1. Python-NumPy Tutorial
CIS 581

2. Intro to NumPy
A Python-based Matlab-style library for scientific calculation
Uses array as basic operation units
For MATLAB Users:
Array -- Matrix
Indexing & Slicing
Row Major -- Column Major

3. NumPy Arrays
A NumPy array is a grid of values, all of the same type. The shape of an array is a tuple of integers giving the size of array along each dimension
Creating arrays:
np.array([])
np.zeros, np.ones, np.full, np.eye, np.empty, …
np.arange
np.random
Code

4. Datatypes Some functions require specific datatypes to run properly
np.uint8 -- for most images, .png, .jpg, etc.
np.int16/32/64 -- integers of different bits length
np.float16/32/64 -- floats of different bits length
np.bool -- boolean value
Manipulation: Code

5. Slicing Python indexing starts from 0
Slicing a[0:i] will have a[i] excluded
Default in slicing
Minus value in slicing
Use a[0, :] and a[0, …] to omit other axis
Use bool value as index
Code

6. Element-wise functions
+, -, *, /, &, |
np.cos, np.sin, np.tan, np.radians, np.angles
np.acos, np.asin, np.atan, np.atan2
np.round, np.ceil, np.floor
np.cumsum
np.log, np.log2, np.log10, np.exp
np.bitwise_and, np.bitwise_or

7. Shape and broadcasting
Most numpy functions has shape constraints for inputs (e.g., input element must equal for most element-wise functions)
Use np.reshape() to manipulate the shape
Broadcasting
NumPy will automatically repeat array to meet shape requirements
Start from the last dimension, must fulfill basic rules

8. Other important functions
np.sum, np.mean, np.std, np.max, np.min
axis, keep_dims
np.sort, np.argsort
np.where
np.linalg
Linear algebra module
np.random
Generate random variables