This repository contains the slides, exercises, and answers for Python and Dask: Scaling the Dataframe. The goal of this tutorial is to teach you, someone with experience working with tabular data in pandas, to scale to problems that do not fit in memory on a single machine.
Python's most popular data science libraries—pandas, numpy, and scikit-learn—were designed to run on a single computer, and in some cases, using a single processor. Whether this computer is a laptop or a server with 96 cores, your compute and memory are constrained by the size of the biggest computer you have access to.
In this course, you'll learn how to use Dask, a Python library for parallel and distributed computing, to bypass this constraint by scaling our compute and memory across multiple cores. Dask provides integrations with Python libraries like pandas, numpy, and scikit-learn so you can scale your computations without having to learn completely new libraries or significantly refactoring your code.
Workshop assumes that participants have intermediate-level programming ability
in Python and experience using Pandas. Participants should know the difference
between a dict, list, and tuple. Familiarity with control-flow
(if/else/for/while) and error handling (try/catch) are required.
No statistics background is required.
If you have a stable Internet connection and the free Binder service isn't under too much load, the easiest way to interactively run the slides and try the exercises is to click the Binder badge (make sure you open in a new window). Keep in mind that Binder aggresively shuts down idle instances so you'll need to refresh the link if you're idle for too long.
You may view the HTML versions of slides and the answers directly in your browser on Github though you will not be able to run them interactively:
- Lesson 1 - Introduction to Dask
- Lesson 2 - Dask DataFrames
- Lesson 3 - Profiling with the Dask Dashboard
- Lesson 4 - Dask Schedulers
If you're taking the course, want to follow along with the slides and do the
exercises, and may not have Internet access, download and
install the Anaconda Python 3 distribution and conda package manager
ahead of time:
https://www.anaconda.com/download/
Download the latest version of the course materials here.
Alternatively, you may clone the course repository using git:
$ git clone --depth 1 https://github.com/dgerlanc/dask-scaling-dataframe.git
The remainder of the installation requires that you use the command line.
To complete the course exercises, you must use conda to install the
dependencies specified in the environment.yml file in the repository:
$ conda env create -f environment.yml
This will create an conda environment called dask-scaling-the-dataframe which may be
"activated" with the following commands:
- Linux and Mac:
conda activate dask-scaling-the-dataframe - Windows:
activate dask-scaling-the-dataframe
Once you've activated the environment your prompt will probably look something like this:
(dask-scaling-the-dataframe) $We won't show the environment name to the left of the prompt for subsequent shell commands.
The entire course is designed to use jupyter notebooks. Start the
notebook server to get started:
$ jupyter labOptional
The Dask JupyterLab extension allows you to view the distributed scheduler dashboard from within JupyterLab. It is not required to complete the course, though you may find it useful.
If you want to enable the Dask extension for JupyterLab run the following before you start JupyterLab:
$ jupyter labextension install dask-labextension
$ jupyter serverextension enable dask_labextension