ploting 2D numpy array with seaborn jointplot

Question

Is there a way to plot a 2D numpy.array with sns.jointplot?

Here is my array:

a = np.array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
    1., 0., 0., 0., 0., 0., 1., 1., 2., 1., 2., 1., 1., 1., 0., 0.,
    0., 0., 0., 0., 0.],
   [0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 0., 0.,
    1., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0.,
    0., 0., 0., 0., 0.],
   [0., 0., 2., 1., 2., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
    0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.,
    0., 0., 0., 0., 0.],
   [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
    0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,
    1., 1., 1., 0., 0.],
   [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
    0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,
    0., 0., 0., 0., 0.]])

I tried to do df = pd.DataFrame(a), and I get the expected matrix, but then sns.jointplot(df) failed, because I don't know what to use for x and y. It should look like this: https://seaborn.pydata.org/examples/joint_kde.html

any help is highly appreciated. thanks.

Edit: the plot I want is a graphical representation of the values stored in a. The values represent amount of people on locations, and the 2 axis of the data are the x and y coordinates of the floor.

Could you clarify what you're trying to plot? Is it each sub-array plotted against its index value? — sacuL
– sacuL, Commented Nov 6, 2018 at 0:23
I did an edit to the question. does this clarify what I need? — otmezger
– otmezger, Commented Nov 6, 2018 at 0:26

user8408080 · Accepted Answer · 2018-11-06 01:22:08Z

2

sns.jointplot does not take a 2D array as an argument. It takes (x,y) coordinates. If you follow the example, that you posted and plot x1 and x2 as a scatterplot you get this:

Now if you do the jointplot you get:

And now you see, that this plots point densities. But the points are from a R->R function. Your 2D array is a R²->R function. If you want it to be reinterpreted as point densities, then you turn it into a R->R function, that looks the same as the old R²->R function when you calculate its density.

Luckily you only have whole numbers, so if you for example want the value 2 at array coordinates [0,24] then you just need two points at the coordinates (0,24) in order to receive the value 2 in density. So all we need to do is find out how many points should be where and then we get the needed two arrays:

#initiate
y1 = np.array([])
y2 = np.array([])

#create locations
for i in range(1,a.max()+1):
    for j in range(i):
        locs = np.where(a==i)
        try:
            y1 = np.append(y1, locs[0])
            y2 = np.append(y2, locs[1])
        except: #this number doesn't exist in the array
            break

for every number n in your array, this will create n points at the right position. You can then call:

g2 = sns.jointplot(y1, y2, kind="kde")

and get your result:

EDIT Just saw your edit. Well the easiest way to just show your array would be

import matplotlib.pyplot as plt
plt.imshow(a)

which gives:

EDIT2 after looking a bit more into your problem: It should be way easier and more exact to store the coordinates of the people in the room.

edited Nov 6, 2018 at 1:22

answered Nov 6, 2018 at 1:00

user8408080

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3 Comments

otmezger Over a year ago

Thank you for the great answer. Now I understand the basics of that plot. Funy enough, I ended with R2->R because I also did the heat map. You are right with your second edit, this is actually the original format of the data :-)

user8408080 Over a year ago

Nice, then you don't have to deal with the Shenannigans of creating new arrays just to simulate densities :D

Eli Korvigo Over a year ago

Downvoting for np.append in a loop.

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