#对纯数字的list进行数学运算，用array
import numpy as np
a = [1,2,3,4]
d=np.array(a)

print(d+1)
print(d*2)
print(sum(d))

# 连续两次掷有4个面的骰子
sample=1000
x = np.random.randint(1,5,size=(2,sample))
print(x)

y = np.random.rand(5) # 默认范围是0到1
y

np.random.shuffle(y) # 打乱顺序
y
np.random.permutation(y) # 打乱顺序,
y

import numpy as np
arr = np.arange(9).reshape((3, 3))
np.random.permutation(arr) # 只对第一维度进行打乱

import numpy as np
a=np.array(np.random.randint(10,size=(3,4)))
print(a)
a[1,::-2]  # 切片的应用

b = np.array(np.random.randint(20,size=(4,1)))
A = np.array(np.random.randint(20,size=(4,4)))
np.linalg.inv(A)

# 求解 Ax = b
np.linalg.solve(A,b)
np.linalg.inv(A)@b ## 更慢

import numpy as np
a=[['a','a','a'],['b','b','b']] ## 2个成员
b=[[1,2,3],[1,2,3]] ## 3个成员
## 纵向展开
print (np.vstack([a,b]),'\n Dimension: ',np.vstack([a,b]).shape)
## 横向展开
print(np.hstack([a,b]),'\n Dimension: ',np.hstack([a,b]).shape)
## 网格展开
print(np.dstack([a,b]),'\n Dimension: ',np.dstack([a,b]).shape)

import pandas as pd
pd.options.mode.chained_assignment = None # 防止出现SettingWithCopyWarning
l1=[1,2,3,4]
l2=[21,18,22,19]
l3=['Adam','Bob','Cinda','David']

dict3={'ID':l1,'Age':l2,'Name':l3}
df = pd.DataFrame(dict3)
df

# 简化写法
df = pd.DataFrame(list(zip(l1, l2,l3)),
               columns =['ID', 'Age', 'Name'])
df

# A和B约定见面，各自将迟到0-1小时。先来者等待15分钟后将离开。
# A和B见到的概率多大？

import numpy as np
import collections
import matplotlib.pyplot as plt
import seaborn as sns

ab = np.random.rand(2,10000)
print(ab)

f, axs = plt.subplots(1,3)

sns.histplot(ab[0],stat='probability',ax=axs[0])
sns.histplot(ab[1],stat='probability',ax=axs[1])
sns.histplot(ab[0]-ab[1],stat='probability',ax=axs[2],color='yellow')
plt.tight_layout() # 调整子图之间的间距

# 从分布的积分图形得到概率
f, axs = plt.subplots(1)

sns.ecdfplot(abs(ab[0]-ab[1]),ax=axs) # cumulative distribution function
x_special = 0.25

for line in axs.get_lines():
    x,y = line.get_data()
    ind = np.argwhere(x > x_special)[0,0]  # first index where x is larger than x_special
    axs.text(x_special,y[ind], f' {y[ind]:.4f}', ha='left', va='top') # maybe color=line.get_color()
axs.axvline(x_special, linestyle='--', color='#cfcfcf', lw=2, alpha=0.75)
plt.show()