Numpy To Weak To Calculate A Precise Mean Value

This question is very similar to this post - but not exactly I have some data in a .csv file. The data has precision to the 4th digit (#.####). Calculating the mean in Excel or SAS

Solution 1:

To get exact decimal numbers, you need to use decimal arithmetic instead of binary. Python provides the decimal module for this.

If you want to continue to use numpy for the calculations and simply round the result, you can still do this with decimal. You do it in two steps, rounding to a large number of digits to eliminate the accumulated error, then rounding to the desired precision. The quantize method is used for rounding.

from decimal import Decimal,ROUND_HALF_UP
ten_places = Decimal('0.0000000001')
four_places = Decimal('0.0001')
mean = 4.2516499999999994
print Decimal(mean).quantize(ten_places).quantize(four_places, rounding=ROUND_HALF_UP)
4.2517

Solution 2:

The result value of average is a double. When you print out a double, by default all digits are printed. What you see here is the result of limited digital precision, which is not a problem of numpy, but a general computing problem. When you care of the presentation of your float value, use "%.4f" % avg_val. There is also a package for rational numbers, to avoid representing fractions as real numbers, but I guess that's not what you're looking for.

For your second statement, summarizing all the values by hand and then dividing it, I suppose you're using python 2.7 and all your input values are integer. In that way, you would have an integer division, which truncates everything after the dot, resulting in another integer value.