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https://github.com/udityamerit/python-librearies-for-data-science
Python libraries for data science enable efficient data manipulation, analysis, and modeling. Key libraries include NumPy for numerical computing, pandas for data handling, Matplotlib for visualization, Scikit-learn for machine learning, TensorFlow for deep learning, and BeautifulSoup/requests for web scraping. These libraries simplify complex data
https://github.com/udityamerit/python-librearies-for-data-science
beautifulsoup data data-science data-science-libraries machine-learning matplotlib numpy pandas requests scikit-learn scikitlearn-machine-learning tensorflow
Last synced: about 3 hours ago
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Python libraries for data science enable efficient data manipulation, analysis, and modeling. Key libraries include NumPy for numerical computing, pandas for data handling, Matplotlib for visualization, Scikit-learn for machine learning, TensorFlow for deep learning, and BeautifulSoup/requests for web scraping. These libraries simplify complex data
- Host: GitHub
- URL: https://github.com/udityamerit/python-librearies-for-data-science
- Owner: udityamerit
- License: mit
- Created: 2024-09-07T06:41:12.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-10-17T16:18:21.000Z (22 days ago)
- Last Synced: 2024-10-20T00:02:00.970Z (20 days ago)
- Topics: beautifulsoup, data, data-science, data-science-libraries, machine-learning, matplotlib, numpy, pandas, requests, scikit-learn, scikitlearn-machine-learning, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 4.99 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](#installation)
- [Libraries Overview](#libraries-overview)
- [NumPy](#numpy)
- [pandas](#pandas)
- [Matplotlib](#matplotlib)
- [Web Scraping Tools](#web-scraping-tools)
- [Scikit-learn](#scikit-learn)
- [TensorFlow](#tensorflow)
- [Contributing](#contributing)
- [License](#license)
## Installation
You can install the required libraries using `pip`. Run the following command:
```bash
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
```bash
pip install numpy
```
#### Basic Usage
```python
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
```bash
pip install pandas
```
#### Basic Usage
```python
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
```bash
pip install matplotlib
```
#### Basic Usage
```python
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
```bash
pip install beautifulsoup4 requests
```
#### Basic Usage
```python
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
```bash
pip install scikit-learn
```
#### Basic Usage
```python
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
```bash
pip install tensorflow
```
#### Basic Usage
```python
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.