Data structures are how computer programs store information. Theses information can be processed, analyzed
and visualized easily from the programme. Scientific data can be large and complex and may require data structures appropriate for scientific programming. In Astronomy, the fits file is one of the most widely used data-storing medium, it can store a lot of information including the coordinates, the precious time, a very large cataelog table, multi-dimension data cube, etc.. These data, when it is opened by the programme, shall be recognised and easily managed by the programme.
In Python, there are pre-defined advanced data structure depending on the kind of data you wish to store. You will have to choose data structures that best meet your requirements for the problem you are trying to solve. In this section, I will go through specifically examine three Python data structures: datetime, lists, tuples, sets, and dictionaries.
Python's simple types are summarized in the following table:
| Type | Example | Description |
|---|---|---|
int |
x = 1 |
integers (i.e., whole numbers) |
float |
x = 1.0 |
floating-point numbers (i.e., real numbers) |
complex |
x = 1 + 2j |
Complex numbers (i.e., numbers with real and imaginary part) |
bool |
x = True |
Boolean: True/False values |
str |
x = 'abc' |
String: characters or text |
NoneType |
x = None |
Special object indicating nulls |
We'll take a quick look at each of these in turn.
In [1]:
a = 1 # integer
b = 1.1 #floating point numbers
c = True; d = False # Boolean (logical expression)
e = "Hello" # Strings
Python implements seven basic binary arithmetic operators, two of which can double as unary operators. They are summarized in the following table:
| Operator | Name | Description |
|---|---|---|
a + b |
Addition | Sum of a and b |
a - b |
Subtraction | Difference of a and b |
a * b |
Multiplication | Product of a and b |
a / b |
True division | Quotient of a and b |
a // b |
Floor division | Quotient of a and b, removing fractional parts |
a % b |
Modulus | Integer remainder after division of a by b |
a ** b |
Exponentiation | a raised to the power of b |
-a |
Negation | The negative of a |
+a |
Unary plus | a unchanged (rarely used) |
These operators can be used and combined in intuitive ways, using standard parentheses to group operations. For example:
In [2]:
# addition, subtraction, multiplication
(4 + 8) * (6.5 - 3)
Out[2]:
# Python 2
print type("Hello World!")
<type 'str'>
# this is a byte string
print type(u"Hello World!")
<type 'unicode'>
# this is a Unicode string
# Python 3
print(type("Hello World!"))
<class 'str'>
# this is a Unicode string
print(type(b"Hello World!"))
<class 'bytes'>
# this is a byte string
| Type Name | Example | Add Element | Get Element | Set Element | Description |
|---|---|---|---|---|---|
list |
[1, 2, 3] |
x.append(1) | x[0] | x[0]=2 | Ordered collection |
tuple |
(1, 2, 3) |
no altering | x[0] | no altering | Immutable ordered collection |
dict |
{'a':1, 'b':2, 'c':3} |
x['new_key'] = 4 or x.update({'new_key'=4} | x['a'] | x['a']=2 | Unordered (key,value) mapping |
set |
{1, 2, 3} |
x.add(4) | no indexing | no indexing | Unordered collection of unique values |
A Python list is a sequence of values (elements) that are usually the same kind of item. They are in order and mutable. Mutable means they can be changed after they are created, of course, this implies you can exchange the order of the elements inside it. This is a Python list of prime numbers smaller than 50:
In [3]:
x = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
In [4]:
print(x)
print(x[0])
A single colon a[:] means a copy of the whole array.
a[start:] return tuple of items start through the rest of the array.
a[:end]return tuple of items from the beginning through end-1.
In [5]:
print(x[1:2])
print(x[:])
print(x[:2])
print(x[1:])
more interestingly, they have negative index
a[-1] means last item in the array
a[-2:] means last two items in the array
a[:-2] means everything except the last two items
In [6]:
print(x[-1])
print(x[-2])
print(x[-2:])
print(x[:-2])
You may reversed a list with xxx[::-1].
In [7]:
print(x[::-1])
In [8]:
print(x + [0,1])
print([0,1] + x)
print([0,1] * 5)
In [9]:
print(x[::-1])
y = sorted(x[::-1])
print(y)
In [10]:
print(x)
x.append('A')
print(x)
In [11]:
print(x)
x.insert(5,'B') # insert 'B' between x[4] and x[5], results in x[5] = 'B'
print(x)
In [12]:
print(x);
x.pop(5); # Removed the x[5] item and return it
print(x);
x.pop(-1); # Removed the last item and return it
print(x)
A Python tuple is similar to a list. The elements are in order but fixed once they are created. In other words, they are immutable. The tuple can store differently type of elements.
It is defined with parentheses : (xx,xx,xx).
The elements are called using a square bracket with an index starting from zero : x[y], 0..N.
You can slice the array using colon, in this case a[start:end] means items start up to end-1.
In [13]:
corr = (22.28552, 114.15769)
print(corr)
In [14]:
corr[0] = 10
Dictionary is more flexible than list and its index is a string, it is defined with curly bracket:
data = {'k1' : y1 , 'k2' : y2 , 'k3' : y3 }
k1, k2, k3 are called keys while y1,y2 and y3 are elements.
It is defined with a pair of curly bracket or the dict() fuction: data = {} or data = dict()
data = {'k1' : y1 , 'k2' : y2 , 'k3' : y3 }. dict() function : data = dict(k1=y1, k2=y2, k3=y3).data = {k: v for k, v in (('k1', y1),('k2',y2),('k3',y3))}.The elements are called using a square bracket with an index string : data[key].
data['k1']=1 # Updates if 'k1' exists, else adds the element with index 'k1'
data.update({'k1':1})
data.update(dict(k1=1))
data.update(k1=1)
Multiple values : data.update({'k3':3,'k4':4}) # Updates 'k3' and adds 'k4'
data3 = {}data3.update(data) # Modifies data3, not datadata3.update(data2) # Modifies data3, not data2del data[key] # Removes specific element in a dictionarydata.pop(key) # Removes the key & returns the valuedata.clear() # Clears entire dictionarykey in data # Return a booleanfor key in data: # Iterates just through the keys, ignoring the valuesfor key, value in d.items(): # Iterates through the pairs
In [15]:
# Creating an empty dictionary
location = {}
print(location)
In [16]:
# Defined with a curly bracket
location = {
'Berlin': (52.5170365, 13.3888599),
'London': (51.5073219, -0.1276474),
'Sydney': (-33.8548157, 151.2164539),
'Tokyo': (34.2255804, 139.294774527387),
'Paris': (48.8566101, 2.3514992),
'Moscow': (46.7323875, -117.0001651)
}
print(location)
In [17]:
# Update
location.update({'Hong Kong': (22.2793278, 114.1628131)})
print(location)
In [18]:
# Call element
location['Tokyo']
Out[18]:
In [19]:
# Delete element
del location['Hong Kong']
location
Out[19]:
In [20]:
for key, value in location.items():
print(key, value)
In [21]:
### More on slicing in list and tuple
start=2
end=5
step=2
print("Original:", x)
print("items start through end-1 :", x[start:end]) # items start through end-1
print("items start through the rest of the array :", x[start:]) # items start through the rest of the array
print("items from the beginning through end-1 :", x[:end]) # items from the beginning through end-1
print("whole array :", x[:]) # whole array
print("last item in the array :", x[-1]) # last item in the array
print("last two items in the array :", x[-2:]) # last two items in the array
print("everything except the last two items :", x[:-2]) # everything except the last two items
print("start through not past end, by step", x[start:end:step]) # start through no01-Python-Syntaxt past end, by step
In addition to the standard numerical operations, Python includes operators to perform bitwise logical operations on integers. These are much less commonly used than the standard arithmetic operations, but it's useful to know that they exist. The six bitwise operators are summarized in the following table:
| Operator | Name | Description |
|---|---|---|
a & b |
Bitwise AND | Bits defined in both a and b |
a | b |
Bitwise OR | Bits defined in a or b or both |
a ^ b |
Bitwise XOR | Bits defined in a or b but not both |
a << b |
Bit shift left | Shift bits of a left by b units |
a >> b |
Bit shift right | Shift bits of a right by b units |
~a |
Bitwise NOT | Bitwise negation of a |
These operations shows Python are so easy to use compared to lower-level languages such as C. In C, we need to manually constructing a loop over the list and checking for equality of each value. In Python, you just type what you want to know, easy to type but hard to debug, just like English grammar.