Use pd.to_numeric with errors=”coerce” # Setup s = pd.Series([‘1’, ‘2’, ‘3’, ‘4’, ‘.’]) s 0 1 1 2 2 3 3 4 4 . dtype: object pd.to_numeric(s, errors=”coerce”) 0 1.0 1 2.0 2 3.0 3 4.0 4 NaN dtype: float64 If you need the NaNs filled in, use Series.fillna. pd.to_numeric(s, errors=”coerce”).fillna(0, downcast=”infer”) 0 1 1 … Read more
So, I found out that the class property [NSDecimalNumber notANumber] is just for this purpose. In some languages NaN != NaN, but this isn’t the case in Cocoa.
Your reasoning is that Double.POSITIVE_INFINITY should not be equal to itself because it is “likely” to have been obtained as the result of a loss of accuracy. This line of reasoning applies to all of floating-point. Any finite value can be obtained as the result of an inaccurate operation. That did not push the IEEE … Read more
Try this: import pandas as pd import numpy as np from pandas import * >>> L = [4, nan ,6] >>> df = Series(L) >>> df 0 4 1 NaN 2 6 >>> if(pd.isnull(df[1])): print “Found” Found >>> if(np.isnan(df[1])): print “Found” Found
To answer the question as asked: Is there any modern browser that raises exceptions on NaN propagation (ie multiplying or adding a number to NaN), or that can be configured to do so? No. Javascript is a very forgiving language and doesn’t care if you want to multiply Math.PI by ‘potato’ (hint: it’s NaN). It’s … Read more
Update 2 New to ECMAScript 6 is the Object.is() function. This is designed to be a further enhancement of the === check. One of the benefits of this new function is that Object.is(NaN, NaN) will now return true. If you’re able to utilize ECMAScript 6, then this would be the most readable and consistent solution … Read more
A[A==NDV]=numpy.nan A==NDV will produce a boolean array that can be used as an index for A
Short Answer For ECMAScript-5 Users: #1 if(x !== x) { console.info(‘x is NaN.’); } else { console.info(‘x is NOT a NaN.’); } For people using ECMAScript-6: #2 Number.isNaN(x); And For consistency purpose across ECMAScript 5 & 6 both, you can also use this polyfill for Number.isNan #3 //Polyfill from MDN Number.isNaN = Number.isNaN || function(value) … Read more
No loops required: print(a) [[ 0.93230948 nan 0.47773439 0.76998063] [ 0.94460779 0.87882456 0.79615838 0.56282885] [ 0.94272934 0.48615268 0.06196785 nan] [ 0.64940216 0.74414127 nan nan]] #Obtain mean of columns as you need, nanmean is convenient. col_mean = np.nanmean(a, axis=0) print(col_mean) [ 0.86726219 0.7030395 0.44528687 0.66640474] #Find indices that you need to replace inds = np.where(np.isnan(a)) #Place … Read more
nan not being equal to nan is part of the definition of nan, so that part’s easy. As for nan in [nan] being True, that’s because identity is tested before equality for containment in lists. You’re comparing the same two objects. If you tried the same thing with two different nans, you’d get False: >>> … Read more