Sorting, Grouping, and Aggregating Data

Pandas makes it easy to organize records and calculate summary statistics across categories. These operations are common in sales reports, financial analysis, and business intelligence.

Sorting Records

The sort_values() method orders records according to one or more columns.

df = pd.DataFrame({

"Product": ["Laptop", "Tablet", "Phone", "Monitor"],

"Sales": [120000, 75000, 95000, 60000]

})

sorted_df = df.sort_values(

by="Sales",

ascending=False

)

print(sorted_df)

Setting ascending=False places the highest sales values first.

Grouping Data

Suppose you have sales records from multiple regions.

sales_data = pd.DataFrame({

"Region": ["North", "South", "North", "West", "South"],

"Sales": [50000, 70000, 65000, 45000, 80000]

})

region_sales = sales_data.groupby("Region")["Sales"].sum()

print(region_sales)

The groupby() method groups rows by region, while sum() calculates total sales for each group.

Applying Multiple Aggregations

You can calculate several statistics in a single operation.

summary = sales_data.groupby("Region").agg(

Total_Sales=("Sales", "sum"),

Average_Sales=("Sales", "mean"),

Maximum_Sales=("Sales", "max"),

Record_Count=("Sales", "count")

)

print(summary)

This produces a summary table with total, average, and maximum sales, along with the number of records in each region.

Grouping and aggregation help transform detailed transaction data into meaningful business summaries.