Python: how to create a regression function that predicts X values based on asymptotic regression using scipy-optimize?

See more:

Python

I have a dataset consisting of two columns X and Y.

|    X    |    Y    |
| ------- | ------- |
|    0    |    0    |
|    1    |   2207  |
|    2    |   2407  |
|    5    |   2570  |
|    7    |   2621  |
|   10    |   2723  |
|   20    |   2847  |
|   30    |   2909  |
|   40    |   2939  |
|   50    |   2963  |

The values in the second column Y are certain figures estimated from column X.
The problem comes back when I have to predict a new value not present in the column Y and that consequently I go back to the corresponding value in column X. To find the values within the range of the column Y written above I use interpolation.
I need that the regression function allow me to predict all the values I want which are greater than the values in column Y.
For now I have used a software (MyCurveFit) which gives me the values of the columns X and Y,creating a graph and a function.

The most similar function I think is Asymptotic Regression but I don't know how to develop it.

What I have tried:

So, I'd like to know what the equation was that, if you put a new value in the Y column, allow me to continue the line obtained by Asymptotic Regression and consequently predicting the X result.

|    X    |    Y    |
| ------- | ------- |
|    ?    |   2980  |
|    ?    |   2995  |
|    ?    |   2999  |
|    ?    |   3005  |

As a programming language I'm using Python. Thank you all!