Extract, modify, and write attribute data in vector datasets¶

Vector datasets combine geometries with attribute data stored in a table-like structure. This example shows how to extract, insert, and modify attribute data in vector datasets to prepare them for further analysis. For instance, it calculates the area of all federal states in Germany and adds a random levelized cost of electricity to each state.

Import required packages

GeoKit is imported to provide the required functionality for working with vector data.

In [1]:

import geokit.core.vector
import geokit.core.geom
import geokit.core.srs
import numpy as np
import pandas as pd
import pathlib
from geokit.core.get_test_data import get_test_data

import geokit.core.vector import geokit.core.geom import geokit.core.srs import numpy as np import pandas as pd import pathlib from geokit.core.get_test_data import get_test_data

In [2]:

# Path to shapefile folder
path_to_shape_file = get_test_data(
    file_name="gadm36_DEU_1.shp",
)

# Load vector dataset from a Shapefile as a pandas DataFrame and reproject to EPSG:3035
data_frame_germany: pd.DataFrame = geokit.core.vector.extractFeatures(path_to_shape_file, srs=3035)

# Path to shapefile folder path_to_shape_file = get_test_data( file_name="gadm36_DEU_1.shp", ) # Load vector dataset from a Shapefile as a pandas DataFrame and reproject to EPSG:3035 data_frame_germany: pd.DataFrame = geokit.core.vector.extractFeatures(path_to_shape_file, srs=3035)

Add attributes to the vector dataset

In [3]:

# Add average LCOE column with random values

# Generate random LCOE values for each row
n_rows = data_frame_germany.loc[:, "geom"].shape[0]
lcoe_values = np.random.uniform(low=30.0, high=80.0, size=n_rows)

# Add average LCOE column with random values # Generate random LCOE values for each row n_rows = data_frame_germany.loc[:, "geom"].shape[0] lcoe_values = np.random.uniform(low=30.0, high=80.0, size=n_rows)

In [4]:

# Calculate the size of each geometry (area)
areas_m2 = data_frame_germany.loc[:, "geom"].apply(lambda g: g.GetArea())

# Add new column (LCOE and area)to the vector dataset
data_frame_germany.loc[:, "avg_LCOE"] = pd.Series(lcoe_values)
data_frame_germany.loc[:, "area_km2"] = pd.Series(areas_m2 / 1e6)

# Show updated dataset
data_frame_germany

# Calculate the size of each geometry (area) areas_m2 = data_frame_germany.loc[:, "geom"].apply(lambda g: g.GetArea()) # Add new column (LCOE and area)to the vector dataset data_frame_germany.loc[:, "avg_LCOE"] = pd.Series(lcoe_values) data_frame_germany.loc[:, "area_km2"] = pd.Series(areas_m2 / 1e6) # Show updated dataset data_frame_germany

Out[4]:

	geom	GID_0	NAME_0	GID_1	NAME_1	VARNAME_1	NL_NAME_1	TYPE_1	ENGTYPE_1	CC_1	HASC_1	avg_LCOE	area_km2
0	MULTIPOLYGON (((4223953.58970637 2731509.51891...	DEU	Germany	DEU.1_1	Baden-Württemberg	None	None	Land	State	08	DE.BW	50.405873	36074.255068
1	POLYGON ((4301467.52857983 2715580.87627153,43...	DEU	Germany	DEU.2_1	Bayern	Bavaria	None	Freistaat	None	09	DE.BY	53.105215	70544.892566
2	POLYGON ((4536790.77005341 3258960.86848328,45...	DEU	Germany	DEU.3_1	Berlin	None	None	Land	State	11	DE.BE	47.308120	891.862007
3	MULTIPOLYGON (((4475872.02032226 3238148.49116...	DEU	Germany	DEU.4_1	Brandenburg	None	None	Land	State	12	DE.BR	64.756372	29654.223234
4	MULTIPOLYGON (((4234577.75904604 3327007.62237...	DEU	Germany	DEU.5_1	Bremen	None	None	Freie Hansestadt	State	04	DE.HB	76.230227	399.805154
5	MULTIPOLYGON (((4334970.2892255 3379166.259019...	DEU	Germany	DEU.6_1	Hamburg	None	None	Freie und Hansestadt	State	02	DE.HH	55.083314	736.380193
6	MULTIPOLYGON (((4225654.7277815 2946381.549486...	DEU	Germany	DEU.7_1	Hessen	Hesse	None	Land	State	06	DE.HE	54.703907	21115.145261
7	MULTIPOLYGON (((4419026.73328481 3437109.38907...	DEU	Germany	DEU.8_1	Mecklenburg-Vorpommern	Mecklenburg-West Pomerania	None	Land	State	13	DE.MV	42.634180	23376.544328
8	MULTIPOLYGON (((4106781.29483516 3394915.76735...	DEU	Germany	DEU.9_1	Niedersachsen	Lower Saxony	None	Land	State	03	DE.NI	42.152628	47672.270673
9	POLYGON ((4183914.44196412 3066634.81647491,41...	DEU	Germany	DEU.10_1	Nordrhein-Westfalen	North Rhine-Westphalia	None	Land	State	05	DE.NW	59.164844	34115.675424
10	POLYGON ((4078665.76666303 2942934.90358417,40...	DEU	Germany	DEU.11_1	Rheinland-Pfalz	Rhineland-Palatinate	None	Land	State	07	DE.RP	31.529621	19856.379920
11	POLYGON ((4090806.83436472 2898538.79416255,40...	DEU	Germany	DEU.12_1	Saarland	None	None	Land	State	10	DE.SL	34.020833	2571.007667
12	POLYGON ((4658284.00379539 3090217.58617005,46...	DEU	Germany	DEU.14_1	Sachsen	Saxony	None	Freistaat	State	14	DE.SN	51.328672	18448.852315
13	MULTIPOLYGON (((4421662.49154591 3121957.38272...	DEU	Germany	DEU.13_1	Sachsen-Anhalt	Saxony-Anhalt	None	Land	State	15	DE.ST	68.976334	20552.378984
14	MULTIPOLYGON (((4289942.76376931 3396511.97997...	DEU	Germany	DEU.15_1	Schleswig-Holstein	None	None	Land	State	01	DE.SH	66.342641	15628.669632
15	POLYGON ((4442016.26070027 3033676.79953545,44...	DEU	Germany	DEU.16_1	Thüringen	Thuringia	None	Freistaat	State	16	DE.TH	64.359434	16201.007412

Inspect attributes

In [5]:

data_frame_germany[["avg_LCOE", "area_km2"]].describe()

data_frame_germany[["avg_LCOE", "area_km2"]].describe()

Out[5]:

	avg_LCOE	area_km2
count	16.000000	16.000000
mean	53.881389	22364.959365
std	12.546485	18717.109604
min	31.529621	399.805154
25%	46.139635	12364.254140
50%	53.904561	20204.379452
75%	64.458668	30769.586282
max	76.230227	70544.892566

Filter attributes

In [6]:

# Select regions with low average LCOE
low_cost_regions = data_frame_germany[data_frame_germany["avg_LCOE"] < 50]

# Show selected regions
print("Regions with low average LCOE (< 50):")
print(low_cost_regions["NAME_1"])


# Select regions higher than average area size
average_area = data_frame_germany["area_km2"].mean()

large_regions = data_frame_germany[data_frame_germany["area_km2"] > average_area]

# Show selected regions
print("")
print("Regions with area larger than average:")
print(large_regions["NAME_1"])

# Select regions with low average LCOE low_cost_regions = data_frame_germany[data_frame_germany["avg_LCOE"] < 50] # Show selected regions print("Regions with low average LCOE (< 50):") print(low_cost_regions["NAME_1"]) # Select regions higher than average area size average_area = data_frame_germany["area_km2"].mean() large_regions = data_frame_germany[data_frame_germany["area_km2"] > average_area] # Show selected regions print("") print("Regions with area larger than average:") print(large_regions["NAME_1"])

Regions with low average LCOE (< 50):
2                     Berlin
7     Mecklenburg-Vorpommern
8              Niedersachsen
10           Rheinland-Pfalz
11                  Saarland
Name: NAME_1, dtype: object

Regions with area larger than average:
0         Baden-Württemberg
1                    Bayern
3               Brandenburg
7    Mecklenburg-Vorpommern
8             Niedersachsen
9       Nordrhein-Westfalen
Name: NAME_1, dtype: object

In [7]:

# Add average LCOE column with random values

# Generate random LCOE values for each row
n_rows = data_frame_germany.geom.shape[0]
lcoe_values = np.random.uniform(low=30.0, high=80.0, size=n_rows)

# Calculate the size of each geometry (area)
areas_m2 = data_frame_germany.geom.apply(lambda g: g.GetArea())

# Add new column (LCOE and area)to the vector dataset
data_frame_germany["avg_LCOE"] = pd.Series(lcoe_values)
data_frame_germany["area_km2"] = pd.Series(areas_m2 / 1e6)

# Show updated dataset
data_frame_germany

# Add average LCOE column with random values # Generate random LCOE values for each row n_rows = data_frame_germany.geom.shape[0] lcoe_values = np.random.uniform(low=30.0, high=80.0, size=n_rows) # Calculate the size of each geometry (area) areas_m2 = data_frame_germany.geom.apply(lambda g: g.GetArea()) # Add new column (LCOE and area)to the vector dataset data_frame_germany["avg_LCOE"] = pd.Series(lcoe_values) data_frame_germany["area_km2"] = pd.Series(areas_m2 / 1e6) # Show updated dataset data_frame_germany

Out[7]:

	geom	GID_0	NAME_0	GID_1	NAME_1	VARNAME_1	NL_NAME_1	TYPE_1	ENGTYPE_1	CC_1	HASC_1	avg_LCOE	area_km2
0	MULTIPOLYGON (((4223953.58970637 2731509.51891...	DEU	Germany	DEU.1_1	Baden-Württemberg	None	None	Land	State	08	DE.BW	48.977513	36074.255068
1	POLYGON ((4301467.52857983 2715580.87627153,43...	DEU	Germany	DEU.2_1	Bayern	Bavaria	None	Freistaat	None	09	DE.BY	64.640346	70544.892566
2	POLYGON ((4536790.77005341 3258960.86848328,45...	DEU	Germany	DEU.3_1	Berlin	None	None	Land	State	11	DE.BE	38.102770	891.862007
3	MULTIPOLYGON (((4475872.02032226 3238148.49116...	DEU	Germany	DEU.4_1	Brandenburg	None	None	Land	State	12	DE.BR	71.825262	29654.223234
4	MULTIPOLYGON (((4234577.75904604 3327007.62237...	DEU	Germany	DEU.5_1	Bremen	None	None	Freie Hansestadt	State	04	DE.HB	72.808301	399.805154
5	MULTIPOLYGON (((4334970.2892255 3379166.259019...	DEU	Germany	DEU.6_1	Hamburg	None	None	Freie und Hansestadt	State	02	DE.HH	66.691763	736.380193
6	MULTIPOLYGON (((4225654.7277815 2946381.549486...	DEU	Germany	DEU.7_1	Hessen	Hesse	None	Land	State	06	DE.HE	56.282231	21115.145261
7	MULTIPOLYGON (((4419026.73328481 3437109.38907...	DEU	Germany	DEU.8_1	Mecklenburg-Vorpommern	Mecklenburg-West Pomerania	None	Land	State	13	DE.MV	40.142658	23376.544328
8	MULTIPOLYGON (((4106781.29483516 3394915.76735...	DEU	Germany	DEU.9_1	Niedersachsen	Lower Saxony	None	Land	State	03	DE.NI	79.209917	47672.270673
9	POLYGON ((4183914.44196412 3066634.81647491,41...	DEU	Germany	DEU.10_1	Nordrhein-Westfalen	North Rhine-Westphalia	None	Land	State	05	DE.NW	59.416601	34115.675424
10	POLYGON ((4078665.76666303 2942934.90358417,40...	DEU	Germany	DEU.11_1	Rheinland-Pfalz	Rhineland-Palatinate	None	Land	State	07	DE.RP	40.169873	19856.379920
11	POLYGON ((4090806.83436472 2898538.79416255,40...	DEU	Germany	DEU.12_1	Saarland	None	None	Land	State	10	DE.SL	42.997241	2571.007667
12	POLYGON ((4658284.00379539 3090217.58617005,46...	DEU	Germany	DEU.14_1	Sachsen	Saxony	None	Freistaat	State	14	DE.SN	33.709525	18448.852315
13	MULTIPOLYGON (((4421662.49154591 3121957.38272...	DEU	Germany	DEU.13_1	Sachsen-Anhalt	Saxony-Anhalt	None	Land	State	15	DE.ST	39.990045	20552.378984
14	MULTIPOLYGON (((4289942.76376931 3396511.97997...	DEU	Germany	DEU.15_1	Schleswig-Holstein	None	None	Land	State	01	DE.SH	44.795939	15628.669632
15	POLYGON ((4442016.26070027 3033676.79953545,44...	DEU	Germany	DEU.16_1	Thüringen	Thuringia	None	Freistaat	State	16	DE.TH	49.970084	16201.007412

Visualizing vector attributes¶

In [8]:

# colored by LCOE
import matplotlib.pyplot as plt

# Create two subplots for side-by-side comparison
fig, ax = plt.subplots(ncols=2, figsize=(10, 5))
ax_handle_lcoe = geokit.core.geom.drawGeoms(
    data_frame_germany,
    colorBy="avg_LCOE",
    srs=3035,
    figsize=(6, 4),
    cbarTitle="Average LCOE [€/MWh]",
    ax=ax[0],
)

# colored by area Size

ax_handle_area = geokit.core.geom.drawGeoms(
    data_frame_germany,
    colorBy="area_km2",
    srs=3035,
    figsize=(6, 4),
    cbarTitle="Area Size [km²]",
    ax=ax[1],
)

# colored by LCOE import matplotlib.pyplot as plt # Create two subplots for side-by-side comparison fig, ax = plt.subplots(ncols=2, figsize=(10, 5)) ax_handle_lcoe = geokit.core.geom.drawGeoms( data_frame_germany, colorBy="avg_LCOE", srs=3035, figsize=(6, 4), cbarTitle="Average LCOE [€/MWh]", ax=ax[0], ) # colored by area Size ax_handle_area = geokit.core.geom.drawGeoms( data_frame_germany, colorBy="area_km2", srs=3035, figsize=(6, 4), cbarTitle="Area Size [km²]", ax=ax[1], )

Store Vector File¶

Lastly, you can save the new vector file as a shapefile.

In [9]:

# Simple vector creation
geokit.core.vector.createVector(geoms=data_frame_germany, output="updated_vector_file.shp")

# Simple vector creation geokit.core.vector.createVector(geoms=data_frame_germany, output="updated_vector_file.shp")

Out[9]:

'/home/docs/checkouts/readthedocs.org/user_builds/geokit/checkouts/latest/docs/Examples/_02_vector/updated_vector_file.shp'