Apprendre Scheduling and Batch Processing with Python | Automating Government Workflows with Python

Batch processing is a powerful tool for government analysts who need to automate repetitive tasks across large datasets. In many government workflows, you often face the challenge of processing similar data from multiple sources, such as service usage records from different regions or time periods. Instead of analyzing each dataset manually, batch processing allows you to automate these tasks using loops and functions, saving time and reducing the risk of human error. This approach is especially useful when you need to apply the same analysis or transformation to multiple datasets, ensuring consistency and efficiency across your work.


              1234567891011121314
            
# Process service usage data for multiple regions using a loop

regions = ["North", "South", "East", "West"]
usage_data = {
    "North": [120, 130, 125],
    "South": [110, 115, 117],
    "East": [140, 145, 142],
    "West": [100, 105, 102]
}

for region in regions:
    data = usage_data[region]
    avg_usage = sum(data) / len(data)
    print(f"Average usage in {region}: {avg_usage}")

To structure your code for batch processing, you should organize your workflow into clear, repeatable steps. Define functions that handle individual tasks, such as processing a single dataset or calculating summary statistics. Use loops to iterate over each dataset, calling your function for each item. Error handling is critical in batch processing; if an error occurs with one dataset, you want your script to log the error and continue processing the remaining items, rather than stopping entirely. This way, you can review any issues afterward and ensure that the majority of your data is still processed successfully.


              1234567891011121314151617181920212223
            
# Collect results from each batch into a summary list, with error handling

def calculate_average(data):
    return sum(data) / len(data)

regions = ["North", "South", "East", "West"]
usage_data = {
    "North": [120, 130, 125],
    "South": [110, 115, 117],
    "East": [140, 145, 142],
    "West": [100, 105, 102]
}

summary = []

for region in regions:
    try:
        avg = calculate_average(usage_data[region])
        summary.append({"region": region, "average_usage": avg})
    except Exception as e:
        print(f"Error processing {region}: {e}")

print("Batch summary:", summary)

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 4

Demandez à l'IA

Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion

Suggested prompts:

Can you explain how error handling works in this batch processing example?

What are some best practices for organizing batch processing code?

How can I modify this code to process additional regions or datasets?

Glissez pour afficher le menu


              1234567891011121314
            
# Process service usage data for multiple regions using a loop

regions = ["North", "South", "East", "West"]
usage_data = {
    "North": [120, 130, 125],
    "South": [110, 115, 117],
    "East": [140, 145, 142],
    "West": [100, 105, 102]
}

for region in regions:
    data = usage_data[region]
    avg_usage = sum(data) / len(data)
    print(f"Average usage in {region}: {avg_usage}")


              1234567891011121314151617181920212223
            
# Collect results from each batch into a summary list, with error handling

def calculate_average(data):
    return sum(data) / len(data)

regions = ["North", "South", "East", "West"]
usage_data = {
    "North": [120, 130, 125],
    "South": [110, 115, 117],
    "East": [140, 145, 142],
    "West": [100, 105, 102]
}

summary = []

for region in regions:
    try:
        avg = calculate_average(usage_data[region])
        summary.append({"region": region, "average_usage": avg})
    except Exception as e:
        print(f"Error processing {region}: {e}")

print("Batch summary:", summary)

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 4

Scheduling and Batch Processing with Python

1. What is batch processing, and why is it useful?

2. How can you handle errors during batch processing in Python?

3. What is the benefit of collecting results into a summary structure?

Scheduling and Batch Processing with Python

1. What is batch processing, and why is it useful?

2. How can you handle errors during batch processing in Python?

3. What is the benefit of collecting results into a summary structure?