[청년취업사관학교 새싹]핀테커스 수업 6주차(10/6)

<수업 내용>

boxplot

import numpy as np
import matplotlib.pyplot as plt

def box_plot_test():
    n_student = 100
    math_scores = np.random.normal(loc=50, scale=10, size=(100,))

    fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(20, 15))
    # axes = axes.flatten()
    axes[0, 0].boxplot(math_scores)
    axes[0, 0].set_title('basic')
    axes[0, 1].boxplot(math_scores, notch=True)
    axes[0, 2].boxplot(math_scores, notch=True, whis=2)
    axes[0, 3].boxplot(math_scores, notch=True, whis=1, sym='bx')

    axes[1, 0].boxplot(math_scores, notch=True, showfliers=False)
    axes[1, 1].boxplot(math_scores, notch=True, showfliers=False, vert=False)

    median_props = {'linewidth': 2, 'color': 'k'}
    axes[1, 2].boxplot(math_scores, medianprops=median_props)

    box_props = {'linestyle': '--', 'color': 'k', 'alpha': 0.7}
    axes[1, 3].boxplot(math_scores, medianprops=median_props, boxprops=box_props)

    whisker_props = {'linestyle': '--', 'color': 'tab:blue', 'alpha': 0.8}
    axes[2, 0].boxplot(math_scores, medianprops=median_props, boxprops=box_props,
                       whiskerprops=whisker_props, capprops=whisker_props)
    
box_plot_test()

import numpy as np
import matplotlib.pyplot as plt


n_student=100
math_score=np.random.normal(loc=50,scale=15, size=(100,1))
chem_score=np.random.normal(loc=70,scale=10,size=(n_student,1))
phy_score=np.random.normal(loc=30, scale=12, size=(n_student,1))
pro_score=np.random.normal(loc=80, scale=5, size=(n_student,1))

data=np.hstack((math_score,chem_score,phy_score,pro_score))

fig,ax=plt.subplots(figsize=(10,7))
ax.set_ylim([0,100])

ax.boxplot(data)

실습

n_student=100
math_score=np.random.normal(loc=50,scale=15, size=(100,1))
chem_score=np.random.normal(loc=70,scale=10,size=(n_student,1))
phy_score=np.random.normal(loc=30, scale=12, size=(n_student,1))
pro_score=np.random.normal(loc=80, scale=5, size=(n_student,1))

data=np.hstack((math_score,chem_score,phy_score,pro_score))

fig,ax=plt.subplots(figsize=(10,7))
ax.set_ylim([0,100])
whisker_props = {'linestyle': '--', 'color': 'tab:blue', 'alpha': 0.8}
ax.boxplot(data,notch=True, whis=1, whiskerprops=whisker_props, capprops=whisker_props)
ax.set_xticklabels(['Math', 'English','Pysics','Programming'], rotation=30, fontsize=15)

실습 정답

plt.style.use('seaborn')

n_student=100
math_score=np.random.normal(loc=50,scale=15, size=(100,1))
chem_score=np.random.normal(loc=70,scale=10,size=(n_student,1))
phy_score=np.random.normal(loc=30, scale=12, size=(n_student,1))
pro_score=np.random.normal(loc=80, scale=5, size=(n_student,1))

data=np.hstack((math_score,chem_score,phy_score,pro_score))


medianprops={'linewidth':1.5,'color':'tab:red'}
boxprops={'linewidth':1.5,'color':'k','alpha':0.7}
whisker_props = {'linestyle': '--', 'color': 'tab:blue', 'alpha': 0.8}

fig,ax=plt.subplots(figsize=(10,7))
ax.set_ylim([0,100])
ax.boxplot(data,notch=True, whis=1,medianprops=medianprops,boxprops=boxprops, whiskerprops=whisker_props, capprops=whisker_props)

ax.set_xticklabels(['Math', 'English','Pysics','Programming'], rotation=30, fontsize=15)
major_yticks=np.arange(0,101,20)
minor_yticks=np.arange(0,101,5)

ax.set_yticks(major_yticks)
ax.set_yticks(minor_yticks, minor=True)

ax.tick_params(labelsize=20)
ax.tick_params(axis='x',labelrotation=10)

ax.grid(axis='y',linewidth=2)
ax.grid(axis='y',which='minor',linewidth=2,linestyle=':')

ax.grid(axis='x',linewidth=0)

plt.style.use('seaborn')

n_student=100
math_score=np.random.normal(loc=50,scale=15, size=(100,1))
chem_score=np.random.normal(loc=70,scale=10,size=(n_student,1))
phy_score=np.random.normal(loc=30, scale=12, size=(n_student,1))
pro_score=np.random.normal(loc=80, scale=5, size=(n_student,1))

data=np.hstack((math_score,chem_score,phy_score,pro_score))


medianprops={'linewidth':1.5,'color':'tab:red'}
boxprops={'linewidth':1.5,'color':'k','alpha':0.7}
whisker_props = {'linestyle': '--', 'color': 'tab:blue', 'alpha': 0.8}

fig,ax=plt.subplots(2,1,figsize=(10,7))
ax[0].boxplot(data)
ax[1].violinplot(data)

ax[0].set_ylim([0,100])
ax[1].set_ylim([0,100])
ax[0].tick_params(labelsize=20,bottom=False,labelbottom=False)
ax[1].tick_params(labelsize=20)
## fig.subplots_adjust(hspace=0.1)

Numpy

1. 파이썬 메소드 dir

a=[1,2,3]
for arr in dir(a):
  print(arr)

2. 파이썬 리스트와 numpy array 차이

#파이썬으로 리스트의 원소들 더하기
u=[1,2,3]
v=[4,5,6]
W=u+v
W

>>[1,2,3,4,5,6]

w=[0]*len(u)
for data_idx  in range(len(u)):
  w[data_idx]=u[data_idx]+v[data_idx]
print(w)

>>[5,6,7]

#넘파이로 array의 원소들 더하기
u=np.array([1,2,3])
v=np.array([4,5,6])
w=u+v
w

>>array([5, 7, 9])

3. numpy array의 shape확인 하기

#numpy array의 shape확인 하기
scalar_np=np.array(3.14)
vec_np=np.array([1,2,3])
mat_np=np.array([[1,2],[3,4]])
tensor_np=np.array([[[1,2,3],[4,5,6]],[[11,12,13],[14,15,16]]])

print(scalar_np.shape)
print(vec_np.shape)
print(mat_np.shape)
print(tensor_np.shape)

>> 
()
(3,)
(2, 2)
(2, 2, 3)

numpy array 생성하기

np.zeros((2,3))

>>
array([[0., 0., 0.],
       [0., 0., 0.]])
       
np.ones((2,3))

>>
array([[1., 1., 1.],
       [1., 1., 1.]])
       
np.full((2,3),3.14)

>>
array([[3.14, 3.14, 3.14],
       [3.14, 3.14, 3.14]])
       
np.arange(2,5)

>>
array([2, 3, 4])

np.arange(2,10,2) #마지막 2는 원소의 간격

>>
array([2, 4, 6, 8])

np.linspace(0,1,5) # 마지막 5는 출력할 원소의 개수

>> 
array([0.  , 0.25, 0.5 , 0.75, 1.  ])

난수 생성

fig, ax=plt.subplots(figsize=(10,5))

random_value=np.random.randn(300) # 표준정규분포를 따르는 난수 300개 생성
ax.hist(random_value, bins=20)

# 200X3 matrix의 정규분포를 따르는 난수 생성(3개의 열의 평균이 각각 -2,0,3이고 표준편차가 1,2,3 
normal=np.random.normal(loc=[-2,0,3], scale=[1,2,3], size=(200,3))
normal

# 3개의 열이 모두 평균이 -2 표준편차가 1
normal=np.random.normal(loc=-2, scale=1, size=(200,3))
normal

from numpy.random import uniform
plt.style.use('seaborn')
fig, ax=plt.subplots(figsize=(10,5))

uniform=np.random.rand(1000)#0,1 사이에서 1000개의 숫자를 임의로 뽑는다
ax.hist(uniform, bins=20)

from numpy.random import uniform
plt.style.use('seaborn')
fig, ax=plt.subplots(figsize=(10,5))
#구간내에서 size만큼의 숫자를 임의로 생성
uniform=np.random.uniform(low=-10,high=10,size=10000)
ax.hist(uniform,bins=20)

ranint=np.random.randint(low=0,high=7,size=20) #정수만 임의로 생성
ranint

>>
array([1, 2, 6, 3, 3, 3, 3, 4, 5, 0, 5, 0, 1, 5, 3, 6, 5, 2, 2, 4])

array의 shape

A=np.ones(shape=(10,))
B=np.ones(shape=(3,4))
C=np.ones(shape=(3,4,5))
D=np.ones(shape=(2,3,4,5,6))

print(A.size)
print(B.size)
print(C.size)
print(D.size)

>>
10
12
60
720

#reshape
a=np.arange(6)
b=np.reshape(a,(2,3))

print(a)
print(b)

>>
[0 1 2 3 4 5]
g
[[0 1 2]
 [3 4 5]]

a=np.arange(24)
b=np.reshape(a,(2,3,4))

print(a)
print(b)

>>
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]

[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]

a=np.arange(12)

b=a.reshape((2,-1))
c=a.reshape((3,-1))
d=a.reshape((4,-1))
e=a.reshape((6,-1))

print(b.shape, c.shape, d.shape, e.shape)

>>
(2, 6) (3, 4) (4, 3) (6, 2)

#size에 shape까지 설정할 수 있다
a=np.random.randint(0,10,size=(2,2))
a

>>
array([[8, 9],
       [1, 5]])

# flatten의 기능       
M=np.arange(9)
N=M.reshape((3,3))
O=N.flatten()

print(M)
print(N)
print(O)

>>
[0 1 2 3 4 5 6 7 8]

[[0 1 2]
 [3 4 5]
 [6 7 8]]
 
[0 1 2 3 4 5 6 7 8]

M=np.arange(27)
N=M.reshape((3,3,3))
O=N.flatten()

print(M)
print(N)
print(O)

>>
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26]

[[[ 0  1  2]
  [ 3  4  5]
  [ 6  7  8]]

 [[ 9 10 11]
  [12 13 14]
  [15 16 17]]

 [[18 19 20]
  [21 22 23]
  [24 25 26]]]
  
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26]

BroadCasting

• shape이 서로 다른 두 matrix의 산술연산을 가능하게 한다
  
  

• 차원이 같은 shape이 다른 텐서

A=np.arange(9).reshape(3,3) #2차 텐서
B=10*np.arange(3).reshape(-1,3) #2차 텐서

print(A)
print(B)

>>
[[0 1 2]
 [3 4 5]
 [6 7 8]]
 
[[ 0 10 20]]

C=A+B
print(C)

>>
[[ 0 11 22]
 [ 3 14 25]
 [ 6 17 28]]
 
A=np.arange(3).reshape(3,-1)
B=10*np.arange(3).reshape(-1,3)
print(A)
print(B)

>>
[[0]
 [1]
 [2]]
 
[[ 0 10 20]]

C=A+B
print(C)

>>
[[ 0 10 20]
 [ 1 11 21]
 [ 2 12 22]]

A=np.arange(18).reshape(2,3,3)
B=10*np.arange(9).reshape(1,3,3)
print(A)
print(B)
>>
[[[ 0  1  2]
  [ 3  4  5]
  [ 6  7  8]]

 [[ 9 10 11]
  [12 13 14]
  [15 16 17]]]
  
[[[ 0 10 20]
  [30 40 50]
  [60 70 80]]]
  
C=A+B
print(C)
>>
[[[ 0 11 22]
  [33 44 55]
  [66 77 88]]

 [[ 9 20 31]
  [42 53 64]
  [75 86 97]]]

• 차원이 다른 텐서
  

a=np.array(3) #스칼라
u=np.arange(5) #벡터
print(a)
print(u)
print(a*u)

>>
3
[0 1 2 3 4]
[ 0  3  6  9 12]

• 안쪽 수치가 동일해야 한다
  

**(2,3,4)와 연산이 가능한 텐서는 (2,3,4), (3,4), (4,) (3,1)이다

a=np.arange(24).reshape(2,3,4)
u=np.arange(0,120,10).reshape(3,4)
print(a)
print(u)
print(a+u)

>>
[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]
  
[[  0  10  20  30]
 [ 40  50  60  70]
 [ 80  90 100 110]]
 
[[[  0  11  22  33]
  [ 44  55  66  77]
  [ 88  99 110 121]]

 [[ 12  23  34  45]
  [ 56  67  78  89]
  [100 111 122 133]]]

array의 indexing & slicing

a=np.arange(12).reshape(3,4)
a[2] # 3행 출력

>>
array([ 8,  9, 10, 11])

a=np.arange(12).reshape(3,4)
a[0,1]

>>
1

a=np.arange(12).reshape(3,4)
print(a)
print(a[1:3,2])

>>
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
 
[ 6 10]