Suman Saha’s Post

𝐌𝐞𝐬𝐬𝐲 𝐜𝐨𝐥𝐮𝐦𝐧 𝐧𝐚𝐦𝐞𝐬. 𝐄𝐱𝐭𝐫𝐚 𝐬𝐩𝐚𝐜𝐞𝐬. 𝐌𝐢𝐱𝐞𝐝 𝐔𝐏𝐏𝐄𝐑𝐂𝐀𝐒𝐄 𝐚𝐧𝐝 𝐥𝐨𝐰𝐞𝐫𝐜𝐚𝐬𝐞. 𝐒𝐨𝐮𝐧𝐝 𝐟𝐚𝐦𝐢𝐥𝐢𝐚𝐫? These 12 Python string methods can fix all of that — sometimes in just one line of code. While learning Python for data analytics, I realized that small string methods like .𝐬𝐭𝐫𝐢𝐩(), .𝐥𝐨𝐰𝐞𝐫(), .𝐫𝐞𝐩𝐥𝐚𝐜𝐞(), .𝐬𝐩𝐥𝐢𝐭(), 𝐚𝐧𝐝 .𝐣𝐨𝐢𝐧() are extremely useful for cleaning text data before analysis. Strong fundamentals make advanced work easier. #Python #DataAnalytics #DataCleaning #PythonForDataAnalysis #LearningPython #AspiringDataAnalyst #PythonTips #DataScience #CodingJourney

INSTEAD OF WASTING TIME AND TRYING TO GET FIGURES. WHY NOT USING CODE?? Sometimes, lecturers or organizations need to generate different sets of questions for multiple candidates, especially when working with matrices. However, this often requires a lot of manual effort and can be time-consuming. Why not simplify the process using NumPy in Python? With just a few lines of code, you can easily generate multiple variations of matrix-based questions efficiently and save valuable time. #randint is an inbuilt function of the random module of numpy #Syntax: np.random.randint(start, stop (rows, columns)) a=np.random.randint(2,30, (3,3)) b=np.random.randint(2,30, (3,3)) c=np.random.randint(2,30, (3,3)) d=np.random.randint(2,30, (3,3)) e=np.random.randint(2,30, (3,3)) #DataScience #Python #NumPy #Education #Automation

🚀 ✨ 𝐃𝐀𝐘 9: 𝐖𝐎𝐑𝐊𝐈𝐍𝐆 𝐖𝐈𝐓𝐇 𝐒𝐓𝐑𝐈𝐍𝐆𝐒 ✨ Today, I explored another important concept in Python — 💻 𝐒𝐭𝐫𝐢𝐧𝐠𝐬 and how to manipulate text data. 🔹 📘 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐒𝐭𝐫𝐢𝐧𝐠𝐬? Strings are sequences of characters used to store and work with 𝐭𝐞𝐱𝐭 𝐝𝐚𝐭𝐚. 🔹 ⚙️ 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 ✔️ 𝐒𝐭𝐫𝐢𝐧𝐠 𝐢𝐧𝐝𝐞𝐱𝐢𝐧𝐠 & 𝐬𝐥𝐢𝐜𝐢𝐧𝐠 ✔️ Common methods like 𝐮𝐩𝐩𝐞𝐫(), 𝐥𝐨𝐰𝐞𝐫(), 𝐬𝐭𝐫𝐢𝐩() ✔️ 𝐂𝐨𝐧𝐜𝐚𝐭𝐞𝐧𝐚𝐭𝐢𝐨𝐧 & 𝐟𝐨𝐫𝐦𝐚𝐭𝐭𝐢𝐧𝐠 🔹 🧠 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Strings are everywhere — from 𝐮𝐬𝐞𝐫 𝐢𝐧𝐩𝐮𝐭 to 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠, making them a must-know concept. 💡 𝐒𝐦𝐚𝐥𝐥 𝐬𝐭𝐫𝐢𝐧𝐠 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 = 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐬! 💪 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐜𝐨𝐦𝐟𝐨𝐫𝐭𝐚𝐛𝐥𝐞 with handling text in Python! 🚀 𝐊𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐤𝐞𝐞𝐩 𝐠𝐫𝐨𝐰𝐢𝐧𝐠! #Python #Day9 #CodingJourney #Strings #LearningPython #Programming #Consistency 🚀

6 Python Libraries To Boost Your Workflow 1. Tenacity: Offers retry strategies for script resilience. 2. Polars: A fast DataFrame library for large datasets. 3. Shelve: Creates persistent, file-backed dictionaries. 4. Rich: Beautifies terminal output with tables and progress bars. 5. Pendulum: Simplifies datetime management with UTC standards. 6. Inline-Snapshot: Records and updates test values in code. Small tools like these often make a big difference—sometimes it's the unexpected library that solves your biggest headache. New to Python? Learn the basics (nl) 👉 https://lnkd.in/emBgb9gf #GeoICT #Python #GIS #SpatialData #OpenSource

I used to look at charts and graphs without truly understanding them. Today, I can explain what the data is actually saying. 📊 I recently worked on a Data Visualization project using Python, where I explored how raw data can be transformed into meaningful insights. At first, it felt confusing — so many libraries, so many plots. But step by step, I started understanding the purpose behind each visualization. Now I can: ✔ Identify patterns in data ✔ Understand distributions ✔ Analyze relationships between variables This project helped me realize that data is not just numbers — it tells a story. And visualization is the language that helps us understand that story. 🔗 Project Link: https://lnkd.in/d6xcbmqs #DataScience #Python #DataAnalytics #LearningJourney #Visualization

📈 Matplotlib Explained (Visualization Library) Matplotlib is used to create basic plots. 🔹 Important Functions: ✔ plot() → Line chart ✔ bar() → Bar chart ✔ scatter() → Scatter plot ✔ hist() → Histogram ✔ title() → Add title ✔ xlabel(), ylabel() → Axis labels 💡 Visualization helps to understand data easily. #Matplotlib #DataVisualization #Python

𝐖𝐡𝐲 𝐏𝐲𝐭𝐡𝐨𝐧 𝐈𝐬 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐅𝐮𝐧 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐖𝐨𝐫𝐤👨💻 Recently started using Python for simple data tasks, and one thing I noticed quickly — it makes working with data much easier than doing everything manually. Even basic things like loading a dataset, checking missing values, or calculating averages become much faster with libraries like pandas. Today I practiced reading a dataset, exploring columns, and getting quick summary statistics. Small steps, but it’s interesting to see how quickly you can start extracting useful information from raw data. Slowly getting more comfortable using Python as a tool for analysis rather than just writing code. #Python #DataAnalytics #LearningByDoing #FinalYear

𝐀 𝐬𝐦𝐚𝐥𝐥 𝐭𝐡𝐢𝐧𝐠 𝐢𝐧 𝐩𝐚𝐧𝐝𝐚𝐬 𝐭𝐡𝐚𝐭 𝐬𝐚𝐯𝐞𝐝 𝐚 𝐥𝐨𝐭 𝐨𝐟 𝐭𝐢𝐦𝐞 While working with a dataset in Python today, I came across something simple but very useful — value_counts() in pandas. Instead of writing multiple filters or loops just to see how frequently different values appear in a column, value_counts() gives a quick frequency breakdown instantly. For example, if you want to see how many records belong to each category, city, or product type, one line can show the entire distribution. It’s a small function, but it makes exploring a new dataset much faster. Slowly realizing that data analysis is really about knowing these small but powerful tools. #Python #Pandas #DataAnalytics #LearningJourney

✅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

Today I implemented Serialize and Deserialize Binary Tree in Python. The goal of the problem is to convert a binary tree into a string so it can be stored or transferred, and later reconstruct the exact same tree from that string. I used a BFS (level order traversal) approach for this. Idea: • Traverse the tree level by level using a queue. • Store node values in a list. • For missing children, store a special marker (#) so the structure of the tree is preserved. • Join the list into a single string. For deserialization, the process is reversed: • Split the string back into values. • Rebuild the tree using a queue. • Attach left and right children in the same order they were stored. What I liked about this problem is that it shows how important it is to preserve structure, not just values. Without storing null nodes, reconstructing the same tree would be impossible. Time Complexity: O(N) Space Complexity: O(N) Problems like this are a good reminder that tree problems often combine traversal, data representation, and careful reconstruction logic. #DSA #BinaryTree #Python #Algorithms #CodingInterview