Lesson3-table-join.rst

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.

.. ipython:: python
  :suppress:

    # THIS CODE WILL BE RUNNING IN BACKGROUND
    # ---------------------------------------
    import gdal
    import pandas as pd
    import geopandas as gpd
    from shapely.geometry import Point
    from geopandas.tools import geocode
    import os
    fp = os.path.join(os.path.abspath('data'), "addresses.txt")
    data = pd.read_csv(fp, sep=';')
    key = 'AIzaSyAwNVHAtkbKlPs-EEs3OYqbnxzaYfDF2_8'
    geo = geocode(data['address'], api_key=key)

.. ipython:: python

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

    # Let's see what we have
    join.head()

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

.. ipython:: python

    type(join)

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?