Python Euclidian Distance for K nearest

Hello, I am looking for someone who can develop a python script (preferably in Jupyter notebook) that can calculate the Euclidian distance of records in a dataset and select the K nearest neighbor record based on the smallest Euclidian distance. Details: 1. Dataset is large with over 550,000 records 2. Script will import the dataset from a .xls/csv file. 3. A weighted Euclidian distance will be calculated based on a defined weight placed on 5 variables in the data set. 4 of the variables are nominal (i.e. Site IDs, or part number) and therefore the distance of one of these nominal variables between records would be a 1 = no match, 0=match. 4. For each record, the K nearest neighbor record (i.e. smallest weighted Euclidian distance) will be identified. The actual Euclidian distance will also be referenced. 5. The dataset with the additional fields for K-nearest record's id and its Euclidian distance can be exported to excel. 6. # Notes describing each step should be referenced in the python script. I have attached a file with a sample of the data set (sheet 1). Yellow fields are the variables that are used for the distance calculation between records. On Tab 2 the weights for each variable to apply in the Euclidian calculation are referenced. If you are interested, please respond with the following: a) Your fixed price quote for development of the python script. b) Date when you could have the script developed. (needed asap) c) Any questions you have regarding the request.