In this post, we work through building up a cubic spline from first principles first in Python, and then converting the Python code into an Alteryx macro. As a bit of a thought experiment, I wondered how hard it would be to create a cubic spline interpolation within Alteryx.

As a bit of a thought experiment, I wondered how hard it would be to create a cubic spline interpolation within Alteryx. As with many of my experiments *BaseA* rules apply.

Stealing an idea from Tasha Alfano, I thought I would do it in both Python and Alteryx from first principles. A quick shout out to MathAPI - a handy site and used to render all the LaTeX to SVG.

So let’s start by reviewing how to create a cubic spline and then build it up. I chose to use the algorithm as described in Wikiversity. Specifically with type II simple boundary conditions. I’m not going through the maths but will define the steps to build the spline.

First, step is given an *X* array and a *Y* array of equal length *n* (greater than 2), we want to build a *tridiagonal matrix* which we will then solve to produce the coefficients for the piece-wise spline. The goal of the spline is that it hits every point *(x, y)* and that the first and second derivatives match at these points too.

Sticking with notation in the paper, lets define `H`

to be an `n-1`

length array of the differences in `X`

:

for

A tridiagonal matrix is a square matrix where all values except for the main diagonal and first diagonals below and above this. For example:

```
1 2 0 0
2 3 2 0
0 2 3 2
0 0 2 1
```

One advantage of a tridiagonal matrix is that they are fairly straight forward to invert and solve linear equations based on them. For the sake of coding up the algorithm - let’s define `B`

to be the `n`

length array holding the diagonal elements, `A`

to be the `n-1`

length array of the diagonal above this and `C`

to be the `n-1`

length array of the diagonal below:

```
b0 c0 0 0
a0 b1 c1 0
0 a1 b2 c2
0 0 a2 b3
```

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Today you're going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates. We gonna use Python OS remove( ) method to remove the duplicates on our drive. Well, that's simple you just call remove ( ) with a parameter of the name of the file you wanna remove done.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc.. You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like __init__, __call__, __str__ etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

The OS module is a python module that provides the interface for interacting with the underlying operating system that Python is running.