Python practice: Column Operations, Math & Type Casting

Load "sales.csv". Create sales["CalcRev"] = sales["Units"] * sales["Price"]. Print int(sales["CalcRev"].sum()):

Load "sales.csv". Create sales["Tax"] = (sales["Revenue"] * 0.18).round(2). Print round(sales["Tax"].sum(), 2):

Given df = pd.DataFrame({"ID": ["1", "2"], "Qty": ["10", "20"]}), cast df["Qty"] = df["Qty"].astype(int) and print df["Qty"].sum():

Load "sales.csv". Map directors: heads = {"North": "Arjun", "South": "Kavita", "East": "Debashis", "West": "Meera"}. Create sales["Director"] = sales["Region"].map(heads). Print sales["Director"].iloc[0]:

Load "sales.csv". Apply function lambda r: "High" if r["Revenue"] >= 30000 else "Standard" with axis=1 into sales["Tier"]. Print sales["Tier"].value_counts()["High"]:

Load "sales.csv". Rename columns using sales = sales.rename(columns={"OrderID": "InvoiceNo", "Rep": "Agent"}). Print list(sales.columns)[0] on line 1 and list(sales.columns)[3] on line 2:

Load "sales.csv". Drop the "Category" column using clean = sales.drop(columns=["Category"]). Print len(clean.columns):

Load "sales.csv". Compute ranking sales["RevRank"] = sales["Revenue"].rank(ascending=False, method="min").astype(int). Print sales["RevRank"].min():