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Python Data Types | Strings Numbers Collections

Master strings, numbers, collections, and datetime for effective programming

Python Data Types | Strings Numbers Collections

In Python, almost everything is an object. This includes variables like strings and integers and containers like lists and dictionaries. Data types are simply how you classify your objects—each type comes with specific properties and functions that define how you can work with that data.

Strings and Characters

Strings and characters are fundamental data types in Python. A character is essentially a string with a single number, letter, or special character such as ‘!’ or ‘@’. A string is a collection of characters that may or may not form readable words and sentences.

name = "Python Programming"
character = "P"
print(name)  # Output: Python Programming
print(character)  # Output: P

Numbers

Python supports four main types of numbers:

  • Integer: Whole numbers without decimal points (e.g., 5, -3, 100)
  • Long Integer: Very large whole numbers
  • Float: Numbers with decimal points (e.g., 3.14, -2.5)
  • Complex: Numbers with real and imaginary parts
integer_num = 42
float_num = 3.14
complex_num = 2 + 3j

print(type(integer_num))  # <class 'int'>
print(type(float_num))    # <class 'float'>
print(type(complex_num))  # <class 'complex'>

Collections

Collections are data types used when you want to store multiple items. Python offers four main collection types:

  • List: Ordered, changeable, allows duplicates
  • Dictionary: Ordered, changeable, key-value pairs
  • Tuple: Ordered, unchangeable, allows duplicates
  • Set: Unordered, changeable, no duplicates
# List example
fruits = ["apple", "banana", "cherry"]

# Dictionary example
person = {"name": "John", "age": 30, "city": "New York"}

# Tuple example
coordinates = (10, 20)

# Set example
unique_numbers = {1, 2, 3, 4, 5}

Date and Time

Date time data types are used for dates and times. While you can store dates as strings, using proper datetime objects provides built-in functionality for formatting, arithmetic, and manipulation.

from datetime import datetime, timedelta

# Current date and time
now = datetime.now()
print(now)

# Add one day
tomorrow = now + timedelta(days=1)
print(tomorrow)

# Format date
formatted_date = now.strftime("%Y-%m-%d %H:%M:%S")
print(formatted_date)

Advantage: Using datetime objects instead of strings gives you automatic date arithmetic, formatting options, and timezone handling.

Frequently Asked Questions

Find answers to common questions

Converting between different Python data types, particularly strings and integers, is a common requirement in programming. Python provides built-in functions that facilitate these conversions: `str()` for converting to a string and `int()` for converting to an integer. Understanding these conversions is essential for data manipulation and validation in your applications. ### Converting Integers to Strings To convert an integer to a string, you can use the `str()` function. For example: ```python number = 10 string_number = str(number) print(type(string_number)) # Output: <class 'str'> ``` This is handy when you need to concatenate numbers with other strings or output them in a user-friendly format. ### Converting Strings to Integers To convert a string that represents an integer back to an integer type, use the `int()` function. However, ensure that the string is a valid representation of an integer; otherwise, a `ValueError` will be raised. For example: ```python string_number = "20" integer_number = int(string_number) print(type(integer_number)) # Output: <class 'int'> ``` If the string contains non-numeric characters, you can handle the exception as follows: ```python try: integer_number = int(string_number) except ValueError: print("Invalid input: not an integer string") ``` ### Best Practices 1. **Input Validation**: Always validate input before conversion. You can use regular expressions to check if a string contains only numeric characters. 2. **Use Try-Except Blocks**: When converting user input, wrap your conversion logic in a try-except block to gracefully handle errors. 3. **Avoid Implicit Conversions**: Python does not allow automatic type conversion between incompatible types (e.g., adding a string and an integer) to avoid unexpected results. Always be explicit in your conversions. ### Real-World Example Consider a scenario in a web application where users input their age as a string from a form. You need to store this age as an integer in your database: ```python age_input = request.form['age'] # Age is received as a string try: age = int(age_input) # Store age in the database except ValueError: # Handle invalid input print("Please enter a valid age.") ``` In summary, converting between strings and integers in Python is straightforward but requires careful consideration of input types and error handling to ensure robust applications.

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