Iterators allow sequential access to elements in lists, strings, dictionaries, sets, and custom objects. For lists, you can directly access elements by index or loop through each element. For strings, you can iterate over each character. When iterating over dictionaries, you can access keys, values, or both using methods like .keys() and .items(). Real-life examples of iterators include processing data from files like CSVs, analyzing social media data, processing large datasets for analysis, and extracting data from webpages during web scraping.

1. Examples Of Iterators
Presentation by:
SHAMRAIZ TASAWAR
02-131232-112

2. Iterating over lists
Direct Access
Access elements using indexes
Sequential Access
Loop through each element one by one
Output Output

3. Iterating over strings
• .
Output
Input
• Strings can be iterated over using a for loop.
• Each character in the string can be accessed individually.
• String methods and built-in functions can also be used during
iteration

4. Iterating over dictionaries
• .
Input Output
• Use a for loop to iterate over key-value pairs
• Access keys, values, or both using the .keys(), .values(), or
items() methods.

5. Iterating over sets
Input Output
• Iterating over sets allows for efficient processing of unique
elements.
• Sets provide a distinct collection of items for iteration.

6. Creating custom iterators
Custom iterators in Python allow you to define your own iteration behavior for objects. You can create
custom iterators by implementing the __iter__() and __next__() methods in your class.
Here's how you can create a custom iterator:
Input
Output

7. Real Life Examples Of Iterators
Processing Data from Files:
Iteration is commonly used to process data
stored in files.
Example: Reading a CSV file line by line
and extracting useful information like sales
data, customer information, or log files.
Social Media Analytics:
Python can be used to iterate over social
media data, extracting insights, analyzing
trends, and identifying patterns.
Data Processing and Analysis:
Python libraries like NumPy, Pandas, and
Matplotlib use iteration for data processing
and analysis tasks.
Example: Iterating over large datasets to
perform operations like filtering,
aggregating, and visualizing data.
Web Scraping:
Python's iteration capabilities are often
employed in web scraping projects.
Example: Extracting information from
webpages by iterating over the HTML
structure to collect data such as news
articles, product details, or job postings.

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