标签：tps map unit ble set imp info inf panda
ABS suburbs data of AUS
1. Dissolve
Merge polygons with the same attribute of "SA2_NAME16".
>>> import arcpy
>>> mxd = arcpy.mapping.MapDocument("CURRENT")
>>> df = arcpy.mapping.ListDataFrames(mxd)[0]
>>> lyrs = arcpy.mapping.ListLayers(df)
>>> for lyr in lyrs:
... arcpy.Dissolve_management(lyr, "Dissolve_" + lyr.name, ‘SA2_NAME16‘, ‘#‘, ‘MULTI_PART‘, ‘DISSOLVE_LINES‘)
...
2. Add Centroid XY
After using the "Add Geometry Attributes" tool, we should close shp files and add them again and will see the results. (Sometimes it can show directly, WTF!!!)
... >>> for lyr in lyrs: ... arcpy.AddGeometryAttributes_management(lyr, "CENTROID") ...
3. Add a state field
Before merging those polygons, we should point a specific field storing state info.
Before doing this, atrribute window should be closed, or it won‘t work.
...
>>> for i in range(1, 9):
... arcpy.AddField_management(lyrs[i], "State", "TEXT")
...
>>> # file name like "Suburbs_MB_2016_NSW"
>>> # we want to get "NSW"
>>> for i in range(1, 9):
... cursor = arcpy.UpdateCursor(lyrs[i])
... fn = lyrs[i].name
... for row in cursor:
... row.setValue("State", fn[fn.rfind("_")+1:])
... cursor.updateRow(row)
...
4. Merge the whole polygons into one
>>> mxd = arcpy.mapping.MapDocument("CURRENT")
>>> df = arcpy.mapping.ListDataFrames(mxd)[0]
>>> lyrs = arcpy.mapping.ListLayers(df)
>>> arcpy.Merge_management(lyrs, "Suburbs_MB_2016_AUS")
5. Export table to csv file
Tool: Export Feature Attribute to ASCII
ref: Export an attribute table to .txt using arcpy.
>>> arcpy.ExportXYv_stats(‘Suburbs_MB_2016_AUS‘, ‘SA2_NAME16;CENTROID_X;CENTROID_Y;State;Shape_Area‘, ‘COMMA‘, r‘D:\Twitter Data\Data\test\2.csv‘, ‘ADD_FIELD_NAMES‘)
6. Get specific columns
Based on pandas lib.
>>> df = pd.read_csv(r"D:\Twitter Data\Data\test\2.csv")
>>> df.head()
XCoord YCoord ... STATE SHAPE_AREA
0 117.899601 -35.008360 ... WA 0.003012
1 118.207172 -34.718972 ... WA 0.394533
2 115.865812 -31.834866 ... WA 0.000638
3 115.677976 -31.600241 ... WA 0.003104
4 115.836085 -32.019166 ... WA 0.000518
[5 rows x 7 columns]
>>> df.columns
Index([‘XCoord‘, ‘YCoord‘, ‘SA2_NAME16‘, ‘CENTROID_X‘, ‘CENTROID_Y‘, ‘STATE‘,
‘SHAPE_AREA‘],
dtype=‘object‘)
>>> df1 = df[[‘SA2_NAME16‘, ‘CENTROID_X‘, ‘CENTROID_Y‘, ‘STATE‘, ‘SHAPE_AREA‘]]
>>> df1.columns
Index([‘SA2_NAME16‘, ‘CENTROID_X‘, ‘CENTROID_Y‘, ‘STATE‘, ‘SHAPE_AREA‘], dtype=‘object‘)
>>> df1.head()
SA2_NAME16 CENTROID_X ... STATE SHAPE_AREA
0 Albany 117.899601 ... WA 0.003012
1 Albany Region 118.207172 ... WA 0.394533
2 Alexander Heights - Koondoola 115.865812 ... WA 0.000638
3 Alkimos - Eglinton 115.677976 ... WA 0.003104
4 Applecross - Ardross 115.836085 ... WA 0.000518
[5 rows x 5 columns]
>>> df1.to_csv(r"D:\Twitter Data\Data\test\Suburbs_AUS.csv", index=False)
7.
【444】Data Analysis (shp, arcpy)
标签：tps map unit ble set imp info inf panda
原文地址：https://www.cnblogs.com/alex-bn-lee/p/11723919.html
