DASK: A Guide to Process Large Datasets using Parallelization | by Paras Varshney | Towards Data Science

If you are dealing with a large amount of data and you are worried that Pandas’ data frame is unable to load it or NumPy arrays get stuck in between and you even need a much better and parallelized solution for your data processing and training machine learning models then dask open up a solution to this problem. Before diving into that, let’s see what actually is dask?
Before diving-in deep, have you ever heard about Lazy-Loading? Check out how Vaex is dominating the market of loading huge datasets.
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Dask is an extremely efficient open-source project that uses existing Python Apis and knowledge structures that makes it straightforward to modify between Numpy, Pandas, Scikit-learn into their Dask-powered equivalents. Also, Dask’s schedulers scale to thousand-node clusters and its algorithms are tested on a number of the most important supercomputers within the world.
Does quality comes pre-installed inside your Anaconda but for pip you can get the complete one using this command:
Conda installation for Dask:
!conda install dask
pip installation for Dask:
!pip install “dask[complete]”
Dask helps to parallelize Arrays, DataFrames, and Machine Learning for dealing with a large amount of data as:
Arrays: Parallelized Numpy
# Arrays implement the Numpy APIimport dask.array as dax = da.random.random(size=(10000, 10000), chunks=(1000, 1000))x + x.T - x.mean(axis=0)
DataFrame: Parallelized Pandas
# Dataframes implement the Pandas APIimport dask.dataframe as dddf = dd.read_csv('financial_dataset.csv')df.groupby(df.amount).balance.sum()
Machine Learning: Parallelized Scikit-Learn
# Dask-ML implements the Scikit-Learn APIfrom dask_ml.linear_model import LogisticRegressionlr = LogisticRegression()lr.fit(train, test)
Most of the Dask API is very similar to the Pandas API so you can directly use the data frames of the Pandas in Dusk with a very similar command. To generate a discrete data frame you can just simply call the `read_csv()` method in the same way you used to call in Pandas or can easily convert a Pandas DataFrame into a Dask DataFrame.
import dask.dataframe as ddfdd = ddf.from_pandas(df, npartitions=N)
For the following benchmarking the machine used had a standard 4-core processor which stays standard for testing both frameworks.
I have done a very simple yet interesting benchmark to show how fast is Dask DataFrame as compared to a traditional Pandas DataFrame for reading a dataset from a .csv file having 5 million records.
Results: To read a 5M data file of size over 600MB Pandas DataFrame took around 6.2 seconds whereas the same task is performed by Dask DataFrame in much much less than a second time due to its impressive parallelization capabilities.
Note: This test was done on a small dataset, but as soon as the size of data increases, this time difference in reading data gets exponentially high.
You can use the code below to alter the benchmarking for a bigger dataset.
In this Benchmark, I generated a 1Trillion sized array of random numbers using both Numpy Array as well as Dask Array.
Result: As expected the results were pretty obvious as Numpy Array took a bit less than 8 seconds to compute whereas the Dask Array took negligible time!
You can try out the same benchmark using the code below
Dask helps in doing data analysis faster because it parallelizes the existing frameworks like Pandas, Numpy, Scikit-Learn, and process data parallelly using the full potential of your machine’s CPU. You can try experimenting with the amazing features of Dask here.
The combination of Lazy Loading with Parallel Processing is really a deadly combination and helps you to utilize the full potential of your system whenever required, to know more you can read this article about Vaex.