Note that in matlab’s hist(x, vec), vec difines the bin-centers, while in matlab histc(x, vec) vec defines the bin-edges of the histogram. Numpy’s histogram seems to work with bin-edges. Is this difference important to you? It should be easy to convert from one to the other, and you might have to add an extra Inf … Read more
Using FacetGrid directly is not recommended. Instead, use other figure-level methods like seaborn.displot seaborn.FacetGrid.map works with figure-level methods. seaborn: Building structured multi-plot grids Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.3, seaborn 0.11.2 Option 1 Use plt. instead of ax. In the OP, the vlines are going to ax for the histplot, but here, the … Read more
Matplotlib uses its own format for dates/times, but also provides simple functions to convert which are provided in the dates module. It also provides various Locators and Formatters that take care of placing the ticks on the axis and formatting the corresponding labels. This should get you started: import random import matplotlib.pyplot as plt import … Read more
use logspace() to create a geometric sequence, and pass it to bins parameter. And set the scale of xaxis to log scale. import pylab as pl import numpy as np data = np.random.normal(size=10000) pl.hist(data, bins=np.logspace(np.log10(0.1),np.log10(1.0), 50)) pl.gca().set_xscale(“log”) pl.show()
If you’re willing to do everything in Python, you can compute the histogram and build a contour plot in one script : import numpy as np import matplotlib.pyplot as plt # load the data M = np.loadtxt(‘datafile.dat’, skiprows=1) # compute 2d histogram bins_x = 100 bins_y = 100 H, xedges, yedges = np.histogram2d(M[:,0], M[:,1], [bins_x, … Read more
You can make use of pd.cut to partition the values into bins corresponding to each interval and then take each interval’s total counts using pd.value_counts. Plot a bar graph later, additionally replace the X-axis tick labels with the category name to which that particular tick belongs. out = pd.cut(s, bins=[0, 0.35, 0.7, 1], include_lowest=True) ax … Read more
The other answers just don’t do it for me. The benefit of using plt.bar over plt.hist is that bar can use align=’center’: import numpy as np import matplotlib.pyplot as plt arr = np.array([ 0., 2., 0., 0., 0., 0., 3., 0., 0., 0., 0., 0., 0., 0., 0., 2., 0., 0., 0., 0., 0., 1., … Read more