Sample datasets¶

Two kinds of CSV live here:

Synthetic dumps — the notebooks generate their data inline (so every run is reproducible and 100% offline). These three files are the same data dumped to disk, so you can practise pd.read_csv against a real file instead of building one in memory.
Small real datasets — permissively licensed, bundled so the optional “📊 try it on real data” sections in the notebooks also run offline.

Synthetic dumps¶

File	Rows	Schema	Used by
`support_ops.csv`	60	`channel`, `month`, `month_num`, `tickets_total`, `tickets_auto`, `automation_rate`, `latency_ms`, `satisfaction`, `cost_per_ticket`	NB 47 (capstone analytics)
`api_log.csv`	50	`request_id`, `model`, `segment`, `quarter`, `tokens_in`, `tokens_out`, `latency_ms`	NB 7 (pandas fundamentals)
`customer_feedback.csv`	15	`id`, `text`, `sentiment`, `topic`	sample mirroring the inline data in NB 17 & NB 28

Real datasets (permissively licensed)¶

File	Rows	What it is	Used by	Licence
`penguins.csv`	344	Palmer Penguins — species, island, bill/flipper measurements, body mass, sex (includes real missing values)	NB 9 (visualization)	CC0
`bike_sharing_daily.csv`	731	UCI Bike Sharing — daily rental counts (2011–2012) with weather + calendar features	NB 26 (demand forecasting)	CC BY 4.0

Attribution (required by CC BY 4.0). bike_sharing_daily.csv is the day.csv file from the UCI Bike Sharing Dataset by Hadi Fanaee-T & João Gama (2013) — https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset. Numeric weather columns are normalised as documented there (temp ÷ 41 °C, atemp ÷ 50 °C, hum ÷ 100, windspeed ÷ 67). Palmer Penguins (Horst, Hill & Gorman) is released CC0 via the Palmer Station LTER.

Loading¶

import pandas as pd
df = pd.read_csv("data/penguins.csv")   # or any file above
df.head()

All files are small on purpose — they fit on a screen and travel with this repo. For real-world projects you’d point pd.read_csv at a much bigger file (or a database connection); the technique is identical. See the course README’s Datasets → Going further section for more real datasets you can load in one line.