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first we import pandas LSPD and check the version to be at least 2.0 we have here an invoice CSV 5 with 5 columns and one million rows which we read as usual which takes numpy as backend then we use Pi Arrow as backend and read the CSV file for the second time We compare the first five rows of each data frame and when we check the data type of the numpy we see that it has converted the integer items to float because of the null value which Pi Arrow does not and 100 100 null values natively we check the time it uh to and the execution time of both versions and compare the execution time and we if we check and compare them together we see that on this machine at least it is 16 times faster using pi Arrow as backend
