import threading
import time

def spider():
    #.....
    time.sleep(0.02)
def main1():
    for i in range(100):
        spider()
def main2():
    thread_list = []
    for i in range(100):
        thread = threading.Thread(target = spider)
        thread.start()
        thread_list.append(thread)
    for t in thread_list:
        t.join()
if __name__ == "__main__":
    start = time.time()
    main1()
    end = time.time()
    print("time1 :{:.4f}".format(end-start))
    
    start = time.time()
    main2()
    end = time.time()
    print("time2 :{:4f}".format(end-start))

def fib(n):
    a,b = 0,1
    while b<n: 
        a,b = b,a+b
        yield a
print(fib(100))
for i in fib(100):
    print(i)

class Solution:
    def removeElement(self, nums: List[int], val: int) -> int:
        return len(list(filter(lambda x:x!=val,nums)))

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

dataset = pd.read_csv('./datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)

classifier.score(X_test,y_test)

import matplotlib.pyplot as plt
import numpy as np

ypoints = np.array([1,3,4,5,8,9,6,1,3,4,5,2,4])

plt.plot(ypoints, marker = 'o') # "o"代表实心圆
plt.show()

fmt = '[marker][line][color]'

# Definition for a binary tree node.
# class TreeNode:
#     def __init__(self, val=0, left=None, right=None):
#         self.val = val
#         self.left = left
#         self.right = right
class Solution:
    def isValidBST(self, root: TreeNode) -> bool:
        return self.search(root,-(232),232)
    def search(self,root,mins,maxs):
        if root == None:
            return True
        if root.val > mins and root.val < maxs:
            pass
        else:
            return False
        return all([self.search(root.left,mins,root.val),self.search(root.right,root.val,maxs)])