Library

Datasets

20 practice datasets used in the lessons. Every one is synthetic — generated for teaching, with invented names and deliberate mess where a lesson needs it. None contains real people's data.

Branch sales exports (synthetic)

March sales exported separately by three branches of an office-supplies shop (Riyadh, Jeddah, Dammam), plus a product catalogue in JSON. Deliberately messy in places, like real exports: the Jeddah file has a blank line, a duplicated row and two product names with stray spaces and lower case; the Dammam file has one quantity written as "n/a" and one row with an empty unit price.

Fields (5)

date: Sale date, YYYY-MM-DD (March 2026)
product: Product name as typed by the branch (may have stray spaces or different case)
quantity: Units sold, a whole number as text (one Dammam row says n/a)
unit_price: Price per unit in SAR with two decimals (one Dammam row is empty)
products.json: Object mapping each product name to {category, list_price}

data/sales/riyadh.csv · 25 lines · 0.8 KB

date,product,quantity,unit_price
2026-03-01,Filing cabinet,10,610.00
2026-03-02,Office chair,10,449.00
2026-03-03,Notebook A5,6,12.50
2026-03-05,Gel pen (box),1,24.00
2026-03-07,Monitor stand,10,135.00
2026-03-07,LED strip,10,59.00
2026-03-09,Office chair,3,449.00
2026-03-12,Sticky notes,2,8.75
2026-03-13,Sticky notes,2,8.75
2026-03-16,Office chair,5,449.00
2026-03-17,Desk lamp,1,89.00

data/sales/jeddah.csv · 21 lines · 0.7 KB

date,product,quantity,unit_price
2026-03-06,Desk lamp,1,89.00
2026-03-07,Monitor stand,6,135.00
2026-03-11,Desk lamp,3,89.00
2026-03-12,  led strip ,1,59.00
2026-03-16,Monitor stand,10,135.00
2026-03-16,Notebook A5,1,12.50

2026-03-16,Monitor stand,10,135.00
2026-03-17,Sticky notes,1,8.75
2026-03-19,Gel pen (box),1,24.00
2026-03-20,Office chair,1,449.00

data/sales/dammam.csv · 17 lines · 0.5 KB

date,product,quantity,unit_price
2026-03-01,Gel pen (box),6,24.00
2026-03-06,Office chair,10,449.00
2026-03-07,Notebook A5,4,12.50
2026-03-09,Filing cabinet,4,610.00
2026-03-16,LED strip,1,59.00
2026-03-16,Notebook A5,n/a,12.50
2026-03-16,Desk lamp,1,89.00
2026-03-17,Gel pen (box),2,24.00
2026-03-17,Notebook A5,4,12.50
2026-03-20,Sticky notes,3,
2026-03-20,Office chair,2,449.00

data/products.json · 34 lines · 0.6 KB

{
  "Desk lamp": {
    "category": "Lighting",
    "list_price": 89.0
  },
  "Office chair": {
    "category": "Furniture",
    "list_price": 449.0
  },
  "Notebook A5": {
    "category": "Stationery",
    "list_price": 12.5

Used in: 8. Paths and folders with pathlib · 8. Reading CSV files · 8. Small data pipelines · 8. Mini project: consolidating branch sales

Generated for Python Quest with a seeded script; the shop, products and figures are invented.

Web server access log (synthetic)
360 lines from one morning of a shop website's access log. Each line is: date, time, HTTP method, path, status code and response time such as 132ms, separated by single spaces.
Fields (5)
date time
2026-04-02 and the time HH:MM:SS
method
GET or POST
path
Requested page, e.g. /products or /api/stock
status
HTTP status code: 200, 201, 301, 404 or 500
duration
Response time in milliseconds with an ms suffix, e.g. 87ms
data/access.log · 360 lines · 14.8 KB
```
2026-04-02 08:02:05 GET /api/prices 200 27ms
2026-04-02 08:03:25 GET /help 200 58ms
2026-04-02 08:05:00 GET /login 200 29ms
2026-04-02 08:06:37 GET /help 200 235ms
2026-04-02 08:07:33 GET / 200 91ms
2026-04-02 08:08:44 GET /help 200 116ms
2026-04-02 08:10:20 GET /cart 200 97ms
2026-04-02 08:11:25 POST /login 200 57ms
2026-04-02 08:11:42 POST /checkout 200 174ms
2026-04-02 08:12:25 GET /checkout 301 108ms
2026-04-02 08:12:53 GET /cart 404 111ms
2026-04-02 08:14:15 GET / 200 47ms
```
Open in the playground Download access.log
Used in: 8. Processing files line by line
Generated for Python Quest with a seeded script; no real traffic was recorded.

Staff records with errors (synthetic)

An HR export of 30 employees for practising validation. It contains deliberate problems: an empty email, a salary written as 12k, an empty salary, negative and implausibly high weekly hours, a department with stray spaces and lower case, a duplicated employee id and an empty name.

Fields (7)

employee_id: Id such as E107 (should be unique)
name: Full name
department: Sales, Finance, Support, Warehouse or IT
email: Work email address
salary: Monthly salary in SAR as a whole number
hours_per_week: Contracted hours, expected between 1 and 60
start_date: YYYY-MM-DD

data/staff.csv · 31 lines · 2.3 KB

employee_id,name,department,email,salary,hours_per_week,start_date
E101,Huda Qasim,Warehouse,huda.qasim@qamar-trading.example,13750,40,2024-12-25
E102,Omar Khoury,IT,omar.khoury@qamar-trading.example,9250,45,2021-06-11
E103,Lina Aziz,Sales,lina.aziz@qamar-trading.example,16250,30,2025-06-13
E104,Faris Mansour,Support,,13250,20,2019-03-15
E105,Maya Darwish,IT,maya.darwish@qamar-trading.example,16500,40,2018-09-17
E106,Sami Rahman,Warehouse,sami.rahman@qamar-trading.example,14750,40,2021-09-18
E107,Rana Aziz,Sales,rana.aziz@qamar-trading.example,17000,40,2019-04-22
E108,Yusuf Haddad,Warehouse,yusuf.haddad@qamar-trading.example,12k,40,2015-09-27
E109,Dana Farouk,Warehouse,dana.farouk@qamar-trading.example,17250,40,2019-07-03
E110,Karim Khoury,Finance,karim.khoury@qamar-trading.example,12500,30,2016-06-04
E111,Noor Haddad,IT,noor.haddad@qamar-trading.example,19500,40,2022-09-04

Used in: 8. Validating records

Generated for Python Quest with a seeded script; all names, emails and salaries are invented.

Customer orders in nested JSON (synthetic)
Twelve orders from an office-supplies shop as one JSON document: a top-level object with the store name, currency and a list of orders. Each order has a nested customer object, a list of line items, a paid flag and a coupon that may be null.
Fields (6)
order_id
Order reference such as ORD-5004
date
Order date, YYYY-MM-DD
customer
Object with id, name and city
items
List of objects with sku, name, qty and unit_price
paid
true or false
coupon
A coupon code or null
data/orders.json · 312 lines · 6.3 KB
```
{
  "store": "Qamar Office Supplies",
  "currency": "SAR",
  "orders": [
    {
      "order_id": "ORD-5001",
      "date": "2026-05-27",
      "customer": {
        "id": "C-202",
        "name": "Blue Dune Cafe",
        "city": "Jeddah"
      },
```
Open in the playground Download orders.json
Used in: 8. Working with JSON
Generated for Python Quest with a seeded script; customers and orders are invented.
Store sales 2024 (synthetic)
813 order lines from a fictional chain of eight homeware and electronics stores across five Saudi regions, one row per order line for the whole of 2024. Clean and consistently typed: use it for selecting, grouping, dates and charts. Prices are in SAR; some November orders carry a 20% promotional price.
Fields (8)
order_id
Unique order line number (integer, 10001 upwards)
date
Order date, YYYY-MM-DD (2024-01-01 to 2024-12-31)
store_id
Store code S01–S08 (see da-stores)
region
Central, Western, Eastern, Northern or Southern
product
Product name (12 products, see da-products)
category
Electronics, Home, Stationery or Grocery
units
Units sold on the line (1–15)
unit_price
Selling price per unit in SAR
data/store_sales.csv · 814 lines · 46.2 KB
```
order_id,date,store_id,region,product,category,units,unit_price
10001,2024-01-01,S01,Central,USB-C Cable,Electronics,2,35.00
10002,2024-01-01,S04,Western,USB-C Cable,Electronics,2,35.00
10003,2024-01-02,S04,Western,Arabic Coffee,Grocery,2,42.00
10004,2024-01-03,S07,Northern,USB-C Cable,Electronics,1,35.00
10005,2024-01-04,S06,Eastern,Notebook,Stationery,4,12.00
10006,2024-01-04,S03,Western,Gel Pens,Stationery,1,18.00
10007,2024-01-04,S03,Western,Wireless Mouse,Electronics,4,79.00
10008,2024-01-04,S01,Central,Throw Pillow,Home,3,65.00
10009,2024-01-05,S01,Central,Coffee Mug,Home,2,25.00
10010,2024-01-05,S06,Eastern,Desk Organizer,Stationery,1,55.00
10011,2024-01-06,S07,Northern,USB-C Cable,Electronics,1,35.00
```
Open in the playground Download store_sales.csv
Used in: 14. Selecting and filtering · 14. Transforming columns · 14. Grouping and aggregating · 14. Combining data and 2 more
Generated for Python Quest with a seeded Python script (random.Random with a fixed seed per file, standard library only). Stores, managers, suppliers, students and figures are invented; no real records were used.
Store directory (synthetic)
Lookup table for the store sales dataset: one row per store. Includes S09, a store opened in 2025 that has no 2024 sales — useful for seeing the difference between inner, left and right merges.
Fields (5)
store_id
Store code S01–S09 (key for merging with da-store-sales)
city
City the store is in
manager
Store manager (invented name)
size_sqm
Sales floor area in square metres
opened_year
Year the store opened
data/stores.csv · 10 lines · 0.3 KB
```
store_id,city,manager,size_sqm,opened_year
S01,Riyadh,Hala Qasim,420,2016
S02,Buraydah,Faris Otaibi,260,2019
S03,Jeddah,Mona Saleh,510,2015
S04,Makkah,Yousef Bakr,300,2020
S05,Dammam,Reem Fahad,380,2017
S06,Khobar,Tariq Nasser,240,2021
S07,Tabuk,Lama Harbi,210,2018
S08,Abha,Omar Shehri,230,2022
S09,Hail,Dana Shammari,190,2025
```
Open in the playground Download stores.csv
Used in: 14. Combining data
Generated for Python Quest with a seeded Python script (random.Random with a fixed seed per file, standard library only). Stores, managers, suppliers, students and figures are invented; no real records were used.
Product catalogue (synthetic)
Lookup table for the store sales dataset: unit cost and supplier per product. Contains one product (Plant Pot) that never appears in the sales, so a left merge from the catalogue shows an unsold item.
Fields (4)
product
Product name (key for merging with da-store-sales)
category
Product category
unit_cost
What the chain pays per unit, SAR
supplier
Supplier company (invented)
data/products.csv · 14 lines · 0.6 KB
```
product,category,unit_cost,supplier
Wireless Mouse,Electronics,41.00,Nadeem Supplies
USB-C Cable,Electronics,14.00,Nadeem Supplies
Desk Lamp,Electronics,66.00,Brightline Trading
Headphones,Electronics,150.00,Brightline Trading
Coffee Mug,Home,9.00,Oasis Homeware
Throw Pillow,Home,28.00,Oasis Homeware
Wall Clock,Home,47.00,Oasis Homeware
Notebook,Stationery,4.50,Qalam Paper Co
Gel Pens,Stationery,7.00,Qalam Paper Co
Desk Organizer,Stationery,26.00,Qalam Paper Co
Arabic Coffee,Grocery,25.00,Najd Foods
```
Open in the playground Download products.csv
Used in: 14. Combining data
Generated for Python Quest with a seeded Python script (random.Random with a fixed seed per file, standard library only). Stores, managers, suppliers, students and figures are invented; no real records were used.
Raw sales export, Jan–Jun 2025 (synthetic, messy)
432 order lines exported from a till system before any cleaning. Deliberately messy: region names have inconsistent capitalisation and stray spaces, some `units` are blank, some `unit_price` values are text such as `SAR 25.00` or `n/a`, and 12 rows are exact duplicates. Used in the end-to-end mini project.
Fields (6)
order_id
Order line number; duplicated rows repeat it
order_date
Order date, YYYY-MM-DD (first half of 2025)
region
Region name with inconsistent case and spacing
product
Product name
units
Units sold; blank when the till did not record it
unit_price
Price per unit as text: mostly `25.00`, sometimes `SAR 25.00` or `n/a`
data/sales_raw.csv · 433 lines · 19.0 KB
```
order_id,order_date,region,product,units,unit_price
50151,2025-01-01,Eastern,Coffee Mug,1,25.00
50159,2025-01-01,Western,Coffee Mug,11,25.00
50080,2025-01-02,Central,Throw Pillow,1,65.00
50080,2025-01-02,Central,Throw Pillow,1,65.00
50128,2025-01-02,Central,Wall Clock,12,89.00
50308,2025-01-02,Central,Gel Pens,1,18.00
50152,2025-01-03,Western,Notebook,5,12.00
50219,2025-01-03,Northern,Gel Pens,10,18.00
50286,2025-01-03,Southern  ,Dates Box,11,60.00
50117,2025-01-04,CENTRAL,Wireless Mouse,9,79.00
50236,2025-01-04,Eastern  ,Gel Pens,4,18.00
```
Open in the playground Download sales_raw.csv
Used in: 14. Communicating findings
Generated for Python Quest with a seeded Python script (random.Random with a fixed seed per file, standard library only). Stores, managers, suppliers, students and figures are invented; no real records were used.
Employee survey (synthetic, messy)
246 responses to an internal staff survey at a fictional company. Messy on purpose: department names vary in case and spacing, some years and satisfaction scores are blank, `commute_minutes` contains the text `unknown` and two implausible values (1 and 900), `would_recommend` mixes `Yes`, `yes` and ` Yes`, and six responses were submitted twice.
Fields (7)
respondent_id
Respondent number 1–240; duplicates repeat it
department
Sales, Finance, IT, Operations, HR or Marketing (inconsistent case/spaces)
years_at_company
Whole years at the company; sometimes blank
remote_days
Days per week worked remotely (0–5)
satisfaction
Job satisfaction 1 (low) to 5 (high); sometimes blank
commute_minutes
One-way commute in minutes as text; includes `unknown` and outliers
would_recommend
Would recommend the company as an employer: Yes/No with inconsistent case and spaces
data/employee_survey.csv · 247 lines · 5.8 KB
```
respondent_id,department,years_at_company,remote_days,satisfaction,commute_minutes,would_recommend
1,HR,1,2,5,68,Yes
2,Operations,0,2,5,41,Yes
3,Finance,0,2,3,32,Yes
4,Sales,8,2,3,5,no
5,IT,6,5,5,32,Yes
6,operations,1,4,3,39,Yes
7,Operations,2,2,5,32,yes
8,finance,0,4,5,17,Yes
9,OPERATIONS,0,0,3,57,No
10,Sales,8,1,3,37,Yes
11,Operations,3,2,2,16,No
```
Open in the playground Download employee_survey.csv
Used in: 14. Cleaning data · 14. Grouping and aggregating
Generated for Python Quest with a seeded Python script (random.Random with a fixed seed per file, standard library only). Stores, managers, suppliers, students and figures are invented; no real records were used.
Website traffic 2024 (synthetic)
Daily visits and sign-ups for three pages (home, pricing, blog) of a fictional software company's website for every day of 2024 (1,098 rows). Traffic grows through the year, dips on Fridays and Saturdays, and jumps during a September campaign — good for dates, resampling and line charts.
Fields (4)
date
Day, YYYY-MM-DD
page
home, pricing or blog
visits
Visits to the page that day
signups
Free-trial sign-ups started from that page that day
data/web_traffic.csv · 1099 lines · 25.4 KB
```
date,page,visits,signups
2024-01-01,home,758,11
2024-01-01,pricing,264,15
2024-01-01,blog,404,1
2024-01-02,home,901,11
2024-01-02,pricing,250,24
2024-01-02,blog,397,2
2024-01-03,home,848,11
2024-01-03,pricing,278,15
2024-01-03,blog,398,3
2024-01-04,home,771,10
2024-01-04,pricing,254,13
```
Open in the playground Download web_traffic.csv
Used in: 14. Dates in pandas · 14. Visualising data
Generated for Python Quest with a seeded Python script (random.Random with a fixed seed per file, standard library only). Stores, managers, suppliers, students and figures are invented; no real records were used.
Warehouse sensor readings (synthetic)
Hourly temperature and humidity from three warehouse sensors (A, B, C) for 1–7 March 2024 (504 rows). About 2% of temperatures are blank, and one reading of 85.0 °C is a sensor fault rather than a real temperature.
Fields (4)
timestamp
Reading time, YYYY-MM-DD HH:MM (hourly)
sensor
Sensor id: A, B or C
temperature_c
Temperature in °C; blank when the reading failed
humidity_pct
Relative humidity in percent
data/sensor_readings.csv · 505 lines · 13.3 KB
```
timestamp,sensor,temperature_c,humidity_pct
2024-03-01 00:00,A,,52
2024-03-01 00:00,B,20.8,50
2024-03-01 00:00,C,18.1,50
2024-03-01 01:00,A,19.1,53
2024-03-01 01:00,B,20.8,52
2024-03-01 01:00,C,18.1,54
2024-03-01 02:00,A,18.1,52
2024-03-01 02:00,B,19.9,54
2024-03-01 02:00,C,17.5,52
2024-03-01 03:00,A,18.0,54
2024-03-01 03:00,B,20.5,52
```
Open in the playground Download sensor_readings.csv
Generated for Python Quest with a seeded Python script (random.Random with a fixed seed per file, standard library only). Stores, managers, suppliers, students and figures are invented; no real records were used.
Student scores (synthetic)
End-of-term scores for 90 students in three Grade 10 classes (10A, 10B, 10C): maths, science and English out of 100, plus attendance percentage. Clean; names are invented first names with an initial.
Fields (7)
student_id
Student number 2401–2490
name
Invented first name and initial
class
10A, 10B or 10C
math
Maths score 0–100
science
Science score 0–100
english
English score 0–100
attendance_pct
Attendance percentage for the term
data/student_scores.csv · 91 lines · 2.7 KB
```
student_id,name,class,math,science,english,attendance_pct
2401,Aisha A.,10A,100,100,100,96
2402,Iman F.,10A,55,65,54,76
2403,Rana K.,10A,57,60,71,90
2404,Nasser P.,10A,51,59,60,90
2405,Joud U.,10A,88,61,69,95
2406,Ghada Z.,10A,62,75,80,100
2407,Nouf E.,10A,69,67,62,96
2408,Zaid J.,10A,66,73,54,93
2409,Sara O.,10A,86,69,78,97
2410,Eyad T.,10A,78,90,82,89
2411,Layan Y.,10A,67,82,97,89
```
Open in the playground Download student_scores.csv
Used in: 14. DataFrames and loading data · 14. Transforming columns · 14. Visualising data
Generated for Python Quest with a seeded Python script (random.Random with a fixed seed per file, standard library only). Stores, managers, suppliers, students and figures are invented; no real records were used.
Branch sales exports (synthetic)
Three March sales exports from the North, South and West branches of an invented wholesale company. The files describe the same kind of record but disagree on details: the South file has its columns in a different order, the West file uses headers such as "Order ID" with spaces and capitals, and the West file contains one duplicated order. Used to practise combining exports.
Fields (4)
order_id
Order reference, prefixed with the branch letter (header "Order ID" in the West file)
date
Order date, YYYY-MM-DD
customer
Invented customer business name
amount
Order value in SAR, two decimals
data/exports/sales_north.csv · 5 lines · 0.2 KB
```
order_id,date,customer,amount
N-1001,2026-03-02,Al-Rawda Bakery,420.00
N-1002,2026-03-03,Sahel Pharmacy,135.50
N-1003,2026-03-05,Najd Office Supplies,980.00
N-1004,2026-03-09,Al-Rawda Bakery,212.75
```
data/exports/sales_south.csv · 4 lines · 0.1 KB
```
customer,amount,order_id,date
Corniche Cafe,310.00,S-2001,2026-03-01
Tihama Garage,1250.00,S-2002,2026-03-04
Corniche Cafe,95.25,S-2003,2026-03-08
```
data/exports/sales_west.csv · 5 lines · 0.2 KB
```
Order ID,Date,Customer,Amount
W-3001,2026-03-02,Hijaz Textiles,640.00
W-3002,2026-03-06,Yanbu Marine Parts,1875.40
W-3002,2026-03-06,Yanbu Marine Parts,1875.40
W-3003,2026-03-10,Hijaz Textiles,88.00
```
Open in the playground Download sales_north.csv Download sales_south.csv Download sales_west.csv
Used in: 15. Combining exports
Written by hand for Python Quest; every business name and figure is invented.
Monthly branch exports inbox (synthetic)
An inbox folder as a sales system might leave it: September 2026 exports from three branches, one August export that belongs to the previous month, and a README. The exports are deliberately messy: the South file has different column order and header spelling, a duplicated order, an amount of "n/a" and an impossible date (2026-09-31); the East file has a negative amount and a missing branch. Used in the monthly report mini project.
Fields (6)
order_id
Order reference, prefixed with the branch letter
date
Order date, YYYY-MM-DD (one invalid date included)
branch
Branch name: North, South, East or West (one blank)
category
Product category: Bakery, Beverages or Household
amount
Order value in SAR (one "n/a" and one negative value included)
status
paid or refunded
data/inbox/branch-north_2026-09.csv · 6 lines · 0.3 KB
```
order_id,date,branch,category,amount,status
N-501,2026-09-01,North,Bakery,320.50,paid
N-502,2026-09-03,North,Beverages,145.00,paid
N-503,2026-09-07,North,Bakery,98.25,refunded
N-504,2026-09-15,North,Household,410.00,paid
N-505,2026-09-22,North,Beverages,76.40,paid
```
data/inbox/branch-south_2026-09.csv · 6 lines · 0.3 KB
```
Category,Amount,Order ID,Date,Branch,Status
Household,1200.00,S-701,2026-09-02,South,paid
Bakery,215.75,S-702,2026-09-05,South,paid
Beverages,n/a,S-703,2026-09-09,South,paid
Household,330.00,S-704,2026-09-31,South,paid
Bakery,215.75,S-702,2026-09-05,South,paid
```
data/inbox/branch-east_2026-09.csv · 5 lines · 0.2 KB
```
order_id,date,branch,category,amount,status
E-301,2026-09-04,East,Beverages,560.00,paid
E-302,2026-09-11,East,Household,-45.00,paid
E-303,2026-09-18,East,Bakery,189.90,paid
E-304,2026-09-25,,Beverages,240.00,paid
```
data/inbox/branch-west_2026-08.csv · 3 lines · 0.1 KB
```
order_id,date,branch,category,amount,status
W-901,2026-08-28,West,Bakery,150.00,paid
W-902,2026-08-30,West,Household,72.00,paid
```
data/inbox/README.txt · 2 lines · 0.1 KB
```
Branch exports are dropped here by the sales system at the start of each month.
File names: branch-<name>_<YYYY-MM>.csv
```
Open in the playground Download branch-north_2026-09.csv Download branch-south_2026-09.csv Download branch-east_2026-09.csv Download branch-west_2026-08.csv Download README.txt
Used in: 15. Mini project: the monthly operations report
Written by hand for Python Quest; branches, orders and amounts are invented.
Office supplies orders 2025 (synthetic)
546 order lines from Desert Rose Office Supplies, a fictional retailer selling ten products across five Saudi regions, one row per order line for the whole of 2025. Clean and consistently typed, with a seasonal rise towards the end of the year. Prices are in SAR. Used by the sales dashboard project.
Fields (7)
order_id
Unique order line number (integer, 50001 upwards)
date
Order date, YYYY-MM-DD (2025-01-01 to 2025-12-31), rows in date order
region
Central, Western, Eastern, Northern or Southern
product
Product name (10 products)
category
Electronics, Furniture, Home or Stationery
units
Units sold on the line (1–8)
unit_price
Selling price per unit in SAR
data/sales_orders.csv · 547 lines · 29.0 KB
```
order_id,date,region,product,category,units,unit_price
50001,2025-01-01,Western,Notebook Pack,Stationery,8,35
50002,2025-01-02,Eastern,Monitor 27in,Electronics,3,899
50003,2025-01-03,Central,Office Chair,Furniture,2,549
50004,2025-01-05,Eastern,Coffee Mug Set,Home,4,59
50005,2025-01-05,Central,Gel Pens (12),Stationery,6,24
50006,2025-01-06,Central,Notebook Pack,Stationery,2,35
50007,2025-01-06,Northern,Notebook Pack,Stationery,6,35
50008,2025-01-06,Central,Gel Pens (12),Stationery,5,24
50009,2025-01-06,Southern,Wireless Mouse,Electronics,8,79
50010,2025-01-07,Central,Wireless Mouse,Electronics,6,79
50011,2025-01-07,Eastern,Bookshelf,Furniture,4,410
```
Open in the playground Download sales_orders.csv
Used in: 17. Project: sales dashboard with pandas and matplotlib
Generated for Python Quest with a seeded script (fixed-seed pseudo-random generator, standard library only). The business, products and figures are invented; no real records were used.
Web shop access log (synthetic)
One hour (09:00–09:59 on 14 March 2026) of web server access log from a fictional online grocery shop, about 400 lines in a common access-log format. Mostly well-formed, but includes realistic problems: a log-rotation marker, a blank line, a truncated line, a line with no byte count, a status code typed with letters and a line missing its IP and timestamp. 304 responses record their size as '-'. Contains bursts of 5xx errors at 09:17, 09:42 and 09:51. IP addresses come from the reserved documentation ranges.
Fields (5)
ip
Client IP address (documentation ranges 192.0.2.x, 198.51.100.x, 203.0.113.x)
time
Request time in brackets, e.g. [14/Mar/2026:09:42:05 +0300]
request
Quoted method, path (sometimes with a ?query string) and protocol
status
HTTP status code (200, 301, 302, 304, 404, 500, 503)
size
Response size in bytes, or '-' when no body was sent
data/access.log · 400 lines · 32.2 KB
```
192.0.2.39 - - [14/Mar/2026:09:00:08 +0300] "POST /cart HTTP/1.1" 200 822
198.51.100.48 - - [14/Mar/2026:09:00:10 +0300] "GET /static/app.css HTTP/1.1" 200 24590
198.51.100.70 - - [14/Mar/2026:09:00:15 +0300] "GET /products?page=2 HTTP/1.1" 200 17967
203.0.113.10 - - [14/Mar/2026:09:00:18 +0300] "GET /products HTTP/1.1" 200 18236
192.0.2.21 - - [14/Mar/2026:09:00:21 +0300] "GET /search?q=dates HTTP/1.1" 200 9243
203.0.113.17 - - [14/Mar/2026:09:00:34 +0300] "POST /checkout HTTP/1.1" 200 1357
198.51.100.26 - - [14/Mar/2026:09:00:44 +0300] "GET /products HTTP/1.1" 200 18580
198.51.100.26 - - [14/Mar/2026:09:01:02 +0300] "GET /static/logo.png HTTP/1.1" 200 13123
198.51.100.15 - - [14/Mar/2026:09:01:03 +0300] "GET /cart HTTP/1.1" 200 3478
203.0.113.59 - - [14/Mar/2026:09:01:07 +0300] "GET / HTTP/1.1" 200 5290
192.0.2.57 - - [14/Mar/2026:09:01:17 +0300] "GET /static/logo.png HTTP/1.1" 200 13145
198.51.100.81 - - [14/Mar/2026:09:01:39 +0300] "GET /products?page=2 HTTP/1.1" 200 18294
```
Open in the playground Download access.log
Used in: 17. Project: web server log analyzer
Generated for Python Quest with a seeded Python script (standard library only). Businesses, people, products and figures are invented; no real records were used.
Customer sign-ups, uncleaned (synthetic)
68 customer rows exported from a fictional shop's sign-up form, deliberately messy: names in mixed capitals with stray and doubled spaces, upper-case emails with leading spaces, three invalid or missing emails, phone numbers in seven different Saudi mobile formats (plus two invalid and two missing), signup dates in three formats (YYYY-MM-DD, DD/MM/YYYY, '7 Jun 2025') with an impossible date, a non-date and blanks, inconsistent city capitalisation, one blank name, and five customers entered twice with the same email (usually in different case or with stray spaces).
Fields (6)
customer_id
Customer code C1001–C1064 (repeated on duplicate entries)
name
Full name as typed (inconsistent case and spacing, one blank)
email
Email as typed (case and spaces vary; some invalid or empty)
phone
Saudi mobile number in one of several formats, or invalid/empty
signup_date
Sign-up date as YYYY-MM-DD, DD/MM/YYYY or D Mon YYYY; some invalid or empty
city
City name (inconsistent case, trailing spaces, one empty)
data/customers_messy.csv · 69 lines · 5.2 KB
```
customer_id,name,email,phone,signup_date,city
C1001,AHMED AL-HARBI ,ahmed.harbi0@example.com,(056) 299 0954,12 Apr 2025,Mecca
C1002,SARA KARIM,sara.karim@example.com,966592508795,19 Sep 2024,Abha
C1003,Huda Al-Qahtani, HUDA.QAHTANI@EXAMPLE.ORG,966518004882,2024-07-20,Dammam
C1004,Faisal Saleh ,faisal.saleh3@example.org,(056) 225 6741,07/06/2025,Khobar
C1004,faisal saleh,  FAISAL.SALEH3@EXAMPLE.ORG ,056-225-6741,7 Jun 2025,Khobar
C1005,Lina Al-Mutairi,lina.mutairi@example.com,(058) 679 7633,2025-09-08,ABHA
C1006,Omar Mansour,omar.mansourexample.com,966518798449,22/11/2025,Abha
C1007,NOURA AL-SHEHRI,noura.shehri6@mail.example.net,0577836108,2 Jan 2024,Riyadh
C1008,Khalid Othman, KHALID.OTHMAN@EXAMPLE.ORG,051-550-4992,2025-03-01,Riyadh 
C1009,reem al-zahrani ,reem.zahrani@example.org,05512,2024-11-22,Riyadh
C1010,YOUSEF NASSER,yousef.nasser9@example.org,051-462-0745,,Medina
```
Open in the playground Download customers_messy.csv
Used in: 17. Project: customer data-cleaning pipeline
Generated for Python Quest with a seeded Python script (standard library only). Businesses, people, products and figures are invented; no real records were used.
Oasis Coffee Roasters customers (synthetic, uncleaned)
42 customer rows for a fictional speciality coffee roaster: 40 real customers plus two duplicates (one exact repeat, one re-entered with a lower-case id and different capitals). Names, cities and segments have inconsistent capitals and stray spaces; one email and two join dates are blank. Used by the capstone project.
Fields (6)
customer_id
Customer code C001–C040 (one duplicate typed as " c012")
name
Person or business name (mixed case, leading or doubled spaces)
email
Email address (some upper-case, one blank)
city
Saudi city (mixed case, stray spaces)
segment
Retail, Wholesale or Cafe (mixed case, stray spaces)
joined
Date the customer joined, YYYY-MM-DD (two blank)
data/capstone_customers.csv · 43 lines · 3.0 KB
```
customer_id,name,email,city,segment,joined
C001, Saad Al-Ghamdi,saad.al.ghamdi@inbox.example,Jeddah,retail,2024-02-22
C002,Lama Rashed,lama.rashed@inbox.example,jeddah, Retail  ,2024-04-12
C003, Najd Office Hub,najd.office.hub@biz.example,Medina,wholesale,2022-04-16
C004, Bandar Khoury,bandar.khoury@mail.example,jeddah,Retail,2023-06-10
C005,dune cafe,dune.cafe@biz.example,Abha,Cafe,2023-04-24
C006,sultan al-ghamdi,sultan.al.ghamdi@inbox.example,RIYADH,Retail,2022-06-11
C007, Ghada Mansour,ghada.mansour@inbox.example,ABHA, Retail  ,2024-05-02
C008,Abeer Rashed,abeer.rashed@inbox.example,RIYADH,Retail,2024-10-14
C009,Rana Al-Shehri,rana.al.shehri@inbox.example,Khobar,Retail,
C010,palm corner cafe,palm.corner.cafe@biz.example,Jeddah,Cafe,2023-05-08
C011,Coral  Bay Hotel,CORAL.BAY.HOTEL@BIZ.EXAMPLE,Dammam,Wholesale,2024-06-15
```
Open in the playground Download capstone_customers.csv
Used in: 17. Capstone: end-to-end business analytics
Generated for Python Quest with a seeded Node.js script (fixed-seed pseudo-random generator, standard library only). The company, customers, products and figures are invented; no real records were used.
Oasis Coffee Roasters order lines 2025 (synthetic, uncleaned)
290 order lines (147 orders) for 2025 with a rise towards the end of the year. Deliberately messy: six duplicated export rows, customer ids and SKUs in lower case or with spaces, status written several ways, eight cancelled lines, two impossible or wrongly formatted dates, three bad quantities (0, -1, blank), two unknown customers and one unknown SKU. Used by the capstone project.
Fields (7)
line_id
Unique order line id L0001 upwards (repeated on duplicated rows)
order_id
Order number O1001 upwards; an order can have several lines
date
Order date, YYYY-MM-DD (two invalid)
customer_id
Customer code (case and spaces vary; two unknown)
sku
Product code (case varies; one unknown)
qty
Units ordered (three invalid values)
status
completed or cancelled, in inconsistent capitals and spacing
data/capstone_orders.csv · 291 lines · 13.8 KB
```
line_id,order_id,date,customer_id,sku,qty,status
L0001,O1001,2025-01-01,C016,bean-col,1,completed
L0002,O1002,2025-01-12,C019,ACC-FLT,7,completed
L0003,O1002,2025-01-12,C019,ACC-MUG,8,completed
L0004,O1002,2025-01-12,C019,BEAN-ETH,9,completed
L0005,O1003,2025-01-07,C037,eqp-grd,3,completed
L0006,O1003,2025-01-07,C037,bean-bra,2,completed
L0007,O1004,2025-01-20,C034,BEAN-DEC,2,completed
L0008,O1004,2025-01-20,C034,TEA-MNT,2,completed
L0009,O1005,2025-01-07,C033,EQP-V60,3,completed
L0010,O1005,2025-01-07,C033,BEAN-YEM,1,completed
L0011,O1005,2025-01-07,C033,BEAN-COL,2,completed 
```
Open in the playground Download capstone_orders.csv
Used in: 17. Capstone: end-to-end business analytics
Generated for Python Quest with a seeded Node.js script (fixed-seed pseudo-random generator, standard library only). The company, customers, products and figures are invented; no real records were used.
Oasis Coffee Roasters product list (synthetic)
12 products as a JSON list of objects. Small inconsistencies to clean: one SKU in lower case with a leading space, categories in mixed case with a trailing space, one price stored as a string and one name with a trailing space. Used by the capstone project.
Fields (4)
sku
Product code, e.g. BEAN-ETH
name
Product name
category
Coffee beans, Tea, Equipment or Accessories
unit_price
Price in SAR (a number, once a string)
data/capstone_products.json · 74 lines · 1.4 KB
```
[
  {
    "sku": "BEAN-ETH",
    "name": "Ethiopia Yirgacheffe beans 250g",
    "category": "Coffee beans",
    "unit_price": 58
  },
  {
    "sku": " bean-col",
    "name": "Colombia Huila beans 250g",
    "category": "Coffee beans",
    "unit_price": 49
```
Open in the playground Download capstone_products.json
Used in: 17. Capstone: end-to-end business analytics
Generated for Python Quest with a seeded Node.js script (fixed-seed pseudo-random generator, standard library only). The company, customers, products and figures are invented; no real records were used.

Branch sales exports (synthetic)

Web server access log (synthetic)

Staff records with errors (synthetic)

Customer orders in nested JSON (synthetic)

Store sales 2024 (synthetic)

Store directory (synthetic)

Product catalogue (synthetic)

Raw sales export, Jan–Jun 2025 (synthetic, messy)

Employee survey (synthetic, messy)

Website traffic 2024 (synthetic)

Warehouse sensor readings (synthetic)

Student scores (synthetic)

Branch sales exports (synthetic)

Monthly branch exports inbox (synthetic)

Office supplies orders 2025 (synthetic)

Web shop access log (synthetic)

Customer sign-ups, uncleaned (synthetic)

Oasis Coffee Roasters customers (synthetic, uncleaned)

Oasis Coffee Roasters order lines 2025 (synthetic, uncleaned)

Oasis Coffee Roasters product list (synthetic)