Cleaning Column Names with Pandas in Python

𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 🐍 | 𝗡𝘂𝗺𝗣𝘆 – 𝗦𝘂𝗺 & 𝗣𝗿𝗼𝗱 ➕✖️ | 📅 𝗗𝗮𝘆 𝟲𝟯 🚀 Today’s task: ✅ 𝗧𝗮𝗸𝗲 a 2D array (matrix). ✅ 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 sum across rows. ✅ 𝗧𝗵𝗲𝗻 take product of the result. Core idea from the code: 𝙣𝙪𝙢𝙥𝙮.𝙨𝙪𝙢(𝙖𝙧𝙧, 𝙖𝙭𝙞𝙨=0) ➡️ Adds elements column-wise Then: 𝙣𝙪𝙢𝙥𝙮.𝙥𝙧𝙤𝙙(...) ➡️ Multiplies all resulting values Example concept: Matrix: [[1 2] [3 4]] Step 1 → Sum (axis=0) [1+3, 2+4] → [4, 6] Step 2 → Product 4 * 6 = 24 💡 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Understanding axis is key: • axis=0 → column-wise • axis=1 → row-wise Strong candidates understand: • Reduction operations • Combining multiple NumPy functions • Data aggregation patterns Because real-world data tasks are all about: Transform → Aggregate → Compute Master these patterns — and NumPy becomes your superpower. #Python #NumPy #InterviewPrep #HackerRank #DataScience #ProblemSolving #DailyCoding #Consistency

📢💡 𝐃𝐚𝐲 𝟗 – 𝐫𝐞𝐩𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧() 𝐯𝐬 𝐜𝐨𝐚𝐥𝐞𝐬𝐜𝐞() 🤔 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 ✔️ Demonstrate partition control impact on small dataset (interview favorite). 📍 𝐈𝐧𝐩𝐮𝐭 𝐝𝐚𝐭𝐚 : 𝐩𝐲𝐭𝐡𝐨𝐧 data = list(range(1, 21)) # 20 numbers 📤 𝐄𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐨𝐮𝐭𝐩𝐮𝐭 👉 Original partitions: 4 👉 After repartition(2): 2 👉 After coalesce(8): 8 (no shuffle) 🧠 𝐄𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧 ✔️ 𝐫𝐞𝐩𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧(n) = shuffle + resize (even distribution) ✔️ 𝐜𝐨𝐚𝐥𝐞𝐬𝐜𝐞(n) = merge partitions (reduce only, no shuffle) ✔️ 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰: Use coalesce for reduce, repartition for increase. #python #Spark #Pyspark #Dataengineering #Bigdata #learnmore #pythonmcq #programmingwithpython #mcq #spark #Practicewithme

📊 Day 19 — 60 Days Data Analytics Challenge Today I learned about Crosstab in Pandas, which helps summarize data by showing the relationship between two categorical variables. 🔍 What I practiced today: • Creating cross-tabulations using pd.crosstab() • Understanding category-wise data distribution • Using margins=True to include total values • Improving table readability with row and column labels This feature is very helpful during Exploratory Data Analysis (EDA) because it allows us to quickly compare categories and identify patterns in the dataset. #DataAnalytics #Python #Pandas #60DaysChallenge #LearningJourney

Trying to simplify Pandas data exploration & filtering in my own way 📊 - Quick look → "head()", "tail()" - Overview → "info()", "describe()" - Selecting data → columns & rows - Filtering → conditions using masks One thing that confused me earlier: 👉 "iloc" is similar to "loc", but it uses index positions (numbers), and the stop index is not included. 👉 In practice, "loc" is used more often because it’s label-based and easier to read. Refer the below carousel for better understanding. #Python #Pandas #DataAnalytics

Day 6/10 🚀 This is where your data starts to take shape. Collections — the backbone of every Python program. Without the right one? Slower code, messy logic. With the right one? Faster lookups, cleaner design. 📋 What I covered today: 01 → Lists — slicing & comprehensions 02 → Tuples — immutability & unpacking 03 → Dictionaries — CRUD & O(1) lookup 04 → Sets — unique values & operations 05 → Frozenset 06 → Advanced — defaultdict, Counter, namedtuple 07 → Iterators — iter() & next() 08 → Mini Project — Inventory Management System Built a simple system using dictionaries to manage stock & pricing — a real-world pattern used in inventory and data pipelines. Day 1 ✅ Day 2 ✅ Day 3 ✅ Day 4 ✅ Day 5 ✅ Day 6 ✅ 4 more to go. Drop a 🐍 if you’ve ever used a list when a set would’ve been better 😄 #Python #Collections #DataEngineering #LearningInPublic #CleanCode #10DaysOfPython #DataStructures

Missing data is one of the most common challenges in data analysis. But the goal isn’t just to remove it, it’s to handle it intelligently. With Pandas, you can: • Drop unnecessary data • Fill missing values with mean/median • Use forward fill for time-series • Apply interpolation for trends The right approach depends on your dataset and business context. Clean data is the foundation of reliable insights. Read the full post here: https://lnkd.in/euXnbWa5 #Python #Pandas #DataCleaning #DataAnalytics #DataScience

𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 🐍 | 𝗡𝘂𝗺𝗣𝘆 – 𝗖𝗼𝗻𝗰𝗮𝘁𝗲𝗻𝗮𝘁𝗲 🔗 | 📅 𝗗𝗮𝘆 𝟱𝟲 🚀 Today’s task: ✅ 𝗧𝗮𝗸𝗲 𝟮 𝗺𝗮𝘁𝗿𝗶𝗰𝗲𝘀. ✅ 𝗖𝗼𝗻𝘃𝗲𝗿𝘁 𝘁𝗵𝗲𝗺 𝗶𝗻𝘁𝗼 NumPy arrays. ✅ 𝗝𝗼𝗶𝗻 𝘁𝗵𝗲𝗺 𝗶𝗻𝘁𝗼 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗺𝗮𝘁𝗿𝗶𝘅. Only if you understand array concatenation. Core idea from the code: 𝙣𝙥.𝙘𝙤𝙣𝙘𝙖𝙩𝙚𝙣𝙖𝙩𝙚((𝙖𝙧𝙧1, 𝙖𝙧𝙧2), 𝙖𝙭𝙞𝙨=0) This joins arrays row-wise. Meaning: Matrix A (N × P) Matrix B (M × P) After concatenation → Result (N+M × P) Example concept: A 1 2 3 4 5 6 B 7 8 9 Result 1 2 3 4 5 6 7 8 9 💡 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Concatenate = merge arrays along an axis Strong candidates understand: • Array dimensions • Axis operations (rows vs columns) • How NumPy handles structured data Because in data processing, combining datasets is a common task. Better structure. Better analysis. #Python #NumPy #InterviewPrep #HackerRank #DataAnalytics #DataStructures #DailyCoding #Consistency

📢⚡𝐃𝐚𝐲 𝟏𝟕 – 𝐰𝐡𝐞𝐫𝐞() + 𝐍𝐔𝐋𝐋 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 🤔 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 ✔️ Find orders with missing amount OR amount < 1000. 📍 𝐈𝐧𝐩𝐮𝐭 𝐝𝐚𝐭𝐚 : 𝐩𝐲𝐭𝐡𝐨𝐧 orders_data = [ (1, "Idli", 50.0), (2, "Dosa", None), (3, "Coffee", 120.0), (4, "Sambar", 25.0) ] 📤 𝐄𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐨𝐮𝐭𝐩𝐮𝐭 +---+------+------+ | id|product|amount| +---+------+------+ | 2| Dosa| null| +---+------+------+ 💡 𝐄𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧 ✔️ where() = alias for filter() ✔️ isNull() handles missing data ✔️ Interview: Always check NULLs in production! #python #Spark #Pyspark #Dataengineering #Bigdata #learnmore #pythonmcq #programmingwithpython #mcq #spark #Practicewithme

𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 🐍 | 𝗡𝘂𝗺𝗣𝘆 – 𝗠𝗶𝗻 & 𝗠𝗮𝘅 🔍 | 📅 𝗗𝗮𝘆 𝟲𝟰 🚀 Today’s task: ✅ 𝗧𝗮𝗸𝗲 a 2D array (matrix). ✅ 𝗙𝗶𝗻𝗱 minimum of each row. ✅ 𝗧𝗵𝗲𝗻 find the maximum among those values. Core idea from the code: 𝙣𝙪𝙢𝙥𝙮.𝙢𝙞𝙣(𝙖𝙧𝙧, 𝙖𝙭𝙞𝙨=1) ➡️ Finds minimum in each row Then: 𝙣𝙪𝙢𝙥𝙮.𝙢𝙖𝙭(...) ➡️ Picks the maximum from those minimum values Example concept: Matrix: [[2 5] [3 7] [1 3]] Step 1 → Row-wise min [2, 3, 1] Step 2 → Max of result max(2, 3, 1) = 3 💡 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: This is a classic pattern: 👉 Min → then Max Strong candidates understand: • axis=1 → row-wise operations • Chaining NumPy functions • Data reduction strategies Because many real problems are about: Finding optimal values from constraints Learn to combine operations — that’s where real power lies. #Python #NumPy #InterviewPrep #HackerRank #DataScience #ProblemSolving #DailyCoding #Consistency

Unlock the power of your data! In this comprehensive lecture, we dive deep into the two most essential Python libraries for data visualization: Matplotlib and Seaborn. Whether you are a budding Data Scientist, a researcher, or a student, being able to communicate insights through clear, professional charts is a non-negotiable skill. We start with the foundational "low-level" control of Matplotlib and move into the "high-level" statistical elegance of Seaborn. 🔍 What You’ll Learn Matplotlib Fundamentals: Understanding the Figure vs. Axes hierarchy. Customization: How to tweak colors, labels, legends, and annotations. Seaborn Essentials: Creating complex statistical plots (Violin, Heatmaps, FacetGrids) with just a few lines of code. Best Practices: Choosing the right chart type for your data (Comparison vs. Distribution vs. Relationship). https://lnkd.in/gk6btxj3