![]() 4e-3 equates to 0.004, because 10 to the -3 is one thousandth, and then we have four of those, which is why we have that 4 there.Ġ4:28 Sometimes the negative form is a little less intuitive, but with a bit of practice, it will make sense. So here, we’re going to define the number j as 4, and then use an e, to denote scientific notation, and a 3.Ġ3:58 And you can see that equals 4000.0 because we have a 4 at the beginning, and then we have 3 powers of 10, which it’s multiplied by, so it’s 4 * 1000.Ġ4:10 The powers can also be negative, as seen here. If you’re not aware of what scientific notation is, a little bit of research on Wikipedia will help you understand it, but the general form is that there will be a number at the beginning and then the second part will be the powers of 10 which are being applied to that number. Sometimes floor division gives you the right number, and other times floor division gives you the wrong number.Ġ3:06 It’s possible to take any number, including an int and turn it into a float using the float() keyword.Ġ3:17 This can be seen here by making i = float(3), and then if we look at i, we can see i has the value 3.0.Ġ3:28 Now, something else which is useful is being able to define numbers using scientific notation. So here, if we take c and divide it by 4, the answer should be 2.5 but we can see it’s also 2.Ġ2:50 This is something you need to be aware of when choosing which division operator you’re going to make use of. Python does have the integer division operator, which is the double divide sign ( //), as seen here, and we can see the result is 2.Ġ2:32 But there is another thing to be aware of with this- it will always give you an integer result, which may not be the exact answer you’re looking for. The result of division will always be a float, even if the answer could be an int.Ġ2:02 So, obviously, in that case, 10 divided by 4 is 2.5, so the answer would need to be a floating-point number, but if d is redefined to be 5, and we make f = c / d again,Ġ2:19 we can see that f is a float even though the answer could be an int. Both of those are ints, but if we make e = c / d,Ġ1:49 we can see the result, which is e, which is a float. Coming up, you’ll see that the result of division will always be a float.Ġ1:29 We’re going to define some variables to work with to make the division clear to see. So while that could be represented as an int, it is a float. But there are some limitations to floating-point numbers, as you will see at the end of this section, and this is something you will fall foul of at some point and need to be aware of.Ġ0:51 The simplest way to define a floating-point number in Python is to create a variable with a decimal point and a number after it, such as seen here, where we have a = 4.2.Ġ1:03 You can see that the value is 4.2, and entering type(a) shows a and a floating-point number has been created.Ġ1:13 A number which has got a decimal after the point, even if it’s 0, will still be a float. And while you’ve seen some maths in the previous section, any division operation will return a float, as you will see in a little more detail later on,Ġ0:30 and they can be defined using scientific notation, such as seen onscreen here with 4 * 10 to the power of 3, which is equal to 4,000. These are represented differently in Python’s memory than the integers we’ve already seen. The float() returns a floating-point number based on a number or a string.00:06 Now, what is a floating-point number? Well, a floating-point number is any number with a decimal point. in binary.īecause of this, Python can only use approximate float representations for those numbers. Some numbers have a finite binary representation, but some don’t, e.g., 0.1. ![]() įor the sake of simplicity, significant digits are all digits except leading and trailing zeros.įor example, 0.25 has two significant digits, 0.125 has three significant digits, and 12.25 has four significant digits. 11 bits for exponent 1.5e-5 1.5 x 10 -5 (exponent is -5) the range is. #Float python 64 bits#Technically, Python uses 64 bits as follows: Unlike the integer type, the float type uses a fixed number of bytes. ![]() Python float uses 8 bytes (or 64 bits) to represent real numbers. The C double type usually implements IEEE 754 double-precision binary float, which is also called binary64. Python uses the float class to represent real numbers.ĬPython implements float using C double type. #Float python how to#Summary: in this tutorial, you’ll learn about the Python float type, how Python represents floating-point numbers, and how to test the floating-point number for equality. ![]()
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