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Brisk introduction to Python

This is an introduction designed for those of us who already know a dynamic programming language fairly well. MATLAB and the R language are examples of dynamic programming languages.

Numbers

There are two types of numbers in Python: integer and floating point. In Python, an integer is an object of type int, and a float is an object of type float.

>>> a = 99
>>> type(a)
<class 'int'>
>>> b = 99.0
>>> type(b)
<class 'float'>

You can create ints and floats by using int and float like this:

>>> float('1')
1.0
>>> float(1)
1.0
>>> int('1')
1
>>> int(1)
1

+, -, * or / on a mix of floats and ints, give floats:

>>> a + b
198.0
>>> a * b
9801.0

Dividing an int by an int also gives a float – but this is only true by default for Python >= 3 (see [1]):

>>> 1 / 2
0.5

If you only want the integer part of the division, use //

>>> 1 // 2
0
>>> 1.0 // 2.0
0.0

Python has built-in function called round:

>>> round(5.0 / 2.0)
2

The % operator on numbers gives you the remainder of integer division (also known as the modulus):

>>> 5 % 2
1

>>> 5.0 % 2.0
1.0

True and False

True and False are special objects in Python. They are of type bool (for Boolean).

>>> type(True)
<class 'bool'>
>>> type(False)
<class 'bool'>
>>> True == False
False
>>> True == True
True
>>> False == False
True

You can use the logical operators and, or and not to express logic about Boolean values:

>>> True and True
True
>>> True and False
False
>>> True or False
True
>>> False or False
False
>>> not True
False
>>> True and not False
True

None

None is also a special object in Python. By convention, Python often uses None to mean that no valid value resulted from an operation, or to signal that we don’t have a value for a parameter.

>>> type(None)
<class 'NoneType'>

Unlike most other values in Python, the default display output from None, is nothing:

>>> None

Equals

As for MATLAB and R, = is for assignment, == is for testing equality.

>>> a = 1
>>> a
1
>>> a == 1
True

Like R, Python uses != for testing that objects are not equal. This is different from MATLAB, which uses ~=:

>>> a != 1
False

“If” statements, blocks and indention

A conditional statement in Python looks like this:

>>> my_var = 10
>>> if my_var == 10:
...     print("The conditional is True!")
...     print("my_var does equal 10")
...
The conditional is True!
my_var does equal 10

The first line of the conditional statement, that contains the conditional test, ends in a colon. Call this the if test. There follow some lines indented relative to the if test. Call these indented lines the if block. Python executes the statements in the if block only when the if test evaluates to True. For example, in this case, the if test evaluates to False, and the block does not execute:

>>> my_var = 11
>>> # This time the conditional evaluates to False
>>> if my_var == 10:  # the "if test"
...     # The indented lines are the "if block"
...     print("The conditional is True!")
...     print("my_var does equal 10")
...

The first line that returns to the same level of indentation as the if test line, closes the if block.

Unless the if block has a further indented block (for example, another if block), then all the lines in the block must have the same indentation.

See note [2] for equivalent if statements in R and MATLAB.

The if block may be followed by another block where the conditional is else:. This block will only run if the initial conditional test evaluates to False.

>>> my_var = 11
>>> if my_var == 10:
...     print("The conditional is True!")
...     print("my_var does equal 10")
... else:
...     print("The conditional is False!")
...     print("my_var does not equal 10")
...
The conditional is False!
my_var does not equal 10

There may be other conditional tests, with associated conditional blocks. These tests use the contraction elif conditional_test, where elif is a contraction for else if:

>>> my_var = 12
>>> if my_var == 10:
...     print("The conditional is True!")
...     print("my_var does equal 10")
... elif my_var == 11:
...     print("The second conditional is True!")
...     print("my_var does equal 11")
... elif my_var == 12:
...     print("The third conditional is True!")
...     print("my_var does equal 12")
... else:
...     print("All conditionals are False!")
...     print("my_var does not equal 10, 11 or 12")
...
The third conditional is True!
my_var does equal 12

“While” statements

while statements are another example with an initial test followed by an indented block. Here’s an example where we find the largest Fibonacci number less than 1000:

>>> last_but_1 = 0
>>> fibonacci = 1
>>> while fibonacci < 1000:
...     last_but_2 = last_but_1
...     last_but_1 = fibonacci
...     fibonacci = last_but_2 + last_but_1
...
>>> print("Largest Fibonacci < 1000 is", last_but_1)
Largest Fibonacci < 1000 is 987

Notice the initial while test: while fibonacci < 1000:, followed by the indented while block. Unlike the if statement, Python will continue to run the statements in the while block until the conditional in the while test evaluates to False.

Lists

Make a list like this:

>>> my_list = [9, 4, 7, 0, 8]
>>> my_list
[9, 4, 7, 0, 8]

>>> type(my_list)
<class 'list'>

A list element can be any type of object, including another list:

>>> mixed_list = [9, 3.0, True, my_list]
>>> mixed_list
[9, 3.0, True, [9, 4, 7, 0, 8]]

>>> type(mixed_list)
<class 'list'>

A Python list is like a cell array in MATLAB, or a list in R.

“for” loops and iteration

We can iterate over a list. To iterate, means to fetch one element after another from some container, such as a list. We can use a for loop to iterate over a list:

>>> for e in my_list:
...     print(e)
...
9
4
7
0
8

The for loop has the same form as if statements and while loops, with a first line ending in a colon, followed by an indented block.

The first line in the for loop is of form: for loop_variable in container:. The container is the container from which we will fetch the elements. At each iteration of the for loop, Python gets a new element from the container to put into the loop variable. For each element in the container, Python executes the for block.

Note [3] shows equivalent for loops in Python, R and MATLAB.

See Range for a common way of writing a for loop that iterates over a sequence of integers.

Lists are sequences

A sequence is a category of Python objects that have a defined element order, have a length, are iterable, can be indexed with integers, and sliced (see below). If object s is a sequence, then:

• s has a length that can be found with len(s);
• we can iterate over the elements in s with for element in s: # do something with element;
• we can return the element at position n with s[n];
• we can get another sequence by slicing s. For example, s[0:n] will give a new sequence containing the first n elements of s.

>>> # Has a length
>>> len(my_list)
5

>>> # Is iterable
>>> for e in my_list:
...     print(e)
9
4
7
0
8

>>> # Can be indexed
>>> my_list[1]
4
>>> # Can be sliced
>>> my_list[0:2]
[9, 4]

Python indices are 0-based

Indices for Python sequences start at 0. For Python, the first element is at index 0, the second element is at index 1, and so on:

>>> my_list[0]
9
>>> my_list[1]
4

Negative indices

Negative numbers as indices count back from the end of the list. For example, use index -1 to return the last element in the list:

>>> my_list
[9, 4, 7, 0, 8]
>>> my_list[-1]
8

This is the third from last element:

>>> my_list[-3]
7

Lists are mutable

A list is a mutable object. Mutable means, that we can change the elements in the list, without creating a new list.

>>> my_list[1] = 99
>>> my_list
[9, 99, 7, 0, 8]

In Python, variable names point to an object.

When you do another_variable = a_variable, you are telling the name another_variable to point to the same object as the name a_variable. When objects are mutable, this can be confusing:

>>> another_list = my_list
>>> another_list
[9, 99, 7, 0, 8]

my_list points to a list object in memory. When you do another_list = my_list, it tells Python that another_list points to the same object. So, if we modify the list, pointed to by my_list, we also modify the value of another_list, because my_list and another_list point at the same list.

>>> my_list[1] = 101
>>> another_list
[9, 101, 7, 0, 8]

Adding lists

Adding two lists with + returns a new list that is the concatenation of the two lists:

>>> new_list = my_list + [False, 1, 2]
>>> new_list
[9, 101, 7, 0, 8, False, 1, 2]

Appending and removing elements

You can append elements with the append method.

A method is a function attached to the object. See Functions for more on functions in Python.

We can see that append is a method by displaying the value of my_list.append:

>>> my_list.append
<built-in method append of list object at 0x...>

To call the method, we add parentheses, surrounding any arguments we want to pass into the method. In this case we want to pass in the element to append:

>>> my_list.append(20)
>>> my_list
[9, 101, 7, 0, 8, 20]

Note that the append method does not return the list, it just changes the list in-place. Python returns None from the append method:

>>> result = my_list.append(42)
>>> result == None
True

This is also true for some other methods that modify the list in-place, such as the sort method:

>>> new_list = [10, 1, 3]
>>> result = new_list.sort()
>>> # Return value is None
>>> result == None
True
>>> # But the original list now in ascending order from sort
>>> new_list
[1, 3, 10]

You can remove elements from the list with the pop method:

>>> # Remove and return the last element of the list
>>> my_list.pop()
42
>>> my_list
[9, 101, 7, 0, 8, 20]
>>> # Remove and return the third element of the list
>>> my_list.pop(2)
7
>>> my_list
[9, 101, 0, 8, 20]

Slicing

You can return slices from any sequence, including lists, by putting a slice specifier in square brackets. For example, this returns the first 3 elements of the list:

>>> my_list[0:3]
[9, 101, 0]

The first number after the square bracket and before the colon is the start index. In this case we start at the first element (element at index 0). The second number, after the colon, is the stop index. This is the end index plus one. So we return elements at index 0, 1 and 2. That is, elements up to, but not including 3.

If you omit the first number (the start index) Python assumes 0:

>>> my_list[:3]
[9, 101, 0]

If you omit the second number, Python assumes the length of the list as the stop index.

>>> my_list[2:]
[0, 8, 20]
>>> my_list[2:len(my_list)]
[0, 8, 20]

You can omit both numbers, in which case you return all the elements of the list. This can be useful if you want to make a new list that contains the same elements as the first:

>>> another_list = my_list[:]
>>> another_list
[9, 101, 0, 8, 20]

Because this is a new list object, you can change the original list without changing the new list:

>>> my_list[1] = 999
>>> another_list
[9, 101, 0, 8, 20]

You can also specify a second colon, and a third number. This third number is the step size. For example, to get every second element of the list:

>>> my_list[0:len(my_list):2]
[9, 0, 20]

>>> # Length of list assumed as stop index if omitted
>>> my_list[0::2]
[9, 0, 20]

You can use negative numbers for the start and stop indices. As for indexing, negative start and stop values count back from the end of the list:

>>> my_list
[9, 999, 0, 8, 20]
>>> my_list[-4:-2]
[999, 0]

Negative numbers for the step count backwards from the start to the stop index:

>>> my_list[4:1:-1]
[20, 8, 0]

If you have a negative step size, and you don’t specify the start index, then the start index defaults to the last element in the list. If you don’t specify the stop index, it defaults to one prior to index 0:

>>> my_list
[9, 999, 0, 8, 20]
>>> my_list[-1:1:-1]
[20, 8, 0]
>>> my_list[:1:-1]
[20, 8, 0]
>>> my_list[-2::-1]
[8, 0, 999, 9]

One consequence that is worth remembering is that the following idiom gives you a reversed copy of the list:

>>> my_list[::-1]
[20, 8, 0, 999, 9]

Tuples

Tuples are almost the same as lists, except they are not mutable. That is, you cannot change the elements of a tuple, or change the number of elements.

>>> my_tuple = (9, 4, 7, 0, 8)
>>> my_tuple
(9, 4, 7, 0, 8)

>>> # This raises a TypeError
>>> # my_tuple[1] = 99

>>> # This raises an AttributeError, because tuples have no append method
>>> # my_tuple.append(20)

Here’s an empty tuple:

>>> empty_tuple = ()
>>> empty_tuple
()

A tuple with two elements:

>>> two_tuple = (1, 5)
>>> two_tuple
(1, 5)

There is a little complication when making a tuple with one element:

>>> not_a_tuple = (1)
>>> not_a_tuple
1

This is because Python can’t tell that you meant this to be a tuple, rather than an expression with parentheses round it:

>>> not_a_tuple = (1 + 5 + 3)
>>> not_a_tuple
9

To tell Python that you mean this to be a length-one tuple, add a comma after the element, and before the closing parenthesis:

>>> one_tuple = (1,)
>>> one_tuple
(1,)

Strings

Make a string like this:

>>> my_string = 'interesting text'
>>> my_string
'interesting text'

You can use single quotes or double quotes for your string, the two strings are the same:

>>> another_string = "interesting text"
>>> another_string
'interesting text'
>>> my_string == another_string
True

Convert other objects to strings using str:

>>> # Convert integer to string
>>> str(9)
'9'
>>> # Convert floating point value to string
>>> str(1.2)
'1.2'

Strings are sequences

Like lists, strings are sequences (have length, can be iterated, can index, can slice).

>>> # Length
>>> len(my_string)
16

>>> # Iterable
>>> for c in my_string:
...     print(c)
i
n
t
e
r
e
s
t
i
n
g
<BLANKLINE>
t
e
x
t

>>> # Can index
>>> my_string[1]
'n'

>>> # Can slice
>>> my_string[1:5]
'nter'

Strings are immutable

Unlike lists, strings are immutable. You cannot change the characters within a string:

>>> # Raises a TypeError
>>> # my_string[1] = 'N'

Adding strings

>>> my_string + ' with added insight'
'interesting text with added insight'

String methods

Strings have lots of interesting methods. In IPython, try tab-complete on a string variable name, followed by a period – e.g. type my_string., followed by the tab key. See also the list of string methods in the Python docs.

One interesting method is replace. It returns a new string that is a copy of the input, but replacing instances of one string with another:

>>> another_string = my_string.replace('interesting', 'extraordinary')
>>> another_string
'extraordinary text'

Notice that the original string has not changed (it’s immutable):

>>> my_string
'interesting text'

Use the split method to break a string into a list of strings. By default, split will split the string at any white space (spaces, tab characters or line breaks):

>>> my_string.split()
['interesting', 'text']

Pass a character to split to split the string at that character:

>>> another_example = 'one:two:three'
>>> another_example.split(":")
['one', 'two', 'three']

The strip method returns a new string with spaces, tabs and end of line characters removed from the beginning and end of the string:

>>> # A string with a newline character at the end
>>> my_string = ' a string\n'
>>> my_string
' a string\n'
>>> my_string.strip()
'a string'

Inserting values into strings

See: Inserting values into strings.

Range

range in Python 3 returns a range object. It is a sequence, and so it is rather like a list [4]. When you use range with one argument, the argument value is the stop index. For example, to make a range object generating the numbers from 0 up to but not including 5:

>>> my_range = range(5)
>>> my_range
range(0, 5)

You can make a range object into a list by using list:

>>> list(range(5))
[0, 1, 2, 3, 4]

A range object is a sequence:

>>> # Has a length
>>> len(my_range)
5
>>> # Is iterable
>>> for e in my_range:
...    print(e)
0
1
2
3
4
>>> # Can be indexed
>>> my_range[1]
1
>>> # Can be sliced
>>> my_range[0:2]
range(0, 2)

Set the start element for range by passing two arguments:

>>> my_range = range(1, 7)
>>> my_range
range(1, 7)
>>> list(my_range)
[1, 2, 3, 4, 5, 6]

Set the step size with a third argument:

>>> my_range = range(1, 7, 2)
>>> my_range
range(1, 7, 2)
>>> list(my_range)
[1, 3, 5]

One common use of range is to iterate over a sequence of numbers in a for loop:

>>> for i in range(5):
...    print(i)
...
0
1
2
3
4

Sets

Sets are collections of unique elements, with no defined order. Python reserves the right to order set elements in any way it chooses:

>>> # Only unique elements collected in the set
>>> my_set = set((5, 3, 1, 3))
>>> my_set  # doctest: +SKIP
{1, 5, 3}

Because there is no defined order, you cannot index into a set:

>>> # Raises a TypeError
>>> # my_set[1]

You can add elements to a set with the add method:

>>> my_set.add(10)
>>> my_set  # doctest: +SKIP
{1, 3, 5, 10}

Because set elements must be unique, if you add an element already in the set, this does not change the set:

>>> my_set.add(5)
>>> my_set  # doctest: +SKIP
{1, 3, 5, 10}

You can iterate over a set, but the order of elements is arbitrary. You cannot rely on the same order in any two runs of your program:

>>> # The order of elements is arbitrary
>>> for element in my_set:  # doctest: +SKIP
...     print(element)
1
3
5

Look at the methods of the set object for interesting operations such as difference, union, intersection etc.

Sets, lists and tuples are containers

A container is a Python object for which you can test an element for membership. So, if an object c is a container then we can test if an element is in the container with true_or_false = element in c.

Be careful – the word in has different meanings in for element in c: and true_or_false = element in c. With for element in c:, in is a part of the for loop syntax. With true_or_false = element in c, in triggers a test of membership, returning True or False.

>>> 5 in my_set
True
>>> 11 in my_set
False

You can use not in to test if an element is not in a container:

>>> 11 not in my_set
True

Lists and tuples are also containers:

>>> 9 in [9, 4, 7, 0, 8]
True
>>> 3 in (1, 3, 5)
True

Dictionaries

A dictionary is an unordered collection of key / value pairs. The key is something that identifies the element, and the value is the value corresponding to the particular key.

>>> # This is an empty dictionary
>>> software = {}

Here we insert a new key / value mapping into the dictionary. The key is a string – 'MATLAB' – and the corresponding value is an integer 50:

>>> software['MATLAB'] = 50
>>> software
{'MATLAB': 50}

Now we insert another key / value mapping:

>>> software['Python'] = 100
>>> software  #doctest: +SKIP
{'Python': 100, 'MATLAB': 50}

Get the value corresponding to a key by indexing the dictionary with the key:

>>> software['Python']
100

We can iterate over the keys in the dictionary, but the order of the keys is arbitrary. Python returns the keys in any order it chooses, and we can’t rely on the order being the same in any two runs of our program:

>>> for key in software.keys():  #doctest: +SKIP
...     print(key)
MATLAB
Python

We can also iterate over the values, with the same constraint, that the order is arbitrary:

>>> for value in software.values():  #doctest: +SKIP
...     print(value)
...
50
100

We can use the items method to iterate over the key / value pairs. In this case each element is a tuple of length two, where the first element is the key and the second element is the value:

>>> for key_value in software.items():  #doctest: +SKIP
...     print(key_value)
('MATLAB', 50)
('Python', 100)

One way to construct a dictionary is with curly brackets, using colons to separate the key and value, and commas to separate the pairs:

>>> software = {'MATLAB': 50, 'Python': 100}
>>> software  #doctest: +SKIP
{'Python': 100, 'MATLAB': 50}

Keys must be unique. A later key / value pair will overwrite an earlier key / value pair that had the same key:

>>> software = {'MATLAB': 50, 'Python': 100, 'MATLAB': 45}
>>> software  # doctest: +SKIP
{'Python': 100, 'MATLAB': 45}

Dictionaries are containers

Dictionaries are also containers. Python takes the elements in the container to be the dictionary keys. This is a convenient way to test if you already have a key in a dictionary:

>>> 'MATLAB' in software
True
>>> 'happiness' in software
False

“for”, “while”, “continue” and “break”

for statements and while statement are loops, because Python keeps executing the for or while block until the for runs out of elements or the while condition is False. You can break out of a loop using the break statement:

>>> for i in range(10):
...     if i == 6:
...         break
...     print(i)
...
0
1
2
3
4
5

The continue statement short-circuits execution of the current iteration of the for or while block, to continue with the next iteration:

>>> for i in range(10):
...     if i == 6:
...         continue
...     print(i)
0
1
2
3
4
5
7
8
9

See “for” and “while”, “break” and “else:” for more on loops and break.

Functions

Here we define our first function in Python:

>>> def my_function(an_argument):
...     return an_argument + 1

The function definition begins with the def keyword followed by a space. There follows the name of the function my_function. Next we have an open parenthesis, followed by a specification of the arguments that the function expects to be passed to it. In this case, the function expects a single argument. In our case, the value of the input argument will be attached to the name an_argument when the function starts to execute. Last, we have an indented block, with code that will run when the function is called. We can return a value from the function using the return statement.

>>> my_function(10)
11

We called my_function by appending the opening parenthesis, and the arguments, followed by the closing parenthesis. The function began to execute with the variable an_argument set to 10. It returned 10 + 1 = 11.

A function need not accept any arguments:

>>> def my_second_function():
...     return 42
...
>>> my_second_function()
42

A function does not need to have a return statement. If there is no return statement, the function returns None:

>>> def function_with_no_return():
...     # Function with no return statement
...     a = 1
...
>>> function_with_no_return() == None
True

A function can have more than one argument:

>>> def my_third_function(first_argument, second_argument):
...     return first_argument + second_argument
...
>>> my_third_function(10, 42)
52

Default values for function arguments

The function definition can give a default value for a function argument:

>>> def my_fourth_function(first_argument, extra_argument=101):
...     return first_argument + extra_argument

This function, like my_third_function, has two arguments, and we can call it the same way that we call my_third_function:

>>> my_fourth_function(10, 42)
52

But, we can also omit the second argument, because it has a default value. In that case the argument will get its default value:

>>> my_fourth_function(10)  # Pass one argument, get default for second
111

So far we have passed in arguments by position, the first argument in our call becoming the first argument in the function, and so on. We can also pass in arguments by name. For example, we could pass in extra_argument by giving the parameter name and value, like this:

>>> my_fourth_function(10, extra_argument=202)
212

Passing arguments this way can make the code easier to read, because the name of the argument often gives a good clue as to its purpose in the function. It can also be useful with functions having many parameters with default values; in that case using the argument name makes it easier to pass in one or few values that are different from the defaults.

Functions are objects too

Remember that everything in Python is an object. The function is itself an object, where the name of the function is a variable, that refers to the function:

>>> my_fourth_function
<function my_fourth_function at 0x...>

>>> type(my_fourth_function)
<class 'function'>

We call the function by adding the open parenthesis followed by the arguments and the close parenthesis:

>>> my_fourth_function(10)
111

We can make a new name to point to this same function as easily as we can could with any other Python variable:

>>> another_reference_to_func4 = my_fourth_function
>>> type(another_reference_to_func4)
<class 'function'>
>>> # We call this function using the new name
>>> another_reference_to_func4(10)
111

Sorting

The Python function sorted returns a sorted list from something that Python can iterate over:

>>> sorted('adcea')
['a', 'a', 'c', 'd', 'e']

>>> sorted((1, 5, 3, 2))
[1, 2, 3, 5]

In order to do the sorting, Python compares the elements with one_element < another_element. For example, to do the sort above, Python needed results like:

>>> 3 < 5
True

Sometimes you want to order the objects in some other way than simply comparing the elements. If so, then you can define a sort function. A sort function is a function that accepts an element as its argument, and returns a sort value for that element. Python does the sorting, not on the elements themselves, but on the returned sort value for each element.

For example, let’s say we have first and last names stored as tuples:

>>> people = [('JB', 'Poline'), ('Matthew', 'Brett'), ('Mark', 'DEsposito')]

By default, Python compares tuples by comparing the first value first, then the second value, and so on. This means for our case that we are sorting on the first name:

>>> ('Matthew', 'Brett') > ('Mark', 'DEsposito')
True

>>> sorted(people)
[('JB', 'Poline'), ('Mark', 'DEsposito'), ('Matthew', 'Brett')]

That may not be what you want. You might want to sort by the last name, which is the second value in the tuple. In that case you can make a sort function, that accepts the element as an input (the tuple in this case), and returns a value:

>>> def get_last_name(person):
...     return person[1]  # The last name

Remember everything in Python is an object. The function we have just defined is also an object, with name get_last_name:

>>> get_last_name
<function get_last_name at 0x...>

We can pass this value to the sorted function as a sort function. We will pass this in using the sort function parameter name, which is key:

>>> sorted(people, key=get_last_name)
[('Matthew', 'Brett'), ('Mark', 'DEsposito'), ('JB', 'Poline')]

Files

You can open a file in several different modes. The mode specifies whether you want to read or write the file, and whether the data in the file is, or will be, text data (string) or binary data (bytes). For example, here we open a file for Writing Text (wt):

>>> my_file = open("a_text_file.txt", "wt")

If we had wanted to write binary (byte) data, we would have used wb for the mode (Write Binary).

As usual, you can explore this new file object in IPython by appending the object name with a period, and pressing the tab key to get a list of attributes and methods – e.g. myfile. followed by tab.

To write to a file, use the write method.

>>> # Write a line of text with a newline character at the end
>>> # The method returns the number of characters written
>>> my_file.write("MATLAB is good for matrices\n")
28
>>> # Another line
>>> my_file.write("Python is good for coding\n")
26

You should close the file when you’ve finished with it:

>>> my_file.close()

To read a file, open the file in read mode:

>>> # Open file in Read Text mode
>>> my_file2 = open("a_text_file.txt", "rt")

You can read all the contents in one shot by calling the read method without arguments:

>>> contents = my_file2.read()
>>> print(contents)
MATLAB is good for matrices
Python is good for coding
<BLANKLINE>

Remember to close the file afterwards:

>>> my_file2.close()

An open text file object is also iterable, meaning, that you can ask the file object to return its contents line by line, in a for loop. Let’s open the file again to show this in action:

>>> my_file2 = open("a_text_file.txt", "rt")
>>> for line in my_file2:  # iterating over the file object
...     print("Line is:", line)
...
Line is: MATLAB is good for matrices
<BLANKLINE>
Line is: Python is good for coding
<BLANKLINE>
>>> my_file2.close()

Footnotes

[1]

Python 3 returns a floating point value from dividing two integers, but the default for Python 2 is to return the integer part of the division. Thus, in Python 2, 1 / 2 == 1 // 2 == 0. If your code may run on Python 2, remember to add the statement from __future__ import division at the top of your code files, to make sure you get the Python 3 behavior when dividing integers.

[2]

Here is an if statement in Python: # Python my_var = 10 if my_var == 10: print("The conditional is True!") print("my_var does equal 10") The equivalent in R is: # R my_var = 10 if (my_var == 10) { print("The conditional is True!") print("my_var does equal 10") } For MATLAB: % MATLAB my_var = 10; if my_var == 10 disp('The conditional is True!'); disp('my_var does equal 10'); end For Python, the indentation defines the block. For R and MATLAB, indentation is an optional way of formatting the code to make it look nicer: # R my_var = 10 if (my_var == 10) { # Indentation is optional print("The conditional is True!") print("my_var does equal 10") } % MATLAB my_var = 10; if my_var == 10 % Indentation is optional disp('The conditional is True!'); disp('my_var does equal 10'); end

[3]	Here is a for loop in Python: # Python for element in [9, 4, 7, 0, 8]: print(element) The equivalents in R and MATLAB are: # R for (element in list(9, 4, 7, 0, 8)) { print(element) } % MATLAB for element = {9, 4, 7, 0, 8} disp(e); end

[4]

In Python 2, range returns a list. You can often use a Python 3 range object in the same way you could use a list, so this difference between Python 2 and 3 may not matter for the person using the code. There are things that you can do with lists that you cannot do with ranges, such as adding. For example range(4) + range(5) will work in Python 2 (adding lists), but fail in Python 3 (you cannot add range objects).