Before pandas, it helps to understand plain Python’s core tools. Almost every automation script is built from lists, dictionaries and loops.

Lists: an ordered column

regions = ["North", "South", "East", "West"]
print(regions[0])        # North  (counting starts at 0)
print(len(regions))      # 4
regions.append("Central")
print(regions[-1])       # Central (last item)

A list is like one Excel column: ordered, and you can add, remove and sort items.

Dictionaries: a lookup table

managers = {"North": "Vikram", "South": "Lakshmi", "East": "Priya"}
print(managers["North"])             # Vikram  — like VLOOKUP
print(managers.get("West", "None"))  # None    — IFERROR built in
managers["West"] = "Farhan"          # add or update

Loops: do something for every item

sales = [12500, 8400, 15200, 6100]
total = 0
for amount in sales:
    total += amount
print(total)          # 42200  — like SUM

big = [s for s in sales if s > 10000]   # like FILTER
print(big)            # [12500, 15200]

Putting them together: totals by region

rows = [("North", 12500), ("South", 8400), ("North", 15200), ("East", 6100)]
totals = {}
for region, amount in rows:
    totals[region] = totals.get(region, 0) + amount
print(totals)   # {"North": 27700, "South": 8400, "East": 6100}  — a pivot table

Next step: pandas for Excel users, which does all of this on whole tables.