Using a histogram is one solution but it involves binning the data. This is not necessary for plotting a CDF of empirical data. Let F(x) be the count of how many entries are less than x then it goes up by one, exactly where we see a measurement. Thus, if we sort our samples then … Read more
Python 3.x does have reduce, you just have to do a from functools import reduce. It also has “dict comprehensions”, which have exactly the syntax in your example. Python 2.7 and 3.x also have a Counter class which does exactly what you want: from collections import Counter cnt = Counter(“abracadabra”) In Python 2.6 or earlier, … Read more
Comparing histograms is quite a subject in itself. You’ve got two big classes of comparison functions : bin-to-bin comparison and cross-bin comparison. Bin-to-bin comparison : As you stated, standard sum of differences is quite bad. There’s an improvement: the Chi-squared distance. If H1.red[0] = 0.001 and H2.red[0] = 0.011, then H2.red[0] is much more important … Read more
I’m on a roll, just found an even simpler way to do it using the by keyword in the hist method: df[‘N’].hist(by=df[‘Letter’]) That’s a very handy little shortcut for quickly scanning your grouped data! For future visitors, the product of this call is the following chart:
You can use the function for plotting histograms like this: a = np.random.random_integers(0,10,20) #example list of values plt.hist(a) plt.show() Or you can use myDictionary just like this: plt.bar(myDictionary.keys(), myDictionary.values(), width, color=”g”)
This should be possible at about 8 elements (1 AVX2 vector) per 2.5 clock cycles or so (per core) on a modern x86-64 like Skylake or Zen 2, using AVX2. Or per 2 clocks with unrolling. Or on your Piledriver CPU, maybe 1x 16-byte vector of indexes per 3 clocks with AVX1 _mm_cmpeq_epi32. The general … Read more
A bin is range that represents the width of a single bar of the histogram along the X-axis. You could also call this the interval. (Wikipedia defines them more formally as “disjoint categories”.) The Numpy histogram function doesn’t draw the histogram, but it computes the occurrences of input data that fall within each bin, which … Read more