🐍 NumPy – Array Manipulation
Array manipulation is a core part of working with NumPy.
It allows you to modify, reshape, combine, and transform arrays efficiently without writing complex loops.
These operations are widely used in data science, machine learning, and scientific computing in Python.
What is Array Manipulation?
Array manipulation refers to changing the structure or content of NumPy arrays without losing data.
You can:
- Reshape arrays
- Join multiple arrays
- Split arrays
- Sort values
- Insert or delete elements
- Transpose matrices
🟢 1. Reshaping Arrays
Change the shape of an array without changing data.
import numpy as np
arr = np.arange(6)
new_arr = arr.reshape(2, 3)
print(new_arr)
🟢 2. Flattening Arrays
Convert multidimensional array into 1D.
arr = np.array([[1, 2], [3, 4]])
print(arr.flatten())
🟢 3. Ravel Function
Similar to flatten but returns a view.
print(arr.ravel())
🟡 4. Transpose
Swap rows and columns.
arr = np.array([[1, 2], [3, 4]])
print(arr.T)
🟡 5. Concatenation
Join arrays together.
a = np.array([1, 2])
b = np.array([3, 4])
print(np.concatenate((a, b)))
🟡 6. Stack Arrays
Vertical Stack
print(np.vstack((a, b)))
Horizontal Stack
print(np.hstack((a, b)))
🔵 7. Splitting Arrays
Divide arrays into multiple parts.
arr = np.array([1, 2, 3, 4, 5, 6])
print(np.array_split(arr, 3))
🔵 8. Inserting Elements
arr = np.array([1, 2, 3])
print(np.insert(arr, 1, 99))
Output:
[ 1 99 2 3]
🔵 9. Deleting Elements
arr = np.array([1, 2, 3])
print(np.delete(arr, 1))
Output:
[1 3]
🟣 10. Sorting Arrays
arr = np.array([3, 1, 2])
print(np.sort(arr))
Output:
[1 2 3]
🟣 11. Unique Values
arr = np.array([1, 1, 2, 3, 3])
print(np.unique(arr))
🧠 Why Array Manipulation is Important
1. Data Preprocessing
Used before feeding data into ML models.
2. Efficient Computation
Avoids loops and manual data handling.
3. Data Cleaning
Remove duplicates, reshape datasets, and fix structure.
4. Machine Learning
Models require properly shaped data.
📊 Common Manipulation Functions
| Function | Purpose |
|---|---|
| reshape() | Change shape |
| flatten() | Convert to 1D |
| ravel() | Flatten view |
| transpose() | Swap axes |
| concatenate() | Join arrays |
| split() | Divide arrays |
| insert() | Add elements |
| delete() | Remove elements |
| sort() | Sort values |
🚀 Real-World Example
Preparing Dataset
import numpy as np
data = np.array([
[1, 2, 3],
[4, 5, 6]
])
data = data.T
print(data)
Used in:
- Data preprocessing
- Image processing
- Machine learning pipelines
⚡ Best Practices
- Use reshape instead of manual restructuring
- Prefer vectorized operations
- Avoid unnecessary copies
- Use ravel() for memory efficiency
- Always check array dimensions
🧾 Summary
NumPy array manipulation allows you to efficiently transform data structures.
Key operations include:
- Reshaping
- Splitting
- Joining
- Sorting
- Transposing
- Inserting and deleting
These tools are essential for working with numerical data in Python.
🏁 Conclusion
Array manipulation is a powerful feature of NumPy that simplifies data processing and improves performance.
Mastering these techniques is essential for data science, machine learning, and advanced Python programming.
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