Add column with constant value to pandas dataframe [duplicate]

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

With modern pandas you can just do:

df['new'] = 0

The reason this puts NaN into a column is because df.index and the Index of your right-hand-side object are different. @zach shows the proper way to assign a new column of zeros. In general, pandas tries to do as much alignment of indices as possible. One downside is that when indices are not aligned you get NaN wherever they aren't aligned. Play around with the reindex and align methods to gain some intuition for alignment works with objects that have partially, totally, and not-aligned-all aligned indices. For example here's how DataFrame.align() works with partially aligned indices:

In [7]: from pandas import DataFrame

In [8]: from numpy.random import randint

In [9]: df = DataFrame({'a': randint(3, size=10)})

In [10]:

In [10]: df
Out[10]:
   a
0  0
1  2
2  0
3  1
4  0
5  0
6  0
7  0
8  0
9  0

In [11]: s = df.a[:5]

In [12]: dfa, sa = df.align(s, axis=0)

In [13]: dfa
Out[13]:
   a
0  0
1  2
2  0
3  1
4  0
5  0
6  0
7  0
8  0
9  0

In [14]: sa
Out[14]:
0     0
1     2
2     0
3     1
4     0
5   NaN
6   NaN
7   NaN
8   NaN
9   NaN
Name: a, dtype: float64

Here is another one liner using lambdas (create column with constant value = 10)

df['newCol'] = df.apply(lambda x: 10, axis=1)

before

df
    A           B           C
1   1.764052    0.400157    0.978738
2   2.240893    1.867558    -0.977278
3   0.950088    -0.151357   -0.103219

after

df
        A           B           C           newCol
    1   1.764052    0.400157    0.978738    10
    2   2.240893    1.867558    -0.977278   10
    3   0.950088    -0.151357   -0.103219   10