I recently published my jupyter notebook in github. All the visualisations of the notebook are visible except Folium heatmap. In the error it specifies to turn on File>Trust in Jupyter notebook.
I tried doing that and then pushed the notebook but still the error persists. I have attached the image for refrence. Thanks in advance for the help.
Trying to use blitting inside a jupyter notebook does not update the figure, only the first image is shown instead of the animation updating the figure.
Blitting as defined in the matplotlib documentation https://matplotlib.org/3.5.0/tutorials/advanced/blitting.html
The notebook can be downloaded from here
Do you know if there is a possibility to make that work ?
The first command in my Jupyter notebook is the %matplotlib notebook. This has been working fine but curiously the figures being drawn today are only partial plots. (The inline option without interactivity display complete plots).
When I pan, I can see more sections of the image.
I would like to see the entire plot and I am not sure what has caused this change in display
When image is first displayed it appears like this:
Image created upon running the cell
After panning it appears like this:
New section of image displayed upon panning
NOTE: This started to occur after updating to Matplotlib version 3.3.0.
For the current time I have reverted to Matplotlib version 3.1.0. But if the latest has some dependencies that I am missing which is causing the issue, I would like to learn how to figure those out.
I do not receive any errors or warnings upon import matplotlib or pyplot when working with 3.3.0
This is not a duplicate as suggested. I am not trying to switch backends. I am just trying to use the %matplotlib notebook formulation. Moreover, the solutions suggested in that other question (place the%matplotlib notebook before the the from matplotlib import pylplot as plt OR trying call the magic command twice in a row) do not work for me. The behavior (blank notebook charts) remains.
place the%matplotlib notebook before the the from matplotlib import pylplot as plt OR trying call the magic command twice in a row
When I use %matplotlib inline my charts always show, but they're generally on the small side, and have no ability to zoom/pan.
When I use %matplotlib notebook I get zoomable, pannable charts, but I sometimes encounter problems where the charts display as blank.
Background:
PythonAnywhere notebook 3.6 Python. Using update Google Chrome from Windows 7.
If I reopen my existing notebook, even restart the kernel and re-run all the cells, all my .plot() appear blank. But if I contemporaneously start a brand new 3.6 notebook, %matplotlib notebook plots work fine. Killing all the jupyter-related processes doesn't help either.
This does not always occur. But if one chart appears blank, they all appear blank.
I would like to use the notebook magic, but this problem is pretty profound.
I have found some stability on using %matplotlib notebook, after including plt.close(), before all my plots.
A pretty dirty solution, I know, but it's working. Here Windows 10, Python 3.6.2 and Jupyter 6.03 (updated Jupyter haven't solved the issue).
Hope this helps.
%matplotlib qt will do your job.
I installed anaconda to use pandas and scipy. I reading and watching pandas tutorials and they all say to open the ipython notebook using
ipython notebook --pylab==inline
but when I do that I get a message saying
"Support for specifying --pylab on the command line has been removed. Please use '%pylab = inline' or '%matplotlib =inline' in the notebook itself"
But that does not work. Then when I try "plot(arange(10))" I get a message saying "name 'plot' is not defined." I trying plotting data from a .csv file and got
"matplotlib.axes._subplots.AxesSubplot at 0xebf8b70".
What should I do?
I believe the pylab magic was removed when they transitioned from IPython to a more general Jupyter notebook.
Try:
%matplotlib inline
Also when you get a message like:
"matplotlib.axes._subplots.AxesSubplot at 0xebf8b70".
That's just IPython displaying the object. You need to specify IPython display it. Hence the matplotlib inline magic.