Dask Parallel Computing

Parallel and distributed computing with Dask for scaling Pandas and NumPy operations to larger datasets and clusters.

Description

This project demonstrates Dask, a library for parallel computing in Python. It covers Dask arrays, DataFrames, delayed computations, distributed computing, and scaling workflows. Perfect for working with larger-than-memory datasets and parallel processing.

Features

Core Features

• Parallel arrays and DataFrames
• Delayed and bag computations
• Distributed computing
• Task scheduling
• Memory-efficient operations

Advanced Features

• Dask Bags for unstructured data (JSON, text, logs)
• Advanced DataFrame operations (joins, window functions, time series)
• Machine learning with parallel training
• Performance profiling and optimization
• Complex data transformations
• Multi-file parallel processing
• Time series resampling and rolling operations
• Hyperparameter tuning with distributed computing

Technologies

• Python
• Dask
• Pandas
• NumPy
• Jupyter Notebook

Difficulty Level

Intermediate

Installation

1. Install the required packages:

pip install -r requirements.txt

2. Launch Jupyter Notebook:

jupyter notebook

3. Open the notebooks in the notebooks/ directory to explore the examples.

Project Structure

dask-parallel/
├── README.md
├── requirements.txt
├── .gitignore
├── notebooks/
│   ├── 01_dask_arrays.ipynb
│   ├── 02_dask_dataframes.ipynb
│   ├── 03_delayed_computations.ipynb
│   ├── 04_distributed_computing.ipynb
│   ├── 05_task_scheduling.ipynb
│   ├── 06_dask_bags.ipynb
│   ├── 07_advanced_dataframes.ipynb
│   └── 08_dask_ml.ipynb
├── scripts/
│   ├── parallel_processing.py
│   ├── memory_efficient_ops.py
│   ├── distributed_workflow.py
│   ├── performance_profiling.py
│   ├── advanced_data_processing.py
│   └── generate_advanced_data.py
└── data/
    └── (generated data files)

Usage Examples

Dask Arrays

import dask.array as da

# Create a large array
x = da.random.random((10000, 10000), chunks=(1000, 1000))
result = (x + 1).sum()
print(result.compute())

Dask DataFrames

import dask.dataframe as dd

# Read large CSV file
df = dd.read_csv('data/large_file.csv')
result = df.groupby('column').sum().compute()

Delayed Computations

from dask import delayed

@delayed
def process_data(x):
    return x * 2

results = [process_data(i) for i in range(10)]
final = sum(results)
print(final.compute())

Advanced: Time Series Processing

import dask.dataframe as dd

# Read and resample time series data
df = dd.read_csv('data/timeseries_data.csv', parse_dates=['timestamp'])
df = df.set_index('timestamp')
daily = df.resample('1D').agg({'value': 'mean'}).compute()

Advanced: Machine Learning

from dask import delayed, compute
from sklearn.ensemble import RandomForestClassifier

@delayed
def train_model(X, y):
    model = RandomForestClassifier()
    model.fit(X, y)
    return model

# Train multiple models in parallel
models = [train_model(X, y) for _ in range(5)]
trained_models = compute(*models)

Generating Advanced Data

To generate advanced sample datasets for testing:

python scripts/generate_advanced_data.py

This will create:

• Large time series datasets
• Transaction data
• Machine learning datasets
• JSON/nested data
• Multiple batch files for parallel processing
• Network/graph data

License

This project is provided for educational purposes. Content used for educational purposes only.

Contact

For questions or support, visit rskworld.in or contact:

• Email: help@rskworld.in
• Phone: +91 93305 39277