conda install -c conda-forge geopandas   

conda install -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge geopandas                   

import geopandas as gpdfrom matplotlib import pyplot as pltdata = gpd.read_file(r"E:gisData行政区划数据2019省.shp")#读取磁盘上的矢量文件#data = gpd.read_file("shapefile/china.gdb", layer="province")#读取gdb中的矢量数据print(data.crs)  # 查看数据对应的投影信息print(data.head())  # 查看前5行数据data.plot()plt.show()#简单展示

# 对应shapely.geometry中的Point，用于表示单个点，下面我们创建一个由若干Point对象组成cq = geopandas.GeoSeries([geometry.Point(110, 60),                          geometry.Point(110.5, 50.4),                          geometry.Point(120, 55),                          geometry.Point(107.8, 54.6),                          geometry.Point(114.6, 50)],                         crs="EPSG:4326",  # 指定坐标系为WGS 1984                         index=["一号", "二号", "三号", "四号", "五号"],  # 相关的索引                         )# 导出数据为shapefile文件cq.to_file("./output/{}.shp".format(os.path.basename(__file__).replace(".py", "")),           driver="ESRI Shapefile",           encoding="utf-8")

# 这里shapely.geometry.LineString([(x1, y1), (x2, y2), ...])用于创建多点按顺序连接而成的线段cq = geopandas.GeoSeries([geometry.LineString([(0, 0), (1, 1), (1, 0)]),                          geometry.LineString([(0.5, 2), (0, 1), (-1, 0)])],                         crs="EPSG:4326",                         index=["一号线", "b"])cq.to_file("./output/{}.shp".format(os.path.basename(__file__).replace(".py", "")),           driver="ESRI Shapefile",           encoding="utf-8")

# 对应shapely.geometry中的Polygon，用于表示面，下面我们创建一个由若干Polygon对象组成cq = geopandas.GeoSeries([geometry.Polygon([(14, 14), (13, 18), (20, 11), (18, 10)]),                          geometry.Polygon([(0, 0), (10, 0), (10, 10), (0, 10)],                                           [((1, 3), (5, 3), (5, 1), (1, 1)),                                            ((9, 9), (9, 8), (8, 8), (8, 9))]),                          geometry.Polygon([(11, 2), (11, 10), (12, 10), (12, 2)])                          ],                         index=["简单面", "复杂面", "c区"],  # 构建一个索引字段                         crs="EPSG:4326",  # 坐标系是：WGS 1984                         )cq.to_file("./output/{}.shp".format(os.path.basename(__file__).replace(".py", "")),           driver="ESRI Shapefile",           encoding="utf-8")

# -*- coding: utf-8 -*-import pandas as pdimport geopandas as gpdfrom shapely.geometry import Pointfrom matplotlib import pyplot as pltfrom matplotlib.ticker import FuncFormatter# 读取数据file_path = "./data-use/高程数据.csv"rankings_colname = ["name", "mark", "longitude", "latitude", "height"];df = pd.read_csv(file_path, header=None, names=rankings_colname)# print(df.head(5))#输出前五行数据查看xy = [Point(xy) for xy in zip(df["longitude"], df["latitude"])]pts = gpd.GeoSeries(xy)  # 创建点要素数据集#保存为SHP文件pts.to_file("./output/展高程点.shp", driver="ESRI Shapefile", encoding="utf-8")"""fig是用来设置图像大小参数，ax是行列有多少个点"""fig, ax = plt.subplots(figsize=(8, 6))  # 返回一个包含figure和axes对象的元组ax = pts.plot(ax=ax,              facecolor="white",              edgecolor="black",              marker="X",              linewidth=0.5,  # 内外符号比例系数              markersize=12,              label="高程点")# 地图标注new_texts = [plt.text(x_ + 1, y_ + 1, text, fontsize=8) for x_, y_, text in             zip(df["longitude"], df["latitude"], df["name"])]# 设置坐标轴def formatnum(x, pos):    # return "$%.1f$x$10^{4}$" % (x / 10000)#科学计数法显示    return int(x)  # 取整显示formatter = FuncFormatter(formatnum)ax.yaxis.set_major_formatter(formatter)# 美观起见隐藏顶部与右侧边框线ax.spines["right"].set_visible(False)ax.spines["top"].set_visible(False)plt.grid(True, alpha=0.4)  # 显示网格，透明度为50%ax.legend(title="图例", loc="lower right", ncol=1, shadow=True) # 添加图例plt.title("展高程点", fontdict={"weight": "normal", "size": 20}) # 设置图名&改变图标题字体# 保存图片plt.savefig("images/展高程点.png", dpi=300, bbox_inches="tight", pad_inches=0)plt.show()

import geopandas as gpdimport pandas as pdfrom shapely.geometry import Polygonfrom matplotlib import pyplot as pltraw = pd.read_excel("./data-use/甘肃省地震带.xls")  # 原始数据# 转换为面要素output = raw.groupby("id")     .apply(lambda df: Polygon([(x, y) for x, y in zip(df["longitude"], df["latitude"])]))     .to_frame(name="geometry")# 转换为GeoDataFrameoutput = gpd.GeoDataFrame(output, crs="EPSG:4326")output.plot()# 地图标注new_longitude = raw.groupby("name", as_index=False,)["longitude"].mean()new_latitude = raw.groupby("name", as_index=False)["latitude"].mean()new_df = pd.merge(pd.DataFrame(new_longitude),pd.DataFrame(new_latitude))new_texts = [plt.text(x_ , y_ , text, fontsize=8) for x_, y_, text in             zip(new_df["longitude"], new_df["latitude"], new_df["name"])]# 导出shapefileoutput.to_file("output/地震带.shp")  plt.show()

import osimport shapelyimport geopandas as gpdimport matplotlib.pyplot as pltpolygon = shapely.geometry.Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])# 分别绘制多边形、多边形正向缓冲区，坐标系是WGS1984，单位是度cq = gpd.GeoSeries([polygon,                    polygon.buffer(distance=1),                    polygon.buffer(distance=3)],                   crs="EPSG:4326")# 导出数据为shapefile文件cq.to_file("./output/{}.shp".format(os.path.basename(__file__).replace(".py", "")),           driver="ESRI Shapefile",           encoding="utf-8")ax = cq.plot(alpha=0.2)ax.axis("off")  # 取消坐标轴的显示plt.show()