Rick Berg - 1 month ago 5x

Python Question

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
```

Source (Stackoverflow)

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