I am working with Matplotlib and came across with Object Oriented Method to create plots with Matplotlib. So I wrote the following code in Jupyter Notebook
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
x=np.linspace(0,5,11)
y=x**2
fig=plt.figure()
axes=fig.add_axes([0.1,0.1,0.8,0.8])
axes.plot(x,y)
But I don't get any plot after running it. I tried looking other answers but couldn't solve my problem. So, my question is how do Object Oriented Interface in Matplotlib actually works and how and why it is better than functional method of Matplotlib?
Thanks.
I am having a really weird issue with using the %matplotlib inline code in my jupyter notebook for plotting graphs using both pyplot and the pandas plotting function.
The problem is they show up without any axes, and basically just show the graph area without anything aside from data points.
I found adding:
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
reverse it, but I find it odd that should do that every time as the effect disappears as soon as I run %matplotlib inlinecommand.
an example could be
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
plt.scatter(A,A)
plt.tight_layout()
plt.xlabel('here')
plt.show()
This would generate the graph below:
Weird enough if I uses the savefig it get plotted with the axis, if I uses the right-click -> new output -> save as figure, I also get the graph with the figures !!
like this:
Can anyone help me understand what is wrong, which global setting did I mess up, and how do I revert it?
(I don't remember messing around with any settings aside from some settings for pandas, but don't think they should have had an impact)
as mentioned running mpl.rcParams.update(mpl.rcParamsDefault) command does bring it back to normal until I run %matplotlib inline` again !!
Any help would be much appreciated.
Okay I am sorry I think I can answer the question myself now.
With the helpfull #Mr. T asking for the imgur link made me realize what was going on. I had starting using the dark jupyter lab theme, and the graph would generate plots with transparent background, ie. the text and lines where there, but I just couldn't see them.
The trick is to change the background color preferably globally, but that will be a task for tomorrow.
i follow the google course about machine learning. i'm on this part : pandas
But on my mac when i want to generate a chart with this command :
california_housing_dataframe.hist('housing_median_age')
it doesn't work. The python icon appear but nothing is displaying on the screen.
i have see some tips with the backend parameter into matplotlibrc but mine is equals to MacOSX and it should work ?
Thanks for help
To elaborate on the comment from T. Kelly:
You need to call plt.show(). This worked for me:
import matplotlib.pyplot as plt
california_housing_dataframe.hist('housing_median_age')
plt.show()
I'm following Google ML Crash Course(I think you are also following it based on the variable name).
I too encountered the same problem.
When I call
california_housing_dataframe.hist('housing_median_age')
It is not showing any histogram. Instead it is showing
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x12bb814e0>]],
dtype=object)
To show the histogram, add this line in your imports:
%matplotlib inline
It should show the histogram.
So I am trying to simply copy a code from the following link:
http://rajeshrinet.github.io/blog/2014/ising-model/
It is the first block of code that produces the 4 subplots. It already has the lines %matplotlib inline and plt.show() which are the normal reasons that plots won't show up. Also, no errors are produced to guide me in what is going wrong. Does anyone see what the problem is? Thank you for your time.
I'm asking this question because I can't solve one problem in Python/Django (actually in pure Python it's ok) which leads to RuntimeError: tcl_asyncdelete async handler deleted by the wrong thread. This is somehow related to the way how I render matplotlib plots in Django. The way I do it is:
...
import matplotlib.pyplot as plt
...
fig = plt.figure()
...
plt.close()
I extremely minimized my code. But the catch is - even if I have just one line of code:
fig = plt.figure()
I see this RuntimeError happening. I hope I could solve the problem, If I knew the correct way of closing/cleaning/destroying plots in Python/Django.
By default matplotlib uses TK gui toolkit, when you're rendering an image without using the toolkit (i.e. into a file or a string), matplotlib still instantiates a window that doesn't get displayed, causing all kinds of problems. In order to avoid that, you should use an Agg backend. It can be activated like so --
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot
For more information please refer to matplotlib documentation -- http://matplotlib.org/faq/howto_faq.html#matplotlib-in-a-web-application-server
The above (accepted) answer is a solution in a terminal environment. If you debug in an IDE, you still might wanna use 'TkAgg' for displaying data. In order to prevent this issue, apply these two simple rules:
everytime you display your data, initiate a new fig = plt.figure()
don't close old figures manually (e.g. when using a debug mode)
Example code:
import matplotlib
matplotlib.use('TkAgg')
from matplotlib import pyplot as plt
fig = plt.figure()
plt.plot(data[:,:,:3])
plt.show()
This proves to be the a good intermediate solution under MacOS and PyCharm IDE.
If you don't need to show plots while debugging, the following works:
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
However, if you would like to plot while debugging, you need to do 3 steps:
1.Keep backend to 'TKAgg' as follows:
import matplotlib
matplotlib.use('TKAgg')
from matplot.lib import pyplot as plt
or simply
import matplotlib.pyplot as plt
2.As Fábio also mentioned, you need to add fig(no. #i)=plt.figure(no.#i) for each figure #i. As the following example for plot no.#1, add:
fig1 = plt.figure(1)
plt.plot(yourX,yourY)
plt.show()
3.Add breakpoints. You need to add two breakpoints at least, one somewhere at the beginning of your codes (before the first plot), and the other breakpoint at a point where you would like all plots (before to the second breakpoint) are plotted. All figures are plotted and you even don't need to close any figure manually.
For me, this happened due to parallel access to data by both Matplotlib and by Tensorboard, after Tensorboard's server was running for a week straight.
Rebotting tensorboard tensorboard --logdir . --samples_per_plugin images=100 solved this for me.
I encountered this problem when plotting graphs live with matplotlib in my tkinter application.
The easiest solution I found, was to always delete subplots. I found you didn't need to instantiate a new figure, you only needed to delete the old subplot (using del subplot), then remake it.
Before plotting a new graph, make sure to delete the old subplot.
Example:
f = Figure(figsize=(5,5), dpi=100)
a = f.add_subplot(111)
(For Loop code that updates graph every 5 seconds):
del a #delete subplot
a = f.add_subplot(111) #redefine subplot
Finding this simple solution to fix this "async handler bug" was excruciatingly painful, I hope this helps someone else :)