Intro to Python Dask: Easy Big Data Analytics with Pandas!

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In this video, you will learn how to use Dask, a Python module that enables pandas code to run in parallel on your local machine or scaled out to multiple machines. No dataframe or numpy size limits and super-fast execution. Just pip install and go! It's that easy! Does it really work? Find out!

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See my Master Databricks and Apache Spark series:
  1. Bryan Cafferky
  2. Python
  3. pandas
  4. Dask
  5. Python programming
  6. data engineering
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Комментарии
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15:30 the rounding difference is probably because you use the full dataset in ddf and only a part in pdf. Very great introduction in Dusk! At the moment I am only working with numpy for data engineering (Deep Learning with Images). Would you say it makes sense to save images to pandas dataframes? It would probably make a lot of stuff easier and by using Dask even fast because of the parallelization.

arturkunz
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Thanks for awesome introduction of Python Dask

atanu
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how reliable it is to use it in production for data ingestion?

sawantamang
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When do you realize you have to leverage Dask on a DF - What error message would you gte?

KOMPAJAM
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Thanks for your video! I 've recently exploring DASK and realized that it only has read_sql_table, but no read_sql_query function. I used to read sql queries into python by using pyodbc/sqlalchemy, but it looks like it's not possible with DASK.

jamiew
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Do I need know python prior this course ?

I
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Great video as usual, thank you. But after about a week of hammering the subject, I could not load a data table from an Azure SQL database ☹️… back to pandas… (having to do loops to deal with the memory limits 🤦‍♂️)

ericxls