SciPy provides a large number of functions that operate on numpy arrays and are useful for different types of scientific and engineering applications such as:

Custering.
Discrete Fourier Analysis.
Interpolation.
Linear algebra.
Signal and Image processing.
Optimization.
Sparse matrix and sparse linear algebra.

1 Optimization example

Optimization example



In [9]:

    
try:
    import scipy
except:
    !pip3 install scipy

try:
    import numpy as np
except:
    !pip3 install numpy
    import numpy as np
    
try:
    import matplotlib.pyplot as plt
except:
    !pip3 install matplotlib
    import matplotlib.pyplot as plt

# http://www.scipy-lectures.org/advanced/mathematical_optimization/
from scipy import optimize



In [6]:

    
def f(x):
    return -np.exp(-(x - .7)**2)



In [7]:

    
sol = optimize.brent(f)
print('min =', sol, '\nx =', f(sol))









    



min = 0.6999999997839409 
x = -1.0



In [10]:

    
x = np.arange(-10, 10, 0.1)
plt.plot(x, f(x))
plt.plot([sol],[f(sol)], 'ro')
plt.show()

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Optimization example