Code understanding python

Question

I am having a hard time to understand this line of code written in python:

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

Does ascending = False mean it's descending?
Also the [1::] refers to what here? Just trying to get the right idea.

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]

Explanation :

First filter by boolean indexing and condition:

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

Then sort_values , ascending = False means descending :

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

Remove first row by slicing (last : is not necessary, because default step is 1 ), better is 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

Get first column called 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')

Convert index to 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]