CODEWITHSUNDEEP
pythonpandasformattingnumber-formatting
Michael K
Michael K

Reputation: 2288

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

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.

Upvotes: 43

Views: 40494

Answers (4)

MarC
MarC

Reputation: 29

This is a solution with Pandas 3.9 that applies to dataframes with str, int and float columns

# Format numeric columns with thousands and decimal separators
df_formateado = df.copy()

# Format to numeric columns only
for col in df_formateado.select_dtypes(include=['float64', 'int64']).columns:
    df_formateado[col] = df_formateado[col].apply(lambda x: f"{x:,.2f}" if isinstance(x, float) else f"{x:,}")

# DataFrame formatted 
print(df_formateado)

Upvotes: 0

ostrokach
ostrokach

Reputation: 19952

Starting with Pandas 1.3.0, you can specify df.style.format(thousands=',') to use commas to separate thousands in floats, complex numbers, and integers.

See docs: Styler.format

Upvotes: 8

John
John

Reputation: 1445

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

Upvotes: 12

unutbu
unutbu

Reputation: 879709

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

Upvotes: 22

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