Reading and writing binary data in python
The second argument you see — mode — tells the interpreter and developer which way the file will be used. The current information stored within the file is also displayed — or printed — for us to view. Once this has been done, you can move on to call the objects functions. The two most common functions are read and write. Naturally, if you open the text file — or look at it — using Python you will see only the text we told the interpreter to add.
If you need to extract a string that contains all characters in the file, you can use the following method: The output of that command will display all the text inside the file, the same text we told the interpreter to add earlier.
For example, with the following code the interpreter will read the first five characters of stored data and return it as a string: If you want to read a file line by line — as opposed to pulling the content of the entire file at once — then you use the readline function. You would execute the readline function as many times as possible to get the data you were looking for. Each time you run the method, it will return a string of characters that contains a single line of information from the file.
If we wanted to return only the third line in the file, we would use this: But what if we wanted to return every line in the file, properly separated? You would use the same function, only in a new form. This is called the file. Notice how each line is separated accordingly? Note that this is not the ideal way to show users the content in a file.
When you want to read — or return — all the lines from a file in a more memory efficient, and fast manner, you can use the loop over method. The advantage to using this method is that the related code is both simple and easy to read. This method is used to add information or content to an existing file. To start a new line after you write data to the file, you can add an EOL character. Obviously, this will amend our current file to include the two new lines of text.
What this does is close the file completely, terminating resources in use, in turn freeing them up for the system to deploy elsewhere. Notice how we have used this in several of our examples to end interaction with a file? This is good practice. Feel free to copy the code and try it out for yourself in a Python interpreter make sure you have any named files created and accessible first. You can also work with file objects using the with statement.
It is designed to provide much cleaner syntax and exceptions handling when you are working with code. One bonus of using this method is that any files opened will be closed automatically after you are done. This leaves less to worry about during cleanup. You can also call upon other methods while using this statement. For instance, you can do something like loop over a file object:.
What this is designed to do, is split the string contained in variable data whenever the interpreter encounters a space character. You can actually split your text using any character you wish - such as a colon, for instance. If you wanted to use a colon instead of a space to split your text, you would simply change line. The reason the words are presented in this manner is because they are stored — and returned — as an array.
The rest of the examples in this section will assume that a file object called f has already been created. Otherwise, at most size bytes are read and returned. If the end of the file has been reached, f. This makes the return value unambiguous; if f. For reading lines from a file, you can loop over the file object. This is memory efficient, fast, and leads to simple code:.
If you want to read all the lines of a file in a list you can also use list f or f. In text files those opened without a b in the mode string , only seeks relative to the beginning of the file are allowed the exception being seeking to the very file end with seek 0, 2 and the only valid offset values are those returned from the f.
Any other offset value produces undefined behaviour. It is good practice to use the with keyword when dealing with file objects. This has the advantage that the file is properly closed after its suite finishes, even if an exception is raised on the way.
It is also much shorter than writing equivalent try - finally blocks:. File objects have some additional methods, such as isatty and truncate which are less frequently used; consult the Library Reference for a complete guide to file objects.
The standard module called json can take Python data hierarchies, and convert them to string representations; this process is called serializing. Reconstructing the data from the string representation is called deserializing. Between serializing and deserializing, the string representing the object may have been stored in a file or data, or sent over a network connection to some distant machine.
The JSON format is commonly used by modern applications to allow for data exchange. Many programmers are already familiar with it, which makes it a good choice for interoperability.
If you have an object x , you can view its JSON string representation with a simple line of code:. Another variant of the dumps function, called dump , simply serializes the object to a text file. So if f is a text file object opened for writing, we can do this:.
To decode the object again, if f is a text file object which has been opened for reading:. This simple serialization technique can handle lists and dictionaries, but serializing arbitrary class instances in JSON requires a bit of extra effort. The reference for the json module contains an explanation of this. Contrary to JSON , pickle is a protocol which allows the serialization of arbitrarily complex Python objects. As such, it is specific to Python and cannot be used to communicate with applications written in other languages.
It is also insecure by default: Enter search terms or a module, class or function name. Note use of 'end' on previous line This is the first line of the file. Second line of the file. See also pickle - the pickle module Contrary to JSON , pickle is a protocol which allows the serialization of arbitrarily complex Python objects. Table Of Contents 7.
Input and Output 7. Fancier Output Formatting 7. Old string formatting 7. Reading and Writing Files 7. Methods of File Objects 7.