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Matplotlib colorbar: some ticks appear without labels

I'm using plr.scatter and logariphmic scale, and i'm trying to add some specific tick values to the colorbar, but it seems to work really arbitrary. See the example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib
from matplotlib.ticker import LogFormatter
x, y = np.meshgrid(np.linspace(0, 1, 30), np.linspace(0, 1, 30))
z = x**2 + 15*y**3 + 1.5
plt.figure(figsize=(9, 4.5))
plt.scatter(x, y, c=z, cmap=cm.jet, norm=matplotlib.colors.LogNorm(), vmin=1, vmax=20)
formatter = LogFormatter(10, labelOnlyBase=False)
cbar = plt.colorbar(ticks=[1, 2, 5, 10, 15, 20], format=formatter)
This code produced all the required major ticks, plus some minor ticks, but only labeled 1 and 10, while I need all numbers to be seen in colorbar. At first I though it was due to the fact that 1 and 10 are integer powers of 10, and other number are not, but...
...if I change the log base to 2, we can see tick labels at 1 and 2, which are powers of 2, but we also see labels at 5, 10 and 20, which are not. 15 did not appear this time too, but if I try adding 17 it works (not shown on the picture, but it does)
formatter = LogFormatter(2, labelOnlyBase=False)
What is this sorcery and how do I make matplotlib add exactly the labels I want to the ticks? I can do it manually by using
cbar.ax.set_yticklabels(['1', '2', '5', '10', '15', '20'])
but it seems redundant. Is there a better way?

You can format any axis ticks with formatter. Below is the example .
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib
from matplotlib.colors import LogNorm
x, y = np.meshgrid(np.linspace(0, 1, 30), np.linspace(0, 1, 30))
z = x**2 + 15*y**3 + 1.5
f, ax = plt.subplots(figsize=(9, 4.5))
p = plt.scatter(x, y, c=z, cmap=cm.jet, norm=LogNorm(vmin=1, vmax=20) )
v1 = np.linspace(z.min(), z.max(), 8, endpoint=True)
cbar=plt.colorbar(ticks=v1)
cbar.ax.set_yticklabels(["{:4.2f}".format(i) for i in v1]) # add the labels

LogFormatter and its subclasses use the minor_thresholds parameter to decide when to hide non-decade tick labels to prevent overcrowding. By default this will hide nearly all non-decade labels, but you can increase it to allow more labels to appear.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LogFormatter
from matplotlib.colors import LogNorm
x, y = np.meshgrid(np.linspace(0, 1, 30), np.linspace(0, 1, 30))
z = x**2 + 15*y**3 + 1.5
plt.figure(figsize=(9, 4.5))
cnorm = LogNorm(vmin=1, vmax=20)
plt.scatter(x, y, c=z, cmap=cm.jet, norm=cnorm)
# define minor_thresholds to be >= the range of the color scale
decades = np.ceil(np.log10(cnorm.vmax / cnorm.vmin))
formatter = LogFormatter(10, minor_thresholds=(decades, decades))
cbar = plt.colorbar(ticks=[1, 2, 5, 10, 15, 20], format=formatter)

How to get arithmetically growing minor ticks with matplotlib?

The following snippet creates a list myHLines of (y) values that is arithmetically growing.
I want to use them as minor y ticks in a matplotlib plot.
How can I do this?
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame({'a': [1, 3, 10, 30, 100]})
myMin = df.a.min()
myMax = df.a.max()
ratio = 3
myHLines = [myMin * ratio ** i for i in range(1000) if myMin * ratio ** i < myMax]
print("myHLines=", myHLines)
# myHLines= [1, 3, 9, 27, 81]
plt.plot(df, '-o', markersize=2, c='r')
plt.show()

Is the scale of the y-axis you want to achieve the y-axis shown in the graph below?
plt.plot(df, '-o', markersize=2, c='r')
locs, labels = plt.yticks()
new_y = sorted(myHLines + locs.tolist()[1:-1])
# print(new_y)
plt.yticks(new_y)
plt.show()

How do i extend trend line in matplotlib plot?

Here is a part of the plot that I have
I need to create TrendLine that would be extended to the 3th
quarter of this plot... I can's think of any solution.
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
x = [1, 8, 12, 20]
y = [1, 8.4, 12.5, 20]
fig = plt.figure(figsize=(20,20))
ax = fig.add_subplot()
ax.set_xlim(-30, 30)
ax.set_ylim(-20, 20)
plt.subplot().spines['left'].set_position('center')
plt.subplot().spines['bottom'].set_position('center')
plt.plot(x,y, 'b.', ms=20)
plt.minorticks_on()
ax.grid(True, which='both')
mean_line = ax.plot()
z = np.polyfit(x, y, 1)
p = np.poly1d(z)
plt.plot(x,p(x),"r--")
plt.show()

I don't think reverse x and y would do the job, it would be limited to the poly1d that pass (0,0)
I think the extending method should be using the fitted line itself.
so a more general method is extend the x and use the poly1d(z) to calculate an extended line. z is description of the fitted line, so feeding x value to z would draw the line.
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings('ignore')
x = [1, 8, 12, 20]
y = [1, 8.4, 12.5, 20]
# make an xx that with from -20 to 20
#xx =np.array(x)
#xx = sorted(np.concatenate((-xx, xx), axis=0))
xx = [-20, 20] # also work
fig, ax = plt.subplots(figsize=(10,10))
ax.set_xlim(-30, 30)
ax.set_ylim(-20, 20)
plt.subplot().spines['left'].set_position('center')
plt.subplot().spines['bottom'].set_position('center')
plt.subplot().spines['right'].set_color('none')
plt.subplot().spines['top'].set_color('none')
plt.plot(x,y, 'b.', ms=20)
plt.minorticks_on()
#ax.grid(True, which='both')
plt.subplot().grid(True, which='both')
mean_line = ax.plot()
z = np.polyfit(x, y, 1)
p = np.poly1d(z)
plt.plot(xx,p(xx),"r--")
plt.show()
if you zoomin near the (0,0), you should see it's not passing the origin point.
zoomed in near (0,0)
result image

I don't have any experience with trendlines, but I created a composite of existing x and y values with different signs and drew the following graph.
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
x = [1, 8, 12, 20]
y = [1, 8.4, 12.5, 20]
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot()
ax.set_xlim(-30, 30)
ax.set_ylim(-20, 20)
plt.subplot().spines['left'].set_position('center')
plt.subplot().spines['bottom'].set_position('center')
plt.plot(x,y, 'b.', ms=20)
plt.minorticks_on()
ax.grid(True, which='both')
mean_line = ax.plot()
# update
xx =np.array(x)
xx = sorted(np.concatenate((-xx, xx), axis=0))
yy =np.array(y)
yy = sorted(np.concatenate((-yy, yy), axis=0))
z = np.polyfit(xx, yy, 1)
p = np.poly1d(z)
plt.plot(xx,p(xx),"r--")
plt.show()

How to update a plot or graph in matplotlib

I would like to know how to update a graph and or plot in matplotlib every few seconds. Code:
import matplotlib.pyplot as plt
import numpy as np
axes = plt.gca()
axes.set_xlim([0,5])
axes.set_ylim([0,100])
X = [0, 1, 2, 3, 4, 5]
Y = [15, 30, 45, 60, 75, 90]
plt.plot(X, Y)
plt.xlabel('Time spent studying (hours)')
plt.ylabel('Score (percentage)')
plt.show()

What you have written is correct , but in order to make your code dynamic , you can put the code in a function and pass the X and Y coordinates to the function . One example as shown below
def GrapgPlot(X, Y):
"Your code"
GrapgPlot([0, 1, 2, 3, 4, 5],[90, 30, 45, 60, 75, 90])
In the plot if you are certain that X axis will not change than you can fix X axis in the code and take only Y axis values as a list from the user as an input and pass it in the function as an argument.
else the best way if you do want user interaction . Update the X and Y axis list with a loop and pass X and Y values in the function as an argument

Used time.sleep(1) for being able to see the changes and reversed Y for new data to be updated. Hopefully this is what you want:
%matplotlib notebook
import time
import matplotlib.pyplot as plt
X = [0, 1, 2, 3, 4, 5]
Y = [15, 30, 45, 60, 75, 90]
fig, ax = plt.subplots()
ax.set_xlim([0,5])
ax.set_ylim([0,100])
ax.set_xlabel('Time spent studying (hours)')
ax.set_ylabel('Score (percentage)')
l, = ax.plot(X, Y)
for ydata in [Y, Y[::-1]]*2:
l.set_ydata(ydata)
fig.canvas.draw()
time.sleep(0.5)

How to correctly generate a 3d histogram using numpy or matplotlib built in functions in python?

This is more of a general question about 3d histogram creation in python.
I have attempted to create a 3d histogram using the X and Y arrays in the following code
import matplotlib
import pylab
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
from matplotlib import cm
def threedhist():
X = [1, 3, 5, 8, 6, 7, 1, 2, 4, 5]
Y = [3, 4, 3, 6, 5, 3, 1, 2, 3, 8]
fig = pylab.figure()
ax = Axes3D(fig)
ax.hist([X, Y], bins=10, range=[[0, 10], [0, 10]])
plt.xlabel('X')
plt.ylabel('Y')
plt.zlabel('Frequency')
plt.title('Histogram')
plt.show()
However, I am getting the following error
Traceback (most recent call last):
File "<pyshell#0>", line 1, in <module>
a3dhistogram()
File "C:/Users/ckiser/Desktop/Projects/Tom/Python Files/threedhistogram.py", line 24, in a3dhistogram
ax.hist([X, Y], bins=10, range=[[0, 10], [0, 10]])
File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 7668, in hist
m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
File "C:\Python27\lib\site-packages\numpy\lib\function_base.py", line 169, in histogram
mn, mx = [mi+0.0 for mi in range]
TypeError: can only concatenate list (not "float") to list
I have tried the code with and without the "[" in the line
ax.hist([X, Y], bins=10, range=[[0, 10], [0, 10]])
I have also tried the function from numpy without success
H, xedges, yedges = np.histogram2d(x, y, bins = (10, 10))
Am I missing a step or a parameter? Any advice would be greatly appreciated.

I posted this in a related thread about colored 3d bar plots, but I think it's also relevant here as I couldn't find a complete answer for what I needed in either thread. This code generates a histogram scatterplot for any sort of x-y data. The height represents the frequency of values in that bin. So, for example, if you had many data point where (x,y) = (20,20) it would be high and red. If you had few data points in the bin where (x,y) = (100,100) it would be low and blue.
Note: result will vary substantially depending on how much data you have and how many bins your choose for you histogram. Adjust accordingly!
xAmplitudes = #your data here
yAmplitudes = #your other data here
x = np.array(xAmplitudes) #turn x,y data into numpy arrays
y = np.array(yAmplitudes)
fig = plt.figure() #create a canvas, tell matplotlib it's 3d
ax = fig.add_subplot(111, projection='3d')
#make histogram stuff - set bins - I choose 20x20 because I have a lot of data
hist, xedges, yedges = np.histogram2d(x, y, bins=(20,20))
xpos, ypos = np.meshgrid(xedges[:-1]+xedges[1:], yedges[:-1]+yedges[1:])
xpos = xpos.flatten()/2.
ypos = ypos.flatten()/2.
zpos = np.zeros_like (xpos)
dx = xedges [1] - xedges [0]
dy = yedges [1] - yedges [0]
dz = hist.flatten()
cmap = cm.get_cmap('jet') # Get desired colormap - you can change this!
max_height = np.max(dz) # get range of colorbars so we can normalize
min_height = np.min(dz)
# scale each z to [0,1], and get their rgb values
rgba = [cmap((k-min_height)/max_height) for k in dz]
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=rgba, zsort='average')
plt.title("X vs. Y Amplitudes for ____ Data")
plt.xlabel("My X data source")
plt.ylabel("My Y data source")
plt.savefig("Your_title_goes_here")
plt.show()
The results for about 75k data points of mine are below. Note, you can drag and drop to different perspectives and may want to save multiple views for presentations, posterity.

Have a look at
https://matplotlib.org/stable/gallery/mplot3d/hist3d.html, this has a working example script.
I've improved the code at that link to be more of a histogram:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = [1, 3, 5, 8, 6, 7, 1, 2, 4, 5]
y = [3, 4, 3, 6, 5, 3, 1, 2, 3, 8]
hist, xedges, yedges = np.histogram2d(x, y, bins=(4,4))
xpos, ypos = np.meshgrid(xedges[:-1]+xedges[1:], yedges[:-1]+yedges[1:])
xpos = xpos.flatten()/2.
ypos = ypos.flatten()/2.
zpos = np.zeros_like (xpos)
dx = xedges [1] - xedges [0]
dy = yedges [1] - yedges [0]
dz = hist.flatten()
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')
plt.xlabel ("X")
plt.ylabel ("Y")
plt.show()
I'm not sure how to do it with Axes3D.hist ().

In this answer there is a solution for 2D and 3D Histograms of scattered points. The usage is simple:
points, sub = hist2d_scatter( radius, density, bins=4 )
points, sub = hist3d_scatter( temperature, density, radius, bins=4 )
Where sub is a matplotlib "Subplot" instance (3D or not) and pointscontains the points used for the scatter plot.

I've added to #lxop's answer to allow for arbitrary size buckets:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.array([0, 2, 5, 10, 2, 3, 5, 2, 8, 10, 11])
y = np.array([0, 2, 5, 10, 6, 4, 2, 2, 5, 10, 11])
# This example actually counts the number of unique elements.
binsOne = sorted(set(x))
binsTwo = sorted(set(y))
# Just change binsOne and binsTwo to lists.
hist, xedges, yedges = np.histogram2d(x, y, bins=[binsOne, binsTwo])
# The start of each bucket.
xpos, ypos = np.meshgrid(xedges[:-1], yedges[:-1])
xpos = xpos.flatten()
ypos = ypos.flatten()
zpos = np.zeros_like(xpos)
# The width of each bucket.
dx, dy = np.meshgrid(xedges[1:] - xedges[:-1], yedges[1:] - yedges[:-1])
dx = dx.flatten()
dy = dy.flatten()
dz = hist.flatten()
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')