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Python is a toolbox for programmers. It's great for all sorts of things, from building apps to talking to computers. One of the reasons it's so powerful is because of all the different tools it has, like numbers, text, and special collections. We'll explore these tools and how to use them and Python data types.

Overview of Python Data Types

Python treats information like tools too, with different categories for numbers, text, and even special collections. These categories are called data types, and they help keep your code tidy and ensure you use the right tool for the job.
 

Have you ever wondered how computers know what kind of information you give them? Python has a unique function called "type()." You provide it with something, and it finds if it's a number, text, or even a fancy list of things. As a result, this helps keep things organized and ensures your code uses the right tool for the job.

List of Basic Data Types in Python

Python has a toolbox full of different types to hold kinds of information. There are 4 data types in Python, numbers (integers, floats, complex), labels (strings), true/false flags (booleans), and even lists (like shopping lists), sets (unique items, like your sock drawer), and dictionaries (like an address book with names and keys).;

Numeric Data Types

In this Python data types, you can store different numbers. Python has various categories for whole numbers (integers), numbers with decimals, and even unique numbers that include imaginary parts (complex).
 

• Whole Numbers (Integers): These are basic counting numbers, like 1, 10, or -5. They can be positive, or negative, but they don't have any decimal points.
• Decimals (Floats): These numbers can have a decimal point, like 3.14 (pi!) or 75.25. Moreover, they are useful for measurements or things that can divide into smaller parts.
• Complex: These are the unique numbers including an imaginary unit, represented by the letter "j.” They are used in advanced math but you probably won't need them for everyday coding.

Here are some examples:
 

• Integers: num1 = 10, num2 = -20
• Decimals: fnum = 2.71, fnum1 = 99.99
• Complex: num3 = 3 + 5j, numcom = 1 - 2j (The "j" means it's an imaginary part)
   

By using these different Python data types, you can perform all sorts of calculations and solve problems with numbers in Python.

Textual Data Types Strings

In Python, you can store and play around with text, like words, sentences, or whole stories. The primary way to do this is with strings. You can write your string using single quotes ('), double quotes ("), or even triple quotes (''' or "’) for really long pieces of text. Apart from that, once you make a string, you can't change its order or content.
 

1. Finding Your Letters
    

Suppose each letter in your string has a unique number, like a secret code. This is called its index. Moreover, the first letter has an index of 0, the second has an index of 1, and so on. You can use this code to find typical letters:

stringname[index] - This lets you peek at the letter at a certain index.
 

For example, the string "Python" has "y" at index 1 and "Pyth" (the first four letters) from index 0 to 3 (remember, it doesn't pick number 4!).
 

2. Secret Agent Techniques
    

• String Glue: Want to join two strings together? Use the plus sign (+) like string glue. However, this is called concatenation.
   

For instance, you can combine "Analytic" and "Vidya" to get "AnalyticVidya."
 

• String Repeater: Need to repeat a string a bunch of times? Use the asterisk (*).
   

If you write "python" * 3, you'll get "pythonpythonpython.”
 

3. Text
    

• Letter Search: Want to know if a letter is hiding in your string? Use the in-operator. For example, "p" is in "python" (True!), but "f" isn't (False!).

• String Size Scanner: The len() function is your X-ray vision to see how many letters are in your string.
   

For example, "Python" has a length of 6.
 

Boolean Data Type

One of the known Python data types after textual data types is Boolean. In Python, you can answer yes or no questions using booleans. These are like tiny switches, either turned on (True) or off (False). Moreover, they help your code make decisions based on conditions.
 

• Making Choices: Booleans come from comparisons, like checking if one number is greater than another (True or False?).
• Logic Gates: You can also combine booleans using logical operators like AND, OR, and NOT. 

Let's see how booleans work with comparisons and logical operations by using the basic data types in Python with examples:
 

• Truths and Falsehoods: We set a to True and b to False.
• Double Trouble (AND): a and b check if BOTH a and b are True. Since one is False, the result is False (like needing two keys to open a door).
• Maybe Maybe (OR): a or b checks if AT LEAST ONE of a or b is True. Since a is True, the result is True (like needing only one key to open a door).
• The Flipper (NOT): not a flips the value of a. Since a is True, not a becomes False (like a light switch).

We assign numbers to x and y. 
 

• x > y checks if x is greater than y (False).
• x <= y checks if x is less than or equal to y (True).
• x == y checks if x is equal to y (False).
• x != y checks if x is NOT equal to y (True).

By understanding booleans and logical operations, you can turn your Python code into a decision-making machine.

Collection Data Types

Python data types have unique tools called collections to help you store and organize multiple things together. 

Lists: 
 

• A list is a tool that can hold anything – numbers, words, even other tools. You can change what's inside and the order of things.
  • Make a list: my_list = [1, 2, "hello", True]
  • Find things: my_list[0] gives you the first item.
  • Add things: my_list.append("new item") adds to the end.
  • Insert things: my_list.insert(2, "inserted item") adds at position 2.
  • Count things: my_list.count(2) tells you how many 2s are there.
  • Reverse order: my_list.reverse() flips the order.
  • Sort things: my_list.sort() arranges items in order.

Tuples: 
 

• Once you put things in, you can't change them.
  • Make a tuple: my_tuple = (1, 2, "hello")
  • Find things: my_tuple[1] gives you the second item.
  • Unlike lists, you can't add, remove, or change items in a tuple.

Dictionaries: 
 

• A dictionary is a tool with labels on each item. You use the key to find the value.
  • Make a dictionary: my_dict = {"name": "Alice", "age": 30}
  • Find things: my_dict["name"] gives you "Alice".
  • Add things: my_dict["city"] = "New York" adds a new label-item pair.
  • Remove things: del my_dict["age"] removes the "age" label and its value.

Sets: 
 

• A set is a tool that only holds unique items. No duplicates allowed.
  • Make a set: my_set = {1, 2, 3, 2} (only 1, 2, and 3 will be in the set)
  • Check if something is inside: if 2 in my_set:
  • Combine sets: set1.union(set2) combines two sets.
  • Find common items: set1.intersection(set2) finds items in both sets.

By understanding these collection types, you can organize your data effectively in Python.

Conclusion

Python data types are the building blocks of any Python program. They classify data into various categories like numbers, text, logical values, and collections, each with its own characteristics and operations. Understanding these data types is crucial for effectively manipulating and processing information. By mastering the nuances of integers, floats, strings, booleans, lists, tuples, dictionaries, and sets, you prepare yourself with the fundamental tools to create robust and efficient Python applications.