Math Floor And Math Ceiling In Javascript

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Python’s ceil () function rounds a number to the nearest integer or whole number. Python floor () rounds decimal places to the nearest whole number. These two functions are part of the Python math library.

While programming in Python, you may come across a scenario where you want to round a number to the nearest whole number.

This is where the Python math.floor () and math.ceil () methods come into play. You can use math.floor () to calculate the integer closest to a decimal number. The math.ceil () method rounds a number to the nearest whole number.

This tutorial will cover using floor and ceil to return the minimum or maximum value of a given value. Esamineremo esempi di ciascuno di questi metodi in un programma per mostrare come funzionano.

Funzione Python Floor

Il metodo Python math.floor () arrotonda a number per difetto at the intero pi√π neighbor. This method takes one argument: the number you want to return. In Python 3, math.floor () returns an integer value.

Calculating the floor of a number is a common mathematical function in Python. The plane of a number refers to the nearest Python integer that is less than or equal to the number . To put it another way, the floor of a number is the number rounded to its nearest integer value.

The Python math module includes a method that can be used to calculate the floor of a number: math.floor (). math.floor () accepts one parameter, which is the number whose floor value you want to calculate.

Here is the syntax for floor () function:

The Python division function floor () is part of the math library. To use it, you must first import the math library. You can import the math library into your program using an Python import statement .

Floor () Python example

Let’s say we are working in a bar. We want to create a calculator that rounds the quantities of grain we have available to their number nearest integer. This makes it easier for us to determine how much coffee we have left.

We could round the amount of a bean to the nearest whole number using the following code:

Our code returns the smallest integer closest to 250.92, that is: 250.

In the first line, we import the math library. Then we define a Python variable called quantity. This variable stores the amount of grain we have in stock. We use the function math.floor () to round the variable quantity to the nearest integer.

In this case, the integer closest to 250.92 is equal to 250. Our code returned 250.

We can use the method math.floor () on negative numbers. Let’s say we write a program that calculates how many beans we will have left at the end of the month.

Our program has predicted that, given the number of sales we’ve seen so far, we’re going to have a negative amount of beans. In other words, we’re going to run out of beans. We print the output of math.floor () to the console.

We want to round our value to the nearest whole number. This will allow us to know the quantity of beans to order, depending on the current demand:

Our code returns: -26.

The program defaults to rounding our negative value to the nearest integer, which in this case is -26.

Pyt hon Ceil

The math.ceil () method is the opposite of the math.floor () method. math.ceil () rounds a number to the nearest whole number. Like math.floor (), math.ceil () returns an integer value.

While floor rounds a number to the nearest integer, ceil rounds a number to the nearest integer.

Here is the syntax of the math.ceil () method:

The syntax of the ceil function is the same as that of math. floor (). Both methods take one parameter: the number that you want to process using the method. The ceil () function returns the maximum number of a float, or the nearest largest integer.

ceil () Python Example

Let’s discuss an example of the math.ceil () method in action. Let’s say we’ve decided that we want to calculate the maximum value for each amount of grain. In other words, we want to know the smallest whole above each amount of bean. We want to calculate this so we know how many beans to order in our next shipment.

We could use the following code to round the amount of beans we have to the nearest whole number:

Our code returns: 23. The math.ceil () function rounded our quantity to the nearest integer not less than the quantity, which in this case is 23.

Similarly, you can use math.ceil () on negative numbers. Let’s use the sample program we talked about earlier to show how this works. Instead of finding the minimum value of our remaining quantity, we want to find the maximum value. We could do it using this program:

Our code returns: -26. Our program rounded our projection of the quantity to the nearest integer, which in this case was -26.

Python Floor Division and Ceil vs. Round

The Python round () method finds the nearest number, which can include decimal places, while math.floor () and < em> ceil () respectively round to the nearest integer ().

If you want to round a number like 105.2529 to two decimal places, you should use round () instead of floor () or ceil ().

If you want to learn more using the round (), take a look at our tutorial on Python Rounding .

Conclusion

The math.floor () method rounds a number to the nearest whole number. The math.ceil () method rounds a number to the nearest integer.

This tutorial has been covered on the use of the two math functions. floor () and math.ceil () to round numbers in Python. We looked at an example of each of these methods in a program.

To learn more about coding in Python, read our How To Learn Python Help .

Math Floor And Math Ceiling In Javascript __dict__: Questions

__dict__

How do I merge two dictionaries in a single expression (taking union of dictionaries)?

5 answers

Carl Meyer By Carl Meyer

I have two Python dictionaries, and I want to write a single expression that returns these two dictionaries, merged (i.e. taking the union). The update() method would be what I need, if it returned its result instead of modifying a dictionary in-place.

>>> x = {"a": 1, "b": 2}
>>> y = {"b": 10, "c": 11}
>>> z = x.update(y)
>>> print(z)
None
>>> x
{"a": 1, "b": 10, "c": 11}

How can I get that final merged dictionary in z, not x?

(To be extra-clear, the last-one-wins conflict-handling of dict.update() is what I"m looking for as well.)

5839

Answer #1

How can I merge two Python dictionaries in a single expression?

For dictionaries x and y, z becomes a shallowly-merged dictionary with values from y replacing those from x.

  • In Python 3.9.0 or greater (released 17 October 2020): PEP-584, discussed here, was implemented and provides the simplest method:

    z = x | y          # NOTE: 3.9+ ONLY
    
  • In Python 3.5 or greater:

    z = {**x, **y}
    
  • In Python 2, (or 3.4 or lower) write a function:

    def merge_two_dicts(x, y):
        z = x.copy()   # start with keys and values of x
        z.update(y)    # modifies z with keys and values of y
        return z
    

    and now:

    z = merge_two_dicts(x, y)
    

Explanation

Say you have two dictionaries and you want to merge them into a new dictionary without altering the original dictionaries:

x = {"a": 1, "b": 2}
y = {"b": 3, "c": 4}

The desired result is to get a new dictionary (z) with the values merged, and the second dictionary"s values overwriting those from the first.

>>> z
{"a": 1, "b": 3, "c": 4}

A new syntax for this, proposed in PEP 448 and available as of Python 3.5, is

z = {**x, **y}

And it is indeed a single expression.

Note that we can merge in with literal notation as well:

z = {**x, "foo": 1, "bar": 2, **y}

and now:

>>> z
{"a": 1, "b": 3, "foo": 1, "bar": 2, "c": 4}

It is now showing as implemented in the release schedule for 3.5, PEP 478, and it has now made its way into the What"s New in Python 3.5 document.

However, since many organizations are still on Python 2, you may wish to do this in a backward-compatible way. The classically Pythonic way, available in Python 2 and Python 3.0-3.4, is to do this as a two-step process:

z = x.copy()
z.update(y) # which returns None since it mutates z

In both approaches, y will come second and its values will replace x"s values, thus b will point to 3 in our final result.

Not yet on Python 3.5, but want a single expression

If you are not yet on Python 3.5 or need to write backward-compatible code, and you want this in a single expression, the most performant while the correct approach is to put it in a function:

def merge_two_dicts(x, y):
    """Given two dictionaries, merge them into a new dict as a shallow copy."""
    z = x.copy()
    z.update(y)
    return z

and then you have a single expression:

z = merge_two_dicts(x, y)

You can also make a function to merge an arbitrary number of dictionaries, from zero to a very large number:

def merge_dicts(*dict_args):
    """
    Given any number of dictionaries, shallow copy and merge into a new dict,
    precedence goes to key-value pairs in latter dictionaries.
    """
    result = {}
    for dictionary in dict_args:
        result.update(dictionary)
    return result

This function will work in Python 2 and 3 for all dictionaries. e.g. given dictionaries a to g:

z = merge_dicts(a, b, c, d, e, f, g) 

and key-value pairs in g will take precedence over dictionaries a to f, and so on.

Critiques of Other Answers

Don"t use what you see in the formerly accepted answer:

z = dict(x.items() + y.items())

In Python 2, you create two lists in memory for each dict, create a third list in memory with length equal to the length of the first two put together, and then discard all three lists to create the dict. In Python 3, this will fail because you"re adding two dict_items objects together, not two lists -

>>> c = dict(a.items() + b.items())
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: "dict_items" and "dict_items"

and you would have to explicitly create them as lists, e.g. z = dict(list(x.items()) + list(y.items())). This is a waste of resources and computation power.

Similarly, taking the union of items() in Python 3 (viewitems() in Python 2.7) will also fail when values are unhashable objects (like lists, for example). Even if your values are hashable, since sets are semantically unordered, the behavior is undefined in regards to precedence. So don"t do this:

>>> c = dict(a.items() | b.items())

This example demonstrates what happens when values are unhashable:

>>> x = {"a": []}
>>> y = {"b": []}
>>> dict(x.items() | y.items())
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: "list"

Here"s an example where y should have precedence, but instead the value from x is retained due to the arbitrary order of sets:

>>> x = {"a": 2}
>>> y = {"a": 1}
>>> dict(x.items() | y.items())
{"a": 2}

Another hack you should not use:

z = dict(x, **y)

This uses the dict constructor and is very fast and memory-efficient (even slightly more so than our two-step process) but unless you know precisely what is happening here (that is, the second dict is being passed as keyword arguments to the dict constructor), it"s difficult to read, it"s not the intended usage, and so it is not Pythonic.

Here"s an example of the usage being remediated in django.

Dictionaries are intended to take hashable keys (e.g. frozensets or tuples), but this method fails in Python 3 when keys are not strings.

>>> c = dict(a, **b)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: keyword arguments must be strings

From the mailing list, Guido van Rossum, the creator of the language, wrote:

I am fine with declaring dict({}, **{1:3}) illegal, since after all it is abuse of the ** mechanism.

and

Apparently dict(x, **y) is going around as "cool hack" for "call x.update(y) and return x". Personally, I find it more despicable than cool.

It is my understanding (as well as the understanding of the creator of the language) that the intended usage for dict(**y) is for creating dictionaries for readability purposes, e.g.:

dict(a=1, b=10, c=11)

instead of

{"a": 1, "b": 10, "c": 11}

Response to comments

Despite what Guido says, dict(x, **y) is in line with the dict specification, which btw. works for both Python 2 and 3. The fact that this only works for string keys is a direct consequence of how keyword parameters work and not a short-coming of dict. Nor is using the ** operator in this place an abuse of the mechanism, in fact, ** was designed precisely to pass dictionaries as keywords.

Again, it doesn"t work for 3 when keys are not strings. The implicit calling contract is that namespaces take ordinary dictionaries, while users must only pass keyword arguments that are strings. All other callables enforced it. dict broke this consistency in Python 2:

>>> foo(**{("a", "b"): None})
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: foo() keywords must be strings
>>> dict(**{("a", "b"): None})
{("a", "b"): None}

This inconsistency was bad given other implementations of Python (PyPy, Jython, IronPython). Thus it was fixed in Python 3, as this usage could be a breaking change.

I submit to you that it is malicious incompetence to intentionally write code that only works in one version of a language or that only works given certain arbitrary constraints.

More comments:

dict(x.items() + y.items()) is still the most readable solution for Python 2. Readability counts.

My response: merge_two_dicts(x, y) actually seems much clearer to me, if we"re actually concerned about readability. And it is not forward compatible, as Python 2 is increasingly deprecated.

{**x, **y} does not seem to handle nested dictionaries. the contents of nested keys are simply overwritten, not merged [...] I ended up being burnt by these answers that do not merge recursively and I was surprised no one mentioned it. In my interpretation of the word "merging" these answers describe "updating one dict with another", and not merging.

Yes. I must refer you back to the question, which is asking for a shallow merge of two dictionaries, with the first"s values being overwritten by the second"s - in a single expression.

Assuming two dictionaries of dictionaries, one might recursively merge them in a single function, but you should be careful not to modify the dictionaries from either source, and the surest way to avoid that is to make a copy when assigning values. As keys must be hashable and are usually therefore immutable, it is pointless to copy them:

from copy import deepcopy

def dict_of_dicts_merge(x, y):
    z = {}
    overlapping_keys = x.keys() & y.keys()
    for key in overlapping_keys:
        z[key] = dict_of_dicts_merge(x[key], y[key])
    for key in x.keys() - overlapping_keys:
        z[key] = deepcopy(x[key])
    for key in y.keys() - overlapping_keys:
        z[key] = deepcopy(y[key])
    return z

Usage:

>>> x = {"a":{1:{}}, "b": {2:{}}}
>>> y = {"b":{10:{}}, "c": {11:{}}}
>>> dict_of_dicts_merge(x, y)
{"b": {2: {}, 10: {}}, "a": {1: {}}, "c": {11: {}}}

Coming up with contingencies for other value types is far beyond the scope of this question, so I will point you at my answer to the canonical question on a "Dictionaries of dictionaries merge".

Less Performant But Correct Ad-hocs

These approaches are less performant, but they will provide correct behavior. They will be much less performant than copy and update or the new unpacking because they iterate through each key-value pair at a higher level of abstraction, but they do respect the order of precedence (latter dictionaries have precedence)

You can also chain the dictionaries manually inside a dict comprehension:

{k: v for d in dicts for k, v in d.items()} # iteritems in Python 2.7

or in Python 2.6 (and perhaps as early as 2.4 when generator expressions were introduced):

dict((k, v) for d in dicts for k, v in d.items()) # iteritems in Python 2

itertools.chain will chain the iterators over the key-value pairs in the correct order:

from itertools import chain
z = dict(chain(x.items(), y.items())) # iteritems in Python 2

Performance Analysis

I"m only going to do the performance analysis of the usages known to behave correctly. (Self-contained so you can copy and paste yourself.)

from timeit import repeat
from itertools import chain

x = dict.fromkeys("abcdefg")
y = dict.fromkeys("efghijk")

def merge_two_dicts(x, y):
    z = x.copy()
    z.update(y)
    return z

min(repeat(lambda: {**x, **y}))
min(repeat(lambda: merge_two_dicts(x, y)))
min(repeat(lambda: {k: v for d in (x, y) for k, v in d.items()}))
min(repeat(lambda: dict(chain(x.items(), y.items()))))
min(repeat(lambda: dict(item for d in (x, y) for item in d.items())))

In Python 3.8.1, NixOS:

>>> min(repeat(lambda: {**x, **y}))
1.0804965235292912
>>> min(repeat(lambda: merge_two_dicts(x, y)))
1.636518670246005
>>> min(repeat(lambda: {k: v for d in (x, y) for k, v in d.items()}))
3.1779992282390594
>>> min(repeat(lambda: dict(chain(x.items(), y.items()))))
2.740647904574871
>>> min(repeat(lambda: dict(item for d in (x, y) for item in d.items())))
4.266070580109954
$ uname -a
Linux nixos 4.19.113 #1-NixOS SMP Wed Mar 25 07:06:15 UTC 2020 x86_64 GNU/Linux

Resources on Dictionaries

5839

Answer #2

In your case, what you can do is:

z = dict(list(x.items()) + list(y.items()))

This will, as you want it, put the final dict in z, and make the value for key b be properly overridden by the second (y) dict"s value:

>>> x = {"a":1, "b": 2}
>>> y = {"b":10, "c": 11}
>>> z = dict(list(x.items()) + list(y.items()))
>>> z
{"a": 1, "c": 11, "b": 10}

If you use Python 2, you can even remove the list() calls. To create z:

>>> z = dict(x.items() + y.items())
>>> z
{"a": 1, "c": 11, "b": 10}

If you use Python version 3.9.0a4 or greater, then you can directly use:

x = {"a":1, "b": 2}
y = {"b":10, "c": 11}
z = x | y
print(z)
{"a": 1, "c": 11, "b": 10}

5839

Answer #3

An alternative:

z = x.copy()
z.update(y)

Math Floor And Math Ceiling In Javascript ceil: Questions

ceil

Is there a ceiling equivalent of // operator in Python?

1 answers

I found out about the // operator in Python which in Python 3 does division with floor.

Is there an operator which divides with ceil instead? (I know about the / operator which in Python 3 does floating point division.)

165

Answer #1

You can just do upside-down floor division:

def ceildiv(a, b):
    return -(-a // b)

This works because Python"s division operator does floor division (unlike in C, where integer division truncates the fractional part).

This also works with Python"s big integers, because there"s no (lossy) floating-point conversion.

Here"s a demonstration:

>>> from __future__ import division   # a/b is float division
>>> from math import ceil
>>> b = 3
>>> for a in range(-7, 8):
...     print(["%d/%d" % (a, b), int(ceil(a / b)), -(-a // b)])
... 
["-7/3", -2, -2]
["-6/3", -2, -2]
["-5/3", -1, -1]
["-4/3", -1, -1]
["-3/3", -1, -1]
["-2/3", 0, 0]
["-1/3", 0, 0]
["0/3", 0, 0]
["1/3", 1, 1]
["2/3", 1, 1]
["3/3", 1, 1]
["4/3", 2, 2]
["5/3", 2, 2]
["6/3", 2, 2]
["7/3", 3, 3]

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