Overview

Dataset statistics

Number of variables6
Number of observations482
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.6 KiB
Average record size in memory52.3 B

Variable types

Numeric4
Text2

Alerts

NO. is highly overall correlated with 노선번호High correlation
노선번호 is highly overall correlated with NO.High correlation
NO. has unique valuesUnique

Reproduction

Analysis started2024-03-14 03:25:14.326966
Analysis finished2024-03-14 03:25:15.968228
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

NO.
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct482
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.5
Minimum1
Maximum482
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-03-14T12:25:16.253976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.05
Q1121.25
median241.5
Q3361.75
95-th percentile457.95
Maximum482
Range481
Interquartile range (IQR)240.5

Descriptive statistics

Standard deviation139.28568
Coefficient of variation (CV)0.57675229
Kurtosis-1.2
Mean241.5
Median Absolute Deviation (MAD)120.5
Skewness0
Sum116403
Variance19400.5
MonotonicityStrictly increasing
2024-03-14T12:25:16.371670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
363 1
 
0.2%
331 1
 
0.2%
330 1
 
0.2%
329 1
 
0.2%
328 1
 
0.2%
327 1
 
0.2%
326 1
 
0.2%
325 1
 
0.2%
324 1
 
0.2%
Other values (472) 472
97.9%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
482 1
0.2%
481 1
0.2%
480 1
0.2%
479 1
0.2%
478 1
0.2%
477 1
0.2%
476 1
0.2%
475 1
0.2%
474 1
0.2%
473 1
0.2%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean560.41909
Minimum15
Maximum1089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-03-14T12:25:16.480948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile49
Q160
median721
Q3741
95-th percentile795
Maximum1089
Range1074
Interquartile range (IQR)681

Descriptive statistics

Standard deviation306.02667
Coefficient of variation (CV)0.54606754
Kurtosis-0.75637608
Mean560.41909
Median Absolute Deviation (MAD)22
Skewness-1.0335634
Sum270122
Variance93652.323
MonotonicityIncreasing
2024-03-14T12:25:16.592884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 70
 
14.5%
743 28
 
5.8%
745 27
 
5.6%
740 22
 
4.6%
714 19
 
3.9%
55 18
 
3.7%
711 17
 
3.5%
721 17
 
3.5%
15 17
 
3.5%
736 16
 
3.3%
Other values (42) 231
47.9%
ValueCountFrequency (%)
15 17
 
3.5%
37 3
 
0.6%
49 70
14.5%
55 18
 
3.7%
60 16
 
3.3%
635 5
 
1.0%
643 3
 
0.6%
697 1
 
0.2%
701 7
 
1.5%
702 3
 
0.6%
ValueCountFrequency (%)
1089 4
 
0.8%
897 3
 
0.6%
861 5
 
1.0%
799 8
 
1.7%
796 3
 
0.6%
795 12
2.5%
792 3
 
0.6%
751 4
 
0.8%
749 15
3.1%
745 27
5.6%

교 량 명
Text

Distinct458
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2024-03-14T12:25:16.870289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.5145228
Min length2

Characters and Unicode

Total characters1694
Distinct characters208
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

Unique437 ?
Unique (%)90.7%

Sample

1st row월산교(신)
2nd row온천교
3rd row성두교
4th row돌담교
5th row석교2교
ValueCountFrequency (%)
신월교 3
 
0.6%
신성교 3
 
0.6%
신기교 3
 
0.6%
금성교 2
 
0.4%
산내교 2
 
0.4%
서산교 2
 
0.4%
원촌교 2
 
0.4%
밤티교 2
 
0.4%
모정교 2
 
0.4%
유천교 2
 
0.4%
Other values (449) 460
95.2%
2024-03-14T12:25:17.299756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 484
28.6%
2 51
 
3.0%
1 48
 
2.8%
산 45
 
2.7%
신 27
 
1.6%
월 27
 
1.6%
천 25
 
1.5%
동 25
 
1.5%
대 25
 
1.5%
용 23
 
1.4%
Other values (198) 914
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1526
90.1%
Decimal Number 116
 
6.8%
Open Punctuation 19
 
1.1%
Close Punctuation 19
 
1.1%
Uppercase Letter 12
 
0.7%
Space Separator 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 484
31.7%
산 45
 
2.9%
신 27
 
1.8%
월 27
 
1.8%
천 25
 
1.6%
동 25
 
1.6%
대 25
 
1.6%
용 23
 
1.5%
상 23
 
1.5%
암 22
 
1.4%
Other values (188) 800
52.4%
Decimal Number
ValueCountFrequency (%)
2 51
44.0%
1 48
41.4%
3 11
 
9.5%
4 5
 
4.3%
5 1
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
C 6
50.0%
I 6
50.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1526
90.1%
Common 156
 
9.2%
Latin 12
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 484
31.7%
산 45
 
2.9%
신 27
 
1.8%
월 27
 
1.8%
천 25
 
1.6%
동 25
 
1.6%
대 25
 
1.6%
용 23
 
1.5%
상 23
 
1.5%
암 22
 
1.4%
Other values (188) 800
52.4%
Common
ValueCountFrequency (%)
2 51
32.7%
1 48
30.8%
( 19
 
12.2%
) 19
 
12.2%
3 11
 
7.1%
4 5
 
3.2%
2
 
1.3%
5 1
 
0.6%
Latin
ValueCountFrequency (%)
C 6
50.0%
I 6
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1526
90.1%
ASCII 168
 
9.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 484
31.7%
산 45
 
2.9%
신 27
 
1.8%
월 27
 
1.8%
천 25
 
1.6%
동 25
 
1.6%
대 25
 
1.6%
용 23
 
1.5%
상 23
 
1.5%
암 22
 
1.4%
Other values (188) 800
52.4%
ASCII
ValueCountFrequency (%)
2 51
30.4%
1 48
28.6%
( 19
 
11.3%
) 19
 
11.3%
3 11
 
6.5%
C 6
 
3.6%
I 6
 
3.6%
4 5
 
3.0%
2
 
1.2%
5 1
 
0.6%

위 치
Text

Distinct310
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2024-03-14T12:25:17.649749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8
Min length7

Characters and Unicode

Total characters3856
Distinct characters181
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)43.2%

Sample

1st row고창 고창 월산
2nd row고창 고창 월암
3rd row고창 고창 성두
4th row고창 고창 율계
5th row고창 고창 석교
ValueCountFrequency (%)
진안 73
 
5.1%
완주 65
 
4.5%
정읍 45
 
3.1%
남원 45
 
3.1%
고창 44
 
3.1%
장수 42
 
2.9%
임실 40
 
2.8%
김제 35
 
2.4%
익산 32
 
2.2%
순창 27
 
1.9%
Other values (377) 992
68.9%
2024-03-14T12:25:18.086697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
960
24.9%
산 203
 
5.3%
주 121
 
3.1%
안 103
 
2.7%
정 94
 
2.4%
진 78
 
2.0%
창 77
 
2.0%
천 76
 
2.0%
수 69
 
1.8%
완 66
 
1.7%
Other values (171) 2009
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2896
75.1%
Space Separator 960
 
24.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 203
 
7.0%
주 121
 
4.2%
안 103
 
3.6%
정 94
 
3.2%
진 78
 
2.7%
창 77
 
2.7%
천 76
 
2.6%
수 69
 
2.4%
완 66
 
2.3%
남 63
 
2.2%
Other values (170) 1946
67.2%
Space Separator
ValueCountFrequency (%)
960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2896
75.1%
Common 960
 
24.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 203
 
7.0%
주 121
 
4.2%
안 103
 
3.6%
정 94
 
3.2%
진 78
 
2.7%
창 77
 
2.7%
천 76
 
2.6%
수 69
 
2.4%
완 66
 
2.3%
남 63
 
2.2%
Other values (170) 1946
67.2%
Common
ValueCountFrequency (%)
960
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2896
75.1%
ASCII 960
 
24.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
960
100.0%
Hangul
ValueCountFrequency (%)
산 203
 
7.0%
주 121
 
4.2%
안 103
 
3.6%
정 94
 
3.2%
진 78
 
2.7%
창 77
 
2.7%
천 76
 
2.6%
수 69
 
2.4%
완 66
 
2.3%
남 63
 
2.2%
Other values (170) 1946
67.2%

연장(m)
Real number (ℝ)

Distinct198
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.824689
Minimum6
Maximum1226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-03-14T12:25:18.205254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q121
median37
Q375.15
95-th percentile207.625
Maximum1226
Range1220
Interquartile range (IQR)54.15

Descriptive statistics

Standard deviation88.136951
Coefficient of variation (CV)1.3596201
Kurtosis66.476266
Mean64.824689
Median Absolute Deviation (MAD)19
Skewness6.2379994
Sum31245.5
Variance7768.1222
MonotonicityNot monotonic
2024-03-14T12:25:18.330512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 20
 
4.1%
40.0 19
 
3.9%
30.0 18
 
3.7%
24.0 14
 
2.9%
90.0 14
 
2.9%
36.0 13
 
2.7%
120.0 12
 
2.5%
60.0 11
 
2.3%
15.0 10
 
2.1%
50.0 10
 
2.1%
Other values (188) 341
70.7%
ValueCountFrequency (%)
6.0 1
 
0.2%
7.0 3
0.6%
7.5 1
 
0.2%
8.0 3
0.6%
9.0 2
 
0.4%
9.5 1
 
0.2%
9.6 1
 
0.2%
10.0 6
1.2%
10.3 1
 
0.2%
10.4 1
 
0.2%
ValueCountFrequency (%)
1226.0 1
0.2%
600.0 1
0.2%
480.9 1
0.2%
350.2 1
0.2%
350.0 1
0.2%
340.7 1
0.2%
333.3 1
0.2%
320.0 1
0.2%
315.0 2
0.4%
301.0 1
0.2%

교폭(m)
Real number (ℝ)

Distinct76
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.16888
Minimum4
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-03-14T12:25:18.459034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.1
Q19
median10
Q311
95-th percentile21
Maximum36
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.3574042
Coefficient of variation (CV)0.39013798
Kurtosis6.4317945
Mean11.16888
Median Absolute Deviation (MAD)1
Skewness2.2855567
Sum5383.4
Variance18.986971
MonotonicityNot monotonic
2024-03-14T12:25:18.571695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 95
19.7%
11.0 75
15.6%
9.0 44
 
9.1%
8.0 23
 
4.8%
9.5 23
 
4.8%
8.5 22
 
4.6%
7.5 18
 
3.7%
19.4 17
 
3.5%
12.0 14
 
2.9%
10.5 13
 
2.7%
Other values (66) 138
28.6%
ValueCountFrequency (%)
4.0 1
 
0.2%
4.5 1
 
0.2%
4.8 1
 
0.2%
5.0 1
 
0.2%
5.1 2
 
0.4%
5.4 2
 
0.4%
5.5 1
 
0.2%
5.6 2
 
0.4%
5.9 1
 
0.2%
6.0 6
1.2%
ValueCountFrequency (%)
36.0 1
0.2%
35.5 1
0.2%
29.4 1
0.2%
28.6 1
0.2%
28.0 1
0.2%
27.2 1
0.2%
25.8 1
0.2%
25.0 2
0.4%
24.9 1
0.2%
23.9 1
0.2%

Interactions

2024-03-14T12:25:15.526789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:14.561038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:14.912304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:15.206395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:15.605759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:14.642040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:14.987144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:15.277108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:15.685278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:14.732473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:15.056404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:15.375218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:15.759180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:14.834341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:15.128660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:25:15.450659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:25:18.650235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NO.노선번호연장(m)교폭(m)
NO.1.0000.8010.1610.408
노선번호0.8011.0000.0630.249
연장(m)0.1610.0631.0000.000
교폭(m)0.4080.2490.0001.000
2024-03-14T12:25:18.726569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NO.노선번호연장(m)교폭(m)
NO.1.0000.998-0.071-0.172
노선번호0.9981.000-0.077-0.170
연장(m)-0.071-0.0771.0000.100
교폭(m)-0.172-0.1700.1001.000

Missing values

2024-03-14T12:25:15.854499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:25:15.934614image/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

NO.노선번호교 량 명위 치연장(m)교폭(m)
0115월산교(신)고창 고창 월산20.025.0
1215온천교고창 고창 월암40.025.0
2315성두교고창 고창 성두285.324.9
3415돌담교고창 고창 율계39.919.4
4515석교2교고창 고창 석교11.219.4
5615학전교고창 고창 석교80.028.6
6715고창IC교고창 고창 주곡40.019.4
7815고수교고창 고창 도산90.319.4
8915청솔교고창 고창 도산120.019.4
91015아산3교고창 아산 상갑80.219.4
NO.노선번호교 량 명위 치연장(m)교폭(m)
472473861학천교남원 산내 부운60.011.0
473474861내령교남원 산내 내령11.210.0
474475861산내교남원 산내 대정75.011.0
475476897대방3교순창 복흥 대방15.09.0
476477897대방2교순창 복흥 대방31.99.0
477478897대방1교순창 복흥 정산7.57.5
4784791089방곡교무주 무풍 덕지18.09.3
4794801089속동교무주 무풍 은산12.011.0
4804811089철목교무주 무풍 철목75.011.0
4814821089호봉교무주 무풍 현내57.613.0