Create dataframes based on column values and delete rows in current dataframe that were used in creating new ones

Asked May 23 '23 at 14:56

Active May 23 '23 at 15:47

Viewed 39 times

I have a Dataframe with an id column of products that I need to separate into new dataframes, saving them on separated csvs and keep the dataframe with the rows that were not used to save in another csv, excluding the one that is now in the new dataframes. What's the best way to do this?

Df_original

    Id value Product_id
0    1   123         7
1    2   514         9
2    3   234         1
3    4   912         9
4    5   325         1
5    6   301         7
6    7   325         1
7    8   301         9

then i create two new dataframes for product id 7 and 9

Df_new_pd_7

    Id value Product_id
0    1   123         7
5    6   301         7

Df_new_pd_9

Id value Product_id
1    2   514         9
3    4   912         9
7    8   301         9

so my original dataframe will look like this

Df_original

    Id value Product_id
2    3   234         1
4    5   325         1
6    7   325         1

edited May 23 '23 at 15:47

asked May 23 '23 at 14:56

Otávio Augusto

`dfs = {k:d for k,d in df.groupby('Product_id')}`, then `dfs[7]` and so on... – Quang Hoang May 23 '23 at 15:00
1

@G.Anderson that removes the selected column from the dataframe and leaves the none mentioned columns. The OP is wanting to filter into new dataframes – stefan_aus_hannover May 23 '23 at 15:00
@QuangHoang what about if there are more than 3 product IDs in the intial DF? – stefan_aus_hannover May 23 '23 at 15:05
@stefan_aus_hannover yes, that works for any number of `Product_id`. – Quang Hoang May 23 '23 at 17:00
@QuangHoang if I would need 2 of 4 separated out? – stefan_aus_hannover May 23 '23 at 18:42

Create dataframes based on column values and delete rows in current dataframe that were used in creating new ones

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