bro's coding

import numpy as np import matplotlib.pyplot as plt plt.subplot(2,2,1) plt.plot([1,2,3,4]) plt.subplot(2,2,2) plt.scatter(np.random.randn(100),np.random.randn(100)) plt.subplot(2,2,3) plt.bar(range(10),range(10)) plt.subplot(2,2,4) plt.imshow(np.random.randn(64,64)) matplotlib.subplot(그래프 여러개 그리기 1) axes[0,0].plot([1,2,3,4]) import numpy as np import matplotlib.pyplot as plt axes[0,0].plot([1,2,3..

axes[0,0].plot([1,2,3,4]) import numpy as np import matplotlib.pyplot as plt axes[0,0].plot([1,2,3,4]) axes[0,1].scatter(np.random.randn(100),np.random.randn(100)) axes[1,0].bar(range(10),range(10)) axes[1,1].imshow(np.random.randn(64,64)) fig.suptitle('Test',fontsize=20) axes[0,0].set_title('Test1') matplotlib.subplot(그래프 여러개 그리기 2) import numpy as np import matplotlib.pyplot as plt plt.subplot..

import numpy as np import matplotlib.pyplot as plt x=np.arange(-1,1,0.1) y=np.arange(-1,1,0.1) X,Y=np.meshgrid(x,y) Z=np.exp(-(X**2+Y**2)) CS=plt.contourf(X,Y,Z,levels=np.arange(-1,1,0.05),alpha=0.5,cmap='seismic') plt.colorbar() CS=plt.contour(X,Y,Z,levels=np.arange(-1,1,0.05)) plt.clabel(CS,inline=2,fontsize=10) https://broscoding.tistory.com/81 matplotlib.contour(등고선 그리기 1) import numpy as np..

import numpy as np import matplotlib.pyplot as plt # x,y의 구간 설정 x=np.arange(-1,1,0.1) y=np.arange(-1,1,0.1) # x,y에 대한 관계좌표 설정 X,Y=np.meshgrid(x,y) # X,Y에 관한 Z값 설정 Z=np.sin(X*Y) #var = plt.contour(X, Y, Z, 레벨 설정) CS=plt.contour(X,Y,Z,levels=np.arange(-1,1,0.2)) #주석 달아주기 plt.clabel(CS,inline=2,fontsize=10) # 컬러바 생성 plt.colorbar() https://broscoding.tistory.com/82 matplotlib.contourf(등고선 그리기 2) imp..

import matplotlib.pyplot as plt img=plt.imread('제목 없음.png') plt.imshow(img*[2.,1.,1.],vmax=1)

import matplotlib.pyplot as plt img=plt.imread('제목 없음.png') plt.imshow(img.mean(axis=2),cmap='gray',vmin=0,vmax=1) plt.colorbar()

a = np.ones([4,3]) a ''' array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]) ''' a+[1,2,3] ''' [2,3,4] [2,3,4] [2,3,4] [2,3,4] ''' https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html Broadcasting — NumPy v1.17 Manual Broadcasting Note See this article for illustrations of broadcasting concepts. The term broadcasting describes how numpy treats arrays with different shapes d..

a = np.arange(12).reshape(4,3) a ''' array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) ''' 1/a ''' array([[ inf, 1. , 0.5 ], [0.33333333, 0.25 , 0.2 ], [0.16666667, 0.14285714, 0.125 ], [0.11111111, 0.1 , 0.09090909]]) '''

import numpy as np a = np.array([1,2,3,4,5]) a+1 [2,3,4,5,6] a*4 [4,8,12,16,20]

import numpy as np iris_labels = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'] iris = np.loadtxt('iris.csv', skiprows=1, delimiter=',', converters={4: lambda s: iris_labels.index(s.decode())}) # iris = np.loadtxt('iris.csv', skiprows=1, delimiter=',', # converters={4: lambda s: labels.index(s)}, encoding='utf-8') # latin1, ascii, utf-8, cp949