Python Libraries for Data Science

This repository provides an introduction to key Python libraries used in data science, including their installation and basic usage. It covers libraries such as NumPy, pandas, Matplotlib, Scikit-learn, TensorFlow, and tools for web scraping. Each section includes installation commands, basic commands, and a simple example to help you get started.

Table of Contents

• Installation
• Libraries Overview
  • NumPy
  • pandas
  • Matplotlib
  • Web Scraping Tools
  • Scikit-learn
  • TensorFlow
• Contributing
• License

Installation

You can install the required libraries using pip. Run the following command:

pip install numpy pandas matplotlib scikit-learn tensorflow beautifulsoup4 requests

This will install:

• NumPy for numerical computing
• pandas for data manipulation
• Matplotlib for data visualization
• scikit-learn for machine learning
• TensorFlow for deep learning
• BeautifulSoup and requests for web scraping

Libraries Overview

NumPy

NumPy is the fundamental package for numerical computing in Python. It provides support for arrays, matrices, and many mathematical functions.

Installation

pip install numpy

Basic Usage

import numpy as np

# Create an array
arr = np.array([1, 2, 3])
print(arr)

# Perform operations
print(np.mean(arr))

pandas

pandas is an open-source library that provides high-performance data manipulation and analysis tools, particularly DataFrames.

Installation

pip install pandas

Basic Usage

import pandas as pd

# Create a DataFrame
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

# Perform basic operations
print(df.describe())

Matplotlib

Matplotlib is a plotting library used for creating static, interactive, and animated visualizations in Python.

Installation

pip install matplotlib

Basic Usage

import matplotlib.pyplot as plt

# Create a simple plot
plt.plot([1, 2, 3], [4, 5, 6])
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Plot')
plt.show()

Web Scraping Tools

BeautifulSoup and requests are essential libraries for web scraping, allowing you to extract data from websites.

Installation

pip install beautifulsoup4 requests

Basic Usage

import requests
from bs4 import BeautifulSoup

# Fetch content from a webpage
url = 'https://example.com'
response = requests.get(url)

# Parse HTML content
soup = BeautifulSoup(response.text, 'html.parser')
print(soup.title.text)

Scikit-learn

Scikit-learn is a library for machine learning, offering simple and efficient tools for data analysis and modeling.

Installation

pip install scikit-learn

Basic Usage

from sklearn.linear_model import LinearRegression
import numpy as np

# Sample data
X = np.array([[1], [2], [3]])
y = np.array([2, 4, 6])

# Create a model and fit it
model = LinearRegression()
model.fit(X, y)

# Make a prediction
print(model.predict([[4]]))

TensorFlow

TensorFlow is an open-source platform for machine learning and deep learning, commonly used for building neural networks.

Installation

pip install tensorflow

Basic Usage

import tensorflow as tf

# Create a constant tensor
hello = tf.constant('Hello, TensorFlow!')
print(hello.numpy())

Contributing

If you'd like to contribute, feel free to fork the repository and submit a pull request. For major changes, please open an issue to discuss what you would like to change.

License

This project is licensed under the MIT License.