Yes — use math.nan. >>> from math import nan >>> print(nan) nan >>> print(nan + 2) nan >>> nan == nan False >>> import math >>> math.isnan(nan) True Before Python 3.5, one could use float(“nan”) (case insensitive). Note that checking to see if two things that are NaN are equal to one another will always … Read more
Pandas NaT behaves like a floating-point NaN, in that it’s not equal to itself. Instead, you can use pandas.isnull: In [21]: pandas.isnull(pandas.NaT) Out[21]: True This also returns True for None and NaN. Technically, you could also check for Pandas NaT with x != x, following a common pattern used for floating-point NaN. However, this is … Read more
Where A is your 2D array: import numpy as np A[np.isnan(A)] = 0 The function isnan produces a bool array indicating where the NaN values are. A boolean array can by used to index an array of the same shape. Think of it like a mask.
Taken from this page: “NaN” stands for “not a number”. “Nan” is produced if a floating point operation has some input parameters that cause the operation to produce some undefined result. For example, 0.0 divided by 0.0 is arithmetically undefined. Taking the square root of a negative number is also undefined.
np.count_nonzero(~np.isnan(data)) ~ inverts the boolean matrix returned from np.isnan. np.count_nonzero counts values that is not 0\false. .sum should give the same result. But maybe more clearly to use count_nonzero Testing speed: In [23]: data = np.random.random((10000,10000)) In [24]: data[[np.random.random_integers(0,10000, 100)],:][:, [np.random.random_integers(0,99, 100)]] = np.nan In [25]: %timeit data.size – np.count_nonzero(np.isnan(data)) 1 loops, best of 3: … Read more
You can use DataFrame.any with parameter axis=1 for check at least one True in row by DataFrame.isna with boolean indexing: df1 = df[df.isna().any(axis=1)] d = {‘filename’: [‘M66_MI_NSRh35d32kpoints.dat’, ‘F71_sMI_DMRI51d.dat’, ‘F62_sMI_St22d7.dat’, ‘F41_Car_HOC498d.dat’, ‘F78_MI_547d.dat’], ‘alpha1’: [0.8016, 0.0, 1.721, 1.167, 1.897], ‘alpha2’: [0.9283, 0.0, 3.833, 2.809, 5.459], ‘gamma1’: [1.0, np.nan, 0.23748000000000002, 0.36419, 0.095319], ‘gamma2’: [0.074804, 0.0, 0.15, 0.3, np.nan], … Read more
Simplest of all solutions: filtered_df = df[df[‘var2’].isnull()] This filters and gives you rows which has only NaN values in ‘var2’ column.
According to MDN’s document say that Note: Technically speaking, includes() uses the sameValueZero algorithm to determine whether the given element is found. const x = NaN, y = NaN; console.log(x == y); // false -> using ‘loose’ equality console.log(x === y); // false -> using ‘strict’ equality console.log([x].indexOf(y)); // -1 (false) -> using ‘strict’ equality … Read more
When an operation results in a quiet NaN, there is no indication that anything is unusual until the program checks the result and sees a NaN. That is, computation continues without any signal from the floating point unit (FPU) or library if floating-point is implemented in software. A signalling NaN will produce a signal, usually … Read more
NaN is used as a placeholder for missing data consistently in pandas, consistency is good. I usually read/translate NaN as “missing”. Also see the ‘working with missing data’ section in the docs. Wes writes in the docs ‘choice of NA-representation’: After years of production use [NaN] has proven, at least in my opinion, to be … Read more