Can you format pandas integers for display, like `pd.options.display.float_format` for floats?

Question

I've seen this and this on formatting floating-point numbers for display in pandas, but I'm interested in doing the same thing for integers.

Right now, I have:

pd.options.display.float_format = '{:,.2f}'.format 

That works on the floats in my data, but will either leave annoying trailing zeroes on integers that are cast to floats, or I'll have plain integers that don't get formatted with commas.

The pandas docs mention a SeriesFormatter class about which I haven't been able to find any information.

Alternatively, if there's a way to write a single string formatter that will format floats as '{:,.2f}' and floats with zero trailing decimal as '{:,d}', that'd work too.

Answer 1

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 

Answer 2

Another option for Jupyter notebooks is to use df.style.format('{:,}'), but it only works on a single dataframe as far as I know, so you would have to call this every time:

table.style.format('{:,}')           col1       col2 0s   9,246,452  6,669,310 >0   2,513,002  5,090,144  table        col1     col2 0s  9246452  6669310 >0  2513002  5090144 

Styling — pandas 1.1.2 documentation