CODEWITHSUNDEEP
pythonnumpymatplotlibscipy
5o3x
5o3x

Reputation: 41

Using .mean() in numpy

I was given code and I'm familiar with numpy, but this one line really has me stuck looking for an answer.

plt.contourf(lat,lev,T.mean(0).mean(-1),extend='both')

T is a 4 dimensional variable dependent on time, lat, lon, lev.

My question is, what does the T.mean(0).mean(-1) do?

Thanks!

Upvotes: 0

Views: 1228

Answers (3)

CT Zhu
CT Zhu

Reputation: 54340

Here are some examples, which I hope will explain what is going on:

In [191]:
#the data
a=np.random.random((3,3,3))
print a
[[[ 0.21715561  0.23384392  0.21248607]
  [ 0.10788638  0.61387072  0.56579586]
  [ 0.6027137   0.77929822  0.80993495]]

 [[ 0.36330373  0.26790271  0.79011397]
  [ 0.01571846  0.99187387  0.1301911 ]
  [ 0.18856381  0.09577381  0.03728304]]

 [[ 0.18849473  0.16550599  0.41999887]
  [ 0.65009076  0.39260551  0.92284577]
  [ 0.92642505  0.46513472  0.77273484]]]
In [192]:
#mean() returns the grand mean
a.mean()
Out[192]:
0.44176096869094533
In [193]:
#mean(0) returns the mean along the 1st axis
a.mean(0)
Out[193]:
array([[ 0.25631803,  0.22241754,  0.47419964],
       [ 0.25789853,  0.6661167 ,  0.53961091],
       [ 0.57256752,  0.44673558,  0.53998427]])
In [195]:
#what is this doing?
a.mean(-1)
Out[195]:
array([[ 0.22116187,  0.42918432,  0.73064896],
       [ 0.47377347,  0.37926114,  0.10720688],
       [ 0.25799986,  0.65518068,  0.72143154]])
In [196]:
#it is returning the mean along the last axis, in this case, the 3rd axis
a.mean(2)
Out[196]:
array([[ 0.22116187,  0.42918432,  0.73064896],
       [ 0.47377347,  0.37926114,  0.10720688],
       [ 0.25799986,  0.65518068,  0.72143154]])
In [197]:
#Ok, this is now clear: calculate the mean along the 1st axis first, then calculate the mean along the last axis of the resultant. 
a.mean(0).mean(-1)
Out[197]:
array([ 0.31764507,  0.48787538,  0.51976246])

IMO, using T as a variable name is probably not a good idea. .T() means transpose in numpy.

Upvotes: 2

ndt
ndt

Reputation: 396

It's the axis along which to take the mean.

>>> import numpy
>>> arr = numpy.array([[1,2,3,4],[5,6,7,8]])
>>> arr.mean(0) == [(1+5)/2, (2+6)/2, (3+7)/2, (4+8)/2]
array([ True,  True,  True,  True], dtype=bool)
>>> arr.mean(1) == [(1+2+3+4)/4, (5+6+7+8)/4]
array([ True,  True], dtype=bool)

Upvotes: 2

AMacK
AMacK

Reputation: 1396

The value passed to mean specifies the axis along which to take the mean. Therefore, T.mean(0) takes the mean along the 0th axis and returns a 3D array. The .mean(-1) then performs the mean along the last axis of the newly created 3D array, returning a 2D array.

Which is conveniently ideal for contourf.

Upvotes: 4

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