If I understand your question correctly, then you probably want a density estimate along with the histogram: X <- c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)) hist(X, prob=TRUE) # prob=TRUE for probabilities not counts lines(density(X)) # add a density estimate with defaults lines(density(X, adjust=2), lty=”dotted”) # add another “smoother” density Edit a long while … Read more
Use the Figure.savefig() method, like so: ax = s.hist() # s is an instance of Series fig = ax.get_figure() fig.savefig(‘/path/to/figure.pdf’) It doesn’t have to end in pdf, there are many options. Check out the documentation. Alternatively, you can use the pyplot interface and just call the savefig as a function to save the most recently … Read more
import matplotlib.pyplot as plt import numpy as np mu, sigma = 100, 15 x = mu + sigma * np.random.randn(10000) hist, bins = np.histogram(x, bins=50) width = 0.7 * (bins[1] – bins[0]) center = (bins[:-1] + bins[1:]) / 2 plt.bar(center, hist, align=’center’, width=width) plt.show() The object-oriented interface is also straightforward: fig, ax = plt.subplots() ax.bar(center, … 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
This is not a completely responsive answer but it is very simple. It illustrates an alternate method to display marginal densities and also how to use alpha levels for graphical output that supports transparency: scatter <- qplot(x,y, data=xy) + scale_x_continuous(limits=c(min(x),max(x))) + scale_y_continuous(limits=c(min(y),max(y))) + geom_rug(col=rgb(.5,0,0,alpha=.2)) scatter
It appears that the network hasn’t learned anything in the layers one to three. The last layer does change, so that means that there either may be something wrong with the gradients (if you’re tampering with them manually), you’re constraining learning to the last layer by optimizing only its weights or the last layer really … Read more
Actually, it’s quite easy: instead of the number of bins you can give a list with the bin boundaries. They can be unequally distributed, too: plt.hist(data, bins=[0, 10, 20, 30, 40, 50, 100]) If you just want them equally distributed, you can simply use range: plt.hist(data, bins=range(min(data), max(data) + binwidth, binwidth)) Added to original answer … Read more
yes, and its quick and simple though very hidden: binwidth=5 bin(x,width)=width*floor(x/width) plot ‘datafile’ using (bin($1,binwidth)):(1.0) smooth freq with boxes check out help smooth freq to see why the above makes a histogram to deal with ranges just set the xrange variable.
In 2.7 and 3.1, there is the special Counter (dict subclass) for this purpose. >>> from collections import Counter >>> Counter([‘apple’,’red’,’apple’,’red’,’red’,’pear’]) Counter({‘red’: 3, ‘apple’: 2, ‘pear’: 1})
Here is an even simpler solution using base graphics and alpha-blending (which does not work on all graphics devices): set.seed(42) p1 <- hist(rnorm(500,4)) # centered at 4 p2 <- hist(rnorm(500,6)) # centered at 6 plot( p1, col=rgb(0,0,1,1/4), xlim=c(0,10)) # first histogram plot( p2, col=rgb(1,0,0,1/4), xlim=c(0,10), add=T) # second The key is that the colours are … Read more