To boost productivity, we often rely on various Python efficiency tools in our daily work. As a well-established programming language, Python can automate a wide range of routine tasks. To make project development more convenient, here are a few highly recommended Python efficiency tools.
1. Pandas – For Data Analysis
Pandas is a powerful toolkit for analyzing structured data. It is built on top of NumPy (which provides high-performance matrix operations) and is used for data mining and data analysis, while also offering data cleaning capabilities.
bash
# 1. Install the package $ pip install pandas
python
# 2. Enter Python's interactive mode # $ python -i # 3. Using Pandas >>> import pandas as pd >>> df = pd.DataFrame() >>> print(df) # 4. Output Empty DataFrame Columns: [] Index: []
2. Selenium – For Automated Testing
Selenium is a tool for testing web applications from an end-user’s perspective. By running tests across different browsers, it’s easier to identify browser incompatibilities. It supports many browsers.
Here’s a simple test that opens a browser and visits Google’s homepage:
python
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/"
browser.get(website_URL)
refreshrate = int(3) # Refresh Google homepage every 3 seconds.
# It will keep running until you stop the interpreter.
while True:
time.sleep(refreshrate)
browser.refresh()
3. Flask – A Micro Web Framework
Flask is a lightweight and customizable framework written in Python. It’s more flexible, lightweight, secure, and easier to learn compared to other similar frameworks. Flask is currently a very popular web framework. Developers can use Python to quickly implement a website or web service.
python
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello_world():
return 'Hello, World!'
4. Scrapy – For Web Scraping
Scrapy provides powerful support, enabling you to precisely scrape information from websites. It’s extremely practical.
Nowadays, most developers use scraping tools to automate data extraction. Scrapy can be used when writing scraping code.
Starting the Scrapy Shell is very straightforward:
bash
scrapy shell
We can try to extract the value of the search button on Baidu’s homepage. First, find the class used by the button; an ‘Inspect Element’ shows the class is “bt1”.
Specifically, perform the following operations:
python
response = fetch("https://baidu.com")
response.css(".bt1::text").extract_first()
# ==> "Search"
5. Requests – For Making API Calls
Requests is a powerful HTTP library. With it, you can easily send requests without manually adding query strings to URLs. It also offers many other features, such as authorization handling, JSON/XML parsing, session handling, etc.
Official example:
python
>>> r = requests.get('https://api.github.com/user', auth=('user', 'pass'))
>>> r.status_code
200
>>> r.headers['content-type']
'application/json; charset=utf8'
>>> r.encoding
'utf-8'
>>> r.text
'{"type":"User"...'
>>> r.json()
{'private_gists': 419, 'total_private_repos': 77, ...}
6. Faker – For Generating Fake Data
Faker is a Python package that generates fake data for you. Whether you need to bootstrap a database, create good-looking XML documents, fill in your persistence for stress testing, or anonymize data taken from a production service, Faker is perfect for the job.
With it, you can quickly generate fake names, addresses, descriptions, and more! Take the following script as an example; it creates a contact entry containing a name, address, and some descriptive text:
Installation:
bash
pip install Faker
Usage:
python
from faker import Faker fake = Faker() fake.name() fake.address() fake.text()
7. Pillow – For Image Processing
Pillow, a Python image processing library, has quite powerful image manipulation capabilities. It’s useful for when you need to perform image processing tasks. After all, as developers, we should choose more powerful image processing tools.
Simple example:
python
from PIL import Image, ImageFilter
try:
original = Image.open("Lenna.png")
blurred = original.filter(ImageFilter.BLUR)
original.show()
blurred.show()
blurred.save("blurred.png")
except:
print("Unable to load image")
Effective tools can help us complete tasks more quickly. Therefore, I’m sharing these tools that I find useful, hoping these 7 Python efficiency tools will be helpful to you too!