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SciPy Tutorial 🔬

Scientific Computing

SciPy (Scientific Python) builds on NumPy and provides a large collection of algorithms for scientific computing, including optimization, integration, interpolation, eigenvalue problems, and more.

✨ Key Submodules

scipy.constants: Physical constants
scipy.optimize: Optimization algorithms
scipy.stats: Statistical functions
scipy.integrate: Integration routines
scipy.interpolate: Interpolation tools
scipy.linalg: Linear algebra

📉 Example: Optimization

Finding the minimum value of a function.

python
from scipy.optimize import minimize

def eqn(x):
  return x**2 + x + 2

mymin = minimize(eqn, 0, method='BFGS')
print(mymin.x) # Value of x that minimizes the function

📏 Constants

SciPy provides many built-in scientific constants.

python
from scipy import constants

print(constants.pi)      # 3.14159...
print(constants.c)       # Speed of light
print(constants.h)       # Planck constant
print(constants.kilo)    # 1000.0

✨ Pro Tip

Always use SciPy's linalg module instead of NumPy's linalg if you need advanced linear algebra features, as it is more complete.