nan

问题 It seems that the IEEE 754 standard defines 16,777,214 32-bit floating point values as NaNs, or 0.4% of all possible values. I wonder what is the rationale for reserving so many useful values, while only 2 ones essentially needed: one for signaling and one for quiet NaN. Sorry if this question is trivial, I couldn't find any explanation on the internet. 回答1: 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

问题 I have a 2D numpy array. Some of the values in this array are NaN . I want to perform certain operations using this array. For example consider the array: [[ 0. 43. 67. 0. 38.] [ 100. 86. 96. 100. 94.] [ 76. 79. 83. 89. 56.] [ 88. NaN 67. 89. 81.] [ 94. 79. 67. 89. 69.] [ 88. 79. 58. 72. 63.] [ 76. 79. 71. 67. 56.] [ 71. 71. NaN 56. 100.]] I am trying to take each row, one at a time, sort it in reversed order to get max 3 values from the row and take their average. The code I tried is: #

问题 Given a pandas dataframe containing possible NaN values scattered here and there: Question: How do I determine which columns contain NaN values? In particular, can I get a list of the column names containing NaNs? 回答1: UPDATE: using Pandas 0.22.0 Newer Pandas versions have new methods 'DataFrame.isna()' and 'DataFrame.notna()' In [71]: df Out[71]: a b c 0 NaN 7.0 0 1 0.0 NaN 4 2 2.0 NaN 4 3 1.0 7.0 0 4 1.0 3.0 9 5 7.0 4.0 9 6 2.0 6.0 9 7 9.0 6.0 4 8 3.0 0.0 9 9 9.0 0.0 1 In [72]: df.isna()

问题 This question already has answers here : Why does typeof NaN return 'number'? (21 answers) Closed 4 years ago . I have a piece of JavaScript code which is expected to set an integer value to a variable. Something is broken, so when I try to do alert(A); , it returns NaN . isNaN(A); returns true. But if I alert(typeof(A)); , it says number . So how can a variable be a number and not a number at the same time? Maybe I misunderstood what NaN really is? Edit: thanks to the answers, I see that I

问题 Soooooo isNaN is apparently broken in JavaScript, with things like: isNaN('') isNaN(' ') isNaN(true) isNaN(false) isNaN([0]) Returning false, when they appear to all be... Not a Number... In ECMAScript 6, the draft includes a new Number.isNaN but it looks like (imo) that this is also broken... I would expect Number.isNaN('RAWRRR') To return true, since it's a string, and cannot be converted to a number... However... It seems that things that I would consider... not a number, are indeed, not,

问题 I have a dataframe with ~300K rows and ~40 columns. I want to find out if any rows contain null values - and put these 'null'-rows into a separate dataframe so that I could explore them easily. I can create a mask explicitly: mask = False for col in df.columns: mask = mask | df[col].isnull() dfnulls = df[mask] Or I can do something like: df.ix[df.index[(df.T == np.nan).sum() > 1]] Is there a more elegant way of doing it (locating rows with nulls in them)? 回答1: [Updated to adapt to modern

问题 I have a variable x and I want to test if x is set to NaN. How do I do that? My first instinct is probably to, you know, test it, like this: if (x === NaN) { ... Silly rabbit, no, that would be far too easy. NaN is like NULL in SQL, it is not equal to anything, even itself. But look, there is a function called isNaN() -- maybe that will do it! No, so far as I can tell, isNaN() is utterly worthless. For example, isNaN([""]) properly returns false, but isNaN(["."]) returns true. You don't want

问题 In Python Pandas, what's the best way to check whether a DataFrame has one (or more) NaN values? I know about the function pd.isnan , but this returns a DataFrame of booleans for each element. This post right here doesn't exactly answer my question either. 回答1: jwilner's response is spot on. I was exploring to see if there's a faster option, since in my experience, summing flat arrays is (strangely) faster than counting. This code seems faster: df.isnull().values.any() For example: In [2]: df

问题 In Python Pandas, what's the best way to check whether a DataFrame has one (or more) NaN values? I know about the function pd.isnan , but this returns a DataFrame of booleans for each element. This post right here doesn't exactly answer my question either. 回答1: jwilner's response is spot on. I was exploring to see if there's a faster option, since in my experience, summing flat arrays is (strangely) faster than counting. This code seems faster: df.isnull().values.any() For example: In [2]: df

问题 I'm using TensorFlow and I modified the tutorial example to take my RGB images. The algorithm works flawlessly out of the box on the new image set, until suddenly (still converging, it's around 92% accuracy usually), it crashes with the error that ReluGrad received non-finite values. Debugging shows that nothing unusual happens with the numbers until very suddenly, for unknown reason, the error is thrown. Adding print "max W vales: %g %g %g %g"%(tf.reduce_max(tf.abs(W_conv1)).eval(),tf.reduce