cudf.Series.round#

Series.round(decimals=0, how='half_even')[source]#

Round to a variable number of decimal places.

Parameters:

decimalsint, dict, Series: Number of decimal places to round each column to. This parameter must be an int for a Series. For a DataFrame, a dict or a Series are also valid inputs. 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.
howstr, optional: Type of rounding. Can be either “half_even” (default) or “half_up” rounding.

Returns:

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

Examples

Series

>>> s = cudf.Series([0.1, 1.4, 2.9])
>>> s.round()
0    0.0
1    1.0
2    3.0
dtype: float64

DataFrame

>>> df = cudf.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 keys and the number of decimal places as values.

>>> 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 the index and the number of decimal places as the values.

>>> decimals = cudf.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