The return values of plt.hist are: Returns: tuple : (n, bins, patches) or ([n0, n1, …], bins, [patches0, patches1,…]) So all you need to do is capture the return values appropriately. For example: import numpy as np import matplotlib.pyplot as plt # generate some uniformly distributed data x = np.random.rand(1000) # create the histogram (n, … Read more
SELECT COUNT(grade) FROM table GROUP BY grade ORDER BY grade Haven’t verified it, but it should work.It will not, however, show count for 6s grade, since it’s not present in the table at all…
Using scipy, you could use stats.gaussian_kde to estimate the probability density function: import matplotlib.pyplot as plt import numpy as np import scipy.stats as stats noise = np.random.normal(0, 1, (1000, )) density = stats.gaussian_kde(noise) n, x, _ = plt.hist(noise, bins=np.linspace(-3, 3, 50), histtype=u’step’, density=True) plt.plot(x, density(x)) plt.show()
Let’s try something like this: fig = plt.figure(figsize = (15,20)) ax = fig.gca() df.hist(ax = ax)
When plotting a normalized histogram, the area under the curve should sum to 1, not the height. In [44]: import matplotlib.pyplot as plt k=(3,3,3,3) x, bins, p=plt.hist(k, density=True) # used to be normed=True in older versions from numpy import * plt.xticks( arange(10) ) # 10 ticks on x axis plt.show() In [45]: print bins [ … Read more
Histogram equalization is a non-linear process. Channel splitting and equalizing each channel separately is not the proper way for equalization of contrast. Equalization involves Intensity values of the image not the color components. So for a simple RGB color image, HE should not be applied individually on each channel. Rather, it should be applied such … Read more
Here is my aim at a more complete answer including choosing the color map and a logarithmic normalization of the color axis. import matplotlib.pyplot as plt import matplotlib.cm as cm from matplotlib.colors import LogNorm import numpy as np x, y, z = np.loadtxt(‘data.txt’, unpack=True) N = int(len(z)**.5) z = z.reshape(N, N) plt.imshow(z+10, extent=(np.amin(x), np.amax(x), np.amin(y), … Read more
EDIT: After your comments this actually makes perfect sense why you don’t get a histogram of each different value. There are 1.4 million rows, and ten discrete buckets. So apparently each bucket is exactly 10% (to within what you can see in the plot). A quick rerun of your data: In [25]: df.hist(column=’Trip_distance’) Prints out … Read more
Grafana v5+ provides direct support for representing Prometheus histograms as heatmap. http://docs.grafana.org/features/panels/heatmap/#histograms-and-buckets Heatmaps are preferred over histogram because a histogram does not show you how the trend changes over time. So if you have a time-series histogram, then use the heatmap panel to picture it. To get you started, here is an example (for Prometheus … Read more