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How do i remove the output text, that comes before a graph, when using plt.xticks(rotation=90) with sns.barplot [duplicate]

I have an array A of shape (1000, 2000). I use matplotlib.pyplot to plot the array, which means 1000 curves, using
import matplotlib.pyplot as plt
plt(A)
The figure is fine but there are a thousand lines of:
<matplotlib.lines.Line2D at 0xXXXXXXXX>
Can I disable this output?

This output is what the plt function is returning (I presume here you meant to write plt.plot(A)). To suppress this output assign the return object a name:
_ = plt.plot(A)
_ is often used to indicate a temporary object which is not going to be used later on. Note that this output you are seeing will only appear in the interpreter, and not when you run the script from outside the interpreter.

You can also suppress the output by use of ; at the end (assuming you are doing this in some sort of interactive environment)
plot(A);

plt.show()
This way there is no need to create unnecessary variables.
E.g.:
import matplotlib.pyplot as plt
plt.plot(A)
plt.show()

use a semi-colon after the plot command
eg:
plt.imshow(image,cmap);
will display the graph and stop the verbose

To ignore warnings
import warnings
warnings.filterwarnings("ignore")
This will resolve your issue.

Plotting a large number of stacked spheres generated from python-- mayavi? paraview and pyevtk? How to transmute .npy to .vtk?

I want to produce plots like this, except with many more particles. Matplotlib is woefully inadequate.
Right now I am using mayavi in python 3.5 running through a jupyter notebook. As I need to plot 5x10^5 spheres it will not be practical, since time is a limiting factor already at 2x10^4 spheres.
Here is my python code to produce the mayavi plot. I have a numpy array of values [a,r,x,y,z]. It's not relevant what the first quantity is for this problem.
"""VISUALIZATION WITH MAYAVI"""
#I think this is too slow to be practical.
#view particles with mayavi
import mayavi
from mayavi import mlab
%gui qt
def plot_sphere(p): #feed it p and get back a sphere \n",
t1,R,a,b,c = p
[phi,theta] = np.mgrid[0:2*np.pi:12j,0:np.pi:12j] #increase the numbers before j for better resolution but more time
x = R*np.cos(phi)*np.sin(theta) + a
y = R*np.sin(phi)*np.sin(theta) + b
z = R*np.cos(theta) + c
return mlab.mesh(x, y, z)
#run s over all particles and plot it
def view(particles):
for p in particles:
plot_sphere(p)
view(spheres)
This code produces plots like this:
I have been told I should look into writing my numpy arrays to .vtk files using evtk, then visualizing these in paraview. I downloaded paraview and read this, but perhaps my version of python is limiting me? First, install pyevtk-- okay:
I tried conda install -c opengeostat pyevtk=1.0.0, but it fails due to incompatibility with my python version. I looked for details but could not find any.
Next I downloaded the repository [here][https://pypi.python.org/pypi/PyEVTK/1.0.0], then used pip to install it successfully.
Next I put evtk.py, vtk.py, hl.py, and xml.py, and tried some examples in the repository-- none of them work. Seemingly there is some problem with
from .vtk import *
type commands. I tried replacing all of these in the four .py files with
from evtk import vtk
from vtk import *
and such, but no luck. Long story short, I can't get pyevtk working to export my numpy arrays as .vtk files. I could use some help in this regard, or better yet I would love a different option to get my numpy arrays rendered by paraview. Any help is appreciated !

Ok, I solved my own problem. This image is made using paraview, after converting numpy arrays to a .vtu object using pyevtk.
Out of the box, the repository did not work, there was some problem with importing the modules inside the four .py files, so I modified them all. Instead of from .vtk import *, I changed it to from vtk import *, and so on, in every module in the library. evtk.py was not able to import a class from xml.py, so I just copied it and pasted, then deleted xml.py. After some tinkering and clueless modifying to make the errors go away, eventually it worked.

matplotlib: savefig omits text and axes labels in eps

If I execute the following code, the resulting eps-file does not contain tick labels or axes labels.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,2*np.pi,100)
y = np.sin(x)
plt.figure()
plt.plot(x,y)
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.savefig('image.eps', format='eps')
As some additional information: I am using version 4.3.0 of the anaconda distribution and execute my code in Spyder 3.1..2. I tested the two backends TkAgg and Qt5Agg, the result is the same for both.
Any ideas how I can get a correct eps-file?
Alternatively: which other vector graphics format could I use if I want to import into MS Word later?

So I think that you will find your answer here: https://stackoverflow.com/a/20817722/2402281
It might be the case that your matplotlib backend does not support that operation. You can change the matplotlib backend as follows:
# The default backend; one of GTK GTKAgg GTKCairo GTK3Agg GTK3Cairo
# CocoaAgg MacOSX Qt4Agg Qt5Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG
import matplotlib
matplotlib.use('PS')
or in the matplotlibrc (see the documentation).

I encountered same question. The eps file is fine when I plot time series or scatter plot. But when I am plotting geospatial plots using dataframe data, the saved eps file contain no texts and tick labels. So I simply tried:
import matplotlib
matplotlib.use('PS')
matplotlib.rcParams['text.usetex'] = True
It seems that if you turn the text.usetex to True, it may help.
Btw, my Matplotlib is 3.4.2, and operation system is MacOS, and I am using Spyder with python 3.7. Actually I don't know why it sometimes works sometimes does not work, and why it works after I adding matplotlib.rcParams['text.usetex'] = True...It may be because I used alpha when I plot the geospatial plots (like set alpha=5 in ax.scatter). I will put some tiny examples later.

Ubuntu and Matplotlib

Hello I am using the Ubuntu Server 14.04 LTS (HVM), SSD Volume Type instance from amazon aws and I am running python 2.7.9 and the latest version of matplotlib. I am trying to plot the sine function and then save the figure to a png in the home directory. Below is my code:
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0,2*np.pi,100)
y = np.sin(x)
plt.plot(x,y)
plt.savefig('Sine')
after I save the figure I use WinSCP to move the png file to my local desktop so that I can open it. But when I open the file I only see the black box with x and y tick marks.
Am I just using the wrong backend, or is the problem egregiously more severe?

I believe your problem comes from the fact that you are actually plotting nothing to your plot because x is empty. The step you use is too large in your np.arange. The third argument to np.arange is the step or increment use to build the array, unlike matlab linspace function, for which the third argument is the number of points generated.
Try this instead:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0,2*np.pi,0.01)
y = np.sin(x)
plt.plot(x,y)
plt.savefig('Sine.png')
which results in this png (in Ubuntu 15.04, Python 2.7.9, matplotlib 1.4.2):
update(2015-07-28):
Regarding the backend, as suggested in the pyplot documentation:
If format is None and fname is a string, the output format is deduced from the extension of the filename. If the filename has no extension, the value of the rc parameter savefig.format is used.
If fname is not a string, remember to specify format to ensure that the correct backend is used.
So maybe to explicitly specifying an extension to the file will help solving the issue regarding the backend (I've updated the code accordingly). By default, the backend TkAgg is used on my machine, so there was no problem plotting with the default settings.

Reduce the size of .eps figure made using matplotlib

Today I was doing a report for a course and I needed to include a figure of a contour plot of some field. I did this with matplotlib (ignore the chaotic header):
import numpy as np
import matplotlib
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
from matplotlib.mlab import griddata
import matplotlib.pyplot as plt
import numpy.ma as ma
from numpy.random import uniform
from matplotlib.colors import LogNorm
fig = plt.figure()
data = np.genfromtxt('Isocurvas.txt')
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
rc('text', usetex=True)
rc('font', family='serif')
x = data[:,0]
y = data[:,1]
z = data[:,2]
# define grid.
xi = np.linspace(0.02,1, 100)
yi = np.linspace(0.02,1.3, 100)
# grid the data.
zi = griddata(x,y,z,xi,yi)
# contour the gridded data.
CS = plt.contour(xi,yi,zi,25,linewidths=0,colors='k')
CS = plt.contourf(xi,yi,zi,25,cmap=plt.cm.jet)
plt.colorbar() # draw colorbar
# plot data points.
plt.scatter(x,y,marker='o',c='b',s=0)
plt.xlim(0.01,1)
plt.ylim(0.01,1.3)
plt.ylabel(r'$t$')
plt.xlabel(r'$x$')
plt.title(r' Contour de $\rho(x,t)$')
plt.savefig("Isocurvas.eps", format="eps")
plt.show()
where "Isocurvas.txt" is a 3 column file, which I really don't want to touch (eliminate data, or something like that, wouldn't work for me). My problem was that the figure size was 1.8 Mb, which is too much for me. The figure itself was bigger than the whole rest of the report, and when I opened the pdf it wasn't very smooth .
So , my question is :
Are there any ways of reducing this size without a sacrifice on the quality of the figure?. I'm looking for any solution, not necessarily python related.
This is the .png figure, with a slight variation on parameters. using .png you can see the pixels, which i don't like very much, so it is preferable pdf or eps.
Thank you.

The scatter plot is what's causing your large size. Using the EPS backend, I used your data to create the figures. Here's the filesizes that I got:
Straight from your example: 1.5Mb
Without the scatter plot: 249Kb
With a raster scatter plot: 249Kb
In your particular example it's unclear why you want the scatter (not visible). But for future problems, you can use the rasterized=True keyword on the call to plt.scatter to activate a raster mode. In your example you have 12625 points in the scatter plot, and in vector mode that's going to take a bit of space.
Another trick that I use to trim down vector images from matplotlib is the following:
Save figure as EPS
Run epstopdf (available with a TeX distribution) on the resulting file
This will generally give you a smaller pdf than matplotlib's default, and the quality is unchanged. For your example, using the EPS file without the scatter, it produced a pdf with 73 Kb, which seems quite reasonable. If you really want a vector scatter command, running epstopdf on the original 1.5 Mb EPS file produced a pdf with 198 Kb in my system.

I'm not sure if it helps with size, but if your willing to try the matplotlib 1.2 release candidate there is a new backend for producing PGF images (designed to slot straight into latex seamlessly). You can find the docs for that here: http://matplotlib.org/1.2.0/users/whats_new.html#pgf-tikz-backend
If you do decide to give it a shot and you have any questions, I'm probably not the best person to talk to, so would recommend emailing the matplotlib-users mailing list.
HTH,

Try removing the scatter plot of your data. They do not appear to be visible in your final figure (because you made them size 0) and may be taking up space in your eps.
EDITED: to completely change the answer because I read the question wrong.