DrinkData’s Post

An in-process analytics database, DuckDB can work with surprisingly large data sets without having to maintain a distributed multiserver system. Best of all? You can analyze data directly from your Python app. #DataAnalytics #Databases #Python by Joab Jackson

It's a great time to be a python developer, duckdb, a DBMS coded in C++, designed to handle big datasets whilst being run on a single computer, can be installed with a quick pip install duckdb. "In 2021, H20.ai tested DuckDB in a set of benchmarks comparing the processing speed for various database-like tools popular in open source data science. The testers ran five queries across 10 million rows and nine columns (about 0.5GB). Duck completed the task in a mere two seconds. That was surprising for a database running on a single computer. Even more surprising, it chewed through 100 million rows (5GB) in 14 seconds. https://lnkd.in/gdjGbeg5

ICYMI, today was a huge day for Python and for Excel with the announcement of General Availability of Python in Excel. It's no surprise that security is a top priority: "The Python code runs in its own hypervisor isolated container using Azure Container Instances and secure, source-built packages from Anaconda, Inc. through a secure software supply chain." More: https://lnkd.in/gA2zdExZ #data #analytics #excel #python #anaconda

Big news for the data community! Microsoft has just launched Python in Excel, making it easier than ever to automate tasks and deepen our analyses directly within Excel. This powerful integration opens up new possibilities for data professionals to enhance productivity and unlock advanced insights! #PythonInExcel #DataAnalysis #Automation #Productivity

🚀 Python Series : pt 1🚀 As per the latest poll : Python won !! So here we go. Python series has been started from today ! It’s like 1 py post a day #pyprepseries Python is essential for data analysts for several reasons🚨🚨🚨 1. **Versatility**: Python offers a wide range of libraries and tools specifically designed for data analysis, such as Pandas, NumPy, and SciPy. These libraries provide powerful data manipulation, analysis, and visualization capabilities. 2. **Ease of Learning and Use**: Python is known for its simplicity and readability, making it accessible to beginners and experienced programmers alike. Its syntax resembles pseudocode, making it intuitive and easy to understand. 3. **Community Support**: Python has a vast and active community of developers and data analysts who contribute to its libraries and tools. This means that there is extensive documentation, tutorials, and online resources available to help users learn and solve problems. 4. **Integration**: Python seamlessly integrates with other technologies and tools commonly used in data analysis, such as SQL databases, Hadoop, Spark, and machine learning frameworks like TensorFlow and scikit-learn. 5. **Data Visualization**: Python provides powerful data visualization libraries like Matplotlib, Seaborn, and Plotly, which enable analysts to create informative and visually appealing plots and charts to communicate insights effectively. 👉 *Data Analyst Course Rode Map Follow me on https://lnkd.in/dX4MFB2d 🚨 For personal Guidance follow me on LinkedIn Madhuja Chatterjee #DataScience #DataAnalyst #DataAnalytics #DataDriven #DataInsights #Analytics #BigData #DataVisualization #Statistics #MachineLearning #DataMining #PythonDataAnalysis #DataSkills #DataAnalyticsLife #BusinessIntelligence #SQL #DataTools #dataanalysisskills Pdf credit and idea : Pushpraj Singh Rathod

Using Apache Spark with Python, emphasizing its role compared to Celery: 🚀 Python in Distributed Computing: Apache Spark vs. Celery Have you ever wondered if Apache Spark can handle distributed tasks in Python, like Celery? Let’s break down the differences and when to use each. Apache Spark with Python (PySpark) 🔹 Spark is a powerful framework for large-scale data processing. With PySpark, you can handle vast datasets and perform tasks like ETL, data analysis, and machine learning across clusters. 🔹 Spark is fault-tolerant and optimized for batch processing on distributed datasets, making it a go-to for big data solutions. Celery for Task-Based Workflows 🔹 Celery, on the other hand, is designed for task scheduling and asynchronous processing. Often paired with web apps, it’s perfect for jobs like sending notifications or running background tasks. 🔹 Unlike Spark, Celery is better for real-time, individual tasks rather than large-scale data processing. Key Takeaway ⭐Use Spark for big data processing, ETL, and machine learning pipelines. ⭐Use Celery for managing individual tasks, especially in web applications. #BigData #Python #ApacheSpark #Celery #DistributedComputing #DataScience #MachineLearning #WebDevelopment 🔗 https://lnkd.in/eHjFCzMv

Must-know Python concepts to help you get ready for a Data Engineering round!!! 👇 1. What are Python data types, and how do they differ in memory usage? 2. Explain list comprehensions and their benefits in Python. 3. How does Python’s memory management work, and what is the role of the garbage collector? 4. Describe the difference between *args and **kwargs in function arguments. 5. What is the Global Interpreter Lock (GIL), and how does it impact concurrency? 6. Explain the purpose of Python’s `map()`, `filter()`, and `reduce()` functions. 7. How does exception handling work in Python, and why is it important? 8. Describe Python decorators and common use cases for them. 9. What are generators, and how do they differ from regular functions? 10. Explain the difference between deep copy and shallow copy in Python. 11. How would you optimize Python code for performance? 12. What is the purpose of the `__init__()` method in Python classes? 13. Describe how you would manage and handle large datasets with Python. 14. Explain the role of virtual environments and package management in Python. 15. What are the benefits of using pandas for data manipulation in Python? Add more from your side!! #softwareengineering #dataengineering #bigdata #database #performance #distributedsystem #parallelcomputing #spark #python #datastructure #interview #hadoop #ETL #datascience #machinelearning #devops #apis #dataanalytics Zach Morris Sumit Mittal Shashank Databricks Snowflake Andreas Apache Spark

3 reasons you should learn to use Python for data analysis with Excel https://lnkd.in/gh8ktzEW PS: it is more than three reasons. money to program with python using data sheets as a structure data objects is a way to think completely differently and how to model your business. for getting changes your brain and VBA, was never an elegant tool for programming it was just a simple macro language. Combining tools allow people to build hybrid systems thick and scale in the cloud. It's not like we're asking you to learn Cobol 😉 #excel #gpt #python #apis

Microsoft Python in Excel is now available, combining the power of Python’s advanced analytics with Excel's ease of use, offering a new way for users to perform sophisticated data analysis directly within Excel.

Hello Pythonistas! Let's kickstart the week with some Python magic! 🐍🚀 Are you looking to level up your database connectivity skills? Check out this informative article on how to establish a connection with SQLite using Python. Learn how to create tables, perform CRUD operations, execute queries, work with transactions, and more. Take your Python programming game to the next level by mastering SQLite! https://lnkd.in/dHrc7CyC #Python #SQLite #DatabaseConnectivity #PythonProgramming