将X，Y，Z列表简化为X，Y列表并检索XY PTS的最小和最大X

Question

I have large data sets of XYZ data I need to extract only the XY as PTS and find the minimum and maximum. The real data sets are floats... ( I still struggle to understand the list comprehension I guess... ) This is part of a bigger problem with Ascii Grid Files, that Im trying to resolve by creating a larger XYZ data set... (Which is a poor approach I know...)

# I have got this... xyz data...
PTSXYZ = [[1,1,3],[4,4,2],[6,4,1],[6,6,5]]
# I want to get this....xy data   PTSXY = [[1,1],[4,4],[6,4],[6,6]]
# I have Tried.... this ??
PTSXY = [PTSXYZ[0][i],PTSXYZ[1][i] for i in PTSXYZ] # Addressing Wrong ?

#Then If I want the Minimum & Maximum  X Values..
print min(PTSXY[:][0]),max(PTSXY[:][0])

Answer 1

For your first question, you can use list comprehension to create a new list based on the first two elements of PTSXYZ :

PTSXY = [i[:2] for i in PTSXYZ]
print PTSXY
# [[1, 1], [4, 4], [6, 4], [6, 6]]

You might also want to consider a generator, which won't store the entire list in memory:

PTSXY = (i[:2] for i in PTSXYZ)

For the second question, you can get the min and max by creating a list of X elements and taking their min and max:

print min(i[0] for i in PTSXY)
# 1
print max(i[0] for i in PTSXY)
# 6

Answer 2

Retrieving the XY slice

For retrieving from the XYZ matrix the XY matrix you can proceed with slicing :

import numpy as np
a = np.array([[1, 1, 3], [4, 4, 2], [6, 4, 1], [6, 6, 5]])
a[:, :2]

With its output being:

array([[1, 1],
       [4, 4],
       [6, 4],
       [6, 6]])

Retrieving the max and min from x

Then, for retrieving the min and max of the X you can use numpy.min and numpy.max :

_max, _min = np.max(a[:, 0]), np.min(a[:, 0])

Obtaining:

6, 1

How numpy efficiency differs from list comprehension

Let's run a test to see how the difference between numpy and list comprehension using numpy:

import numpy as np
a = np.random.randint(1000, size=(1000, 3))
%timeit [[i[0], i[1]] for i in a]
>>> 483 µs ± 10.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit a[:,:2]
>>> 391 ns ± 6.29 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

As expected, numpy is amazingly faster.

Answer 3

Once you get the XY list as @101 explained, you can get the minimum and maximum in a similar way:

PTSXY = [[i[0], i[1]] for i in PTSXYZ]
minimum = min([i[0] for i in PTSXY])
maximum = max([i[0] for i in PTSXY])