A function is a block of organized, reusable code that is used to perform a single, related action. Functions provide better modularity for your application and a high degree of code reusing.
As you already know, Python gives you many built-in functions like printetc. These functions are called user-defined functions. You can define functions to provide the required functionality. Here are simple rules to define a function in Python. Function blocks begin with the keyword def followed by the function name and parentheses.
Any input parameters or arguments should be placed within these parentheses. You can also define parameters inside these parentheses. The first statement of a function can be an optional statement - the documentation string of the function or docstring. The statement return [expression] exits a function, optionally passing back an expression to the caller.
A return statement with no arguments is the same as return None. By default, parameters have a positional behavior and you need to inform them in the same order that they were defined. Defining a function gives it a name, specifies the parameters that are to be included in the function and structures the blocks of code.
Once the basic structure of a function is finalized, you can execute it by calling it from another function or directly from the Python prompt. All parameters arguments in the Python language are passed by reference. It means if you change what a parameter refers to within a function, the change also reflects back in the calling function. Here, we are maintaining reference of the passed object and appending values in the same object. There is one more example where argument is being passed by reference and the reference is being overwritten inside the called function.
The parameter mylist is local to the function changeme. Changing mylist within the function does not affect mylist. Required arguments are the arguments passed to a function in correct positional order. Here, the number of arguments in the function call should match exactly with the function definition. Keyword arguments are related to the function calls. When you use keyword arguments in a function call, the caller identifies the arguments by the parameter name.
This allows you to skip arguments or place them out of order because the Python interpreter is able to use the keywords provided to match the values with parameters. The following example gives a clearer picture. Note that the order of parameters does not matter.
A default argument is an argument that assumes a default value if a value is not provided in the function call for that argument. You may need to process a function for more arguments than you specified while defining the function.
These arguments are called variable-length arguments and are not named in the function definition, unlike required and default arguments. This tuple remains empty if no additional arguments are specified during the function call. These functions are called anonymous because they are not declared in the standard manner by using the def keyword.
You can use the lambda keyword to create small anonymous functions. Lambda forms can take any number of arguments but return just one value in the form of an expression. They cannot contain commands or multiple expressions. Lambda functions have their own local namespace and cannot access variables other than those in their parameter list and those in the global namespace.
All the examples given below are not returning any value. All variables in a program may not be accessible at all locations in that program.Every value in Python has a data type. Data types are a classification of data that tells the compiler or the interpreter how you want to use the data. The type defines the operations that can be done on the data and the structure in which you want the data to be stored. In data science, you will often need to change the type of your data, so that it becomes easier to use and work with.
This tutorial will tackle some of the important and most frequently used data structures, and you will learn to change their types to suit your need.Python Musings: Converting String Data to Binary
More specifically, you will learn:. Python has many data types. You must have already seen and worked with some of them. You can make use of lists, tuples, dictionary, and sets that are data structures where you can store a collection of values. Data conversion in Python can happen in two ways: either you tell the compiler to convert a data type to some other type explicitly, or the compiler understands this by itself and does it for you. In the former case, you're performing an explicit data type conversion, whereas, in the latter, you're doing an implicit data type conversion.
Implicit conversion or coercion is when data type conversion takes place either during compilation or during run time and is handled directly by Python for you. Let's see an example:. Tip : you can use the type function in Python to check the data type of an object. This is the implicit data conversion. This is due to a broader concept of type promotion in computer science.
How to convert binary to decimal in Python
Simply put, this is a defense mechanism of the compiler that allows you to perform operations whenever possible by converting your data into a different supertype without the loss of information. That means that the conversion from float to integer is not done because then the compiler will need to remove the fractional part leading to the loss of information.
Explicit conversion also known as type casting is when data type conversion takes place because you clearly defined it in your program. You basically force an expression to be of a specific type. The general form of an explicit data type conversion is as follows:. Note : as you can imagine, with explicit data type conversion, there is a risk of information loss since you're forcing an expression to be of a specific type.
With all of this in mind, you can dig into some of the commonly used explicit data type conversions Primitive data structures are the building blocks for data manipulation and contain pure, simple values of data.
Python has four primitive variable types:.We have looked at simple numbers and operations before. In this article you will learn how numbers work inside the computer and a some of magic to go along with that More detailed: While this is not directly useful in web applications or most desktop applications, it is very useful to know. In this article you will learn how to use binary numbers in Python, how to convert them to decimals and how to do bitwise operations on them. Related course: Python Programming Bootcamp: Go from zero to hero.
Binary Left Shift and Binary Right Shift Multiplication by a factor two and division by a factor of two is very easy in binary. We simply shift the bits left or right. We shift left below:. Before shifting 0,1,0,1 we have the number 5.
After shifting 1,0,1,0 we have the number By changing the inputs you will have the same results as the image above. We can do the AND operator on a sequence:.
The first one compares the bits, the second one compares both statements and should be used as if a and b then; In detail:. The first is a bitwise logical and. It compares the bits, if both are 1 it will be 1, otherwise 0. The second is not a bitwise logical and operator and simply the normal and operator. If A and B then true.
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Are there any canned Python methods to convert an Integer or Long into a binary string in Python? There are a myriad of dec2bin functions out on Google Format spec docs for Python 2.
Format spec docs for Python 3. If you're looking for bin as an equivalent to hexit was added in python 2. For a more general philosophy, no language or library will give its user base everything that they desire. If you're working in an environment that doesn't provide exactly what you need, you should be collecting snippets of code as you develop to ensure you never have to write the same thing twice.
Such as, for example, the pseudo-code:.
Binary to decimal and vice-versa in python
Just keep in mind that's a generic bit of pseudo-code which may not be the most efficient way of doing it though, with the iterations you seem to be proposing, it won't make much difference. It's really just meant as a guideline on how it could be done. This function can convert a positive integer as large asrepresented as string ''. A simple way to do that is to use string format, see this page.
Unless I'm misunderstanding what you mean by binary string I think the module you are looking for is struct. Here is the code I've just implemented. This is not a method but you can use it as a ready-to-use function! Here's yet another way using regular math, no loops, only recursion. Trivial case 0 returns nothing. If you are willing to give up "pure" Python but gain a lot of firepower, there is Sage - example here :.
You'll note that it returns as a string, so to use it as a number you'd want to do something like. E is input decimal data, M is the binary orders.Sometimes you may have a quantitative variable in your data set and you might want to discretize it or bin it or categorize it based on the values of the variable.
For example, let us say you have measurements of height and want to discretize it such that it is 0 or 1 depending on if the height is below or above a certain value of height. We will see examples of discretizing or binning a quantitative variable in two ways. Let us create a numpy array with 10 integers. We will also use random seed to reproduce the random numbers.
Let us consider a simple binning, where we use 50 as threshold to bin our data into two categories. One with values less than 50 are in the 0 category and the ones above 50 are in the 1 category. The bins argument is a list and therefore we can specify multiple binning or discretizing conditions.
In the example below, we bin the quantitative variable in to three categories. It gives us three categories as we wanted, 0 category with values less than 50, 1 category with value less than and category 3 with more than Here is an example with four categories using digitize.
Pandas cut function is a powerful function for categorize a quantitative variable. Let us first make a Pandas data frame with height variable using the random number we generated above.
Let us categorize the height variable into four categories using Pandas cut function. In addition to that, we need to specify bins such that height values between 0 and 25 are in one category, values between 25 and 50 are in second category and so on.
Python bin() Function
Let us save the binned variable as another variable in the original dataframe. Check the type of each Pandas variable using df. Note how we specify the bins with Pandas cut, we need to specify both lower and upper end of the bins for categorizing.
Let us see another Pandas cut example, but this time let us specify labels for each categorical variable that Pandas cut provides. In this Pandas cut example, we provide the labels as integers. Since we want to have four bins or categories, we provide the bin labels as [0,1,2,3].
In the above Pandas cut example, we used integers as labels. However, we can use more descriptive categories like this as well.The bytes type in Python is immutable and stores a sequence of values ranging from 8-bits. You can get the value of a single byte by using an index like an array, but the values can not be modified.
To create a mutable object you need to use the bytearray type. With a bytearray you can do everything you can with other mutables like push, pop, insert, append, delete, and sort. The io. BytesIO inherits from io. BufferedReader class comes with functions like readwritepeekgetvalue. It is a general buffer of bytes that you can work with. Alternatively, you could explicitly call open and close, but if you do it this way you will need to do the error handling yourself and ensure the file is always closed, even if there is an error during writing.
I don't recommend this method unless you have a strong reason. You can move to a specific position in file before reading or writing using seek. You can pass a single parameter to seek and it will move to that position, relative to the beginning of the file.
Format strings can be helpful to visualize or output byte values. Format strings require an integer value so the byte will have to be converted to an integer first. Packing and unpacking requires a string that defines how the binary data is structured. It needs to know which bytes represent values. It needs to know whether the entire set of bytes represets characters or if it is a sequence of 4-byte integers.
It can be structured in any number of ways. The format strings can be simple or complex. In this example I am packing a single four-byte integer followed by two characters.
The letters i and c represent integers and characters. You might need to know what byte order your system uses. Byte order refers to big endian or little endian. The sys module can provide that value. View the discussion thread. Skip to main content. Working with Binary Data in Python.
All examples are in Python 3 and many will not work in Python 2. Video: Bytes and Bytearray tutorial for Python 3 The Bytes Type The bytes type in Python is immutable and stores a sequence of values ranging from 8-bits. BytesIO Binary data and strings are different types, so a str must be encoded to binary using ascii, utf-8, or other.
Default is unsigned. Struct Packing and Unpacking Packing and unpacking requires a string that defines how the binary data is structured. Here it specifies the data is structured with a single four-byte integer followed by two characters. Find out what byte order your system uses import sys print "Native byteorder: ", sys. Exercise: Rewrite this code to compare the files part at a time so it will not run out of RAM with large files.The Python interpreter has a number of functions and types built into it that are always available.
They are listed here in alphabetical order. Return the absolute value of a number. The argument may be an integer or a floating point number.
If the argument is a complex number, its magnitude is returned. Return True if all elements of the iterable are true or if the iterable is empty.
Equivalent to:. Return True if any element of the iterable is true. If the iterable is empty, return False. This generates a string similar to that returned by repr in Python 2.
The result is a valid Python expression. Some examples:. See also format for more information. Return a Boolean value, i. If x is false or omitted, this returns False ; otherwise it returns True. The bool class is a subclass of int see Numeric Types — int, float, complex.
It cannot be subclassed further. Its only instances are False and True see Boolean Values. Changed in version 3. This function drops you into the debugger at the call site. Specifically, it calls sys. By default, sys. However, sys. Raises an auditing event builtins. Return a new array of bytes. It has most of the usual methods of mutable sequences, described in Mutable Sequence Typesas well as most methods that the bytes type has, see Bytes and Bytearray Operations.
The optional source parameter can be used to initialize the array in a few different ways:. If it is a stringyou must also give the encoding and optionally, errors parameters; bytearray then converts the string to bytes using str. If it is an integerthe array will have that size and will be initialized with null bytes. If it is an object conforming to the buffer interface, a read-only buffer of the object will be used to initialize the bytes array.
Accordingly, constructor arguments are interpreted as for bytearray. Bytes objects can also be created with literals, see String and Bytes literals. Return True if the object argument appears callable, False if not.