前言：这个小技巧前面白化的推文也使用过，专门拿出来写一篇
版本 python 3.9
代码语言：javascript复制import numpy as np
import geopandas as gpd
import matplotlib.pyplot as plt
# 读取全国地图数据
china_map = gpd.read_file('/home/mw/input/china1656/china_map/china_map/China_Province_2022.shp')
china_map省 | 省级码 | 省类型 | ENG_NAME | VAR_NAME | FIRST_GID | FIRST_TYPE | year | geometry | |
|---|---|---|---|---|---|---|---|---|---|
0 | 北京市 | 110000 | 直辖市 | Beijing | Běi Jīng | 110000 | Municipality | 2022 | POLYGON ((117.38335 40.22647, 117.38557 40.224... |
1 | 天津市 | 120000 | 直辖市 | Tianjin | Tiān Jīn | 120000 | Municipality | 2022 | POLYGON ((117.56937 40.19153, 117.56744 40.189... |
2 | 河北省 | 130000 | 省 | Hebei | Hé Běi | 130000 | Province | 2022 | MULTIPOLYGON (((118.26945 38.98097, 118.26871 ... |
3 | 山西省 | 140000 | 省 | Shanxi | Shān Xī | 140000 | Province | 2022 | POLYGON ((114.13714 40.73445, 114.13860 40.732... |
4 | 内蒙古自治区 | 150000 | 自治区 | Neimenggu | Nèi Měng Gǔ | 150000 | Autonomous Region | 2022 | POLYGON ((121.49813 53.32607, 121.50116 53.321... |
5 | 辽宁省 | 210000 | 省 | Liaoning | Liáo Níng | 210000 | Province | 2022 | MULTIPOLYGON (((121.03521 38.87021, 121.03528 ... |
6 | 吉林省 | 220000 | 省 | Jilin | Jí Lín | 220000 | Province | 2022 | POLYGON ((123.90309 46.29744, 123.90283 46.294... |
7 | 黑龙江省 | 230000 | 省 | Heilongjiang | Hēi Lóng Jiāng | 230000 | Province | 2022 | POLYGON ((123.40249 53.53506, 123.40471 53.535... |
8 | 上海市 | 310000 | 直辖市 | Shanghai | Shàng Hǎi | 310000 | Municipality | 2022 | MULTIPOLYGON (((121.87476 31.63516, 121.87542 ... |
9 | 浙江省 | 330000 | 省 | Zhejiang | Zhè Jiāng | 330000 | Province | 2022 | MULTIPOLYGON (((120.47933 27.15321, 120.48163 ... |
10 | 安徽省 | 340000 | 省 | Anhui | ān Huī | 340000 | Province | 2022 | POLYGON ((116.42485 34.65234, 116.43225 34.642... |
11 | 福建省 | 350000 | 省 | Fujian | Fú Jiàn | 350000 | Province | 2022 | MULTIPOLYGON (((117.29228 23.59563, 117.29206 ... |
12 | 江西省 | 360000 | 省 | Jiangxi | Jiāng Xī | 360000 | Province | 2022 | POLYGON ((116.68416 30.07160, 116.68576 30.070... |
13 | 山东省 | 370000 | 省 | Shandong | Shān Dōng | 370000 | Province | 2022 | MULTIPOLYGON (((119.92414 35.62384, 119.92294 ... |
14 | 河南省 | 410000 | 省 | Henan | Hé Nán | 410000 | Province | 2022 | MULTIPOLYGON (((111.02770 33.17911, 111.02767 ... |
15 | 湖北省 | 420000 | 省 | Hubei | Hú Běi | 420000 | Province | 2022 | MULTIPOLYGON (((113.12740 29.43223, 113.11645 ... |
16 | 湖南省 | 430000 | 省 | Hunan | Hú Nán | 430000 | Province | 2022 | MULTIPOLYGON (((109.47771 26.84005, 109.47793 ... |
17 | 广东省 | 440000 | 省 | Guangdong | Guǎng Dōng | 440000 | Province | 2022 | MULTIPOLYGON (((110.59023 20.37852, 110.59232 ... |
18 | 广西壮族自治区 | 450000 | 自治区 | Guangxi | Guǎng Xī | 450000 | Autonomous Region | 2022 | MULTIPOLYGON (((109.20674 20.91898, 109.20686 ... |
19 | 海南省 | 460000 | 省 | Hainan | Hǎi Nán | 460000 | Province | 2022 | MULTIPOLYGON (((112.04381 3.83812, 112.01370 3... |
20 | 重庆市 | 500000 | 直辖市 | Chongqing | Chóng Qìng | 500000 | Municipality | 2022 | POLYGON ((109.57960 31.72849, 109.58644 31.725... |
21 | 四川省 | 510000 | 省 | Sichuan | Sì Chuān | 510000 | Province | 2022 | POLYGON ((102.95840 34.27996, 102.95933 34.270... |
22 | 贵州省 | 520000 | 省 | Guizhou | Guì Zhōu | 520000 | Province | 2022 | MULTIPOLYGON (((105.09467 24.92520, 105.09458 ... |
23 | 云南省 | 530000 | 省 | Yunnan | Yún Nán | 530000 | Province | 2022 | POLYGON ((99.11276 29.21149, 99.11737 29.20723... |
24 | 西藏自治区 | 540000 | 自治区 | Xizang | Xī Zàng | 540000 | Autonomous Region | 2022 | POLYGON ((88.38821 36.47854, 88.38945 36.47845... |
25 | 陕西省 | 610000 | 省 | Shaanxi | Shǎn Xī | 610000 | Province | 2022 | POLYGON ((108.13454 36.57919, 108.13418 36.580... |
26 | 甘肃省 | 620000 | 省 | Gansu | Gān Sù | 620000 | Province | 2022 | POLYGON ((97.19051 42.76287, 97.23601 42.67222... |
27 | 青海省 | 630000 | 省 | Qinghai | Qīng Hǎi | 630000 | Province | 2022 | POLYGON ((100.91694 38.17344, 100.91780 38.173... |
28 | 宁夏回族自治区 | 640000 | 自治区 | Ningxia | Níng Xià Huí Zú | 640000 | Autonomous Region | 2022 | MULTIPOLYGON (((106.06218 35.43728, 106.06239 ... |
29 | 新疆维吾尔自治区 | 650000 | 自治区 | Xinjiang | Xīn Jiāng | 650000 | Autonomous Region | 2022 | POLYGON ((87.79720 49.18060, 87.81916 49.17268... |
30 | 台湾省 | 710000 | 省 | Taiwan | Tái Wān | 710000 | Province | 2022 | MULTIPOLYGON (((123.69793 25.92930, 123.69726 ... |
31 | 香港特别行政区 | 810000 | 特别行政区 | HongKong | Hong Kong | 810000 | Special District | 2022 | MULTIPOLYGON (((114.22665 22.54375, 114.22661 ... |
32 | 澳门特别行政区 | 820000 | 特别行政区 | Aomen | ào Mén | 820000 | Special District | 2022 | MULTIPOLYGON (((113.55346 22.21547, 113.55374 ... |
33 | 江苏省 | 320000 | 省 | Jiangsu | Jiāng Sū | 320000 | Province | 2022 | MULTIPOLYGON (((121.56617 32.22928, 121.56693 ... |
# 获取行政区域数据
tj = china_map[china_map['省'] == '天津市']
# 保存裁剪后的tjshp文件
tj.to_file('/home/mw/project/tj.shp')---------------------------------------------------------------------------
CPLE_AppDefinedError Traceback (most recent call last)
fiona/ogrext.pyx in fiona.ogrext.WritingSession.start()
fiona/_err.pyx in fiona._err.exc_wrap_int()
CPLE_AppDefinedError: Failed to create field name '省': cannot convert to ISO-8859-1
During handling of the above exception, another exception occurred:
SchemaError Traceback (most recent call last)
<ipython-input-10-634bcee8b14b> in <module>
2 tj = china_map[china_map['省'] == '天津市']
3 # 保存裁剪后的tjshp文件
----> 4 tj.to_file('/home/mw/project/tj.shp')
/opt/conda/lib/python3.9/site-packages/geopandas/geodataframe.py in to_file(self, filename, driver, schema, index, **kwargs)
1112 from geopandas.io.file import _to_file
1113
-> 1114 _to_file(self, filename, driver, schema, index, **kwargs)
1115
1116 def set_crs(self, crs=None, epsg=None, inplace=False, allow_override=False):
/opt/conda/lib/python3.9/site-packages/geopandas/io/file.py in _to_file(df, filename, driver, schema, index, mode, crs, **kwargs)
391 elif crs:
392 crs_wkt = crs.to_wkt("WKT1_GDAL")
--> 393 with fiona.open(
394 filename, mode=mode, driver=driver, crs_wkt=crs_wkt, schema=schema, **kwargs
395 ) as colxn:
/opt/conda/lib/python3.9/site-packages/fiona/env.py in wrapper(*args, **kwargs)
406 def wrapper(*args, **kwargs):
407 if local._env:
--> 408 return f(*args, **kwargs)
409 else:
410 if isinstance(args[0], str):
/opt/conda/lib/python3.9/site-packages/fiona/__init__.py in open(fp, mode, driver, schema, crs, encoding, layer, vfs, enabled_drivers, crs_wkt, **kwargs)
270 else:
271 this_schema = None
--> 272 c = Collection(path, mode, crs=crs, driver=driver, schema=this_schema,
273 encoding=encoding, layer=layer, enabled_drivers=enabled_drivers, crs_wkt=crs_wkt,
274 **kwargs)
/opt/conda/lib/python3.9/site-packages/fiona/collection.py in __init__(self, path, mode, driver, schema, crs, encoding, layer, vsi, archive, enabled_drivers, crs_wkt, ignore_fields, ignore_geometry, **kwargs)
163 elif self.mode in ('a', 'w'):
164 self.session = WritingSession()
--> 165 self.session.start(self, **kwargs)
166 except IOError:
167 self.session = None
fiona/ogrext.pyx in fiona.ogrext.WritingSession.start()
SchemaError: Failed to create field name '省': cannot convert to ISO-8859-1报错原因是由于Fiona库在写入shapefile文件时，遇到了无法转换为ISO-8859-1编码的字符。Shapefile的标准不支持UTF-8编码，因此在处理包含非ISO-8859-1字符（例如中文）的字段时，可能会出现问题。 下面介绍两种方法
方法一：改变列名
代码语言：javascript复制tj1 = tj.rename(columns={'省': 'Province','省级码':'code','省类型':'type'})
tj1.to_file('/home/mw/project/tj1.shp')shp = gpd.read_file('/home/mw/project/tj1.shp',crs=None)
shp.plot()
plt.show()
好像一只烧鸭
方法二：存取为支持UTF-8编码的GeoJSON格式
代码语言：javascript复制# 获取行政区域数据
tj = china_map[china_map['省'] == '天津市']
tj2.to_file('/home/mw/project/tj2.geojson', driver='GeoJSON')
/opt/conda/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
pd.Int64Index,
# 读取GeoJSON文件
tj2 = gpd.read_file('/home/mw/project/tj2.geojson')
# 绘制GeoDataFrame
tj2.plot()
plt.show()烧鸭二号完成。可喜可贺。
