Pandas enumerate groups in descending order

Question

I've the following column:

   column
0      10
1      10
2       8
3       8
4       6
5       6

My goal is to find the today unique values (3 in this case) and create a new column which would create the following

   new_column
0           3
1           3
2           2
3           2
4           1
5           1

The numbering starts from length of unique values (3) and same number is repeated if current row is same as previous row based on original column. Number gets decreased as row value changes. All unique values in original column have same number of rows (2 rows for each unique value in this case).

My solution was to groupby the original column and create a new list like below:

i=1
new_time=[]
for j, v in df.groupby('column'):
    new_time.append([i]*2)
    i=i+1

Then I'd flatten the list sort in decreasing order. Any other simpler solution?

Thanks.

Answer 1

pd.factorize

i, u = pd.factorize(df.column)
df.assign(new=len(u) - i)

   column  new
0      10    3
1      10    3
2       8    2
3       8    2
4       6    1
5       6    1

dict.setdefault

d = {}
for k in df.column:
    d.setdefault(k, len(d))

df.assign(new=len(d) - df.column.map(d))

Answer 2

使用GroupBy.ngroup有ascending=False：

df.groupby('column', sort=False).ngroup(ascending=False)+1

0    3
1    3
2    2
3    2
4    1
5    1
dtype: int64

对于看起来像这样的 DataFrame，

df = pd.DataFrame({'column': [10, 10, 8, 8, 10, 10]})

. . .如果只对连续的值进行分组，您需要修改您的石斑鱼：

(df.groupby(df['column'].ne(df['column'].shift()).cumsum(), sort=False)
   .ngroup(ascending=False)
   .add(1))

0    3
1    3
2    2
3    2
4    1
5    1
dtype: int64