python - Inserting LiDAR points (from laspy) in GeoDataFrame without using a numpy array?

Working with Python 3.6 (Anaconda) under Windows using a laspy fork that works with Python 3 (https://github.com/sethrh/laspy).

I am loading a LAS file to a GeoPandas GeoDataFrame to eventually perform a spatial join (using sjoin) with a polygon layer. I am sure there is a more elegant way to do what I am doing using the kludge below:

import numpy as np
from laspy.file import File
from pandas import DataFrame
from geopandas import GeoDataFrame
from shapely.geometry import Point

#Read LAS file
inFile = File("s428_7568.las", mode = "r")

#Import LAS into numpy array (X=raw integer value x=scaled float value)

lidar_points = np.array((inFile.x,inFile.y,inFile.z,inFile.intensity,
               inFile.raw_classification,inFile.scan_angle_rank)).transpose()

#Transform to pandas DataFrame
lidar_df=DataFrame(lidar_points)

#Transform to geopandas GeoDataFrame
crs = None
geometry = [Point(xyz) for xyz in zip(inFile.x,inFile.y,inFile.z)]
lidar_geodf = GeoDataFrame(lidar_df, crs=crs, geometry=geometry)

lidar_geodf.crs = {'init' :'epsg:2959'} # set correct spatial reference

So, is there a better way, more efficient, more pythonesque way of injecting the .las file points (from laspy) to the GeoPandas dataframe without passing through a numpy array?

Answer

So, is there a better way, more efficient, more pythonesque way of injecting the .las file points (from laspy) to the GeoPandas dataframe without passing through a numpy array?

No, and I'm not sure why you think this is the least efficient way to go. laspy is underneath the covers making a memoryview to the data and the Numpy array is a wrapper over that. There isn't much copying going on at all. You could provide more background on why you think this is the bottleneck.

Note that the latest 1.5.0 laspy release has included Seth's and other's patches to be compatible with Python 3.5/3.6.