🔍 Learn Arrays • Slice • Reshape • Analyze

• Project Overview
• Skills Demonstrated
• Technical Implementation
• Key Business Insights Discovered
• Visualizations Created
• Advanced Python Techniques Demonstrated
• Business Value Demonstrated
• Quick Access
• Author

This beginner-friendly NumPy project focuses on:

✔️ NumPy array creation
✔️ Slicing, indexing & reshaping
✔️ Vectorized operations for performance
✔️ Boolean conditions for filtering

📌 A portfolio-ready project for Python, Data Analytics & Machine Learning beginners.

flowchart TD
    A[Start] --> B[Create NumPy Array]
    B --> C[Check Shape & Size]
    C --> D[Slice Required Elements]
    D --> E[Reshape into Column Matrix]
    E --> F[Apply Vectorized Operations]
    F --> G[Filter Values using Condition]
    G --> H[Print Output & Analyze]

import numpy as np

array_1 = np.array([10, 20, 30, 40, 50])
print("Original Array:", array_1)

print("Shape:", array_1.shape)
print("Size:", array_1.size)

print("Sliced Array:", array_1[1:4])

reshaped_array = array_1.reshape(5, 1)
print("\nReshaped Array (5x1):\n", reshaped_array)

multiplied_array = array_1 * 2
print("\nArray after Multiplying by 2:", multiplied_array)

filtered_array = array_1[array_1 > 25]
print("\nValues greater than 25:", filtered_array)

Original Array: [10 20 30 40 50]
Shape: (5,)
Size: 5
Sliced Array: [20 30 40]
Reshaped Array (5x1):
 [[10]
 [20]
 [30]
 [40]
 [50]]
Array after Multiplying by 2: [20 40 60 80 100]
Values greater than 25: [30 40 50]

📁 Folder Location:
Documentation/NumPy_Project01_Portfolio_Pack/

• Vectorized computation
• Efficient memory usage
• Conditional filtering
• Data restructuring for ML
• Clean output formatting

✔ Faster computation than Excel-like operations
✔ Reliable filtered data for business decisions
✔ Smooth integration with ML pipelines
✔ Supports cost-efficient analytics workflows

🔍 Key Observations:

• Array indexing & slicing se hum specific range ke values nikal sakte hain
• Reshaping se 1D array ko matrix form me convert karna easy hai
• Vectorized operations har element par one-shot me apply ho jate hain
• Boolean filter se sirf condition-match hone wale elements milte hain

• ⚡ Vectorization > Loops for numerical operations – faster & cleaner code
• 📐 Arrays ko easily reshape kiya ja sakta hai according to model/input needs
• 🧹 Boolean filtering se data cleaning & subsetting bahut asaan ho jata hai
• 📊 Yehi concepts aage chal kar Pandas, ML models & data pipelines me use hote hain

NumPy arrays:

• 🔹 Fast, flexible & memory-efficient hote hain
• 🔹 Real-world data processing ke liye backbone ka kaam karte hain
• 🔹 Is project ne aapko array creation → manipulation → analysis ka poora flow dikhaya

Yeh project Data Analytics & Machine Learning ke liye ek perfect starting point hai 🚀

• 📈 Visualize results using Matplotlib / Seaborn
• 🧮 Add more complex operations (mean, std, variance, etc.)
• 📂 Work with 2D / 3D arrays (matrices & tensors)
• 🧠 Link this with a small ML model as next step

• 📄 Complete Project Overview (PDF): 👉 Open PDF on GitHub

• 📦 NumPy Project 01 Portfolio Pack: 👉 [Documentation + PPT + Images](https://github.com/Ashwin18-Of

📄 Full Report PDF:
Documentation/Complete Project Overview.pdf

📦 Portfolio Pack (PDF + PPT + Images):
Documentation/NumPy_Project01_Portfolio_Pack/

Ashwin Panbude
📍 India | Data Analyst

🔗 LinkedIn Profile:
www.linkedin.com/in/ashwin-panbude-27b824253

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“Small consistent projects build a strong Data Analyst mindset.” 🚀