Matplotlib - label each bin

Sure! To set the ticks, just, well... Set the ticks (see matplotlib.pyplot.xticks or ax.set_xticks). (Also, you don't need to manually set the facecolor of the patches. You can just pass in a keyword argument.)

For the rest, you'll need to do some slightly more fancy things with the labeling, but matplotlib makes it fairly easy.

As an example:

import matplotlib.pyplot as pltimport numpy as npfrom matplotlib.ticker import FormatStrFormatterdata = np.random.randn(82)fig, ax = plt.subplots()counts, bins, patches = ax.hist(data, facecolor='yellow', edgecolor='gray')# Set the ticks to be at the edges of the bins.ax.set_xticks(bins)# Set the xaxis's tick labels to be formatted with 1 decimal place...ax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))# Change the colors of bars at the edges...twentyfifth, seventyfifth = np.percentile(data, [25, 75])for patch, rightside, leftside in zip(patches, bins[1:], bins[:-1]):    if rightside < twentyfifth:        patch.set_facecolor('green')    elif leftside > seventyfifth:        patch.set_facecolor('red')# Label the raw counts and the percentages below the x-axis...bin_centers = 0.5 * np.diff(bins) + bins[:-1]for count, x in zip(counts, bin_centers):    # Label the raw counts    ax.annotate(str(count), xy=(x, 0), xycoords=('data', 'axes fraction'),        xytext=(0, -18), textcoords='offset points', va='top', ha='center')    # Label the percentages    percent = '%0.0f%%' % (100 * float(count) / counts.sum())    ax.annotate(percent, xy=(x, 0), xycoords=('data', 'axes fraction'),        xytext=(0, -32), textcoords='offset points', va='top', ha='center')# Give ourselves some more room at the bottom of the plotplt.subplots_adjust(bottom=0.15)plt.show()

To add SI prefixes to your axis labels you want to use QuantiPhy. In fact, in its documentation it has an example that shows how to do this exact thing: MatPlotLib Example.

I think you would add something like this to your code:

from matplotlib.ticker import FuncFormatterfrom quantiphy import Quantitytime_fmtr = FuncFormatter(lambda v, p: Quantity(v, 's').render(prec=2))ax.xaxis.set_major_formatter(time_fmtr)

One thing I wanted to add to the plots in the histogram with "density = True" was the relative frequency values for each bin, search but I couldn't find a function that would do that. A solution I made follows as image:

The function:

def label_densityHist(ax, n, bins, x=4, y=0.01, r=2, **kwargs):"""Add labels,relative value of bin, to each bin in a density histogram .:param ax: Object axe of matplotlib        The axis to plot.:param n: list, array of int, float        The values of the histogram bins.:param bins: list, array of int, float        The edges of the bins.:param x: int, float        Related the x position of the bin labels. The higher, the lower the value on the x-axis.        Default: 4:param y: int, float        Related the y position of the bin labels. The higher, the greater the value on the y-axis.        Default: 0.01:param r: int        Number of decimal places.        Default: 2:param **kwargs: Text properties in matplotlib:return: NoneExampleimport matplotlib.pyplot as pltimport numpy as npdados = np.random.randn(100)axe = plt.gca()n, bins, _ = axe.hist(x=dados, edgecolor='black')label_densityHist(axe,n, bins)plt.show()Example:import matplotlib.pyplot as pltimport numpy as npdados = np.random.randn(100)axe = plt.gca()n, bins, _ = axe.hist(x=dados, edgecolor='black')label_densityHist(axe,n, bins, x=6, fontsize='large')plt.show()Reference:[1]https://matplotlib.org/3.1.1/api/text_api.html#matplotlib.text.Text"""k = []# calculate the relative frequency of each binfor i in range(0,len(n)):    k.append((bins[i+1]-bins[i])*n[i])# roundedk = around(k,r); #print(k)# plot the label/text to each binfor i in range(0, len(n)):    x_pos = (bins[i + 1] - bins[i]) / x + bins[i]    y_pos = n[i] + (n[i] * y)    label = str(k[i]) # relative frequency of each bin    ax.text(x_pos, y_pos, label, kwargs)