mm_*_*der 8 python pandas geopandas
您好,我正在尝试将 X 和 Y 坐标列表转换为线。我想通过groupby
ID 和时间映射这些数据。只要我grouby
一列,我的代码就会成功执行,但两列是我遇到错误的地方。我参考了这个问题。
以下是一些示例数据:
ID X Y Hour
1 -87.78976 41.97658 16
1 -87.66991 41.92355 16
1 -87.59887 41.708447 17
2 -87.73956 41.876827 16
2 -87.68161 41.79886 16
2 -87.5999 41.7083 16
3 -87.59918 41.708485 17
3 -87.59857 41.708393 17
3 -87.64391 41.675133 17
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这是我的代码:
df = pd.read_csv("snow_gps.csv", sep=';')
#zip the coordinates into a point object and convert to a GeoData Frame
geometry = [Point(xy) for xy in zip(df.X, df.Y)]
geo_df = GeoDataFrame(df, geometry=geometry)
# aggregate these points with the GrouBy
geo_df = geo_df.groupby(['track_seg_point_id', 'Hour'])['geometry'].apply(lambda x: LineString(x.tolist()))
geo_df = GeoDataFrame(geo_df, geometry='geometry')
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这是错误:ValueError: LineStrings must have at least 2坐标元组
这是我试图得到的最终结果:
ID Hour geometry
1 16 LINESTRING (-87.78976 41.97658, -87.66991 41.9...
1 17 LINESTRING (-87.78964000000001 41.976634999999...
1 18 LINESTRING (-87.78958 41.97663499999999, -87.6...
2 16 LINESTRING (-87.78958 41.976612, -87.669785 41...
2 17 LINESTRING (-87.78958 41.976624, -87.66978 41....
3 16 LINESTRING (-87.78958 41.97666, -87.6695199999...
3 17 LINESTRING (-87.78954 41.976665, -87.66927 41....
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请在如何对多个参数进行分组方面有任何建议或想法。
小智 7
你的代码很好,问题是你的数据。
可以看到,如果按 ID 和 Hour 分组,则只有 1 个点与 ID 为 1 和小时为 17 的分组。 LineString 必须由 1 个或多个点组成(必须至少有 2 个坐标元组)。我在您的示例数据中添加了另一点:
ID X Y Hour
1 -87.78976 41.97658 16
1 -87.66991 41.92355 16
1 -87.59887 41.708447 17
1 -87.48234 41.677342 17
2 -87.73956 41.876827 16
2 -87.68161 41.79886 16
2 -87.5999 41.7083 16
3 -87.59918 41.708485 17
3 -87.59857 41.708393 17
3 -87.64391 41.675133 17
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正如您在下面看到的,下面的代码几乎与您的相同:
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point, LineString, shape
df = pd.read_csv("snow_gps.csv", sep='\s*,\s*')
#zip the coordinates into a point object and convert to a GeoData Frame
geometry = [Point(xy) for xy in zip(df.X, df.Y)]
geo_df = gpd.GeoDataFrame(df, geometry=geometry)
geo_df2 = geo_df.groupby(['ID', 'Hour'])['geometry'].apply(lambda x: LineString(x.tolist()))
geo_df2 = gpd.GeoDataFrame(geo_df2, geometry='geometry')
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