CODEWITHSUNDEEP
pythonpandasgeopandasfoliumgeopy
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Reputation: 1402

Geopandas read geometry from geo_interface as JSON column in Dataframe

I have a GeoDataFrame with a geometry column (of polygons) and few other columns used for plotting polygons and their marker popups on the map. I exported this dataframe by using gdf.__geo_interface__ as a column geo and other attributes to a CSV file using to_csv on the complete DataFrame.

The geo column looks like

{'type': 'FeatureCollection', 'features': [{'id': '1', 'type': 'Feature', 'properties': {...}}

How can I read back from the CSV file and get the GeoDataFrame using the CSV? Specifically, how can I create back the original columns (polygons and attributes) that I had in the GeoDataFrame?

Upvotes: 1

Views: 1550

Answers (1)

Serge de Gosson de Varennes
Serge de Gosson de Varennes

Reputation: 11474

Given the following situation:

from shapely.geometry import Point
d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
gdf

you can define a function that will flatten any json by:

def flatten_nested_json_df(df):
    df = df.reset_index()
    s = (df.applymap(type) == list).all()
    list_columns = s[s].index.tolist()
    
    s = (df.applymap(type) == dict).all()
    dict_columns = s[s].index.tolist()

    
    while len(list_columns) > 0 or len(dict_columns) > 0:
        new_columns = []

        for col in dict_columns:
            horiz_exploded = pd.json_normalize(df[col]).add_prefix(f'{col}.')
            horiz_exploded.index = df.index
            df = pd.concat([df, horiz_exploded], axis=1).drop(columns=[col])
            new_columns.extend(horiz_exploded.columns) # inplace

        for col in list_columns:
            #print(f"exploding: {col}")
            df = df.drop(columns=[col]).join(df[col].explode().to_frame())
            new_columns.append(col)

        s = (df[new_columns].applymap(type) == list).all()
        list_columns = s[s].index.tolist()

        s = (df[new_columns].applymap(type) == dict).all()
        dict_columns = s[s].index.tolist()
    return df

Now, you used geo = gdf.__geo_interface__ which returned something like:

{'type': 'FeatureCollection',
 'features': [{'id': '0',
   'type': 'Feature',
   'properties': {'col1': 'name1'},
   'geometry': {'type': 'Point', 'coordinates': (1.0, 2.0)},
   'bbox': (1.0, 2.0, 1.0, 2.0)},
  {'id': '1',
   'type': 'Feature',
   'properties': {'col1': 'name2'},
   'geometry': {'type': 'Point', 'coordinates': (2.0, 1.0)},
   'bbox': (2.0, 1.0, 2.0, 1.0)}],
 'bbox': (1.0, 1.0, 2.0, 2.0)}

Note that I called it geo. Then, do this:

json = json.dumps(geo) 
df = pd.json_normalize(geo)
flatten_nested_json_df(df)

Which will give you:

index               type                  bbox features.id features.type  \
0      0  FeatureCollection  (1.0, 1.0, 2.0, 2.0)           0       Feature   
0      0  FeatureCollection  (1.0, 1.0, 2.0, 2.0)           1       Feature   

          features.bbox features.properties.col1 features.geometry.type  \
0  (1.0, 2.0, 1.0, 2.0)                    name1                  Point   
0  (2.0, 1.0, 2.0, 1.0)                    name2                  Point   

  features.geometry.coordinates  
0                    (1.0, 2.0)  
0                    (2.0, 1.0)

Upvotes: 1

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