Comparing two pandas series for floating point near-equality?

Question

I can compare two Pandas series for exact equality using pandas.Series.equals. Is there a corresponding function or parameter that will check if the elements are equal to some ε of precision?

Answer 1

You can use numpy.allclose:

numpy.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)

Returns True if two arrays are element-wise equal within a tolerance.

The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b.

numpy works well with pandas.Series objects, so if you have two of them - s1 and s2, you can simply do:

np.allclose(s1, s2, atol=...) 

Where atol is your tolerance value.

Answer 2

Numpy works well with pandas Series. However one has to be careful with the order of indices (or columns and indices for pandas DataFrame)

For example

series_1 = pd.Series(data=[0,1], index=['a','b'])
series_2 = pd.Series(data=[1,0], index=['b','a']) 
np.allclose(series_1,series_2)

will return False

A workaround is to use the index of one pandas series

np.allclose(series_1, series_2.loc[series_1.index])