NumPy Array Shape
📐 NumPy — Array Shape
The shape of a NumPy array tells how many rows, columns, and dimensions the array has.
It is represented as a tuple:
1. Checking Shape of an Array
Use .shape attribute:
Output:
Meaning → 1D array with 4 elements.
2. Shape of a 2-D Array
Output:
Meaning → 2 rows × 3 columns.
3. Shape of a 3-D Array
Output:
Meaning → 2 blocks, each with 2 rows and 2 columns.
🔄 Changing Shape — reshape()
You can change array shape without changing data.
⭐ Important Rule: Total elements must remain the SAME
❌ Invalid:
🤖 Auto Dimension — Use -1
NumPy automatically calculates the missing dimension if you use -1.
🧱 Flattening an Array (Convert to 1-D)
Output:
📌 Reshape vs Resize
| Feature | reshape() |
resize() |
|---|---|---|
| Returns new array | ✅ Yes | ❌ Modifies original |
| Data preservation | Yes if compatible | May repeat or trim data |
Example:
🧠 Summary Table
| Operation | Example |
|---|---|
| Get array shape | arr.shape |
| Reshape array | arr.reshape(2,3) |
| Auto reshape | arr.reshape(2, -1) |
| Flatten array | arr.reshape(-1) |
| Resize original array | arr.resize(3,3) |
