Notice it will convert your float type to object

df.DollarAmount.apply(lambda x : "{:,}".format(x))
Out[509]: 
0    5,721.48
1     4,000.0
2     4,769.0
3      824.07
4       643.6
5       620.0
Name: DollarAmount, dtype: object

When formatting a number with , you can just use '{:,}'.format:

n = 10000
print '{:,}'.format(n)
n = 1000.1
print '{:,}'.format(n)

In pandas, you can use the formatters parameter to to_html as discussed here.

num_format = lambda x: '{:,}'.format(x)
def build_formatters(df, format):
    return {
        column:format 
        for column, dtype in df.dtypes.items()
        if dtype in [ np.dtype('int64'), np.dtype('float64') ] 
    }
formatters = build_formatters(data_frame, num_format)
data_frame.to_html(formatters=formatters)

Adding the thousands separator has actually been discussed quite a bit on stackoverflow. You can read here or here.

Probably not the solution you're looking for but you could convert to string and just remove all .'s

To make all your floats show comma separators by default in pandas versions 0.23 through 0.25 set the following:

pd.options.display.float_format = '{:,}'.format

https://pandas.pydata.org/pandas-docs/version/0.23.4/options.html

In pandas version 1.0 this leads to some strange formatting in some cases.

pandas (as of 0.20.1) does not allow overriding the default integer format in an easy way. It is hard coded in pandas.io.formats.format.IntArrayFormatter (the lambda function):

class IntArrayFormatter(GenericArrayFormatter):

    def _format_strings(self):
        formatter = self.formatter or (lambda x: '% d' % x)
        fmt_values = [formatter(x) for x in self.values]
        return fmt_values

I'm assuming what you're actually asking for is how you can override the format for all integers: modify (i.e. "monkey patch") the IntArrayFormatter to print integer values with thousands separated by comma as follows:

import pandas

class _IntArrayFormatter(pandas.io.formats.format.GenericArrayFormatter):

    def _format_strings(self):
        formatter = self.formatter or (lambda x: ' {:,}'.format(x))
        fmt_values = [formatter(x) for x in self.values]
        return fmt_values

pandas.io.formats.format.IntArrayFormatter = _IntArrayFormatter

Note:

• before 0.20.0, the formatters were in pandas.formats.format.
• before 0.18.1, the formatters were in pandas.core.format.

Aside

For floats you do not need to jump through those hoops since there is a configuration option for it:

display.float_format: The callable should accept a floating point number and return a string with the desired format of the number. This is used in some places like SeriesFormatter. See core.format.EngFormatter for an example.

Comma is the default separator. If you want to choose your own separator you can do this by declaring the sep parameter of pandas to_csv() method.
df.to_csv(sep=',')

If you goal is to create thousand separators and export them back into a csv you can follow this example:

import pandas as pd
df = pd.DataFrame([[12172083.89, 1341.4078,   -9568703.592,    10323.7222],
[21661725.86, -1770.2725,  12669066.38, 14669.7118]],columns=['A','B','C','D'])
for c in df.columns:
    df[c] = df[c].apply(lambda x : '{0:,}'.format(x))
df.to_csv(sep='\t')

If you just want pandas to show separators when printed out:

pd.options.display.float_format = '{:,}'.format
print(df)

Pass param thousands=',' to read_csv to read those values as thousands:

In [27]:
import pandas as pd
import io

t="""id;value
0;123,123
1;221,323,330
2;32,001"""
pd.read_csv(io.StringIO(t), thousands=r',', sep=';')

Out[27]:
   id      value
0   0     123123
1   1  221323330
2   2      32001