Introduction to Dask

Dask is a flexible library for parallel computing in Python. It is designed to scale from a single computer to a large cluster, making it popular among data scientists and engineers who need to handle large datasets or perform complex computations.

Key Features of Dask

• Parallel computing: Execute tasks in parallel, efficiently using multiple cores or machines.
• Scalability: Works seamlessly on small datasets on a laptop and can scale up to large datasets on a cluster.
• Integrations: Integrated with popular Python libraries such as NumPy, pandas, and scikit-learn.

Useful Dask APIs and Examples

Dask Arrays

  import dask.array as da

  # Create a large random Dask array
  x = da.random.random((10000, 10000), chunks=(1000, 1000))

  # Compute the mean along an axis
  result = x.mean(axis=0).compute()
  print(result)

Dask DataFrame

  import dask.dataframe as dd

  # Create a Dask DataFrame from a CSV file
  df = dd.read_csv('large_dataset.csv')

  # Perform operations on the Dask DataFrame
  df_filtered = df[df['column'] > 0]
  df_grouped = df_filtered.groupby('category').sum().compute()
  print(df_grouped)

Dask Delayed

  from dask import delayed

  # Define a function to be executed in parallel
  @delayed
  def add(x, y):
      return x + y

  # Create delayed objects
  result = add(1, 2)

  # Compute the result
  final_result = result.compute()
  print(final_result)

Dask Bag

  import dask.bag as db

  # Create a Dask Bag from a list of elements
  bag = db.from_sequence([1, 2, 3, 4, 5])

  # Perform a map operation on the Dask Bag
  result = bag.map(lambda x: x * 2).compute()
  print(result)

Dask ML

  import dask_ml

  from dask_ml.linear_model import LogisticRegression
  from dask_ml.datasets import make_classification

  # Create a synthetic dataset
  X, y = make_classification(n_samples=10000, n_features=20, chunks=1000)

  # Create and train a Dask-ML model
  clf = LogisticRegression()
  clf.fit(X, y)

  # Make predictions
  predictions = clf.predict(X).compute()
  print(predictions)

Application Example with Multiple APIs

Let’s put together an application that utilizes multiple Dask APIs.

  import dask.array as da
  import dask.dataframe as dd
  import dask.bag as db
  from dask import delayed
  import dask_ml
  from dask_ml.linear_model import LogisticRegression
  from dask_ml.datasets import make_classification

  # Dask Array operations
  x = da.random.random((10000, 10000), chunks=(1000, 1000))
  mean_result = x.mean(axis=0).compute()

  # Dask DataFrame operations
  df = dd.read_csv('large_dataset.csv')
  df_filtered = df[df['column'] > 0]
  df_grouped = df_filtered.groupby('category').sum().compute()

  # Dask Bag operations
  bag = db.from_sequence([1, 2, 3, 4, 5])
  bag_result = bag.map(lambda x: x * 2).compute()

  # Dask Delayed operations
  @delayed
  def add(x, y):
      return x + y
  delayed_result = add(1, 2).compute()

  # Dask ML operations
  X, y = make_classification(n_samples=10000, n_features=20, chunks=1000)
  clf = LogisticRegression()
  clf.fit(X, y)
  ml_predictions = clf.predict(X).compute()

  # Print all results
  print("Dask Array Mean Result:", mean_result)
  print("Dask DataFrame Grouped Result:", df_grouped)
  print("Dask Bag Result:", bag_result)
  print("Dask Delayed Result:", delayed_result)
  print("Dask ML Predictions:", ml_predictions)

By combining multiple Dask APIs, you can build powerful and scalable data-processing pipelines that handle a variety of tasks efficiently.

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