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 plt
import numpy as np
from matplotlib.ticker import FormatStrFormatter

data = 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 plot
plt.subplots_adjust(bottom=0.15)
plt.show()

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: None


Example

import matplotlib.pyplot as plt
import numpy as np

dados = 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 plt
import numpy as np


dados = 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 bin
for i in range(0,len(n)):
    k.append((bins[i+1]-bins[i])*n[i])

# rounded
k = around(k,r); #print(k)

# plot the label/text to each bin
for 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)