How to Store Multiple Data Items

Scenario

As a programmer, there are instances where you would need a single variable to hold multiple values. There are many answers to this situation because there are different designed solutions to different scenarios of this problem.

There are many built-in data types (list, set, and dictionary) that can help us that best suits the given situation.

We will be looking at 3 basic practices.

Where is this data stored?

In this chapter, our multiple items are temporarily stored within our program. When our program is done executing our code, we cannot have access to our data.

This is called non-persistent storage.

If we do not need to remember our previous state of our program, we often store data (values stored in variables and such) into the RAM of our computer. The RAM allows a very quick retrieval of data when a program is running.

If we want to save data from our program to be accessible without the program or we want to remember what happened in the previous execution, we would need to create an external file or create and use a database.

Option 1 - Using lists

• Items are not consistent in data type

• Items can repeat

• Item location / address does not matter; therefore, it will be just ordered based on time of insertion

• Items should be available to be accessed based on their index location

• Items at certain index should be modifiable

# Example of a list data

example = ['Marshall', 'Park', 4, ['Freya', 'Goji'], 'White']

A list in python can contain any type of data.

Each item added will have a numeric index (starting at 0).

• example[0] will contain a String data of 'Marshall'

• example[3] will contain a List data containing ['Freya,' 'Goji']

# Example being modified

example = ['Marshall', 'Park', 4, ['Freya', 'Goji'], 'White']

example[2] = 5 # now example is: ['Marshall', 'Park', 5, ['Freya', 'Goji'], 'White']

Lists are built-in data type in Python that offers strong set of operations and methods to provide a way to have data collection.

Option 2 - Using sets

Main Reason: To check if some item exists, checking against a set is faster than checking against a list

• Sets can handle different data types, but most often each item in a set should be a same data type

  • Sets also cannot contain mutable data types
• There should be no duplicates; each items are unique

• Order of insertion does not matter

• When you need an implementation of a mathematical set so that you can perform set operations (Python documentation link)
• We can convert a list with duplicate items to set to remove duplicates

• Items are still addable and removable in a set

# Example of a set

allergies = {'Dairy', 'Shellfish', 'Peanuts'}

list_allergies = ['Dairy', 'Shellfish', 'Peanuts']

print("Is the person allergic to dust?")
print("Dust" in allergies) # Outputs: False
print("Dust" in list_allergies) # Outputs: False

The code above currently has two collection of data: a set and a list.

It is currently trying to see if the person is allergic to dust given the two collections with same item values.

With respect to performance (speed of the program), the set based collection will outperform the list.

Reasoning:

• Because each individual item in a set is unique, Python generates an integer address for each item.

  • This is the reason that sets do not have duplicates; duplicates would generate the same integer address. This concept of integer addressing is called hashing.
• When we are checking if dust is an item in the set of of allergies by executing: "Dust" in allergies, Python would hash the value of dust and try to access the location of it from allergies. Since that address is not part of the set, it can immediately return a value of False

• When dust is being searched through the list, Python starts the search from the beginning of the list, and it compares every item against dust until found. If it is never found, then we reach the end of the list to return False

Therefore, a set would need to do a single hashing operation to see if that value is in the set or not. However, a list would have to check every single value against the searching value to see if it exists in the list. As the size of the list grows, the number of comparisons Python needs to do would be far larger than the number of comparisons a set would have done.

Option 3 - Countable Unique Items

For this situation, let's look at an example first.

# Unique, but countable

inventory = {
    'pencil' : 2,
    'pen' : 1,
    'erasor' : 1
    'white-out' : 1,
}

The code above is an example of using a dictionary.

A dictionary will always store two types of information for each item in its collection.

1. Address (commonly known as "key") -> Dictionary allows us to use immutable customized hashing for our items in the dictionary to act as an index for dictionary items.

2. Value -> Dictionary allows us to associate any type of data located at our custom address

In Python programming, dictionaries are a way to create a key+value pairs of data.