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Parallel Programming with Dask in Python

MittelSchwierigkeitsgrad

Aktualisierte 04.2024

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

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PythonProgramming4 Std.15 Videos51 Übungen4,150 XP4,271Leistungsnachweis

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Kursbeschreibung

Use Parallel Processing to Speed Up Your Python Code

With this 4-hour course, you’ll discover how parallel processing with Dask in Python can make your workflows faster.

When working with big data, you’ll face two common obstacles: using too much memory and long runtimes. The Dask library can lower your memory use by loading chunks of data only when needed. It can lower runtimes by using all your available computing cores in parallel. Best of all, it requires very few changes to your existing Python code.

Analyze Big Structured Data Using Dask DataFrames

In this course, you use Dask to analyze Spotify song data, process images of sign language gestures, calculate trends in weather data, analyze audio recordings, and train machine learning models on big data.

You’ll start by learning the basics of Dask, exploring how parallel processing in Python can speed up almost any code. Next, you’ll explore Dask DataFrames and arrays and how to use them to analyze big structured data.

Train machine learning models using Dask-ML

As you progress through the 51 exercises in this course, you’ll learn how to process any type of data, using Dask bags to work with unstructured and structured data. Finally, you’ll learn how to use Dask in Python to train machine learning models and improve your computing speeds.

Voraussetzungen

Data Manipulation with pandas Python Toolbox

1

Lazy Evaluation and Parallel Computing

Kapitel starten

Introduction to Dask

Lazy evaluation

Delaying functions

Task graphs and scheduling methods

What are the different schedulers?

Plotting the task graph

Building delayed pipelines

Analyzing songs on Spotify

How danceable are songs these days?

Most popular songs

2

Parallel Processing of Big, Structured Data

Kapitel starten

Dask arrays

Dask array chunksizes

Loading and processing photos

An image processing pipeline

Dask DataFrames

Creating Dask dataframes from CSVs

Read Dask DataFrames from Parquet

Summertime grooves

Multidimensional arrays

Exploring HDF5 files

Dask arrays from HDF5 datasets

Dask arrays from Zarr datasets

Exploratory data analysis with xarray

Monthly mean temperatures

Calculating the trend in European temperatures

3

Dask Bags for Unstructured Data

Kapitel starten

Introduction to Dask bags

Creating a Dask bag

Creating a bag from saved text

String operations

Dask bag operations

Loading JSON data

Filtering Dask bags

Chaining operations

Converting unstructured data to a DataFrame

Restructuring a dictionary

Converting to DataFrame

Using any data in Dask bags

Loading wav data

Constructing custom Dask bags

Processing unstructured audio

4

Dask Machine Learning and Final Pieces

Kapitel starten

Using processes and threads

Which scheduler will be used?

Clusters and clients

Training machine learning models on big datasets

Using Dask to train a linear model

Making lazy predictions

Preprocessing big datasets

Lazily transforming training data

Lazy train-test split

Parallel Programming with Dask in Python

Kurs
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