1.2.2. Basic types Numerical types


Python supports the following numerical, scalar types:

>>> 1 + 1
>>> a = 4
>>> type(a)
<class 'int'>
>>> c = 2.1
>>> type(c)
<class 'float'>
>>> a = 1.5 + 0.5j
>>> a.real
>>> a.imag
>>> type(1. + 0j)
<class 'complex'>
>>> 3 > 4
>>> test = (3 > 4)
>>> test
>>> type(test)
<class 'bool'>


A Python shell can therefore replace your pocket calculator, with the basic arithmetic operations +, -, *, /, % (modulo) natively implemented

>>> 7 * 3.
>>> 2**10
>>> 8 % 3

Type conversion (casting):

>>> float(1)
1.0 Containers


Python provides many efficient types of containers, in which collections of objects can be stored.



A list is an ordered collection of objects, that may have different types. For example:

>>> colors = ['red', 'blue', 'green', 'black', 'white']
>>> type(colors)
<class 'list'>

Indexing: accessing individual objects contained in the list:

>>> colors[2]

Counting from the end with negative indices:

>>> colors[-1]
>>> colors[-2]


Indexing starts at 0 (as in C), not at 1 (as in Fortran or Matlab)!

Slicing: obtaining sublists of regularly-spaced elements:

>>> colors
['red', 'blue', 'green', 'black', 'white']
>>> colors[2:4]
['green', 'black']


Note that colors[start:stop] contains the elements with indices i such as start<= i < stop (i ranging from start to stop-1). Therefore, colors[start:stop] has (stop - start) elements.

Slicing syntax: colors[start:stop:stride]


All slicing parameters are optional:

>>> colors
['red', 'blue', 'green', 'black', 'white']
>>> colors[3:]
['black', 'white']
>>> colors[:3]
['red', 'blue', 'green']
>>> colors[::2]
['red', 'green', 'white']

Lists are mutable objects and can be modified:

>>> colors[0] = 'yellow'
>>> colors
['yellow', 'blue', 'green', 'black', 'white']
>>> colors[2:4] = ['gray', 'purple']
>>> colors
['yellow', 'blue', 'gray', 'purple', 'white']


The elements of a list may have different types:

>>> colors = [3, -200, 'hello']
>>> colors
[3, -200, 'hello']
>>> colors[1], colors[2]
(-200, 'hello')


For collections of numerical data that all have the same type, it is often more efficient to use the array type provided by the numpy module. A NumPy array is a chunk of memory containing fixed-sized items. With NumPy arrays, operations on elements can be faster because elements are regularly spaced in memory and more operations are performed through specialized C functions instead of Python loops.


Python offers a large panel of functions to modify lists, or query them. Here are a few examples; for more details, see https://docs.python.org/3/tutorial/datastructures.html#more-on-lists

Add and remove elements:

>>> colors = ['red', 'blue', 'green', 'black', 'white']
>>> colors.append('pink')
>>> colors
['red', 'blue', 'green', 'black', 'white', 'pink']
>>> colors.pop() # removes and returns the last item
>>> colors
['red', 'blue', 'green', 'black', 'white']
>>> colors.extend(['pink', 'purple']) # extend colors, in-place
>>> colors
['red', 'blue', 'green', 'black', 'white', 'pink', 'purple']
>>> colors = colors[:-2]
>>> colors
['red', 'blue', 'green', 'black', 'white']


>>> rcolors = colors[::-1]
>>> rcolors
['white', 'black', 'green', 'blue', 'red']
>>> rcolors2 = list(colors) # new object that is a copy of colors in a different memory area
>>> rcolors2
['red', 'blue', 'green', 'black', 'white']
>>> rcolors2.reverse() # in-place; reversing rcolors2 does not affect colors
>>> rcolors2
['white', 'black', 'green', 'blue', 'red']

Concatenate and repeat lists:

>>> rcolors + colors
['white', 'black', 'green', 'blue', 'red', 'red', 'blue', 'green', 'black', 'white']
>>> rcolors * 2
['white', 'black', 'green', 'blue', 'red', 'white', 'black', 'green', 'blue', 'red']



>>> sorted(rcolors) # new object
['black', 'blue', 'green', 'red', 'white']
>>> rcolors
['white', 'black', 'green', 'blue', 'red']
>>> rcolors.sort() # in-place
>>> rcolors
['black', 'blue', 'green', 'red', 'white']


Different string syntaxes (simple, double or triple quotes):

s = 'Hello, how are you?'
s = "Hi, what's up"
s = '''Hello,
how are you''' # tripling the quotes allows the
# string to span more than one line
s = """Hi,
what's up?"""
In [2]: 'Hi, what's up?'
Cell In[2], line 1
'Hi, what's up?'
SyntaxError: unterminated string literal (detected at line 1)

This syntax error can be avoided by enclosing the string in double quotes instead of single quotes. Alternatively, one can prepend a backslash to the second single quote. Other uses of the backslash are, e.g., the newline character \n and the tab character \t.


Strings are collections like lists. Hence they can be indexed and sliced, using the same syntax and rules.


>>> a = "hello"
>>> a[0]
>>> a[1]
>>> a[-1]


(Remember that negative indices correspond to counting from the right end.)


>>> a = "hello, world!"
>>> a[3:6] # 3rd to 6th (excluded) elements: elements 3, 4, 5
>>> a[2:10:2] # Syntax: a[start:stop:step]
'lo o'
>>> a[::3] # every three characters, from beginning to end
'hl r!'


Accents and special characters can also be handled as in Python 3 strings consist of Unicode characters.

A string is an immutable object and it is not possible to modify its contents. One may however create new strings from the original one.

In [3]: a = "hello, world!"
In [4]: a.replace('l', 'z', 1)
Out[4]: 'hezlo, world!'


Strings have many useful methods, such as a.replace as seen above. Remember the a. object-oriented notation and use tab completion or help(str) to search for new methods.

See also

Python offers advanced possibilities for manipulating strings, looking for patterns or formatting. The interested reader is referred to https://docs.python.org/3/library/stdtypes.html#string-methods and https://docs.python.org/3/library/string.html#format-string-syntax

String formatting:

>>> 'An integer: %i; a float: %f; another string: %s' % (1, 0.1, 'string') # with more values use tuple after %
'An integer: 1; a float: 0.100000; another string: string'
>>> i = 102
>>> filename = 'processing_of_dataset_%d.txt' % i # no need for tuples with just one value after %
>>> filename



A dictionary is basically an efficient table that maps keys to values.

>>> tel = {'emmanuelle': 5752, 'sebastian': 5578}
>>> tel['francis'] = 5915
>>> tel
{'emmanuelle': 5752, 'sebastian': 5578, 'francis': 5915}
>>> tel['sebastian']
>>> tel.keys()
dict_keys(['emmanuelle', 'sebastian', 'francis'])
>>> tel.values()
dict_values([5752, 5578, 5915])
>>> 'francis' in tel


It can be used to conveniently store and retrieve values associated with a name (a string for a date, a name, etc.). See https://docs.python.org/3/tutorial/datastructures.html#dictionaries for more information.

A dictionary can have keys (resp. values) with different types:

>>> d = {'a':1, 'b':2, 3:'hello'}
>>> d
{'a': 1, 'b': 2, 3: 'hello'}

More container types


Tuples are basically immutable lists. The elements of a tuple are written between parentheses, or just separated by commas:

>>> t = 12345, 54321, 'hello!'
>>> t[0]
>>> t
(12345, 54321, 'hello!')
>>> u = (0, 2)

Sets: unordered, unique items:

>>> s = set(('a', 'b', 'c', 'a'))
>>> s
{'a', 'b', 'c'}
>>> s.difference(('a', 'b'))
{'c'} Assignment operator


Python library reference says:

Assignment statements are used to (re)bind names to values and to modify attributes or items of mutable objects.

In short, it works as follows (simple assignment):

  1. an expression on the right hand side is evaluated, the corresponding object is created/obtained

  2. a name on the left hand side is assigned, or bound, to the r.h.s. object

Things to note:

  • a single object can have several names bound to it:

    In [5]: a = [1, 2, 3]
  • to change a list in place, use indexing/slices:

    In [6]: a = [1, 2, 3]
  • the key concept here is mutable vs. immutable

    • mutable objects can be changed in place

    • immutable objects cannot be modified once created

See also

A very good and detailed explanation of the above issues can be found in David M. Beazley’s article Types and Objects in Python.