當我嘗試將 csv 檔案作為地理資料框上傳時收到此錯誤。根據此站點上的其他問題解決方案，此方法應該可以解決問題。
這是我用來執行的代碼：將檔案作為 gdf ??上傳，然后生成一個子集資料框，其中僅存在一些列。
cp_union = gpd.read_file(r'C:\Users\User\Desktop\CPAWS\terrestrial_outputs\cp_union.csv')
cp_union.crs = 'epsg:3005'
cp_trimmed = cp_union[['COSEWIC_status','reason_for_designation','cnm_eng','iucn_cat','mgmt_e','status_e','classification','sq_m']]
如標題中所述，我收到的錯誤是：ValueError: GeoDataFrame does not support multiple columns using the geometry column name 'geometry'.將 gdf ??保存為 csv 然后將其重新加載為 gdf ??的程序中是否存在會導致創建額外幾何列的部分？
編輯
在另一個腳本中，我加載了與 pd 資料框相同的 csv 檔案。這是該 pd 資料框中的第一行資料。
Unnamed: 0 0
fid_critic 0
scntfc_nm Castilleja victoriae
cnm_eng Victoria's Owl-clover
cnm_fren Castilléjie de Victoria
cswc_pop NaN
ch_stat Final
cb_site_nm Cattle Point
ch_detail Detailed Polygon
ch_variant NaN
ch_method NaN
hectares 0.8559
utm_zone 10
utm_east 478383
utm_north 5365043
latitude 48.438164
longitude -123.29226
shape_1 0.0
objectid 10251681.0
area_sqm 8478.6733
feat_len 326.5008
fid_protec -1
name_e NaN
name_f NaN
aichi_t11 NaN
iucn_cat NaN
oecm NaN
o_area 0.0
loc_e NaN
loc_f NaN
type_e NaN
mgmt_e NaN
gov_type NaN
legisl_e NaN
status_e NaN
protdate 0
delisdate 0
owner_e NaN
owner_f NaN
subs_right NaN
comments NaN
url NaN
shape_leng 0.0
protected 0
shape_le_1 320.859687
shape_area 6499.790343
geometry POLYGON ((1200735.4438 384059.0133999996, 1200...
COSEWIC_status Endangered
reason_for_designation This small annual herb is confined to a very s...
sq_m 6499.790343
classification c
Name: 0, dtype: object
所以我在這里唯一的理論是，當您將 gdf ??保存為 csv 時，csv 包含一個稱為幾何的列。然后，當您將該 csv 作為 gdf ??加載時，geopandas 會嘗試在 csv 中已經存在的幾何列之上創建一個新的幾何列。我可能完全錯了。即使是這種情況，我也不知道如何解決這個問題。
謝謝您的幫助！
uj5u.com熱心網友回復：
- 使用您的示例資料創建 CSV。必須替換幾何，因為樣本不是有效的 WKT 字串
- 重新產生你的錯誤
- 通過使用pandas加載然后轉換為geopandas來解決
解決方案
df = pd.read_csv(f)
cp_union = gpd.GeoDataFrame(
df.loc[:, [c for c in df.columns if c != "geometry"]],
geometry=gpd.GeoSeries.from_wkt(df["geometry"]),
crs="epsg:3005",
)
完整代碼
import pandas as pd
import geopandas as gpd
import io
from pathlib import Path
# fmt: off
df_q = pd.read_csv(io.StringIO("""Unnamed: 0 0
fid_critic 0
scntfc_nm Castilleja victoriae
cnm_eng Victoria's Owl-clover
cnm_fren Castilléjie de Victoria
cswc_pop NaN
ch_stat Final
cb_site_nm Cattle Point
ch_detail Detailed Polygon
ch_variant NaN
ch_method NaN
hectares 0.8559
utm_zone 10
utm_east 478383
utm_north 5365043
latitude 48.438164
longitude -123.29226
shape_1 0.0
objectid 10251681.0
area_sqm 8478.6733
feat_len 326.5008
fid_protec -1
name_e NaN
name_f NaN
aichi_t11 NaN
iucn_cat NaN
oecm NaN
o_area 0.0
loc_e NaN
loc_f NaN
type_e NaN
mgmt_e NaN
gov_type NaN
legisl_e NaN
status_e NaN
protdate 0
delisdate 0
owner_e NaN
owner_f NaN
subs_right NaN
comments NaN
url NaN
shape_leng 0.0
protected 0
shape_le_1 320.859687
shape_area 6499.790343
geometry POLYGON ((5769135.557632876 7083849.386658552, 5843426.213336911 7098018.122146672, 5852821.812968816 7081377.7312996285, 5914814.478616157 7091734.620966213, 5883751.009067913 7017032.330573363, 5902031.719573214 6983898.953064103, 5864452.659165712 6922039.030140929, 5829585.402576889 6878872.269967912, 5835906.522449658 6846685.714836724, 5800391.382286092 6827305.509709548, 5765261.646424723 6876008.057438379, 5765261.402301509 6876010.894933639, 5765264.431247815 6876008.786040769, 5760553.056402712 6927522.42488809, 5720896.599172597 6983360.181762057, 5755349.303491102 7039380.015177476, 5769135.557632876 7083849.386658552))
COSEWIC_status Endangered
reason_for_designation This small annual herb is confined to a very s...
sq_m 6499.790343
classification c"""), sep="\s\s ", engine="python", header=None).set_index(0).T
# fmt: on
# generate a CSV file from sample data
f = Path.cwd().joinpath("SO_q.csv")
df_q.to_csv(f, index=False)
# replicate issue...
try:
gpd.read_file(f)
except ValueError as e:
print(e)
# now the actual solution
df = pd.read_csv(f)
cp_union = gpd.GeoDataFrame(
df.loc[:, [c for c in df.columns if c != "geometry"]],
geometry=gpd.GeoSeries.from_wkt(df["geometry"]),
crs="epsg:3005",
)
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