Table join¶

Table joins are again something that you need to really frequently when doing GIS analyses. Combining data from different tables based on common key attribute can be done easily in Pandas/Geopandas using .merge() -function.

Let’s continue with our geocoding task and join the data and geo DataFrames together based on common column address. Parameter on is used to determine the common key in the tables. If your key in the first table would be named differently than in the other one, you can also specify them separately for each table by using left_on and right_on -parameters.

# Join tables by using a key column 'address'
In [1]: join = geo.merge(data, on='address')

# Let's see what we have
In [2]: join.head()
Out[2]: 
                                             address  \
0              Kampinkuja 1, 00100 Helsinki, Finland   
1               Kaivokatu 8, 00101 Helsinki, Finland   
2  Hermanstads strandsväg 1, 00580 Helsingfors, F...   
3                  Itäväylä, 00900 Helsinki, Finland   
4         Tyynenmerenkatu 9, 00220 Helsinki, Finland   

                               geometry    id  
0         POINT (24.9301701 60.1683731)  1001  
1         POINT (24.9418933 60.1698665)  1002  
2  POINT (24.9774004 60.18735880000001)  1003  
3  POINT (25.0919641 60.21448089999999)  1004  
4         POINT (24.9214846 60.1565781)  1005  

Let’s also check the data type of our new join table

In [3]: type(join)
Out[3]: geopandas.geodataframe.GeoDataFrame

As a result we have a new GeoDataFrame called join where we now have all original columns plus a new column for geometry.

Now it is easy to save our address points into a Shapefile

# Output file path
outfp = r"/home/geo/addresses.shp"

# Save to Shapefile
join.to_file(outfp)

That’s it. Now we have successfully geocoded those addresses into Points and made a Shapefile out of them.

Todo

Task:

Make a map out of the points. What do you think that the addresses are representing?