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

You could try direct assignment (assuming your dataframe is named df):

for col in l:
    df[col] = 0

Or use the DataFrame's assign method, which is a slightly cleaner way of doing it if l can contain a value, an array or any pandas Series constructor.

# create a dictionary of column names and the value you want
d = dict.fromkeys(l, 0)
df.assign(**d)

Pandas Documentation on the assign method : http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html

Pandas tries to broadcast the numpy array to fit your dataframe in ways that are not helpful in this case.

To avoid this you could make a list of the arrays.

df["matrix"] = [np.zeros((20,20)) for _ in df.index]

Or use apply to get the value you want in each cell

df["matrix"] = df.apply(lambda _: np.zeros((20, 20)), axis=1)

There's a trick here: when DataFrame (or Series) constructor is passed a scalar as the first argument this value is propogated:

In [11]: pd.DataFrame(0, index=np.arange(1, 4), columns=np.arange(24))
Out[11]:
   0   1   2   3   4   5   6   7   8   9  ...  14  15  16  17  18  19  20  21  22  23
1   0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   0   0   0   0   0
2   0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   0   0   0   0   0
3   0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   0   0   0   0   0

[3 rows x 24 columns]

Note: np.arange is numpy's answer to python's range.

Use Setting with enlargement:

df.loc[len(df)] = 0
print (df)
   A  B  C  D  E
1  1  2  0  1  0
2  0  0  0  1 -1
3  1  1  3 -5  2
4 -3  4  2  6  0
5  2  4  1  9 -1
6  0  0  0  0  0

Or DataFrame.append with Series filled by 0 and index by columns of DataFrame:

df = df.append(pd.Series(0, index=df.columns), ignore_index=True)

This can be accomplished by mapping a string format object to the column of floats:

df.colName.map('{:.2f}'.format)

(Credit to exp1orer)

Create and fill a pandas dataframe with zeros

feature_list = ["foo", "bar", 37]
df = pd.DataFrame(0, index=np.arange(7), columns=feature_list) 
print(df) 

which prints:

   foo  bar  37
0    0    0   0
1    0    0   0
2    0    0   0
3    0    0   0
4    0    0   0
5    0    0   0
6    0    0   0

You can create the append df with concat

out = pd.concat([df,pd.DataFrame({'D':[7]})]).fillna(0)
out
     A    B    C    D
0  1.0  2.0  3.0  0.0
1  4.0  5.0  6.0  0.0
0  0.0  0.0  0.0  7.0

I believe you need assign with dictionary for set new columns names:

subset = subset.assign(**dict.fromkeys(['IRNotional','IPNotional'], 0))
#you can define each column separately
#subset = subset.assign(**{'IRNotional': 0, 'IPNotional': 1})

Or simplier:

subset['IRNotional'] = 0
subset['IPNotional'] = 0

Now when i do similar things to another dataframe i get zeros columns with a mix NaN and zeros rows as shown below. This is really strange.

I think problem is different index values, so is necessary create same indices, else for not matched indices get NaNs:

subset['IPNotional']=pd.DataFrame(numpy.zeros(shape=(len(subset),1)), index=subset.index)