Summarizing and Grouping Operational Data
Understanding how to summarize and group operational data is essential for making informed decisions in operations management. Grouping and aggregating data allows you to quickly answer questions such as which products are top sellers, which teams are meeting their targets, or how different branches are performing. For example, you might want to know the total sales by product or the average order value per day. These insights help you identify trends, measure performance, and allocate resources more effectively.
123456789101112import pandas as pd # Sample sales data data = { "item": ["Widget", "Gadget", "Widget", "Widget", "Gadget"], "quantity": [10, 5, 7, 3, 2] } df = pd.DataFrame(data) # Group by item and sum the quantity sold for each total_quantity_per_item = df.groupby("item")["quantity"].sum() print(total_quantity_per_item)
Aggregation functions like sum, mean, and count are powerful tools for operations managers. The sum function helps you find totals, such as total sales or total hours worked. The mean function is used to calculate averages, such as average sales per day or average delivery time. The count function helps you determine how many times a certain event occurred, such as the number of completed orders. By applying these functions to grouped data, you can quickly generate key performance indicators and spot operational issues or successes.
123456789101112import pandas as pd # Sample order data orders = { "order_id": [101, 102, 103, 104, 105, 106], "status": ["Completed", "Pending", "Completed", "Pending", "Completed", "Completed"] } orders_df = pd.DataFrame(orders) # Group by status and count the number of orders in each category order_counts = orders_df.groupby("status")["order_id"].count() print(order_counts)
1. What does the groupby function do in pandas?
2. Which aggregation function would you use to find the average sales per day?
3. How can grouping data help operations managers make better decisions?
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Understanding how to summarize and group operational data is essential for making informed decisions in operations management. Grouping and aggregating data allows you to quickly answer questions such as which products are top sellers, which teams are meeting their targets, or how different branches are performing. For example, you might want to know the total sales by product or the average order value per day. These insights help you identify trends, measure performance, and allocate resources more effectively.
123456789101112import pandas as pd # Sample sales data data = { "item": ["Widget", "Gadget", "Widget", "Widget", "Gadget"], "quantity": [10, 5, 7, 3, 2] } df = pd.DataFrame(data) # Group by item and sum the quantity sold for each total_quantity_per_item = df.groupby("item")["quantity"].sum() print(total_quantity_per_item)
Aggregation functions like sum, mean, and count are powerful tools for operations managers. The sum function helps you find totals, such as total sales or total hours worked. The mean function is used to calculate averages, such as average sales per day or average delivery time. The count function helps you determine how many times a certain event occurred, such as the number of completed orders. By applying these functions to grouped data, you can quickly generate key performance indicators and spot operational issues or successes.
123456789101112import pandas as pd # Sample order data orders = { "order_id": [101, 102, 103, 104, 105, 106], "status": ["Completed", "Pending", "Completed", "Pending", "Completed", "Completed"] } orders_df = pd.DataFrame(orders) # Group by status and count the number of orders in each category order_counts = orders_df.groupby("status")["order_id"].count() print(order_counts)
1. What does the groupby function do in pandas?
2. Which aggregation function would you use to find the average sales per day?
3. How can grouping data help operations managers make better decisions?
Obrigado pelo seu feedback!
