Dictionaries, sets, files, and modules
Last updated
Last updated
Dictionaries are a compound type different from the sequence types we studied in the Strings, lists, and tuples chapter. They are Python’s built-in mapping type. They map keys, which can be any immutable type, to values, which can be any type, just like the values of a list or tuple.
Note
Other names for dictionaries in computer science include maps, symbol tables, and associative arrays. The pairs of values are referred to as name-value, key-value, field-value, or attribute-value pairs.
As an example, we will create a dictionary to translate English words into Spanish. For this dictionary, the keys are strings.
One way to create a dictionary is to start with the empty dictionary and add key-value pairs. The empty dictionary is denoted with a pair of curly braces, {}:
The first assignment creates a dictionary named eng2sp; the other assignments add new key-value pairs to the dictionary. We can print the current value of the dictionary in the usual way:
The key-value pairs of the dictionary are seperated by commas. Each pair contains a key and a value separated by a colon.
The order of the pairs may not be what you expected. Python uses complex algorithms to determine where the key-value pairs are stored in a dictionary. For our purposes we can think of this ordering as unpredicatable, so you should not try to rely on it. Instead, look up values by using a known key.
Another way to create a dictionary is to provide a list of key-value pairs using the same syntax as the previous output:
It doesn’t matter what order we write the pairs. The values in a dictionary are accessed with keys, not with indices, so ordering is unimportant.
Here is how we use a key to look up the corresponding value:
The key 'two' yields the value 'dos'.
The del statement removes a key-value pair from a dictionary. For example, the following dictionary contains the names of various fruits and the number of each fruit in stock:
If someone buys all of the pears, we can remove the entry from the dictionary:
Or if we’re expecting more pears soon, we might just change the value associated with pears:
The len function also works on dictionaries; it returns the number of key-value pairs:
The in operator returns True if the key appears in the dictionary and False otherwise:
This operator can be very useful, since looking up a non-existant key in a dictionary causes a runtime error:
To address this problem, the built-in get method provides a default value that is returned when a key is not found:
Python’s built-in sorted function returns a list of a dictionaries keys in sorted order:
Because dictionaries are mutable, you need to be aware of aliasing. Whenever two variables refer to the same object, changes to one affect the other.
If you want to modify a dictionary and keep a copy of the original, use the copy method. For example, opposites is a dictionary that contains pairs of opposites:
an_alias and opposites refer to the same object; a_copy refers to a fresh copy of the same dictionary. If we modify alias, opposites is also changed:
If we modify a_copy, opposites is unchanged:
A set is a Python data type that holds an unordered collection of unique elements. It implements the set abstract data type which is in turn based on the mathematical concept of a finite set. As with dictionaries, Python uses curly braces to indicate a set, but with elements instead of key-value pairs:
To create an empty set, you can not use empty curly braces.
Instead, use the set type converter function without an argument.
Sets contain a unique collection of elements of any type. You can add to a set using its add method, and test for membership with the in operator.
Since sets hold unique collections of elements, you can use the set type conversion function to remove duplicates from a list.
This book is aimed at aspiring web developers, so much of our focus will be on creating dynamic web pages using Python. Web pages are stored in text files, which are essentially files containing a string of text. The ability to process and format text is quite important to us. That’s what this section is about.
format method for stringsThe easiest and most powerful way to format a string in Python 3 is to use the format method.
The key idea is that one provides a formatter string which contains placeholder fields, ... {0} ... {1} ... {2} ... etc. The format method of a string uses the numbers as indexes into its arguments, and substitutes the appropriate argument into each placeholder field.
Each of the placeholders can also contain a format specification — it is always introduced by the : symbol. This can control things like
whether the field is aligned left <, centered ^, or right >
the width allocated to the field within the result string (a number like 10)
the type of conversion (we’ll initially only force conversion to float, f, as we did in line 11 of the code above, or perhaps we’ll ask integer numbers to be converted to hexadecimal using x)
if the type conversion is a float, you can also specify how many decimal places are wanted (typically, .2f is useful for working with currencies to two decimal places.)
You can have multiple placeholders indexing the same argument, or perhaps even have extra arguments that are not referenced at all:
This produces the following:
As you might expect, you’ll get an index error if your placeholders refer to arguments that you do not provide:
In addition to positional arguments in format strings, named arguments are also supported:
Notice that the order of the arguments to the format method example doesn’t correspond with the order they appear in the format string. These are keywords, as with dictionaries, so their order is not relevant.
Old style format strings
Earlier versions of Python used a cryptic way to format strings. It is considered deprecated and will eventually disappear from the language.
While we won’t use it in this book, you will still see it around in lots of existing Python code, so it is good to be aware of it.
The syntax for the old string formatting operation looks like this:
To see how this works, here are a few examples:
While a program is running, its data is stored in random access memory (RAM). RAM is extremely fast, but it is also volatile, which means that when the program ends, or the computer shuts down, data in RAM disappears. To make data available the next time you turn on your computer and start your program, you have to write it to a non-volatile storage medium, such a hard drive, usb drive, or CD-RW.
Data on non-volatile storage media is stored in named locations on the media called files. By reading and writing files, programs can save information between program runs. A file is a block of data stored in the file system of the computer’s operating system.
To use a file, you have to open it. When you’re done, you have to close it. When you open the file, you have to decide ahead of time whether you want to read data from the file or write data to it. If you plan to write data to the file you have to choose between starting a new version of the file or writing data at the end of what was already there. This second option for writing to the file is called appending. The first option destroys any previously existing data in the file.
open functionThe open function takes two arguments. The first is the name of the file, and the second is the mode. Mode 'w' means that we are opening the file for writing. Mode 'r' means reading, and mode 'a' means appending.
Let’s begin with an example that shows these three modes in operation:
Opening a file creates what we call a file descriptor. In this example, the variable myfile refers to the new descriptor object. Our program calls methods on the descriptor, and this makes changes to the actual file which is located in non-volatile storage.
The first line opens the test.txt for writing. If there is no file named test.txt on the disk, it will be created. If there already is one, it will be replaced by the file we are writing and any previous data in it will be lost.
To put data in the file we invoke the write method on the file descriptor. We do this three times in the example above, but in bigger programs, the three separate calls to write will usually be replaced by a loop that writes many more lines into the file. The write method returns the number of bytes (characters) written to the file.
Closing the file handle tells the system that we are done writing and makes the disk file available for reading by other programs (or by our own program).
We finish this example by openning test.txt for reading. We then call the read method, assigning the contents of the file, which is a string, to a variable named contents, and finally print contents to see that it is indeed what we wrote to the file previously.
If we want to add to an already existing file, use the append mode.
If we try to open a file that doesn’t exist, we get an error:
There is nothing wrong with the syntax of the line that resulted in the error. The error occurred because the file did not exist. Errors like these are called exceptions. Most modern programming languages provide support for dealing with situations like this. The process is called exception handling.
In Python, exceptions are handled with the try ... except statement.
In this example we try to open the data file for reading. If it succeeds, we use the read() method to read the file contents as a string into the variable mydata and close the file. If an IOError exception occurs, we still create mydata as an empty string and continue on with the program.
Python file descriptors have three methods for reading in data from a file. We’ve already seen the read() method, which returns the entire contents of the file as a single string. For really big files this may not be what you want.
The readline() method returns one line of the file at a time. Each time you call it readline() returns the next line. Calls made to readline() after reaching the end of the file return an empty string ('').
This is a handy pattern for our toolbox. In bigger programs, we’d squeeze more extensive logic into the body of the loop at line 8 — for example, if each line of the file contained the name and email address of one of our friends, perhaps we’d split the line into some pieces and call a function to send the friend a party invitation.
On line 8 we suppress the newline character that print usually appends to our strings. Why? This is because the string already has its own newline: the readline method in line 3 returns everything up to and including the newline character. This also explains the end-of-file detection logic: when there are no more lines to be read from the file, readline returns an empty string — one that does not even have a newline at the end, hence it’s length is 0.
It is often useful to fetch data from a disk file and turn it into a list of lines. Suppose we have a file containing our friends and their email addresses, one per line in the file. But we’d like the lines sorted into alphabetical order. A good plan is to read everything into a list of lines, then sort the list, and then write the sorted list back to another file:
The readlines method in line 2 reads all the lines and returns a list of the strings.
We could have used the template from the previous section to read each line one-at-a-time, and to build up the list ourselves, but it is a lot easier to use the method that the Python implementors gave us!
Many useful line-processing programs will read a text file line-at-a-time and do some minor processing as they write the lines to an output file. They might number the lines in the output file, or insert extra blank lines after every 60 lines to make it convenient for printing on sheets of paper, or extract some specific columns only from each line in the source file, or only print lines that contain a specific substring. We call this kind of program a filter.
Here is a filter that copies one file to another, omitting any lines that begin with #:
The continue statement at line 9 skips over the remaining lines in the current iteration of the loop, but the loop will still iterate. This style looks a bit contrived here, but it is often useful to say “get the lines we’re not concerned with out of the way early, so that we have cleaner more focussed logic in the meaty part of the loop that might be written around line 11.”
Thus, if text is the empty string, the loop exits. If the first character of text is a hash mark, the flow of execution goes to the top of the loop, ready to start processing the next line. Only if both conditions fail do we fall through to do the processing at line 11, in this example, writing the line into the new file.
Let’s consider one more case: suppose your original file contained empty lines. At line 6 above, would this program find the first empty line in the file, and terminate immediately? No! Recall that readline always includes the newline character in the string it returns. It is only when we try to read beyond the end of the file that we get back the empty string of length 0.
repr() and eval() functionsPython has a built-in function named repr that takes a Python object as an argument and returns a string representation of that object. For Python’s built-in types, the string representation of an object can be evaluated using the built-in eval function to recreate the object.
The way this works is easiest to demonstrate by example.
The list object, mylist is converted into a string representation using the repr function, and this string representation is than converted back into a Python list object using the eval function (which evaluates the string representation).
While we will learn much better ways to achieve the goal of storing Python objects into data files later, repr and eval provide us with an easy to understand tool for writing and then reading back Python data to files that we can use now.
A module is a file containing Python definitions and statements intended for use in other Python programs. There are many Python modules that come with Python as part of the standard library. We have seen two of these already, the doctest module and the string module.
All we need to create a module is a text file with a .py extension on the filename:
We can now use our module in both scripts and the Python shell. To do so, we must first import the module. There are two ways to do this:
and:
In the first example, remove_at is called just like the functions we have seen previously. In the second example the name of the module and a dot (.) are written before the function name.
Notice that in either case we do not include the .py file extension when importing. Python expects the file names of Python modules to end in .py, so the file extention is not included in the import statement.
The use of modules makes it possible to break up very large programs into manageable sized parts, and to keep related parts together.
A namespace is a syntactic container which permits the same name to be used in different modules or functions (and as we will see soon, in classes and methods).
Each module determines its own namespace, so we can use the same name in multiple modules without causing an identification problem.
We can now import both modules and access question and answer in each:
If we had used from module1 import * and from module2 import * instead, we would have a naming collision and would not be able to access question and answer from module1.
Functions also have their own namespace:
Running this program produces the following output:
The three n’s here do not collide since they are each in a different namespace.
Namespaces permit several programmers to work on the same project without having naming collisions.
Variables defined inside a module are called attributes of the module. They are accessed by using the dot operator ( .). The question attribute of module1 and module2 are accessed using module1.question and module2.question.
Modules contain functions as well as attributes, and the dot operator is used to access them in the same way. seqtools.remove_at refers to the remove_at function in the seqtools module.
In Chapter 7 we introduced the find function from the string module. The string module contains many other useful functions:
You should use pydoc to browse the other functions and attributes in the string module.
dictionary
A collection of key-value pairs that maps from keys to values. The keys can be any immutable type, and the values can be any type.file
A named entity, usually stored on a hard drive, floppy disk, or CD-ROM, that contains a stream of characters.file system
A method for naming, accessing, and organizing files and the data they contain.fully qualified name
A name that is prefixed by some namespace identifier and the dot operator, or by an instance object, e.g. math.sqrt or f.open('myfile.txt', 'r').handle
An object in our program that is connected to an underlying resource (e.g. a file). The file handle lets our program manipulate / read/ write / close the actual file that is on our disk.import
A statement which permits functions and variables defined in a Python script to be brought into the environment of another script or a running Python shell.For example, assume the following is in a script named tryme.py:
Now begin a python shell from within the same directory where tryme.py is located:
Three names are defined in tryme.py: print_thrice, n, and s. If we try to access any of these in the shell without first importing, we get an error:
If we import everything from tryme.py, however, we can use everything defined in it:
Note that you do not include the .py from the script name in the import statement.key
A data item that is mapped to a value in a dictionary. Keys are used to look up values in a dictionary.key-value pair
One of the pairs of items in a dictionary. Values are looked up in a dictionary by key.mapping type
A mapping type is a data type comprised of a collection of keys and associated values. Python’s only built-in mapping type is the dictionary. Dictionaries implement the associative array abstract data type.mode
A distinct method of operation within a computer program. Files in Python can be openned in one of three modes: read ('r'), write ('w'), and append ('a').non-volatile memory
Memory that can maintain its state without power. Hard drives, flash drives, and rewritable compact disks (CD-RW) are each examples of non-volatile memory.path
A sequence of directory names that specifies the exact location of a file.set
A collection of unique, unordered elements.text file
A file that contains printable characters organized into lines separated by newline characters.volatile memory
Memory which requires an electrical current to maintain state. The main memory or RAM of a computer is volatile. Information stored in RAM is lost when the computer is turned off.
