Introduction to Python Part II

Table of Contents

• Dictionaries
• Looping Techniques
  • More on Conditions
  • Comparing Sequences and Other Types
• Modules
  • Executing modules as scripts
  • The Module Search Path
  • “Compiled” Python files
  • Standard Modules
• Input and Output
  • Output Formatting
  • The String format() Method

Dictionaries

Another useful data type built into Python is the dictionary (see Mapping Types — dict). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend().

It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {}. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del. If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.

Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead). To check whether a single key is in the dictionary, use the in keyword.

Here is a small example using a dictionary:

tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel

tel['jack']

del tel['sape']
tel['irv'] = 4127
tel

list(tel)

sorted(tel)

'guido' in tel

'jack' not in tel

The dict() constructor builds dictionaries directly from sequences of key-value pairs:

dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

{x: x**2 for x in (2, 4, 6)}

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

dict(sape=4139, guido=4127, jack=4098)

Looping Techniques

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method.

knights = {'gallahad': 'the pure', 'robin': 'the brave'}
for k, v in knights.items():
    print(k, v)

gallahad the pure
robin the brave

When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.

for i, v in enumerate(['tic', 'tac', 'toe']):
    print(i, v)

0 tic
1 tac
2 toe

To loop over two or more sequences at the same time, the entries can be paired with the zip() function.

questions = ['name', 'quest', 'favorite color']
answers = ['lancelot', 'the holy grail', 'blue']
for q, a in zip(questions, answers):
    print('What is your {0}?  It is {1}.'.format(q, a))

What is your name?  It is lancelot.
What is your quest?  It is the holy grail.
What is your favorite color?  It is blue.

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.

for i in reversed(range(1, 10, 2)):
    print(i)

9
7
5
3
1

To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.

basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
for i in sorted(basket):
    print(i)

apple
apple
banana
orange
orange
pear

Using set() on a sequence eliminates duplicate elements. The use of sorted() in combination with set() over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order.

basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
for f in sorted(set(basket)):
    print(f)

apple
banana
orange
pear

It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.

import math
raw_data = [56.2, float('NaN'), 51.7, 55.3, 52.5, float('NaN'), 47.8]
filtered_data = []
for value in raw_data:
    if not math.isnan(value):
        filtered_data.append(value)

filtered_data
[56.2, 51.7, 55.3, 52.5, 47.8]

More on Conditions

The conditions used in while and if statements can contain any operators, not just comparisons.

The comparison operators in and not in are membership tests that determine whether a value is in (or not in) a container. The operators is and is not compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators.

Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c.

Comparisons may be combined using the Boolean operators and and or, and the outcome of a comparison (or of any other Boolean expression) may be negated with not. These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C. As always, parentheses can be used to express the desired composition.

The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C. When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,

string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
non_null = string1 or string2 or string3
non_null
'Trondheim'

Note that in Python, unlike C, assignment inside expressions must be done explicitly with the walrus operator :=. This avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

Comparing Sequences and Other Types

Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type:

(1, 2, 3)              < (1, 2, 4)
[1, 2, 3]              < [1, 2, 4]
'ABC' < 'C' < 'Pascal' < 'Python'
(1, 2, 3, 4)           < (1, 2, 4)
(1, 2)                 < (1, 2, -1)
(1, 2, 3)             == (1.0, 2.0, 3.0)
(1, 2, ('aa', 'ab'))   < (1, 2, ('abc', 'a'), 4)

Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception.

Modules

A module can contain executable statements as well as function definitions. These statements are intended to initialize the module. They are executed only the first time the module name is encountered in an import statement. 1 (They are also run if the file is executed as a script.)

Each module has its own private namespace, which is used as the global namespace by all functions defined in the module. Thus, the author of a module can use global variables in the module without worrying about accidental clashes with a user’s global variables. On the other hand, if you know what you are doing you can touch a module’s global variables with the same notation used to refer to its functions, modname.itemname.

Modules can import other modules. It is customary but not required to place all import statements at the beginning of a module (or script, for that matter). The imported module names, if placed at the top level of a module (outside any functions or classes), are added to the module’s global namespace.

There is a variant of the import statement that imports names from a module directly into the importing module’s namespace. For example:

from fibo import fib, fib2
fib(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377

This does not introduce the module name from which the imports are taken in the local namespace (so in the example, fibo is not defined).

There is even a variant to import all names that a module defines:

from fibo import *
fib(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377

This imports all names except those beginning with an underscore (_). In most cases Python programmers do not use this facility since it introduces an unknown set of names into the interpreter, possibly hiding some things you have already defined.

Note that in general the practice of importing * from a module or package is frowned upon, since it often causes poorly readable code. However, it is okay to use it to save typing in interactive sessions.

If the module name is followed by as, then the name following as is bound directly to the imported module.

import fibo as fib
fib.fib(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377

This is effectively importing the module in the same way that import fibo will do, with the only difference of it being available as fib.

It can also be used when utilising from with similar effects:

from fibo import fib as fibonacci
fibonacci(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377

Note For efficiency reasons, each module is only imported once per interpreter session. Therefore, if you change your modules, you must restart the interpreter – or, if it’s just one module you want to test interactively, use importlib.reload(), e.g. import importlib; importlib.reload(modulename).

Executing modules as scripts

When you run a Python module with

python fibo.py <arguments> the code in the module will be executed, just as if you imported it, but with the name set to "main". That means that by adding this code at the end of your module:

if __name__ == "__main__":
    import sys
    fib(int(sys.argv[1]))

you can make the file usable as a script as well as an importable module, because the code that parses the command line only runs if the module is executed as the “main” file:

python fibo.py 50
0 1 1 2 3 5 8 13 21 34

If the module is imported, the code is not run:

import fibo

This is often used either to provide a convenient user interface to a module, or for testing purposes (running the module as a script executes a test suite).

The Module Search Path

When a module named spam is imported, the interpreter first searches for a built-in module with that name. These module names are listed in sys.builtin_module_names. If not found, it then searches for a file named spam.py in a list of directories given by the variable sys.path. sys.path is initialized from these locations:

The directory containing the input script (or the current directory when no file is specified).

PYTHONPATH (a list of directory names, with the same syntax as the shell variable PATH).

The installation-dependent default (by convention including a site-packages directory, handled by the site module).

More details are at The initialization of the sys.path module search path.

Note On file systems which support symlinks, the directory containing the input script is calculated after the symlink is followed. In other words the directory containing the symlink is not added to the module search path. After initialization, Python programs can modify sys.path. The directory containing the script being run is placed at the beginning of the search path, ahead of the standard library path. This means that scripts in that directory will be loaded instead of modules of the same name in the library directory. This is an error unless the replacement is intended. See section Standard Modules for more information.

“Compiled” Python files

To speed up loading modules, Python caches the compiled version of each module in the pycache directory under the name module.version.pyc, where the version encodes the format of the compiled file; it generally contains the Python version number. For example, in CPython release 3.3 the compiled version of spam.py would be cached as pycache/spam.cpython-33.pyc. This naming convention allows compiled modules from different releases and different versions of Python to coexist.

Python checks the modification date of the source against the compiled version to see if it’s out of date and needs to be recompiled. This is a completely automatic process. Also, the compiled modules are platform-independent, so the same library can be shared among systems with different architectures.

Python does not check the cache in two circumstances. First, it always recompiles and does not store the result for the module that’s loaded directly from the command line. Second, it does not check the cache if there is no source module. To support a non-source (compiled only) distribution, the compiled module must be in the source directory, and there must not be a source module.

Some tips for experts:

You can use the -O or -OO switches on the Python command to reduce the size of a compiled module. The -O switch removes assert statements, the -OO switch removes both assert statements and doc strings. Since some programs may rely on having these available, you should only use this option if you know what you’re doing. “Optimized” modules have an opt- tag and are usually smaller. Future releases may change the effects of optimization.

A program doesn’t run any faster when it is read from a .pyc file than when it is read from a .py file; the only thing that’s faster about .pyc files is the speed with which they are loaded.

The module compileall can create .pyc files for all modules in a directory.

There is more detail on this process, including a flow chart of the decisions, in PEP 3147.

Standard Modules

Python comes with a library of standard modules, described in a separate document, the Python Library Reference (“Library Reference” hereafter). Some modules are built into the interpreter; these provide access to operations that are not part of the core of the language but are nevertheless built in, either for efficiency or to provide access to operating system primitives such as system calls. The set of such modules is a configuration option which also depends on the underlying platform. For example, the winreg module is only provided on Windows systems. One particular module deserves some attention: sys, which is built into every Python interpreter. The variables sys.ps1 and sys.ps2 define the strings used as primary and secondary prompts:

import sys
sys.ps1
'>>> '
sys.ps2
'... '
sys.ps1 = 'C> '
C> print('Yuck!')
Yuck!
C>

These two variables are only defined if the interpreter is in interactive mode.

The variable sys.path is a list of strings that determines the interpreter’s search path for modules. It is initialized to a default path taken from the environment variable PYTHONPATH, or from a built-in default if PYTHONPATH is not set. You can modify it using standard list operations:

import sys
sys.path.append('/ufs/guido/lib/python')

Input and Output

There are several ways to present the output of a program; data can be printed in a human-readable form, or written to a file for future use. This section will discuss some of the possibilities.

Output Formatting

So far we’ve encountered two ways of writing values: expression statements and the print() function. (A third way is using the write() method of file objects; the standard output file can be referenced as sys.stdout. See the Library Reference for more information on this.)

Often you’ll want more control over the formatting of your output than simply printing space-separated values. There are several ways to format output.

To use formatted string literals, begin a string with f or F before the opening quotation mark or triple quotation mark. Inside this string, you can write a Python expression between { and } characters that can refer to variables or literal values.

year = 2016
event = 'Referendum'
f'Results of the {year} {event}'
'Results of the 2016 Referendum'

The str.format() method of strings requires more manual effort. You’ll still use { and } to mark where a variable will be substituted and can provide detailed formatting directives, but you’ll also need to provide the information to be formatted.

yes_votes = 42_572_654
no_votes = 43_132_495
percentage = yes_votes / (yes_votes + no_votes)
'{:-9} YES votes  {:2.2%}'.format(yes_votes, percentage)
' 42572654 YES votes  49.67%'

Finally, you can do all the string handling yourself by using string slicing and concatenation operations to create any layout you can imagine. The string type has some methods that perform useful operations for padding strings to a given column width.

When you don’t need fancy output but just want a quick display of some variables for debugging purposes, you can convert any value to a string with the repr() or str() functions.

The str() function is meant to return representations of values which are fairly human-readable, while repr() is meant to generate representations which can be read by the interpreter (or will force a SyntaxError if there is no equivalent syntax). For objects which don’t have a particular representation for human consumption, str() will return the same value as repr(). Many values, such as numbers or structures like lists and dictionaries, have the same representation using either function. Strings, in particular, have two distinct representations.

Some examples:

s = 'Hello, world.'
str(s)
'Hello, world.'
repr(s)
"'Hello, world.'"
str(1/7)
'0.14285714285714285'
x = 10 * 3.25
y = 200 * 200
s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'
print(s)
The value of x is 32.5, and y is 40000...
# The repr() of a string adds string quotes and backslashes:
hello = 'hello, world\n'
hellos = repr(hello)
print(hellos)
'hello, world\n'
# The argument to repr() may be any Python object:
repr((x, y, ('spam', 'eggs')))
"(32.5, 40000, ('spam', 'eggs'))"

The string module contains a Template class that offers yet another way to substitute values into strings, using placeholders like $x and replacing them with values from a dictionary, but offers much less control of the formatting.

The String format() Method

Basic usage of the str.format() method looks like this:

print('We are the {} who say "{}!"'.format('knights', 'Ni'))
We are the knights who say "Ni!"

The brackets and characters within them (called format fields) are replaced with the objects passed into the str.format() method. A number in the brackets can be used to refer to the position of the object passed into the str.format() method.

print('{0} and {1}'.format('spam', 'eggs'))
spam and eggs
print('{1} and {0}'.format('spam', 'eggs'))
eggs and spam

If keyword arguments are used in the str.format() method, their values are referred to by using the name of the argument.

print('This {food} is {adjective}.'.format(
      food='spam', adjective='absolutely horrible'))
This spam is absolutely horrible.

Positional and keyword arguments can be arbitrarily combined:

print('The story of {0}, {1}, and {other}.'.format('Bill', 'Manfred',
                                                   other='Georg'))
The story of Bill, Manfred, and Georg.

If you have a really long format string that you don’t want to split up, it would be nice if you could reference the variables to be formatted by name instead of by position. This can be done by simply passing the dict and using square brackets '[]' to access the keys.

table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
print('Jack: {0[Jack]:d}; Sjoerd: {0[Sjoerd]:d}; '
      'Dcab: {0[Dcab]:d}'.format(table))
Jack: 4098; Sjoerd: 4127; Dcab: 8637678

This could also be done by passing the table dictionary as keyword arguments with the ** notation.

table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
print('Jack: {Jack:d}; Sjoerd: {Sjoerd:d}; Dcab: {Dcab:d}'.format(**table))
Jack: 4098; Sjoerd: 4127; Dcab: 8637678

This is particularly useful in combination with the built-in function vars(), which returns a dictionary containing all local variables.

As an example, the following lines produce a tidily aligned set of columns giving integers and their squares and cubes:

for x in range(1, 11):
    print('{0:2d} {1:3d} {2:4d}'.format(x, x*x, x*x*x))

 1   1    1
 2   4    8
 3   9   27
 4  16   64
 5  25  125
 6  36  216
 7  49  343
 8  64  512
 9  81  729
10 100 1000

For a complete overview of string formatting with str.format(), see Format String Syntax.