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How to Round Numbers in Python

While you are dealing with data, sometimes you may come across a biased dataset. In statistics, bias is whereby the expected value of the results differs from the true underlying quantitative parameter being estimated. Working with such data can be dangerous and can lead you to incorrect conclusions. To learn more about various other concepts of Python, go through our  Python Tutorials.There are many types of biases such as selection bias, reporting bias, sampling bias and so on. Similarly, rounding bias is related to numeric data. In this article we will see:Why is it important to know the ways to round numbersHow to use various strategies to round numbersHow data is affected by rounding itHow to use NumPy arrays and Pandas DataFrames to round numbersLet us first learn about Python’s built-in rounding process.About Python’s Built-in round() FunctionPython offers a built-in round() function which rounds off a number to the given number of digits and makes rounding of numbers easier. The function round() accepts two numeric arguments, n and n digits and then returns the number n after rounding it to ndigits. If the number of digits are not provided for round off, the function rounds off the number n to the nearest integer.Suppose, you want to round off a number, say 4.5. It will be rounded to the nearest whole number which is 5. However, the number 4.74 will be rounded to one decimal place to give 4.7.It is important to quickly and readily round numbers while you are working with floats which have many decimal places. The inbuilt Python function round() makes it simple and easy.Syntaxround(number, number of digits)The parameters in the round() function are:number - number to be roundednumber of digits (Optional) - number of digits up to which the given number is to be rounded.The second parameter is optional. In case, if it is missing then round() function returns:For an integer, 12, it rounds off to 12For a decimal number, if the last digit after the decimal point is >=5 it will round off to the next whole number, and if <5 it will round off to the floor integerLet us look into an example where the second parameter is missing.# For integers print(round(12))   # For floating point print(round(21.7))   print(round(21.4))The output will be:12 22 21Now, if the second parameter is present.# when the (ndigit+1)th digit is =5 print(round(5.465, 2))   # when the (ndigit+1)th digit is >=5 print(round(5.476, 2))     # when the (ndigit+1)th digit is <5 print(round(5.473, 2))The output will be:5.46 5.48 5.47A practical application of round() functionThere is always a mismatch between fractions and decimals. The rounding of functions can be used to handle such cases. While converting fractions to decimals, we generally get many digits after the decimal point such as for ⅙ we get 0.166666667 but we use either two or three digits to the right of the decimal point. This is where the round function saves the day.For example:x = 1/3 print(x) print(round(x, 2))The output will be:0.3333333333333333 0.33Some errors and exceptions associated with this functionFor example,print(round("x", 2))The output will be:--------------------------------------------------------------------------- TypeError                                 Traceback (most recent call last) <ipython-input-9-6fc428ecf419> in <module>() ----> 1 print(round("x", 2)) TypeError: type str doesn't define __round__ methodAnother example,print(round(1.5)) print(round(2)) print(round(2.5))The output will be:2 2 2The function round() rounds 1.5 up to 2, and 2.5 down to 2. This is not a bug, the round() function behaves this way. In this article you will learn a few other ways to round a number. Let us look at the variety of methods to round a number.Diverse Methods for RoundingThere are many ways to round a number with its own advantages and disadvantages. Here we will learn some of the techniques to rounding a number.TruncationTruncation, as the name means to shorten things. It is one of the simplest methods to round a number which involves truncating a number to a given number of digits. In this method, each digit after a given position is replaced with 0. Let us look into some examples.ValueTruncated ToResult19.345Tens place1019.345Ones place1919.345Tenths place19.319.345Hundredths place19.34The truncate() function can be used for positive as well as negative numbers:>>> truncate(19.5) 19.0 >>> truncate(-2.852, 1) -2.8 >>> truncate(2.825, 2) 2.82The truncate() function can also be used to truncate digits towards the left of the decimal point by passing a negative number.>>> truncate(235.7, -1) 230.0 >>> truncate(-1936.37, -3) -1000.0When a positive number is truncated, we are basically rounding it down. Similarly, when we truncate a negative number, the number is rounded up. Let us look at the various rounding methods.Rounding UpThere is another strategy called “rounding up” where a number is rounded up to a specified number of digits. For example:ValueRound Up ToResult12.345Tens place2018.345Ones place1918.345Tenths place18.418.345Hundredths place18.35The term ceiling is used in mathematics to explain the nearest integer which is greater than or equal to a particular given number. In Python, for “rounding up” we use two functions namely,ceil() function, andmath() functionA non-integer number lies between two consecutive integers. For example, considering a number 5.2, this will lie between 4 and 5. Here, ceiling is the higher endpoint of the interval, whereas floor is the lower one. Therefore, ceiling of 5.2 is 5, and floor of 5.2 is 4. However, the ceiling of 5 is 5.In Python, the function to implement the ceiling function is the math.ceil() function. It always returns the closest integer which is greater than or equal to its input.>>> import math >>> math.ceil(5.2) 6 >>> math.ceil(5) 5 >>> math.ceil(-0.5) 0If you notice you will see that the ceiling of -0.5 is 0, and not -1.Let us look into a short code to implement the “rounding up” strategy using round_up() function:def round_up(n, decimals=0):     multiplier = 10 ** decimals     return math.ceil(n * multiplier) / multiplierLet’s look at how round_up() function works with various inputs:>>> round_up(3.1) 4.0 >>> round_up(3.23, 1) 3.3 >>> round_up(3.543, 2) 3.55You can pass negative values  to decimals, just like we did in truncation.>>> round_up(32.45, -1) 40.0 >>> round_up(3352, -2) 3400You can follow the diagram below to understand round up and round down. Round up to the right and down to the left.Rounding up always rounds a number to the right on the number line, and rounding down always rounds a number to the left on the number line.Rounding DownSimilar to rounding up we have another strategy called rounding down whereValueRounded Down ToResult19.345Tens place1019.345Ones place1919.345Tenths place19.319.345Hundredths place19.34In Python, rounding down can be implemented using a similar algorithm as we truncate or round up. Firstly you will have to shift the decimal point and then round an integer. Lastly shift the decimal point back.math.ceil() is used to round up to the ceiling of the number once the decimal point is shifted. For “rounding down” we first need to round the floor of the number once the decimal point is shifted.>>> math.floor(1.2) 1 >>> math.floor(-0.5) -1Here’s the definition of round_down():def round_down(n, decimals=0):     multiplier = 10 ** decimals return math.floor(n * multiplier) / multiplierThis is quite similar to round_up() function. Here we are using math.floor() instead of math.ceil().>>> round_down(1.5) 1 >>> round_down(1.48, 1) 1.4 >>> round_down(-0.5) -1Rounding a number up or down has extreme effects in a large dataset. After rounding up or down, you can actually remove a lot of precision as well as alter computations.Rounding Half UpThe “rounding half up” strategy rounds every number to the nearest number with the specified precision, and breaks ties by rounding up. Here are some examples:ValueRound Half Up ToResult19.825Tens place1019.825Ones place2019.825Tenths place19.819.825Hundredths place19.83In Python, rounding half up strategy can be implemented by shifting the decimal point to the right by the desired number of places. In this case you will have to determine whether the digit after the shifted decimal point is less than or greater than equal to 5.You can add 0.5 to the value which is shifted and then round it down with the math.floor() function.def round_half_up(n, decimals=0):     multiplier = 10 ** decimals return math.floor(n*multiplier + 0.5) / multiplierIf you notice you might see that round_half_up() looks similar to round_down. The only difference is to add 0.5 after shifting the decimal point so that the result of rounding down matches with the expected value.>>> round_half_up(19.23, 1) 19.2 >>> round_half_up(19.28, 1) 19.3 >>> round_half_up(19.25, 1) 19.3Rounding Half DownIn this method of rounding, it rounds to the nearest number similarly like “rounding half up” method, the difference is that it breaks ties by rounding to the lesser of the two numbers. Here are some examples:ValueRound Half Down ToResult16.825Tens place1716.825Ones place1716.825Tenths place16.816.825Hundredths place16.82In Python, “rounding half down” strategy can be implemented by replacing math.floor() in the round_half_up() function with math.ceil() and then by subtracting 0.5 instead of adding:def round_half_down(n, decimals=0):     multiplier = 10 ** decimals return math.ceil(n*multiplier - 0.5) / multiplierLet us look into some test cases.>>> round_half_down(1.5) 1.0 >>> round_half_down(-1.5) -2.0 >>> round_half_down(2.25, 1) 2.2In general there are no bias for both round_half_up() and round_half_down(). However, rounding of data with more number of ties results in bias. Let us consider an example to understand better.>>> data = [-2.15, 1.45, 4.35, -12.75]Let us compute the mean of these numbers:>>> statistics.mean(data) -2.275Now let us compute the mean on the data after rounding to one decimal place with round_half_up() and round_half_down():>>> rhu_data = [round_half_up(n, 1) for n in data] >>> statistics.mean(rhu_data) -2.2249999999999996 >>> rhd_data = [round_half_down(n, 1) for n in data] >>> statistics.mean(rhd_data) -2.325The round_half_up() function results in a round towards positive infinity bias, and round_half_down() results in a round towards negative infinity bias.Rounding Half Away From ZeroIf you have noticed carefully while going through round_half_up() and round_half_down(), neither of the two is symmetric around zero:>>> round_half_up(1.5) 2.0 >>> round_half_up(-1.5) -1.0 >>> round_half_down(1.5) 1.0 >>> round_half_down(-1.5) -2.0In order to introduce symmetry, you can always round a tie away from zero. The table mentioned below illustrates it clearly:ValueRound Half Away From Zero ToResult16.25Tens place2016.25Ones place1616.25Tenths place16.3-16.25Tens place-20-16.25Ones place-16-16.25Tenths place-16.3The implementation of “rounding half away from zero” strategy on a number n is very simple. All you need to do is start as usual by shifting the decimal point to the right a given number of places and then notice the digit d immediately to the right of the decimal place in this new number. Here, there are four cases to consider:If n is positive and d >= 5, round upIf n is positive and d < 5, round downIf n is negative and d >= 5, round downIf n is negative and d < 5, round upAfter rounding as per the rules mentioned above, you can shift the decimal place back to the left.There is a question which might come to your mind - How do you handle situations where the number of positive and negative ties are drastically different? The answer to this question brings us full circle to the function that deceived us at the beginning of this article: Python’s built-in  round() function.Rounding Half To EvenThere is a way to mitigate rounding bias while you are rounding values in a dataset. You can simply round ties to the nearest even number at the desired precision. Let us look at some examples:ValueRound Half To Even ToResult16.255Tens place2016.255Ones place1616.255Tenths place16.216.255Hundredths place16.26To prove that round() really does round to even, let us try on a few different values:>>> round(4.5) 4 >>> round(3.5) 4 >>> round(1.75, 1) 1.8 >>> round(1.65, 1) 1.6The Decimal ClassThe  decimal module in Python is one of those features of the language which you might not be aware of if you have just started learning Python. Decimal “is based on a floating-point model which was designed with people in mind, and necessarily has a paramount guiding principle – computers must provide an arithmetic that works in the same way as the arithmetic that people learn at school.” – except from the decimal arithmetic specification. Some of the benefits of the decimal module are mentioned below -Exact decimal representation: 0.1 is actually 0.1, and 0.1 + 0.1 + 0.1 - 0.3 returns 0, as expected.Preservation of significant digits: When you add 1.50 and 2.30, the result is 3.80 with the trailing zero maintained to indicate significance.User-alterable precision: The default precision of the decimal module is twenty-eight digits, but this value can be altered by the user to match the problem at hand.Let us see how rounding works in the decimal module.>>> import decimal >>> decimal.getcontext() Context(     prec=28,     rounding=ROUND_HALF_EVEN,     Emin=-999999,     Emax=999999,     capitals=1,     clamp=0,     flags=[],     traps=[         InvalidOperation,         DivisionByZero,         Overflow     ] )The function decimal.getcontext() returns a context object which represents the default context of the decimal module. It also includes the default precision and the default rounding strategy.In the above example, you will see that the default rounding strategy for the decimal module is ROUND_HALF_EVEN. It allows to align with the built-in round() functionLet us create a new Decimal instance by passing a string containing the desired value and declare a number using the decimal module’s Decimal class.>>> from decimal import Decimal >>> Decimal("0.1") Decimal('0.1')You may create a Decimal instance from a floating-point number but in that case, a floating-point representation error will be introduced. For example, this is what happens when you create a Decimal instance from the floating-point number 0.1>>> Decimal(0.1) Decimal('0.1000000000000000055511151231257827021181583404541015625')You may create Decimal instances from strings containing the decimal numbers you need in order to maintain exact precision.Rounding a Decimal using the .quantize() method:>>> Decimal("1.85").quantize(Decimal("1.0")) Decimal('1.8')The Decimal("1.0") argument in .quantize() allows to determine the number of decimal places in order to round the number. As 1.0 has one decimal place, the number 1.85 rounds to a single decimal place. Rounding half to even is the default strategy, hence the result is 1.8.Decimal class:>>> Decimal("2.775").quantize(Decimal("1.00")) Decimal('2.78')Decimal module provides another benefit. After performing arithmetic the rounding is taken care of automatically and also the significant digits are preserved.>>> decimal.getcontext().prec = 2 >>> Decimal("2.23") + Decimal("1.12") Decimal('3.4')To change the default rounding strategy, you can set the decimal.getcontect().rounding property to any one of several  flags. The following table summarizes these flags and which rounding strategy they implement:FlagRounding Strategydecimal.ROUND_CEILINGRounding updecimal.ROUND_FLOORRounding downdecimal.ROUND_DOWNTruncationdecimal.ROUND_UPRounding away from zerodecimal.ROUND_HALF_UPRounding half away from zerodecimal.ROUND_HALF_DOWNRounding half towards zerodecimal.ROUND_HALF_EVENRounding half to evendecimal.ROUND_05UPRounding up and rounding towards zeroRounding NumPy ArraysIn Data Science and scientific computation, most of the times we store data as a  NumPy array. One of the most powerful features of NumPy is the use of  vectorization and broadcasting to apply operations to an entire array at once instead of one element at a time.Let’s generate some data by creating a 3×4 NumPy array of pseudo-random numbers:>>> import numpy as np >>> np.random.seed(444) >>> data = np.random.randn(3, 4) >>> data array([[ 0.35743992,  0.3775384 ,  1.38233789,  1.17554883],        [-0.9392757 , -1.14315015, -0.54243951, -0.54870808], [ 0.20851975, 0.21268956, 1.26802054, -0.80730293]])Here, first we seed the np.random module to reproduce the output easily. Then a 3×4 NumPy array of floating-point numbers is created with np.random.randn().Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.To round all of the values in the data array, pass data as the argument to the  np.around() function. The desired number of decimal places is set with the decimals keyword argument. In this case, round half to even strategy is used similar to Python’s built-in round() function.To round the data in your array to integers, NumPy offers several options which are mentioned below:numpy.ceil()numpy.floor()numpy.trunc()numpy.rint()The np.ceil() function rounds every value in the array to the nearest integer greater than or equal to the original value:>>> np.ceil(data) array([[ 1.,  1.,  2.,  2.],        [-0., -1., -0., -0.], [ 1., 1., 2., -0.]])Look at the code carefully, we have a new number! Negative zero! Let us now take a look at Pandas library, widely used in Data Science with Python.Rounding Pandas Series and DataFramePandas has been a game-changer for data analytics and data science. The two main data structures in Pandas are Dataframe and Series. Dataframe works like an Excel spreadsheet whereas you can consider Series to be columns in a spreadsheet. Series.round() and DataFrame.round() methods. Let us look at an example.Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.>>> import pandas as pd >>> # Re-seed np.random if you closed your REPL since the last example >>> np.random.seed(444) >>> series = pd.Series(np.random.randn(4)) >>> series 0    0.357440 1    0.377538 2    1.382338 3    1.175549 dtype: float64 >>> series.round(2) 0    0.36 1    0.38 2    1.38 3    1.18 dtype: float64 >>> df = pd.DataFrame(np.random.randn(3, 3), columns=["A", "B", "C"]) >>> df           A         B         C 0 -0.939276 -1.143150 -0.542440 1 -0.548708  0.208520  0.212690 2  1.268021 -0.807303 -3.303072 >>> df.round(3)        A      B      C 0 -0.939 -1.143 -0.542 1 -0.549  0.209  0.213 2  1.268 -0.807 -3.303 The DataFrame.round() method can also accept a dictionary or a Series, to specify a different precision for each column. For instance, the following examples show how to round the first column of df to one decimal place, the second to two, and the third to three decimal places: >>> # Specify column-by-column precision with a dictionary >>> df.round({"A": 1, "B": 2, "C": 3})      A     B      C 0 -0.9 -1.14 -0.542 1 -0.5  0.21  0.213 2  1.3 -0.81 -3.303 >>> # Specify column-by-column precision with a Series >>> decimals = pd.Series([1, 2, 3], index=["A", "B", "C"]) >>> df.round(decimals)      A     B      C 0 -0.9 -1.14 -0.542 1 -0.5  0.21  0.213 2  1.3 -0.81 -3.303 If you need more rounding flexibility, you can apply NumPy's floor(), ceil(), and print() functions to Pandas Series and DataFrame objects: >>> np.floor(df)      A    B    C 0 -1.0 -2.0 -1.0 1 -1.0  0.0  0.0 2  1.0 -1.0 -4.0 >>> np.ceil(df)      A    B    C 0 -0.0 -1.0 -0.0 1 -0.0  1.0  1.0 2  2.0 -0.0 -3.0 >>> np.rint(df)      A    B    C 0 -1.0 -1.0 -1.0 1 -1.0  0.0  0.0 2  1.0 -1.0 -3.0 The modified round_half_up() function from the previous section will also work here: >>> round_half_up(df, decimals=2)       A     B     C 0 -0.94 -1.14 -0.54 1 -0.55  0.21  0.21 2 1.27 -0.81 -3.30Best Practices and ApplicationsNow that you have come across most of the rounding techniques, let us learn some of the best practices to make sure we round numbers in the correct way.Generate More Data and Round LaterSuppose you are dealing with a large set of data, storage can be a problem at times. For example, in an industrial oven you would want to measure the temperature every ten seconds accurate to eight decimal places, using a temperature sensor. These readings will help to avoid large fluctuations which may lead to failure of any heating element or components. We can write a Python script to compare the readings and check for large fluctuations.There will be a large number of readings as they are being recorded each and everyday. You may consider to maintain three decimal places of precision. But again, removing too much precision may result in a change in the calculation. However, if you have enough space, you can easily store the entire data at full precision. With less storage, it is always better to store at least two or three decimal places of precision which are required for calculation.In the end, once you are done computing the daily average of the temperature, you may calculate it to the maximum precision available and finally round the result.Currency Exchange and RegulationsWhenever we purchase an item from a particular place, the tax amount paid against the amount of the item depends largely on geographical factors. An item which costs you $2 may cost you less (say $1.8)  if you buy the same item from a different state. It is due to regulations set forth by the local government.In another case, when the minimum unit of currency at the accounting level in a country is smaller than the lowest unit of physical currency,  Swedish rounding is done. You can find a list of such rounding methods used by various countries if you look up on the  internet.If you want to design any such software for calculating currencies, keep in mind to check the local laws and regulations applicable in your present location.Reduce errorAs you are rounding numbers in a large datasets used in complex computations, your primary concern should be to limit the growth of the error due to rounding.SummaryIn this article we have seen a few methods to round numbers, out of those “rounding half to even” strategy minimizes rounding bias the best. We are lucky to have Python, NumPy, and Pandas already have built-in rounding functions to use this strategy. Here, we have learned about -Several rounding strategies, and how to implement in pure Python.Every rounding strategy inherently introduces a rounding bias, and the “rounding half to even” strategy mitigates this bias well, most of the time.You can round NumPy arrays and Pandas Series and DataFrame objects.If you enjoyed reading this article and found it to be interesting, leave a comment. To learn more about rounding numbers and other features of Python, join our  Python certification course.
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How to Round Numbers in Python

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How to Round Numbers in Python

While you are dealing with data, sometimes you may come across a biased dataset. In statistics, bias is whereby the expected value of the results differs from the true underlying quantitative parameter being estimated. Working with such data can be dangerous and can lead you to incorrect conclusions. To learn more about various other concepts of Python, go through our  Python Tutorials.

There are many types of biases such as selection bias, reporting bias, sampling bias and so on. Similarly, rounding bias is related to numeric data. In this article we will see:

  • Why is it important to know the ways to round numbers
  • How to use various strategies to round numbers
  • How data is affected by rounding it
  • How to use NumPy arrays and Pandas DataFrames to round numbers

Let us first learn about Python’s built-in rounding process.

About Python’s Built-in round() Function

Python offers a built-in round() function which rounds off a number to the given number of digits and makes rounding of numbers easier. The function round() accepts two numeric arguments, n and n digits and then returns the number n after rounding it to ndigits. If the number of digits are not provided for round off, the function rounds off the number n to the nearest integer.

Suppose, you want to round off a number, say 4.5. It will be rounded to the nearest whole number which is 5. However, the number 4.74 will be rounded to one decimal place to give 4.7.

It is important to quickly and readily round numbers while you are working with floats which have many decimal places. The inbuilt Python function round() makes it simple and easy.

Syntax

round(number, number of digits)

The parameters in the round() function are:

  1. number - number to be rounded
  2. number of digits (Optional) - number of digits up to which the given number is to be rounded.

The second parameter is optional. In case, if it is missing then round() function returns:

  • For an integer, 12, it rounds off to 12
  • For a decimal number, if the last digit after the decimal point is >=5 it will round off to the next whole number, and if <5 it will round off to the floor integer

Let us look into an example where the second parameter is missing.

# For integers
print(round(12))
 
# For floating point
print(round(21.7))  
print(round(21.4))

The output will be:

12
22
21

Now, if the second parameter is present.

# when the (ndigit+1)th digit is =5 
print(round(5.465, 2)) 
  
# when the (ndigit+1)th digit is >=5 
print(round(5.476, 2))   
  
# when the (ndigit+1)th digit is <5 
print(round(5.473, 2))

The output will be:

5.46 
5.48 
5.47

A practical application of round() function
There is always a mismatch between fractions and decimals. The rounding of functions can be used to handle such cases. While converting fractions to decimals, we generally get many digits after the decimal point such as for ⅙ we get 0.166666667 but we use either two or three digits to the right of the decimal point. This is where the round function saves the day.

For example:

x = 1/3
print(x)
print(round(x, 2))

The output will be:

0.3333333333333333 
0.33

Some errors and exceptions associated with this function
For example,

print(round("x", 2))

The output will be:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-9-6fc428ecf419> in <module>()
----> 1 print(round("x", 2))
TypeError: type str doesn't define __round__ method

Another example,

print(round(1.5))
print(round(2))
print(round(2.5))

The output will be:

2
2
2

The function round() rounds 1.5 up to 2, and 2.5 down to 2. This is not a bug, the round() function behaves this way. In this article you will learn a few other ways to round a number. Let us look at the variety of methods to round a number.

Diverse Methods for Rounding

There are many ways to round a number with its own advantages and disadvantages. Here we will learn some of the techniques to rounding a number.

Truncation

Truncation, as the name means to shorten things. It is one of the simplest methods to round a number which involves truncating a number to a given number of digits. In this method, each digit after a given position is replaced with 0. Let us look into some examples.

ValueTruncated ToResult
19.345Tens place10
19.345Ones place19
19.345Tenths place19.3
19.345Hundredths place19.34

The truncate() function can be used for positive as well as negative numbers:

>>> truncate(19.5)
19.0

>>> truncate(-2.8521)
-2.8

>>> truncate(2.8252)
2.82

The truncate() function can also be used to truncate digits towards the left of the decimal point by passing a negative number.

>>> truncate(235.7, -1)
230.0

>>> truncate(-1936.37-3)
-1000.0

When a positive number is truncated, we are basically rounding it down. Similarly, when we truncate a negative number, the number is rounded up. Let us look at the various rounding methods.

Rounding Up

There is another strategy called “rounding up” where a number is rounded up to a specified number of digits. For example:

ValueRound Up ToResult
12.345Tens place20
18.345Ones place19
18.345Tenths place18.4
18.345Hundredths place18.35

The term ceiling is used in mathematics to explain the nearest integer which is greater than or equal to a particular given number. In Python, for “rounding up” we use two functions namely,

  1. ceil() function, and
  2. math() function

A non-integer number lies between two consecutive integers. For example, considering a number 5.2, this will lie between 4 and 5. Here, ceiling is the higher endpoint of the interval, whereas floor is the lower one. Therefore, ceiling of 5.2 is 5, and floor of 5.2 is 4. However, the ceiling of 5 is 5.

In Python, the function to implement the ceiling function is the math.ceil() function. It always returns the closest integer which is greater than or equal to its input.

>>> import math

>>> math.ceil(5.2)
6

>>> math.ceil(5)
5

>>> math.ceil(-0.5)
0

If you notice you will see that the ceiling of -0.5 is 0, and not -1.
Let us look into a short code to implement the “rounding up” strategy using round_up() function:

def round_up(n, decimals=0): 
    multiplier = 10 ** decimals 
    return math.ceil(n * multiplier) / multiplier

Let’s look at how round_up() function works with various inputs:

>>> round_up(3.1)
4.0

>>> round_up(3.231)
3.3

>>> round_up(3.5432)
3.55

You can pass negative values  to decimals, just like we did in truncation.

>>> round_up(32.45, -1)
40.0

>>> round_up(3352-2)
3400

You can follow the diagram below to understand round up and round down. Round up to the right and down to the left.

The diagram which helps to better understand Rounding Up and Rounding Down in Python

Rounding up always rounds a number to the right on the number line, and rounding down always rounds a number to the left on the number line.

Rounding Down

Similar to rounding up we have another strategy called rounding down where

ValueRounded Down ToResult
19.345Tens place10
19.345Ones place19
19.345Tenths place19.3
19.345Hundredths place19.34

In Python, rounding down can be implemented using a similar algorithm as we truncate or round up. Firstly you will have to shift the decimal point and then round an integer. Lastly shift the decimal point back.

math.ceil() is used to round up to the ceiling of the number once the decimal point is shifted. For “rounding down” we first need to round the floor of the number once the decimal point is shifted.

>>> math.floor(1.2)
1

>>> math.floor(-0.5)
-1

Here’s the definition of round_down():

def round_down(n, decimals=0):
    multiplier = 10 ** decimals
return math.floor(n * multiplier) / multiplier

This is quite similar to round_up() function. Here we are using math.floor() instead of math.ceil().

>>> round_down(1.5)
1

>>> round_down(1.481)
1.4

>>> round_down(-0.5)
-1

Rounding a number up or down has extreme effects in a large dataset. After rounding up or down, you can actually remove a lot of precision as well as alter computations.

Rounding Half Up

The “rounding half up” strategy rounds every number to the nearest number with the specified precision, and breaks ties by rounding up. Here are some examples:

ValueRound Half Up ToResult
19.825Tens place10
19.825Ones place20
19.825Tenths place19.8
19.825Hundredths place19.83

In Python, rounding half up strategy can be implemented by shifting the decimal point to the right by the desired number of places. In this case you will have to determine whether the digit after the shifted decimal point is less than or greater than equal to 5.

You can add 0.5 to the value which is shifted and then round it down with the math.floor() function.

def round_half_up(n, decimals=0):
    multiplier = 10 ** decimals
return math.floor(n*multiplier + 0.5) / multiplier

If you notice you might see that round_half_up() looks similar to round_down. The only difference is to add 0.5 after shifting the decimal point so that the result of rounding down matches with the expected value.

>>> round_half_up(19.23, 1)
19.2

>>> round_half_up(19.281)
19.3

>>> round_half_up(19.251)
19.3

Rounding Half Down

In this method of rounding, it rounds to the nearest number similarly like “rounding half up” method, the difference is that it breaks ties by rounding to the lesser of the two numbers. Here are some examples:

ValueRound Half Down ToResult
16.825Tens place17
16.825Ones place17
16.825Tenths place16.8
16.825Hundredths place16.82

In Python, “rounding half down” strategy can be implemented by replacing math.floor() in the round_half_up() function with math.ceil() and then by subtracting 0.5 instead of adding:

def round_half_down(n, decimals=0):
    multiplier = 10 ** decimals
return math.ceil(n*multiplier - 0.5) / multiplier

Let us look into some test cases.

>>> round_half_down(1.5)
1.0

>>> round_half_down(-1.5)
-2.0

>>> round_half_down(2.251)
2.2

In general there are no bias for both round_half_up() and round_half_down(). However, rounding of data with more number of ties results in bias. Let us consider an example to understand better.

>>> data = [-2.151.454.35-12.75]

Let us compute the mean of these numbers:

>>> statistics.mean(data)
-2.275

Now let us compute the mean on the data after rounding to one decimal place with round_half_up() and round_half_down():

>>> rhu_data = [round_half_up(n, 1for n in data]
>>> statistics.mean(rhu_data)
-2.2249999999999996

>>> rhd_data = [round_half_down(n, 1for n in data]
>>> statistics.mean(rhd_data)
-2.325

The round_half_up() function results in a round towards positive infinity bias, and round_half_down() results in a round towards negative infinity bias.

Rounding Half Away From Zero

If you have noticed carefully while going through round_half_up() and round_half_down(), neither of the two is symmetric around zero:

>>> round_half_up(1.5)
2.0

>>> round_half_up(-1.5)
-1.0

>>> round_half_down(1.5)
1.0

>>> round_half_down(-1.5)
-2.0

In order to introduce symmetry, you can always round a tie away from zero. The table mentioned below illustrates it clearly:

ValueRound Half Away From Zero ToResult
16.25Tens place20
16.25Ones place16
16.25Tenths place16.3
-16.25Tens place-20
-16.25Ones place-16
-16.25Tenths place-16.3

The implementation of “rounding half away from zero” strategy on a number n is very simple. All you need to do is start as usual by shifting the decimal point to the right a given number of places and then notice the digit d immediately to the right of the decimal place in this new number. Here, there are four cases to consider:

  1. If n is positive and d >= 5, round up
  2. If n is positive and d < 5, round down
  3. If n is negative and d >= 5, round down
  4. If n is negative and d < 5, round up

After rounding as per the rules mentioned above, you can shift the decimal place back to the left.

There is a question which might come to your mind - How do you handle situations where the number of positive and negative ties are drastically different? The answer to this question brings us full circle to the function that deceived us at the beginning of this article: Python’s built-in  round() function.

Rounding Half To Even

There is a way to mitigate rounding bias while you are rounding values in a dataset. You can simply round ties to the nearest even number at the desired precision. Let us look at some examples:

ValueRound Half To Even ToResult
16.255Tens place20
16.255Ones place16
16.255Tenths place16.2
16.255Hundredths place16.26

To prove that round() really does round to even, let us try on a few different values:

>>> round(4.5)
4

>>> round(3.5)
4

>>> round(1.751)
1.8

>>> round(1.651)
1.6

The Decimal Class

The  decimal module in Python is one of those features of the language which you might not be aware of if you have just started learning Python. Decimal “is based on a floating-point model which was designed with people in mind, and necessarily has a paramount guiding principle – computers must provide an arithmetic that works in the same way as the arithmetic that people learn at school.” – except from the decimal arithmetic specification. 

Some of the benefits of the decimal module are mentioned below -

  • Exact decimal representation: 0.1 is actually 0.1, and 0.1 + 0.1 + 0.1 - 0.3 returns 0, as expected.

  • Preservation of significant digits: When you add 1.50 and 2.30, the result is 3.80 with the trailing zero maintained to indicate significance.

  • User-alterable precision: The default precision of the decimal module is twenty-eight digits, but this value can be altered by the user to match the problem at hand.

Let us see how rounding works in the decimal module.

>>> import decimal
>>> decimal.getcontext()
Context(
    prec=28,
    rounding=ROUND_HALF_EVEN,
    Emin=-999999,
    Emax=999999,
    capitals=1,
    clamp=0,
    flags=[],
    traps=[
        InvalidOperation,
        DivisionByZero,
        Overflow
    ]
)

The function decimal.getcontext() returns a context object which represents the default context of the decimal module. It also includes the default precision and the default rounding strategy.

In the above example, you will see that the default rounding strategy for the decimal module is ROUND_HALF_EVEN. It allows to align with the built-in round() function

Let us create a new Decimal instance by passing a string containing the desired value and declare a number using the decimal module’s Decimal class.

>>> from decimal import Decimal
>>> Decimal("0.1")
Decimal('0.1')

You may create a Decimal instance from a floating-point number but in that case, a floating-point representation error will be introduced. For example, this is what happens when you create a Decimal instance from the floating-point number 0.1

>>> Decimal(0.1)
Decimal('0.1000000000000000055511151231257827021181583404541015625')

You may create Decimal instances from strings containing the decimal numbers you need in order to maintain exact precision.

Rounding a Decimal using the .quantize() method:

>>> Decimal("1.85").quantize(Decimal("1.0"))
Decimal('1.8')

The Decimal("1.0") argument in .quantize() allows to determine the number of decimal places in order to round the number. As 1.0 has one decimal place, the number 1.85 rounds to a single decimal place. Rounding half to even is the default strategy, hence the result is 1.8.

Decimal class:

>>> Decimal("2.775").quantize(Decimal("1.00"))
Decimal('2.78')

Decimal module provides another benefit. After performing arithmetic the rounding is taken care of automatically and also the significant digits are preserved.

>>> decimal.getcontext().prec = 2
>>> Decimal("2.23") + Decimal("1.12")
Decimal('3.4')

To change the default rounding strategy, you can set the decimal.getcontect().rounding property to any one of several  flags. The following table summarizes these flags and which rounding strategy they implement:

FlagRounding Strategy
decimal.ROUND_CEILINGRounding up
decimal.ROUND_FLOORRounding down
decimal.ROUND_DOWNTruncation
decimal.ROUND_UPRounding away from zero
decimal.ROUND_HALF_UPRounding half away from zero
decimal.ROUND_HALF_DOWNRounding half towards zero
decimal.ROUND_HALF_EVENRounding half to even
decimal.ROUND_05UPRounding up and rounding towards zero

Rounding NumPy Arrays

In Data Science and scientific computation, most of the times we store data as a  NumPy array. One of the most powerful features of NumPy is the use of  vectorization and broadcasting to apply operations to an entire array at once instead of one element at a time.

Let’s generate some data by creating a 3×4 NumPy array of pseudo-random numbers:

>>> import numpy as np
>>> np.random.seed(444)

>>> data = np.random.randn(34)
>>> data
array([[ 0.35743992,  0.3775384 ,  1.38233789,  1.17554883],
       [-0.9392757 , -1.14315015, -0.54243951, -0.54870808],
       [ 0.20851975, 0.21268956, 1.26802054, -0.80730293]])

Here, first we seed the np.random module to reproduce the output easily. Then a 3×4 NumPy array of floating-point numbers is created with np.random.randn().

Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.

To round all of the values in the data array, pass data as the argument to the  np.around() function. The desired number of decimal places is set with the decimals keyword argument. In this case, round half to even strategy is used similar to Python’s built-in round() function.

To round the data in your array to integers, NumPy offers several options which are mentioned below:

The np.ceil() function rounds every value in the array to the nearest integer greater than or equal to the original value:

>>> np.ceil(data)
array([[ 1.,  1.,  2.,  2.],
       [-0., -1., -0., -0.],
       [ 1., 1., 2., -0.]])

Look at the code carefully, we have a new number! Negative zero! Let us now take a look at Pandas library, widely used in Data Science with Python.

Rounding Pandas Series and DataFrame

Pandas has been a game-changer for data analytics and data science. The two main data structures in Pandas are Dataframe and Series. Dataframe works like an Excel spreadsheet whereas you can consider Series to be columns in a spreadsheet. Series.round() and DataFrame.round() methods. Let us look at an example.

Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.

>>> import pandas as pd

>>> # Re-seed np.random if you closed your REPL since the last example
>>> np.random.seed(444)

>>> series = pd.Series(np.random.randn(4))
>>> series
0    0.357440
1    0.377538
2    1.382338
3    1.175549
dtype: float64

>>> series.round(2)
0    0.36
1    0.38
2    1.38
3    1.18
dtype: float64

>>> df = pd.DataFrame(np.random.randn(33), columns=["A""B""C"])
>>> df
          A         B         C
0 -0.939276 -1.143150 -0.542440
1 -0.548708  0.208520  0.212690
2  1.268021 -0.807303 -3.303072

>>> df.round(3)
       A      B      C
0 -0.939 -1.143 -0.542
1 -0.549  0.209  0.213
2  1.268 -0.807 -3.303

The DataFrame.round() method can also accept a dictionary or a Series, to specify a different precision for each column. For instance, the following examples show how to round the first column of df to one decimal place, the second to two, and the third to three decimal places:
>>> # Specify column-by-column precision with a dictionary
>>> df.round({"A"1"B"2"C"3})
     A     B      C
0 -0.9 -1.14 -0.542
1 -0.5  0.21  0.213
2  1.3 -0.81 -3.303

>>> # Specify column-by-column precision with a Series
>>> decimals = pd.Series([123], index=["A""B""C"])
>>> df.round(decimals)
     A     B      C
0 -0.9 -1.14 -0.542
1 -0.5  0.21  0.213
2  1.3 -0.81 -3.303

If you need more rounding flexibility, you can apply NumPy's floor(), ceil(), and print() functions to Pandas Series and DataFrame objects:
>>> np.floor(df)
     A    B    C
0 -1.0 -2.0 -1.0
1 -1.0  0.0  0.0
2  1.0 -1.0 -4.0

>>> np.ceil(df)
     A    B    C
0 -0.0 -1.0 -0.0
1 -0.0  1.0  1.0
2  2.0 -0.0 -3.0

>>> np.rint(df)
     A    B    C
0 -1.0 -1.0 -1.0
1 -1.0  0.0  0.0
2  1.0 -1.0 -3.0

The modified round_half_up() function from the previous section will also work here:
>>> round_half_up(df, decimals=2)
      A     B     C
0 -0.94 -1.14 -0.54
1 -0.55  0.21  0.21
2 1.27 -0.81 -3.30

Best Practices and Applications

Now that you have come across most of the rounding techniques, let us learn some of the best practices to make sure we round numbers in the correct way.

Generate More Data and Round Later

Suppose you are dealing with a large set of data, storage can be a problem at times. For example, in an industrial oven you would want to measure the temperature every ten seconds accurate to eight decimal places, using a temperature sensor. These readings will help to avoid large fluctuations which may lead to failure of any heating element or components. We can write a Python script to compare the readings and check for large fluctuations.

There will be a large number of readings as they are being recorded each and everyday. You may consider to maintain three decimal places of precision. But again, removing too much precision may result in a change in the calculation. However, if you have enough space, you can easily store the entire data at full precision. With less storage, it is always better to store at least two or three decimal places of precision which are required for calculation.

In the end, once you are done computing the daily average of the temperature, you may calculate it to the maximum precision available and finally round the result.

Currency Exchange and Regulations

Whenever we purchase an item from a particular place, the tax amount paid against the amount of the item depends largely on geographical factors. An item which costs you $2 may cost you less (say $1.8)  if you buy the same item from a different state. It is due to regulations set forth by the local government.

In another case, when the minimum unit of currency at the accounting level in a country is smaller than the lowest unit of physical currency,  Swedish rounding is done. You can find a list of such rounding methods used by various countries if you look up on the  internet.

If you want to design any such software for calculating currencies, keep in mind to check the local laws and regulations applicable in your present location.

Reduce error

As you are rounding numbers in a large datasets used in complex computations, your primary concern should be to limit the growth of the error due to rounding.

Summary

In this article we have seen a few methods to round numbers, out of those “rounding half to even” strategy minimizes rounding bias the best. We are lucky to have Python, NumPy, and Pandas already have built-in rounding functions to use this strategy. Here, we have learned about -

  • Several rounding strategies, and how to implement in pure Python.
  • Every rounding strategy inherently introduces a rounding bias, and the “rounding half to even” strategy mitigates this bias well, most of the time.
  • You can round NumPy arrays and Pandas Series and DataFrame objects.

If you enjoyed reading this article and found it to be interesting, leave a comment. To learn more about rounding numbers and other features of Python, join our  Python certification course.

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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Its integration with Django gives a Python framework. It also has keyword auto-completion, good debugging tool, syntax highlighting and indentation. Pros Free open source Robust IDE feature set Auto-completion of codes and analysis Smart indentation Interactive console shortcuts Integrated with Django configuration  Platform independent Cons: User interface is not great  Visual studioKey Features: It is categorized as an IDE, is a full-featured IDE developed by Microsoft. It is compatible with Windows and Mac OS only and comes with free as well as paid versions. It has its own marketplace for extensions. PTVS(Python Tools for Visual Studio) offers various features as in coding for Python development, IntelliSense, debugging, refactoring etc. Pros Easy and less tedious installation for development purposes Cons Spacious files  Not supported on Linux Visual studio code Key Features: VS code is a code editor and is way more different from VS. It is a free open source code editor developed by Microsoft can be run on platforms such as Windows, Linux and Mac OS X.  It has a full-featured editor that is highly configurable with Python compatibility for software development. Python tools can be added to enable coding in Python.VS code is integrated with Git which promotes it to perform operations like push, commit directly from the editor itself. It also has electron framework for Node JS applications running on the Blink browser engine. It is enclosed with smart code completion with function definition, imported modules and variable types. Apart from these, VS code also comes with syntax highlighting, a debugging console and proprietary IntelliSense code auto completion. After installing Python, VS code recognizes Python files and libraries immediately.  Pros Free and available on every platform  Small, light-weight but highly extensible Huge compatibility Has a powerful code management system Enables debugging from the editor Multi-language support  Extensive libraries Smart user interface and an acceptable layout Cons Slow search engine Tedious launch time Not a native app just like Atom WingKey Features: Wing is also one of the powerful IDEs today and comes with a lot of good features. It is an open source IDE used commercially. It also is constituted with a strong framework and has a strong debugger and smart editor for Python development making it fast, accurate and fun to perform. It comes with a 30 day trial version. It supports text driven development with unit test, PyTest and Django testing framework.  Pros Open source Find and go-to definition Customizable and extensible Auto-code completion Quick Troubleshoot  Source browser shows all the variables used in the script Powerful debugger  Good refactoring  Cons Not capable of supporting dark themes Wing interface is quite intimidating Commercial version is expensive Python-specific IDEs and Editors Anaconda - Jupyter NotebooksKey Features: It is also an open source IDE with a server-client structure, used to create and edit the codes of a Python. Once it is saved, you can share live code equations, visualizations and text. It has anaconda distribution i.e., libraries are preinstalled so downloading the anaconda itself does the task. It supports Python and R language which are installed by default at installation.  This IDE is again used for data science learning. Quite easy to use, it is not just used as an editor but also as an educational tool or presentation. It supports numerical simulation, machine  learning visualization and statistical modelling. Pros Free Open source  Good user interface Server-client structure Educational tool- Data science, Machine learning  Supports numerical simulation  Enables to create, write, edit and insert images Combines code, text and images Integrated libraries - Matplotlib, NumPy, Pandas Multi-language support Auto code completion Cons Sometimes slow loading is experienced Google Colaboratory Key Features: It is the simplest web IDE used for Python. It gives a free GPU access. Instead of downloading heavy files and tedious launch time, one can directly update the files from Colab to the drive. All you need to do is log in to your google account and open Colab. There is no need for extra setup. Unlike other IDEs no files are required to download. Google provides free computation resources with Colaboratory. It is designed for creating machine learning models. For compilation and execution, all you need to do is to update Python package and get started.   Pros Available to all Code can be run without any interruption Highly user interactive No heavy file downloads Integrated libraries Multi-language support Updated in google drive Update the Python package for execution  Runs on cloud Comments can be added in cells Can import Jupiter or IPython notebooks Cons  All colaboratory files are to be stored in google drive Install all specific libraries No access to unsaved files once the session is over Pycharm Key Features: Developed by Jet Brains and one of the widely used full-featured Python IDE, this is a cross-platform IDE for Python programming and  is well-integrated with Python console and IPython Notebook. It is supported by Windows, Linux, Mac OS and other platforms as well. It has massive productivity and saves ample amount of time. It comes with smart code navigation, code editor, good debugging tool, quick refactoring etc. and supports Python web development frameworks such as Angular JS, JavaScript, CSS, HTML  and live editing functions. The paid version offers advanced features such as full database management and a multitude Framework than the community version such as Django, Flask, Google App, Engine, Pyramid and web2py. Pros Great supportive community Brilliant performance. Amazing editing tools Robust debugging tool Smart code navigation Quick and safe refactoring  Built in developer tools Error detection and fix up suggestions Customizable interface Available in free and paid version Cons Slow loading  Installation is quite difficult and may hang up in between SpyderKey Features: It is an open source IDE supported on all platforms. Ranked as one of the best Python compilers, it supports syntax highlighting, auto completion of codes just like Pycharm. It offers an advanced level of editing, debugging, quick diagnose, troubleshoot and many data exploration features. To get started with Spyder, one needs to install anaconda distribution which is basically used in data science and machine learning. Just like Pycharm it has IntelliSense auto-completion of code. Spyder is built on a structured and powerful framework which makes it one of the best IDE used so far. It is most commonly used for scientific development. Pros Free open source IDE Quick troubleshoot Active framework Smart editing and debugging Syntax is automatically highlighted Auto completion of codes Good for data science and machine learning Structured framework Integrates common Python data science libraries like SciPy, NumPy, and Matplotlib Finds and eliminates bottlenecks Explores and edits variables directly from GUI  Performs well in multi-language editor and auto completion mode Cons Spyder is not capable to configure a specific warning Too many plugins degrades its performance ThonnyKey Features: Thonny is another IDE best suited for beginners for Python development and provides a good virtual environment. It is supported on all platforms. It gives a simple debugger with F5, F6 and F7 keys for debugging. Also, Thonny supports highlighting errors, good representation of function calls, auto code completion and smart indentation. It even allows the developers to configure their code and shell commands. by default,  in Thonny Python is pre-installed as it downloads with its own version of Python.  Pros Simple Graphical user interface.  Free open source IDE Best for beginners Simple debugger with F5, F6, F7 Keys Tackles issues with Python interpreters Highlights syntax error Auto-completion of code Good representation of function calls User can change reference mode easily Step through expression evaluation Reply and resolve to comments Cons Interface is not that good for developers Confined to text editing No template support Slow plugin creation Too basic IDE for software development Which Python IDE is right for you? Requirements vary from programmer to programmer. It is one’s own choice to pick the right tool that is best suited for the task at hand. Beginners need to use a simple tool with few customizations whereas experts require tools with advanced features to bring new updates. Few suggestions are listed below:- Beginners should start with IDLE and Thonny as they do not have complex features and are pretty easy to learn. For data science learners Jupyter Notebooks and Google Colaboratory is preferred. Generally, large scale enterprises prefer the paid versions of IDEs like PyCharm, Atom, Sublime Text etc. in order to get extensive service support from the company. Also, they provide easy finance options and manpower. On the other hand, middle and small scale enterprises tend to look for open source tools which provides them with excellent features. Some of such IDEs are Spyder, Pydev, IDLE and Visual Studio. Conclusion Today, Python stands out as one of the most popular programming languages worldwide. IDE being a program dedicated to software development has made it easier for developers to build, execute, and debug their codes. Code editors can only be used for editing codes whereas an IDE is a feature rich editor which has inbuilt text editor, compiler, debugging tool and libraries. Different IDEs and code editors are detailed in this article along with their merits and demerits. Some are suitable for beginners because of their lightweight nature and simplicity like IDLE, Thonny whereas experts require advance featured ones for building software.  For learning purposes say data science, machine learning Jupyter and Google Colaboratory are strongly recommended. Again there are large scale enterprises who prefer PyCharm, Atom, Sublime Text for software development. On the other hand, small scale enterprises prefer Spyder, Pydev, IDLE and Visual Studio. Hence,the type of IDE or code editor that should be used completely depends upon the requirement of the programmer . To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course. 
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Top 10 Python IDEs and Code Editors

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Scala Vs Kotlin

Ever-changing requirements in coding have always been happening, ones that cause programmers to change their minds about using the appropriate programming language and tools to code. Java has been there for a long time, a really long time, 24 years ago. It is relatively easy to use, write, compile, debug, and learn than other programming languages. However, its certain inhibitions like slow performance, unavailability of any support for low-level programming, possessing poor features in GUI 4, and having no control over garbage collection is putting Java developers in a dilemma on choosing an alternative to Java, such as JetBrains’ programming language, Kotlin, presently an officially supported language for Android development or Scala, an all-purpose programming language supporting functional programming and a strong static type system. Today, we will discuss how developers can decide to choose Scala or Kotlin as an alternative to Java. We will briefly talk about Scala and Kotlin separately and talk about their application before moving forward to looking at the differences, advantages, and disadvantages of both and finally have you decide which one of these two suits your requirements. User’s requirement Before we begin, here is a question for the readers, ‘What are you looking for in the next programming language that you will use?’ It is an obvious question because the programming purposes drive the actual basis and need of developing a language. Do you need a language that strives to better Java or use a language that lets you do things that aren’t possible in Java? If it is the first reason, then Scala might be the best one for you, otherwise, it is a simplified programming language like Kotlin. Now let us first briefly discuss Scala and Kotlin individually. ScalaDeveloped by Martin Odersky, the first version of Scala was launched in the year 2003 and is a classic example of a  general-purpose, object-oriented computer language, offering a wide range of functional programming language features and a strong static type system. Inspired from Java itself, Scala, as the name suggests, is highly scalable and this very feature sets Scala apart from other programming languages. When we say that Scala is inspired from Java, that means developers can code Scala in the same way they do for Java. Additionally, Scala makes it possible to use numerous Java and libraries within itself as well. It is designed to be able to use an elegant, concise and type-safe method to express common programming patterns. Scala is a very popular programming language amongst developers and rising up its ranks in the world of technology. Although Scala comes with a number of plus points, there are some which make it a bit ineffective. Here are the strengths and weaknesses of Scala. Strengths: Full Support for Pattern Matching, Macros, and Higher-Kinded Types Has a very flexible code syntax Gets a bigger Community Support Enables overloading operators Weaknesses: Slow in compilation Challenging Binary Compilation Not so proficient in the Management of Null SafetyKotlin Developed by JetBrains, Kotlin was released on February 2012 as an open-source language. Until now, there have been two released versions with the latest one being Kotlin 1.2, the most stable version that was released on November 28, 2017. Since Kotlin is extremely compatible with Java 6 the latest version of Java on Android, it has gained critical acclaim on Android worldwide and additionally, it offers various key features that are prepared only for Java 8 and not even Java 6 developers have access to that. Kotlin provides seamless and flawless interoperability with Java. That means, developers can easily call Java codes from Kotlin and same goes the other way around. The built-in null safety feature avoids showing the NullPointerException (NPE) that makes developing android apps easy and joyful, something every android programmer wants. Below mentioned are the key pointers on the strengths and weaknesses of Kotlin. Strengths Takes a Functional Programming Approach and Object-Oriented Programming style(OOP) Style  Has Higher-Order Functions Short, Neat, and Verbose-Free Expression  Supported by JetBrains and Google. Weaknesses: More limited Pattern Matching Additional Runtime Size Initial Readability of Code Shortage of Official Support Smaller Support Community. Ease of learning: Scala vs Kotlin Scala is a powerful programming language packed with superior features and possesses a flexible syntax. It is not an easy language to learn and is a nightmare for newcomers. Kotlin, on the other hand, has been reported to have been an easy-to-learn language for many Java developers as getting started with Kotlin is relatively easy and so is writing codes. Even though it is a comparatively easier language to learn and code with, Kotlin lacks the solid set of features that is common in Scala. It might take less time to learn a programming language, but the most important thing to look for is a comprehensive array of features. Scala, even though a very difficult language to learn, is cherished by the developers as it lets them do things that cannot be done in Kotlin Here are the major differences between Scala and Kotlin: ScalaKotlinType inferenceEfficientImmutabilityExtension FunctionsSingleton objectMassive InteroperabilityConcurrency controlLessens Crashes at RuntimeString interpolationSmart Cast FunctionHigher-order functionSafe and ReliableCase classes and Pattern matching Lazy computationLow adoption costRich collection setMaking the appropriate choice of languageNow, whether you may like a programming language or not, if that very language helps you get the best out of your job, then you will have to live with it. These are the facts about getting the best results. The outcome is the main factor in you deciding the appropriate language for your job. Kotlin is the only option for Android development as Android doesn’t use JVM, so any old JVM-compatible language will not work in Android. Kotlin has it all what it takes to compile, debug, and run the software on Android because of which it is in-built into Android Studio. However, Kotlin is not so usable outside Android development. If you are one of the developers who like working with Eclipse for your IDE, then Scala IDE is better than the Kotlin Plugin even if you can make Eclipse work with both the languages with limitations. Scala IDE is more advanced than the Kotlin plugin and is easier to set up. Some developers found it quite difficult to make the Kotlin plugin work. This case is quite the same with NetBeans. Kotlin is still getting there but is already popular amongst Java developers as it offers an easier transition than Scala. Kotlin is still maturing, but many Java people find adopting it is an easier transition than Scala is.  Scala, however, is for developers who are focused more on discovering new ideas while Kotlin is for those who want to get results. Kotlin stresses fast compilation but is more restrictive while Scala gives a lot of flexibility. Go for Scala if you breathe functional programming! It has more appropriate features for this type of programming than Kotlin does. Scala supports currying and partial application, the methods of breaking down functions requiring multiple arguments offering more flexibility. Go for the one that is the most appropriate one for your work, style of working and what you are aiming at. Think before you leap. The Outcome At the end of the day, all that matters is what you want to use the language for. While Scala goes well for the projects that require a combination of functional, OOP style programming languages, and where programmers need to handle lots of data or complex modelling, Kotlin becomes the best choice when you want something less frustrating than Java while developing apps because using Kotlin makes app development less cumbersome and a great thing to work on. It is just like a better-looking version of Java with less lengthy codes. 
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Scala Vs Kotlin

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Xcode vs Swift

Xcode and Swift are two different products developed by Apple for macOS, iOS, iPadOS, watchOS, and tvOS. While Xcode is an integrated development environment (IDE) for macOS containing a suite of software development tools to develop software for macOS, iOS, iPadOS, watchOS, and tvOS, Swift is a general-purpose, multi-paradigm, compiled programming language developed iOS, macOS, watchOS, tvOS, Linux, and z/OS. So it is clear that they can not be compared with each other. On the contrary, Swift is compatible with Xcode as Swift v 5.1, the default version of Swift is included in Xcode v 11. In this article, we will go through what Xcode and Swift are in general and cover their features strengths and weaknesses followed by how Swift is compatible with Xcode. XcodeIt was first released in 2003 as version 1 with the latest stable one being version 10.2.1 released on 17 April 2019. It can be downloaded from the Mac App Store and is free to use for macOS Mojave users. Registered developers may download the preview releases and previous versions of the suite using via the Apple Developer website.  Overview of the major featuresSupport: Programming languages such as C, C++, Objective-C, Objective-C++, Java, AppleScript, Python, Ruby, ResEdit (Rez), and Swift are supported by Xcode with source code along with support for a variety of programming models including Cocoa, Carbo, and Java. Not only that, there is additional support via third parties for GNU Pascal, Free Pascal, Ada, C#, Perl, and D Capability: Xcode can build fat binary files that include the code for various architectures in the Mach-O executable format. Known as universal binary files, these allow the application to run on both PowerPC and Intel-based (x86) platforms including both 32-bit and 64-bit codes Compiling and debugging: Xcode uses the iOS SDK to compile and debug applications for iOS that run on ARM architecture processors GUI tool: Xcode comprises of the GUI tool, Instruments that runs dynamic tracing framework on the top of DTrace, a dynamic tracing framework designed by Sun Microsystems and released as a part of OpenSolaris. Advantages and disadvantages of Xcode: Xcode is designed by Apple and will only work with Apple operating systems: macOS, iOS, iPadOS, watchOS, and tvOS. Since its release in 2003, Xcode has made significant improvements and the latest version, Xcode 10.2.1 has all the features that are needed to perform continuous integration. Let us have a look at the pros of using Xcode: Equipped with a well designed and easy to use UI creator Excellent for code completion Using Xcode, a developer can learn profiling and heap analysis in a natural way Xcode’s simulator lets you easily test your app while you build it in an environment that simulates your iPhone The app store has a wide range of audience who are willing to pay for apps. Now, the cons: Clunky and outdated Objective C makes it more frustrating if you are habituated to use a modern language No support for tabbed work environments makes it difficult to work with multiple windows Hardly any information can be found online to solve problems due to a previous Apple NDA on Xcode development It is a complicated process to export your app onto a device Will only work with Apple operating systems The App Store approval process can be annoyingly lengthy.SwiftSwift was launched at Apple's 2014 Worldwide Developers Conference as a general-purpose, multi-paradigm, compiled programming language for iOS, macOS, watchOS, tvOS, Linux, and z/OS Being a new entry these operating systems, Swift accelerates on the best parts of C and Objective C without being held back by its compatibility. It utilises safe patterns for programming, adding more features to it, thus making programming easier and more flexible. By developing their existing debugger, compiler and framework infrastructure, it took quite some time to create the base for Swift. Furthermore, Automatic Reference Counting was used to simplify the memory management part. The framework stack which was once built upon a solid framework of Cocoa and Foundation has undergone significant changes and is now completely regulated and refurbished. Developers who have worked with Objective-C do find Swift quite similar. Objective-C’s dynamic object model and its comprehensively named parameters provide a lot of control to Swift.  Developers can use Swift to have access to the existing Cocoa framework in addition to the mix and match interoperability with an objective C code. Swift uses this common rule to offer multiple new features in combination with object-oriented and procedural portions of the language. The idea is to create the best possible language for a wide range of uses, varying from desktop and mobile apps, systems programming, and scaling up to cloud services. The designing of Swift was done to make sure that developers find it easy to maintain and write correct programs. Coding done in Xcode is safe, fast and expressive. Swift offers a host of features that give developers the control needed to make the code easy to read and write. Furthermore, Apple made Swift to be easily understandable to help developers avoid making mistakes while coding and make the code look organised, along with the modules that give namespaces and eliminate headers. Since Swift uses some features present in other languages, one of them being named parameters written with clean syntax that makes the APIs much easier to maintain and read. Here are some of the additional features of Swift: Multiple return values and Tuples Generics Short and quick iterations over a collection or range Structs that support extensions, methods and protocols Functional programming patterns Advanced control flow Powerful error handling. These features are systematically designed to make them work together resulting in creating a powerful but fun-to-use language. Advantages and disadvantages of Swift: Pros of using the Swift Programming language: Easy to read and maintain: The Swift program codes are based on natural English as it has borrowed syntaxes from other programming languages. This makes the language more expressive Scalable: Users can add more features to Swift, making it a scalable programming language. In the future, Swift is what Apple is relying on and not Objective C Concise: Swift does not include long lines of code and that favours the developers who want a concise syntax, thus increasing the development and testing rate of the program Safety and improved performance: It is almost 40% better than the Objective-C when speed and performance are taken into consideration as it is easy to tackle the bugs which lead to safer programming Cross-device support: This language is capable of handling a wide range of Apple platforms such as iOS, iOS X, macOS, tvOS, and watchOS. Automatic Memory Management: This feature present in Swift prevents memory leaks and helps in optimizing the application’s performance that is done by using Automatic Reference Counting. Cons of Swift: Compatibility issues: The updated versions Swift is found to a bit unstable with the newer versions of Apple leading to a few issues. Switching to a newer version of Swift is the fix but that is costly Speed Issues: This is relevant to the earlier versions of the Swift programming language Less in number: The number of Swift developers is limited as Swift is a new programming language Delay in uploading apps: Developers will be facing delays over their apps written in Swift to be uploaded to the App Store only after iOS 8 and Xcode 6 are released. The estimated time for release is reported to be September-October, 2014. Conclusion So as we discussed both Xcode and Swift, it is clear that they cannot be compared to each other. In fact, they both complement each other to deliver impressive results without any headaches. Apple relies on both quite a lot and it is certain to have Swift and Xcode the perfect combination of a robust application and a user-friendly programming language.
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Xcode vs Swift

Xcode and Swift are two different products develop... Read More

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