Compared to everyone who has heard words such as automated production lines and automated offices, machines can complete various tasks on their own without human intervention, which greatly improves work efficiency.
There are various automation scripts in the programming world to complete different tasks.
In particular, Python is very suitable for writing automated scripts because its syntax is simple and easy to understand, and it has a rich third-party tool library.
This time we use Python to implement several automation scenarios, which may be used in your work.
This script can capture text from web pages and then automatically read it by voice. This is a good choice when you want to listen to news.
The code is divided into two parts. The first is to crawl the web page text through a crawler, and the second is to read the text aloud through a reading tool.
Required third-party libraries:
Beautiful Soup - classic HTML/XML text parser, used to extract crawled web page information
requests - easy to use in reverse Tian's HTTP tool, used to send requests to web pages to obtain data
Pyttsx3 - Convert text to speech, and control the rate, frequency and speech
import pyttsx3 import requests from bs4 import BeautifulSoup engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices') newVoiceRate = 130 ## Reduce The Speech Rate engine.setProperty('rate',newVoiceRate) engine.setProperty('voice', voices[1].id) def speak(audio): engine.say(audio) engine.runAndWait() text = str(input("Paste articlen")) res = requests.get(text) soup = BeautifulSoup(res.text,'html.parser') articles = [] for i in range(len(soup.select('.p'))): article = soup.select('.p')[i].getText().strip() articles.append(article) text = " ".join(articles) speak(text) # engine.save_to_file(text, 'test.mp3') ## If you want to save the speech as a audio file engine.runAndWait()
This script can convert color pictures into pencil sketches, which has good effects on portraits and scenery.
And it only takes a few lines of code to generate it with one click, which is suitable for batch operations and is very fast.
Required third-party libraries:
Opencv - computer vision tool that can achieve diversified image and video processing, with Python interface
""" Photo Sketching Using Python """ import cv2 img = cv2.imread("elon.jpg") ## Image to Gray Image gray_image = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ## Gray Image to Inverted Gray Image inverted_gray_image = 255-gray_image ## Blurring The Inverted Gray Image blurred_inverted_gray_image = cv2.GaussianBlur(inverted_gray_image, (19,19),0) ## Inverting the blurred image inverted_blurred_image = 255-blurred_inverted_gray_image ### Preparing Photo sketching sketck = cv2.divide(gray_image, inverted_blurred_image,scale= 256.0) cv2.imshow("Original Image",img) cv2.imshow("Pencil Sketch", sketck) cv2.waitKey(0)
This script can help us send emails in batches at regular intervals. The email content and attachments can also be customized and adjusted, which is very practical.
Compared with email clients, the advantage of Python scripts is that they can deploy email services intelligently, in batches, and with high customization.
Required third-party libraries:
Email - used to manage email messages
Smtlib - sends emails to the SMTP server, which defines an SMTP client session object , this object can send emails to any computer with an SMTP or ESMTP listening program on the Internet
Pandas - a tool for data analysis and cleaning
import smtplib from email.message import EmailMessage import pandas as pd def send_email(remail, rsubject, rcontent): email = EmailMessage()## Creating a object for EmailMessage email['from'] = 'The Pythoneer Here'## Person who is sending email['to'] = remail## Whom we are sending email['subject'] = rsubject ## Subject of email email.set_content(rcontent) ## content of email with smtplib.SMTP(host='smtp.gmail.com',port=587)as smtp: smtp.ehlo() ## server object smtp.starttls() ## used to send data between server and client smtp.login("deltadelta371@gmail.com","delta@371") ## login id and password of gmail smtp.send_message(email)## Sending email print("email send to ",remail)## Printing success message if __name__ == '__main__': df = pd.read_excel('list.xlsx') length = len(df)+1 for index, item in df.iterrows(): email = item[0] subject = item[1] content = item[2] send_email(email,subject,content)
Data exploration is the first step in a data science project. You need to understand the basic information of the data to further analyze the deeper value.
Generally, we will use tools such as pandas and matplotlib to explore data, but we need to write a lot of code ourselves. If we want to improve efficiency, Dtale is a good choice.
Dtale is characterized by generating automated analysis reports with one line of code. It combines the Flask backend and React frontend to provide us with an easy way to view and analyze Pandas data structures.
We can use Dtale on Jupyter.
Required third-party libraries:
Dtale - Automatically generate analysis reports
### Importing Seaborn Library For Some Datasets import seaborn as sns ### Printing Inbuilt Datasets of Seaborn Library print(sns.get_dataset_names()) ### Loading Titanic Dataset df=sns.load_dataset('titanic') ### Importing The Library import dtale #### Generating Quick Summary dtale.show(df)
from win10toast import ToastNotifier import time toaster = ToastNotifier() header = input("What You Want Me To Remembern") text = input("Releated Messagen") time_min=float(input("In how many minutes?n")) time_min = time_min * 60 print("Setting up reminder..") time.sleep(2) print("all set!") time.sleep(time_min) toaster.show_toast(f"{header}", f"{text}", duration=10, threaded=True) while toaster.notification_active(): time.sleep(0.005)
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