# 1.2.2. Basic types¶

## 1.2.2.1. Numerical types¶

Tip

Python supports the following numerical, scalar types:

Integer: ```>>> 1 + 1 2 >>> a = 4 >>> type(a) ``` ```>>> c = 2.1 >>> type(c) ``` ```>>> a = 1.5 + 0.5j >>> a.real 1.5 >>> a.imag 0.5 >>> type(1. + 0j) ``` ```>>> 3 > 4 False >>> test = (3 > 4) >>> test False >>> type(test) ```

Tip

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

```>>> 7 * 3.
21.0
>>> 2**10
1024
>>> 8 % 3
2
```

Type conversion (casting):

```>>> float(1)
1.0
```

Warning

Integer division

In Python 2:

```>>> 3 / 2
1
```

In Python 3:

```>>> 3 / 2
1.5
```

To be safe: use floats:

```>>> 3 / 2.
1.5

>>> a = 3
>>> b = 2
>>> a / b # In Python 2
1
>>> a / float(b)
1.5
```

Future behavior: to always get the behavior of Python3

```>>> from __future__ import division
>>> 3 / 2
1.5
```

Tip

If you explicitly want integer division use //:

```>>> 3.0 // 2
1.0
```

Note

The behaviour of the division operator has changed in Python 3.

## 1.2.2.2. Containers¶

Tip

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

### 1.2.2.2.1. Lists¶

Tip

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

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

Indexing: accessing individual objects contained in the list:

```>>> l[2]
'green'
```

Counting from the end with negative indices:

```>>> l[-1]
'white'
>>> l[-2]
'black'
```

Warning

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

Slicing: obtaining sublists of regularly-spaced elements:

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

Warning

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

Slicing syntax: l[start:stop:stride]

Tip

All slicing parameters are optional:

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

Lists are mutable objects and can be modified:

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

Note

The elements of a list may have different types:

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

Tip

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.

Tip

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/tutorial/datastructures.html#more-on-lists

Add and remove elements:

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

Reverse:

```>>> r = L[::-1]
>>> r
['white', 'black', 'green', 'blue', 'red']
>>> r2 = list(L)
>>> r2
['red', 'blue', 'green', 'black', 'white']
>>> r2.reverse() # in-place
>>> r2
['white', 'black', 'green', 'blue', 'red']
```

Concatenate and repeat lists:

```>>> r + L
['white', 'black', 'green', 'blue', 'red', 'red', 'blue', 'green', 'black', 'white']
>>> r * 2
['white', 'black', 'green', 'blue', 'red', 'white', 'black', 'green', 'blue', 'red']
```

Tip

Sort:

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

Methods and Object-Oriented Programming

The notation r.method() (e.g. r.append(3) and L.pop()) is our first example of object-oriented programming (OOP). Being a list, the object r owns the method function that is called using the notation .. No further knowledge of OOP than understanding the notation . is necessary for going through this tutorial.

Discovering methods:

Reminder: in Ipython: tab-completion (press tab)

```In [28]: r.<TAB>
r.__class__         r.__imul__          r.__setitem__
r.__contains__      r.__init__          r.__setslice__
r.__delattr__       r.__iter__          r.__sizeof__
r.__delitem__       r.__le__            r.__str__
r.__delslice__      r.__len__           r.__subclasshook__
r.__doc__           r.__lt__            r.append
r.__eq__            r.__mul__           r.count
r.__format__        r.__ne__            r.extend
r.__ge__            r.__new__           r.index
r.__getattribute__  r.__reduce__        r.insert
r.__getitem__       r.__reduce_ex__     r.pop
r.__getslice__      r.__repr__          r.remove
r.__gt__            r.__reversed__      r.reverse
r.__hash__          r.__rmul__          r.sort
```

### 1.2.2.2.2. Strings¶

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

```s = 'Hello, how are you?'
s = "Hi, what's up"
s = '''Hello,                 # tripling the quotes allows the
how are you'''         # the string to span more than one line
s = """Hi,
what's up?"""
```
```In [1]: 'Hi, what's up?'
------------------------------------------------------------
File "<ipython console>", line 1
'Hi, what's up?'
^
SyntaxError: invalid syntax
```

The newline character is \n, and the tab character is \t.

Tip

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

Indexing:

```>>> a = "hello"
>>> a[0]
'h'
>>> a[1]
'e'
>>> a[-1]
'o'
```

Tip

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

Slicing:

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

Tip

Accents and special characters can also be handled in Unicode strings (see https://docs.python.org/tutorial/introduction.html#unicode-strings).

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 [53]: a = "hello, world!"
In [54]: a[2] = 'z'
---------------------------------------------------------------------------
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'str' object does not support item assignment

In [55]: a.replace('l', 'z', 1)
Out[55]: 'hezlo, world!'
In [56]: a.replace('l', 'z')
Out[56]: 'hezzo, worzd!'
```

Tip

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.

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

String formatting:

```>>> 'An integer: %i; a float: %f; another string: %s' % (1, 0.1, 'string')
'An integer: 1; a float: 0.100000; another string: string'

>>> i = 102
>>> filename = 'processing_of_dataset_%d.txt' % i
>>> filename
'processing_of_dataset_102.txt'
```

### 1.2.2.2.3. Dictionaries¶

Tip

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

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

Tip

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/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, 3: 'hello', 'b': 2}
```

### 1.2.2.2.4. More container types¶

Tuples

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

Sets: unordered, unique items:

```>>> s = set(('a', 'b', 'c', 'a'))
>>> s
set(['a', 'c', 'b'])
>>> s.difference(('a', 'b'))
set(['c'])
```

## 1.2.2.3. Assignment operator¶

Tip

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 [1]: a = [1, 2, 3]
In [2]: b = a
In [3]: a
Out[3]: [1, 2, 3]
In [4]: b
Out[4]: [1, 2, 3]
In [5]: a is b
Out[5]: True
In [6]: b[1] = 'hi!'
In [7]: a
Out[7]: [1, 'hi!', 3]
```
• to change a list in place, use indexing/slices:

```In [1]: a = [1, 2, 3]
In [3]: a
Out[3]: [1, 2, 3]
In [4]: a = ['a', 'b', 'c'] # Creates another object.
In [5]: a
Out[5]: ['a', 'b', 'c']
In [6]: id(a)
Out[6]: 138641676
In [7]: a[:] = [1, 2, 3] # Modifies object in place.
In [8]: a
Out[8]: [1, 2, 3]
In [9]: id(a)
Out[9]: 138641676 # Same as in Out[6], yours will differ...
```
• the key concept here is mutable vs. immutable

• mutable objects can be changed in place
• immutable objects cannot be modified once created