Python has a rich ecosystem of libraries. They cater to diverse applications such as data analysis, machine learning, and web development. Furthermore, they support more applications. Below is a categorized list of important Python libraries with examples of how to use them.
1. Data Manipulation and Analysis
Pandas
Used for data manipulation and analysis.
import pandas as pd # Create a DataFrame data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]} df = pd.DataFrame(data) print(df.head()) # View the first few rows
NumPy
For numerical computations and working with arrays.
import numpy as np # Create a NumPy array array = np.array([1, 2, 3, 4]) print(np.mean(array)) # Compute the mean
2. Visualization
Matplotlib
Basic plotting library for data visualization.
import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [10, 20, 25, 30] plt.plot(x, y) plt.xlabel("X-axis") plt.ylabel("Y-axis") plt.title("Line Plot") plt.show()
Seaborn
High-level data visualization library built on Matplotlib.
import seaborn as sns import pandas as pd # Sample data data = pd.DataFrame({'Category': ['A', 'B', 'C'], 'Values': [10, 20, 15]}) sns.barplot(x='Category', y='Values', data=data) plt.show()
3. Machine Learning
Scikit-learn
A robust library for machine learning tasks.
from sklearn.linear_model import LinearRegression
# Model and data
model = LinearRegression()
X = [[1], [2], [3]]
y = [1, 2, 3]
model.fit(X, y)
print(model.predict([[4]])) # Predict new value
TensorFlow
A powerful library for deep learning.
import tensorflow as tf # Simple constant in TensorFlow x = tf.constant([5, 6, 7]) print(x.numpy()) # Output: [5 6 7]
4. Web Development
Flask
Microframework for building web applications.
from flask import Flask app = Flask(__name__) @app.route('/') def home(): return "Hello, Flask!" if __name__ == "__main__": app.run(debug=True)
Django
A full-stack framework for web development.
- Use
django-admin startproject <project_name>to initialize a project.
5. Data Science and Natural Language Processing
NLTK
For natural language processing.
import nltk from nltk.tokenize import word_tokenize sentence = "This is an example sentence." print(word_tokenize(sentence))
SpaCy
Efficient NLP library.
import spacy nlp = spacy.load("en_core_web_sm") doc = nlp("Natural language processing is fun!") for token in doc: print(token.text, token.pos_)
6. Web Scraping
BeautifulSoup
For parsing HTML and XML documents.
from bs4 import BeautifulSoup html = "<html><body><h1>Hello, World!</h1></body></html>" soup = BeautifulSoup(html, 'html.parser') print(soup.h1.text) # Output: Hello, World!
Requests
For making HTTP requests.
import requests response = requests.get("https://example.com") print(response.status_code) # Check the status code
7. Automation and Productivity
Selenium
Automates browser tasks.
from selenium import webdriver driver = webdriver.Chrome() driver.get("https://example.com") print(driver.title) # Get the page title driver.quit()
OpenPyXL
For Excel file manipulation.
from openpyxl import Workbook wb = Workbook() sheet = wb.active sheet["A1"] = "Hello" wb.save("example.xlsx")
8. Graphs and Networks
NetworkX
For graph creation and manipulation.
import networkx as nx G = nx.Graph() G.add_edge("A", "B") print(G.edges) # Output: [('A', 'B')]
9. Testing
Pytest
For unit testing.
def add(x, y): return x + y def test_add(): assert add(2, 3) == 5
10. Other Specialized Libraries
OpenCV (Computer Vision):
import cv2 image = cv2.imread("example.jpg") cv2.imshow("Image", image) cv2.waitKey(0) cv2.destroyAllWindows()
PyTorch (Deep Learning):
import torch x = torch.tensor([1.0, 2.0]) print(x * 2)These libraries represent just a fraction of the Python ecosystem. Each library has extensive documentation and community support to help you get started.
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