@@ -33,8 +33,8 @@ __ https://pypi.python.org/pypi/nzpyida
33
33
How the spatial functions work
34
34
------------------------------
35
35
36
- The nzpyida-spatial functions translate geopandas-like syntax into SQL and uses a middleware API (pypyodbc/JayDeBeApi)
37
- to send it to an ODBC or JDBC-connected database for execution.
36
+ The nzpyida-spatial functions translate geopandas-like syntax into SQL and uses a middleware API (nzpy/ pypyodbc/JayDeBeApi)
37
+ to send it to an nzpy, ODBC or JDBC-connected database for execution.
38
38
The results are fetched and formatted into the corresponding data structure, for example, a GeoPandas.GeoDataframe.
39
39
40
40
The following scenario illustrates how nzpyida works.
@@ -50,12 +50,13 @@ IdaDataBase object, because it holds the connection.
50
50
Now let us compute the area of the counties in the GEO_COUNTY table:
51
51
52
52
>>> idadf[' area' ] = idadf[' SHAPE' ].area()
53
- OBJECTID NAME SHAPE area
54
- 1 Wilbarger MULTIPOLYGON (((-99.4756582604 33.8340108094, ... 0.247254
55
- 2 Austin MULTIPOLYGON (((-96.6219873342 30.0442882117, ... 0.162639
56
- 3 Logan MULTIPOLYGON (((-99.4497297204 46.6316377481, ... 0.306589
57
- 4 La Plata MULTIPOLYGON (((-107.4817473750 37.0000108736,... 0.447591
58
- 5 Randolph MULTIPOLYGON (((-91.2589262966 36.2578866492, ... 0.170844
53
+ >>> idadf[[" NAME" , ' area' ]]
54
+ NAME area
55
+ Wilbarger 0.247254
56
+ Austin 0.162639
57
+ Logan 0.306589
58
+ La Plata 0.447591
59
+ Randolph 0.170844
59
60
60
61
The result of the area will be stored as a new column 'area' in the Ida geo data frame.
61
62
@@ -71,5 +72,5 @@ Here is the SQL request that was executed for this example:
71
72
It's as simple as that!
72
73
73
74
.. toctree ::
74
- geoFrame .rst
75
- geoSeries .rst
75
+ geo_frame .rst
76
+ geo_series .rst
0 commit comments