https://github.com/emmanuel10701/numpy
Numpy
https://github.com/emmanuel10701/numpy
jupyter-notebook machine-learning numpy pandas python
Last synced: 3 months ago
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Numpy
- Host: GitHub
- URL: https://github.com/emmanuel10701/numpy
- Owner: Emmanuel10701
- Created: 2025-01-12T15:53:06.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-28T10:52:54.000Z (12 months ago)
- Last Synced: 2025-01-28T11:35:32.846Z (12 months ago)
- Topics: jupyter-notebook, machine-learning, numpy, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 5.86 KB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# NumPy CRUD Operations Walkthrough
This README focuses on demonstrating **CRUD (Create, Read, Update, Delete)** operations in **NumPy** arrays through a Jupyter Notebook. The notebook showcases essential operations for working with numerical data efficiently and converting them into NumPy arrays.
---
## Table of Contents
1. [Introduction](#introduction)
2. [Setup and Dependencies](#setup-and-dependencies)
3. [CRUD Operations in NumPy](#crud-operations-in-numpy)
- [Create Arrays](#create-arrays)
- [Read Arrays](#read-arrays)
- [Update Arrays](#update-arrays)
- [Delete Elements from Arrays](#delete-elements-from-arrays)
4. [Conclusion](#conclusion)
## Introduction
**NumPy** is a powerful library for numerical computing in Python. This walkthrough covers the **CRUD operations** that form the basis of array manipulation and data analysis. CRUD operations help you manipulate data programmatically and are fundamental when working with arrays in data science and scientific computing.
---
## Setup and Dependencies
Make sure you have the following tools and libraries installed on your machine:
- Python 3.x
- Jupyter Notebook or JupyterLab
- NumPy
### Install NumPy
If NumPy is not installed, you can install it using pip:
```bash
pip install numpy
````
You can launch Jupyter Notebook using:
```bash
jupyter notebook
```
---
## CRUD Operations in NumPy
This section demonstrates how to perform **Create**, **Read**, **Update**, and **Delete** operations on NumPy arrays.
### Create Arrays
Use various NumPy functions to create arrays:
```python
import numpy as np
# Create array from list
arr1 = np.array([1, 2, 3])
print("arr1:", arr1)
# Create array of zeros
arr2 = np.zeros((2, 3))
print("arr2:\n", arr2)
# Create array of ones
arr3 = np.ones((3, 2))
print("arr3:\n", arr3)
# Create array with range of numbers
arr4 = np.arange(0, 10, 2)
print("arr4:", arr4)
```
---
### Read Arrays
Access specific elements, rows, or columns from NumPy arrays:
```python
arr = np.array([[10, 20, 30], [40, 50, 60]])
# Read single element
print(arr[0][1]) # Output: 20
# Read entire row
print(arr[1]) # Output: [40 50 60]
# Read specific column
print(arr[:, 2]) # Output: [30 60]
# Slice array
print(arr[0:2, 1:3]) # Output: [[20 30] [50 60]]
```
---
### Update Arrays
Modify the values of arrays using direct indexing:
```python
arr = np.array([5, 10, 15, 20, 25])
# Update single value
arr[2] = 100
print(arr) # Output: [ 5 10 100 20 25]
# Update multiple values
arr[1:4] = [200, 300, 400]
print(arr) # Output: [ 5 200 300 400 25]
```
---
### Delete Elements from Arrays
Use `np.delete()` to remove elements:
```python
arr = np.array([1, 2, 3, 4, 5])
# Delete element at index 2
new_arr = np.delete(arr, 2)
print(new_arr) # Output: [1 2 4 5]
# 2D array example
arr2d = np.array([[1, 2], [3, 4], [5, 6]])
# Delete row 1 (second row)
new_arr2d = np.delete(arr2d, 1, axis=0)
print(new_arr2d) # Output: [[1 2] [5 6]]
# Delete column 0 (first column)
new_arr2d_col = np.delete(arr2d, 0, axis=1)
print(new_arr2d_col) # Output: [[2] [4] [6]]
```
---
## Conclusion
This walkthrough has demonstrated how to perform basic **CRUD operations** with **NumPy**, a core Python library for numerical computation. Mastering these operations is essential for effective data manipulation, analysis, and preprocessing tasks in data science and machine learning workflows.
---
Happy Coding! 🎉
```
### ✅ How to Use:
1. Save this as `README.md` in your project folder.
2. Run the code snippets in a Jupyter Notebook or any Python environment.
3. Add screenshots, visuals, or outputs if desired for a more interactive guide.
Let me know if you'd like the notebook (`.ipynb`) version or want to include visuals or links to GitHub/Colab.
```