ValueError: GeoDataFrame does not support multiple columns using the geometry column name 'geometry'
ValueError: GeoDataFrame does not support multiple columns using the geometry column name 'geometry'
当我尝试将 csv 文件作为 geodataframe.According 上传到此站点上的其他问题解决方案时，我收到此错误，此方法应该可以解决问题。
这是我用来执行以下操作的代码：将文件上传为 gdf，然后生成仅包含部分列的子集数据框。
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']]
如标题所述，我收到的错误是：ValueError: GeoDataFrame does not support multiple columns using the geometry column name 'geometry'. 将 gdf 保存为 csv 然后将其重新加载为 gdf 的过程中是否存在某些部分会导致创建一个额外的几何列？
编辑
在另一个脚本中，我加载了与 pd 数据帧相同的 csv 文件。这是该 pd 数据帧中的第一行数据。
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
所以我这里唯一的理论是，当您将 gdf 保存为 csv 时，csv 包含一个名为 geometry 的列。然后，当您将该 csv 加载为 gdf 时，geopandas 会尝试在 csv 中已有的几何列之上创建一个新的几何列。我可能完全错了。即使是这样，我也不确定如何解决这个问题。
感谢您的帮助！
- 使用您的示例数据创建 CSV。必须替换 geometry 因为示例不是有效的 WKT 字符串
- re-produced 你的错误
- 通过使用 pandas 加载然后转换为 geopandas
解决
解决方案
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",
)
完整代码
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",
)
当我尝试将 csv 文件作为 geodataframe.According 上传到此站点上的其他问题解决方案时，我收到此错误，此方法应该可以解决问题。
这是我用来执行以下操作的代码：将文件上传为 gdf，然后生成仅包含部分列的子集数据框。
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']]
如标题所述，我收到的错误是：ValueError: GeoDataFrame does not support multiple columns using the geometry column name 'geometry'. 将 gdf 保存为 csv 然后将其重新加载为 gdf 的过程中是否存在某些部分会导致创建一个额外的几何列？
编辑
在另一个脚本中，我加载了与 pd 数据帧相同的 csv 文件。这是该 pd 数据帧中的第一行数据。
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
所以我这里唯一的理论是，当您将 gdf 保存为 csv 时，csv 包含一个名为 geometry 的列。然后，当您将该 csv 加载为 gdf 时，geopandas 会尝试在 csv 中已有的几何列之上创建一个新的几何列。我可能完全错了。即使是这样，我也不确定如何解决这个问题。
感谢您的帮助！
- 使用您的示例数据创建 CSV。必须替换 geometry 因为示例不是有效的 WKT 字符串
- re-produced 你的错误
- 通过使用 pandas 加载然后转换为 geopandas 解决
解决方案
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",
)
完整代码
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",
)