Using pyproj inverse transform to get azimuth, row iteration and lambda apply (I'm close to the answer!)

Question

I am using pyproj inverse transform to add azimuth and distance "info" to an ordered geodataframe.

I am trying to iterate over all the rows in the geodataframe to add the "info" as a tuple to a new column in the geodataframe, but there is something amiss with my lambda function.

Running the following:
Windows 10
conda 4.8.2
Python 3.8.3
shapely 1.7.0 py38hbf43935_3 conda-forge
pyproj 2.6.1.post1 py38h1dd9442_0 conda-forge

Example geodataframe:

%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
from shapely.geometry import LineString
import pyproj

myid = [1, 1, 1, 1, 1]
myorder = [1, 2, 3, 4, 5]
lat = [36.42, 36.4, 36.32, 36.28,36.08]
long = [-118.11, -118.12, -118.07, -117.95, -117.95]
df = pd.DataFrame(list(zip(myid, myorder, lat, long)), columns =['myid', 'myorder', 'lat', 'long']) 
gdf_pt = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['long'], df['lat']))
gdf_pt = gdf_pt.set_crs(epsg=4326)
print(gdf_pt.crs)
display(gdf_pt)
ax = gdf_pt.plot();
ax.set_aspect('equal')
ax.set_xticklabels(ax.get_xticklabels(), rotation=90);

Expected output: I am iterating over i row number, starting at the second row, and then using that row & the previous to calc the azimuths & distances. The results are as expected.

g = pyproj.Geod(ellps='WGS84') 
for i, r in gdf_pt.iloc[1:].iterrows():
    myinfo = g.inv(gdf_pt.long[i], gdf_pt.lat[i], gdf_pt.long[i-1], gdf_pt.lat[i-1])
    gdf_pt.loc[i, 'az_fwd'] = myinfo[0]
    gdf_pt.loc[i, 'az_back'] = myinfo[1]
    gdf_pt.loc[i, 'dist'] = myinfo[2]

display(gdf_pt)

Wrong output, need help here!
I am trying to iterate over i row number, starting at the second row, and then using that row & the previous to calc the azimuths & distances. However, as seen in the table display, it is not passing through i as I expected, when compared to the expected results above. It is just reading the last effort and/or over-writing

g = pyproj.Geod(ellps='WGS84') 
for i, r in gdf_pt.iloc[1:].iterrows():
    gdf_pt['mytuple'] = gdf_pt.apply(lambda x: 
                                     g.inv(gdf_pt.long[i], gdf_pt.lat[i], 
                                           gdf_pt.long[i-1], gdf_pt.lat[i-1]), axis=1)
display(gdf_pt)

I also tried the following, which threw the error shown:

g = pyproj.Geod(ellps='WGS84') 
for i, r in gdf_pt.iloc[1:].iterrows():
    gdf_pt.loc[i, 'mytuple'] = gdf_pt.apply(lambda x: 
                                     g.inv(gdf_pt.long[i], gdf_pt.lat[i], 
                                           gdf_pt.long[i-1], gdf_pt.lat[i-1]), axis=1)
display(gdf_pt)

ValueError: Incompatible indexer with Series

Answer 1

As a general rule, don't iterate over the rows of a dataframe. As a more specific rule, you don't want want to try to assign values in a loop. Since each of your iterations is creating three values, I think your best bet is to make two dataframes and join them together. Note that you really don't need geopandas for this, but since you're using it, I took advantage of it:

import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
from shapely.geometry import LineString
import pyproj

myid = [1, 1, 1, 1, 1]
myorder = [1, 2, 3, 4, 5]
lat = [36.42, 36.4, 36.32, 36.28,36.08]
long = [-118.11, -118.12, -118.07, -117.95, -117.95]
g = pyproj.Geod(ellps='WGS84') 

gdf = (
    pd.DataFrame(list(zip(myid, myorder)), columns =['myid', 'myorder']) 
      .pipe(gpd.GeoDataFrame, geometry=gpd.points_from_xy(long, lat), crs='epsg:4326')
      .join(pd.DataFrame([
          g.inv(p1.x, p1.y, p2.x, p2.y)
          if p1 and p2 else (None, None, None)
          for p1, p2 in zip(gdf.geometry, gdf.geometry.shift(1))
          ], columns=['ax_fwd', 'az_back', 'dist']
      ))
)

And that gives me:

   myid  myorder                     geometry     ax_fwd     az_back          dist
0     1        1  POINT (-118.11000 36.42000)        NaN         NaN           NaN
1     1        2  POINT (-118.12000 36.40000)  22.003207 -157.990857   2393.731315
2     1        3  POINT (-118.07000 36.32000) -26.802568  153.167789   9947.072638
3     1        4  POINT (-117.95000 36.28000) -67.582667  112.346292  11656.645203
4     1        5  POINT (-117.95000 36.08000)   0.000000  180.000000  22192.468253