Replace ones in binary columns with values from another column

You could do something like:

df = pd.DataFrame([df['value']*df['item1'],df['value']*df['item2'],df['value']*df['item3']])
df.columns = ['item1','item2','item3']

EDIT: As this answer will not scale well to many columns as @coldspeed comments, it should be done iterating a loop:

 cols = ['item1','item2','item3']
 for c in cols:
     df[c] *= df['value']
 df.drop('value',axis=1,inplace=True)

Why not just multiply?

df.pop('value').values * df

   item1  item2  item3
0      0      5      0
1      4      0      0
2      0      0      3

DataFrame.pop has the nice effect of in-place removing and returning a column, so you can do this in a single step.

---

if the "item_*" columns have anything besides 1 in them, then you can multiply with bools:

df.pop('value').values * df.astype(bool)

   item1  item2  item3
0      0      5      0
1      4      0      0
2      0      0      3

---

If your DataFrame has other columns, then do this:

df
   value  name  item1  item2  item3
0      4  John      0      1      0
1      5  Mike      1      0      0
2      3  Stan      0      0      1

# cols = df.columns[df.columns.str.startswith('item')]
cols = df.filter(like='item').columns
df[cols] = df.pop('value').values * df[cols]

df
  name  item1  item2  item3
0  John      0      5      0
1  Mike      4      0      0
2  Stan      0      0      3