Delta Lake’s Post

🔍 Diving Deeper into Data with Pandas' loc() Function! 📊 Today, I explored the power of loc() in Pandas to access specific parts of a DataFrame quickly and intuitively. Whether retrieving rows, specific columns, or filtering data by condition, loc() allows for an efficient approach to dataset manipulation in Python! 🚀 Here’s what I learned: Targeted Access: Select rows/columns by labels. Flexible Slicing: Retrieve custom ranges in rows/columns. Selective Filtering: Access data based on specific conditions. Practical use of these capabilities enhances data handling and makes the entire process smoother and faster! 🔥 #DataScience #Python #Pandas #locFunction #DataAnalysis #MachineLearning #DataVisualization #CodingJourney #LinkedInLearning #TechCommunity #100DaysOfCode

When functions modify DataFrames directly, they can affect other parts of your code that need the original data. For instance, in the example below, the maximum humidity value is no longer 62.3 as it was in the original data. To prevent such side effects, create a copy of the DataFrame before applying modifications. This way, you maintain access to both the original and transformed data. 📕 Dive deeper into the post: https://bit.ly/4dZPlhV #pandas #python #datascience

Create a copy of the DataFrame before applying modifications. This way, you maintain access to both the original and transformed data.

#day56 of #90daysofcodechallenge Day 56 Learnings: 📌Learnt about four Collection Data Types in Python and it's different methods. 📌Four Collection Data Types: 1)List - ordered, mutable(can be change), allow duplicate members 2)Tuple - ordered, immutable(can't be change), allow duplicate members 3)Set - unordered, immutable, unindexed, no duplicate members Set items are unchangeable, but we can remove/add items whenever we want 4)Dictionary - ordered, mutable, no duplicate members #JavaProgramming #DSACodingChallenge #CodingJourney #LinkedInCodingChallenge #day56

I made Hal9 take the Maven Analytics Coffee Challenge! -- Hal9 is like #midjourney for #dataanalytics, it enables business users to generate simple data reports in seconds. Would love to see this entry discussed! https://lnkd.in/gUpE9g-D The best part, Hal9 reports are generated with the best #datascience tools like #pandas, #python, #streamlit, and #plotly which then data teams can validate or improve, the future is wild!

I have just submitted my reproduction of Cara Thompson's work, #BarBeRight, to the plotnine contest. Here is the link to the tutorial: https://lnkd.in/gBtktGsR Credit goes to Cara Thompson for her insightful tutorials and superb work on Data Visualization. #plotnine #datascience #rstats #python

HoloViews 1.19.0 adds support for Bars plot with continuous axis. I.e. you can now display timeseries data in Bars plot with axes nicely linked across plots. This change also enables users of dependent libraries like hvPlot and Polars to use this new exciting feature. #python #dataviz #datascience #analytics

Day 85 of #365daysofml "Hello LinkedIn community" Topic covered. 🔽 🔧 Data Structures and Algorithms (DSA): Strings: Covered the basics of string declaration, initialization, user input, built-in functions, the + operator, and string updating. Challenge: Successfully tackled a challenging problem on scoring after flipping a matrix. It was a great exercise in applying my knowledge to a real-world problem! 🔍 Python Concepts: Loops: Mastered while loops and applied them in various basic problems. #machinelearning #cppprogramming #computerscience #pyhtonprogramming

What do we get with txtai out of the box? txtai vector indexes use SQLite + Faiss by default. This enables search with SQL and dynamic columns. Results are standard Python dictionaries and that allows direct integration with Pandas/Polars DataFrames. See how this compares to LangChain: https://lnkd.in/esX88rPR

💡 Day 53 of #100DaysOfProgrammingChallenge 🚀 Challenge Accepted: LeetCode Problem #2878 - Get the Size of a DataFrame 📊 Problem Breakdown: Input: A DataFrame called players. Output: A list containing the DataFrame's dimensions, i.e., [number of rows, number of columns]. Solution Approach: Access DataFrame Shape: Use the .shape attribute of the DataFrame to get its row and column count. Convert to List: Convert this tuple to a list, so the result is compatible with the expected format. Return Result: Return the list representing the DataFrame's size. Example: Input: A DataFrame players with 5 rows and 3 columns. Output: [5, 3] Key Insight: This challenge emphasizes the basics of DataFrame manipulation in Python, especially using Pandas to extract essential information. Perfect for those honing their data handling skills in Python! #LeetCode #Pandas #DataScience #Python #DataFrames #100DaysOfCode #ProgrammingChallenge