rebin | Rebin histograms into new bins using numpy arrays | Data Manipulation library

Aim : Rebin an existing image (FITS file) and write the new entries into a new rebinned image (also a FITS file).

Issue : Rebinned FITS file and the original FITS file seem to have mismatched co-ordinates (figure shown later in the question).

Process : I will briefly describe my process to shed more light. The first step is to read the existing fits file and define numpy arrays

For this data file that I'm working off, I am given pairs of lists where each element represents an age interval, but they're written as strings. For example,

List1 = ['0-9', '10-19', '20-29', '30-39', '40-49', '50-']

List2 = ['0-19', '20-39', '40-']

List1 is used as a template to represent the age intervals for the corresponding data:

A1 = [30, 40, 50, 60, 70, 80]

B1 = [33, 20, 40, 76, 777, 844]

So, for example, the second element of A1 means the value is 40 for the age interval '10-19', the fifth element of B1 means the value is 777 for the interval '40-49'.

It is possible, because of the matching time intervals in List1 with List2, to sum the elements in A1 and B1 so that they now represent the time interval of List2.

A2 = [70, 110, 150]

B2 = [53, 116, 1621]

So now, for example, the second element of A2 (previously A1) represents the value 110 for the age interval '20-39' and the first element of B2 (previously B2) represents 53 for the interval '0-19'.

The data for List1 has been rebinned to match List2's age intervals. This is possible because of the overlapping age intervals. This cannot be done for data representing the following two age intervals:

List3 = ['0-14', '15-29', '30-44', '45-']

List4 = ['0-19', '20-39', '40-']

Because of the format of the data, I don't know how I can check if two lists has overlapping age intervals, which allows for data to be rebinned to represent a new set of age intervals. If anyone could point to me a method or library available in python that is capable of making such a task possible, specifically dealing with number intervals represented as strings, it would be much appreciated. Thank you.

I have a dataframe with a variable (E), where the value in the dataframe is the left edge of the bin, and a set of occupancies for each bin (n) (and the uncertainty squared (v)). At the moment, these are binned from 200 to 2000 in steps of 100 (usually), then binned 2000 to +inf. However these bins are very fine for the plotting I need to perform, and I need to rebin these into 200, 300, 400, 600, 1000, +inf.

Key Point: Because I am reading several sets of data like this from a source, not all my dataframes have entries e.g. for bin 600-700, i.e. some rows will be missing from one dataframe, while another may have entries for them. I need to rebin and sum n and v based on the new bins, while accounting for the fact that my dataframes aren't "regular".

Here's an example dataframe:

I am currently updating some files I have in a directory, with a loop, and I would like to save those file in a different directory.

Here is what I have: