nan

问题 This question is very similar to: SIMD instructions for floating point equality comparison (with NaN == NaN) Although that question focused on 128 bit vectors and had requirements about identifying +0 and -0. I had a feeling I might be able to get this one myself but the intel intrinsics guide page seems to be down :/ My goal is to take an array of doubles and to return whether a NaN is present in the array. I am expecting that the majority of the time that there won't be one, and would like

问题 I have two numpy arrays that contains NaNs: A = np.array([np.nan, 2, np.nan, 3, 4]) B = np.array([ 1 , 2, 3 , 4, np.nan]) are there any smart way using numpy to remove the NaNs in both arrays, and also remove whats on the corresponding index in the other list? Making it look like this: A = array([ 2, 3, ]) B = array([ 2, 4, ]) 回答1: What you could do is add the 2 arrays together this will overwrite with NaN values where they are none, then use this to generate a boolean mask index and then use

问题 I'm trying to sort an array that sometimes has Infinity or NaN . When I use a standard JavaScript array.sort() it seems to sort until it reaches a NaN and then I get random results after that. var array =[.02,.2,-.2,Nan,Infinity,20]; Is there a way to still sort this so that the end result is from negative to positive and still have NaN or Infinity at the end. -.2,.02,.2,20,NaN,Infinity 回答1: You can catch NaN and Infinity using JavaScript's built-in utility functions for those cases: let

问题 I'm trying to sort an array that sometimes has Infinity or NaN . When I use a standard JavaScript array.sort() it seems to sort until it reaches a NaN and then I get random results after that. var array =[.02,.2,-.2,Nan,Infinity,20]; Is there a way to still sort this so that the end result is from negative to positive and still have NaN or Infinity at the end. -.2,.02,.2,20,NaN,Infinity 回答1: You can catch NaN and Infinity using JavaScript's built-in utility functions for those cases: let

问题 I have a dataframe like this : A B C E D --------------- 0 a r g g 1 x 2 x f f r 3 t 3 y I am trying for forward filling using ffill. It is not working cols = df.columns[:4].tolist() df[cols] = df[cols].ffill() I also tried : df[cols] = df[cols].fillna(method='ffill') But it is not getting filled. Is it the empty columns in data causing this issue? Data is mocked. Exact data is different (contains strings,numbers and empty columns) desired o/p: A B C E D --------------- 0 a r g g 1 a r g x 2

问题 I am using scipy.stats.binned_statistic_2d to bin irregular data onto a uniform grid by finding the mean of points within every bin. x,y = np.meshgrid(sort(np.random.uniform(0,1,100)),sort(np.random.uniform(0,1,100))) z = np.sin(x*y) statistic, xedges, yedges, binnumber = sp.stats.binned_statistic_2d(x.ravel(), y.ravel(), values=z.ravel(), statistic='mean',bins=[np.arange(0,1.1,.1), np.arange(0,1.1,.1)]) plt.figure(1) plt.pcolormesh(x,y,z, vmin = 0, vmax = 1) plt.figure(2) plt.pcolormesh

问题 I am using scipy.stats.binned_statistic_2d to bin irregular data onto a uniform grid by finding the mean of points within every bin. x,y = np.meshgrid(sort(np.random.uniform(0,1,100)),sort(np.random.uniform(0,1,100))) z = np.sin(x*y) statistic, xedges, yedges, binnumber = sp.stats.binned_statistic_2d(x.ravel(), y.ravel(), values=z.ravel(), statistic='mean',bins=[np.arange(0,1.1,.1), np.arange(0,1.1,.1)]) plt.figure(1) plt.pcolormesh(x,y,z, vmin = 0, vmax = 1) plt.figure(2) plt.pcolormesh

问题 I want to find the difference between 2 columns of type int in a pandas DataFrame. I am using python 2.7. The columns are as below - >>> df INVOICED_QUANTITY QUANTITY_SHIPPED 0 15 NaN 1 20 NaN 2 7 NaN 3 7 NaN 4 7 NaN Now, I want to subtract QUANTITY_SHIPPED from INVOICED_QUANTITY & I do the below- >>> df['Diff'] = df['QUANTITY_INVOICED'] - df['SHIPPED_QUANTITY'] >>> df QUANTITY_INVOICED SHIPPED_QUANTITY Diff 0 15 NaN NaN 1 20 NaN NaN 2 7 NaN NaN 3 7 NaN NaN 4 7 NaN NaN How do I take care of

问题 Since the gcc option -ffast-math effectively disables NaN and -/+inf , I'm looking for maybe the next best option for representing NaN in my performance-critical math code. Ideally the sentinel value if operated on (add, mul, div, sub, etc..) would yield the sentinel value as NaN would do but I doubt this would be possible since I think NaN is the only value that accomplishes this. -0.0 might not be a good fit as it's also disabled in -ffast-math and could prevent certain optimizations like

问题 I have "None" and "nan" strings scattered in my dataframe. Is there a way to replace all of those with empty string "" or nan so they do not show up when I export the dataframe as excel sheet? Simplified Example: Note: nan in col4 are not strings ID col1 col2 col3 col4 1 Apple nan nan nan 2 None orange None nan 3 None nan banana nan The output should be like this after removing all the "None" and "nan" strings when we replaced them by empty strings "" : ID col1 col2 col3 col4 1 Apple nan 2