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Python Programming Tutorial-Page3

Refer following pages to learn complete Python language tutorial.
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Working with Data

•  Most programs work with data
•  In this section, we look at how Python programmers represent and work with data
•  Common programming idioms
Primitive Datatypes
Python has a few primitive types of data Integers
Floating point numbers
Strings (text)
Obviously, all programs use these
None type
Nothing, nil, null
logfile = None
This is often used as a placeholder for optional program settings or values
if logfile:
logfile.write("Some message")
If you don't assign logfile to something, the above code would crash (undefined variable) Strings (text)

Data Structures

Real programs have more complex data
Example: A holding of stock
100 shares of GOOG at $490.10
An "object" with three parts
Name ("GOOG", a string)
Number of shares (100, an integer)
Price (490.10, a float)


A collection of values grouped together,Tuples are usually used to represent simple records and data structures.
s = ('GOOG',100,490.10)

Tuple contents are ordered (like an array) s = ('GOOG', 100, 490.10)

Tuples are really focused on packing and unpacking data into variables , not storing distinct items in a list Packing multiple values into a tuple s = ('GOOG', 100, 490.10)

To use the tuple elsewhere, you typically unpack its parts into variables
Unpacking values from a tuple
(name, shares, price) = s


A hash table or associative array
A collection of values indexed by "keys"
The keys are like field names
s = {
'name' : 'GOOG',
'shares' : 100,
'price' : 490.10

Getting values: Just use the key names
>>> print s['name'],s['shares']
GOOG 100
Adding/modifying values : Assign to key names
>>> s['shares'] = 75
Deleting a value
>>> del s['date']

When to use a dictionary as a data structure.
Data has many different parts.
The parts will be modified/manipulated.
For example: If you were reading data from a database and each row had 50 fields,
a dictionary could store the contents of each row using descriptive field names.


Programs often have to work many objects. A portfolio of stocks,Spreadsheets and matrices.
Three choices are:
1. Lists (ordered data)
2. Dictionaries (unordered data)
3. Sets (unordered collection)

Lists as a Container

Use a list when the order of data matters.Lists can hold any kind of object.
For example: A list of tuples
portfolio = [
portfolio[0] ('GOOG',100,490.10)

Dicts as a Container

Dictionaries are useful if you want fast random lookups (by key name)
Example: A dictionary of stock prices

prices = {
'GOOG' : 513.25,
'IBM' : 87.22,
>>> prices['IBM']

Dicts : Looking For Items

To test for existence of a key
if key in d:
# Yes
# No
Looking up a value that might not exist
name = d.get(key,default)
>>> prices.get('IBM',0.0)

Dicts and Lists

dict() promotes a list of pairs to a dictionary
prices = dict(pricelist)


a = set([2,3,4])
Holds collection of unordered items
No duplicates, support common set ops
>>> a = set([2,3,4])


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