This is what ยง2.3.2 of the JVM 7 spec has to say about it: The elements of the double value set are exactly the values that can be represented using the double floating-point format defined in the IEEE 754 standard, except that there is only one NaN value (IEEE 754 specifies 253-2 distinct NaN values). … Read more
This seems to achieve my objective: import simplejson >>> simplejson.dumps(d, ignore_nan=True) Out[3]: ‘{“a”: 1, “c”: 3, “b”: 2, “e”: null, “f”: [1, null, 3]}’
Because that’s how floating-point is defined (more generally than just Javascript). See for example: http://en.wikipedia.org/wiki/Floating-point#Infinities http://en.wikipedia.org/wiki/NaN#Creation Crudely speaking, you could think of 1/0 as the limit of 1/x as x tends to zero (from the right). And 0/0 has no reasonable interpretation at all, hence NaN.
The IEEE-754 standard defines a NaN as a number with all ones in the exponent, and a non-zero significand. The highest-order bit in the significand specifies whether the NaN is a signaling or quiet one. The remaining bits of the significand form what is referred to as the payload of the NaN. Whenever one of … Read more
If you do any of the following without horsing around with the floating-point environment, you should get a NaN where you didn’t have one before: 0/0 (either sign on top and bottom) inf/inf (either sign on top and bottom) inf – inf or (-inf) + inf or inf + (-inf) or (-inf) – (-inf) 0 … Read more
It’s defined in the FloatingPointNumber protocol, which both the Float and Double types conform to. Usage is as follows: let d = 3.0 let isNan = d.isNaN // False let d = Double.NaN let isNan = d.isNaN // True If you’re looking for a way to make this check yourself, you can. IEEE defines that … Read more
Posting as it might help future users. As correctly pointed out by others, np.isnan won’t work for object or string dtypes. If you’re using pandas, as mentioned here you can directly use pd.isnull, which should work in your case. import pandas as pd import numpy as np var1 = ” var2 = np.nan >>> type(var1) … Read more
Actually, it turned out to be something stupid. I’m posting this in case anyone else would run into a similar error. cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) is actually a horrible way of computing the cross-entropy. In some samples, certain classes could be excluded with certainty after a while, resulting in y_conv=0 for that sample. That’s normally not … Read more
No, you can’t, at least with current version of NumPy. A nan is a special value for float arrays only. There are talks about introducing a special bit that would allow non-float arrays to store what in practice would correspond to a nan, but so far (2012/10), it’s only talks. In the meantime, you may … Read more
Yes, dropna. See http://pandas.pydata.org/pandas-docs/stable/missing_data.html and the DataFrame.dropna docstring: Definition: DataFrame.dropna(self, axis=0, how=’any’, thresh=None, subset=None) Docstring: Return object with labels on given axis omitted where alternately any or all of the data are missing Parameters ———- axis : {0, 1} how : {‘any’, ‘all’} any : if any NA values are present, drop that label all … Read more