Perception of People at a Party (Fixed Size Dataset)
The "Party Dataset" is an open survey available to everyone to contribute their personal perception of the number of people for seven different terms. This dataset contains only the first 56 responses to allow for consistent analysis.
- Dataset Format: Well-formatted CSV with column headers as the first row
- Dataset Size: 56 rows × 7 columns
- CSV File Location: https://waf.cs.illinois.edu/discovery/party.csv
- Dataset Variables:
Several: number ➜ The perceived number of people at a party when "several people were at the party".A Few: number ➜ The perceived number of people at a party when "a few people were at the party".Many: number ➜ The perceived number of people at a party when "many people were at the party".A Couple: number ➜ The perceived number of people at a party when "a couple of people were at the party".Dozens: number ➜ The perceived number of people at a party when "dozens of people were at the party".Some: number ➜ The perceived number of people at a party when "some people were at the party".Scores Of: number | empty ➜ The perceived number of people at a party when "scores of people were at the party".
- Data Collection: When this dataset was presented to users, the phrases appeared in a random order as a measure to eliminate possible bias.
Using the Party Dataset in Python
The dataset can be loaded using the pandas library in Python:
import pandas as pd
df = pd.read_csv("https://waf.cs.illinois.edu/discovery/party.csv")
df| Several | A Few | Many | A Couple | Dozens | Some | Scores Of | |
|---|---|---|---|---|---|---|---|
| 0 | 5.0 | 5.0 | 10.0 | 4.0 | 12.0 | 15.0 | 25.0 |
| 1 | 5.0 | 3.0 | 12.0 | 2.0 | 24.0 | 4.0 | 35.0 |
| 2 | 5.0 | 4.0 | 15.0 | 2.0 | 30.0 | 10.0 | 50.0 |
| 3 | 5.0 | 3.0 | 10.0 | 2.0 | 24.0 | 6.0 | 30.0 |
| 4 | 4.0 | 3.0 | 20.0 | 2.0 | 36.0 | 12.0 | 200.0 |
| 5 | 15.0 | 8.0 | 30.0 | 12.0 | 80.0 | 20.0 | 100.0 |
| 6 | 5.0 | 3.0 | 7.0 | 6.0 | 20.0 | 10.0 | 100.0 |
| 7 | 15.0 | 7.0 | 68.0 | 2.0 | 56.0 | 8.0 | 20.0 |
| 8 | 3.0 | 5.0 | 20.0 | 10.0 | 50.0 | 5.0 | 80.0 |
| 9 | 3.0 | 5.0 | 7.0 | 10.0 | 50.0 | 20.0 | 100.0 |
| 10 | 7.0 | 4.0 | 25.0 | 2.0 | 24.0 | 4.0 | 2.0 |
| 11 | 6.0 | 3.0 | 50.0 | 2.0 | 36.0 | 15.0 | 100.0 |
| 12 | 50.0 | 5.0 | 100.0 | 15.0 | 30.0 | 20.0 | 500.0 |
| 13 | 12.0 | 6.0 | 15.0 | 2.0 | 24.0 | 8.0 | 50.0 |
| 14 | 7.0 | 4.0 | 20.0 | 2.0 | 24.0 | 5.0 | 100.0 |
| 15 | 5.0 | 3.0 | 40.0 | 2.0 | 60.0 | 20.0 | 100.0 |
| 16 | 15.0 | 6.0 | 80.0 | 4.0 | 32.0 | 30.0 | 120.0 |
| 17 | 10.0 | 8.0 | 50.0 | 5.0 | 60.0 | 15.0 | 80.0 |
| 18 | 30.0 | 15.0 | 40.0 | 8.0 | 60.0 | 20.0 | 80.0 |
| 19 | 7.0 | 4.0 | 15.0 | 2.0 | 12.0 | 10.0 | 10.0 |
| 20 | 4.0 | 5.0 | 20.0 | 2.0 | 36.0 | 8.0 | 50.0 |
| 21 | 10.0 | 5.0 | 20.0 | 7.0 | 28.0 | 11.0 | 30.0 |
| 22 | 15.0 | 5.0 | 30.0 | 6.0 | 70.0 | 20.0 | 100.0 |
| 23 | 10.0 | 15.0 | 25.0 | 5.0 | 40.0 | 30.0 | 100.0 |
| 24 | 9.0 | 5.0 | 20.0 | 4.0 | 36.0 | 6.0 | 200.0 |
| 25 | 7.0 | 4.0 | 15.0 | 2.0 | 36.0 | 10.0 | 80.0 |
| 26 | 5.0 | 3.0 | 20.0 | 2.0 | 12.0 | 5.0 | 0.0 |
| 27 | 8.0 | 6.0 | 10.0 | 6.0 | 24.0 | 8.0 | 30.0 |
| 28 | 7.0 | 3.0 | 30.0 | 7.0 | 10.0 | 7.0 | NaN |
| 29 | 3.0 | 5.0 | 15.0 | 8.0 | 50.0 | 20.0 | 80.0 |
| 30 | 7.0 | 3.0 | 15.0 | 4.0 | 35.0 | 5.0 | 100.0 |
| 31 | 50.0 | 13.0 | 100.0 | 5.0 | 36.0 | 15.0 | 15.0 |
| 32 | 30.0 | 3.0 | 20.0 | 2.0 | 24.0 | 5.0 | 0.0 |
| 33 | 7.0 | 10.0 | 50.0 | 12.0 | 72.0 | 15.0 | 100.0 |
| 34 | 9.0 | 6.0 | 13.0 | 5.0 | 24.0 | 8.0 | 30.0 |
| 35 | 8.0 | 12.0 | 25.0 | 35.0 | 56.0 | 40.0 | 100.0 |
| 36 | 6.0 | 3.0 | 10.0 | 2.0 | 24.0 | 8.0 | 30.0 |
| 37 | 7.0 | 3.0 | 15.0 | 2.0 | 30.0 | 6.0 | 40.0 |
| 38 | 10.0 | 4.0 | 15.0 | 3.0 | 80.0 | 12.0 | 37.0 |
| 39 | 12.0 | 5.0 | 33.0 | 10.0 | 50.0 | 25.0 | 100.0 |
| 40 | 10.0 | 3.0 | 15.0 | 2.0 | 24.0 | 7.0 | 40.0 |
| 41 | 15.0 | 10.0 | 20.0 | 6.0 | 40.0 | 15.0 | 50.0 |
| 42 | 6.0 | 3.0 | 13.0 | 8.0 | 14.0 | 10.0 | 30.0 |
| 43 | 0.4 | 0.2 | 0.8 | 0.1 | 0.6 | 0.5 | 0.7 |
| 44 | 3.0 | 14.0 | 22.0 | 15.0 | 32.0 | 20.0 | 40.0 |
| 45 | 30.0 | 3.0 | 50.0 | 2.0 | 24.0 | 20.0 | 100.0 |
| 46 | 5.0 | 3.0 | 10.0 | 2.0 | 24.0 | 4.0 | 40.0 |
| 47 | 60.0 | 5.0 | 100.0 | 3.0 | 50.0 | 20.0 | NaN |
| 48 | 40.0 | 4.0 | 20.0 | 6.0 | 23.0 | 8.0 | 70.0 |
| 49 | 10.0 | 3.0 | 30.0 | 7.0 | 24.0 | 5.0 | 100.0 |
| 50 | 20.0 | 10.0 | 50.0 | 12.0 | 36.0 | 4.0 | 200.0 |
| 51 | 6.0 | 3.0 | 9.0 | 2.0 | 12.0 | 4.0 | 20.0 |
| 52 | 15.0 | 10.0 | 30.0 | 8.0 | 21.0 | 7.0 | 30.0 |
| 53 | 40.0 | 20.0 | 60.0 | 30.0 | 50.0 | 30.0 | 230.0 |
| 54 | 15.0 | 6.0 | 60.0 | 5.0 | 36.0 | 10.0 | 100.0 |
| 55 | 20.0 | 5.0 | 60.0 | 3.0 | 56.0 | 10.0 | 100.0 |
The full Party Dataset stored in a DataFrame (56 rows).
Pages Using the Party Dataset
- Video Walk-Through & Worksheet: Learn Page: Discovering Properties of Center
- Video Walk-Through & Worksheet: Learn Page: Discovering Properties of Spread
- Video Walk-Through & Worksheet: Learn Page: Histograms, Bar Charts, and Box Plots