1.2.5. Reusing code: scripts and modules

For now, we have typed all instructions in the interpreter. For longer sets of instructions we need to change track and write the code in text files (using a text editor), that we will call either scripts or modules. Use your favorite text editor (provided it offers syntax highlighting for Python), or the editor that comes with the Scientific Python Suite you may be using. Scripts


Let us first write a script, that is a file with a sequence of instructions that are executed each time the script is called. Instructions may be e.g. copied-and-pasted from the interpreter (but take care to respect indentation rules!).

The extension for Python files is .py. Write or copy-and-paste the following lines in a file called test.py

message = "Hello how are you?"
for word in message.split():


Let us now execute the script interactively, that is inside the Ipython interpreter. This is maybe the most common use of scripts in scientific computing.


in Ipython, the syntax to execute a script is %run script.py. For example,

In [1]: %run test.py
In [2]: message
Out[2]: 'Hello how are you?'

The script has been executed. Moreover the variables defined in the script (such as message) are now available inside the interpreter’s namespace.


Other interpreters also offer the possibility to execute scripts (e.g., execfile in the plain Python interpreter, etc.).

It is also possible In order to execute this script as a standalone program, by executing the script inside a shell terminal (Linux/Mac console or cmd Windows console). For example, if we are in the same directory as the test.py file, we can execute this in a console:

$ python test.py


Standalone scripts may also take command-line arguments

In file.py:

import sys
$ python file.py test arguments
['file.py', 'test', 'arguments']


Don’t implement option parsing yourself. Use a dedicated module such as argparse. Importing objects from modules

In [1]: import os
In [2]: os
Out[2]: <module 'os' from '/usr/lib/python2.6/os.pyc'>
In [3]: os.listdir('.')

And also:

In [4]: from os import listdir

Importing shorthands:

In [5]: import numpy as np


from os import *

This is called the star import and please, Do not use it

  • Makes the code harder to read and understand: where do symbols come from?
  • Makes it impossible to guess the functionality by the context and the name (hint: os.name is the name of the OS), and to profit usefully from tab completion.
  • Restricts the variable names you can use: os.name might override name, or vise-versa.
  • Creates possible name clashes between modules.
  • Makes the code impossible to statically check for undefined symbols.


Modules are thus a good way to organize code in a hierarchical way. Actually, all the scientific computing tools we are going to use are modules:

>>> import numpy as np # data arrays
>>> np.linspace(0, 10, 6)
array([ 0., 2., 4., 6., 8., 10.])
>>> import scipy # scientific computing Creating modules


If we want to write larger and better organized programs (compared to simple scripts), where some objects are defined, (variables, functions, classes) and that we want to reuse several times, we have to create our own modules.

Let us create a module demo contained in the file demo.py:

"A demo module."
def print_b():
"Prints b."
def print_a():
"Prints a."
c = 2
d = 2


In this file, we defined two functions print_a and print_b. Suppose we want to call the print_a function from the interpreter. We could execute the file as a script, but since we just want to have access to the function print_a, we are rather going to import it as a module. The syntax is as follows.

In [1]: import demo
In [2]: demo.print_a()
In [3]: demo.print_b()

Importing the module gives access to its objects, using the module.object syntax. Don’t forget to put the module’s name before the object’s name, otherwise Python won’t recognize the instruction.


In [4]: demo?
Type: module
Base Class: <type 'module'>
String Form: <module 'demo' from 'demo.py'>
Namespace: Interactive
File: /home/varoquau/Projects/Python_talks/scipy_2009_tutorial/source/demo.py
A demo module.
In [5]: who
In [6]: whos
Variable Type Data/Info
demo module <module 'demo' from 'demo.py'>
In [7]: dir(demo)
In [8]: demo.<TAB>
demo.c demo.print_a demo.py
demo.d demo.print_b demo.pyc

Importing objects from modules into the main namespace

In [9]: from demo import print_a, print_b
In [10]: whos
Variable Type Data/Info
demo module <module 'demo' from 'demo.py'>
print_a function <function print_a at 0xb7421534>
print_b function <function print_b at 0xb74214c4>
In [11]: print_a()


Module caching

Modules are cached: if you modify demo.py and re-import it in the old session, you will get the old one.


In [10]: reload(demo)

In Python3 instead reload is not builtin, so you have to import the importlib module first and then do:

In [10]: importlib.reload(demo) ‘__main__’ and module loading


Sometimes we want code to be executed when a module is run directly, but not when it is imported by another module. if __name__ == '__main__' allows us to check whether the module is being run directly.

File demo2.py:

def print_b():
"Prints b."
def print_a():
"Prints a."
# print_b() runs on import
if __name__ == '__main__':
# print_a() is only executed when the module is run directly.

Importing it:

In [11]: import demo2
In [12]: import demo2

Running it:

In [13]: %run demo2
a Scripts or modules? How to organize your code


Rule of thumb

  • Sets of instructions that are called several times should be written inside functions for better code reusability.
  • Functions (or other bits of code) that are called from several scripts should be written inside a module, so that only the module is imported in the different scripts (do not copy-and-paste your functions in the different scripts!).

How modules are found and imported

When the import mymodule statement is executed, the module mymodule is searched in a given list of directories. This list includes a list of installation-dependent default path (e.g., /usr/lib/python) as well as the list of directories specified by the environment variable PYTHONPATH.

The list of directories searched by Python is given by the sys.path variable

In [1]: import sys
In [2]: sys.path

Modules must be located in the search path, therefore you can:

  • write your own modules within directories already defined in the search path (e.g. $HOME/.local/lib/python2.7/dist-packages). You may use symbolic links (on Linux) to keep the code somewhere else.

  • modify the environment variable PYTHONPATH to include the directories containing the user-defined modules.


    On Linux/Unix, add the following line to a file read by the shell at startup (e.g. /etc/profile, .profile)

    export PYTHONPATH=$PYTHONPATH:/home/emma/user_defined_modules

    On Windows, http://support.microsoft.com/kb/310519 explains how to handle environment variables.

  • or modify the sys.path variable itself within a Python script.


    import sys
    new_path = '/home/emma/user_defined_modules'
    if new_path not in sys.path:

    This method is not very robust, however, because it makes the code less portable (user-dependent path) and because you have to add the directory to your sys.path each time you want to import from a module in this directory.

See also

See https://docs.python.org/tutorial/modules.html for more information about modules. Packages

A directory that contains many modules is called a package. A package is a module with submodules (which can have submodules themselves, etc.). A special file called __init__.py (which may be empty) tells Python that the directory is a Python package, from which modules can be imported.

$ ls
cluster/ io/ README.txt@ stsci/
__config__.py@ LATEST.txt@ setup.py@ __svn_version__.py@
__config__.pyc lib/ setup.pyc __svn_version__.pyc
constants/ linalg/ setupscons.py@ THANKS.txt@
fftpack/ linsolve/ setupscons.pyc TOCHANGE.txt@
__init__.py@ maxentropy/ signal/ version.py@
__init__.pyc misc/ sparse/ version.pyc
INSTALL.txt@ ndimage/ spatial/ weave/
integrate/ odr/ special/
interpolate/ optimize/ stats/
$ cd ndimage
$ ls
doccer.py@ fourier.pyc interpolation.py@ morphology.pyc setup.pyc
doccer.pyc info.py@ interpolation.pyc _nd_image.so
filters.py@ info.pyc measurements.py@ _ni_support.py@
filters.pyc __init__.py@ measurements.pyc _ni_support.pyc tests/
fourier.py@ __init__.pyc morphology.py@ setup.py@

From Ipython:

In [1]: import scipy
In [2]: scipy.__file__
Out[2]: '/usr/lib/python2.6/dist-packages/scipy/__init__.pyc'
In [3]: import scipy.version
In [4]: scipy.version.version
Out[4]: '0.7.0'
In [5]: import scipy.ndimage.morphology
In [6]: from scipy.ndimage import morphology
In [17]: morphology.binary_dilation?
Type: function
Base Class: <type 'function'>
String Form: <function binary_dilation at 0x9bedd84>
Namespace: Interactive
File: /usr/lib/python2.6/dist-packages/scipy/ndimage/morphology.py
Definition: morphology.binary_dilation(input, structure=None,
iterations=1, mask=None, output=None, border_value=0, origin=0,
Multi-dimensional binary dilation with the given structure.
An output array can optionally be provided. The origin parameter
controls the placement of the filter. If no structuring element is
provided an element is generated with a squared connectivity equal
to one. The dilation operation is repeated iterations times. If
iterations is less than 1, the dilation is repeated until the
result does not change anymore. If a mask is given, only those
elements with a true value at the corresponding mask element are
modified at each iteration. Good practices

  • Use meaningful object names

  • Indentation: no choice!


    Indenting is compulsory in Python! Every command block following a colon bears an additional indentation level with respect to the previous line with a colon. One must therefore indent after def f(): or while:. At the end of such logical blocks, one decreases the indentation depth (and re-increases it if a new block is entered, etc.)

    Strict respect of indentation is the price to pay for getting rid of { or ; characters that delineate logical blocks in other languages. Improper indentation leads to errors such as

    IndentationError: unexpected indent (test.py, line 2)

    All this indentation business can be a bit confusing in the beginning. However, with the clear indentation, and in the absence of extra characters, the resulting code is very nice to read compared to other languages.

  • Indentation depth: Inside your text editor, you may choose to indent with any positive number of spaces (1, 2, 3, 4, …). However, it is considered good practice to indent with 4 spaces. You may configure your editor to map the Tab key to a 4-space indentation.

  • Style guidelines

    Long lines: you should not write very long lines that span over more than (e.g.) 80 characters. Long lines can be broken with the \ character

    >>> long_line = "Here is a very very long line \
    ... that we break in two parts."


    Write well-spaced code: put whitespaces after commas, around arithmetic operators, etc.:

    >>> a = 1 # yes
    >>> a=1 # too cramped

    A certain number of rules for writing “beautiful” code (and more importantly using the same conventions as anybody else!) are given in the Style Guide for Python Code.

Quick read

If you want to do a first quick pass through the Scipy lectures to learn the ecosystem, you can directly skip to the next chapter: NumPy: creating and manipulating numerical data.

The remainder of this chapter is not necessary to follow the rest of the intro part. But be sure to come back and finish this chapter later.