Question:

I'm reading out a text file with some float numbers using np.loadtxt . This is what my numpy array looks like:

x = np.loadtxt(t2) print(x) array([[ 1.00000000e+00, 6.61560000e-13], [ 2.00000000e+00, 3.05350000e-13], [ 3.00000000e+00, 6.22240000e-13], [ 4.00000000e+00, 3.08850000e-13], [ 5.00000000e+00, 1.11170000e-10], [ 6.00000000e+00, 3.82440000e-11], [ 7.00000000e+00, 5.39160000e-11], [ 8.00000000e+00, 1.75910000e-11], [ 9.00000000e+00, 2.27330000e-10]])

I separate out the first column from the second by doing this:

idx, coeffs = zip(*x)

Now, I want to create a mapping of id : coeff, something like this:

mapping = dict(zip(map(int, idx), coeffs)) print(mapping) {1: 6.6155999999999996e-13, 2: 3.0535000000000001e-13, 3: 6.2223999999999998e-13, 4: 3.0884999999999999e-13, 5: 1.1117e-10, 6: 3.8243999999999997e-11, 7: 5.3915999999999998e-11, 8: 1.7591e-11, 9: 2.2733e-10}

As you can see, precision errors have been introduced. For example, 6.61560000e-13 became 6.6155999999999996e-13.

This is what I would like, preferrably:

{1: 6.61560000e-13, 2: 3.05350000e-13, 3: 6.22240000e-13, 4: 3.08850000e-13, ... }

How can I do this? I am working on IPython3, if that helps.

<a href="https://stackoverflow.com/questions/45688226/fix-precision-issues-when-displaying-floats-in-python?noredirect=1#comment78334374_45688226" rel="nofollow">Jean-François Fabre's comment</a> gave me an idea, and I tried it out. Taking into consideration Alexander's suggestion to use a dict comprehension, this worked for me:

x = np.loadtxt(t2) mapping = {int(k) : v for k, v in x.tolist()} print (mapping)

Output:

{1: 6.6156e-13, 2: 3.0535e-13, 3: 6.2224e-13, 4: 3.0885e-13, 5: 1.1117e-10, 6: 3.8244e-11, 7: 5.3916e-11, 8: 1.7591e-11, 9: 2.2733e-10}

The reason this works is because x is of type np.float64. Calling .tolist() converts x to a list of lists, where each element is of type double. np.float64 and double have different __repr__ implementations. The double uses the <a href="http://www.ampl.com/REFS/abstracts.html#rounding" rel="nofollow">David Gay Algorithm</a> to correctly represent these floats, while numpy has a much simpler implementation (mere truncation).

Not sure about the downvote.

After entering your data, you have already 'lost precision':

x = np.array([[ 1.00000000e+00, 6.61560000e-13], [ 2.00000000e+00, 3.05350000e-13], [ 3.00000000e+00, 6.22240000e-13], [ 4.00000000e+00, 3.08850000e-13], [ 5.00000000e+00, 1.11170000e-10], [ 6.00000000e+00, 3.82440000e-11], [ 7.00000000e+00, 5.39160000e-11], [ 8.00000000e+00, 1.75910000e-11], [ 9.00000000e+00, 2.27330000e-10]]) >>> x[0, 1] 6.6155999999999996e-13

Perhaps a simple dict comprehension may be easier:

>>> {int(k): v for k, v in x} {1: 6.6155999999999996e-13, 2: 3.0535000000000001e-13, 3: 6.2223999999999998e-13, 4: 3.0884999999999999e-13, 5: 1.1117e-10, 6: 3.8243999999999997e-11, 7: 5.3915999999999998e-11, 8: 1.7591e-11, 9: 2.2733e-10}

Going by your method itself, you can cast your input (float) array to an int array and then construct dictionary after ziping it.

In [44]: dict(zip(np.asarray(x[:,0], dtype=int).tolist(), x[:,1].tolist())) Out[44]: {1: 6.6156e-13, 2: 3.0535e-13, 3: 6.2224e-13, 4: 3.0885e-13, 5: 1.1117e-10, 6: 3.8244e-11, 7: 5.3916e-11, 8: 1.7591e-11, 9: 2.2733e-10}

P.S. Using Python 3.6.1 in IPython 6.1.0