nan

I have a large amount of data to process with math intensive operations on each data set. Much of it is analogous to image processing. However, since this data is read directly from a physical device, many of the pixel values can be invalid. This makes NaN's property of representing values that are not a number and spreading on arithmetic operations very compelling. However, it also seems to require turning off some optimizations such as gcc's -ffast-math, plus we need to be cross platform. Our current design uses a simple struct that contains a float value and a bool indicating validity.

int i = 0, j = 0; double nan1 = (double)0/0; double nan2 = (double)0/0; double nan3 = (double)i/j; System.out.println(Double.doubleToRawLongBits(nan1) == Double.doubleToRawLongBits(nan2)); System.out.println(Double.doubleToRawLongBits(nan1) == Double.doubleToRawLongBits((double)0/0)); System.out.println(Double.doubleToRawLongBits(nan3) == Double.doubleToRawLongBits(nan2)); output: true true false Please help me how the output came true for first two and false for last one. Please tell me what is actual work of Double.doubleToRawLongBits() method. Garbage Please try to run following code to see

I just want to check if a single cell in Pandas series is null or not. i.e, I'd like to check if a value is NaN . All other answers are for series and arrays, but not for single value. I have tried pandas.notnull , pandas.isnull , numpy.isnan . Is there a solution for a single value only? aerokite 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 STEP 1.) df[df.isnull().any(1)] ----> Will give you dataframe with rows and

In R I can quickly see a count of missing data using the summary command, but the equivalent pandas DataFrame method, describe does not report these values. I gather I can do something like len(mydata.index) - mydata.count() to compute the number of missing values for each column, but I wonder if there's a better idiom (or if my approach is even right). Both describe and info report the count of non-missing values. In [1]: df = DataFrame(np.random.randn(10,2)) In [2]: df.iloc[3:6,0] = np.nan In [3]: df Out[3]: 0 1 0 -0.560342 1.862640 1 -1.237742 0.596384 2 0.603539 -1.561594 3 NaN 3.018954 4

I need to calculate the number of non-NaN elements in a numpy ndarray matrix. How would one efficiently do this in Python? Here is my simple code for achieving this: import numpy as np def numberOfNonNans(data): count = 0 for i in data: if not np.isnan(i): count += 1 return count Is there a built-in function for this in numpy? Efficiency is important because I'm doing Big Data analysis. Thnx for any help! 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

I've been searching around, and there appear to be scattered discussions about NaN s in different programming languages, including some specific cases, but nothing exhaustive or clear. What are the most common operations that would cause a NaN , in Python, which originate while working with NumPy or SciPy? 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 * inf

I am working with this Pandas DataFrame in Python 2.7. File heat Farheit Temp_Rating 1 YesQ 75 N/A 1 NoR 115 N/A 1 YesA 63 N/A 1 NoT 83 41 1 NoY 100 80 1 YesZ 56 12 2 YesQ 111 N/A 2 NoR 60 N/A 2 YesA 19 N/A 2 NoT 106 77 2 NoY 45 21 2 YesZ 40 54 3 YesQ 84 N/A 3 NoR 67 N/A 3 YesA 94 N/A 3 NoT 68 39 3 NoY 63 46 3 YesZ 34 81 I need to replace all NaNs in the Temp_Rating column with the value from the Farheit column. This is what I need: File heat Observation 1 YesQ 75 1 NoR 115 1 YesA 63 1 YesQ 41 1 NoR 80 1 YesA 12 2 YesQ 111 2 NoR 60 2 YesA 19 2 NoT 77 2 NoY 21 2 YesZ 54 3 YesQ 84 3 NoR 67 3