Python Pandas: pd.to_numeric vs astype('Int64') for Data Cleaning

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.

SQL subqueries confused me until I stopped thinking of them as SQL. They're helper functions. In Python, you write a function and call it. In SQL, you nest a query. Same idea, different syntax. Three types. One performance gotcha that will save you from a very slow query. Swipe to see it. Link in comments. #SQL #Python #DataEngineering #PythonDeveloper #LearnSQL

🚀 Python Pro-Tip: Stop using + to join strings. 🛑 If you’re building long strings or SQL queries with +, you’re hurting performance and readability. 📉 The Shortcut: f-Strings (Interpolation) ⚡ It’s faster, cleaner, and allows you to run code right inside the string. ❌ The Messy Way: url = "https://" + domain + "/" + path + "?id=" + str(user_id) ✅ The Clean Way: url = f"https://{domain}/{path}?id={user_id}" Why?? * Readability: It looks like the final result. * Performance: Python optimizes f-strings at runtime better than concatenation. * Power: You can even do math: f"Total: {price * 1.15:.2f}" Are you still using .format() or are you 100% on the f-string train? 👇 #Python #CleanCode #ProgrammingTips #SoftwareEngineering #BackendDev

One concept that often confuses developers (including me at first) is reference behavior in Python. In Python, variables don’t actually store values. They store references to objects in memory. a = [1, 2] b = a Here, a and b are not two separate lists. They both point to the same list in memory. So when you do b[0] = 3 you are modifying the same objects. That means print(a) => [3,2] print(b) => [3,2] 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Understanding references helps avoid unexpected bugs, especially when working with:  • Lists  • Dictionaries  • Objects  • Nested data structures 𝗖𝗼𝗺𝗺𝗼𝗻 𝗠𝗶𝘀𝘁𝗮𝗸𝗲: a = [[0, 0, 0]] * 2 This doesn't create two separate list but creates two references to the same list. so when you do a[0][0] = 3 you may expect the output be a = [[3,0,0],[0,0,0]] But the actual output will be [[3,0,0],[3,0,0]] #Python #ProgrammingConcepts #SoftwareEngineering #CodingTips #LearnPython #DeveloperJourney #TechLearning

As I continue exploring Python, I’m realizing how important built-in data structures are. Here are four core structures : 1️⃣ List An ordered and mutable collection of items. This means I can add, remove, or change elements after creating it. 2️⃣ Tuple An ordered but immutable collection. Once created, the values cannot be changed. Tuples are also more memory-efficient than lists and can even be used as dictionary keys if they contain only immutable elements. 3️⃣ Set An unordered collection of unique elements. Sets automatically remove duplicates and are very useful for operations like union, intersection, and difference, as well as checking if an element exists. 4️⃣ Dictionary A mutable collection of key-value pairs. It’s optimized for fast lookups using unique keys such as strings, numbers, or tuples. Example: {"name": "Maruf", "skill": "Python"} #Python #LearningInPublic #CodingJourney #SoftwareDevelopment

Basic data types in python :- 1) int : to declare integer numbers 2) float : to declare floating point numbers 3) strings : to declare strings 4) Boolean: to compare true and false values 5) list : to list the item into a array , mutable [. ] 6) set : represents in {. } 7) dic : values will be represented in key and values basis 8) tuple: ordered values and values defined we can't change it to get know the type of declared value : my_value = 'Learn something' type (my_value) type () :- function will provide the type of the value declared and defined #learn #python #beginner #content #creator #something #big #dreem #teach

Your #Python #Pandas data frame has a multi-index, and you want to remove one part — not return it to be a regular column? Add drop=True: df.reset_index(level=1) # index level 1 becomes a column in df df.reset_index(level=1, drop=True) # index level 1 is removed

PowerOracle is a Python CLI tool that analyzes historical *Power &all* drawing data to generate statistically optimized number picks. It combines frequency analysis (hot/cold/due numbers), pattern rec… I went from $0.00 to $7.00; not a millionaire, but hey, it paid for itself. Built with Python, scikit-learn, pandas, numpy, rich, and BeautifulSoup, anthropic CCode. https://lnkd.in/eTksia36

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