如何将我的数据集（CSV）划分为子集，或根据值之间的距离添加属性？

Question

我正在研究车辆轨迹的数据集（CSV）。我需要将整个数据集分成几个部分，每个部分只覆盖200英尺的距离

"Vehicle ID","Frame ID","Total Frames","Global Time","Local X","Local Y","Global X","Global Y","V_Len","V_Width","V_Class","V_Vel","V_Acc","Lane_ID","Pre_Veh","Fol_Veh","Spacing","Headway"

2,13,437,1118846980200,16.467,35.381,6451137.641,1873344.962,14.5,4.9,2,40.00,0.00,2,0,0,0.00,0.00
2,14,437,1118846980300,16.447,39.381,6451140.329,1873342.000,14.5,4.9,2,40.00,0.00,2,0,0,0.00,0.00 
2,17,437,1118846980600,16.385,51.381,6451148.395,1873333.115,14.5,4.9,2,40.00,0.00,2,0,0,0.00,0.00 
69,208,504,1118846999700,16.455,174.923,6451232.437,1873241.783,12.5,5.0,2,31.41,-7.71,2,71,78,75.46,2.40
1693,4861,466,1118847465000,39.652,231.988,6451255.460,1873184.174,8.5,6.0,2,33.05,-11.20,4,1689,1699,91.34,2.76
2615,8256,919,1118847804500,17.871,1805.110,6452448.583,1872154.574,16.0,6.9,2,19.98,-0.28,2,2608,2618,45.52,2.28
2616,7597,714,1118847738600,42.468,408.007,6451382.402,1873059.356,17.0,7.4,2,38.35,8.50,4,2604,2620,67.36,1.76
3109,7184,510,1118847697300,57.417,2082.094,6452636.527,1871944.862,14.0,5.9,2,50.00,0.00,5,0,2325,0.00,0.00

我试着取两个点，给出大约200英尺的距离，并设置为数据帧的掩码。但事实并非如此

sx=16.467;
sy=35.381;
ex=15.64;
ey=238.909;

maskdist=((df['Local X'] > sx) & (df['Local X'] <= ex) & (df['Local Y'] > sy) & (df['Local Y']) <= ey)

dist_df=df.copy()
dist_df=dist_df.loc[maskdist]

要么我必须保留为单独的数据集，要么需要添加一个属性来标记截面ID（例如，截面1-0-200ft，2-201-400ft，3-401-600ft。。。10-1800-2000ft.）以便根据截面ID，进一步分析数据帧