Reading .KML with GeoPandas

Question

I am trying to read a .kml file (here is an example of the input):

<name>99</name>
    <Style><LineStyle><color>ff0000ff</color></LineStyle><PolyStyle><fill>0</fill></PolyStyle></Style>
    <ExtendedData><SchemaData schemaUrl="#COMPARABLES_APPARTS">
        <SimpleData name="tessellate">-1</SimpleData>
        <SimpleData name="extrude">0</SimpleData>
        <SimpleData name="visibility">-1</SimpleData>
        <SimpleData name="Titref">T5610/71()</SimpleData>
        <SimpleData name="Mappe">28-17-a-16</SimpleData>
        <SimpleData name="Nature">T</SimpleData>            
        <SimpleData name="Carnet">31</SimpleData>
        <SimpleData name="Bon">14</SimpleData>
        <SimpleData name="DateCB">04-07-2018</SimpleData>
        <SimpleData name="Archive">T</SimpleData>
        <SimpleData name="NUM_TITRE">T/5610/71/</SimpleData>
        <SimpleData name="Titre">T/18403/71/</SimpleData>
        <SimpleData name="Montant">1344000.000000000000000</SimpleData>
        <SimpleData name="Superficie">56</SimpleData>
        <SimpleData name="Prix_unita">24000</SimpleData>
        <SimpleData name="Formalité">بيع كلي</SimpleData>
        <SimpleData name="Date_Acte">2019/02/12</SimpleData>
        <SimpleData name="Type Acte">عقد موثق</SimpleData>
        <SimpleData name="Consista_1">شقة</SimpleData>
        <SimpleData name="Origine">T/5610/71/</SimpleData>
        <SimpleData name="Adresse">البيضاء- حي راسين</SimpleData>
        <SimpleData name="Pdite">نورماندي ايطا36/1</SimpleData>
    </SchemaData></ExtendedData>
      <MultiGeometry><Polygon><outerBoundaryIs><LinearRing><coordinates>-7.64047383421453,33.5879835701317 -7.64066743631093,33.5884293463282 -7.64012881378832,33.5885680296946 -7.64007947841229,33.5884516406187 -7.64008802819036,33.5884487806223 -7.64003671338741,33.5882972444107 -7.64007756187161,33.5882929019069 -7.64007969352916,33.5882919614817 -7.64018375142164,33.5882305516264 -7.64031826698891,33.5881379222242 -7.64023531103129,33.5880555158574 -7.6402575429815,33.5880397802828 -7.64047383421453,33.5879835701317</coordinates></LinearRing></outerBoundaryIs></Polygon></MultiGeometry>
  </Placemark>

I found a suggestion on StackOverflow which I believe only reads the first part of the KML

Here is the code and the output

import geopandas as gpd
gpd.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'

df = gpd.read_file(path_to_data+transac_file+'.kml', driver='KML')

the output is as follows:

   Name Description                                           geometry
0     93              MULTIPOLYGON (((-7.64047 33.58798, -7.64067 33...
1     99              MULTIPOLYGON (((-7.64047 33.58798, -7.64067 33...
2     81              MULTIPOLYGON (((-7.64047 33.58798, -7.64067 33...
3     87              MULTIPOLYGON (((-7.64047 33.58798, -7.64067 33...
4      9              MULTIPOLYGON (((-7.64047 33.58798, -7.64067 33..

df.columns

[134 rows x 3 columns]
Index(['Name', 'Description', 'geometry'], dtype='object')

I can see from the output that the features have not been read.

I tried another way as follows:

gpd.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'    
fp=path_to_data+transac_file+'.kml'  
gdf_list = []
for layer in fiona.listlayers(fp):    
    gdf = gpd.read_file(fp, driver='KML', layer=layer)
    gdf_list.append(gdf)

gdf = gpd.GeoDataFrame(pd.concat(gdf_list, ignore_index=True))

But I am still getiing the same result as before.

How can I extract all features and put them into a dataframe?

Answer 1

This solved my problem. I can see all features in the dataframe.

I just changed the driver from KML to LIBKML

gpd.io.file.fiona.drvsupport.supported_drivers['LIBKML'] = 'rw'

fp=path_to_data+transac_file+'.kml'

gdf_list = []
for layer in fiona.listlayers(fp):    
    gdf = gpd.read_file(fp, driver='LIBKML', layer=layer)
    gdf_list.append(gdf)

gdf = gpd.GeoDataFrame(pd.concat(gdf_list, ignore_index=True))

The output is:

    Name description timestamp begin end altitudeMode  tessellate  extrude  \
0     93        None       NaT   NaT NaT         None          -1        0   
1     99        None       NaT   NaT NaT         None          -1        0   
2     81        None       NaT   NaT NaT         None          -1        0   
3     87        None       NaT   NaT NaT         None          -1        0   
4      9        None       NaT   NaT NaT         None          -1        0   
..   ...         ...       ...   ...  ..          ...         ...      ...   
129   59        None       NaT   NaT NaT         None          -1        0   
130  117        None       NaT   NaT NaT         None          -1        0   
131  118        None       NaT   NaT NaT         None          -1        0   
132  108        None       NaT   NaT NaT         None          -1        0   
133  116        None       NaT   NaT NaT         None          -1        0