Python practice: Reshaping Data: Pivot Tables & Melting

When should an analyst use pd.pivot_table() instead of standard .groupby()?

Load "sales.csv". Create a pivot table summarizing total Revenue with index="Region", columns="Category", values="Revenue", aggfunc="sum", and fill_value=0. Print the total revenue for "North" Sheets (int(pt.loc["North", "Sheets"])):

Load "sales.csv". Build a pivot table of total Revenue with index="Region", columns="Category", aggfunc="sum", fill_value=0, and margins=True. Print the overall grand total (int(pt.loc["All", "All"])):

Load "sales.csv". Build a pivot table with index="Region", columns="Category", values="Units", aggfunc="mean", and fill_value=0. Print the average units for East Profiles (round(pt.loc["East", "Profiles"], 1)):

Load "sales.csv". Notice North has no Profiles sales. Pivot total Revenue by Region and Category with fill_value=0. Print the value of pt.loc["North", "Profiles"] as an integer:

In data analytics and tidy data architecture, why is long format (stacked key-value rows) preferred over wide format (separate column per month)?

Given wide DataFrame df = pd.DataFrame({"Rep": ["Asha", "Ravi"], "Q1": [10000, 15000], "Q2": [12000, 18000], "Q3": [14000, 20000]}), melt into long format with id_vars=["Rep"], value_vars=["Q1", "Q2", "Q3"], var_name="Quarter", value_name="Sales". Print the sum of "Sales":

Load "sales.csv". Create a pivot table with index=["Region", "Rep"], columns="Category", values="Revenue", aggfunc="sum", and fill_value=0. Print the number of rows in the resulting pivot table (len(pt)):