I'm very new to python, and recently started learning seaborn. When I ran the code, there was no track back and the grid was showed up in a new window. But the problem was no image showed for the FaceGrid, the distplot was showning. Not sure what happened, really appreciate if anyone could help me! Thnank you!
import pandas as pd
import seaborn as sns
import matplotlib as plt
train = pd.read_csv("train.csv")
train["Age"] = train["Age"].fillna(train["Age"].median())
#THIS ONE IS NOT SHOWN
sns.FacetGrid(train, col='Survived', row='Pclass', size=2.2, aspect=1.6)
#THIS ONE WAS SHOWED
sns.distplot(train['Age'])
sns.plt.show()
click to see the image
From the seaborn docs it appears that calling sns.FacetGrid initializes the grid. After that you need to map plots onto the grid. Hopefully that helps.
Related
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 want to create a barchart for my dataframe but it doesn't show up, so I made this small script to try out some things and this does display the barchart the way i want. The dataframe is structured the exact same way (I assume) as my big script where all my data is transformed.
Even if I copy paste this code in my other script it doesn't show the the plot
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame({
'soortfout':['totaalnoodstoppen','aantaltrapopen','aantaltrapdicht','aantalrectdicht','aantalphotocellopen','aantalphotocelldicht','aantalsafetyedgeopen', 'aantalsafetyedgeclose'],
'aantalfouten':[19,9,0,0,10,0,0,0],
})
print(df)
df.plot(kind='bar',x='soortfout',y='aantalfouten')
plt.show()
I can't really paste my other code in here since it's pretty big. But is it possible that other code that doesn't even use anything from matplotlib interferes with plotting a chart?
I've tried most other solutions like:
matplotlib.rcParams['backend'] = "Qt4Agg"
Currently using Pycharm 2.5
It does work when i use Jupyter notebook.
I was importing modules that i wasn't using so they were grayed out.
But apparently you shouldn't use import pandas_profiling if you want to plot with matplotlib
Don't import modules that can interfere with plotting like pandas_profiling
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.
When using some of the built in visualization tools in Pandas, one that is very helpful for me is the parallel_coordinates visualization. However, since I have around 18 features in the dataframe, the bottom of the parallel_coords plot gets really messy.
Therefore, I was wondering if anyone knew how to rotate the axis-names to be vertical rather than horizontal as shown here:
I did find a way to use parallel_coords in a polar set up, creating a radar-chart; while that was helpful for getting the different features to be visible, that solution doesn't quite work since whenever the values are close to 0, it becomes almost impossible to see the curve. Furthermore, doing it with the polar coord frame required me to break from using pandas' dataframe which is part of what made the this method so appealing.
Use plt.xticks(rotation=90) should be enough. Here is an example with the “Iris” dataset:
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import parallel_coordinates
data = pd.read_csv('iris.csv')
parallel_coordinates(data, 'Name')
plt.xticks(rotation=90)
plt.show()
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 :)