3. The Quick Python overview
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The purpose of this chapter is to give you a basic feeling for the syntax, semantics, capabilities, and philosophy of the Python language. It has been designed to provide you an initial perspective or conceptual framework on which you’ll be able to add details as you encounter them in the rest of the book.
On an initial read, you needn’t be concerned about working through and understanding the details of the code segments. You’ll be doing fine if you pick up a bit of an idea about what’s being done. The subsequent chapters walk you through the specifics of these features and don’t assume previous knowledge. You can always return to this chapter and work through the examples in the appropriate sections as a review after you’ve read the later chapters.
Python has several built-in data types, such as integers, floats, complex numbers, strings, lists, tuples, dictionaries, and file objects. These data types can be manipulated using language operators, built-in functions, library functions, or a data type’s own methods.
Programmers can also define their own classes and instantiate their own class instances.[] These class instances can be manipulated by programmer-defined methods, as well as the language operators and built-in functions for which the programmer has defined the appropriate special method attributes.
1The Python documentation and this book use the term object to refer to instances of any Python data type, not just what many other languages would call class instances. This is because all Python objects are instances of one class or another.
Python provides conditional and iterative control flow through an if-elif-else construct along with while and for loops. It allows function definition with flexible argument-passing options. Exceptions (errors) can be raised by using the raise statement, and they can be caught and handled by using the try-except-else-finally construct.
Variables (or identifiers) don’t have to be declared and can refer to any built-in data type, user-defined object, function, or module.
Python has several built-in data types, from scalars such as numbers and Booleans to more complex structures such as lists, dictionaries, and files.
3.2.1. Numbers
Python’s four number types are integers, floats, complex numbers, and Booleans:
Integers—1, –3, 42, 355, 888888888888888, –7777777777 (integers aren’t limited in size except by available memory)
Floats—3.0, 31e12, –6e-4
Complex numbers—3 + 2j, –4- 2j, 4.2 + 6.3j
Booleans—True, False
You can manipulate them by using the arithmetic operators: + (addition), – (subtraction), * (multiplication), / (division), ** (exponentiation), and % (modulus).
The following examples use integers:
Division of integers with / 1 results in a float (new in Python 3.x), and division of integers with // 2 results in truncation. Note that integers are of unlimited size 3; they grow as large as you need them to, limited only by the memory available.
These examples work with floats, which are based on the doubles in C:
These examples use complex numbers:
Complex numbers consist of both a real element and an imaginary element, suffixed with j. In the preceding code, variable x is assigned to a complex number 1. You can obtain its “real” part by using the attribute notation x.real and obtain the “imaginary” part with x.imag.
Several built-in functions can operate on numbers. There are also the library module cmath (which contains functions for complex numbers) and the library module math (which contains functions for the other three types):
Built-in functions are always available and are called by using a standard function-calling syntax. In the preceding code, round is called with a float as its input argument 1.
The functions in library modules are made available via the import statement. At 2, the math library module is imported, and its ceil function is called using attribute notation: module.function(arguments).
The following examples use Booleans:
Other than their representation as True and False, Booleans behave like the numbers 1 (True) and 0 (False) 1.
3.2.2. Lists
Python has a powerful built-in list type:
A list can contain a mixture of other types as its elements, including strings, tuples, lists, dictionaries, functions, file objects, and any type of number 1.
A list can be indexed from its front or back. You can also refer to a subsegment, or slice, of a list by using slice notation:
Table 3.1. List indices
You can use this notation to add, remove, and replace elements in a list or to obtain an element or a new list that’s a slice from it:
The size of the list increases or decreases if the new slice is bigger or smaller than the slice it’s replacing 1.
Some built-in functions (len, max, and min), some operators (in, +, and *), the del statement, and the list methods (append, count, extend, index, insert, pop, remove, reverse, and sort) operate on lists:
The operators + and * each create a new list, leaving the original unchanged 1. A list’s methods are called by using attribute notation on the list itself: x.method (arguments) 2.
Some of these operations repeat functionality that can be performed with slice notation, but they improve code readability.
3.2.3. Tuples
Tuples are similar to lists but are immutable—that is, they can’t be modified after they’ve been created. The operators (in, +, and *) and built-in functions (len, max, and min) operate on them the same way as they do on lists because none of them modifies the original. Index and slice notation work the same way for obtaining elements or slices but can’t be used to add, remove, or replace elements. Also, there are only two tuple methods: count and index. An important purpose of tuples is for use as keys for dictionaries. They’re also more efficient to use when you don’t need modifiability.
A one-element tuple 1 needs a comma. A tuple, like a list, can contain a mixture of other types as its elements, including strings, tuples, lists, dictionaries, functions, file objects, and any type of number 2.
A list can be converted to a tuple by using the built-in function tuple:
Conversely, a tuple can be converted to a list by using the built-in function list:
3.2.4. Strings
String processing is one of Python’s strengths. There are many options for delimiting strings:
Strings can be delimited by single (' '), double (" "), triple single (''' '''), or triple double (""" """) quotations and can contain tab (\t) and newline (\n) characters.
Strings are also immutable. The operators and functions that work with them return new strings derived from the original. The operators (in, +, and *) and built-in functions (len, max, and min) operate on strings as they do on lists and tuples. Index and slice notation works the same way for obtaining elements or slices but can’t be used to add, remove, or replace elements.
Strings have several methods to work with their contents, and the re library module also contains functions for working with strings:
The re module 1 provides regular-expression functionality. It provides more sophisticated pattern extraction and replacement capabilities than the string module.
The print function outputs strings. Other Python data types can be easily converted to strings and formatted:
Objects are automatically converted to string representations for printing 1. The % operator 2 provides formatting capability similar to that of C’s sprintf.
3.2.5. Dictionaries
Python’s built-in dictionary data type provides associative array functionality implemented by using hash tables. The built-in len function returns the number of key-value pairs in a dictionary. The del statement can be used to delete a key-value pair. As is the case for lists, several dictionary methods (clear, copy, get, items, keys, update, and values) are available.
Keys must be of an immutable type 2, including numbers, strings, and tuples. Values can be any kind of object, including mutable types such as lists and dictionaries. If you try to access the value of a key that isn’t in the dictionary, a KeyError exception is raised. To avoid this error, the dictionary method get 3 optionally returns a user-definable value when a key isn’t in a dictionary.
3.2.6. Sets
A set in Python is an unordered collection of objects, used in situations where membership and uniqueness in the set are the main things you need to know about that object. Sets behave as collections of dictionary keys without any associated values:
You can create a set by using set on a sequence, like a list 1. When a sequence is made into a set, duplicates are removed 2. The in keyword 3 is used to check for membership of an object in a set.
3.2.7. File objects
A file is accessed through a Python file object:
The open statement 1 creates a file object. Here, the file myfile in the current working directory is being opened in write ("w") mode. After writing two lines to it and closing it 2, you open the same file again, this time in read ("r") mode. The os module 3 provides several functions for moving around the filesystem and working with the pathnames of files and directories. Here, you move to another directory 4. But by referring to the file by an absolute pathname 5, you’re still able to access it.
Several other input/output capabilities are available. You can use the built-in input function to prompt and obtain a string from the user. The sys library module allows access to stdin, stdout, and stderr. The struct library module provides support for reading and writing files that were generated by, or are to be used by, C programs. The Pickle library module delivers data persistence through the ability to easily read and write the Python data types to and from files.
Python has a full range of structures to control code execution and program flow, including common branching and looping structures.
3.3.1. Boolean values and expressions
Python has several ways of expressing Boolean values; the Boolean constant False, 0, the Python nil value None, and empty values (for example, the empty list [] or empty string "") are all taken as False. The Boolean constant True and everything else is considered True.
You can create comparison expressions by using the comparison operators (<, <=, ==, >, >=, !=, is, is not, in, not in) and the logical operators (and, not, or), which all return True or False.
3.3.2. The if-elif-else statement
The block of code after the first True condition (of an if or an elif) is executed. If none of the conditions is True, the block of code after the else is executed:
The elif and else clauses are optional 1, and there can be any number of elif clauses. Python uses indentation to delimit blocks 2. No explicit delimiters, such as brackets or braces, are necessary. Each block consists of one or more statements separated by newlines. All these statements must be at the same level of indentation. The output in the example would be 5 0 10.
3.3.3. The while loop
The while loop is executed as long as the condition (which here is x > y) is True:
This is a shorthand notation. Here, u and v are assigned a value of 0, x is set to 100, and y obtains a value of 30 1. This is the loop block 2. It’s possible for a loop to contain break (which ends the loop) and continue statements (which abort the current iteration of the loop). The output would be 60 40.
3.3.4. The for loop
The for loop is simple but powerful because it’s possible to iterate over any iterable type, such as a list or tuple. Unlike in many languages, Python’s for loop iterates over each of the items in a sequence (for example, a list or tuple), making it more of a foreach loop. The following loop finds the first occurrence of an integer that’s divisible by 7:
x is sequentially assigned each value in the list 1. If x isn’t an integer, the rest of this iteration is aborted by the continue statement 2. Flow control continues with x set to the next item from the list. After the first appropriate integer is found, the loop is ended by the break statement 3. The output would be
3.3.5. Function definition
Python provides flexible mechanisms for passing arguments to functions:
Functions are defined by using the def statement 1. The return statement 2 is what a function uses to return a value. This value can be of any type. If no return statement is encountered, Python’s None value is returned. Function arguments can be entered either by position or by name (keyword). Here, z and y are entered by name 3. Function parameters can be defined with defaults that are used if a function call leaves them out 4. A special parameter can be defined that collects all extra positional arguments in a function call into a tuple 5. Likewise, a special parameter can be defined that collects all extra keyword arguments in a function call into a dictionary 6.
3.3.6. Exceptions
Exceptions (errors) can be caught and handled by using the try-except-else-finally compound statement. This statement can also catch and handle exceptions you define and raise yourself. Any exception that isn’t caught causes the program to exit. This listing shows basic exception handling.
Listing 3.1. File exception.py
Here, you define your own exception type inheriting from the base Exception type 1. If an IOError or EmptyFileError occurs during the execution of the statements in the try block, the associated except block is executed 2. This is where an IOError might be raised 3. Here, you raise the EmptyFileError 4. The else clause is optional 5; it’s executed if no exception occurs in the try block. (Note that in this example, continue statements in the except blocks could have been used instead.) The finally clause is optional 6; it’s executed at the end of the block whether an exception was raised or not.
3.3.7. Context handling using the with keyword
A more streamlined way of encapsulating the try-except-finally pattern is to use the with keyword and a context manager. Python defines context managers for things like file access, and it’s possible for the developer to define custom context managers. One benefit of context managers is that they may (and usually do) have default clean-up actions defined, which always execute whether or not an exception occurs.
This listing shows opening and reading a file by using with and a context manager.
Listing 3.2. File with.py
Here, with establishes a context manager which wraps the open function and the block that follows. In this case, the context manager’s predefined clean-up action closes the file, even if an exception occurs, so as long as the expression in the first line executes without raising an exception, the file is always closed. That code is equivalent to this code:
It’s easy to create your own modules, which can be imported and used in the same way as Python’s built-in library modules. The example in this listing is a simple module with one function that prompts the user to enter a filename and determines the number of times that words occur in this file.
Listing 3.3. File wo.py
Documentation strings, or docstrings, are standard ways of documenting modules, functions, methods, and classes 1. Comments are anything beginning with a # character 2. read returns a string containing all the characters in a file 3, and split returns a list of the words of a string “split out” based on whitespace. You can use a \ to break a long statement across multiple lines 4. This if statement allows the program to be run as a script by typing python wo.py at a command line 5.
If you place a file in one of the directories on the module search path, which can be found in sys.path, it can be imported like any of the built-in library modules by using the import statement:
This function is called 1 by using the same attribute syntax used for library module functions.
Note that if you change the file wo.py on disk, import won’t bring your changes into the same interactive session. You use the reload function from the imp library in this situation:
For larger projects, there is a generalization of the module concept called packages, which allows you to easily group modules in a directory or directory subtree and then import and hierarchically refer to them by using a package.subpackage.module syntax. This entails little more than creating a possibly empty initialization file for each package or subpackage.
Listing 3.4. File sh.py
Classes are defined by using the class keyword 1. The instance initializer method (constructor) for a class is always called __init__ 2. Instance variables x and y are created and initialized here 3. Methods, like functions, are defined by using the def keyword 4. The first argument of any method is by convention called self. When the method is invoked, self is set to the instance that invoked the method. Class Circle inherits from class Shape 5 and is similar to, but not exactly like, a standard class variable 6. A class must, in its initializer, explicitly call the initializer of its base class 7. The __str__ method is used by the print function 8. Other special method attributes permit operator overloading or are employed by built-in methods such as the length (len) function.
Importing this file makes these classes available:
The initializer is implicitly called, and a circle instance is created 1. The print function implicitly uses the special __str__ method 2. Here, you see that the move method of Circle’s parent class Shape is available 3. A method is called by using attribute syntax on the object instance: object.method(). The first (self) parameter is set implicitly.
This chapter is a rapid and very high-level overview of Python; the following chapters provide more detail. This chapter ends the book’s overview of Python.
You may find it valuable to return to this chapter and work through the appropriate examples as a review after you read about the features covered in subsequent chapters.
If this chapter was mostly a review for you, or if you’d like to learn more about only a few features, feel free to jump around, using the index or table of contents.
Index from the front 1 using positive indices (starting with 0 as the first element). Index from the back 2 using negative indices (starting with -1 as the last element). Obtain a slice using [m:n] 3, where m is the inclusive starting point and n is the exclusive ending point (see ). An [:n] slice 4 starts at its beginning, and an [m:] slice goes to a list’s end.
Python provides full support for OOP. is an example that might be the start of a simple shapes module for a drawing program. It’s intended mainly to serve as a reference if you’re already familiar with OOP. The callout notes relate Python’s syntax and semantics to the standard features found in other languages.
You should have a solid understanding of the Python features in this chapter before skipping ahead to .