how do I insert a column at a specific column index in pandas?

see docs: http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.insert.html

using loc = 0 will insert at the beginning

df.insert(loc, column, value)

df = pd.DataFrame({'B': [1, 2, 3], 'C': [4, 5, 6]})

df
Out: 
   B  C
0  1  4
1  2  5
2  3  6

idx = 0
new_col = [7, 8, 9]  # can be a list, a Series, an array or a scalar   
df.insert(loc=idx, column='A', value=new_col)

df
Out: 
   A  B  C
0  7  1  4
1  8  2  5
2  9  3  6

If you want a single value for all rows:

df.insert(0,'name_of_column','')
df['name_of_column'] = value

Edit:

You can also:

df.insert(0,'name_of_column',value)

df.insert(loc, column_name, value)

This will work if there is no other column with the same name. If a column, with your provided name already exists in the dataframe, it will raise a ValueError.

You can pass an optional parameter allow_duplicates with True value to create a new column with already existing column name.

Here is an example:

    >>> df = pd.DataFrame({'b': [1, 2], 'c': [3,4]})
    >>> df
       b  c
    0  1  3
    1  2  4
    >>> df.insert(0, 'a', -1)
    >>> df
       a  b  c
    0 -1  1  3
    1 -1  2  4
    >>> df.insert(0, 'a', -2)
    Traceback (most recent call last):
      File "", line 1, in 
      File "C:\Python39\lib\site-packages\pandas\core\frame.py", line 3760, in insert
        self._mgr.insert(loc, column, value, allow_duplicates=allow_duplicates)
      File "C:\Python39\lib\site-packages\pandas\core\internals\managers.py", line 1191, in insert
        raise ValueError(f"cannot insert {item}, already exists")
    ValueError: cannot insert a, already exists
    >>> df.insert(0, 'a', -2,  allow_duplicates = True)
    >>> df
       a  a  b  c
    0 -2 -1  1  3
    1 -2 -1  2  4

You could try to extract columns as list, massage this as you want, and reindex your dataframe:

>>> cols = df.columns.tolist()
>>> cols = [cols[-1]]+cols[:-1] # or whatever change you need
>>> df.reindex(columns=cols)

   n  l  v
0  0  a  1
1  0  b  2
2  0  c  1
3  0  d  2

EDIT: this can be done in one line ; however, this looks a bit ugly. Maybe some cleaner proposal may come...

>>> df.reindex(columns=['n']+df.columns[:-1].tolist())

   n  l  v
0  0  a  1
1  0  b  2
2  0  c  1
3  0  d  2