-Code Verständnis Python

Question

Ich bin eine harte Zeit, diese Codezeile in Python geschrieben zu verstehen:

drop_index = assets_df[assets_df['id']==Id].sort_values(['date_signed_contract','start_date'], ascending = False)[1::].index.tolist() 

Heisst ascending = False es absteigend?
Auch die [1::] bezieht sich auf was hier? Ich versuche nur, die richtige Idee zu bekommen.

Answer 1

np.random.seed(41) 

N= 10 
L = pd.date_range('2015-01-01', '2016-01-01') 
assets_df = pd.DataFrame({'date_signed_contract': np.random.choice(L, N), 
          'start_date':np.random.choice(L, N), 
          'id':np.random.randint(3, size=N), 
          'col':np.random.randint(10, size=N)}) 
print (assets_df) 
    col date_signed_contract id start_date 
0 4   2015-03-22 1 2015-07-18 
1 1   2015-11-18 2 2015-03-26 
2 2   2015-09-01 2 2015-12-09 
3 3   2015-03-31 1 2015-04-16 
4 4   2015-10-10 0 2015-06-29 
5 5   2016-01-01 1 2015-12-11 
6 4   2015-08-25 1 2015-07-23 
7 5   2015-05-12 1 2015-04-03 
8 7   2015-06-13 2 2015-06-30 
9 6   2015-06-28 2 2015-05-26 

---

Id = 1 
drop_index =(assets_df[assets_df['id']==Id] 
      .sort_values(['date_signed_contract','start_date'], ascending = False).iloc[1:]) 
      .index.tolist()) 
print (drop_index) 
[6, 7, 3, 0] 

Erläuterung:

erste Filter nach boolean indexing und Zustand:

Id = 1 
print (assets_df[assets_df['id']==Id]) 
    col date_signed_contract id start_date 
0 4   2015-03-22 1 2015-07-18 
3 3   2015-03-31 1 2015-04-16 
5 5   2016-01-01 1 2015-12-11 
6 4   2015-08-25 1 2015-07-23 
7 5   2015-05-12 1 2015-04-03 

Dann sort_values bedeuten ascending = Falsedescending:

print (assets_df[assets_df['id']==Id] 
       .sort_values(['date_signed_contract','start_date'], ascending = False)) 
    col date_signed_contract id start_date 
5 5   2016-01-01 1 2015-12-11 
6 4   2015-08-25 1 2015-07-23 
7 5   2015-05-12 1 2015-04-03 
3 3   2015-03-31 1 2015-04-16 
0 4   2015-03-22 1 2015-07-18 

erste Zeile entfernen durch Schneiden (letzte : nicht notwendig ist, weil Standard Schritt 1 ist), ist besser iloc:

print (assets_df[assets_df['id']==Id] 
      .sort_values(['date_signed_contract','start_date'], ascending = False).iloc[1:]) 
    col date_signed_contract id start_date 
6 4   2015-08-25 1 2015-07-23 
7 5   2015-05-12 1 2015-04-03 
3 3   2015-03-31 1 2015-04-16 
0 4   2015-03-22 1 2015-07-18 

Werden erste Spalte namens index:

print (assets_df[assets_df['id']==Id] 
       .sort_values(['date_signed_contract','start_date'], ascending = False).iloc[1:] 
       .index) 
Int64Index([6, 7, 3, 0], dtype='int64') 

Konvertieren index zu list:

print (assets_df[assets_df['id']==Id] 
       .sort_values(['date_signed_contract','start_date'], ascending = False).iloc[1:] 
       .index.tolist()) 
[6, 7, 3, 0]