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Python Tutorial for Robot Framework Test Library Developers
Python Tutorial for Robot Framework Test Library Developers
Copyright © Nokia Siemens Networks 2008-2012
Licensed under the Creative Commons Attribution 3.0 Unported license
Table of Contents
Introduction
- This is self learning material to learn how to program using Python
language. The target is to learn enough Python to be able to start
creating test libraries for Robot Framework.
- Earlier programming experience is expected but not absolutely
necessary.
- The main study material for this training is the excellent Dive Into
Python book which is freely available for on-line reading,
downloading or printing from http://diveintopython.net. It is
targeted for people who already know how to program but do not know
Python before.
- If you are a novice programmer, it might better to start with Think
Python book. It is also available for free and its target audience
is people without any earlier programming knowledge.
- Python Tutorial, available at http://python.org and included in
the standard Python installation at least on Windows, is also very
good. Some of the sections in this training refer to it instead of
or in addition to Dive Into Python.
- Python coding style guidelines are specified in PEP-8. Notice that
the Dive Into Python book uses camelCaseStyle instead of the
recommended underline_style.
- Another highly recommended style guide, covering many essential
Python idioms and techniques, is Code Like a Pythonista:
Idiomatic Python available at
http://python.net/~goodger/projects/pycon/2007/idiomatic/handout.html
- The official Python website at http://python.org is a good place to
find more documentation and Python related information in
general.
- If you need information about Jython, the Java implementation of
Python, you can start from http://jython.org.
- The Definitive Guide to Jython covers Jython in detail and is
useful especially if you are interested about Jython-Java
integration. It is freely available at http://jythonbook.com.
Getting started
Installation
- Most Linux distributions, OS X, and other UNIX like machines have
Python installed by default, but on Windows you probably need to
install it separately. Installers for different platforms can be
found from http://python.org.
- Robot Framework does not yet support Python 3.x versions and this
tutorial is also based on Python 2.x. Any 2.x version up from 2.3 is
sufficient but the latter versions are recommended.
- It is highly recommended that you configure your system so that you
can run Python from command line simply by typing python and pressing
enter.
- On Windows, and possibly on some other systems, this requires
adding Python installation directory into PATH environment
variable. For example Robot Framework User Guide has
instructions on how to do it in its Installation section.
Interactive interpreter
Open the command prompt and type python. On Windows you can
also start the interpreter by selecting Start > All Programs >
Python 2.x.
Statements and expressions can be written in the interpreter.
Pressing enter will interpret the line and possible results are
echoed. Try for example:
>>> 1 + 2
3
Use Ctrl-D to exit on UNIX like machines and Ctrl-Z
and enter on Windows.
- With Python 2.5 and newer you can exit the interpreter also with
command exit().
Dive Into Python has some more examples:
http://diveintopython.net/installing_python/shell.html
Python editors
- Most general purpose text editors (Emacs, VIM, UltraEdit, ...) and
IDEs (Eclipse, Netbeans, ...) can be used to edit Python. There are
also some editors specially for Python.
- The most important editor features are source highlighting and
handling indentation. Make sure your editor of choice supports them
either natively or via Python plugin or mode.
- If you do not know any editor, you can at least get started with
IDLE. It is included in the standard Python installation on
Windows and can be installed also on other systems.
Variables
Basic data types
Python has strings, integers, floating point numbers, Boolean values
(True and False) similarly as most other programming
languages.
Strings can be enclosed into double or single quotes. Different
quotest do not have any difference like they do for example in Perl.
Unicode strings have a special syntax like u"Hyv\xE4\xE4
y\xF6\t\xE4!". Using Unicode with Python is not covered otherwise
in this tutorial.
None is a special value meaning nothing similarly as
null in Java.
Try at least these on the interpreter:
>>> 2 * 2.5
5.0
>>> 'This is easy'
'This is easy'
>>> "Ain't it"
"Ain't it"
Declaring variables
All different values can be assigned to variables. Valid characters
in variable identifiers are letters, underscore, and numbers,
although numbers cannot start the variable name.
A variable needs not to be declared, it starts to exist when a value is
assigned for the first time.
There is no need to specify the variable type either as the type is
got from the assigned variable automatically.
Try it out:
>>> a = 3
>>> a
3
>>> b = 4
>>> a*b
12
>>> greeting = 'Hello'
>>> greeting
'Hello'
>>> greeting.upper()
'HELLO'
It is even possible to assign multiple variables at once:
>>> x, y = 'first', 'second'
>>> x
'first'
>>> y
'second'
First program
Create a file hello.py with your editor of choice and write
this content into it:
print "Hello, world!"
Then execute the file on the console like this:
python hello.py
As a result you should get Hello, world! printed into the
screen. With Robot Framework keywords such messages would end up
into the log file.
For more interesting examples see Dive Into Python:
http://diveintopython.net/getting_to_know_python/index.html
Functions
Creating functions
Creating functions in Python is super easy. This example uses the
interpreter, but you can also write the code into the previous
hello.py file and execute it.
>>> def hello():
... print "Hello, world!"
...
>>> hello()
Hello, world!
Note that in Python code blocks must be indented (four spaces is the
norm and highly recommended) and you close the block simply by
returning to the earlier indentation level. Inside a block you must
use the indentation level consistently.
Notice also that this hello function is actually already a
valid keyword for Robot Framework!
A function with arguments is not that more complicated:
>>> def hello(name):
... print "Hello, %s!" % name
...
>>> hello("Python")
Hello, Python!
>>> hello("Robot Framework")
Hello, Robot Framework!
The hard part in this example is string formatting (i.e. "Hello,
%s!" % name) which uses similar syntax as for example C language.
More information about it can be found e.g. from Dive Into Python:
http://diveintopython.net/native_data_types/formatting_strings.html
Optional and named arguments
Functions can have default values for some or all of its arguments:
>>> def hello(name="World"):
... print "Hello, %s!" % name
...
>>> hello()
Hello, World!
>>> hello("Robot")
Hello, Robot!
If there are several optional arguments, it is also possible to
specify only some of them by giving their name along with the value
as the example below illustrates. Those arguments that do not have
default values cannot be omitted.
>>> def test(a, b=1, c=2, d=3):
... print a, b, c, d
...
>>> test(0)
0 1 2 3
>>> test(0, 42)
0 42 2 3
>>> test(1, c=10)
1 1 10 3
>>> test(2, c=100, d=200)
2 1 100 200
>>> test(b=0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: test() takes at least 1 non-keyword argument (0 given)
Robot Framework keywords can have default values but they are always
used with positional arguments. For example, if the above hello
method was used as a keyword, it could be used with zero or one
argument, and test could be used with one to four arguments.
Dive Into Python explains both optional and named arguments very well:
http://diveintopython.net/power_of_introspection/optional_arguments.html
Variable number of arguments
- Function can also be created so that they take any number of
arguments. This is done by prefixing an argument after required and
optional arguments with an asterisk like *args, and it means that
the specified argument gets all the "extra" arguments as a tuple.
>>> def example(arg1, arg2, *rest):
... print arg1, arg2, rest
...
>>> example(1, 2)
1 2 ()
>>> example(1, 2, 3)
1 2 (3,)
>>> example(1, 2, 3, 4, 5)
1 2 (3, 4, 5)
- Using variable number of arguments works also with Robot Framework
keywords.
- Python tutorial explains everything in this and the prvious section
in detail:
http://docs.python.org/tutorial/controlflow.html#more-on-defining-functions
Returning values
Functions can use return statement to return values that can be
assigned to variables or passed to other functions:
>>> def multiply_by_two(number):
... return number * 2
...
>>> result = multiply_by_two(10)
>>> result
20
>>> result = multiply_by_two(multiply_by_two(2))
>>> result
8
Robot Framework keywords can also return values that can be assigned
to variables and then used as arguments to other keywords.
Documenting functions
In Python functions, as well as classes and modules, are documented with
so called doc strings:
>>> def hello():
... """Prints 'Hello, world!' to the standard output."""
... print "Hello, world!"
...
Interestingly the documentation is available dynamically:
>>> print hello.__doc__
Prints 'Hello, world!' to the standard output.
Doc strings are covered pretty well in Dive Into Python:
http://diveintopython.net/getting_to_know_python/documenting_functions.html
Robot Framework has libdoc.py tool that can generate test library
documentation based on these doc strings. Documenting functions that
are used as keywords is thus very important.
Container data types
- Python has a nice set of container data types built into the
language with a really simple syntax similarly as in Perl and
Ruby. You are going to use them a lot!
- See Dive Into Python for more information and examples than shown
below: http://diveintopython.net/native_data_types
Lists
A list is an ordered collection of items which you normally access
by index.
They also have handy methods like append, insert and
pop to access or alter the list.
>>> x = ['Some', 'strings', 'here']
>>> x[0]
'Some'
>>> x[1]
'strings'
>>> x[-1]
'here'
>>> x[2] = x[2].upper()
>>> x.append(42)
>>> x
['Some', 'strings', 'HERE', 42]
Tuples
A tuple is a list like structure which you cannot alter after creating it.
>>> t = (1, 2, 'x')
>>> t[0]
1
>>> t[-1]
'x'
>>> t[0] = 'new value'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment
Notice that you must use a trailing comma to create a tuple with one
element:
>>> empty = ()
>>> one = (1,)
>>> two = (1, 2)
Dictionaries
A dictionary is an unordered collection of key-value pairs. The same
data structure is often called hashmap.
>>> d = {'x': 'some value', 'a': 1, 'b': 2}
>>> d['a']
1
>>> d['x']
'some value'
>>> d['a'] = d['b']
>>> d['tuple'] = t
>>> d
{'a': 2, 'x': 'some value', 'b': 2, 'tuple': (1, 2, 'x')}
>>> 'x' in d
True
>>> 'z' in d
False
Control Flow
Conditional execution
Python has similar if/elif/else structure as most other
programming languages.
Notice that no parentheses are needed around the expression as in
Java or C.
def is_positive(number):
if number > 0:
return True
return False
def greet(name, time):
if 7 < time < 12:
print 'Good morning %s' % name
elif time < 18:
print 'Good afternoon %s' % name
elif time < 23:
print 'Good night %s' % name
else:
print '%s, you should be sleeping!' % name
Looping
for loops allow iterating over a sequence of items such as
a list. This is probably the loop you are going to use most often.
def greet_many(names):
for name in names:
print 'Hello %s' % name
def count_up(limit):
for num in range(1, limit+1):
if num == limit:
print 'bye!'
else:
print num
while loops iterate as long as given expression is true. Very handy
when waiting some event to occur.
def wait_until_message_received():
msg = try_to_receive_message()
while msg is None:
time.sleep(5)
msg = try_to_receive_message()
return msg
Both for and while loops have typical
continue and break statements that can be used to
end the current iteration or exit the loop altogether.
For more examples and information see:
- Python Tutorial: http://docs.python.org/tutorial/controlflow.html
- Dive Into Python: http://diveintopython.net/file_handling/for_loops.html
List comprehensions
Quite often for loops can be replaced with even more concise list
comprehensions or generator expressions:
>>> numbers = [1, -5, 4, -32, 0, 42]
>>> positive = [ num for num in numbers if num > 0 ]
>>> positive
[1, 4, 42]
>>> sum(num * 2 for num in positive)
94
This syntax might look a bit strange at first but you will love it
very soon. To learn more see, for example, Dive Into Python:
http://diveintopython.net/native_data_types/mapping_lists.html
Modules
Importing modules
- Importing existing Python modules is as simply as saying import
modulename.
- An alternative syntax is from modulename import something.
- Python has a comprehensive standard library and a package
index with external modules so there is plenty of existing code to
be imported. It is recommended to study what is available to avoid
reinventing wheels.
Creating modules
Because every .py file is effectively a Python module, you
have already created at least hello module.
For example if we have the following code in a file called
example.py:
def hello(name="World"):
print "Hello, %s!" % name
if __name__ == "__main__":
hello()
then we can use it in the interpreter (or from other modules) like:
>>> import example
>>> example.hello("Tellus")
Hello, Tellus!
if __name__ == "__main__" block in the previous example is
important because it allows executing the file also as a script like
python example.py.
The automatic __name__ attribute (Python has many of these
as you will see if you study it more) gets value "__main___"
when the file is run as a script and the if block is thus
executed only in that case.
Bigger modules can be organized into several files inside a higher
level module as submodules. In this case the higher level module is
a directory with a special ___init___.py file.
For more information about modules see Python Tutorial:
http://docs.python.org/tutorial/modules.html
Module search path (PYTHONPATH)
- Python modules are not automatically searched everywhere on you
machine. Python has certain default places to search modules for
(e.g. its own library directory which is often in place like
C:\Python26\Lib or /usr/lib/python2.6) and
additionally it looks for them from so called PYTHONPATH.
- PYTHONPATH is most often controlled using an environment
variable with the same name that contains places (mainly
directories) to look for Python modules. It is similar to Java's
CLASSPATH and also to PATH environment variable which
is used by an operating system to look for executable programs.
- PYTHONPATH is important also with Robot Framework because it
can import test libraries only if the module containing the library
can be imported.
Advanced features
Classes and instances
Python is an object-oriented language but as we have seen you do not
need to use classes everywhere like you need to with Java. It is
totally fine to just have a module with functions if that suites
your needs, but object oriented features are often really handy.
The syntax for creating classes and then instances from them is
relatively straightforward:
>>> class MyClass:
... def __init__(self, name):
... self._name = name
... def hello(self, other="World"):
... print "%s says hello to %s." % (self._name, other)
...
>>> c = MyClass('Robot')
>>> c.hello()
Robot says hello to World.
>>> c.hello('Tellus')
Robot says hello to Tellus.
The only surprising part in the syntax is that every class method
must have self as the first argument in the signature. After
you create an instance of the class Python binds the method, and it
also takes care of passing the self argument automatically
so you do not use it when calling the method.
To learn more about classes you can follow an interesting example
from Dive Into Python and/or study detailed information from Python
Tutorial:
Exceptions
Python has an exception system similar to many other languages.
Exceptions are classes and the normal way to raise them is
raise SomeException("Error message").
Exceptions are handled in a try/except block:
try:
f = open(path)
except IOError, err:
print "Opening file %s for reading failed: %s" % (path, err)
The try/except block can have multiple except
branches, an optional else to execute if no exception
occurred, and finally to execute both when an exception
occurred and when it did not.
Compared to Java there are some terminology differences
(raise vs. throw and except
vs. catch) but the biggest real difference is that there are
no checked exceptions. This means that you do not need to add
throws SomeException to methods that may raise an exception.
More information can be found, for example, from Dive Into Python:
http://diveintopython.net/file_handling/index.html
Exceptions are an important part of the Robot Framework Library API
because keywords use them to communicate failures to the framework.
Regular expressions
- Regular expressions are really handy for processing strings which is
a really common need in test automation.
- Python has a really fast regular expression engine and it uses a
syntax derived from Perl's regexp syntax similarly as Java and many
other languages.
- Dive Into Python contains a good introduction again:
http://diveintopython.net/regular_expressions/index.html
- Notice that Python strings also have many useful methods
(e.g. startswith, find, isdigit) so regexps
are not needed as often as in Perl or Ruby.
Unit testing
- Unit testing is important especially when you start having more
code and unit testing your test library code can be a really good
idea.
- Python has several unit testing frameworks. Two of them,
unittest and doctest, are in the standard
library. The former is immediately familiar for anyone who has used
JUnit or some other xUnit framework and the other is interesting
because it allows using function doc strings for tests.
- Dive Into Python has really good chapters about unit testing,
test-driven development (TDD), and refactoring.
Writing test libraries
Robot Framework's test library API is really simple. It is explained
fully in Robot Framework User Guide and this tutorial only covers
the very basic features with an executable example.
Library API basics
The test library can be either a module or a class. In case of a
module, a keyword will be created for each top-level function in the
module. In case of a class, a keyword will be created for each public
method of the class.
The most important ways keywords can interact with the framework have already
been covered in this tutorial:
- Keyword name maps to the function name (case insensitively and
underscores removed).
- Keywords have same arguments as implementing functions.
- Failures are reported by raising exceptions.
- print statement can be used to log messages.
- Values can be returned using the return statement.
- Doc strings are used to document keywords.
Executable example
The example library and associated test data shown below demonstrate
the most important features of the test library API. You can execute
these test cases in your own environment and edit them to test also
other features. A precondition is having Robot Framework installed,
but then you only need to get the library and the data, and
run command pybot example_tests.tsv.
def simple_keyword():
"""Log a message"""
print 'You have used the simplest keyword.'
def greet(name):
"""Logs a friendly greeting to person given as argument"""
print 'Hello %s!' % name
def multiply_by_two(number):
"""Returns the given number multiplied by two
The result is always a floating point number.
This keyword fails if the given `number` cannot be converted to number.
"""
return float(number) * 2
def numbers_should_be_equal(first, second):
print '*DEBUG* Got arguments %s and %s' % (first, second)
if float(first) != float(second):
raise AssertionError('Given numbers are unequal!')
Simple test cases using keywords from ExampleLibrary
*Settings*
Library
ExampleLibrary
*Test Cases*
Simple Test
Simple Keyword
Greet
Robot Framework
Greet
World
Returning Value
${result} =
Multiply By Two
4.1
Numbers Should Be Equal
${result}
8.2
Failing Test
Numbers Should Be Equal
2
2
Numbers Should Be Equal
2
3