Data Wrangling with PySpark for Data Scientists Who Know Pandas - Andrew Ray

"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

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Видео Data Wrangling with PySpark for Data Scientists Who Know Pandas - Andrew Ray канала Databricks