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Kodey
เข้าร่วมเมื่อ 31 มี.ค. 2020
Understand data technologies without the long, boring course. My courses have helped 60,000 students to understand Apache Spark & Python in under an hour.
🔥 In-Depth Apache Spark Tutorials: Dive deep into the world of Apache Spark, the cutting-edge technology powering big data processing. From the fundamentals to advanced techniques, we've got you covered.
🐍 Python for Data Engineers: Master the art of using Python to manipulate and analyze data. Our tutorials will take you from a Python novice to a data engineering pro.
🎓 Comprehensive Courses: Explore structured courses designed to help you build a solid foundation and advance your data engineering career.
🔥 In-Depth Apache Spark Tutorials: Dive deep into the world of Apache Spark, the cutting-edge technology powering big data processing. From the fundamentals to advanced techniques, we've got you covered.
🐍 Python for Data Engineers: Master the art of using Python to manipulate and analyze data. Our tutorials will take you from a Python novice to a data engineering pro.
🎓 Comprehensive Courses: Explore structured courses designed to help you build a solid foundation and advance your data engineering career.
Learn Data Analytics with Polars (Python) in just 2 hours!
Download the file here: drive.google.com/file/d/1Da--emUZ9DQuVa8mN0BLpolJHwtPUtrc/view?usp=sharing
Please use pip install polars==0.17.3 to ensure we are using the same version
Welcome to our exciting course. In this fast-paced and engaging learning experience, I'll take you on a journey through the incredible world of data analysis using the Polars library in Python.
Polars is a high-performance data manipulation library that empowers you to ingest, clean, aggregate, and manipulate data like a pro. Whether you're a seasoned Python developer or just starting out, this course is designed to equip you with the essential skills to derive amazing insights from your data.
Key Course Highlights:
Data Ingestion: Learn how to efficiently load data from various sources, making it easy to work with diverse datasets.
Data Cleaning: Discover techniques to clean and preprocess your data, ensuring it's ready for analysis.
Data Aggregation : Master the art of aggregating and summarizing data, uncovering trends and patterns that drive decision-making.
Data Manipulation: Become proficient in data transformation and manipulation, enabling you to answer complex questions with ease.
No Prior Experience Required: While some minor Python experience is beneficial, it's not a prerequisite. We'll start from the basics and guide you through each step.
By the end of this course, you'll have the confidence to tackle real-world data analysis projects using Polars in Python. Whether you're a data enthusiast, a student, a professional, or anyone seeking to harness the power of data, this course is your gateway to unlocking incredible insights.
Join me on this journey to become a data analysis maestro, and let's embark on a data-driven adventure together. Enrol now and start your path to mastering data analysis with Polars!
Please use pip install polars==0.17.3 to ensure we are using the same version
Welcome to our exciting course. In this fast-paced and engaging learning experience, I'll take you on a journey through the incredible world of data analysis using the Polars library in Python.
Polars is a high-performance data manipulation library that empowers you to ingest, clean, aggregate, and manipulate data like a pro. Whether you're a seasoned Python developer or just starting out, this course is designed to equip you with the essential skills to derive amazing insights from your data.
Key Course Highlights:
Data Ingestion: Learn how to efficiently load data from various sources, making it easy to work with diverse datasets.
Data Cleaning: Discover techniques to clean and preprocess your data, ensuring it's ready for analysis.
Data Aggregation : Master the art of aggregating and summarizing data, uncovering trends and patterns that drive decision-making.
Data Manipulation: Become proficient in data transformation and manipulation, enabling you to answer complex questions with ease.
No Prior Experience Required: While some minor Python experience is beneficial, it's not a prerequisite. We'll start from the basics and guide you through each step.
By the end of this course, you'll have the confidence to tackle real-world data analysis projects using Polars in Python. Whether you're a data enthusiast, a student, a professional, or anyone seeking to harness the power of data, this course is your gateway to unlocking incredible insights.
Join me on this journey to become a data analysis maestro, and let's embark on a data-driven adventure together. Enrol now and start your path to mastering data analysis with Polars!
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If the 'theory' of causality is correct, then every moment of existence is both a 'cause' and and 'effect'. Simultaneously! So, even if this 'theory' is correct (it ain't), what is the point? You cannot identify any distinction between a 'cause' and an 'effect' because(?) there is none. So again, any essential distinction between the two is illusory, but it is pragmatic under certain circumstances/Perspectives. Like making the kiddies believe that a demon will gobble them up if they go down the old abandoned mine. The illusion makes their lives a bit more comfortable. A good scientific reason why 'causality' is not possible is; "The Laws of Nature are not rules controlling the metamorphosis of what is, into what will be (ie; Karma). They are descriptions of patterns that exist, all at once... " - Genius; the Life and Science of Richard Feynman All 'eternity' at once; Here! Now!! Reality is not linear, it is Holistic! ;)
You can use the same version by using: pip install polars==0.17.3. This is the version from April 2023, so there will be some minor changes to syntax since then. The majority of commands will still work, you may need to do some minor tweaking in other areas
Hello, I am unable to run this as you did: def calculate_bytes(row): try: dlbytes = int(row[3]) ulbytes = int(row[4]) return dlbytes + ulbytes except: return 1 df.with_columns( df.apply(calculate_bytes, return_dtype=pl.Int64)).head(5)
Hey there, I have run this in my envvironment & it is working. However, if you install the version of Polars that I used in the course, you should be good to go. They do make minor syntax changes often, but if you learn the core fundamentals here, you'll be able to adjust your code easily going forward! You can use the same version by using: pip install polars==0.17.3 - this is the version from April 2023, so there will be some minor changes to syntax since then
I like your approach.
Thank you for this video.
Have you thought of releasing new videos more often?
Can we become friends?
Your video made my day!
Keep on a good work!
Rare quality content nowadays.
Good luck with your channel!
You should be proud of yourself.
How much would it take to advertise on your channel?
Would you be interested in partnership?
If you could improve the sound a bit, would be nice.
I will put this on repeat!
Greetings from Miami!
What software do you use to edit your videos?
Nice, keep posting.
Can’t get any better!
Let time go by and make your channel thrive!
Brings up old memories.
When to expect a new video from you?
Can you share your e-mail?
Waiting for a new video!
Best wishes from Nebraska!
It’s important to have clear users before creating a dashboard (or its for yourself to answer questions quickly). Otherwise, it’s a waste of time.
🔥🔥🔥
great vid
very informative, thanks
th-cam.com/video/kFrgbC8k5KM/w-d-xo.html
This should always be the first step, and if possible include a data scientist in the creation of a ROI valuation (before work begins - to not waste time).
Agree. Change (& budget) comes from the top. Breaking down data silos is super hard without a shared data infrastructure & OKRs.
To manage data is very annoying in my POV.