30)],'--o')
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(3)) # 큰 눈금간격을 3으로
ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(1)) # 작은 눈금간격을 1로

축 삭제

fig, ax = plt.subplots()
ax.plot([(xi/30)**2 for xi in range(30)],'--o')
ax.xaxis.set_major_locator(mpl.ticker.NullLocator()) # x축 눈금삭제
ax.yaxis.set_major_locator(mpl.ticker.NullLocator()) # y축 눈금삭제

축 범위조정

fig, ax = plt.subplots()
ax.plot([(xi/30)**2 for xi in range(30)],'--o')
ax.set_ylim(-1,2) 
ax.set_xlim(-5,35)
#plt.ylim(-1,2)   위 ax와 같은 코드
#plt.xlim(-5,35)

(-5.0, 35.0)

gcf, gca

plt.plot([1,2,3,2])
fig=plt.gcf()

# gcf: get current figure

fig.suptitle('suptitle')

Text(0.5, 0.98, 'suptitle')

fig

ax = fig.gca()

# gca: get current axes

ax.set_title('title') 
fig

sns scatterplot

sns: aray

sns: wide df

sns: long df

sns을 이용하여 matplotlib 액시즈에 그림 그리기 (\(\star\))

예제1

예제2

mpl 미세먼지 팁(2)

축 간격조정

축 삭제

축 범위조정

gcf, gca