Merging Dictionaries with Summed Values in Python

Technical post: I've been posting some graphs on here, talking about functions and "equivalence". This was all started by working on porting an MLOPs framework from python 3.10 to 3.12, and all the "dependency hell" one has to go through. Then naturally the question arose "What are the boundaries of one project to another, in terms of functions being called etc.,?" This led me down the rabbit hole (not too deep) of what happens when I do something like python -m <module> <somescript>. Specifically, what is a "no op" module, and what kind of ops can we inject, thanks to python being an interpreted language. A few years ago I'd worked on something along similar lines called TracePath, which provided a decorator to do something similar (e.g. who called who, how long it took, etc.). So I merged these two ideas (avoid decorating every function, have an "inspector" module) and ran this on a simple pandas dataframe creation. The resulting function invocation graph is the image attached to this post. When I ran it across the whole workflow (create, load, transform data etc.,), the graph had ~9000 connections. The nice thing is I can specify which modules (e.g. only pandas, or pandas and numpy) should be added to the graph etc. What do you think is the next logical thing to do with something like this? What kind of graphs would well structured software produce? How about badly written software? #graphs #swe #dependencyhell #python

Day 10/365: Building a List from User Input & Finding Basic Stats 🔢📥 Today I wrote a Python program that takes numbers from the user, stores them in a list, and then calculates some basic statistics: sum, average, minimum, and maximum. What the code does step by step: First, I ask the user how many elements they want to enter and store that in n. I create an empty list l and a variable total to keep track of the sum. Using a for loop, I take n inputs from the user: Each number is added to the list using append(). At the same time, I keep adding each number to total to calculate the sum. After the loop: I print the full list. I print the sum using the total variable. Then I calculate the average as total / n and print it. To find the minimum and maximum: I start by assuming both min and max are the first element of the list. I loop through the list and update min if I find a smaller value. Similarly, I update max if I find a larger value. In the end, I print the minimum and maximum numbers in the list. What I learned from this exercise: How to take multiple inputs from a user and store them in a list. How to maintain a running sum while taking inputs. How to manually compute average, minimum, and maximum without using built‑in functions like sum(), min(), or max(). How loops and variables can work together to build simple but useful statistics — a basic idea used a lot in data analysis. Day 10 done ✅ 355 more to go. If you have ideas like extending this to find median, mode, or standard deviation, send them to me — I’d love to try them next. #100DaysOfCode #365DaysOfCode #Python #LogicBuilding #Lists #UserInput #CodingJourney #LearnInPublic #AspiringDeveloper

🚀 Day 5/30 of My LeetCode Journey (Python + SQL) Staying consistent and pushing forward every single day! 💻🔥 🔹 **SQL Problem of the Day** 👉 *Customers Who Never Order* Given two tables `Customers` and `Orders`, write a query to find all customers who never placed any order. 💡 *Key Concept:* LEFT JOIN + filtering NULL values to identify missing relationships. 🔹 **Python Problem of the Day** 👉 *Search Insert Position* Given a sorted array of distinct integers and a target value, return the index if found. If not, return the index where it would be inserted. 💡 *Key Concept:* Binary Search (O(log n)) for efficient searching. Understanding patterns like joins and binary search is making problem-solving faster and cleaner 📈 Day 5 done ✅ Let’s keep going! #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #BinarySearch #ProblemSolving

One of the most common questions beginners ask is: "I’ve learned Python basics... now what?" The beauty of Python isn't just in the syntax; it’s in the incredible ecosystem of libraries that allow you to pivot into almost any field. Whether you want to build AI agents, automate your boring tasks, or dive deep into data, there is a "formula" for it. Here is a quick breakdown of the Python combinations that power the industry today: For Data Fanatics: Python + Pandas = Data Analysis 📊 For AI Pioneers: Python + LangChain = AI Agents 🤖 For Web Architects: Python + Django/Flask = Web Development 🌐 For Automation Kings: Python + Selenium/Airflow = Workflow Magic ⚙️ For Visual Storytellers: Python + Matplotlib = Data Visualization 📈 Which "formula" are you currently working on? I’m personally diving deep into the data side of things, but the more I see what’s possible with Streamlit and FastAPI, the more I realize the possibilities are endless. Let’s discuss in the comments! What’s your favorite Python library to work with right now? #Python #DataScience #WebDevelopment #Programming #TechCommunity #Automation #LearningToCode #DataAnalytics #SoftwareEngineering

🚀 Day 8/30 of My LeetCode Journey (Python + SQL) Staying consistent and leveling up every day! 💻🔥 🔹 **SQL Problem of the Day** 👉 *Find Customer Referee* Given a `Customer` table, write a query to find the names of customers who are either: • Not referred by customer with id = 2 • OR not referred by anyone 💡 *Key Concept:* Filtering with conditions (`!= 2` OR `IS NULL`). 🔹 **Python Problem of the Day** 👉 *Merge Sorted Array* Given two sorted arrays, merge them into a single sorted array in-place without returning a new array. 💡 *Key Concept:* Two-pointer approach from the end for efficient in-place merging. Every problem is helping me think more efficiently and write better code ⚡ Day 8 done ✅ #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #Learning

🚀 Last month, I built and published my first Python package — Pristinizer I wanted to solve a simple but real problem in data science: 👉 Cleaning and understanding raw datasets takes way too much time. So I built Pristinizer, a lightweight Python package that helps streamline data cleaning + EDA in just a few lines of code. 🔍 What Pristinizer does: • Cleans messy datasets (duplicates, missing values, column formatting) • Generates structured dataset summaries • Visualizes missing data (heatmap, matrix, bar chart) ⚙️ Tech Stack: Python • pandas • matplotlib • seaborn 📦 Try it out: >> pip install pristinizer >> import pristinizer as ps df = ps.clean(df) ps.summarize(df) ps.missing_heatmap(df) 🧠 What I learned while building this: • Designing a clean and intuitive API • Structuring a real-world Python package • Publishing to PyPI • Writing proper documentation for users 📌 Next, I’m planning to add: • Outlier detection • Automated preprocessing pipelines • Advanced EDA reports Would love to hear your thoughts or feedback! #Python #DataScience #MachineLearning #OpenSource #Pandas #EDA #Projects

🚀 Python Series – Day 14: File Handling (Read & Write Files) Yesterday, we explored advanced concepts in functions. Today, let’s learn something super practical — how Python works with files 📂 🧠 What is File Handling? File handling allows you to: ✔️ Read data from files ✔️ Write data to files ✔️ Store information permanently 👉 Used in real-world projects like logs, data storage, reports, etc. 📂 Step 1: Open a File file = open("demo.txt", "r") 👉 Modes: "r" → Read "w" → Write (overwrites file) "a" → Append "x" → Create new file 📖 Step 2: Read a File file = open("demo.txt", "r") print(file.read()) file.close() ✍️ Step 3: Write to a File file = open("demo.txt", "w") file.write("Hello, Python!") file.close() ➕ Step 4: Append Data file = open("demo.txt", "a") file.write("\nLearning File Handling 🚀") file.close() 🔥 Best Practice (Important!) Use with statement (auto closes file): with open("demo.txt", "r") as file: data = file.read() print(data) 🎯 Why This is Important? ✔️ Used in data science (CSV, logs) ✔️ Used in real-world applications ✔️ Helps manage large data ⚠️ Pro Tip: Always close files OR use with 👉 Otherwise it may cause memory issues 📌 Tomorrow: Exception Handling (Handle Errors Like a Pro!) Follow me to master Python step-by-step 🚀 #Python #Coding #Programming #DataScience #LearnPython #100DaysOfCode #Tech #MustaqeemSiddiqui

I used to think Data Structures were just a hard exam topic. Then StemLink lectures showed me how they actually work in Python — and everything changed. 🐍 Here's what I learned, broken down simply 👇 --- 🔷 The 4 main data structures in Python: 📋 Lists → ordered, mutable, most used   mylist = []   mylist.append("item")  # add to end   mylist.sort()      # sort in place 📖 Dictionaries → key-value pairs, O(1) lookup   user = {"name": "Abiya", "age": 20} 🔸 Tuples → like lists but NOT mutable   coords = (6.9, 79.8)  # can't change this 🔹 Sets → unique values only, no duplicates   tags = {"python", "dsa", "python"} # stores only once --- 🔷 How data lives inside structures: Everything stored inside these is called an Element. Elements are ordered in groups → accessed using Indexes. mylist[0]    # first item mylist[-1]    # last item mylist[0:3]   # slice from 0 to 2 --- 🔷 How we move through data: Loops + Indexes work together to iterate through elements. for elem in mylist:   # loop through every item   print(elem) mylist[int] = x     # modify by assignment --- 🔷 The big insight: Lists are the most popular data structure in Python. They connect everything — elements, loops, indexes, methods, and assignment — all in one place. Once you understand Lists deeply, the rest makes sense. These fundamentals go directly into the projects I build. Not just theory. Real code. Which Python data structure do you use the most? 👇 #Python #DSA #DataStructures #StemLink #IITColombo #LearnToCode #CS #StudentDeveloper #BuildInPublic #Programming

Your 2020 Python skills are becoming a 2026 bottleneck. I’ve seen brilliant analysts struggle with memory errors and 10-minute wait times for simple joins. The problem isn't their logic; it’s their toolkit. The "Modern Python Stack" for Analysts has fundamentally shifted. If you are still relying 100% on Pandas and Matplotlib, you are leaving performance and interactivity on the table. I’ve fact-checked the production environments of top data teams this year. Here is the Save-Worthy 2026 Python for Analysts Cheat Sheet. 🚀 Polars: The multi-threaded engine that handles 10GB+ datasets on a laptop. 🦆 DuckDB: Run high-speed SQL directly on your local Parquet files. 📊 Plotly Express: Interactive charts that stakeholders can actually explore. ✅ Pydantic V2: Automated data cleaning that's 20x faster than traditional methods. 👇 The Big Debate: Is it finally time to retire import pandas as pd for good, or is it still the king of small-scale EDA? Let’s settle it in the comments. #Python #DataAnalytics #Polars #DuckDB #DataScience #MicrosoftFabric #2026Trends #Coding

Hey there, data fam! 👋 I’m kicking off a new series today to help you master Python in most simplified form. 🆅🅰🆁🅸🅰🅱🅻🅴🆂 🅰🅽🅳 🅽🅰🅼🅸🅽🅶 🆁🆄🅻🅴🆂.🐍 📦 What is a Variable? In Python, variables are symbolic names that act as references to objects stored in memory.  In the simplest terms a variable is just a container for storing data values. You can think of it as a labeled box where you put something important so you can find it later. 🏠 The Real-World Example: Imagine you are moving into a new apartment. You have dozens of cardboard boxes. If you put your kitchen plates in a box but you forgot label it, you'll be digging through 50 boxes just to eat dinner. But if you write "Kitchen_Plates" on the side, you know exactly what is inside and where to find it. Simply said , In Python, the Label is the variable name, and the Plates are the data. 📝 𝐓𝐡𝐞 𝐍𝐚𝐦𝐢𝐧𝐠 𝐑𝐮𝐥𝐞𝐬 We can't just name a variable anything. Python has some ground rules to keep things organized. 👉 Start with a letter or underscore: name or _name is fine. 1name is a big no-no. 👉 No Spaces: Python hates spaces in names. Use an underscore instead (user_age, not user age). 👉 Case Sensitive: Age, age, and AGE are three completely different ! 👉 No Special Characters: Keep @, $, %, and & is no - no . 👉 Reserved Words: We can't use words Python already uses for itself, like if, else, or print. #python #variables #namingrules #pythonsimplified