七个实用的Python自动化代码，别再重复造轮子了！

from selenium import webdriverimport timeimport urllib.requestdriver = webdriver.Chrome()driver.maximize_window()driver.get("https://www.zhihu.com/question/29134042")i = 0while i <h4>② 没事闲的时候，听两个聊天机器人互相聊天</h4><pre class="brush:python;toolbar:false;">from time import sleepimport requestss = input("请主人输入话题：")while True:resp = requests.post("http://www.tuling123.com/openapi/api",data={"key":"4fede3c4384846b9a7d0456a5e1e2943", "info": s, })resp = resp.json()sleep(1)print('小鱼：', resp['text'])s = resp['text']resp = requests.get("http://api.qingyunke.com/api.php", {'key': 'free', 'appid':0, 'msg': s})resp.encoding = 'utf8'resp = resp.json()sleep(1)print('菲菲：', resp['content'])#网上还有一个据说智商比较高的小i机器人，用爬虫的功能来实现一下：import urllib.requestimport rewhile True:x = input("主人：")x = urllib.parse.quote(x)link = urllib.request.urlopen("http://nlp.xiaoi.com/robot/webrobot?&amp;callback=__webrobot_processMsg&amp;data=%7B%22sessionId%22%3A%22ff725c236e5245a3ac825b2dd88a7501%22%2C%22robotId%22%3A%22webbot%22%2C%22userId%22%3A%227cd29df3450745fbbdcf1a462e6c58e6%22%2C%22body%22%3A%7B%22content%22%3A%22" + x + "%22%7D%2C%22type%22%3A%22txt%22%7D")html_doc = link.read().decode()reply_list = re.findall(r'"content":"(.+?)"', html_doc)print("小i：" + reply_list[-1])

import jiebafrom nltk.classify import NaiveBayesClassifier# 需要提前把李白的诗收集一下，放在libai.txt文本中。text1 = open(r"libai.txt", "rb").read()list1 = jieba.cut(text1)result1 = " ".join(list1)# 需要提前把杜甫的诗收集一下，放在dufu.txt文本中。text2 = open(r"dufu.txt", "rb").read()list2 = jieba.cut(text2)result2 = " ".join(list2)# 数据准备libai = result1dufu = result2# 特征提取def word_feats(words):return dict([(word, True) for word in words])libai_features = [(word_feats(lb), 'lb') for lb in libai]dufu_features = [(word_feats(df), 'df') for df in dufu]train_set = libai_features + dufu_features# 训练决策classifier = NaiveBayesClassifier.train(train_set)# 分析测试sentence = input("请输入一句你喜欢的诗：")print("n")seg_list = jieba.cut(sentence)result1 = " ".join(seg_list)words = result1.split(" ")# 统计结果lb = 0df = 0for word in words:classResult = classifier.classify(word_feats(word))if classResult == 'lb':lb = lb + 1if classResult == 'df':df = df + 1# 呈现比例x = float(str(float(lb) / len(words)))y = float(str(float(df) / len(words)))print('李白的可能性：%.2f%%' % (x * 100))print('杜甫的可能性：%.2f%%' % (y * 100))

import randomtemp = [i + 1 for i in range(35)]random.shuffle(temp)i = 0list = []while i <h4>⑤ 自动写检讨书</h4><pre class="brush:python;toolbar:false;">import randomimport xlrdExcelFile = xlrd.open_workbook(r'test.xlsx')sheet = ExcelFile.sheet_by_name('Sheet1')i = []x = input("请输入具体事件：")y = int(input("老师要求的字数："))while len(str(i)) <h4>⑥ 屏幕录相机，抓屏软件</h4><pre class="brush:python;toolbar:false;">from time import sleepfrom PIL import ImageGrabm = int(input("请输入想抓屏几分钟："))m = m * 60n = 1while n <h4>⑦ 制作Gif动图</h4><pre class="brush:python;toolbar:false;">from PIL import Imageim = Image.open("1.jpg")images = []images.append(Image.open('2.jpg'))images.append(Image.open('3.jpg'))im.save('gif.gif', save_all=True, append_images=images, loop=1, duration=1, comment=b"aaabb")