Sarthak's
DigitalPlayground
>>>one question a week
A public practice log for web development and data analysis. Every week I ask one question on X, work through the answers in a Jupyter notebook, and publish the results here as interactive analytics — with the CSVs on GitHub and the raw data on Kaggle.
- questions asked
- 1
- rows analysed
- 314
- CSV files
- 4
- charts drawn
- 4
↑ running on the real CSVs from week 00 · move your cursor over the field
This week's answer
The newest published week, at a glance. Every number below is read straight from the notebook's CSVs when the site builds.
The question
“If I roll a virtual die 10,000 times, does every face really come up one time in six?”
- Every face landed within ±1.8% of the expected 1,667 rolls — the biggest miss was Face 6, up by 29.
- A chi-square goodness-of-fit test gives χ² = 1.03 (5 degrees of freedom, p ≈ 0.96). Nothing to see: the die is fair.
- Rolls simulated
- 10K
- Chi-square
- 1.03
Fig. 01
Rolls per face
Six bars, all within a whisker of the 1,667 a fair die expects.
From a tweet to a chart, every week
Five steps, repeated weekly. The web half is practice in building data interfaces; the notebook half is practice in finding the answer.
Ask
A random question goes out on X — a poll, a reply thread, or a “send me your…” call.
tweetCollect
Replies, votes and any public data that helps answer it get scraped and saved raw.
raw_data.csvAnalyse
Cleaning, joining and counting happen in a Jupyter notebook with pandas.
analysis.ipynbPublish
Tidy result tables go to GitHub; the raw dataset goes to Kaggle for anyone to reuse.
week-NN/*.csvVisualise
This site reads the CSVs at build time and turns them into interactive charts.
/weeks/week-NNEvery question so far
Search by keyword, filter by tag or status. Weeks still collecting answers show up here too.
Take the data, check my work
Nothing here is a screenshot. The numbers behind every chart are public, versioned and downloadable.