#### Overview of this book

Python for Finance
Credits
Acknowledgments
www.PacktPub.com
Preface
Free Chapter
Introduction and Installation of Python
13 Lines of Python to Price a Call Option
Introduction to Modules
Statistical Analysis of Time Series
Index

## Interpolation in SciPy

In the following code example, `x` can be viewed as the x axis with a set of values from 0 to 10, while the vertical axis is y, where `y = exp(-x/3)`. We intend to interpolate between different `y(i)` values by applying two methods: linear and cubic. The following lines of code are an example from SciPy Reference Guide:

```>>>import numpy as np
>>>import matplotlib.pyplot as plt
>>>from scipy.interpolate import interp1d
>>>x = np.linspace(0, 10, 10)
>>>y = np.exp(-x/3.0)
>>>f = interp1d(x, y)
>>>f2 = interp1d(x, y, kind='cubic')
>>>xnew = np.linspace(0, 10, 40)
>>>plt.plot(x,y,'o',xnew,f(xnew),'-', xnew, f2(xnew),'--')
>>>plt.legend(['data', 'linear', 'cubic'], loc='best')
>>>plt.show()
```

In the preceding program, we use the `np.linspace()` function to generate evenly spaced numbers—40 values—over a specified interval, from 0 to 10 in this case. The related output is shown as follows...