binning

问题 I am on the lookout for a faster alternative to R's hist(x, breaks=XXX, plot=FALSE)$count function as I don't need any of the other output that is produced (as I want to use it in an sapply call, requiring 1 million iterations in which this function would be called), e.g. x = runif(100000000, 2.5, 2.6) bincounts = hist(x, breaks=seq(0,3,length.out=100), plot=FALSE)$count Any thoughts? 回答1: A first attempt using table and cut : table(cut(x, breaks=seq(0,3,length.out=100))) It avoids the extra

问题 I have a dataframe with a column called Signal. I want to add a new column to that dataframe and apply a custom function i've built. I'm very new at this and I seem to be having trouble when it comes to passing values that I'm getting out of a data frame column into a function so any help as to my syntax errors or reasoningg would be greatly appreciated! Signal 3.98 3.78 -6.67 -17.6 -18.05 -14.48 -12.25 -13.9 -16.89 -13.3 -13.19 -18.63 -26.36 -26.23 -22.94 -23.23 -15.7 This is my simple

问题 I would like to build the hexbin plot where for every bin is the "ratio between class 1 and class2 points falling into this bin" is plotted (either log or not). x <- rnorm(10000) y <- rnorm(10000) h <- hexbin(x,y) plot(h) l <- as.factor(c( rep(1,2000), rep(2,8000) )) Any suggestions on how to implement this? Is there a way to introduce function to every bin based on bin statistics? 回答1: @cryo111's answer has the most important ingredient - IDs = TRUE . After that it's just a matter of

问题 I'm trying to display geospatial data in a hexagonal grid on a Google Map. In order to do so, given a hexagon tile grid size X I need to be able to convert ( {lat, lng} ) coordinates into the ( {lat, lng} ) centers of the hexagon grid tiles that contain them. In the end, I would like to be able to display data on a Google Map like this: Does anybody have any insight into how this is done? I've tried porting this Python hexagon binning script, binner.py to Javascript but it doesn't seem to be

问题 I have a large two dimensional array arr which I would like to bin over the second axis using numpy. Because np.histogram flattens the array I'm currently using a for loop: import numpy as np arr = np.random.randn(100, 100) nbins = 10 binned = np.empty((arr.shape[0], nbins)) for i in range(arr.shape[0]): binned[i,:] = np.histogram(arr[i,:], bins=nbins)[0] I feel like there should be a more direct and more efficient way to do that within numpy but I failed to find one. 回答1: You could use np

问题 I am looking for a fast formulation to do a numerical binning of a 2D numpy array. By binning I mean calculate submatrix averages or cumulative values. For ex. x = numpy.arange(16).reshape(4, 4) would have been splitted in 4 submatrix of 2x2 each and gives numpy.array([[2.5,4.5],[10.5,12.5]]) where 2.5=numpy.average([0,1,4,5]) etc... How to perform such an operation in an efficient way... I don't have really any ideay how to perform this ... Many thanks... 回答1: You can use a higher

问题 I am looking for a fast formulation to do a numerical binning of a 2D numpy array. By binning I mean calculate submatrix averages or cumulative values. For ex. x = numpy.arange(16).reshape(4, 4) would have been splitted in 4 submatrix of 2x2 each and gives numpy.array([[2.5,4.5],[10.5,12.5]]) where 2.5=numpy.average([0,1,4,5]) etc... How to perform such an operation in an efficient way... I don't have really any ideay how to perform this ... Many thanks... 回答1: You can use a higher

问题 I want to create a binned variable out of a continuous variable. I want 10 bins, with break points set from whatever results from a jenks classification. How do I assign each value to one of these 10 bins? # dataframe w/ values (AllwdAmt) df <- structure(list(X = c(2078L, 2079L, 2080L, 2084L, 2085L, 2086L, 2087L, 2092L, 2093L, 2094L, 2095L, 4084L, 4085L, 4086L, 4087L, 4088L, 4089L, 4091L, 4092L, 4093L, 4094L, 4095L, 4096L, 4097L, 4098L, 4099L, 4727L, 4728L, 4733L, 4734L, 4739L, 4740L, 4741L,

问题 I want to create a binned variable out of a continuous variable. I want 10 bins, with break points set from whatever results from a jenks classification. How do I assign each value to one of these 10 bins? # dataframe w/ values (AllwdAmt) df <- structure(list(X = c(2078L, 2079L, 2080L, 2084L, 2085L, 2086L, 2087L, 2092L, 2093L, 2094L, 2095L, 4084L, 4085L, 4086L, 4087L, 4088L, 4089L, 4091L, 4092L, 4093L, 4094L, 4095L, 4096L, 4097L, 4098L, 4099L, 4727L, 4728L, 4733L, 4734L, 4739L, 4740L, 4741L,

问题 I have a pre-binned frequency table for a rather large dataset. That is, a single column vector of bins and a single column vector of counts associated with those bins. I'd like R to plot a histogram of this data by doing further binning and summing the existing counts. For example, if in the pre-binned data I have something like [(0.01, 5000), (0.02, 231), (0.03, 948)], where the first number is the bin and the second is the count, and I choose 0.04 as the new bin width, I'd expect to get [