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A tool for rounding numbers in complex Python objects

Project description

rounder: Rounding of numbers in complex Python objects

rounder is a lightweight package for rounding float numbers in complex structures, such as dictionaries, lists, tuples, and sets, and any complex object that combines any number of such objects in any nested structure. The code is organized as a Python (Python >= 3.6 is required) package that can be installed from PyPi (pip install rounder), but as it is a one-file package, you can simply download the main module ( and use it directly in your project.

The package is useful mainly for presentation purposes, but in some cases, it can be useful in other situations as well.

rounder offers you four functions for rounding complex objects:

  • round_object(obj, digits=0, use_copy=False), which rounds x to digits decimal places
  • floor_object(obj, use_copy=False), which rounds x down to the nearest integer
  • ceil_object(obj, use_copy=False), which rounds x up to the nearest integer
  • signif_object(obj, digits, use_copy=False), which rounds x to digits significant digits

In addition, rounder comes with a generalized function:

  • map_obj(func, obj, use_copy=False), which runs callable func, which takes a number as an argument and returns a number, to all numbers across the object.

rounder also offers a function for rounding numbers to significant digits:

  • signif(x, digits), which rounds x (either an int or a float) to digits significant digits

You can use signif in a simple way:

>>> import rounder as r
>>> r.signif(1.1212, 3)
>>> r.signif(12.1239112, 5)
>>> r.signif(121212.12, 3)

The package is simple to use, but you have to remember that when you're working with mutable objects, such as dicts or lists, rounding them for printing purposes will affect the original object; no such effect, of course, will occur for immutable types (e.g., tuples and sets). To overcome this effect, simply use use_copy=True in the above functions for rounding objects (not in signif). If you do so, the function will create a copy of the object and work (and return) its deepcopy, not the original object.

You can use rounder functions for rounding floats, but do remember that their behavior is slightly different than that of their builtin and math counterparts, as they do not throw an exception when a non-number object is used.

You can round a list, a tuple, a set (including a frozenset), a float array.array, and a dict:

>>> r.round_object([1.122, 2.4434], 1)
[1.1, 2.4]
>>> r.ceil_object([1.122, 2.4434])
[2, 3]
>>> r.floor_object([1.122, 2.4434])
[1, 2]
>>> r.signif_object([1.1224, 222.4434], 4)
[1.122, 222.4]

>>> r.round_object((1.122, 2.4434), 1)
(1.1, 2.4)
>>> r.round_object({1.122, 2.4434}, 1)
{1.1, 2.4}
>>> r.round_object({"1": 1.122, "q":2.4434}, 1)
{'1': 1.1, 'q': 2.4}

Do remember, however, that array.array works in its own way, so rounding its values is a tricky thing:

>>> import array
>>> arr = array.array("f", (1.122, 2.4434))
>>> r.round_object(arr, 1) == array.array("f", (1.1, 2.4))

Perfect... But:

>>> array.array("f", (1.1, 2.4))
array('f', [1.100000023841858, 2.4000000953674316])

and indeed:

>>> r.round_object(arr, 1)
array('f', [1.100000023841858, 2.4000000953674316])

This is seldom what you want to achieve when rounding numbers, so more often than not, before rounding an array, you should make it a list or a tuple:

>>> arr = array.array("d", (1.122, 2.4434))
>>> r.round_object(arr, 1)
array('d', [1.1, 2.4])

Note that you do not have to worry about having non-roundable objects in a list (or whatever object you're feeding into rounder functions). Your objects can contain objects of any type, and only numbers will be rounded while all others will be remain untouched:

>>> r.round_object([1.122, "string", 2.4434, 2.45454545-2j], 1)
[1.1, 'string', 2.4, (2.5-2j)]

In fact, you can round any object, and the function will simply return it if it cannot be rounded:

>>> r.round_object("string")
>>> r.round_object(lambda x: x**3)(2)
>>> r.round_object(range(10))
range(0, 10)

But most of all, you can apply rounding for any complex object, of any structure. Imagine you have a structure like this:

>>> x = {
...     "items": ["item 1", "item 2", "item 3",],
...     "quantities": {"item 1": 235, "item 2" : 300, "item 3": 17,},
...     "prices": {
...         "item 1": {"$": 32.22534554, "EURO": 41.783234567},
...         "item 2": {"$": 42.26625, "EURO": 51.333578},
...         "item 3": {"$": 2.223043225, "EURO": 2.78098721346}
...     },
...     "income": {
...         "2009": {"$": 3445342.324364, "EURO":   39080.332546},
...         "2010": {"$": 6765675.56665554, "EURO": 78980.34564546},
...     }
... }

Perhaps it does not make much sense, but the point is that to round all the values in this structure, you would need to build a dedicated script for that. With rounder, this is a piece of cake:

>>> rounded_x = r.round_object(x, digits=2, use_copy=True)

And you will get this:

>>> from pprint import pprint
>>> pprint(rounded_x)
{'income': {'2009': {'$': 3445342.32, 'EURO': 39080.33},
            '2010': {'$': 6765675.57, 'EURO': 78980.35}},
 'items': ['item 1', 'item 2', 'item 3'],
 'prices': {'item 1': {'$': 32.23, 'EURO': 41.78},
            'item 2': {'$': 42.27, 'EURO': 51.33},
            'item 3': {'$': 2.22, 'EURO': 2.78}},
 'quantities': {'item 1': 235, 'item 2': 300, 'item 3': 17}}

Piece of cake! Note that we used use_copy=True, which means that rounded_x is a deepcopy of x, so the original dictionary has not been affected anyway.


In addition, rounder offers you a map_object() function, which enables you to run any function that takes a number and returns a number for all numbers in an object. This works like the following:

>>> xy = {"x": [12, 33.3, 45.5, 3543.22], "y": [.45, .3554, .55223, .9911], "expl": "x and y values"}
>>> r.round_object(r.map_object(lambda x: x**3/(1 - 1/x), xy, use_copy=True), 4, use_copy=True)
{'x': [1885.0909, 38069.258, 96313.1475, 44495587353.9829], 'y': [-0.0746, -0.0248, -0.2077, -108.4126], 'expl': 'x and y values'}

You would have achieved the same result had you used round inside the lambda body:

>>> r.map_object(lambda x: round(x**3/(1 - 1/x), 4), xy, use_copy=True)
{'x': [1885.0909, 38069.258, 96313.1475, 44495587353.9829], 'y': [-0.0746, -0.0248, -0.2077, -108.4126], 'expl': 'x and y values'}

The latter approach, actually, will be quicker, as the full recursion is used just once, not twice, as it was done in the former example.

If the function takes additional arguments, you can use a wrapper function to overcome this issue:

>>> def forget(something): pass
>>> def fun(x, to_forget):
...     forget(to_forget)
...     return x**2
>>> def wrapper(x):
...     return fun(x, "this can be forgotten")
>>> r.map_object(wrapper, [2, 2, [3, 3, ], {"a": 5}])
[4, 4, [9, 9], {'a': 25}]

Or even:

>>> r.map_object(lambda x: fun(x, "this can be forgotten"), [2, 2, [3, 3, ], {"a": 5}])
[4, 4, [9, 9], {'a': 25}]


First of all, all these functions will work the very same way as their original counterparts (not for signif, which does not have one):

>>> import math
>>> x = 12345.12345678901234567890
>>> for d in range(10):
...     assert round(x, d) == r.round_object(x, d)
...     assert math.ceil(x) == r.ceil_object(x)
...     assert math.floor(x) == r.floor_object(x)

Immutable types

rounder does work with immutable types! It simply creates a new object, with rounded numbers:

>>> x = {1.12, 4.555}
>>> r.round_object(x)
{1, 5}
>>> r.round_object(frozenset(x))
frozenset({1, 5})
>>> r.round_object((1.12, 4.555))
(1, 5)
>>> r.round_object(({1.1, 1.2}, frozenset({1.444, 2.222})))
({1}, frozenset({1, 2}))

Remember, however, that in the case of sets, you can get a shorter set then the original one:

>>> x = {1.12, 1.99}
>>> r.ceil_object(x)

Generators and other unpickable objects

This should be an extremely rare situation to request to round an object that contains a generator, or any other unpickable object. But if you happen to be in such a situation, be aware of some limitations of rounder functions.

As a rule, mainly for safety, generators are returned unchanged. This is a safe approach for the simple reason that you often choose to use a generator instead of, say, a list when the data you're processing can be too large for your machine's memory to handle. So:

>>> gen = (i**2 for i in range(10))
>>> rounded_gen = r.round_object(gen)
>>> rounded_gen is gen
>>> next(gen)
>>> next(gen)
>>> next(rounded_gen)

You will get the same result for range:

>>> ran = range(10)
>>> r.round_object(ran) is ran

But you have to remember that when you request a deepcopy (with use_copy=True), all elements to be rounded need to be pickable:

>>> gen_2 = (i**2 for i in range(10))
>>> gen_2_copied_rounded = r.round_object(gen_2, use_copy=True)
Traceback (most recent call last):

range() is pickable, so you can request a deepcopy of it in rounder functions.

This is a rare situation, however, to include such objects in an object to be rounded. Remember about the above limitations, and you can either work with the original object (not its copy, so with default use_copy=False), or change it so that all its elements can be pickled.

NumPy and Pandas

rounder does not work with numpy and pandas: they have their own builtin methods for rounding, and using them will be much quicker.


The package is covered with unit pytests, located in the tests/ folder. In addition, the package uses doctests, which are collected here, in this README, and in the main module, These doctests serve mainly documentation purposes, and since they can be run any time during development and before with each release, this helps to check whether all the examples work fine.


The package is OS-independent. Its releases are checked in local machines, on Windows 10 and Ubuntu 20.04 for Windows, and in Pythonista for iPad.

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