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Arguments are optional in functions and multiple arguments must be comma seperated. Functions have a return value. gas x user reviews Functions have a default return value of the aforementioned None placeholder. If one would like to have the return value returned, the suite must include the syntax ‘return value’. Multiple values can be returned in the form of a tuple. The return value can also be ignored by the ‘caller’ in which case the value is simply disposed of. The caller is the identifier of the function ‘def caller(argument):’

The function above has one argument "msg". The user is asked to enter a string because of the input() function. It is passed to the int() function which attempts to convert the input function argument into an integer. If this is not possible, in that the int() function was unable to convert the said input, the exception ValueError will save itself to the err variable. Inside the suite for the exception is a print() function which will output the string saved to the err variable. If the value entered satisfies the try block it is returned to the caller ‘get_int()’. If it is an unsatisfiable return value the loop will then start from the top and try again.

In the statement "age = get_int(‘enter your age: ‘)", the function get_int() is being called and saved to the variable ‘age’. I’m not sure I quite understand what is going on here but aparantly because the get_int function wasn’t given a default value, we have to supply it with the string ‘enter your age: ‘. It is said that Python supports ‘a very sophisticated and flexible syntax for function parameters’. I’m not exactly sure how this is born out, but I’ll take it on its face. Before you go and start making all your favorite new functions, beware that Python has a robust collection of built-in functions. What you are trying to do has probably already been done.

Once you’ve got the module imported you can access all of the functions of the module in your code. I’m assuming that this is all done in your own Python code, subsequently using functions that the imported module is capable of. It seems to me that the interpreter is what is serving all of these functions to be usable in your own code. But I’m not sure.

After importing a module using ‘import moduleName’ we can then use its functions by stating ‘moduleName.functionName(args)’. This uses the ‘dot’ also known as the ‘access atribute’ operator. There are a whole bunch of modules in the standard python library that are available to be used. These modules all use lowercase names so you may want to differentiate your functions by using a different case formation such as ‘camelCase’ or ‘Title-Case.’

Because we imported ‘random.py’ we can now use the functions which are available in the file. power company near me The second line of code chooses a random integer (either 1 or 6) because those were the only arguments passed to it. The third line of code randomly selects an item from the list. I’m assuming these are providing psuedo-random results. Usually, all the import statements are placed at the top of the code you are writing, right after the ‘shebang’ line (which we’ll talk more about later.) For consistency’s sake we should import standard modules first, then third party modules, and then your own modules.

I suppose the use of sys.argv is to allow us to read in commands from the console. These are commands that the user of the program is giving to the program. I think this is different from that of the input() function because that is just reading in a string which is comprised of the key inputs of the user. sys.argv seems to be actually giving commands to the program itself.

Something to note here is that this list is spanning multiples lines, which is ok in Python. This can be done with parenthesized expressions, lists, sets, or dictionary literals (not sure exactly what those are), function call arguments, or a multiline statement in which every character that comes at the end of the line is escaped with a ‘\’.

A note on the round() function: Using the second value as a positive integer gives likely results, however when you invert the value of the rounding integer and can get interesting and useful results. Using a negative rounding integer will push the rounding functions into the other side of the decimal, rounding to the nearest value (whether up or down) depending on the number of places the negative value corresponds to. u save gas station grants pass This isn’t well written in the book, btw.

Secondly, we can create a literal with one argument. If an argument of the same type is given then a new object will be created that is a ‘shallow copy’ of the original object. If an argument of a different type is given then Python attempts a conversion. If the argument falls within the conversion ability of the function then that value is used. However, if this is not possible it raises a ValueError exception. A TypeError is given if the function is unable to deal with such a conversion outright. The built-in str and float types provide integer conversions. Later, we can see that custom data types can also be given this ability.

As we have mentioned before, there are two boolean values. True and False. In similar fashion to that of all other Python data types, one may call this as a function. This is done by using bool(). When you leave out arguments all together it returns False. When you give it an argument it returns a copy of the argument. It will also attempt to convert the argument into a bool.

As previously mentioned, Python provides three logical operators: and, or, not. I still don’t get this next part, but ‘and’, as well as ‘or’ are called short-circuit logic operators and will return the ‘operand’ that determined the result of the boolean. This is something inherent to how logic works, but I am still trying to figure out how it is deciding. The ‘not’ operator will return ‘True’ or ‘False’

These are immutable types. The ‘float’ holds ‘double precision’ floating point numbers with a range depending on the compiler that Python was built with. This means that they have limited precision and can’t be reliably compared for equality. (I’m not exactly sure what this means). Numbers that are of the type ‘float’ are written with a decimal point, or using exponential notation. electricity lab physics Examples: 0.0, 3., -2.4, -2e9, 8.9e-4

Computers natively represent floating-point numbers using base two. Because of this some numbers can be represented exactly such as ‘0.5’, but others can only be represented aproximately such as ‘0.1’ or ‘0.2’. (Make not to learn what the heck this means) Also, the representation is created using a fixed number of bits, consequently there is a limit to the number of digits that can be held (interesting) This creates issues of inexactness with are not specific to Python, but to all programming languages.

Of course, in order to use this one must import sys first. We use the sys.float_info object which has a bunch of attributes. ‘.epsilon’ is effectively the smallest difference that the machine can distinguish between to floating-point numbers. Traditionally this minute value is called Epsilon. Usually, Python will provide reliable accuracy for up to 17 significant digits.

sys.float_info will print to the console a bunch of stats about the ability of the local machine to process floating-point integers. We can convert floating-point integers to integers by using int(). Doing this will just hack off the decimal value. We can also round() which will take in to account the decimal value. We can also use the math library and do math.floor() or math.ceil() which will round down or up, respectively. If a float has a fractional value of 0 it will give us a true bool value when using float.is_integer(). If it is a nonzero value it will return false. A float’s fractional representation can be got using the float.as_integer_ratio() method (which is pretty cool, btw). We can also do all kinds of other sorts of type changing using other methods.

It is important to note that the math module does not work with complex numbers. It was designed this way to keep people from getting complex numbers without knowing it. If you really care to deal with complex numbers you can get the cmath module which provides methods for complex numbers. There are also some nifty complex number specific functions.

Most of the time you don’t need the accuracy that the decimal object provides. The float module should be more than sufficient for most operations. gas x chewables reviews However, in the case that you need it, you can certainly use the decimal object. The decimal module provides immutable Decimal numbers that are as accurate as we want. You may not notice the latency that is involved in Decimal calculations.

As one can clearly see, the decimal.Decimal method is used to create a decimal number type. We can pass arguments that are either an integer or a string, but not floats because they are held inexactly whereas decimals are represented exactly. If we are inputing a string it can also be in exponential notation. Because these things are exact we can use them to compare for exact sameness when neccessary. If we want we can convert a float to a decimal by using the decimal.Decimal.from_float() method. The product is the number which gets closest to the aproximation that the float represents. Math and cmath methods are not appopriate for the decimal data type. But some of the same functionality is provided in the decimal object. The author says something about ‘syntactic sugar’, that sounds yummy.

One thing to note is that when print() is called on something it may format the thing produced for the consol differently than it would otherwise. Using print() prints the string form of the answer. If we don’t use print on a decimal object we get what is known as the ‘representational form’. This goes to show that all Python objects have two output forms. The print() function gives us the human readable string form. Representational form is formatted to be read by the Python interpreter, but many times we will also be able make out the representational form with our human eyes.

As noted before the str data type is immutable and holds a sequence of Unicode characters. We can use it as a function as in str(). Using it in such a way will create string object literals. If we don’t give it an argument it will return an empty string. If we give it a nonstring argument (like an integer) it will convert it into a string. If we give it a string it will return a copy of the string. We can use it also to convert things that are not strings.

This is interesting, a str() function can include two additional arguments beside the thing to be string’d. e gaskell north and south The first being the encoding to use and the other being how to handle encoding errors. This is also legit: Python allows for ‘tripple quoted strings’ which give us the ability to use single and double quotes inside a string with impunity. We can also escape with a \ in order to span lines. Using strings we can also use escape keys to format specific things. We can use single quotes without escaping if we are inside a string which is begun using double quotes. If we use a triple quoted string then we don’t need to escape newlines. If we use a standard quote format, we need to use ‘\n’. But what do you do when you want to use a literal \ in your string? Use ‘raw strings’. Raw strings are quoted or triple quoted strings in which the first quote is preceeded by the letter r. When this is used all characters are taken to be literal therefore it is not necessary to escape. If we want to make a string that takes up more than one line, we can do this the ugly way by adding a + \ between string segments. Or we can simply put () around our normally quoted string. Because things in parentheses have their newlines ignored by the interpreter, it makes for nicer formatting. In fact, it is a poor practice to use an escaped newline to do this, don’t do it.

.py files default to UTF-8 Unicode encoding so we can use all sorts of nifty characters. electricity rate per kwh philippines We can also use escaped codes as well to do the same thing. If we want to know the ‘code point’, the Unicode number that every character is assigned, we can use the ord() function. We can also convert any integer which is representative of the code point by using the built-in chr() function.