Python Programming Jobs in Retail: What the Role Involves

# pip install pandas
import pandas as pd

sales = pd.DataFrame({
    "store":    ["Noida", "Noida", "Delhi", "Delhi", "Noida", "Delhi"],
    "product":  ["Rice 5kg", "Tea 1kg", "Rice 5kg", "Oil 1L", "Oil 1L", "Tea 1kg"],
    "qty":      [10, 4, 7, 12, 6, 5],
    "price":    [450, 520, 450, 180, 180, 520],
})
sales["revenue"] = sales["qty"] * sales["price"]

by_store = sales.groupby("store")["revenue"].sum().sort_values(ascending=False)
top_products = (sales.groupby("product")["revenue"].sum()
                     .sort_values(ascending=False).head(3))

print("Revenue by store:\n", by_store)
print("\nTop products:\n", top_products)

A typical day - Python analyst in retail

09:30  Check overnight data loads completed
10:00  Fix a store file with a changed column name
11:00  Answer: "Why did tea sales fall last week?"
12:30  Lunch
13:30  Improve the weekly sales report script
15:00  Meeting with the category team on promotions
16:30  Add a new store to the reporting pipeline
17:30  Document changes, push code to Git

import pandas as pd

stock = pd.DataFrame({
    "product":         ["Rice 5kg", "Tea 1kg", "Oil 1L"],
    "on_hand":         [40, 12, 90],
    "avg_daily_sales": [8, 3, 9],
    "lead_time_days":  [3, 5, 2],
})

SAFETY_DAYS = 2
stock["reorder_point"] = stock["avg_daily_sales"] * (stock["lead_time_days"] + SAFETY_DAYS)
stock["days_of_stock"] = (stock["on_hand"] / stock["avg_daily_sales"]).round(1)
stock["reorder_now"] = stock["on_hand"] <= stock["reorder_point"]

print(stock[["product", "on_hand", "reorder_point", "days_of_stock", "reorder_now"]])

Reading the reorder table:

reorder_point = daily sales x (lead time + safety days)
  -> the stock level at which you should order

days_of_stock = on hand / daily sales
  -> how many days current stock will last

reorder_now = True
  -> on hand is at or below the reorder point

Business use:
- buyers get a daily list of items to order
- planners spot items with too many days of stock

# Combine all store CSV files in a folder into one Excel report
# pip install pandas openpyxl
from pathlib import Path
import pandas as pd

folder = Path("store_files")          # e.g. noida.csv, delhi.csv
folder.mkdir(exist_ok=True)

# Create two sample files so the script runs anywhere
pd.DataFrame({"product": ["Rice 5kg", "Tea 1kg"], "qty": [10, 4]}).to_csv(folder / "noida.csv", index=False)
pd.DataFrame({"product": ["Rice 5kg", "Oil 1L"], "qty": [7, 12]}).to_csv(folder / "delhi.csv", index=False)

frames = []
for file in sorted(folder.glob("*.csv")):
    df = pd.read_csv(file)
    df["store"] = file.stem.title()
    frames.append(df)

all_sales = pd.concat(frames, ignore_index=True)
summary = all_sales.pivot_table(index="product", columns="store",
                                values="qty", aggfunc="sum", fill_value=0)

with pd.ExcelWriter("weekly_report.xlsx") as writer:
    all_sales.to_excel(writer, sheet_name="raw", index=False)
    summary.to_excel(writer, sheet_name="summary")

print(summary)

Make retail scripts reliable

[ ] Check expected columns exist before processing
[ ] Log how many rows each file had
[ ] Stop with a clear error if a file is missing
[ ] Never overwrite the only copy of raw data
[ ] Keep settings (paths, thresholds) in one place
[ ] Add a README: what it does, how to run it
[ ] Store code in Git, not on one laptop

Script Spec
Name: _______________
Requested by: _______________ team
Problem today: _______________ (manual time per week: ___)

Inputs: files / database / API
Output: Excel / dashboard / alert / table
Schedule: daily / weekly / on demand
Checks: _______________
Owner after launch: _______________

Retail Portfolio Project

Dataset: public retail or sales dataset
Questions to answer:
1. Which products drive most revenue?
2. Which stores or months are weakest?
3. Which items should be reordered?

Deliverables:
[ ] Clean data notebook
[ ] Reorder point script
[ ] Automated Excel or dashboard report
[ ] README with key findings

Weekly Work Log

Requests handled: _______________
Scripts improved: _______________
Hours of manual work saved (estimate): _____
Data issues found: _______________
Next improvement: _______________