I am already having Tkinter(someone said to install a tkinter)
code used:
imports are:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
if u want to view the data-set then it is :
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv")
code used to plot boxplot in jupyter notebook
fig, ax = plt.subplots(figsize = (20,20))
sns.boxplot(data = df,ax = ax)
)
I was supposed to add in my import's
%matplotlib inline
import numpy as np
import os.path
from skimage.io import imread
from skimage import data_dir
img = imread(os.path.join(data_dir, 'checker_bilevel.png'))
import matplotlib.pyplot as plt
#plt.imshow(img, cmap='Blues')
#plt.show()
imgT = img.T
plt.figure(1)
plt.imshow(imgT,cmap='Greys')
#plt.show()
imgR = img.reshape(20,5)
plt.figure(2)
plt.imshow(imgR,cmap='Blues')
plt.show(1)
I read that plt.figure() will create or assign the image a new ID if not explicitly given one. So here, I have given the two figures, ID 1 & 2 respectively. Now I wish to see only one one of the image.
I tried plt.show(1) epecting ONLY the first image will be displayed but both of them are.
What should I write to get only one?
plt.clf() will clear the figure
import matplotlib.pyplot as plt
plt.plot(range(10), 'r')
plt.clf()
plt.plot(range(12), 'g--')
plt.show()
plt.show will show all the figures created. The argument you forces the figure to be shown in a non-blocking way. If you only want to show a particular figure you can write a wrapper function.
import matplotlib.pyplot as plt
figures = [plt.subplots() for i in range(5)]
def show(figNum, figures):
if plt.fignum_exists(figNum):
fig = [f[0] for f in figures if f[0].number == figNum][0]
fig.show()
else:
print('figure not found')
I have the following snippet in the first cell of a Jupyter notebook:
import matplotlib.pyplot as plt
import pandas as pd
import ipywidgets as widgets
import numpy as np
out = widgets.Output()
data = pd.DataFrame(np.random.normal(size = 50))
plt.ioff()
with out:
fig, axes = plt.subplots()
data.hist(ax = axes)
display(fig)
plt.ion()
display(out)
If I restart the kernel and run this first cell, I see this output:
<Figure size 640x480 with 1 Axes>
However, if I run this first cell a second time, I see a matplotlib figure as I intended. This behavior also shows up if I move everything after the import of matplotlib to a second cell, restart the kernel, and rerun the entire notebook.
Is this difference in behavior intentional?
The code rearranging and adding magic command '%matplotlib notebook' work for me.
%matplotlib notebook
import matplotlib.pyplot as plt
import pandas as pd
import ipywidgets as widgets
import numpy as np
out = widgets.Output()
plt.ioff()
fig, axes = plt.subplots()
plt.ion()
data = pd.DataFrame(np.random.normal(size = 50))
data.hist(ax = axes)
display(out)
with out:
display(fig)
I've tried multiple animation sample codes and cannot get any of them working. Here's a basic one I've tried from the Matplotlib documentation:
"""
A simple example of an animated plot
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
x = np.arange(0, 2*np.pi, 0.01) # x-array
line, = ax.plot(x, np.sin(x))
def animate(i):
line.set_ydata(np.sin(x+i/10.0)) # update the data
return line,
#Init only required for blitting to give a clean slate.
def init():
line.set_ydata(np.ma.array(x, mask=True))
return line,
ani = animation.FuncAnimation(fig, animate, np.arange(1, 200), init_func=init,
interval=25, blit=True)
plt.show()
When I execute the above in an IPython Notebook, I just see a blank plot generated. I've tried running this from multiple servers (including Wakari), on multiple machines, using multiple browsers (Chrome, FF, IE).
I can save the animation to an mp4 file just fine and it looks good when played.
Any help is appreciated!
To summarize the options you have:
Using display in a loop Use IPython.display.display(fig) to display a figure in the output. Using a loop you would want to clear the output before a new figure is shown. Note that this technique gives in general not so smooth resluts. I would hence advice to use any of the below.
import matplotlib.pyplot as plt
import matplotlib.animation
import numpy as np
from IPython.display import display, clear_output
t = np.linspace(0,2*np.pi)
x = np.sin(t)
fig, ax = plt.subplots()
l, = ax.plot([0,2*np.pi],[-1,1])
animate = lambda i: l.set_data(t[:i], x[:i])
for i in range(len(x)):
animate(i)
clear_output(wait=True)
display(fig)
plt.show()
%matplotlib notebook Use IPython magic %matplotlib notebook to set the backend to the notebook backend. This will keep the figure alive instead of displaying a static png file and can hence also show animations.
Complete example:
%matplotlib notebook
import matplotlib.pyplot as plt
import matplotlib.animation
import numpy as np
t = np.linspace(0,2*np.pi)
x = np.sin(t)
fig, ax = plt.subplots()
l, = ax.plot([0,2*np.pi],[-1,1])
animate = lambda i: l.set_data(t[:i], x[:i])
ani = matplotlib.animation.FuncAnimation(fig, animate, frames=len(t))
plt.show()
%matplotlib tk Use IPython magic %matplotlib tk to set the backend to the tk backend. This will open the figure in a new plotting window, which is interactive and can thus also show animations.
Complete example:
%matplotlib tk
import matplotlib.pyplot as plt
import matplotlib.animation
import numpy as np
t = np.linspace(0,2*np.pi)
x = np.sin(t)
fig, ax = plt.subplots()
l, = ax.plot([0,2*np.pi],[-1,1])
animate = lambda i: l.set_data(t[:i], x[:i])
ani = matplotlib.animation.FuncAnimation(fig, animate, frames=len(t))
plt.show()
Convert animation to mp4 video:
from IPython.display import HTML
HTML(ani.to_html5_video())
or use plt.rcParams["animation.html"] = "html5" at the beginning of the notebook.
This will require to have ffmpeg video codecs available to convert to HTML5 video. The video is then shown inline. This is therefore compatible with %matplotlib inline backend. Complete example:
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["animation.html"] = "html5"
import matplotlib.animation
import numpy as np
t = np.linspace(0,2*np.pi)
x = np.sin(t)
fig, ax = plt.subplots()
l, = ax.plot([0,2*np.pi],[-1,1])
animate = lambda i: l.set_data(t[:i], x[:i])
ani = matplotlib.animation.FuncAnimation(fig, animate, frames=len(t))
ani
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.animation
import numpy as np
t = np.linspace(0,2*np.pi)
x = np.sin(t)
fig, ax = plt.subplots()
l, = ax.plot([0,2*np.pi],[-1,1])
animate = lambda i: l.set_data(t[:i], x[:i])
ani = matplotlib.animation.FuncAnimation(fig, animate, frames=len(t))
from IPython.display import HTML
HTML(ani.to_html5_video())
Convert animation to JavaScript:
from IPython.display import HTML
HTML(ani.to_jshtml())
or use plt.rcParams["animation.html"] = "jshtml" at the beginning of the notebook.
This will display the animation as HTML with JavaScript. This highly compatible with most new browsers and also with the %matplotlib inline backend. It is available in matplotlib 2.1 or higher.
Complete example:
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["animation.html"] = "jshtml"
import matplotlib.animation
import numpy as np
t = np.linspace(0,2*np.pi)
x = np.sin(t)
fig, ax = plt.subplots()
l, = ax.plot([0,2*np.pi],[-1,1])
animate = lambda i: l.set_data(t[:i], x[:i])
ani = matplotlib.animation.FuncAnimation(fig, animate, frames=len(t))
ani
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.animation
import numpy as np
t = np.linspace(0,2*np.pi)
x = np.sin(t)
fig, ax = plt.subplots()
l, = ax.plot([0,2*np.pi],[-1,1])
animate = lambda i: l.set_data(t[:i], x[:i])
ani = matplotlib.animation.FuncAnimation(fig, animate, frames=len(t))
from IPython.display import HTML
HTML(ani.to_jshtml())
According to this answer, you can get animation (and full interactivity support) working in an IPython notebook enabling the nbagg backend with %matplotlib nbagg.
I was having the exact same problem as you until a moment ago. I am a complete novice, so tcaswell's answer was a bit cryptic to me. Perhaps you figured out what he meant or found your own solution. In case you have not, I will put this here.
I googled "matplotlib inline figures" and found this site, which mentions that you have to enable matplotlib mode. Unfortunately, just using %maplotlib didn't help at all.
Then I typed %matplotlib qt into the IPython console on a lark and it works just fine now, although the plot appears in a separate window.
I ran into this issue as well and found I needed to understand the concept of matplotlib backends, how to enable a specific backend, and which backends work with FuncAnimation. I put together an ipython notebook that explains the details and summarizes which backends work with FuncAnimation on Mac, Windows, and wakari.io. The notebook also summarizes which backends work with the ipython interact() widget, and where plots appear (inline or secondary window) for basic matplotlib plotting. Code and instructions are included so you can reproduce any of the results.
The bottom line is that you can't get an animation created with FuncAnimation to display inline in an ipython notebook. However, you can get it to display in a separate window. It turns out that I needed this to create visualizations for an undergraduate class I am teaching this semester, and while I would much prefer the animations to be inline, at least I was able to create some useful visualizations to show during class.
No inline video in Jupyter at the end of an animation also happens when
HTML(ani.to_html5_video())
is not at the very end of a notebook cell, as the output is then suppressed.
You may use it then as follows
out = HTML(ani.to_html5_video())
and just type out` in a new cell to get the video online.
I ran the following code to get two plots next to each other (it is a minimal working example that you can copy):
import pandas as pd
import numpy as np
from matplotlib.pylab import plt
comp1 = np.random.normal(0,1,size=200)
values = pd.Series(comp1)
plt.close("all")
f = plt.figure()
plt.show()
sp1 = f.add_subplot(2,2,1)
values.hist(bins=100, alpha=0.5, color="r", normed=True)
sp2 = f.add_subplot(2,2,2)
values.plot(kind="kde")
Unfortunately, I then get the following image:
This is also an interesting layout, but I wanted the figures to be next to each other. What did I do wrong? How can I correct it?
For clarity, I could also use this:
import pandas as pd
import numpy as np
from matplotlib.pylab import plt
comp1 = np.random.normal(0,1,size=200)
values = pd.Series(comp1)
plt.close("all")
fig, axes = plt.subplots(2,2)
plt.show()
axes[0,0].hist(values, bins=100, alpha=0.5, color="r", normed=True) # Until here, it works. You get a half-finished correct image of what I was going for (though it is 2x2 here)
axes[0,1].plot(values, kind="kde") # This does not work
Unfortunately, in this approach axes[0,1] refers to the subplot that has a plot method but does not know kind="kde". Please take into consideration that the in the first version plot is executed on the pandas object, whereas in the second version plot is executed on the subplot, which does not work with the kind="kde" parameter.
use ax= argument to set which subplot object to plot:
import pandas as pd
import numpy as np
from matplotlib.pylab import plt
comp1 = np.random.normal(0,1,size=200)
values = pd.Series(comp1)
plt.close("all")
f = plt.figure()
sp1 = f.add_subplot(2,2,1)
values.hist(bins=100, alpha=0.5, color="r", normed=True, ax=sp1)
sp2 = f.add_subplot(2,2,2)
values.plot(kind="kde", ax=sp2)