In Python, there are several types of numbers. There are integer numbers, also known as whole numbers, and floating point numbers, or fractions. Integer numbers do not have a fractional part, while floating point numbers do.
Integers can be negative or positive, and floating point numbers can have a sign (+ or -) and a fractional part (1/10th or 2).
Python has a special type of integer called an unsigned integer. These cannot be negative values! All values must be 0 or greater.
Similarly, there is a special type of floating point number called a float64. This type of number must have up to 7 digits after the decimal place and must be accurate to the 1/10000th place. All other floats are considered float32s and are less accurate.
This article will go into detail about some errors that occur in Python due to incorrect use of integers and floats.
Reasons for nan, infinities, and large values
There are a few reasons you may get these values while coding. Some of them are more serious than others.
Invalid code can result in nan, infinity, and large values. If you have invalid code, like using = when you should use == , then your program will not run properly and may result in one of these unexpected values.
When running the same code on different computers, nan, infinity, and large values can occur. Computer hardware is different so the number of digits stored in memory is different. This can cause one computer to store a number that is too big for another computer to handle.
Network issues can also result in nan, infinity, and large values. If one computer receives incorrect information from another computer then some of your numbers may be too big or small due to the differences in networking equipment.
Solutions for nan, infinities, and large values
Fortunately, there are a few solutions for nan, infinity, and large values. You can replace them with zero, normal numbers, or smaller values depending on your situation.
If you have to keep a value that is NaN, then you can still use your function as long as your function does not depend on the value of the NaN element.
For example, let’s say you have to keep the age of a person, but their age cannot be computed due to lack of information. In this case, you can keep their age as NaN and then tell anyone who asks that they are too old for them to remember their exact age.
When using numerals with large values, make sure to scale them down first before using them in calculations. This way, you are making sure that the numbers are small enough for your domain.
Understand the problem
In some cases, your Julia code may return a value that is too large or small to be represented as a float64 . This can happen when you do any kind of math with nan s or infinity values.
For example:
julia> x = 1.0 + 0.0; julia> y = 1.0 – 0.0; julial> z = x + y; julia> z 2.0
Here, since we are adding two zero values, the result of the calculation is also zero.
Evaluate input data
The next step is to evaluate the input data. Is it a valid number? Is it in the correct format? Are there too many or too few numbers?
Built-in functions like int() , float() , round() and isdigit() are useful for evaluating input data. You can use them separately or in combinations to ensure the data is valid.
For example, int(123) returns 123 as an integer, float(“123”) returns 123 as a floating point number, round(123.4567) returns 124 and isdigit(“1”) returns true .
You can also use more advanced functions like regexp_matches() , regexp_replace() and scan() to evaluate input data.
Replace invalid values with valid ones
In some cases, it is possible to replace the invalid values with valid ones. For example, you could check if a person’s income is below a certain amount and set their income to that amount if it is.
However, in other cases this may not be possible. For example, you could not assume that a person’s weight is zero pounds unless they explicitly state this.
In both of these cases, you can still use pandas to correctly handle the data. By creating new columns that indicate whether someone has an income or weight above a certain value and then using those in calculations, you can safely use pandas to analyze the data.
By doing this, you are able to still get valid results while still dealing with invalid values.
Use the ‘nan’ value
When you need to represent a value as ‘not a number’, use the nan value. This is a built-up value that contains no information except that the number is not a real number.
For example, if you try to calculate 1/0, you will get NaN, because you cannot divide by zero. Similarly, if you try to calculate 0/0, you will get NaN because zero divided by any other number is 0.
You can use the nan in Python 3 by inserting nan as a new variable. In Python 2, you must import it as nan = None .
Use this when you need to represent that there is no valid value for something. For example, if someone asks your app for their BMI but they do not enter a valid height or weight, then return nan .
Use infinity as a limit value
When you are trying to find the limit of a value, you must be careful not to include infinity in your calculations. Mathematically, it is okay to use infinity as a limit value when finding limits.
For example, when calculating .infinity-infinity., you can say that .infinity-infinity. equals infinity, because .infinity-inf Infinity.-Infinity. is equal to infinity.
The problem arises when you try to put infinity into a calculation. For example, if you tried to calculate the limit of by adding 1 to it, the calculation would return an error because infinite minus 1 is infinite.
The solution is to use a special type of infinity called negative infinity (also known as negation). Negative infinity looks like -INF or -0
Ensure valid output format
Once you have checked that your output format is valid, the next step is to make sure your output format is valid for all possible input.
For example, if you are performing a calculation in decimal arithmetic, then your output must be in decimal format. If you were to try and calculate in binary, your output would be invalid.
In cases where you have no control over the output format, like in some software applications, then you can check for invalid output formats using a validation tool. There are many of these available online for free.
In cases where you do have control over the output format, like in programming software modules, then you can implement validation checks within the code.
This is an important check that many do not think about, but it can potentially save you from receiving invalid data due to an invalid output format.
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