# pandas.DataFrame.round¶

DataFrame.round(self, decimals=0, *args, **kwargs) → ’DataFrame’[source]

Round a DataFrame to a variable number of decimal places.

Parameters
decimalsint, dict, Series

Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.

*args

Additional keywords have no effect but might be accepted for compatibility with numpy.

**kwargs

Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns
DataFrame

A DataFrame with the affected columns rounded to the specified number of decimal places.

numpy.around

Round a numpy array to the given number of decimals.

Series.round

Round a Series to the given number of decimals.

Examples

>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
...                   columns=['dogs', 'cats'])
>>> df
dogs  cats
0  0.21  0.32
1  0.01  0.67
2  0.66  0.03
3  0.21  0.18


By providing an integer each column is rounded to the same number of decimal places

>>> df.round(1)
dogs  cats
0   0.2   0.3
1   0.0   0.7
2   0.7   0.0
3   0.2   0.2


With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value

>>> df.round({'dogs': 1, 'cats': 0})
dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0


Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value

>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0