I have a dataframe with n store
columns, here I'm just showing the first 2:
ref_id store_0 store_1
0 100 c b
1 300 d NaN
I want a way to concat only the non-NaN values from store
columns into a new column adding a comma between each value, and finally drop those columns. Desired output is:
ref_id stores
0 100 c,b
1 300 d
Right now I've tried df['stores'] = df['store_0'] + ',' + df['store_1']
with this result:
ref_id store_0 store_1 stores
0 100 c b c,b
1 300 d NaN NaN
You can use:
cols = df.filter(like='store_').columns
df2 = (df
.drop(columns=cols)
.assign(stores=df[cols].agg(lambda s: s.dropna()
.str.cat(sep=','),
axis=1))
)
Or, for in place modification:
cols = df.filter(like='store_').columns
df['stores'] = df[cols].agg(lambda s: s.dropna().str.cat(sep=','), axis=1)
df.drop(columns=cols, inplace=True)
Output:
ref_id stores
0 100 c,b
1 300 d