# Choosing The Right Python Data Type for Analysis

Untitled

In [4]:
import datetime


# Dictionary

Dictionary is used when there exists a mapping relationship, for example, in stock market data, stock prices are linked to a specific date.

In [94]:
stock = {"Header":["Open","High","Low","Close"],"12/12/2015":[32.03,50,40,32]}

In [95]:
stock["12/12/2015"]

Out[95]:
[32.03, 50, 40, 32]

Dictionary provides some useful methods

In [74]:
list(stock.iterkeys())  #key iterator, giving you a list

Out[74]:
['Header', '12/12/2015']
In [78]:
list(stock.iteritems()) #iterm iterator, giving you tuples, representing relations

Out[78]:
[('Header', ['Open', 'High', 'Low', 'Close']),
('12/12/2015', [32.03, 50, 40, 32])]

Dictionary can also be feeded in the constructor of pandas dataframe. We will talk about this in my later posts.

# Tuple

Anything you want to be considered as a whole should use turple becuase it’s immutable. Turple often used in feeding a set of arguments into function caller.

In [68]:
x=12
y=23
z=23.6

point = (x,y,z) # a coordinate can use turple to replesent because the
# position of each element matters


Some people also argues that you can compare tuple to strcut in C/C++ since tuple usually holds heterogeneous collections.

As I mentioned, tuple also used to represent relations in discrete data structure. I can use you an example using dictionary and tuple

In [66]:
# for example, we have y = x^2
f = {1:1,2:4,3:9}
f.items()  # the items method can turn dictionary into a tuple

Out[66]:
[(1, 1), (2, 4), (3, 9)]

# List

List is like array in other programming language. You should use list in situations that uses array. In python, list provides you more methods to perform comprehensive analysis.

## List as stack operation

In [14]:
l = []
l.append(1)
l.append(2)
l.append(3)

In [15]:
l

Out[15]:
[1, 2, 3]
In [22]:
l.pop()  # First in first out

Out[22]:
1
In [17]:
l

Out[17]:
[1, 2]
In [18]:
l.append(4)
l

Out[18]:
[1, 2, 4]
In [19]:
l.pop()

Out[19]:
4
In [20]:
l.pop()

Out[20]:
2
In [21]:
l

Out[21]:
[1]

#### List sorting

In [32]:
l = [2,3,9,4]
l.sort()

In [33]:
l

Out[33]:
[2, 3, 4, 9]

#### List Reversing

In [34]:
l.reverse()

In [35]:
l

Out[35]:
[9, 4, 3, 2]

#### List Extend Method

In [40]:
l.extend([1]) # need to feed a list, and will insert the element to a proper place

In [39]:
l

Out[39]:
[9, 4, 3, 2, 1]

#### Other List Methods

In [42]:
l.remove(1) #remove elements
l

Out[42]:
[9, 4, 3, 2]
In [52]:
l.index(4) # return the position of an element

Out[52]:
1
In [62]:
l.insert(0,2) # insert 2 into index 0
l

Out[62]:
[2, 9, 4, 3, 2]

We will talk about data structure usage more in-depth later in my blog when we go further into analysis.