Customized float formatting in a pandas DataFrame

a simple method using round(), pass the number of digits you want to round to as a parameter.

Assuming that your DataFrame is named 'df':

df.round(2)

output:

   0     1
0  3.00  5.60
1  1.20  3.45

In [188]: df
Out[188]:
       a      b       c
0 1.0000 2.2460  2.0000
1 3.0000 4.4920  6.0000
2 5.0000 6.7380 10.0000

In [189]: pd.options.display.float_format = '{:,.2f}'.format

In [190]: df.apply(lambda x: x.astype(int) if np.allclose(x, x.astype(int)) else x)
Out[190]:
   a    b   c
0  1 2.25   2
1  3 4.49   6
2  5 6.74  10

UPDATE:

In [222]: df
Out[222]:
       0      1
0 3.0000 5.6000
1 1.2000 3.4560

In [223]: df.applymap(lambda x: str(int(x)) if abs(x - int(x)) < 1e-6 else str(round(x,2)))
Out[223]:
     0     1
0    3   5.6
1  1.2  3.46

NOTE: be aware that .applymap() method is pretty slow as it's doing map(func, series) for each series in the DataFrame

In case someone wants a quick way to apply the same precision to all numeric types in the dataframe (while not worrying about str types):

pd.set_option('display.precision', 2)

This works for displaying DataFrame and Styler objects in jupyter notebooks.

A good solution for this to test whether the value has a decimal part and format it accordingly :

pd.options.display.float_format = lambda x : '{:.0f}'.format(x) if int(x) == x else '{:,.2f}'.format(x)

Edit: This will produce an error when NaNs are in your data. Consider instead using round(): pd.options.display.float_format = lambda x : '{:.0f}'.format(x) if round(x,0) == x else '{:,.2f}'.format(x)