Data Wrangling with PySpark for Data Scientists Who Know Pandas – Andrew Ray
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“Data scientists spend more time wrangling data than making models. Traditional tools like Pandas provide a very powerful data manipulation toolset. Transitioning to big data tools like PySpark allows one to work with much larger datasets, but can come at the cost of productivity.
In this session, learn about data wrangling in PySpark from the perspective of an experienced Pandas user. Topics will include best practices, common pitfalls, performance consideration and debugging.
Session hashtag: #SFds12
Learn more:
Developing Custom Machine Learning Algorithms in PySpark
https://databricks.com/blog/2017/08/30/developing-custom-machine-learning-algorithms-in-pyspark.html
Introducing Pandas UDF for PySpark
https://databricks.com/blog/2017/10/30/introducing-vectorized-udfs-for-pyspark.html
Best Practices for Running PySpark
https://databricks.com/session/best-practices-for-running-pyspark
Session Overview:
– Why?
– What Do i get with pyspark?
– Primer
– Important Concepts
– Architecture
– Setup
– Run
– Load CSV
– View Dataframe
– Rename Columns
– Drop Column
– Filtering
– Add Column
– Fill Nulls
– Aggregation
– Standard Transformations
– Keep it in the JVM
– Row Conditional Statements
– Python when Required
– merge/join dataframes
– Pivot table
– Summary Statistics
– histogram
– SQL
– Make sure to
– Things not to do
– If things go wrong
– Thank you
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Read more here: https://databricks.com/product/unified-data-analytics-platform
Connect with us:
Website: https://databricks.com
Facebook: https://www.facebook.com/databricksinc
Twitter: https://twitter.com/databricks
LinkedIn: https://www.linkedin.com/company/databricks
Instagram: https://www.instagram.com/databricksinc/
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he provided with a really good comparison between the two!
Too quiet please fix
Just use koalas.
Which is better in databricks environment?? Python or R or SQL..reply in comments
great presentation!
For those who search a tutorial: https://www.rrighart.com/pyspark/pyspark-in-the-context-of-retail . Disclaimer: I am the author ;-).
Best videos for beginners to start with!
Thank you for such a great presentation
Would this be a good tool for combining large numbers of csvs into a single dataframe quickly and then performing manipulations on that dataframe before outputting a single csv?
19:12, now pandas has an SQL support
great tech video, but volume really …
Thank you for such a great presentation for beginners!
Whats with the volume?
Very good presentation! Thanks
This a great video. Exactly what I'm looking for thanks very much.
Really nice how we see pandas and pyspark functions side-by-side!
7:49
Volume is low! 🙁
Cool talk and key differences nicely illustrated.
LOL good presentation, but unprepared for the Q &A
Must watch Q n A session in the end. I loved it.
by just downloading and writing this code it will not work. You have to create a session.
Hey Andrew could you send me your Github link