我得到了一個結果列表，我想按兩個標準對其進行排序。我對 sys 列進行了第一次排序：systems = {'BzBz_S':0,'BzBz_PD34':1,'MeMe':2}sorted_results = sorted(results, key = lambda e: (systems[e[0]]))并傳遞給 DataFramedf = pd.DataFrame(sorted_results,columns = ['sys','dis','basis','Energy'])這給了我以下輸出：,sys,dis,system,basis,Energy0,BzBz_S,10.0,BzBz_S_10.0,S,0.025624651,BzBz_S,3.2,BzBz_S_3.2,S,1.485102972,BzBz_S,3.3,BzBz_S_3.3,S,-0.250864983,BzBz_S,6.0,BzBz_S_6.0,S,-0.118279754,BzBz_S,3.9,BzBz_S_3.9,S,-2.447052445,BzBz_PD34,0.4,BzBz_PD34_0.4,PD34,-1.881723126,BzBz_PD34,0.2,BzBz_PD34_0.2,PD34,-1.505190347,MeMe,5.0,MeMe_5.0,5,-0.121942838,MeMe,5.4,MeMe_5.4,5,-0.07556324除了 sys 之外，我如何創建第二個排序標準來對 dis 列進行排序，以獲得這樣的最終結果：,sys,dis,system,basis,Energy0,BzBz_S,3.2,BzBz_S_3.2,S,1.485102971,BzBz_S,3.3,BzBz_S_3.3,S,-0.250864982,BzBz_S,3.9,BzBz_S_3.9,S,-2.44705244 3,BzBz_S,6.0,BzBz_S_6.0,S,-0.118279754,BzBz_S,10.0,BzBz_S_10.0,S,0.025624655,BzBz_PD34,0.2,BzBz_PD34_0.2,PD34,-1.505190346,BzBz_PD34,0.4,BzBz_PD34_0.4,PD34,-1.881723127,MeMe,5.0,MeMe_5.0,5,-0.121942838,MeMe,5.4,MeMe_5.4,5,-0.07556324
1 回答
幕布斯6054654
TA貢獻1876條經驗 獲得超7個贊
獲得第一個輸出后，您可以執行以下操作以獲得最終輸出，希望對您有所幫助！
df['sys_cat']=df['sys'].astype('category') #creating a categorical column in the dataframe
d = dict(zip(df.sys_cat,df.sys_cat.cat.codes)) # converting categorical column into codes
# reassigning categories
count=0
for i in d:
d[i]=count
count+=1
df['sys_cat']=df['sys_cat'].map(d).astype(int)
df.sort_values(by=['sys_cat', 'dis'],ascending=[True, True], inplace=True)
df.drop(['sys_cat'], inplace=True, axis=1)
df.reset_index(inplace=True, drop=True)
df
給出：
sys dis system basis Energy
0 BzBz_S 3.2 BzBz_S_3.2 S 1.485103
1 BzBz_S 3.3 BzBz_S_3.3 S -0.250865
2 BzBz_S 3.9 BzBz_S_3.9 S -2.447052
3 BzBz_S 6.0 BzBz_S_6.0 S -0.118280
4 BzBz_S 10.0 BzBz_S_10.0 S 0.025625
5 BzBz_PD34 0.2 BzBz_PD34_0.2 PD34 -1.505190
6 BzBz_PD34 0.4 BzBz_PD34_0.4 PD34 -1.881723
7 MeMe 5.0 MeMe_5.0 5 -0.121943
8 MeMe 5.4 MeMe_5.4 5 -0.075563
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