具有重复的车间ID的数据帧,其中某些车间ID发生两次,而有些发生三次:
我只想根据分配给其区域的最短店铺距离保留唯一的店铺ID。
Area Shop Name Shop Distance Shop ID
0 AAA Ly 86 5d87790c46a77300
1 AAA Hi 230 5ce5522012138400
2 BBB Hi 780 5ce5522012138400
3 CCC Ly 450 5d87790c46a77300
...
91 MMM Ju 43 4f76d0c0e4b01af7
92 MMM Hi 1150 5ce5522012138400
...
使用pandas drop_duplicates drop the row duplicates,但条件基于第一个/最后一个出现的店铺ID,这不允许我按距离排序:
shops_df = shops_df.drop_duplicates(subset='Shop ID', keep= 'first')
我也试着按商店ID分组然后排序,但是sort返回错误:重复
bbtshops_new['C'] = bbtshops_new.groupby('Shop ID')['Shop ID'].cumcount()
bbtshops_new.sort_values(by=['C'], axis=1)
到目前为止,我试着一直做到这个阶段:
# filter all the duplicates into a new df
df_toclean = shops_df[shops_df['Shop ID'].duplicated(keep= False)]
# create a mask for all unique Shop ID
mask = df_toclean['Shop ID'].value_counts()
# create a mask for the Shop ID that occurred 2 times
shop_2 = mask[mask==2].index
# create a mask for the Shop ID that occurred 3 times
shop_3 = mask[mask==3].index
# create a mask for the Shops that are under radius 750
dist_1 = df_toclean['Shop Distance']<=750
# returns results for all the Shop IDs that appeared twice and under radius 750
bbtshops_2 = df_toclean[dist_1 & df_toclean['Shop ID'].isin(shop_2)]
* if i use df_toclean['Shop Distance'].min() instead of dist_1 it returns 0 results
我想我已经做了很长的一段时间了,但仍然没有找到删除副本的方法,有谁知道如何用更短的方式解决这个问题?我是python新手,谢谢你的帮助!
