polars.Series.round#

Series.round(decimals: int = 0, mode: RoundMode = 'half_to_even') → Series[source]#

Round underlying floating point data by decimals digits.

Parameters:
decimals

Number of decimals to round by.

mode{‘half_to_even’, ‘half_away_from_zero’, ‘to_zero’}

The rounding strategy used. A “rounded value” is a value with at most decimals decimal places (e.g. integers when decimals=0, multiples of 0.1 when decimals=1, 0.01 when decimals=2, and so on).

Strategies that start with half_ round all values to the nearest rounded value, only using the strategy to break ties when a value falls exactly between two rounded values (e.g. 0.5 when decimals=0, 0.05 when decimals=1). Other rounding strategies specify explicitly which rounded value is chosen and always apply (not just for tiebreaks).

  • half_to_even (default)

    Round to the nearest value; break ties by choosing the nearest even value. For example, 0.5 rounds to 0, 1.5 rounds to 2, 2.5 rounds to 2. Also known as “banker’s rounding”; this is the default because it tends to minimise cumulative rounding bias.

  • half_away_from_zero

    Round to the nearest value; break ties by rounding away from zero. For example, 0.5 rounds to 1, -0.5 rounds to -1, 2.5 rounds to 3. Also known as “commercial rounding”.

  • to_zero

    Always round (truncate) towards zero, discarding the fractional part beyond decimals. For example, 0.9 rounds to 0, -0.9 rounds to 0, 1.29 rounds to 1.2 (with decimals=1). Equivalent to the truncate() method.

Examples

>>> s = pl.Series("a", [1.12345, 2.56789, 3.901234])
>>> s.round(2)
shape: (3,)
Series: 'a' [f64]
[
    1.12
    2.57
    3.9
]

>>> s = pl.Series([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5])
>>> s.round(mode="half_to_even")
shape: (8,)
Series: '' [f64]
[
    -4.0
    -2.0
    -2.0
    -0.0
    0.0
    2.0
    2.0
    4.0
]
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