Return, first_Match],, second_Match], ,

New_array = np.hstack(purchaseOrderItemDescription)ДЄrray_appendedValues = np.append(new_array, new_list)ДЄrray_appendedValues = np.append(array_appendedValues, array_appendedValues)ДЇirst_func(array_appendedValues.astype(str), array_appendedValues, limit=3)) Global first_Match, second_Match, third_Match, counterДЇor materialElement in range(len(list_MaterialsData)): # Counter to increment the values inside appended list/+1 after each iteration. Percent_Match = process.extract(query, match, limit=limit, scorer=fuzz.token_sort_ratio) List_MaterialsData = np.array(materialDescription) List_POData = np.array(purchaseOrderItemDescription) Would highly appreciate any hints or pieces of advice import pandas as pd My question is - how can I perform the actions described above and after done, write into an Excel file the matching pairs in neighboring columns (1st match number - 1st match %, 2nd match number - 2nd match % and so on)? I am using pandas and numpy to extract and reformat the column data into data frames and then reformat it to numpy arrays for faster performance. But since I will be working with enormous amounts of data, was wondering if there was a way I could first estimate the string similarity ratio via the fuzzy wuzzy module. By estimating the similarity ratio, I have to then filter it by using sort and find the first 3 matches which register the higher percentage (limit = 3), then output the result.

However, my case is a bit trickier - every row from the first spreadsheet column I have to compare with each row from the second spreadsheet column. The goal of my project is to extract data from two separate columns from different spreadsheets, use the fuzzy wuzzy string matching algorithm to estimate the percentage match between the row values and then output the result in Excel. I would like to ask a question regarding a logic that I have been developing for the past couple of weeks, unfortunately was not able to produce the desired result.

Numpy array to excel