Overview

Dataset statistics

Number of variables2
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory19.3 B

Variable types

Numeric1
Text1

Alerts

노선ID has unique valuesUnique
노선명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:37:30.698731
Analysis finished2023-12-10 22:37:30.950590
Duration0.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선ID
Real number (ℝ)

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4100602 × 108
Minimum2.4100204 × 108
Maximum2.4100716 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T07:37:31.034291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.4100204 × 108
5-th percentile2.4100306 × 108
Q12.4100589 × 108
median2.4100658 × 108
Q32.4100703 × 108
95-th percentile2.4100714 × 108
Maximum2.4100716 × 108
Range5125
Interquartile range (IQR)1142

Descriptive statistics

Standard deviation1317.3104
Coefficient of variation (CV)5.4658818 × 10-6
Kurtosis1.2838929
Mean2.4100602 × 108
Median Absolute Deviation (MAD)567
Skewness-1.4649284
Sum1.3737343 × 1010
Variance1735306.7
MonotonicityNot monotonic
2023-12-11T07:37:31.166986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
241006868 1
 
1.8%
241006889 1
 
1.8%
241005960 1
 
1.8%
241005980 1
 
1.8%
241005890 1
 
1.8%
241007070 1
 
1.8%
241005900 1
 
1.8%
241005970 1
 
1.8%
241006973 1
 
1.8%
241007074 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
241002040 1
1.8%
241002680 1
1.8%
241003000 1
1.8%
241003070 1
1.8%
241003550 1
1.8%
241003860 1
1.8%
241003960 1
1.8%
241003980 1
1.8%
241003990 1
1.8%
241004890 1
1.8%
ValueCountFrequency (%)
241007165 1
1.8%
241007164 1
1.8%
241007147 1
1.8%
241007135 1
1.8%
241007096 1
1.8%
241007077 1
1.8%
241007076 1
1.8%
241007075 1
1.8%
241007074 1
1.8%
241007073 1
1.8%

노선명
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-11T07:37:31.360906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length9.6315789
Min length4

Characters and Unicode

Total characters549
Distinct characters78
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st row8877
2nd row9701-1안동-인천공항
3rd rowA4300
4th row8852
5th row7001
ValueCountFrequency (%)
8877 1
 
1.8%
8835안녕동-인천공항 1
 
1.8%
7300의정부-김포공항 1
 
1.8%
8829이천-인천공항 1
 
1.8%
8844진접-인천공항 1
 
1.8%
4000 1
 
1.8%
7100전곡-인천공항 1
 
1.8%
7200의정부-인천공항 1
 
1.8%
8455고양-안성 1
 
1.8%
4200 1
 
1.8%
Other values (47) 47
82.5%
2023-12-11T07:37:31.679633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 71
 
12.9%
- 49
 
8.9%
항 38
 
6.9%
공 38
 
6.9%
8 36
 
6.6%
천 35
 
6.4%
인 33
 
6.0%
4 22
 
4.0%
5 20
 
3.6%
1 19
 
3.5%
Other values (68) 188
34.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
46.6%
Decimal Number 236
43.0%
Dash Punctuation 49
 
8.9%
Close Punctuation 3
 
0.5%
Open Punctuation 3
 
0.5%
Uppercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 38
14.8%
공 38
14.8%
천 35
13.7%
인 33
12.9%
안 6
 
2.3%
포 6
 
2.3%
주 5
 
2.0%
김 5
 
2.0%
역 5
 
2.0%
동 5
 
2.0%
Other values (54) 80
31.2%
Decimal Number
ValueCountFrequency (%)
0 71
30.1%
8 36
15.3%
4 22
 
9.3%
5 20
 
8.5%
1 19
 
8.1%
2 18
 
7.6%
7 17
 
7.2%
3 16
 
6.8%
9 13
 
5.5%
6 4
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 291
53.0%
Hangul 256
46.6%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 38
14.8%
공 38
14.8%
천 35
13.7%
인 33
12.9%
안 6
 
2.3%
포 6
 
2.3%
주 5
 
2.0%
김 5
 
2.0%
역 5
 
2.0%
동 5
 
2.0%
Other values (54) 80
31.2%
Common
ValueCountFrequency (%)
0 71
24.4%
- 49
16.8%
8 36
12.4%
4 22
 
7.6%
5 20
 
6.9%
1 19
 
6.5%
2 18
 
6.2%
7 17
 
5.8%
3 16
 
5.5%
9 13
 
4.5%
Other values (3) 10
 
3.4%
Latin
ValueCountFrequency (%)
A 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 293
53.4%
Hangul 256
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71
24.2%
- 49
16.7%
8 36
12.3%
4 22
 
7.5%
5 20
 
6.8%
1 19
 
6.5%
2 18
 
6.1%
7 17
 
5.8%
3 16
 
5.5%
9 13
 
4.4%
Other values (4) 12
 
4.1%
Hangul
ValueCountFrequency (%)
항 38
14.8%
공 38
14.8%
천 35
13.7%
인 33
12.9%
안 6
 
2.3%
포 6
 
2.3%
주 5
 
2.0%
김 5
 
2.0%
역 5
 
2.0%
동 5
 
2.0%
Other values (54) 80
31.2%

Interactions

2023-12-11T07:37:30.773203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:37:32.031358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선ID노선명
노선ID1.0001.000
노선명1.0001.000

Missing values

2023-12-11T07:37:30.860948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:37:30.923825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

노선ID노선명
02410068688877
12410071359701-1안동-인천공항
2241007076A4300
32410049008852
42410065907001
52410065807000
62410071657000A
72410059105100신흥동-김포공항
82410058808843-1마석-인천공항
92410039909701안동-인천공항
노선ID노선명
472410070754200-1
482410070714100
492410060108834안성-인천공항
502410068948864평택-인천공항
512410048908165
522410068803903북부-인천
532410071478848하남-강변역-인천공항
542410068559500(인천공항-정읍)
552410070961357김포공항-연무대(논산)
562410030004800