Aprenda Scheduling and Automating Business Workflows | Automating Business Analysis Tasks

Workflow automation is a powerful way to streamline business analysis tasks by reducing the need for manual intervention. By running scripts on a schedule, you can ensure that data is processed, analyzed, and reported consistently and on time. Chaining tasks together allows you to create end-to-end processes where the output of one step becomes the input for the next, eliminating repetitive manual steps and minimizing errors. This approach is especially useful for recurring tasks such as daily sales reporting, monthly performance summaries, or regular data cleaning operations.


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import pandas as pd

def clean_data(df):
    # Remove rows with missing 'Sales' values
    df = df.dropna(subset=['Sales'])
    # Convert 'Sales' to numeric
    df['Sales'] = pd.to_numeric(df['Sales'], errors='coerce')
    return df

def summarize_data(df):
    # Calculate total and average sales
    total_sales = df['Sales'].sum()
    average_sales = df['Sales'].mean()
    summary = {
        'Total Sales': total_sales,
        'Average Sales': average_sales
    }
    return summary

def generate_report(summary, region):
    # Create a simple text report
    report = (
        f"Sales Report for {region}\n"
        f"----------------------\n"
        f"Total Sales: {summary['Total Sales']:.2f}\n"
        f"Average Sales: {summary['Average Sales']:.2f}\n"
    )
    print(report)

def workflow(data, region):
    cleaned = clean_data(data)
    summary = summarize_data(cleaned)
    generate_report(summary, region)

# Example usage with sample data for a region
data = pd.DataFrame({
    'Product': ['A', 'B', 'C', 'D'],
    'Sales': ['100', '200', None, '150']
})

workflow(data, 'North Region')

By breaking down your workflow into modular functions, you make each step reusable and easy to test. These functions can be mixed and matched for different scenarios, making it simple to adapt your automation as business needs change. For instance, the same data cleaning and summarization functions can be reused across different datasets or reporting periods, allowing you to build robust, end-to-end automation pipelines.


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import pandas as pd

regions = {
    'North': pd.DataFrame({'Product': ['A', 'B'], 'Sales': ['120', '130']}),
    'South': pd.DataFrame({'Product': ['C', 'D'], 'Sales': ['110', '140']}),
    'East': pd.DataFrame({'Product': ['E', 'F'], 'Sales': ['115', '125']})
}

def clean_data(df):
    df = df.dropna(subset=['Sales'])
    df['Sales'] = pd.to_numeric(df['Sales'], errors='coerce')
    return df

def summarize_data(df):
    total_sales = df['Sales'].sum()
    average_sales = df['Sales'].mean()
    return {'Total Sales': total_sales, 'Average Sales': average_sales}

def generate_report(summary, region):
    report = (
        f"Sales Report for {region}\n"
        f"----------------------\n"
        f"Total Sales: {summary['Total Sales']:.2f}\n"
        f"Average Sales: {summary['Average Sales']:.2f}\n"
    )
    print(report)

def workflow(data, region):
    cleaned = clean_data(data)
    summary = summarize_data(cleaned)
    generate_report(summary, region)

# Run the workflow for each region
for region, data in regions.items():
    workflow(data, region)

1. What is the benefit of chaining multiple analysis steps into a single workflow?

2. How does modular code support business process automation?

3. Fill in the blanks: To automate a workflow, define functions and call them in order.

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 3. Capítulo 6

Pergunte à IA

Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo

Deslize para mostrar o menu


              123456789101112131415161718192021222324252627282930313233343536373839404142
            
import pandas as pd

def clean_data(df):
    # Remove rows with missing 'Sales' values
    df = df.dropna(subset=['Sales'])
    # Convert 'Sales' to numeric
    df['Sales'] = pd.to_numeric(df['Sales'], errors='coerce')
    return df

def summarize_data(df):
    # Calculate total and average sales
    total_sales = df['Sales'].sum()
    average_sales = df['Sales'].mean()
    summary = {
        'Total Sales': total_sales,
        'Average Sales': average_sales
    }
    return summary

def generate_report(summary, region):
    # Create a simple text report
    report = (
        f"Sales Report for {region}\n"
        f"----------------------\n"
        f"Total Sales: {summary['Total Sales']:.2f}\n"
        f"Average Sales: {summary['Average Sales']:.2f}\n"
    )
    print(report)

def workflow(data, region):
    cleaned = clean_data(data)
    summary = summarize_data(cleaned)
    generate_report(summary, region)

# Example usage with sample data for a region
data = pd.DataFrame({
    'Product': ['A', 'B', 'C', 'D'],
    'Sales': ['100', '200', None, '150']
})

workflow(data, 'North Region')


              123456789101112131415161718192021222324252627282930313233343536
            
import pandas as pd

regions = {
    'North': pd.DataFrame({'Product': ['A', 'B'], 'Sales': ['120', '130']}),
    'South': pd.DataFrame({'Product': ['C', 'D'], 'Sales': ['110', '140']}),
    'East': pd.DataFrame({'Product': ['E', 'F'], 'Sales': ['115', '125']})
}

def clean_data(df):
    df = df.dropna(subset=['Sales'])
    df['Sales'] = pd.to_numeric(df['Sales'], errors='coerce')
    return df

def summarize_data(df):
    total_sales = df['Sales'].sum()
    average_sales = df['Sales'].mean()
    return {'Total Sales': total_sales, 'Average Sales': average_sales}

def generate_report(summary, region):
    report = (
        f"Sales Report for {region}\n"
        f"----------------------\n"
        f"Total Sales: {summary['Total Sales']:.2f}\n"
        f"Average Sales: {summary['Average Sales']:.2f}\n"
    )
    print(report)

def workflow(data, region):
    cleaned = clean_data(data)
    summary = summarize_data(cleaned)
    generate_report(summary, region)

# Run the workflow for each region
for region, data in regions.items():
    workflow(data, region)

1. What is the benefit of chaining multiple analysis steps into a single workflow?

2. How does modular code support business process automation?

3. Fill in the blanks: To automate a workflow, define functions and call them in order.

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 3. Capítulo 6

Scheduling and Automating Business Workflows

1. What is the benefit of chaining multiple analysis steps into a single workflow?

2. How does modular code support business process automation?

3. Fill in the blanks: To automate a workflow, define ____ functions and call them in ____ order.

Scheduling and Automating Business Workflows

1. What is the benefit of chaining multiple analysis steps into a single workflow?

2. How does modular code support business process automation?

3. Fill in the blanks: To automate a workflow, define ____ functions and call them in ____ order.

3. Fill in the blanks: To automate a workflow, define functions and call them in order.

3. Fill in the blanks: To automate a workflow, define functions and call them in order.