Pandas TypeError: Fixing Column Name Spaces

Hello Angel One I would like to report a potential edge-case issue in the historical candle API (getCandleData) that I’m facing while running a Python Smark SDK. Following Code is executed at 2026-02-25 10:29:02 Request: {'exchange': 'NSE', 'symboltoken': '99926000', 'interval': 'ONE_MINUTE', 'fromdate': '2026-02-25 10:25', 'todate': '2026-02-25 10:29'} Response: {'message': "From datetime can't be greater than current datetime", 'errorcode': 'AB1012', 'status': False, 'data': None} Angel One

Didn't know you could extract tables from a Word doc using Python until today. python-docx lets you loop through tables, pull cell data, and load it straight into a DataFrame. Spent some time cleaning it up — splitting on ':', transposing, fixing headers — but it worked. Also practiced groupby() and lambda functions inline. Small things but they make the code so much cleaner. Notebook here 👉 https://lnkd.in/dfTwrvqT #Python #Pandas #DataAnalysis #LearningInPublic

Just practiced Pandas and data cleaning hits different when you're working with real messy data. Covered data types, type conversion, handling missing values, replacing inconsistent entries, and using category dtype to save memory — FuelType column went from 11488 bytes to 1460 bytes just by changing the dtype. Notebook here 👉 https://lnkd.in/d3djYPvp #Python #Pandas #DataAnalysis #LearningInPublic

🚀 Day 51 of #100DaysOfCode ✅ Solved: 1689. Partitioning Into Minimum Number of Deci-Binary Numbers Today’s problem looked tricky at first, but the solution turned out to be beautifully simple once the pattern was understood. 🔎 Problem Insight: A deci-binary number contains only digits 0 or 1. To form a number like "82734", think about each digit: If a digit is 8, we need at least 8 deci-binary numbers contributing 1 at that position. If a digit is 3, we need at least 3 deci-binary numbers at that position. 👉 So the minimum number of deci-binary numbers required is simply: 📌 The maximum digit in the given number 💡 Example: Input: "82734" Output: 8 🧠 Python Code: Python Copy code class Solution: def minPartitions(self, n: str) -> int: return int(max(n)) ⏱ Time Complexity: O(N) 💾 Space Complexity: O(1) This problem is a great example of how understanding the pattern simplifies the solution dramatically. #LeetCode #ProblemSolving #Python #DataStructures #CodingJourney #100DaysOfCode

🐍 Day 60 — Bar Charts in Matplotlib Day 60 of #python365ai 📊 Bar charts compare categories. Example: plt.bar(["A", "B", "C"], [5, 7, 3]) plt.show() 📌 Why this matters: Bar charts are common in reports and dashboards. 📘 Practice task: Create a bar chart for three products. #python365ai #BarChart #DataAnalysis #Python

Understanding how data structures behave is crucial for writing reliable and scalable Python code. In my latest article, I explore the real difference between Lists and Tuples in Python and why choosing the right one can impact system stability, performance, and trust in real-world applications. From flexibility to stability, this article explains when data should evolve and when it must remain unchanged. Read the full article here: https://lnkd.in/gvQahst3 #Python #DataScience #Programming #Coding #PythonProgramming #SoftwareEngineering #TechLearning #Developers #DataEngineering #Analytics #MachineLearning #AI #TechCommunity #LearnToCode #CareerGrowth

Troubleshooting my way into Pandas! 🐼 Today, I took a deep dive into Python's most powerful data tool: pandas. Like every developer's journey, it started with a "ModuleNotFoundError," but that was just the first step! Key takeaways from my session: Installation: How to "invite pandas to the party" manually when it's not pre-installed. The DataFrame vs. df: I learned that DataFrame is the blueprint (the class), while df is the specific variable where we store our data. Series vs. DataFrames: A Series is a single column (1D), while a DataFrame is the entire tabular structure (2D). Case Sensitivity: A big lesson—pd.DataFrame() works, but pd.dataframe() does not! Next stop: Cleaning even messier datasets! 🚀 #Python #Pandas #DataScience #LearningJourney #DataAnalytics

Day 7 of my Python journey 🐍🔤 Behind-the-scenes string magic + variable naming rules unlocked today! Decoded: String comparison: Unicode truth revealed ord(): char → ASCII/Unicode number chr(): number → character (reverse magic!) Variable rules: legal names, reserved words Naming styles: snake_case, CamelCase, best practices Key takeaways: Strings compare by Unicode values, not "looks" 🤔 ord('A') = 65, chr(65) = 'A' → perfect pair! ✅ snake_case for Python variables = readability win Valid names prevent sneaky bugs 🐛 Practiced: Unicode explorers, safe variable creators, and style checkers. Code now speaks clearly! 🗣️ Next: Lists, tuples, and data structure adventures! 📚 Week 1 complete → Foundation rock solid! 🪨 #Python #Day7 #StringComparison #Unicode #OrdChr #VariableNaming #PythonRules #CodingJourney #100DaysOfCode #LearnInPublic #CodeNewbie #DeveloperJourney #Hyderabad #PracticeMakesProgress #PythonForBeginners

Some instructions are used more than once. When working with data, certain operations tend to repeat. Cleaning a value. Checking a condition. Transforming a piece of information. Applying the same rule across different parts of a dataset. Writing the same set of instructions every time would quickly make code longer and harder to follow. This is where functions come in. A function is simply a way of grouping a set of instructions under a name so that the same logic can be used again whenever it is needed. Instead of rewriting the steps, the program calls the function and runs those instructions again. For example: def check_pass(score): if score >= 50: return "Pass" return "Fail" Once defined, the same logic can be applied wherever it is needed. check_pass(72) check_pass(43) The instructions stay the same. Only the input changes. Functions don’t introduce new logic. They organize existing logic so it can be reused clearly and consistently. And in larger programs, that organization becomes just as important as the logic itself. Day 28 / 30. #30DaysOfDataScience #Python #Functions #ProgrammingLogic #LearningInPublic

Sigma ➕ Python Bennett Frohock walks through exactly how to set this up, from creating a stored procedure in Snowflake to enabling Python queries in Sigma's connection settings. Once it's running, the use cases get interesting fast. Think gradient boosting models, churn predictions, LLM API calls and geographic data binning, all inside a Sigma workbook. Read the full blog at the link below. https://lnkd.in/grZZa64v