Super simple in-place assignment: df['new'] = 0

For in-place modification, perform direct assignment. This assignment is broadcasted by pandas for each row.

df = pd.DataFrame('x', index=range(4), columns=list('ABC'))
df

   A  B  C
0  x  x  x
1  x  x  x
2  x  x  x
3  x  x  x

df['new'] = 'y'
# Same as,
# df.loc[:, 'new'] = 'y'
df

   A  B  C new
0  x  x  x   y
1  x  x  x   y
2  x  x  x   y
3  x  x  x   y

Note for object columns

If you want to add an column of empty lists, here is my advice:

• Consider not doing this. object columns are bad news in terms of performance. Rethink how your data is structured.
• Consider storing your data in a sparse data structure. More information: sparse data structures

• If you must store a column of lists, ensure not to copy the same reference multiple times.

  # Wrong
  df['new'] = [[]] * len(df)
  # Right
  df['new'] = [[] for _ in range(len(df))]
  

---

Generating a copy: df.assign(new=0)

If you need a copy instead, use DataFrame.assign:

df.assign(new='y')

   A  B  C new
0  x  x  x   y
1  x  x  x   y
2  x  x  x   y
3  x  x  x   y

And, if you need to assign multiple such columns with the same value, this is as simple as,

c = ['new1', 'new2', ...]
df.assign(**dict.fromkeys(c, 'y'))

   A  B  C new1 new2
0  x  x  x    y    y
1  x  x  x    y    y
2  x  x  x    y    y
3  x  x  x    y    y

Multiple column assignment

Finally, if you need to assign multiple columns with different values, you can use assign with a dictionary.

c = {'new1': 'w', 'new2': 'y', 'new3': 'z'}
df.assign(**c)

   A  B  C new1 new2 new3
0  x  x  x    w    y    z
1  x  x  x    w    y    z
2  x  x  x    w    y    z
3  x  x  x    w    y    z