Prevent SQL injection with Python 3.14 t-strings

✅Day 5 – Working with Strings in Python Today I practised "Strings in Python" — one of the most important data types in real-world datasets. Strings are simply text data. ✅Examples: * Customer Name * Email Address * Product Category * City Name ✅What I Learned Today: * How to create strings * String concatenation * Changing case (upper/lower) * Finding text inside a string In data analytics, most datasets contain a lot of text data. Cleaning and manipulating strings is essential before analysis. ✅Today’s lesson reminded me: Understanding text data is just as important as understanding numbers. Building step by step. #Python #DataAnalytics #LearningJourney #BusinessAnalytics #Consistency

Major #dbt Fusion unblocker: Python Models 👇 A lot of projects were unable to migrate to Fusion because they were using Python models. Now, that blocker is gone: Python models are in **Public Preview.** If you don't know what Python Models are in dbt, they are basically dbt models written in Python instead of SQL, and they can be used for transformations that would be impossible (or very hard) to do with SQL alone. You can also perform analyses using tools from the open-source Python ecosystem, including state-of-the-art packages for data science and statistics. #AnalyticsEngineering #DataEngineering

Data scientists often switch between SQL and Pandas, which can make workflows unnecessarily complex. Tools like DuckDB now allow SQL queries directly on DataFrames combining the power of SQL with the flexibility of Python. The image shows a simple way to think about which tool works best for different tasks. For a quick explanation of this approach, read here: https://lnkd.in/eZPTyP2 #DataScience #Python #SQL #Analytics

Understanding data types is the first step to writing better Python programs 💡 In Python, everything is an object — and every object has a data type. 📌 Main Data Types in Python: 🔹 Numeric Types int → Whole numbers (10, -5, 100) float → Decimal numbers (10.5, -3.14) complex → Complex numbers (3+4j) 🔹 Sequence Types str → Text values ("Hello") list → Ordered, changeable collection [1, 2, 3] tuple → Ordered, unchangeable collection (1, 2, 3) 🔹 Set Types set → Unordered, no duplicate values {1, 2, 3} 🔹 Mapping Type dict → Key-value pairs {"name": "Vyshnavi", "age": 22} 🔹 Boolean Type bool → True or False #Python #PythonProgramming #LearnPython #Coding #DataTypes #ProgrammingBasics #DeveloperJourney

Return a #Python #Pandas data frame's index to a regular column with reset_index: df = df.reset_index() • 1-column index? It's now a regular column. • Multi-index? Its columns are all regular columns. reset_index returns a new data frame. It doesn't modify the original.

Every Python object secretly carries a __𝙙𝙞𝙘𝙩__ — a full dictionary to store its attributes. Dictionaries have overhead — they pre-allocate memory and store hash tables. If you're creating millions of objects (like rows from a database, or data points in ML), this overhead adds up enormously. __𝙨𝙡𝙤𝙩𝙨__ tells Python: "this class will only ever have these specific attributes — don't bother with a dictionary." Python then uses a compact array instead, saving 40-50% memory in many cases. 🤔 𝗪𝗵𝗲𝗻 𝗡𝗢𝗧 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁? 𝗜𝗳 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝗮𝗹𝗹𝘆 𝗮𝗱𝗱 𝗻𝗲𝘄 𝗮𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝘀 𝘁𝗼 𝗼𝗯𝗷𝗲𝗰𝘁𝘀 𝗮𝘁 𝗿𝘂𝗻𝘁𝗶𝗺𝗲, __𝘀𝗹𝗼𝘁𝘀__ 𝘄𝗶𝗹𝗹 𝗯𝗹𝗼𝗰𝗸 𝘁𝗵𝗮𝘁. It's a trade-off: flexibility vs memory.

📘 Python Data Types – Strengthening the Basics Today, I revised Python Data Types, which are the foundation for writing clean, efficient, and error-free code. 🔹 What are Data Types? Data types define the kind of data a variable can store and the operations that can be performed on it. Python is dynamically typed, meaning the data type is determined at runtime. 📌 Key Data Types Covered Numeric: int, float, complex Boolean: bool Sequence: str, list, tuple Set: set Mapping: dict NoneType: None 📌 Important Concepts Mutable vs Immutable data types Type checking using type() and isinstance() Type conversion (int, float, str) Real-time usage of lists, dictionaries, and sets 💡 Understanding data types helps in: Writing optimized code Avoiding runtime errors Handling real-world data efficiently Building strong fundamentals, one concept at a time 🚀 #Python #DataTypes #PythonLearning #ProgrammingBasics #DataAnalytics #CodingJourney #TechSkills

Over the last few days I've been building a small data analysis toolkit in Python. The idea is simple: Instead of solving the same data cleaning problems again and again across different projects, I want to create a small reusable set of functions. So far I've implemented a function for cleaning and normalizing column names in Pandas DataFrames: lowercase, removing whitespace, replacing spaces with underscores, etc. Right now I'm working on the next part: handling duplicates. The goal is to build a function that can: - detect duplicates - report them - automatically clean them depending on the chosen action Besides writing the function itself, I'm also focusing on: - writing clear docstrings - input validation - and adding tests It's a small step, but building things from scratch is one of the best ways I've found to really understand how tools work under the hood. More updates soon as the toolkit grows. #python #pandas #dataanalysis #machinelearning #learninginpublic

Day4/30 Topic : Python for Beginners ‎ Here‘s what I learnt ‎key points ; ‎Basic Syntax and Data Types ‎ ‎Comments [single line and multiple line comment] ‎ ‎Data Types in Python [Integer, Strings,Boolean,Floats] ‎ ‎Checking Data Types [ using the ‘type function’ ‎ ‎Variables and Naming Conventions ‎ ‎assign operator [=] for allocating value to variable ‎ ‎Rules for Naming Variables ‎ ‎Basic Operators ‎ ‎Arithmetic Operators [+,-,*,**,/,etc] ‎ ‎Comparison Operators [==. !=, >,<,>=,<=] ‎ ‎Logical Operators [‘not’,’and’, ‘or’] #30daysofTech #Datascience #TsAcademy