There are multiple data types in Python, and it's pivotal to understand them so that you can build Python skills from the ground up. Here is a cheat sheet that lists the most commonly used types, what they are, and an example of what their output would look like: - String: Text data that is wrapped in quotes (e.g. "Hello World" or 'Hello World') - Integer: Positive or negative whole numbers (e.g. 15, -15) - Float: Positive or negative decimal numbers (e.g. 3.14, -1.5) - Boolean: Used for true or false evaluation (e.g. True or False) - List: Ordered, mutable collection of values held within []. Allows duplicates (e.g. [1,2,2,3]) - Tuple: Ordered, immutable collection of values held within () that cannot be changed after creation. Allows duplicates (e.g. (1,2,2,3)) - Set: Unordered, mutable collection of values held within {}. Values are unique and immutable within the set itself (e.g. {1,2,3}) - Dictionary: Key-value pairs where the key is a unique identifier for the value, held within {} (e.g. {"name":"John","age":25}) The application for the different data types is endless, such as converting a list to a set in order to remove duplicates and store a unique set of values from the original list. For example: og_list = [1,2,2,3,4,4,5] new_set = set(og_list) print(new_set) # Output: {1, 2, 3, 4, 5} Once you are familiar with the different data types, you have a foundation that helps you move on to creating more advanced scripts! #Python #PythonTips #LearnPython #Programming #DataEngineering #DataScience #AnalyticsEngineering
Python Data Types Cheat Sheet: Strings, Integers, Floats & More
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🔤 Strings in Python – Quick Guide Strings are used to store text data in Python. They are simple, powerful, and used everywhere — from data cleaning to report generation. Creating Strings s1 = 'Hello' s2 = "Python" s3 = """Multi-line string""" Access & Slicing text = "Python" text[0] # P text[-1] # n text[0:3] # Pyt Common Operations "Hello" + " World" # Concatenation "Hi " * 3 # Repetition Useful String Methods text = " hello world " text.upper() # HELLO WORLD text.lower() # hello world text.strip() # remove spaces text.replace("world","Python") text.split() String Formatting (Best Practice) name = "Maha" print(f"Hello {name}") Important: Strings are immutable (cannot be changed directly) text = "hello" text = "H" + text[1:] #Python #PythonBasics #DataAnalytics #Programming #LearnPython #Coding #DataScience #PythonForBeginners #100DaysOfCode
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🚀 Python Series – Day 10: Strings in Python (Text Handling Basics) Till now, we worked with numbers and collections. But what about text data? 🤔 👉 That’s where Strings come in! 🧠 What is a String? A string is a sequence of characters enclosed in quotes. ✔️ Can use single ' ' or double " " quotes 🔧 Example: name = "Mustaqeem" print(name) 🔁 Access Characters text = "Python" print(text[0]) # P print(text[-1]) # n ✂️ String Slicing text = "Python" print(text[0:3]) # Pyt print(text[2:]) # thon 🔄 String Methods msg = "hello world" print(msg.upper()) # HELLO WORLD print(msg.lower()) # hello world print(msg.title()) # Hello World ❌ Mutability Fails in String Strings are immutable — meaning you cannot change them directly. text = "Python" text[0] = "J" # ❌ Error 👉 This will give an error because strings cannot be modified. ✅ Correct Way (Create New String) text = "Python" new_text = "J" + text[1:] print(new_text) # Jython 🎯 Why Strings are Important? ✔️ Used in almost every program ✔️ Helps in user input & output ✔️ Important for data processing 🔥 Pro Tip: Whenever you want to modify a string 👉 create a new one instead of changing the original ⚡ Quick Challenge: What will be the output? text = "Python" print(text[1:4]) 👇 Comment your answer! 📌 Tomorrow: Dictionaries & Sets (Advanced Data Structures) Follow me to learn Python step-by-step from basics to advanced 🚀 #Python #DataScience #Coding #Programming #LearnPython #Beginners #Tech #MustaqeemSiddiqui
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Dates and times appear in almost every real-world dataset and Python's datetime handling is one of those topics where small gaps in knowledge cause big headaches. Knowing the difference between strftime and strptime, how timedelta enables date arithmetic, when to use naive vs aware datetimes, and how pandas handles datetime columns — these are the details that separate clean, reliable data pipelines from brittle ones that break on unexpected date formats. Master datetime handling early and it stops being a source of bugs and starts being one of the fastest, most routine steps in your data workflow. Read the full post here: https://lnkd.in/ebJytJ9c #Python #DataScience #Programming #Pandas #DataEngineering #Analytics
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🧠 Python Concept: join() for Strings Stop using + in loops 😵💫 ❌ Traditional Way words = ["Python", "is", "awesome"] sentence = "" for word in words: sentence += word + " " print(sentence.strip()) ❌ Problem 👉 Slow 👉 Messy 👉 Hard to read ✅ Pythonic Way words = ["Python", "is", "awesome"] sentence = " ".join(words) print(sentence) 🧒 Simple Explanation Think of join() like a glue 🧴 ➡️ It connects all words ➡️ Uses a separator (" ") ➡️ Gives a clean result 💡 Why This Matters ✔ Much faster than + ✔ Cleaner code ✔ Used in real-world string processing ✔ Avoids unnecessary loops ⚡ Bonus Example data = ["2026", "03", "27"] date = "-".join(data) print(date) 👉 Output: 2026-03-27 🐍 Don’t build strings piece by piece 🐍 Join them smartly #Python #PythonTips #CleanCode #LearnPython #Programming #Join #DeveloperLife #100DaysOfCode
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Python: sort() vs sorted() Have you ever had to pause for a second and think: “Do I need sort() or sorted() here?” 😅 This is the common Python confusions. Let’s clear it up. 🔹 list.sort() ◾ A method (belongs to list objects) ◾ Works only on lists ◾ Sorts the list in-place ◾ Changes the original list ◾ Returns None Example: numbers = [3, 1, 4, 2] numbers.sort() print(numbers) # [1, 2, 3, 4] 🔹 sorted() ◾ A function (built-in Python function) ◾ Returns a new sorted list ◾ Does NOT change the original ◾ Works on any iterable Example: numbers = [3, 1, 4, 2] new_numbers = sorted(numbers) print(new_numbers) # [1, 2, 3, 4] print(numbers) # [3, 1, 4, 2] The key difference: sort() → changes your original data sorted() → keeps your original data safe 💡 Quick way to remember: 👉 If you want to keep the original, use sorted() 👉 If you want to modify the list directly, use sort() #Python #Programming #LearnPython #DataScience #LearningJourney #WomenInTech
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Stop using + to join strings in Python! 🐍 When you are first learning Python, it is tempting to use the + operator to build strings. It looks like this: name = "Gemini" status = "coding" print("Hello, " + name + " is currently " + status + ".") The Problem? In Python, strings are immutable. Every time you use +, Python has to create a brand-new string in memory. If you are doing this inside a big loop, your code will slow down significantly. The Pro Way: f-strings (Fast & Clean) Since Python 3.6, f-strings are the gold standard. They are faster, more readable, and handle data types automatically. The 'Pro' way: print(f"Hello, {name} is currently {status}.") Why use f-strings? Speed: They are evaluated at runtime rather than constant concatenation. Readability: No more messy quotes and plus signs. Power: You can even run simple math or functions inside the curly braces: print(f"Next year is {2026 + 1}") Small changes in your syntax lead to big gains in performance. Are you still using + or have you made the switch to f-strings? Let’s talk Python tips in the comments! 👇 #Python #CodingTips #DataEngineering #SoftwareDevelopment #CleanCode #PythonProgramming
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🚀Today I explored another important concept in Python — Strings 💻 🔹 What is a String? A string is a sequence of characters used to store text data. Anything written inside quotes (' ' or " ") is considered a string in Python. 🔹 How Strings Work: 1️⃣ Each character has a position (index) 2️⃣ We can access characters using indexing 3️⃣ We can extract parts of a string using slicing 4️⃣ We can modify output using built-in methods 👉 Flow: Text → Access/Manipulate → Output 🔹 Operations I explored: ✔️ Indexing Accessing individual characters using position ✔️ Slicing Extracting a part of the string ✔️ String Methods Using built-in functions like upper(), lower(), replace() 🔹 Example 1: Indexing & Slicing text = "Python" print(text[0]) # P print(text[-1]) # n print(text[0:4]) # Pyth 🔹 Example 2: String Methods msg = "hello world" print(msg.upper()) print(msg.replace("world", "Python")) 🔹 Key Concepts I Learned: ✔️ Indexing (positive & negative) ✔️ Slicing ✔️ Built-in string methods ✔️ Immutability (strings cannot be changed directly) 🔹 Why Strings are Important: 💡 Used in user input 💡 Data processing 💡 Text manipulation in real-world applications 🔹 Real-life understanding: Strings are everywhere — from usernames and passwords to messages and data handling in applications Learning step by step and gaining deeper understanding every day 🚀 #Python #CodingJourney #Strings #Programming
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🚀 #python #Ep 2: Understanding #Data Types in Python In Python, everything is an object, and every object has a data type. Data types define what kind of value a variable holds and what operations you can perform on it. 🔗 Code reference: https://lnkd.in/ei6STRqT 🧠 Why Data Types Matter? Prevent errors in your code Help Python understand how to store and process data Make your programs efficient and readable 📌 Common Python Data Types 🔢 Numeric Types int → Whole numbers (10, -5) float → Decimal numbers (3.14) complex → Complex numbers (2+3j) 📝 String (str) Used to store text Example: "Hello Python" ✅ Boolean (bool) Only two values: True or False 📦 Sequence Types list → Ordered & mutable → [1, 2, 3] tuple → Ordered & immutable → (1, 2, 3) 🗂️ Mapping Type dict → Key-value pairs → {"name": "Hari"} 🔁 Set Types set → Unordered & unique values → {1, 2, 3} 💡 Pro Tip Python is dynamically typed, meaning you don’t need to declare data types explicitly — Python figures it out at runtime 🔍 Example x = 10 # int y = 3.14 # float name = "Hari" # str is_active = True # bool 📣 Final Thought Mastering data types is the foundation of Python programming. Once you understand them, everything else becomes easier! #Python #Coding
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