Best Python libraries for beginners

Python is a language that is famous for its huge number of libraries. These libraries help simplify many tasks, from working with data to creating web applications. In this article, we will talk about the best libraries that are suitable for beginners and share useful examples for practice.

1. Requests

If you want to work with data from the Internet, the requests library will be your best friend. It makes it easy to send HTTP requests, receive data, and process them.

How to install:

pip install requests

Example of use:

import requests
response = requests.get("https://api.github.com")
if response.status_code == 200:
    print(response.json())

Why should you try it?

• Easy to use.

• It is often used for parsing web pages, working with APIs, and automating tasks.

• Suitable for creating first projects related to processing data from the Internet.

Tip: Try to write a program that checks the status of popular sites or collects data from open APIs.

2. Pandas

Pandas is a library for working with data. It helps you easily manage tables, analyze and perform transformations.

How to install:

pip install pandas

Example of use:

import pandas as pd

# Creating a table
data = pd.DataFrame({"Name": ["Alice", "Bob"], "Age": [25, 30]})
print(data)

# Reading data from a file
data = pd.read_csv("example.csv")
print(data.head())

Why should you try it?

• Suitable for analyzing large amounts of data.

• Very useful for students studying data processing.

• Used in machine learning tasks and business analytics.

Tip: Start with tasks to analyze simple CSV files. Try to find the average values, maximums and minimums in the data.

3. Matplotlib

Matplotlib allows you to build graphs and charts. It is an ideal tool for data visualization.

How to install:

pip install matplotlib

Example of use:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.plot(x, y, label="Sample schedule")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Chart title")
plt.legend()
plt.show()

Why should you try it?

• Easy integration with other libraries such as Pandas.

• Helps to visualize data and find patterns in them.

• Suitable for creating reports and presentations.

Tip: Try to build a graph based on data from the Pandas table.

4. Flask

Flask is a lightweight framework for creating web applications. It is ideal for beginners who want to understand the basics of web development.

How to install:

pip install flask

Example of use:

from flask import Flask

app = Flask(__name__)

@app.route("/")
def home():
    return "Hello, Flask!"

if __name__ == "__main__":
    app.run(debug=True)

Why should you try it?

• Easy to set up and use.

• Ideal for creating small web applications.

• It allows you to quickly understand how servers and query processing work.

Tip: Create a web application that accepts data from the user and processes it.

You can find more information about Flask in our article

5. BeautifulSoup

BeautifulSoup helps extract data from web pages. It is often used for web scraping.

How to install:

pip install beautifulsoup4

Example of use:

from bs4 import BeautifulSoup
import requests

response = requests.get("https://example.com")
soup = BeautifulSoup(response.text, "html.parser")
print(soup.title.text)

Why should you try it?

• Easy to learn and use.

• Helps you quickly extract the data you need from HTML code.

• Suitable for creating parsers and automating data collection.

Tip: Try to write a parser that collects article titles from a news site.

6. NumPy

NumPy is a library for working with arrays and matrices of numbers. It is often used in scientific calculations and machine learning.

How to install:

pip install numpy

Example of use:

import numpy as np

array = np.array([1, 2, 3, 4, 5])
print(array)

# Example of operations with an array
print(array * 2)

Why should you try it?

• Optimized for mathematical operations.

• It is the basis for other libraries such as Pandas and SciPy.

• Helps to quickly work with large volumes of numbers.

Tip: Try using NumPy to work with multidimensional arrays.

Total

These libraries will help you learn Python and expand its capabilities. Try using them in your projects, experiment with data, and create interesting applications. If you want more examples, visit the website Coursme or our "Kodik" app. Good luck learning Python!