Processing Large Datasets with Dask: A Step‑by‑Step Tutorial

Goal: Learn to handle large‑scale data using Dask.

Learning content: basic Dask concepts and a hands‑on exercise that reads, explores, cleans, filters, aggregates, visualizes, and saves a large CSV file.

Code example:

import dask.dataframe as dd
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt

def create_large_csv(file_path, num_rows=1000000):
    data = {
        'id': range(num_rows),
        'value1': np.random.rand(num_rows),
        'value2': np.random.rand(num_rows),
        'category': np.random.choice(['A', 'B', 'C'], size=num_rows)
    }
    df = pd.DataFrame(data)
    df.to_csv(file_path, index=False)

file_path = 'large_dataset.csv'
create_large_csv(file_path)
print(f"Created large CSV file: {file_path}")

# Read with Dask
dask_df = dd.read_csv(file_path)
print(f"Dask DataFrame head:\n{dask_df.head()}")

# Basic info
print(f"Shape: {dask_df.shape}")
print(f"Columns: {dask_df.columns}")
print(f"Describe:\n{dask_df.describe().compute()}")

# Unique values in 'category'
unique_categories = dask_df['category'].unique().compute()
print(f"Unique categories: {unique_categories}")

# Handle missing values
missing_values = dask_df.isnull().sum().compute()
print(f"Missing values per column:\n{missing_values}")
dask_df = dask_df.fillna(dask_df.mean().compute())
print(f"After filling missing values:\n{dask_df.head()}")

# Filter rows where value1 > 0.5
filtered_df = dask_df[dask_df['value1'] > 0.5]
print(f"Filtered data:\n{filtered_df.head()}")

# Group by category and compute mean
grouped_df = filtered_df.groupby('category').mean().compute()
print(f"Grouped mean by category:\n{grouped_df}")

# Convert to pandas for visualization
pandas_df = filtered_df.compute()
pandas_df['value1'].hist(bins=30)
plt.xlabel('Value1')
plt.ylabel('Frequency')
plt.title('Distribution of Value1')
plt.show()

# Save processed data
output_file_path = 'processed_large_dataset.csv'
filtered_df.to_csv(output_file_path, single_file=True, index=False)
print(f"Processed data saved to: {output_file_path}")

The tutorial walks through each step, demonstrating how Dask enables out‑of‑core computation, efficient aggregation, and seamless integration with pandas for plotting, making it suitable for processing massive CSV files on limited memory.

Conclusion: After completing the practice, you should be able to use Dask to process large datasets, perform cleaning, filtering, aggregation, visualization, and export results.