![Creating NumPy Arrays
• - From Python Lists: np.array([1, 2, 3])
• - From Built-in Methods: np.zeros(), np.ones(),
np.arange(), np.linspace()
• - From Random Module: np.random.rand(),
np.random.randint()](https://image.slidesharecdn.com/numpypresentation-250319212522-a3804070/85/NumPy_Presentation-about-Numerical-python-pptx-3-320.jpg)
![NumPy Indexing and Selection
• - Indexing 2D Array: arr[1,2]
• - Logical Selection: arr[arr > 3]](https://image.slidesharecdn.com/numpypresentation-250319212522-a3804070/85/NumPy_Presentation-about-Numerical-python-pptx-7-320.jpg)
NumPy_Presentation about Numerical python.pptx
- 1. NumPy Understanding NumPy Arrays, Operations, and Indexing
- 2. Introduction to NumPy • NumPy (Numerical Python) is a library for numerical computing in Python. It provides support for multi-dimensional arrays and mathematical functions.
- 3. Creating NumPy Arrays • - From Python Lists: np.array([1, 2, 3]) • - From Built-in Methods: np.zeros(), np.ones(), np.arange(), np.linspace() • - From Random Module: np.random.rand(), np.random.randint()
- 4. Array Attributes and Methods • - Reshape: arr.reshape(3,3) • - Max & Min: arr.max(), arr.min() • - Argmax & Argmin: arr.argmax(), arr.argmin() • - Shape: arr.shape • - Dtype: arr.dtype • - Size: arr.size • - Ndim: arr.ndim
- 5. Operations on Arrays • - Copying: arr.copy() • - Appending & Inserting: np.append(), np.insert() • - Sorting: np.sort() • - Removing: np.delete() • - Concatenation: np.concatenate() • - Splitting: np.split()
- 6. Data Loading & Saving • - Save: np.save('file.npy', arr) • - Load: np.load('file.npy') • - CSV Handling: np.savetxt('file.csv', arr), np.loadtxt('file.csv')
- 7. NumPy Indexing and Selection • - Indexing 2D Array: arr[1,2] • - Logical Selection: arr[arr > 3]
- 8. Broadcasting in NumPy • - Perform operations on arrays of different shapes without manual resizing • - Example: arr + 5, arr1 + arr2 (with different shapes)