Contents

Introduction to Python

Before we get started 1…


Goals

  • learn basic and efficient usage of the python programming language

    • what is python & how to utilize it

    • building blocks of & operations in python

What is Python?

  • Python is a programming language

  • Specifically, it’s a widely used, very flexible, high-level, general-purpose, dynamic programming language

  • That’s a mouthful! Let’s explore each of these points in more detail…

Widely-used

  • Python is the fastest-growing major programming language

  • Top 3 overall (with JavaScript, Java)

High-level

Python features a high level of abstraction

  • Many operations that are explicit in lower-level languages (e.g., C/C++) are implicit in Python

  • E.g., memory allocation, garbage collection, etc.

  • Python lets you write code faster

File reading in Java

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
 
public class ReadFile {
    public static void main(String[] args) throws IOException{
        String fileContents = readEntireFile("./foo.txt");
    }
 
    private static String readEntireFile(String filename) throws IOException {
        FileReader in = new FileReader(filename);
        StringBuilder contents = new StringBuilder();
        char[] buffer = new char[4096];
        int read = 0;
        do {
            contents.append(buffer, 0, read);
            read = in.read(buffer);
        } while (read >= 0);
        return contents.toString();
    }
}

General-purpose

You can do almost everything in Python

  • Comprehensive standard library

  • Enormous ecosystem of third-party packages

  • Widely used in many areas of software development (web, dev-ops, data science, etc.)

Dynamic

Code is interpreted at run-time

  • No compilation process*; code is read line-by-line when executed

  • Eliminates delays between development and execution

  • The downside: poorer performance compared to compiled languages

(Try typing import antigravity into a new cell and running it!)

Variables and data types

  • In Python, we declare a variable by assigning it a value with the = sign

    • Variables are pointers, not data stores!

  • Python supports a variety of data types and structures:

    • booleans

    • numbers (ints, floats, etc.)

    • strings

    • lists

    • dictionaries

    • many others!

  • We don’t specify a variable’s type at assignment

What follows is a short introduction to Python to help beginners to get familiar with this programming language.

It is divided into the following chapters:

Module

Most of the functionality in Python is provided by modules. To use a module in a Python program it first has to be imported. A module can be imported using the import statement. For example, to import the module math, which contains many standard mathematical functions, we can do:

import math

This includes the whole module and makes it available for use later in the program. For example, we can do:

import math

x = math.cos(2 * math.pi)

print(x)

1.0

Importing the whole module us often times unnecessary and can lead to longer loading time or increase the memory consumption. An alternative to the previous method, we can also choose to import only a few selected functions from a module by explicitly listing which ones we want to import:

from math import cos, pi

x = cos(2 * pi)

print(x)

1.0

It is also possible to give an imported module or symbol your own access name with the as additional:

import numpy as np
from math import pi as number_pi

x  = np.rad2deg(number_pi)

print(x)

180.0

Namespaces and imports

  • Python is very serious about maintaining orderly namespaces

  • If you want to use some code outside the current scope, you need to explicitly “import” it

  • Python’s import system often annoys beginners, but it substantially increases code clarity

    • Almost completely eliminates naming conflicts and confusion

Help and Descriptions

Using the function help we can get a description of almost all functions.

help(math.log)

Help on built-in function log in module math:

log(...)
    log(x, [base=math.e])
    Return the logarithm of x to the given base.
    
    If the base not specified, returns the natural logarithm (base e) of x.

math.log(10)

2.302585092994046

math.log(10, 2)

3.3219280948873626

Variables and types

Symbol names

Variable names in Python can contain alphanumerical characters a-z, A-Z, 0-9 and some special characters such as _. Normal variable names must start with a letter.

By convention, variable names start with a lower-case letter, and Class names start with a capital letter.

In addition, there are a number of Python keywords that cannot be used as variable names. These keywords are:

and, as, assert, break, class, continue, def, del, elif, else, except, exec, finally, for, from, global, if, import, in, is, lambda, not, or, pass, print, raise, return, try, while, with, yield

Assignment

The assignment operator in Python is =. Python is a dynamically typed language, so we do not need to specify the type of a variable when we create one.

Assigning a value to a new variable creates the variable:

# variable assignments
x = 1.0

Although not explicitly specified, a variable does have a type associated with it. The type is derived from the value it was assigned.

type(x)

float

If we assign a new value to a variable, its type can change.

x = 1

type(x)

int

If we try to use a variable that has not yet been defined we get an NameError (Note, that we will use in the notebooks try/except blocks to handle the exception, so the notebook doesn’t stop. The code below will try to execute print function and if the NameError occurs the error message will be printed. Otherwise, an error will be raised. Later in this notebook you will learn more about exception handling.):

try:
    print(y)
except(NameError) as err:
    print("NameError", err)
else:
    raise

NameError name 'y' is not defined

Variable names:

  • Can include letters, digits, and underscores

  • Cannot start with a digit

  • Are case sensitive.

This means that, for example:

  • weight0 is a valid variable name, whereas 0weight is not

  • weight and Weight are different variables

Fundamental types & data structures

  • Most code requires more complex structures built out of basic data types

  • Python provides built-in support for many common structures

    • Many additional structures can be found in the collections module

# integers
x = 1
type(x)

int

# float
x = 1.0
type(x)

float

# boolean
b1 = True
b2 = False

type(b1)

bool

# string
s = "hello world"

type(s)

str

Operators and comparisons

Most operators and comparisons in Python work as one would expect:

  • Arithmetic operators +, -, *, /, ** power, % modulo

[1 + 2, 
 1 - 2,
 1 * 2,
 1 % 2]

[3, -1, 2, 1]

In Python 2.7, what kind of division (/) will be executed, depends on the type of the numbers involved. If all numbers are integers, the division will be an integer division, otherwise, it will be a float division. In Python 3 this has been changed and fractions aren’t lost when dividing integers (for integer division you can use another operator, //).

# In Python 3 these two operations will give the same result
# (in Python 2 the first one will be treated as an integer division). 
print(1 / 2)
print(1 / 2.0)

0.5
0.5

# Note! The power operator in python isn't ^, but **
2 ** 2

4

  • The boolean operators are spelled out as words and, not, or.

True and False

False

not False

True

True or False

True

  • Comparison operators >, <, >= (greater or equal), <= (less or equal), == (equal), != (not equal) and is (identical).

2 > 1, 2 < 1

(True, False)

2 > 2, 2 < 2

(False, False)

2 >= 2, 2 <= 2

(True, True)

# equal to
[1,2] == [1,2]

True

# not equal to
2 != 3

True

  • boolean operator

x = True
y = False

print(not x)
print(x and y)
print(x or y)

False
False
True

  • String comparison

"lo W" in "Hello World"

True

"x" not in "Hello World"

True

Shortcut math operation and assignment

a = 2
a = a * 2
print(a)

4

The command a = a * 2, can be shortcut to a *= 2. This also works with +=, -= and /=.

b = 3
b *= 3
print(b)

9

Strings, List and dictionaries

Strings

Strings are the variable type that is used for storing text messages.

s = "Hello world"
type(s)

str

# length of the string: number of characters in string
len(s)

11

# replace a substring in a string with something else
s2 = s.replace("world", "test")
print(s2)

Hello test

We can index a character in a string using []:

s[0]

'H'

Heads up MATLAB users: Indexing start at 0!

We can extract a part of a string using the syntax [start:stop], which extracts characters between index start and stop:

s[0:5]

'Hello'

If we omit either (or both) of start or stop from [start:stop], the default is the beginning and the end of the string, respectively:

s[:5]

'Hello'

s[6:]

'world'

s[:]

'Hello world'

We can also define the step size using the syntax [start:end:step] (the default value for step is 1, as we saw above):

s[::1]

'Hello world'

s[::2]

'Hlowrd'

This technique is called slicing.

String formatting examples

print("str1" + "str2" + "str3")  # strings added with + are concatenated without space

str1str2str3

print("str1" "str2" "str3")      # The print function concatenates strings differently
print("str1", "str2", "str3")    # depending on how the inputs are specified
print(("str1", "str2", "str3"))  # See the three different outputs below

str1str2str3
str1 str2 str3
('str1', 'str2', 'str3')

print("str1", 1.0, False)       # The print function converts all arguments to strings

str1 1.0 False

print("value = %f" %1.0)       # we can use C-style string formatting

value = 1.000000

Python has two string formatting styles. An example of the old style is below, specifier %.2f transforms the input number into a string, that corresponds to a floating point number with 2 decimal places and the specifier %d transforms the input number into a string, corresponding to a decimal number.

s2 = "value1 = %.2f. value2 = %d" % (3.1415, 1.5)

print(s2)

value1 = 3.14. value2 = 1

The same string can be written using the new style string formatting.

s3 = 'value1 = {:.2f}, value2 = {}'.format(3.1415, 1.5)

print(s3)

value1 = 3.14, value2 = 1.5

print("Newlines are indicated by \nAnd tabs by \t.")

print(r"Newlines are indicated by \nAnd tabs by \t. Printed as rawstring")

Newlines are indicated by 
And tabs by 	.
Newlines are indicated by \nAnd tabs by \t. Printed as rawstring

print("Name: {}\nNumber: {}\nString: {}".format("Nipype", 3, 3 * "-"))

Name: Nipype
Number: 3
String: ---

strString = """This is
a multiline
string."""
print(strString)

This is
a multiline
string.

print("This {verb} a {noun}.".format(noun = "test", verb = "is"))

This is a test.

Single Quote

You can specify strings using single quotes such as 'Quote me on this'. All white space i.e. spaces and tabs, within the quotes, are preserved as-is.

Double Quotes

Strings in double quotes work exactly the same way as strings in single quotes. An example is "What's your name?".

Triple Quotes

You can specify multi-line strings using triple quotes - (""" or '''). You can use single quotes and double quotes freely within the triple quotes. An example is:

'''This is a multi-line string. This is the first line.
This is the second line.
"What's your name?," I asked.
He said "Bond, James Bond."
'''

'This is a multi-line string. This is the first line.\nThis is the second line.\n"What\'s your name?," I asked.\nHe said "Bond, James Bond."\n'

List

Lists are very similar to strings, except that each element can be of any type.

The syntax for creating lists in Python is [...]:

l = [1,2,3,4]

print(type(l))
print(l)

<class 'list'>
[1, 2, 3, 4]

We can use the same slicing techniques to manipulate lists as we could use on strings:

print(l)
print(l[1:3])
print(l[::2])

[1, 2, 3, 4]
[2, 3]
[1, 3]

Heads up MATLAB users: Indexing starts at 0!

l[0]

1

Elements in a list do not all have to be of the same type:

l = [1, 'a', 1.0]

print(l)

[1, 'a', 1.0]

Python lists can be inhomogeneous and arbitrarily nested:

nested_list = [1, [2, [3, [4, [5]]]]]

nested_list

[1, [2, [3, [4, [5]]]]]

Lists play a very important role in Python and are for example used in loops and other flow control structures (discussed below). There are a number of convenient functions for generating lists of various types, for example, the range function (note that in Python 3 range creates a generator, so you have to use list function to get a list):

start = 10
stop = 30
step = 2

list(range(start, stop, step))

[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]

# convert a string to a list by type casting:

print(s)

s2 = list(s)

s2

Hello world

['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']

# sorting lists
s2.sort()

print(s2)

[' ', 'H', 'd', 'e', 'l', 'l', 'l', 'o', 'o', 'r', 'w']

Adding, inserting, modifying, and removing elements from lists

# create a new empty list
l = []

# add an elements using `append`
l.append("A")
l.append("d")
l.append("d")

print(l)

['A', 'd', 'd']

We can modify lists by assigning new values to elements in the list. In technical jargon, lists are mutable.

l[1] = "p"
l[2] = "t"

print(l)

['A', 'p', 't']

l[1:3] = ["s", "m"]

print(l)

['A', 's', 'm']

Insert an element at an specific index using insert

l.insert(0, "i")
l.insert(1, "n")
l.insert(2, "s")
l.insert(3, "e")
l.insert(4, "r")
l.insert(5, "t")

print(l)

['i', 'n', 's', 'e', 'r', 't', 'A', 's', 'm']

Remove first element with specific value using ‘remove’

l.remove("A")

print(l)

['i', 'n', 's', 'e', 'r', 't', 's', 'm']

Remove an element at a specific location using del:

del l[7]
del l[6]

print(l)

['i', 'n', 's', 'e', 'r', 't']

Lists

  • An ordered, heterogeneous collection of objects

  • List elements can be accessed by position

Tuples

Tuples are like lists, except that they cannot be modified once created, that is they are immutable.

In Python, tuples are created using the syntax (..., ..., ...), or even ..., ...:

point = (10, 20)

print(type(point))
print(point)

<class 'tuple'>
(10, 20)

If we try to assign a new value to an element in a tuple we get an error:

try:
    point[0] = 20
except(TypeError) as er:
    print("TypeError:", er)
else:
    raise

TypeError: 'tuple' object does not support item assignment

Tuples

  • Very similar to lists

  • Key difference: tuples are immutable

    • They can’t be modified once they’re created

Dictionaries

Dictionaries are also like lists, except that each element is a key-value pair. The syntax for dictionaries is {key1 : value1, ...}:

params = {"parameter1" : 1.0,
          "parameter2" : 2.0,
          "parameter3" : 3.0,}

print(type(params))
print(params)

<class 'dict'>
{'parameter1': 1.0, 'parameter2': 2.0, 'parameter3': 3.0}

Dictionary entries can only be accessed by their key name.

params["parameter2"]

2.0

print("parameter1 = " + str(params["parameter1"]))
print("parameter2 = " + str(params["parameter2"]))
print("parameter3 = " + str(params["parameter3"]))

parameter1 = 1.0
parameter2 = 2.0
parameter3 = 3.0

params["parameter1"] = "A"
params["parameter2"] = "B"

# add a new entry
params["parameter4"] = "D"

print("parameter1 = " + str(params["parameter1"]))
print("parameter2 = " + str(params["parameter2"]))
print("parameter3 = " + str(params["parameter3"]))
print("parameter4 = " + str(params["parameter4"]))

parameter1 = A
parameter2 = B
parameter3 = 3.0
parameter4 = D

Dictionaries (dict)

  • Unordered collection of key-to-value pairs

  • dictionary elements can be accessed by key, but not by position

Everything is an object in Python

  • All of these ‘data types’ are actually just objects in Python

  • Everything is an object in Python!

  • The operations you can perform with a variable depend on the object’s definition

  • E.g., the multiplication operator * is defined for some objects but not others

Indentation

Whitespace is important in Python. Actually, whitespace at the beginning of the line is important. This is called indentation. Leading whitespace (spaces and tabs) at the beginning of the logical line is used to determine the indentation level of the logical line, which in turn is used to determine the grouping of statements.

This means that statements which go together must have the same indentation, for example:

i = 5

print('Value is ', i)
print('I repeat, the value is ', i)

Value is  5
I repeat, the value is  5

Each such set of statements is called a block. We will see examples of how blocks are important later on. One thing you should remember is that wrong indentation rises IndentationError.

Control Flow

Control structures

  • Language features that allow us to control how code is executed

  • Iteration (e.g., for-loops, while statements…)

  • Conditionals (if-then-else statements)

  • Etc…

Conditional statements: if, elif, else

The Python syntax for conditional execution of code use the keywords if, elif (else if), else:

statement1 = False
statement2 = False

if statement1:
    print("statement1 is True")
    
elif statement2:
    print("statement2 is True")
    
else:
    print("statement1 and statement2 are False")

statement1 and statement2 are False

For the first time, here we encountered a peculiar and unusual aspect of the Python programming language: Program blocks are defined by their indentation level. In Python, the extent of a code block is defined by the indentation level (usually a tab or say four white spaces). This means that we have to be careful to indent our code correctly, or else we will get syntax errors.

Examples:

# Good indentation
statement1 = statement2 = True

if statement1:
    if statement2:
        print("both statement1 and statement2 are True")

both statement1 and statement2 are True

# Bad indentation! This would lead to error
#if statement1:
#    if statement2:
#    print("both statement1 and statement2 are True")  # this line is not properly indented

statement1 = False 

if statement1:
    print("printed if statement1 is True")
    
    print("still inside the if block")

if statement1:
    print("printed if statement1 is True")
    
print("now outside the if block")

now outside the if block

Loops

In Python, loops can be programmed in a number of different ways. The most common is the for loop, which is used together with iterable objects, such as lists. The basic syntax is:

for loops

for x in [1,2,3]:
    print(x),

1
2
3

The for loop iterates over the elements of the supplied list and executes the containing block once for each element. Any kind of list can be used in the for loop. For example:

for x in range(4): # by default range start at 0
    print(x),

0
1
2
3

Note: range(4) does not include 4 !

for x in range(-3,3):
    print(x),

-3
-2
-1
0
1
2

for word in ["scientific", "computing", "with", "python"]:
    print(word)

scientific
computing
with
python

To iterate over key-value pairs of a dictionary:

for key, value in params.items():
    print(key + " = " + str(value))

parameter1 = A
parameter2 = B
parameter3 = 3.0
parameter4 = D

Sometimes it is useful to have access to the indices of the values when iterating over a list. We can use the enumerate function for this:

for idx, x in enumerate(range(-3,3)):
    print(idx, x)

0 -3
1 -2
2 -1
3 0
4 1
5 2

break, continue and pass

To control the flow of a certain loop you can also use break, continue and pass.

rangelist = list(range(10))
print(list(rangelist))

for number in rangelist:
    # Check if number is one of
    # the numbers in the tuple.
    if number in [4, 5, 7, 9]:
        # "Break" terminates a for without
        # executing the "else" clause.
        break
    else:
        # "Continue" starts the next iteration
        # of the loop. It's rather useless here,
        # as it's the last statement of the loop.
        print(number)
        continue
else:
    # The "else" clause is optional and is
    # executed only if the loop didn't "break".
    pass # Do nothing

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
0
1
2
3

List comprehensions: Creating lists using for loops:

A convenient and compact way to initialize lists:

l1 = [x**2 for x in range(0,5)]

print(l1)

[0, 1, 4, 9, 16]

while loops:

i = 0

while i < 5:
    print(i)
    
    i = i + 1
    
print("done")

0
1
2
3
4
done

Note that the print "done" statement is not part of the while loop body because of the difference in the indentation.

Functions

A function in Python is defined using the keyword def, followed by a function name, a signature within parentheses (), and a colon :. The following code, with one additional level of indentation, is the function body.

def say_hello():
    # block belonging to the function
    print('hello world')

say_hello() # call the function

hello world

Functions

  • A block of code that only runs when explicitly called

  • Can accept arguments (or parameters) that alter its behavior

  • Can accept any number/type of inputs, but always return a single object

    • Note: functions can return tuples (may look like multiple objects)

Following an example where we also feed two arguments into the function.

def print_max(a, b):
    if a > b:
        print( a, 'is maximum')
    elif a == b:
        print(a, 'is equal to', b)
    else:
        print(b, 'is maximum')

# directly pass literal values
print_max(3, 4)

x = 7
y = 7

# pass variables as arguments
print_max(x, y)

4 is maximum
7 is equal to 7

Very important: Variables inside a function are treated as local variables and therefore don’t interfere with variables outside the scope of the function.

x = 50

def func(x):
    print('x is', x)
    x = 2
    print('Changed local x to', x)

func(x)
print('x is still', x)

x is 50
Changed local x to 2
x is still 50

The local scope of a variable inside a function can be extended with the keyword global.

x = 50

def func():
    global x

    print('x is', x)
    x = 2
    print('Changed global x to', x)

func()
print('Value of x is', x)

x is 50
Changed global x to 2
Value of x is 2

Optionally, but highly recommended, we can define a so called “docstring”, which is a description of the functions purpose and behavior. The docstring should follow directly after the function definition, before the code in the function body.

def func1(s):
    """
    Print a string 's' and tell how many characters it has    
    """
    
    print(s + " has " + str(len(s)) + " characters")

help(func1)

Help on function func1 in module __main__:

func1(s)
    Print a string 's' and tell how many characters it has

func1("test")

test has 4 characters

Functions that return a value use the return keyword:

def square(x):
    """
    Return the square of x.
    """
    return x ** 2

square(4)

16

We can return multiple values from a function using tuples (see above):

def powers(x):
    """
    Return a few powers of x.
    """
    return x ** 2, x ** 3, x ** 4

powers(3)

(9, 27, 81)

And if we know that a function returns multiple outputs, we can store them directly in multiple variables.

x2, x3, x4 = powers(3)

print(x3)

27

Positional vs. keyword arguments

  • Positional arguments are defined by position and must be passed

    • Arguments in the function signature are filled in order

  • Keyword arguments have a default value

    • Arguments can be passed in arbitrary order (after any positional arguments)

Default argument and keyword arguments

In a definition of a function, we can give default values to the arguments the function takes:

def myfunc(x, p=2, debug=False):
    if debug:
        print("evaluating myfunc for x = " + str(x) + " using exponent p = " + str(p))
    return x**p

If we don’t provide a value of the debug argument when calling the the function myfunc it defaults to the value provided in the function definition:

myfunc(5)

25

myfunc(5, debug=True)

evaluating myfunc for x = 5 using exponent p = 2

25

If we explicitly list the name of the arguments in the function calls, they do not need to come in the same order as in the function definition. This is called keyword arguments and is often very useful in functions that take a lot of optional arguments.

myfunc(p=3, debug=True, x=7)

evaluating myfunc for x = 7 using exponent p = 3

343

*args and *kwargs parameters

Sometimes you might want to define a function that can take any number of parameters, i.e. variable number of arguments, this can be achieved by using one (*args) or two (**kwargs) asterisks in the function declaration. *args is used to pass a non-keyworded, variable-length argument list and the **kwargs is used to pass a keyworded, variable-length argument list.

def args_func(arg1, *args):
    print("Formal arg:", arg1)
    for a in args:
        print("additioanl arg:", a)

args_func(1, "two", 3, [1, 2, 3])

Formal arg: 1
additioanl arg: two
additioanl arg: 3
additioanl arg: [1, 2, 3]

def kwargs_func(arg1, **kwargs):
    print("kwargs is now a dictionary...\nType: %s\nContent: %s\n" % (type(kwargs), kwargs))

    print("Formal arg:", arg1)
    for key in kwargs:
        print("another keyword arg: %s: %s" % (key, kwargs[key]))
    
kwargs_func(arg1=1, myarg2="two", myarg3=3)

kwargs is now a dictionary...
Type: <class 'dict'>
Content: {'myarg2': 'two', 'myarg3': 3}

Formal arg: 1
another keyword arg: myarg2: two
another keyword arg: myarg3: 3

Unnamed functions: lambda function

In Python we can also create unnamed functions, using the lambda keyword:

f1 = lambda x: x**2
    
# is equivalent to 

def f2(x):
    return x**2

f1(2), f2(2)

(4, 4)

This technique is useful for example when we want to pass a simple function as an argument to another function, like this:

# map is a built-in python function
list(map(lambda x: x**2, range(-3,4)))

[9, 4, 1, 0, 1, 4, 9]

Classes

Classes are the key features of object-oriented programming. A class is a structure for representing an object and the operations that can be performed on the object.

In Python, a class can contain attributes (variables) and methods (functions).

A class is defined almost like a function, but using the class keyword, and the class definition usually contains a number of class method definitions (a function in a class).

  • Each class method should have an argument self as it first argument. This object is a self-reference.

  • Some class method names have special meaning, for example:

class Point:
    """
    Simple class for representing a point in a Cartesian coordinate system.
    """
    
    def __init__(self, x, y):
        """
        Create a new Point at x, y.
        """
        self.x = x
        self.y = y
        
    def translate(self, dx, dy):
        """
        Translate the point by dx and dy in the x and y direction.
        """
        self.x += dx
        self.y += dy
        
    def __str__(self):
        return("Point at [%f, %f]" % (self.x, self.y))

To create a new instance of a class:

p1 = Point(0, 0)  # this will invoke the __init__ method in the Point class

print(p1)          # this will invoke the __str__ method

Point at [0.000000, 0.000000]

To invoke a class method in the class instance p:

p2 = Point(1, 1)
print(p2)

p2.translate(0.25, 1.5)
print(p2)

Point at [1.000000, 1.000000]
Point at [1.250000, 2.500000]

You can access any value of a class object directly, for example:

print(p1.x)

p1.x = 10

print(p1)

0
Point at [10.000000, 0.000000]

Modules

One of the most important concepts in good programming is to reuse code and avoid repetitions.

The idea is to write functions and classes with a well-defined purpose and scope, and reuse these instead of repeating similar code in different part of a program (modular programming). The result is usually that readability and maintainability of a program are greatly improved. What this means in practice is that our programs have fewer bugs, are easier to extend and debug/troubleshoot.

Python supports modular programming at different levels. Functions and classes are examples of tools for low-level modular programming. Python modules are a higher-level modular programming construct, where we can collect related variables, functions, and classes in a module. A python module is defined in a python file (with file-ending .py), and it can be made accessible to other Python modules and programs using the import statement.

Consider the following example: the file mymodule.py contains simple example implementations of a variable, function and a class:

%%file mymodule.py
"""
Example of a python module. Contains a variable called my_variable,
a function called my_function, and a class called MyClass.
"""

my_variable = 0

def my_function():
    """
    Example function
    """
    return my_variable
    
class MyClass:
    """
    Example class.
    """

    def __init__(self):
        self.variable = my_variable
        
    def set_variable(self, new_value):
        """
        Set self.variable to a new value
        """
        self.variable = new_value
        
    def get_variable(self):
        return self.variable

Writing mymodule.py

Note: %%file is called a cell-magic function and creates a file that has the following lines as content.

We can import the module mymodule into our Python program using import:

import mymodule

Use help(module) to get a summary of what the module provides:

help(mymodule)

Help on module mymodule:

NAME
    mymodule

DESCRIPTION
    Example of a python module. Contains a variable called my_variable,
    a function called my_function, and a class called MyClass.

CLASSES
    builtins.object
        MyClass
    
    class MyClass(builtins.object)
     |  Example class.
     |  
     |  Methods defined here:
     |  
     |  __init__(self)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |  
     |  get_variable(self)
     |  
     |  set_variable(self, new_value)
     |      Set self.variable to a new value
     |  
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |  
     |  __dict__
     |      dictionary for instance variables (if defined)
     |  
     |  __weakref__
     |      list of weak references to the object (if defined)

FUNCTIONS
    my_function()
        Example function

DATA
    my_variable = 0

FILE
    /home/neuro/workshop_weizmann/workshop/prerequisites/mymodule.py

mymodule.my_variable

0

mymodule.my_function() 

0

my_class = mymodule.MyClass() 
my_class.set_variable(10)
my_class.get_variable()

10

If we make changes to the code in mymodule.py, we need to reload it using reload:

from importlib import reload
reload(mymodule)

<module 'mymodule' from '/home/neuro/workshop_weizmann/workshop/prerequisites/mymodule.py'>

Exceptions

In Python errors are managed with a special language construct called “Exceptions”. When errors occur exceptions can be raised, which interrupts the normal program flow and fallback to somewhere else in the code where the closest try-except statement is defined.

To generate an exception we can use the raise statement, which takes an argument that must be an instance of the class BaseExpection or a class derived from it.

try:
    raise Exception("description of the error")
except(Exception) as err:
    print ("Exception:", err)

Exception: description of the error

A typical use of exceptions is to abort functions when some error condition occurs, for example:

def my_function(arguments):

    if not verify(arguments):
        raise Exception("Invalid arguments")
    
    # rest of the code goes here

To gracefully catch errors that are generated by functions and class methods, or by the Python interpreter itself, use the try and except statements:

try:
    # normal code goes here
except:
    # code for error handling goes here
    # this code is not executed unless the code
    # above generated an error

For example:

try:
    print("test")
    # generate an error: the variable test is not defined
    print(test)
except:
    print("Caught an exception")

test
Caught an exception

To get information about the error, we can access the Exception class instance that describes the exception by using for example:

except Exception as e:

try:
    print("test")
    # generate an error: the variable test is not defined
    print(test)
except Exception as e:
    print("Caught an exception:" + str(e))
finally:
    print("This block is executed after the try- and except-block.")

test
Caught an exception:name 'test' is not defined
This block is executed after the try- and except-block.

def some_function():
    try:
        # Division by zero raises an exception
        10 / 0
    except ZeroDivisionError:
        print("Oops, invalid.")
    else:
        # Exception didn't occur, we're good.
        pass
    finally:
        # This is executed after the code block is run
        # and all exceptions have been handled, even
        # if a new exception is raised while handling.
        print("We're done with that.")

some_function()

Oops, invalid.
We're done with that.

You will see more exception handling examples in this and other notebooks.

File I/O

This section should give you a basic knowledge about how to read and write CSV or TXT files. First, let us create a CSV and TXT file about demographic information of 10 subjects (experiment_id, subject_id, gender, age).

%%file demographics.csv
ds102,sub001,F,21.94
ds102,sub002,M,22.79
ds102,sub003,M,19.65
ds102,sub004,M,25.98
ds102,sub005,M,23.24
ds102,sub006,M,23.27
ds102,sub007,D,34.72
ds102,sub008,D,22.22
ds102,sub009,M,22.7
ds102,sub010,D,25.24

Writing demographics.csv

%%file demographics.txt
ds102	sub001	F	21.94
ds102	sub002	M	22.79
ds102	sub003	M	19.65
ds102	sub004	M	25.98
ds102	sub005	M	23.24
ds102	sub006	M	23.27
ds102	sub007	D	34.72
ds102	sub008	D	22.22
ds102	sub009	M	22.7
ds102	sub010	D	25.24

Writing demographics.txt

Reading CSV files

Parsing comma-separated-values (CSV) files is a common task. There are many tools available in Python to deal with this. Let’s start by using the built-in csv module.

import csv

Before you can read or write any kind of file, you first have to open the file and go through its content with a reader function or write the output line by line with a write function.

f = open('demographics.csv','r')   # open the file with reading rights = 'r'
data = [i for i in csv.reader(f) ] # go through file and read each line
f.close()                          # close the file again

for line in data:
    print(line)

['ds102', 'sub001', 'F', '21.94']
['ds102', 'sub002', 'M', '22.79']
['ds102', 'sub003', 'M', '19.65']
['ds102', 'sub004', 'M', '25.98']
['ds102', 'sub005', 'M', '23.24']
['ds102', 'sub006', 'M', '23.27']
['ds102', 'sub007', 'D', '34.72']
['ds102', 'sub008', 'D', '22.22']
['ds102', 'sub009', 'M', '22.7']
['ds102', 'sub010', 'D', '25.24']

Writing CSV files

Now, we want to write the same data without the first experiment_id column in CSV format to a csv-file. First, let’s delete the first column in the dataset.

data_new = [line[1:] for line in data]

for line in data_new:
    print(line)

['sub001', 'F', '21.94']
['sub002', 'M', '22.79']
['sub003', 'M', '19.65']
['sub004', 'M', '25.98']
['sub005', 'M', '23.24']
['sub006', 'M', '23.27']
['sub007', 'D', '34.72']
['sub008', 'D', '22.22']
['sub009', 'M', '22.7']
['sub010', 'D', '25.24']

Now, we first have to open a file again, but this time with writing permissions = 'w'. After it, we can go through the file and write each line to the new csv-file.

f = open('demographics_new.csv','w') # open a file with writing rights = 'w'
fw = csv.writer(f)                   # create csv writer
fw.writerows(data_new)               # write content to file
f.close()                            # close file

Lets now check the content of demographics_new.csv.

!cat demographics_new.csv

sub001,F,21.94

sub002,M,22.79

sub003,M,19.65

sub004,M,25.98

sub005,M,23.24

sub006,M,23.27

sub007,D,34.72

sub008,D,22.22

sub009,M,22.7

sub010,D,25.24

Reading TXT files

The reading of txt files is quite similar to the reading of csv-files. The only difference is in the name of the reading function and the formatting that has to be applied to the input or output.

f = open('demographics.txt','r') # open file with reading rights = 'r'

# go through file and trim the new line '\n' at the end
datatxt = [i.splitlines() for i in f.readlines()]

# go through data and split elements in line by tabulators '\t'
datatxt = [i[0].split('\t') for i in datatxt]

f.close() # close file again

for line in datatxt:
    print(line)

['ds102', 'sub001', 'F', '21.94']
['ds102', 'sub002', 'M', '22.79']
['ds102', 'sub003', 'M', '19.65']
['ds102', 'sub004', 'M', '25.98']
['ds102', 'sub005', 'M', '23.24']
['ds102', 'sub006', 'M', '23.27']
['ds102', 'sub007', 'D', '34.72']
['ds102', 'sub008', 'D', '22.22']
['ds102', 'sub009', 'M', '22.7']
['ds102', 'sub010', 'D', '25.24']

Writing TXT files

The writing of txt files is as follows:

f = open('demograhics_new.txt', 'w') # open file with writing rights = 'w'

datatxt_new = [line[1:] for line in datatxt] # delete first column of array

# Go through datatxt array and write each line with specific format to file
for line in datatxt_new:
    f.write("%s\t%s\t%s\n"%(line[0],line[1],line[2]))

f.close() # close file

with open

The previous methods to open or write a file always required that you also close the file again with the close() function. If you don’t want to worry about this, you can also use the with open approach. For example:

with open('demographics.txt','r') as f:

    datatxt = [i.splitlines() for i in f.readlines()]
    datatxt = [i[0].split('\t') for i in datatxt]

for line in datatxt:
    print(line)

['ds102', 'sub001', 'F', '21.94']
['ds102', 'sub002', 'M', '22.79']
['ds102', 'sub003', 'M', '19.65']
['ds102', 'sub004', 'M', '25.98']
['ds102', 'sub005', 'M', '23.24']
['ds102', 'sub006', 'M', '23.27']
['ds102', 'sub007', 'D', '34.72']
['ds102', 'sub008', 'D', '22.22']
['ds102', 'sub009', 'M', '22.7']
['ds102', 'sub010', 'D', '25.24']

File modes

  • Read-only: r

  • Write-only: w (Create a new file or overwrite existing file)

  • Append a file: a

  • Read and Write: r+

  • Binary mode: b (Use for binary files, especially on Windows)

Why do data science in Python?

Easy to learn

  • Readable, explicit syntax

  • Most packages are very well documented

    • e.g., scikit-learn’s documentation is widely held up as a model

  • A huge number of tutorials, guides, and other educational materials

Comprehensive standard library

  • The Python standard library contains a huge number of high-quality modules

  • When in doubt, check the standard library first before you write your own tools!

  • For example:

    • os: operating system tools

    • re: regular expressions

    • collections: useful data structures

    • multiprocessing: simple parallelization tools

    • pickle: serialization

    • json: reading and writing JSON

Exceptional external libraries

  • Python has very good (often best-in-class) external packages for almost everything

  • Particularly important for data science, which draws on a very broad toolkit

  • Package management is easy (conda, pip)

  • Examples:

    • Web development: flask, Django

    • Database ORMs: SQLAlchemy, Django ORM (w/ adapters for all major DBs)

    • Scraping/parsing text/markup: beautifulsoup, scrapy

    • Natural language processing (NLP): nltk, gensim, textblob

    • Numerical computation and data analysis: numpy, scipy, pandas, xarray

    • Machine learning: scikit-learn, Tensorflow, keras

    • Image processing: pillow, scikit-image, OpenCV

    • Plotting: matplotlib, seaborn, altair, ggplot, Bokeh

    • GUI development: pyQT, wxPython

    • Testing: py.test

    • Etc. etc. etc.

(Relatively) good performance

  • Python is a high-level dynamic language—this comes at a performance cost

  • For many (not all!) data scientists, performance is irrelevant most of the time

  • In general, the less Python code you write yourself, the better your performance will be

    • Much of the standard library consists of Python interfaces to C functions

    • Numpy, scikit-learn, etc. all rely heavily on C/C++ or Fortran

Python vs. other data science languages

  • Python competes for mind share with many other languages

  • Most notably, R

  • To a lesser extent, Matlab, Mathematica, SAS, Julia, Java, Scala, etc.

R

  • R is dominant in traditional statistics and some fields of science

    • Has attracted many SAS, SPSS, and Stata users

  • Exceptional statistics support; hundreds of best-in-class libraries

  • Designed to make data analysis and visualization as easy as possible

  • Slow

  • Language quirks drive many experienced software developers crazy

  • Less support for most things non-data-related

MATLAB

  • A proprietary numerical computing language widely used by engineers

  • Good performance and very active development, but expensive

  • Closed ecosystem, relatively few third-party libraries

    • There is an open-source port (Octave)

  • Not suitable for use as a general-purpose language

So, why Python?

Why choose Python over other languages?

  • Arguably none of these offers the same combination of readability, flexibility, libraries, and performance

  • Python is sometimes described as “the second best language for everything”

  • Doesn’t mean you should always use Python

    • Depends on your needs, community, etc.

You can have your cake and eat it!

  • Many languages—particularly R—now interface seamlessly with Python

  • You can work primarily in Python, fall back on R when you need it (or vice versa)

  • The best of all possible worlds?

The core Python data science stack

  • The Python ecosystem contains tens of thousands of packages

  • Several are very widely used in data science applications:

  • We’ll cover the first three very briefly here

    • Other tutorials will go into greater detail on most of the others

The Jupyter notebook

  • “The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.”

  • Supports many different languages

  • A living document wrapped around a command prompt

  • Various extensions and widgets

Why Jupyter?

  • An easy way to write and share completely reproducible documents

  • Combine code, results, and text in one place

  • You can mix languages

  • Completely interactive: make a change and see what happens

  • Execution order matters

Slideshow mode

  • These slides are actually a live Jupyter notebook

  • We can edit and execute cells on-the-fly

  • The slideshow extension is installed separately; follow the instructions here

Built-in LaTeX support

\[ F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx \]

Highly customizable

Magic functions

  • Jupyter/IPython includes a number of “magic” commands to make life easier

  • Support in-line plotting, timing, debugging, calling other languages, etc.

Combining R and Python with the %R magic

  • In the same notebook

  • And even in the same notebook cell!

  • Can also use an R kernel with the notebook

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Introduction to the jupyter ecosystem & notebooks

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Introduction to NumPy