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목차
1. matplotlib
Python 프로그래밍 언어 및 Numpy 라이브러리를 활용한 플로팅 라이브러리입니다. 범용 GUI 툴킷을 사용하여 애플리케이션에 플롯을 포함하기 위한 객체 지향 API를 제공합니다.
a. linear equation
_, axe = plt.subplots()
x = np.arange(10)
y = x*5+10
axe.plot(x,y)
b. quadratic equation
_, axe = plt.subplots()
x = np.linspace(2,4,10)
y = 2*x*x+5
axe.plot(x,y)
c. normal distribution
_, axe = plt.subplots()
x = np.linspace(-5,5,100)
mu = 0
sig = 1
pi = np.pi
y = (1/(sig*np.sqrt(2*pi)))*(np.exp(-((x-mu)**2)/(2*sig**2)))
axe.plot(x,y)
d. error function
_, axe = plt.subplots()
x = np.linspace(-5,5,100)
mu = 0
sig = 1
y=[]
for v in x:
a = (1 + math.erf((v-mu)/(sig*np.sqrt(2))))*(1/2)
y.append(a)
axe.plot(x,y)
2. Example
a. iris
import pickle
import numpy as np
import matplotlib.pyplot as plt
with open('iris_ndarray.pickle','rb') as f:
iris_np = pickle.load(f)
print(iris_np)
sepal_length = iris_np[:,0]
sepal_width = iris_np[:,1]
petal_length = iris_np[:,2]
petal_width = iris_np[:,3]
# 결과값
[[5.1 3.5 1.4 0.2 0. ]
[4.9 3. 1.4 0.2 0. ]
[4.7 3.2 1.3 0.2 0. ]
[4.6 3.1 1.5 0.2 0. ]
[5. 3.6 1.4 0.2 0. ]
[5.4 3.9 1.7 0.4 0. ]
[4.6 3.4 1.4 0.3 0. ]
[5. 3.4 1.5 0.2 0. ]
[4.4 2.9 1.4 0.2 0. ]
[4.9 3.1 1.5 0.1 0. ]
[5.4 3.7 1.5 0.2 0. ]
[4.8 3.4 1.6 0.2 0. ]
[4.8 3. 1.4 0.1 0. ]
[4.3 3. 1.1 0.1 0. ]
[5.8 4. 1.2 0.2 0. ]
[5.7 4.4 1.5 0.4 0. ]
[5.4 3.9 1.3 0.4 0. ]
[5.1 3.5 1.4 0.3 0. ]
[5.7 3.8 1.7 0.3 0. ]
[5.1 3.8 1.5 0.3 0. ]
[5.4 3.4 1.7 0.2 0. ]
[5.1 3.7 1.5 0.4 0. ]
[4.6 3.6 1. 0.2 0. ]
[5.1 3.3 1.7 0.5 0. ]
[4.8 3.4 1.9 0.2 0. ]
[5. 3. 1.6 0.2 0. ]
[5. 3.4 1.6 0.4 0. ]
[5.2 3.5 1.5 0.2 0. ]
[5.2 3.4 1.4 0.2 0. ]
[4.7 3.2 1.6 0.2 0. ]
[4.8 3.1 1.6 0.2 0. ]
[5.4 3.4 1.5 0.4 0. ]
[5.2 4.1 1.5 0.1 0. ]
[5.5 4.2 1.4 0.2 0. ]
[4.9 3.1 1.5 0.1 0. ]
[5. 3.2 1.2 0.2 0. ]
[5.5 3.5 1.3 0.2 0. ]
[4.9 3.1 1.5 0.1 0. ]
[4.4 3. 1.3 0.2 0. ]
[5.1 3.4 1.5 0.2 0. ]
[5. 3.5 1.3 0.3 0. ]
[4.5 2.3 1.3 0.3 0. ]
[4.4 3.2 1.3 0.2 0. ]
[5. 3.5 1.6 0.6 0. ]
[5.1 3.8 1.9 0.4 0. ]
[4.8 3. 1.4 0.3 0. ]
[5.1 3.8 1.6 0.2 0. ]
[4.6 3.2 1.4 0.2 0. ]
[5.3 3.7 1.5 0.2 0. ]
[5. 3.3 1.4 0.2 0. ]
[7. 3.2 4.7 1.4 1. ]
[6.4 3.2 4.5 1.5 1. ]
[6.9 3.1 4.9 1.5 1. ]
[5.5 2.3 4. 1.3 1. ]
[6.5 2.8 4.6 1.5 1. ]
[5.7 2.8 4.5 1.3 1. ]
[6.3 3.3 4.7 1.6 1. ]
[4.9 2.4 3.3 1. 1. ]
[6.6 2.9 4.6 1.3 1. ]
[5.2 2.7 3.9 1.4 1. ]
[5. 2. 3.5 1. 1. ]
[5.9 3. 4.2 1.5 1. ]
[6. 2.2 4. 1. 1. ]
[6.1 2.9 4.7 1.4 1. ]
[5.6 2.9 3.6 1.3 1. ]
[6.7 3.1 4.4 1.4 1. ]
[5.6 3. 4.5 1.5 1. ]
[5.8 2.7 4.1 1. 1. ]
[6.2 2.2 4.5 1.5 1. ]
[5.6 2.5 3.9 1.1 1. ]
[5.9 3.2 4.8 1.8 1. ]
[6.1 2.8 4. 1.3 1. ]
[6.3 2.5 4.9 1.5 1. ]
[6.1 2.8 4.7 1.2 1. ]
[6.4 2.9 4.3 1.3 1. ]
[6.6 3. 4.4 1.4 1. ]
[6.8 2.8 4.8 1.4 1. ]
[6.7 3. 5. 1.7 1. ]
[6. 2.9 4.5 1.5 1. ]
[5.7 2.6 3.5 1. 1. ]
[5.5 2.4 3.8 1.1 1. ]
[5.5 2.4 3.7 1. 1. ]
[5.8 2.7 3.9 1.2 1. ]
[6. 2.7 5.1 1.6 1. ]
[5.4 3. 4.5 1.5 1. ]
[6. 3.4 4.5 1.6 1. ]
[6.7 3.1 4.7 1.5 1. ]
[6.3 2.3 4.4 1.3 1. ]
[5.6 3. 4.1 1.3 1. ]
[5.5 2.5 4. 1.3 1. ]
[5.5 2.6 4.4 1.2 1. ]
[6.1 3. 4.6 1.4 1. ]
[5.8 2.6 4. 1.2 1. ]
[5. 2.3 3.3 1. 1. ]
[5.6 2.7 4.2 1.3 1. ]
[5.7 3. 4.2 1.2 1. ]
[5.7 2.9 4.2 1.3 1. ]
[6.2 2.9 4.3 1.3 1. ]
[5.1 2.5 3. 1.1 1. ]
[5.7 2.8 4.1 1.3 1. ]
[6.3 3.3 6. 2.5 2. ]
[5.8 2.7 5.1 1.9 2. ]
[7.1 3. 5.9 2.1 2. ]
[6.3 2.9 5.6 1.8 2. ]
[6.5 3. 5.8 2.2 2. ]
[7.6 3. 6.6 2.1 2. ]
[4.9 2.5 4.5 1.7 2. ]
[7.3 2.9 6.3 1.8 2. ]
[6.7 2.5 5.8 1.8 2. ]
[7.2 3.6 6.1 2.5 2. ]
[6.5 3.2 5.1 2. 2. ]
[6.4 2.7 5.3 1.9 2. ]
[6.8 3. 5.5 2.1 2. ]
[5.7 2.5 5. 2. 2. ]
[5.8 2.8 5.1 2.4 2. ]
[6.4 3.2 5.3 2.3 2. ]
[6.5 3. 5.5 1.8 2. ]
[7.7 3.8 6.7 2.2 2. ]
[7.7 2.6 6.9 2.3 2. ]
[6. 2.2 5. 1.5 2. ]
[6.9 3.2 5.7 2.3 2. ]
[5.6 2.8 4.9 2. 2. ]
[7.7 2.8 6.7 2. 2. ]
[6.3 2.7 4.9 1.8 2. ]
[6.7 3.3 5.7 2.1 2. ]
[7.2 3.2 6. 1.8 2. ]
[6.2 2.8 4.8 1.8 2. ]
[6.1 3. 4.9 1.8 2. ]
[6.4 2.8 5.6 2.1 2. ]
[7.2 3. 5.8 1.6 2. ]
[7.4 2.8 6.1 1.9 2. ]
[7.9 3.8 6.4 2. 2. ]
[6.4 2.8 5.6 2.2 2. ]
[6.3 2.8 5.1 1.5 2. ]
[6.1 2.6 5.6 1.4 2. ]
[7.7 3. 6.1 2.3 2. ]
[6.3 3.4 5.6 2.4 2. ]
[6.4 3.1 5.5 1.8 2. ]
[6. 3. 4.8 1.8 2. ]
[6.9 3.1 5.4 2.1 2. ]
[6.7 3.1 5.6 2.4 2. ]
[6.9 3.1 5.1 2.3 2. ]
[5.8 2.7 5.1 1.9 2. ]
[6.8 3.2 5.9 2.3 2. ]
[6.7 3.3 5.7 2.5 2. ]
[6.7 3. 5.2 2.3 2. ]
[6.3 2.5 5. 1.9 2. ]
[6.5 3. 5.2 2. 2. ]
[6.2 3.4 5.4 2.3 2. ]
[5.9 3. 5.1 1.8 2. ]]sepal_ratio = np.divide(sepal_length,sepal_width)
petal_ratio = np.divide(petal_length,petal_width)
print(sepal_ratio.shape)
sepal_ratio_2d = np.expand_dims(sepal_ratio,1)
print(sepal_ratio_2d.shape)
print(petal_ratio.shape)
petal_ratio_2d = petal_ratio[:,np.newaxis]
print(petal_ratio_2d.shape)
concat_result = np.concatenate((sepal_ratio_2d,petal_ratio_2d),axis=1)
print(concat_result)
# 결과값
(150,)
(150, 1)
(150,)
(150, 1)
[[ 1.45714286 7. ]
[ 1.63333333 7. ]
[ 1.46875 6.5 ]
[ 1.48387097 7.5 ]
[ 1.38888889 7. ]
[ 1.38461538 4.25 ]
[ 1.35294118 4.66666667]
[ 1.47058824 7.5 ]
[ 1.51724138 7. ]
[ 1.58064516 15. ]
[ 1.45945946 7.5 ]
[ 1.41176471 8. ]
[ 1.6 14. ]
[ 1.43333333 11. ]
[ 1.45 6. ]
[ 1.29545455 3.75 ]
[ 1.38461538 3.25 ]
[ 1.45714286 4.66666667]
[ 1.5 5.66666667]
[ 1.34210526 5. ]
[ 1.58823529 8.5 ]
[ 1.37837838 3.75 ]
[ 1.27777778 5. ]
[ 1.54545455 3.4 ]
[ 1.41176471 9.5 ]
[ 1.66666667 8. ]
[ 1.47058824 4. ]
[ 1.48571429 7.5 ]
[ 1.52941176 7. ]
[ 1.46875 8. ]
[ 1.5483871 8. ]
[ 1.58823529 3.75 ]
[ 1.26829268 15. ]
[ 1.30952381 7. ]
[ 1.58064516 15. ]
[ 1.5625 6. ]
[ 1.57142857 6.5 ]
[ 1.58064516 15. ]
[ 1.46666667 6.5 ]
[ 1.5 7.5 ]
[ 1.42857143 4.33333333]
[ 1.95652174 4.33333333]
[ 1.375 6.5 ]
[ 1.42857143 2.66666667]
[ 1.34210526 4.75 ]
[ 1.6 4.66666667]
[ 1.34210526 8. ]
[ 1.4375 7. ]
[ 1.43243243 7.5 ]
[ 1.51515152 7. ]
[ 2.1875 3.35714286]
[ 2. 3. ]
[ 2.22580645 3.26666667]
[ 2.39130435 3.07692308]
[ 2.32142857 3.06666667]
[ 2.03571429 3.46153846]
[ 1.90909091 2.9375 ]
[ 2.04166667 3.3 ]
[ 2.27586207 3.53846154]
[ 1.92592593 2.78571429]
[ 2.5 3.5 ]
[ 1.96666667 2.8 ]
[ 2.72727273 4. ]
[ 2.10344828 3.35714286]
[ 1.93103448 2.76923077]
[ 2.16129032 3.14285714]
[ 1.86666667 3. ]
[ 2.14814815 4.1 ]
[ 2.81818182 3. ]
[ 2.24 3.54545455]
[ 1.84375 2.66666667]
[ 2.17857143 3.07692308]
[ 2.52 3.26666667]
[ 2.17857143 3.91666667]
[ 2.20689655 3.30769231]
[ 2.2 3.14285714]
[ 2.42857143 3.42857143]
[ 2.23333333 2.94117647]
[ 2.06896552 3. ]
[ 2.19230769 3.5 ]
[ 2.29166667 3.45454545]
[ 2.29166667 3.7 ]
[ 2.14814815 3.25 ]
[ 2.22222222 3.1875 ]
[ 1.8 3. ]
[ 1.76470588 2.8125 ]
[ 2.16129032 3.13333333]
[ 2.73913043 3.38461538]
[ 1.86666667 3.15384615]
[ 2.2 3.07692308]
[ 2.11538462 3.66666667]
[ 2.03333333 3.28571429]
[ 2.23076923 3.33333333]
[ 2.17391304 3.3 ]
[ 2.07407407 3.23076923]
[ 1.9 3.5 ]
[ 1.96551724 3.23076923]
[ 2.13793103 3.30769231]
[ 2.04 2.72727273]
[ 2.03571429 3.15384615]
[ 1.90909091 2.4 ]
[ 2.14814815 2.68421053]
[ 2.36666667 2.80952381]
[ 2.17241379 3.11111111]
[ 2.16666667 2.63636364]
[ 2.53333333 3.14285714]
[ 1.96 2.64705882]
[ 2.51724138 3.5 ]
[ 2.68 3.22222222]
[ 2. 2.44 ]
[ 2.03125 2.55 ]
[ 2.37037037 2.78947368]
[ 2.26666667 2.61904762]
[ 2.28 2.5 ]
[ 2.07142857 2.125 ]
[ 2. 2.30434783]
[ 2.16666667 3.05555556]
[ 2.02631579 3.04545455]
[ 2.96153846 3. ]
[ 2.72727273 3.33333333]
[ 2.15625 2.47826087]
[ 2. 2.45 ]
[ 2.75 3.35 ]
[ 2.33333333 2.72222222]
[ 2.03030303 2.71428571]
[ 2.25 3.33333333]
[ 2.21428571 2.66666667]
[ 2.03333333 2.72222222]
[ 2.28571429 2.66666667]
[ 2.4 3.625 ]
[ 2.64285714 3.21052632]
[ 2.07894737 3.2 ]
[ 2.28571429 2.54545455]
[ 2.25 3.4 ]
[ 2.34615385 4. ]
[ 2.56666667 2.65217391]
[ 1.85294118 2.33333333]
[ 2.06451613 3.05555556]
[ 2. 2.66666667]
[ 2.22580645 2.57142857]
[ 2.16129032 2.33333333]
[ 2.22580645 2.2173913 ]
[ 2.14814815 2.68421053]
[ 2.125 2.56521739]
[ 2.03030303 2.28 ]
[ 2.23333333 2.26086957]
[ 2.52 2.63157895]
[ 2.16666667 2.6 ]
[ 1.82352941 2.34782609]
[ 1.96666667 2.83333333]]filter_class0 = iris_np[:,-1]==0.0
iris_np_class0 = iris_np[filter_class0]
filter_class1 = iris_np[:,-1]==1.0
iris_np_class1 = iris_np[filter_class1]
filter_class2 = iris_np[:,-1]==2.0
iris_np_class2 = iris_np[filter_class2]
_, axe = plt.subplots()
axe.set_xlim(0,8)
axe.set_ylim(0,3.0)
axe.scatter(iris_np_class0[:,2],iris_np_class0[:,3])
axe.scatter(iris_np_class1[:,2],iris_np_class1[:,3])
axe.scatter(iris_np_class2[:,2],iris_np_class2[:,3])
