Top 10 Amazing Python Tools To Make Your Life Easier
As a developer, there are moments when I feel as though my list of sprint chores is never-ending. Many of the duties frequently recur, which contributes to the issue. Python Tools have a role in this. Python is a fantastic all-purpose language that is highly powerful yet reads effortlessly (nearly like English). There are so many Python tools, libraries, and supported IDEs that you can discover a package to help solve your difficulty in dealing with those repeating tasks. Additionally, Python provides excellent community support if you can’t discover it on your own. This post highlights a few Python tools I always have on hand to help me with the major chores I need to complete regularly, making my life easier. Let’s start now.
Top Python Tools
1. Simple Web Frameworks with Flask
You must configure a web server. Have two seconds to spare? Because it takes that long to launch a basic Python web server:
python -m http.server 8000
That’s all there is to it; now your browser can connect to a functioning server. But this might be too straightforward for a small web application. Bring on Flask.
Python was used to create the micro web framework Flask. Its lack of a database abstraction layer, form validations, and mail support makes it a “micro” application. As a result, it’s ideal if you only want to serve up a straightforward API. Fortunately, it includes a sizable collection of extensions you can plug and play with.
Use the script below to set up a Flask API server:
from flask import Flask
from flask import jsonify
app = Flask(__name__)
@app.route('/')
def root():
return jsonify
(
app_name="Best python tool Flask",
app_user="Enter user here"
)
You can easily develop it on your personal computer if you want to. Run this program by typing python server.py and saving it as server.py.
FLASK_APP=flask.py flask run
Finally, the following JSON should appear when you open the URL in a browser:
{"app_name" : "Top 10 Python Tools", "app_user" : "ActiveState"}
2. API Calls with Requests
Requests is a strong HTTP library. With it, virtually anything that involves HTTP requests can be automated, including API calls, so you don’t have to make them manually using a rest client. In addition, it also has helpful features like handling sessions, JSON/XML parsing, and authorization handling.
Let’s take a quick look at an instance of using the GitHub API, which is protected by an authorization wall:
import requests
requests.get('https://api.github.com/user')
==> <Response [401]>
Because we attempted to access the API without providing the necessary authorization credentials, we received a 401 “Unauthorized Error” message. So let’s give it another go and ensure you enter a legitimate username and password.
import requests
requests.get('https://api.github.com/user', auth=('user', 'pass'))
==> <Response [200]>
This time, a 200 “Ok/Success” message is received. The Requests package provides both deep and broad functionality. Consult the documentation for more details regarding its capabilities.
3. Command Line Packaging with Click As developers
We create a lot of scripts to simplify our work, such as those that get external IP addresses and ping servers to see if they’re still running or looking up the time. So, naturally, you must navigate to the script’s directory before running it. Want to make a point? Ignore it! You’ll give up once you’ve parsed every user choice.
Any Python script exposing functionality to the command line can be packaged using Click. Once packaged, your script is immediately accessible from the terminal.
Let’s examine an illustration:
"""It's a program that Repeats NAME with greet for a total of x times."""
import click
@click.command()
@click.option('--x', default=1, help='write your number here.')
@click.option('--name', prompt='Name -',
help='The person you want to greet is Name.')
def Welcome to (x, name):
for x in range(x):
click.echo(Welcome to %s!' % name)
if __name__ == '__main__':
Welcome to()
The method option makes the command aware of the names of its arguments. Here, the arguments count and name are both in the open.
Finally, we’ll refer to our script as follows:
python hello.py --x=5 Your name: TechVidvan Welcome to TechVidvan! Welcome to TechVidvan! Welcome to TechVidvan! Welcome to TechVidvan!
4. Automated Testing with Selenium
A testing framework called Selenium is used to create automated test cases. The Python package offers API-like access to practically all Selenium functions, despite being designed in Java.
Selenium can automate various operations on your computer, including launching a browser, dragging and dropping files, and much more. Selenium is generally used to automate the testing of an application’s user interface.
Let’s take a brief look at an example that demonstrates how to launch a browser and access Google’s home page:
from selenium import webdriver
import time
browser = webdriver.Chrome(executable_path ="C:\Program Files (x86)\Google\Chrome\chromedriver.exe")
website_URL ="https://www.google.co.in/"
brower.get(website_URL)
refreshrate = int(15)
# This would keep running until you stop the compiler.
while True:
time.sleep(refreshrate)
browser.refresh()
The Google homepage is now refreshed in the browser every 15 seconds by this script.
5. Data Analytics with Pandas
Pandas is a straightforward but effective data analytics tool. It may be used to read and clean up massive amounts of data and analyze it statistically. Furthermore, the data can be instantly generated into summaries or even divided.
When you’re done with the analysis, you may use third-party libraries like Matplotlib to show the results.
The nice thing about Pandas is that NumPy, another fantastic data analysis tool used to apply numerical approaches to data, is built on top of it. The majority of NumPy methods are, therefore, functions that are already present in Pandas.
6. Web Scraping with Scrapy
You can “scrape” or extract information from web pages using the powerful application known as Scrapy. However, it is not efficient to manually extract significant volumes of information from numerous websites or web pages. Instead, programmers use “spiders” or “crawlers,” which scrape data from online pages, to automate the process.
Using HTML elements or CSS classes, Scrapy offers simple ways and packages for information extraction. The following command will launch the Scrapy Shell, an interactive mode that enables you to experiment with various techniques:
scrapy shell
Let’s attempt to determine the worth of the search button on the Google homepage as a basic experiment. To accomplish this, we first locate the class that the button belongs to. The class is “gb1,” as shown with a simple “Inspect Element” command.
Do the following in the interactive Scrapy Shell:
response = fetch("https://google.com")
response.css(".gb1::text").extract_first()
==> "Search"
7. Generating Fake Data with Faker
Without a doubt, this is the most practical tool I have. I only use Faker whenever I need to add dummy data or fill in the blanks on websites.
Using it, you can create fictitious names, addresses, descriptions, and more! For instance, the script that follows creates a contact entry with a name, address, and brief description:
from faker import Faker fake = Faker() fake.name() # 'Lucy Cechtelar' fake.address() # '426 Jordy Lodge # Cartwrightshire, SC 88120-6700' fake.text() # 'Sint velit eveniet. Rerum atque repellat voluptatem quia rerum. Numquam excepturi # beatae sint laudantium consequatur. Magni occaecati itaque sint et sit tempore. Nesciunt # amet quidem. Iusto deleniti cum autem ad quia aperiam. # A consectetur quos aliquam. In iste aliquid et aut similique suscipit. Consequatur qui # quaerat iste minus hic expedita. Consequuntur error magni et laboriosam. Aut aspernatur
8. Image Processing with Pillow
I frequently have to alter photographs to suit my needs better. For example, I might blur specifics, combine several images, or produce thumbnails. My go-to tool as a developer is “Pillow,” a potent Python image processing tool, rather than a GUI program like Photoshop.
I frequently mix my Pillow scripts with Click and then use the command line to access them. Useful for accelerating routine image processing activities.
Here is a little illustration of how to blur images:
from PIL import Image, ImageFilter
try:
original = Image.open("Logo.png")
# Blur the image
blurred = original.filter(ImageFilter.BLUR)
# Display both images
original.show()
blurred.show()
blurred.save("blurred.png")
except:
print "Unable to load image"
9. Date and Time Parsing with Pendulum
Working with date and time formats is never enjoyable. Working with different time zones and odd hours is something I despise. The pendulum is a powerhouse, though the built-in Python DateTime package performs a respectable job. It offers a quicker processing interface that is more intuitive. It enables formatting, DateTime operations, and timezone conversion.
To adjust UTC and obtain the time in 3 different time zones, you need to enter this:
from datetime import datetime
import pendulum
utc = pendulum.timezone('UTC')
pst = pendulum.timezone('America/Los_Angeles')
ist = pendulum.timezone('Asia/Calcutta')
10. Code Template Creation with CookieCutter
It’s a glorified cheat sheet, Cookiecutter! It is a command-line tool that generates projects from project templates known as “cookiecutters”.
You can use it to create project templates and share them with your team (or open-source them). All team members can now utilize your project as the foundation for their own, making only the necessary changes to meet their needs.
If you manage a Python project or are interested in publishing a project to PyPI, Audrey/cookiecutter-package is one of the most popular cookie cutters. You can use it to obtain a “skeleton” Python package that includes tests, distributions, and documentation. You can then modify this package to create your own to finish your project and even have it automatically published to PyPI.
Conclusion
With TechVidvan, Getting to know a programming language’s inner workings is the best approach to mastering it. These are some of the most common developer tools you need to be familiar with, regardless of your degree of Python expertise or how long you’ve been using it. These are the Top 10 Python Tools that will improve the efficiency of your daily tasks.