空间特定属性

1import pandas as pd
2
3# 打开一个示例GPS追踪（.txt）文件
4df = pd.read_csv('go_track_trackspoints.csv')
5
6# 将时间戳列转换为datetime数据类型
7df['timestamp'] = pd.to_datetime(df['time'])
8
9# 使用.dt访问器提取时间值
10df['hour'] = df['timestamp'].dt.hour
11df['minute'] = df['timestamp'].dt.minute
12df['second'] = df['timestamp'].dt.second
13
14print(df.head().to_html())

1import geopandas as gpd
2
3countries = gpd.read_file('countries.geojson')
4
5print(countries.crs)

1import geopandas as gpd
2
3# 打开数据集
4gdf = gpd.read_file('countries.geojson')
5
6# 投影到 ESRI:54034
7gdf = gdf.to_crs('ESRI:54034')
8
9# 计算面积（单位：平方千米）
10gdf['area'] = gdf.area / 1e6
11
12# 获取面积最大的6个国家
13biggest = gdf.sort_values(by='area', ascending=False).head(6)
14
15# 创建字典以水平放置图例
16legend_kwds = {'loc': 'upper center', 'bbox_to_anchor': (0.7, 0.45), 'ncol': 2}
17
18# 重新投影回 WGS84（便于可视化）
19biggest = biggest.to_crs(4326)
20ax = biggest.plot(figsize=(10, 8), column='sovereignt', legend=True, legend_kwds=legend_kwds)
21ax.set_ylabel('纬度')
22ax.set_xlabel('经度')
23
24# 保存图像
25ax.figure.savefig('./output/biggest_countries.png', dpi=300)
26
27# 输出面积信息
28print(biggest[['name', 'area']].to_html())