For details, see the Pint measurements tutorial

handling uncertainties in Uncertainties

For propagation of uncertainty calculations without units , we use  Uncertainties: a Python package written by Eric. O. Lebigot.  The uncertainties module returns its result with the uncertainty specified by linear error propagation theory, correctly taking into account any direct correlations between variables.  This is in fact what Pint uses under the hood! 
If you need still more advanced approaches to propagation of uncertainty, Lebigot recommends trying  soerp (second-order approximations) and mcerp (Monte-Carlo approach).  I haven't yet done so.

Importing and exporting data from spreadsheet files

There are many ways to import, export, and represent data in files. Here we provide just enough to get you started. 

CSV data files

For specificity, let's assume that the data values were first entered in a spreadsheet consisting of one or more columns — one for each measured variable — and a few initial 'header' rows of text providing information about the data that we need to skip over when loading numerical values, then  exported as a '.csv' text file (where CSV stands for 'comma-separated-variables') .
Table  \ref{177785} shows what the data in the file  650 nm calibration with error.csv  looks like when viewed in  spreadsheet form. To see what the data looks like as a text file, first click on the Data icon to the left of the table to reveal the data file, then instruct your web browser to open the file with a plain text editor (instead of a spreadsheet program).