Creating Spatial Enabled Dataframe from pandas dataframe with Latitude and Longitude coordinates?

MichaelWallace3

I have a pandas dataframe with latitude and longitude columns. I would like to convert it ultimately to a feature class. In the conversion to a spatial data frame, I do the following. 

 

sdf = arcgis.features.GeoAccessor.from_xy(dff, x_column='longitude', y_column='latitude', sr=4326)

 

I used this tutorial as a guide https://developers.arcgis.com/python/guide/part2-working-with-geometries/#Create-an-SeDF-object-with-geometries

and keep getting this exception message

raise Exception("Spatial column not defined, please use `set_geometry`")

Exception: Spatial column not defined, please use `set_geometry`

jcarlson

Here we go.

The Standard Method

 

from arcgis.features import GeoAccessor
import pandas as pd
from numpy.random import rand

lats = rand(5) * 45 + 30
lons = rand(5) * 45 + 30

df = pd.DataFrame({'lat':lats, 'lon':lons})

 

Here's df:

 

|   |       lat |       lon |
|--:|----------:|----------:|
| 0 | 66.212195 | 42.930500 |
| 1 | 58.392814 | 74.032695 |
| 2 | 44.315964 | 32.602737 |
| 3 | 69.484491 | 58.170186 |
| 4 | 33.592844 | 62.363671 |

 

Using the standard from_xy seems to work as intended.

GeoAccessor.from_xy(df, 'lon', 'lat').spatial.plot()

Results in:

This even works when the lat/lon values are null or clearly invalid ( > 180 ), it still "works", it just doesn't render correctly on the map.

 

Let's Pretend that Didn't Work: Alternate Method

I have had it happen where the normal conversion process just wouldn't work. To work around that, we'll wrangle the lat/lon values into a string of the same format that the SEDF uses.

 

df['SHAPE'] = '"spatialReference": {"wkid": 4326}, {"x":' + df['lon'].astype('str') + ', "y":' + df['lat'].astype('str') + '}'

 

This results in a new column 'SHAPE' with values like this:

'{"spatialReference": {"wkid": 4326}, "x": 67.91584605411032, "y": 34.142203783078585 }

Which you may recognize as the JSON text of point geometries, seen in SEDFs. Let's now try to assign this as the geometry column.

sdf = GeoAccessor.from_df(df, geometry_column='SHAPE')
sdf.spatial.plot()

Which results in (different iteration of script, so my random values have shifted):

Give this alternate method a shot. If that doesn't work, you may be in a pickle. Or you may need to take a harder look at the input data.