I have the following dataframe:

d_test = {
    'random_staff' : ['gfda', 'fsd','gec', 'erw', 'gd', 'kjhk', 'fd', 'kui'],
    'cluster_number' : [1, 2, 3, 3, 2, 1, 4, 2]
}
df_test = pd.DataFrame(d_test)

cluster_number column contains values from 1 to n. Some values could have repetition but no missing values are presented. For example above such values are: 1, 2, 3, 4.

I want to be able to select some value from cluster_number column and change every occurrence of this value to set of unique values. No missing value should be presented. For example if we select value 2 then desirable outcome for cluster_number is [1, 2, 3, 3, 5, 1, 4, 6]. Note we had three 2 in the column. We kept first one as 2 we change next occurrence of 2 to 5 and we changed last occurrence of 2 to 6.

I wrote code for the logic above and it works fine:

cluster_number_to_change = 2
max_cluster = max(df_test['cluster_number'])
first_iter = True
i = cluster_number_to_change
for index, row in df_test.iterrows():
    if row['cluster_number'] == cluster_number_to_change:
        df_test.loc[index, 'cluster_number'] = i
        if first_iter:
            i = max_cluster   1
            first_iter = False
        else:
            i  = 1

But it is written as for-loop and I am trying understand if can be transformed in form of pandas .apply method (or any other effective vectorized solution).

CodePudding user response：

Using boolean indexing:

# get cluster #2
m1 = df_test['cluster_number'].eq(2)
# identify duplicates
m2 = df_test['cluster_number'].duplicated()

# increment duplicates using the max as reference
df_test.loc[m1&m2, 'cluster_number']  = (
 m2.where(m1).cumsum()
   .add(df_test['cluster_number'].max())
   .convert_dtypes()
)

print(df_test)

Output:

  random_staff  cluster_number
0         gfda               1
1          fsd               2
2          gec               3
3          erw               3
4           gd               5
5         kjhk               1
6           fd               4
7          kui               6