Rick Berg Rick Berg - 2 months ago 14
Python Question

Number duplicates sequentially in Pandas DataFrame

I have a Pandas DataFrame that has a column that is basically a foreign key, as below:

Index | f_key | values
0 | 1 | red
1 | 2 | blue
2 | 1 | green
3 | 2 | yellow
4 | 3 | orange
5 | 1 | violet


What I would like is to add a column that labels the repeated foreign keys sequentially, as in "dup_number" below:

Index | dup_number | f_key | values
0 | 1 | 1 | red
1 | 1 | 2 | blue
2 | 2 | 1 | green
3 | 2 | 2 | yellow
4 | 1 | 3 | orange
5 | 3 | 1 | violet


The rows can be reordered if needed, I just need to get the "dup_number" keys in there. I wrote following code, which works fine, it gives me a Series which I can then add into the DataFrame, but it is very slow (that for loop is what kills the time), and I feel like it's way more complicated than is needed:

df = pd.DataFrame({'f_key': [1,2,1,2,3,1], 'values': ['red', 'blue', 'green', 'yellow', 'orange', 'violet']})
df_unique = df['f_key'].drop_duplicates().reset_index(drop=True)
dup_number = pd.DataFrame(columns = ['dup_number', 'temp_index'])
for n in np.arange(len(df_unique)):
sub_df = df.loc[df['f_key'] == df_unique[n]].reset_index()
dup_index = pd.DataFrame({'dup_number': sub_df.index.values[:]+1, 'temp_index': sub_df['index']})
dup_number = dup_number.append(dup_index)
dup_number = dup_number.set_index(dup_number['temp_index'].astype(int))
dup_number = dup_number['dup_number'].sort_index()


Any suggestions on faster/simpler ways to do this are appreciated!

Answer

You can use cumcount()

df['dup_number'] = df.groupby(['f_key']).cumcount()+1

           f_key  values  dup_number
    0      1     red           1
    1      2    blue           1
    2      1   green           2
    3      2  yellow           2
    4      3  orange           1
    5      1  violet           3