Filtering GeoDataFrame rows with list of strings in GeoPandas

Question

I want to filter GeoPandas GeoDataFrame rows by a list containing 4 'matches';
list_of_names = ['BE36_1000_1349', 'BE36_1000_1350', 'BE36_1000_1449', 'BE36_1000_1450'].

I want to return four complete rows that have the index_tile column matching the 4 elements in the above list.

print (topo50_df.filter(like=list_of_names))
--------------------------------------------
TypeError: 'in <string>' requires string as left operand, not list

print (topo50_df.query(str(list_of_names))) 
--------------------------------------------
KeyError(f"None of [{key}] are in the [{axis_name}]") KeyError: "None of 
[Index(['BE36_1000_1349', 'BE36_1000_1350', 'BE36_1000_1449', 
'BE36_1000_1450'], dtype='object')] are in the [index]"

import geopandas as gpd
from shapely.geometry import Polygon

imagery_bb_polygon_df = gpd.GeoDataFrame(index=[0], crs=("epsg:2193"), geometry=[Polygon(list_of_points)])
topo50_df = gpd.read_file(topo50_geometry_file)

intersection_matches = topo50_df.overlay(imagery_bb_polygon_df, how='intersection', keep_geom_type=False)
list_of_names = list(intersection_matches.loc[1:4, 'index_tile'])

My GeoDataFrame named topo50_ref looks like this:

                index_tile sheet_code  scale  tile                                           geometry
210648  BE36_1000_1349       BE36   1000  1349  POLYGON ((1875520.000 5792640.000, 1875040.000...
210649  BE36_1000_1350       BE36   1000  1350  POLYGON ((1876000.000 5792640.000, 1875520.000...
210698  BE36_1000_1449       BE36   1000  1449  POLYGON ((1875520.000 5791920.000, 1875040.000...
210699  BE36_1000_1450       BE36   1000  1450  POLYGON ((1876000.000 5791920.000, 1875520.000...
<class 'geopandas.geodataframe.GeoDataFrame'>

Answer 1

Use isin, see Use a list of values to select rows from a Pandas dataframe:

import geopandas as gpd

list_of_names = ['2262', '2361', '2161', '2039', '2132']

df = gpd.read_file(r"C:\GIS\data\testdata\ak_riks_lines.shp")
#df.shape
#Out: (294, 16)

fieldname = 'KOM_KOD'

df2 = df[df[fieldname].isin(list_of_names)]
#df2.shape
#(5, 16)

Answer 2

Another approach utilizes the .query(), that was also mentioned in the Use a list of values to select rows from a Pandas dataframe.

import geopandas as gpd

list_of_names = ['Type 1', 'Type 3']

_layer = "C:/Documents/Python Scripts/geopandas/layer.shp"

layer = gpd.read_file(_layer)
print(layer)

   fid    Type                                           geometry
0  1.0  Type 1  POLYGON ((413827.594 5289710.674, 610390.807 5...
1  2.0  Type 2  POLYGON ((1118850.852 5483173.520, 1415245.854...
2  3.0  Type 3  POLYGON ((413827.594 5289710.674, 613697.864 4...

layer_filtered = layer.query('Type in @list_of_names')
print(layer_filtered)

   fid    Type                                           geometry
0  1.0  Type 1  POLYGON ((413827.594 5289710.674, 610390.807 5...
2  3.0  Type 3  POLYGON ((413827.594 5289710.674, 613697.864 4...

As mentioned in Indexing and selecting data | Performance of query():

DataFrame.query() using numexpr is slightly faster than Python for large frames.

---

Why are you getting that error? Because you are missing 'index_tile in ..., the vital component for the .query().

If I do the same i.e. layer.query(str('@list_of_values')) I will get exactly your error:

    raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Type 1', 'Type 3'], dtype='object')] are in the [index]"

So, you need to apply a tiny change:

layer_filtered = layer.query('index_tile in @list_of_names')
print(layer_filtered)

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References:

• pandas: Why query() instead of bracket operator?
• How to filter Pandas dataframe using 'in' and 'not in' like in SQL