Python libraries list
Python libraries list
I'll respond in English and provide a list of popular Python libraries, which is at least 300 words. Here's the list:
Data Analysis
NumPy: The fundamental package for scientific computing with Python. Pandas: A library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. SciPy: A free open-source software for scientific and engineering applications that includes modules for optimization, linear algebra, integration, interpolation, and more.Machine Learning
scikit-learn: A machine learning library for the Python programming language. TensorFlow: An open-source software library for numerical computation, particularly well-suited to large-scale Machine Learning tasks. Keras: A high-level neural networks API that can run on top of TensorFlow or Theano.Web Development
Flask: A micro web framework written in Python. Django: A free and open-source web framework written in Python. Pyramid: A small, fast, flexible and modular web development framework for Python.Mathematics
SymPy: A Python library for symbolic mathematics. Mpmath: A pure Python library for arbitrary-precision arithmetic. SageMath: A free open-source mathematics software system based on Python.Graphics and Gaming
Pygame: A set of Python modules designed for writing video games. Pyglet: A cross-platform windowing and multimedia library for Python. Pillow: The friendly PIL fork, a powerful and easy-to-use library for opening, manipulating, and saving many different image file formats.Networking
Scapy: A powerful interactive packet manipulation program and library. Twisted: An event-driven networking framework written in Python. Paramiko: A set of Python modules which provide a high-level interface to the SSHv2 protocol.Audio and Video
Pydub: A simple and easy-to-use Python library for manipulating audio. MoviePy: A Python module for video editing. OpenCV: A computer vision library with applications in various fields such as image processing, object detection, facial recognition, etc.Database
SQLAlchemy: A SQL toolkit for Python that provides a high-level database abstraction. Django ORM (Object-Relational Mapping): A powerful way to interact with databases using Python code instead of writing raw SQL. PyODBC: An ODBC library for Python that allows you to connect to any data source that has an ODBC driver.Scientific Computing
SciPy: A free open-source software for scientific and engineering applications that includes modules for optimization, linear algebra, integration, interpolation, and more. Astropy: A Python package which provides a common interface to many astronomy-specific functions such as celestial coordinate conversions, atmospheric corrections, etc. Maya: A 3D computer animation, modeling, simulation, and rendering software developed by Autodesk.Utilities
requests: A simple HTTP library for human beings. beautifulsoup4: A Python library that creates parse trees from XML or HTML documents. logging: Python's standard library module for logging events in your program.This is not an exhaustive list, but it includes many popular and well-established Python libraries across various domains.
Python third party libraries examples
I'd be happy to provide an example of using Python's third-party libraries!
In the following examples, we'll look at some popular third-party libraries that are widely used in the Python community.
Example 1: NumPy
One of the most commonly used scientific computing libraries is NumPy (Numerical Python). It provides support for large, multi-dimensional arrays and matrices, and it has a wide range of high-level mathematical functions to manipulate these arrays. This library is very useful when working with scientific data.
import numpy as np
Create an array
x = np.array([1, 2, 3, 4, 5])
Calculate the mean of the array
mean = np.mean(x)
print(mean) # Output: 3.0
Calculate the median of the array
median = np.median(x)
print(median) # Output: 3.0
Example 2: Pandas
Another extremely useful library is Pandas, which provides high-performance data structures and functions for working with structured (tabular) data. It's particularly useful when dealing with data that has been read in from a CSV file or other table-based format.
import pandas as pd
Read in a CSV file
df = pd.read_csv("data.csv")
Display the first few rows of the dataframe
print(df.head())
Calculate the mean of column 'A'
mean_value = df['A'].mean()
print(mean_value)
Example 3: Matplotlib
When creating visualizations for your data, it's often helpful to have a library like Matplotlib. This allows you to create high-quality plots and charts from your data.
import matplotlib.pyplot as plt
Create a simple line plot
plt.plot([1, 2, 3], [4, 5, 6])
Display the plot
plt.show()
Example 4: Requests
If you're working with web-scraping or API integrations, you'll likely need to make HTTP requests. The Requests library makes this process easy.
import requests
Make a GET request
response = requests.get("http://www.example.com")
Get the content of the response
content = response.content
print(content)
Example 5: Scikit-learn
For machine learning tasks, you can use Scikit-Learn to create and train models. This is particularly useful when working with supervised learning.
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
Create a logistic regression model
logreg = LogisticRegression()
Train the model
logreg.fit(X_train, y_train)
Evaluate the model on the testing set
print(logreg.score(X_test, y_test))
I hope this helps illustrate some of the many amazing things you can do with Python and its third-party libraries!
