Distributed programming divides computation across networked machines or processors. A familiar path begins with pandas: its DataFrame API supports SQL-like selection, filtering, aggregation, ordering, and joins, then provides a practical bridge toward multiprocessing or distributed computing. section 2.3.1
Joining product data
Suppose three warehouse tables describe product details, descriptions, and manufacturers. Merge them by product_id in pandas, preserve intermediate transformations, and prepare the combined dataset for analysis or machine learning. The same operation can be expressed as a SQL join. section 2.3.1
Choose distributed processing when the single-machine approach becomes limiting:
- Pandas offers control over transformation stages and preserves characteristics such as data types and ordering. section 2.3.1
- Pandas can be slow and cannot process data larger than available computer memory, creating efficiency and scalability limits. section 2.3.1
Listing 2.4 Merging datasets in pandas
import pandas as pd
df1 = pd.DataFrame({'product_id': [1, 2, 3, 4],
'product_name': ['Product A',
'Product B',
'Product C',
'Product D'],
'price': [10.99, 20.99, 15.99, 8.99]}) #1
df2 = pd.DataFrame({'product_id': [1, 2, 3, 4],
'product_description': ['A great product',
'A high-quality product',
'A reliable product',
'An affordable product'],
'category': ['Category A', 'Category B',
'Category C', 'Category D']}) #2
df3 = pd.DataFrame({'product_id': [1, 2, 3, 4],
'manufacturer': ['Manufacturer A', 'Manufacturer B',
'Manufacturer C', 'Manufacturer D'],
'weight': [1.5, 2.0, 1.8, 1.2]}) #3
merged_df = pd.merge(df1, df2, on='product_id') #4
merged_df = pd.merge(merged_df, df3, on='product_id') #5
print(merged_df)
Inspect a concrete pandas merge that models the controlled, staged workflow preceding larger-scale processing. section 2.3.1
