Data Correlation in Excel may need Python

This is a task in Microsoft Excel to correlate 2 different databases. May require Python which I have and can use. Should be able to put in any dataset that corresponds to these column rules. I have put a bunch of detailed information on the ruleset with examples to try to be as clear as I can and have sample file for example so one can see how data looks and what it should look like when processed. Need this as soon as possible but in 3 days from time contract is awarded. Summary: You will be stepping through the profiles in column L,M,N,O and looking for correlations (full and partial matches) in column C,D,E,F. Based on a ruleset will score the partial or full matches as described later. Will use 3 examples through the steps to demonstrate how this works. Column C,D,E,F refer to “Leftside data” and column L,M,N,O refer to “Rightside data”. We check each Rightside data with the Leftside data for matches. There is always more Leftside data so it makes sense to do it this way. 1) Check each entry of column L (full names) with Column C and score 2) Check each entry of column M (street #) with column D and score 3) Check each entry of column N (Zip) with column E and score 4) Check each entry of column O (number) with column F and score 5) Total score and copy data 6) Mark data where appropriate 7) Put in value for column B and color coordinate where appropriate Detailed information: Scoring: 1) Check each entry of column L (full names) with Column C. Things like Jr Jr. Sr Sr. II III IV Esq Esq. are not last names and the word before it should be considered last name in these cases when we see these. last name + first letter of another word = 0.5 Column H adjacent to the column C it last name + another word matches other than last name = 1.5 Column H adjacent to the column C it matches. exact name match (name matches exactly like other name) = 2.5 column H adjacent to the column C it matches. *If score value is not at least 0.5 here, move to next potential leftside match entry (skip steps 2 thru 7 for this entry) Ex. 1) “Joe Smith II” to “H Joe Smith Jr” should score as a 1.5 since Jr and II is ignored for last name and we find “Smith” and “Smith”. “Joe” in both of them gives us another word match. Ex. 2) “J Smith Jr.” and “H Joe Smith Jr” would also score a 1.5 since the word “Jr” match as well as the last name “Smith” Ex. 3) “Joe Smith” to “H Joseph Smith II” this would be scored as a 0.5 since last name and 1st letter of another word ‘J’ match Ex. 4) “Joe Smith” and “Joe Smith” would score as a 2.5 2) Check each entry of column M (street #) with column D If street # matches then put in a 1 value in column I adjacent to the column D it matches. Ex.) “1216” matches “1216” 3) Check each entry of column N (Zip) with column E A zip code match occurs when first 5 digits match in column N and E. Input zip code match = 1 column J adjacent to the column E it matches . Ex.) “90210” matches “90210” 4) Check each entry in column O (number) with column F and score… If difference in values is 2 (could be +/-2), score column G 0.5 next to corresponding and adjacent column F. If difference in values is 1 or 0 (could be +/-1 or 0), score column G 1.5 next to corresponding and adjacent column F. Ex.) “21” and “22” are given a 1.5 value Ex.) “21 and 23” are given a 0.5 value Ex.) “21” and “20” are given a 1.5 value Ex. “21” and “21” are given a 1.5 value All other values compared to “21” score a zero additional details will be provided for other steps....