使用 GeoPandas 将坐标列表转换为多边形

Question

I got lists of coordinates in the csv file (please click the pic). How should I convert them to polygons in GeoDataFrame?

Below is the coordinates of one polygon and I have thousands rows of this.

[118.103198,24.527338],[118.103224,24.527373],[118.103236,24.527366],[118.103209,24.527331],[118.103198,24.527338]

I tried the following codes:

def bike_fence_format(s):
    s = s.replace('[', '').replace(']', '').split(',')
    return s

df['FENCE_LOC'] = df['FENCE_LOC'].apply(bike_fence_format)

df['LAT'] = df['FENCE_LOC'].apply(lambda x: x[1::2])
df['LON'] = df['FENCE_LOC'].apply(lambda x: x[::2])

df['geom'] = Polygon(zip(df['LON'].astype(str),df['LAT'].astype(str)))

But I failed in the last step, since df['LON'] returns 'series' not 'string' type. How should I get over this problem? It's better if there is an easier way to achieve my goal.

Answer 1

Recreated a sample df of what your.csv file would give (depending on how your read it in with.read_csv()).

import pandas as pd
import geopandas as gpd

df = pd.DataFrame({'FENCE_LOC': ['[32250,175889],[33913,180757],[29909,182124],[28246,177257],[32250,175889]', 
                  '[32250,175889],[33913,180757],[29909,182124],[28246,177257],[32250,175889]', 
                  '[32250,175889],[33913,180757],[29909,182124],[28246,177257],[32250,175889]']}, index=[0, 1, 2])

Modified your function slightly because we want numeric values, not strings

def bike_fence_format(s):
    s = s.replace('[', '').replace(']', '').split(',')
    s = [float(x) for x in s]

    return s

df['FENCE_LOC'] = df['FENCE_LOC'].apply(bike_fence_format)


df['LAT'] = df['FENCE_LOC'].apply(lambda x: x[1::2])
df['LON'] = df['FENCE_LOC'].apply(lambda x: x[::2])

We can use some list comprehensions to build a list of Shapely polygons.

geom_list = [(x, y) for x, y  in zip(df['LON'],df['LAT'])]

geom_list_2 = [Polygon(tuple(zip(x, y))) for x, y in geom_list]

Finally, we can create a gdf using our list of Shapely polygons.

polygon_gdf =  gpd.GeoDataFrame(geometry=geom_list_2)

Answer 2

To make available a small representative dataset similar to what the OP posts as an image, I create this rows of data (sorry for too many decimal digits):

[[-2247824.100899419,-4996167.43201861],[-2247824.100899419,-4996067.43201861],[-2247724.100899419,-4996067.43201861],[-2247724.100899419,-4996167.43201861],[-2247824.100899419,-4996167.43201861]]
[[-2247724.100899419,-4996167.43201861],[-2247724.100899419,-4996067.43201861],[-2247624.100899419,-4996067.43201861],[-2247624.100899419,-4996167.43201861],[-2247724.100899419,-4996167.43201861]]
[[-2247624.100899419,-4996167.43201861],[-2247624.100899419,-4996067.43201861],[-2247524.100899419,-4996067.43201861],[-2247524.100899419,-4996167.43201861],[-2247624.100899419,-4996167.43201861]]
[[-2247824.100899419,-4996067.43201861],[-2247824.100899419,-4995967.43201861],[-2247724.100899419,-4995967.43201861],[-2247724.100899419,-4996067.43201861],[-2247824.100899419,-4996067.43201861]]
[[-2247724.100899419,-4996067.43201861],[-2247724.100899419,-4995967.43201861],[-2247624.100899419,-4995967.43201861],[-2247624.100899419,-4996067.43201861],[-2247724.100899419,-4996067.43201861]]
[[-2247624.100899419,-4996067.43201861],[-2247624.100899419,-4995967.43201861],[-2247524.100899419,-4995967.43201861],[-2247524.100899419,-4996067.43201861],[-2247624.100899419,-4996067.43201861]]
[[-2247824.100899419,-4995967.43201861],[-2247824.100899419,-4995867.43201861],[-2247724.100899419,-4995867.43201861],[-2247724.100899419,-4995967.43201861],[-2247824.100899419,-4995967.43201861]]
[[-2247724.100899419,-4995967.43201861],[-2247724.100899419,-4995867.43201861],[-2247624.100899419,-4995867.43201861],[-2247624.100899419,-4995967.43201861],[-2247724.100899419,-4995967.43201861]]
[[-2247624.100899419,-4995967.43201861],[-2247624.100899419,-4995867.43201861],[-2247524.100899419,-4995867.43201861],[-2247524.100899419,-4995967.43201861],[-2247624.100899419,-4995967.43201861]]

This data is saved as polygon_data.csv file.

For the code, modules are loaded first as

import geopandas as gpd
import pandas as pd
from shapely.geometry import Polygon

Then, the data is read to create a dataframe by pandas.read_csv() . To get each row of data into a single column of the dataframe, delimiter="x" is used. Since there is no x within any row of data, the whole row of data as a long string is the result.

df3 = pd.read_csv('polygon_data.csv', header=None, index_col=None, delimiter="x")

To view the content of df3 , you can run

df3.head()

and get single column (with header: 0) dataframe:

                                                   0
0  [[-2247824.100899419,-4996167.43201861],[-2247...
1  [[-2247724.100899419,-4996167.43201861],[-2247...
2  [[-2247624.100899419,-4996167.43201861],[-2247...
3  [[-2247824.100899419,-4996067.43201861],[-2247...
4  [[-2247724.100899419,-4996067.43201861],[-2247...

Next, df3 is used to create a geoDataFrame. Data in each row of df3 is used to create a Polygon object to act as the geometry of the geoDataFrame polygon_df3 .

geometry = [Polygon(eval(xy_string)) for xy_string in df3[0]]
polygon_df3 = gpd.GeoDataFrame(df3, \
                   #crs={'init': 'epsg:4326'}, #uncomment this if (x,y) is long/lat
                   geometry=geometry)

Finally, the geoDataFrame can be plotted with a simple command:

# this plot the geoDataFrame
polygon_df3.plot(edgecolor='black')

In this particular case with my proposed data, the output plot is: