import pandas as pd
pd.options.display.float_format = '${:,.2f}'.format
df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
                  index=['foo','bar','baz','quux'],
                  columns=['cost'])
print(df)

yields

        cost
foo  234.57
baz  $345.68
quux $456.79

but this only works if you want every float to be formatted with a dollar sign.

Otherwise, if you want dollar formatting for some floats only, then I think you'll have to pre-modify the dataframe (converting those floats to strings):

import pandas as pd
df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
                  index=['foo','bar','baz','quux'],
                  columns=['cost'])
df['foo'] = df['cost']
df['cost'] = df['cost'].map('${:,.2f}'.format)
print(df)

yields

         cost       foo
foo   $123.46  123.4567
bar   $234.57  234.5678
baz   $345.68  345.6789
quux  $456.79  456.7890

you can use Series.str.zfill() method:

df['column_name'] = df['column_name'].astype(str).str.zfill(4)

Demo:

In [29]: df = pd.DataFrame({'a':[1,2], 'b':[3,234]})

In [30]: df
Out[30]:
   a    b
0  1    3
1  2  234

In [31]: df['b'] = df['b'].astype(str).str.zfill(4)

In [32]: df
Out[32]:
   a     b
0  1  0003
1  2  0234

You could monkey-patch pandas.io.formats.format.IntArrayFormatter:

import contextlib
import numpy as np
import pandas as pd
import pandas.io.formats.format as pf
np.random.seed(2015)

@contextlib.contextmanager
def custom_formatting():
    orig_float_format = pd.options.display.float_format
    orig_int_format = pf.IntArrayFormatter

    pd.options.display.float_format = '{:0,.2f}'.format
    class IntArrayFormatter(pf.GenericArrayFormatter):
        def _format_strings(self):
            formatter = self.formatter or '{:,d}'.format
            fmt_values = [formatter(x) for x in self.values]
            return fmt_values
    pf.IntArrayFormatter = IntArrayFormatter
    yield
    pd.options.display.float_format = orig_float_format
    pf.IntArrayFormatter = orig_int_format


df = pd.DataFrame(np.random.randint(10000, size=(5,3)), columns=list('ABC'))
df['D'] = np.random.random(df.shape[0])*10000

with custom_formatting():
    print(df)

yields

      A     B     C        D
0 2,658 2,828 4,540 8,961.77
1 9,506 2,734 9,805 2,221.86
2 3,765 4,152 4,583 2,011.82
3 5,244 5,395 7,485 8,656.08
4 9,107 6,033 5,998 2,942.53

while outside of the with-statement:

print(df)

yields

      A     B     C            D
0  2658  2828  4540  8961.765260
1  9506  2734  9805  2221.864779
2  3765  4152  4583  2011.823701
3  5244  5395  7485  8656.075610
4  9107  6033  5998  2942.530551