Overview

Dataset statistics

Number of variables17
Number of observations1070
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory148.5 KiB
Average record size in memory142.1 B

Variable types

Text3
Categorical10
Numeric4

Dataset

Description도로터널 정보를 제공한다.(터널명,도로종류,도로노선명,소재지지번주소,시설물종별구분,터널 형식,시점 형식,시점 상세형식,종점 형식,종점 상세형식,상하행선분리여부,터널연장,터널 폭,높이,차로수,터널준공년도,관리본부명,관리지사명)
URLhttps://www.data.go.kr/data/15063131/fileData.do

Alerts

도로노선명 is highly overall correlated with 터널준공년도 and 2 other fieldsHigh correlation
종점 상세형식 is highly overall correlated with 도로종류 and 3 other fieldsHigh correlation
시점 형식 is highly overall correlated with 도로종류 and 3 other fieldsHigh correlation
터널 형식 is highly overall correlated with 도로종류High correlation
관리본부명 is highly overall correlated with 도로종류 and 1 other fieldsHigh correlation
시설물종별구분 is highly overall correlated with 터널연장 and 1 other fieldsHigh correlation
도로종류 is highly overall correlated with 터널연장 and 12 other fieldsHigh correlation
시점 상세형식 is highly overall correlated with 도로종류 and 3 other fieldsHigh correlation
종점 형식 is highly overall correlated with 도로종류 and 3 other fieldsHigh correlation
차로수 is highly overall correlated with 터널 폭 and 2 other fieldsHigh correlation
터널연장 is highly overall correlated with 도로종류 and 1 other fieldsHigh correlation
터널 폭 is highly overall correlated with 도로종류 and 1 other fieldsHigh correlation
높이 is highly overall correlated with 도로종류 and 1 other fieldsHigh correlation
터널준공년도 is highly overall correlated with 도로종류 and 1 other fieldsHigh correlation
도로종류 is highly imbalanced (91.7%)Imbalance
터널 형식 is highly imbalanced (85.8%)Imbalance
차로수 is highly imbalanced (73.4%)Imbalance
터널명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:57:33.635325
Analysis finished2023-12-12 00:57:37.992102
Duration4.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

터널명
Text

UNIQUE 

Distinct1070
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2023-12-12T09:57:38.243532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length8.735514
Min length5

Characters and Unicode

Total characters9347
Distinct characters219
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1070 ?
Unique (%)100.0%

Sample

1st row소래터널(판교)
2nd row소래터널(일산)
3rd row수암터널(판교)
4th row수암터널(일산)
5th row수리터널(판교)
ValueCountFrequency (%)
소래터널(판교 1
 
0.1%
파천2터널(상주 1
 
0.1%
옥산터널(영덕 1
 
0.1%
지품7터널(영덕 1
 
0.1%
길안1터널(영덕 1
 
0.1%
길안1터널(상주 1
 
0.1%
길안2터널(영덕 1
 
0.1%
길안2터널(상주 1
 
0.1%
길안3터널(영덕 1
 
0.1%
길안3터널(상주 1
 
0.1%
Other values (1060) 1060
99.1%
2023-12-12T09:57:38.750350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
터 1061
 
11.4%
널 1061
 
11.4%
( 1059
 
11.3%
) 1059
 
11.3%
천 251
 
2.7%
산 230
 
2.5%
양 217
 
2.3%
1 216
 
2.3%
2 202
 
2.2%
서 148
 
1.6%
Other values (209) 3843
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6536
69.9%
Open Punctuation 1059
 
11.3%
Close Punctuation 1059
 
11.3%
Decimal Number 687
 
7.3%
Uppercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
터 1061
 
16.2%
널 1061
 
16.2%
천 251
 
3.8%
산 230
 
3.5%
양 217
 
3.3%
서 148
 
2.3%
주 143
 
2.2%
영 138
 
2.1%
울 112
 
1.7%
부 105
 
1.6%
Other values (195) 3070
47.0%
Decimal Number
ValueCountFrequency (%)
1 216
31.4%
2 202
29.4%
3 130
18.9%
4 69
 
10.0%
5 26
 
3.8%
6 16
 
2.3%
7 12
 
1.7%
0 8
 
1.2%
8 4
 
0.6%
9 4
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
C 3
50.0%
I 3
50.0%
Open Punctuation
ValueCountFrequency (%)
( 1059
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1059
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6536
69.9%
Common 2805
30.0%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
터 1061
 
16.2%
널 1061
 
16.2%
천 251
 
3.8%
산 230
 
3.5%
양 217
 
3.3%
서 148
 
2.3%
주 143
 
2.2%
영 138
 
2.1%
울 112
 
1.7%
부 105
 
1.6%
Other values (195) 3070
47.0%
Common
ValueCountFrequency (%)
( 1059
37.8%
) 1059
37.8%
1 216
 
7.7%
2 202
 
7.2%
3 130
 
4.6%
4 69
 
2.5%
5 26
 
0.9%
6 16
 
0.6%
7 12
 
0.4%
0 8
 
0.3%
Other values (2) 8
 
0.3%
Latin
ValueCountFrequency (%)
C 3
50.0%
I 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6536
69.9%
ASCII 2811
30.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
터 1061
 
16.2%
널 1061
 
16.2%
천 251
 
3.8%
산 230
 
3.5%
양 217
 
3.3%
서 148
 
2.3%
주 143
 
2.2%
영 138
 
2.1%
울 112
 
1.7%
부 105
 
1.6%
Other values (195) 3070
47.0%
ASCII
ValueCountFrequency (%)
( 1059
37.7%
) 1059
37.7%
1 216
 
7.7%
2 202
 
7.2%
3 130
 
4.6%
4 69
 
2.5%
5 26
 
0.9%
6 16
 
0.6%
7 12
 
0.4%
0 8
 
0.3%
Other values (4) 14
 
0.5%

도로종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1
1059 
<NA>
 
11

Length

Max length4
Median length1
Mean length1.0308411
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1059
99.0%
<NA> 11
 
1.0%

Length

2023-12-12T09:57:38.917444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:39.025464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1059
99.0%
na 11
 
1.0%

도로노선명
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
서울양양선
118 
순천완주선
74 
상주영덕선
74 
중부내륙선
70 
남해선(영암순천)
 
65
Other values (30)
669 

Length

Max length15
Median length5
Mean length5.4261682
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수도권제1순환선
2nd row수도권제1순환선
3rd row수도권제1순환선
4th row수도권제1순환선
5th row수도권제1순환선

Common Values

ValueCountFrequency (%)
서울양양선 118
 
11.0%
순천완주선 74
 
6.9%
상주영덕선 74
 
6.9%
중부내륙선 70
 
6.5%
남해선(영암순천) 65
 
6.1%
중앙선 59
 
5.5%
광주대구선 57
 
5.3%
부산포항선 54
 
5.0%
통영대전선,중부선 52
 
4.9%
평택제천선 41
 
3.8%
Other values (25) 406
37.9%

Length

2023-12-12T09:57:39.160484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울양양선 118
 
10.9%
순천완주선 74
 
6.8%
상주영덕선 74
 
6.8%
중부내륙선 70
 
6.5%
남해선(영암순천 65
 
6.0%
중앙선 59
 
5.5%
광주대구선 57
 
5.3%
부산포항선 54
 
5.0%
통영대전선,중부선 52
 
4.8%
평택제천선 41
 
3.8%
Other values (26) 417
38.6%

소재지지번주소
Text

Distinct455
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2023-12-12T09:57:39.563015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length15.628972
Min length12

Characters and Unicode

Total characters16723
Distinct characters253
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

Unique90 ?
Unique (%)8.4%

Sample

1st row경기도 시흥시 대야동
2nd row경기도 시흥시 대야동
3rd row경기도 안산시 상록구 수암동
4th row경기도 안양시 만안구 안양동
5th row경기도 안양시 만안구 안양동
ValueCountFrequency (%)
경상북도 183
 
4.3%
강원도 181
 
4.3%
전라남도 158
 
3.7%
경상남도 120
 
2.8%
전라북도 109
 
2.6%
경기도 109
 
2.6%
충청북도 97
 
2.3%
홍천군 38
 
0.9%
충주시 38
 
0.9%
울산광역시 34
 
0.8%
Other values (777) 3151
74.7%
2023-12-12T09:57:40.093677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3257
19.5%
도 1018
 
6.1%
리 966
 
5.8%
면 814
 
4.9%
군 593
 
3.5%
시 539
 
3.2%
경 446
 
2.7%
남 416
 
2.5%
상 414
 
2.5%
북 411
 
2.5%
Other values (243) 7849
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13466
80.5%
Space Separator 3257
 
19.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1018
 
7.6%
리 966
 
7.2%
면 814
 
6.0%
군 593
 
4.4%
시 539
 
4.0%
경 446
 
3.3%
남 416
 
3.1%
상 414
 
3.1%
북 411
 
3.1%
전 329
 
2.4%
Other values (242) 7520
55.8%
Space Separator
ValueCountFrequency (%)
3257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13466
80.5%
Common 3257
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1018
 
7.6%
리 966
 
7.2%
면 814
 
6.0%
군 593
 
4.4%
시 539
 
4.0%
경 446
 
3.3%
남 416
 
3.1%
상 414
 
3.1%
북 411
 
3.1%
전 329
 
2.4%
Other values (242) 7520
55.8%
Common
ValueCountFrequency (%)
3257
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13466
80.5%
ASCII 3257
 
19.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3257
100.0%
Hangul
ValueCountFrequency (%)
도 1018
 
7.6%
리 966
 
7.2%
면 814
 
6.0%
군 593
 
4.4%
시 539
 
4.0%
경 446
 
3.3%
남 416
 
3.1%
상 414
 
3.1%
북 411
 
3.1%
전 329
 
2.4%
Other values (242) 7520
55.8%

시설물종별구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2종
704 
1종
365 
기타
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1종
2nd row1종
3rd row1종
4th row1종
5th row1종

Common Values

ValueCountFrequency (%)
2종 704
65.8%
1종 365
34.1%
기타 1
 
0.1%

Length

2023-12-12T09:57:40.231587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:40.337574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2종 704
65.8%
1종 365
34.1%
기타 1
 
0.1%

터널 형식
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
NATM
1019 
개착식
 
39
기타
 
5
재래식
 
5
TBM
 
2

Length

Max length4
Median length4
Mean length3.9476636
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNATM
2nd rowNATM
3rd rowNATM
4th rowNATM
5th rowNATM

Common Values

ValueCountFrequency (%)
NATM 1019
95.2%
개착식 39
 
3.6%
기타 5
 
0.5%
재래식 5
 
0.5%
TBM 2
 
0.2%

Length

2023-12-12T09:57:40.484535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:40.592699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
natm 1019
95.2%
개착식 39
 
3.6%
기타 5
 
0.5%
재래식 5
 
0.5%
tbm 2
 
0.2%

시점 형식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
면벽형
548 
돌출형
461 
기타형
61 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row면벽형
2nd row면벽형
3rd row돌출형
4th row돌출형
5th row면벽형

Common Values

ValueCountFrequency (%)
면벽형 548
51.2%
돌출형 461
43.1%
기타형 61
 
5.7%

Length

2023-12-12T09:57:40.706928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:40.816195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
면벽형 548
51.2%
돌출형 461
43.1%
기타형 61
 
5.7%

시점 상세형식
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
반중력식
285 
벨마우스식
251 
아치날개식
195 
원통절개식
160 
날개식
68 
Other values (5)
111 

Length

Max length11
Median length5
Mean length4.5775701
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row반중력식
2nd row반중력식
3rd row벨마우스식
4th row벨마우스식
5th row반중력식

Common Values

ValueCountFrequency (%)
반중력식 285
26.6%
벨마우스식 251
23.5%
아치날개식 195
18.2%
원통절개식 160
15.0%
날개식 68
 
6.4%
돌출식 50
 
4.7%
2-ARCH식 40
 
3.7%
BOX식 17
 
1.6%
기타 2
 
0.2%
Bird Beak 형 2
 
0.2%

Length

2023-12-12T09:57:40.938342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:41.074987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
반중력식 285
26.5%
벨마우스식 251
23.4%
아치날개식 195
18.2%
원통절개식 160
14.9%
날개식 68
 
6.3%
돌출식 50
 
4.7%
2-arch식 40
 
3.7%
box식 17
 
1.6%
기타 2
 
0.2%
bird 2
 
0.2%
Other values (2) 4
 
0.4%

종점 형식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
면벽형
562 
돌출형
443 
기타형
65 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row면벽형
2nd row면벽형
3rd row돌출형
4th row돌출형
5th row면벽형

Common Values

ValueCountFrequency (%)
면벽형 562
52.5%
돌출형 443
41.4%
기타형 65
 
6.1%

Length

2023-12-12T09:57:41.221869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:41.326659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
면벽형 562
52.5%
돌출형 443
41.4%
기타형 65
 
6.1%

종점 상세형식
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
반중력식
323 
벨마우스식
239 
아치날개식
197 
원통절개식
174 
날개식
42 
Other values (5)
95 

Length

Max length11
Median length5
Mean length4.6504673
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row반중력식
2nd row반중력식
3rd row벨마우스식
4th row벨마우스식
5th row반중력식

Common Values

ValueCountFrequency (%)
반중력식 323
30.2%
벨마우스식 239
22.3%
아치날개식 197
18.4%
원통절개식 174
16.3%
날개식 42
 
3.9%
2-ARCH식 40
 
3.7%
돌출식 30
 
2.8%
BOX식 17
 
1.6%
Bird Beak 형 6
 
0.6%
기타 2
 
0.2%

Length

2023-12-12T09:57:41.447358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:41.573619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
반중력식 323
29.9%
벨마우스식 239
22.1%
아치날개식 197
18.2%
원통절개식 174
16.1%
날개식 42
 
3.9%
2-arch식 40
 
3.7%
돌출식 30
 
2.8%
box식 17
 
1.6%
bird 6
 
0.6%
beak 6
 
0.6%
Other values (2) 8
 
0.7%

터널연장
Real number (ℝ)

HIGH CORRELATION 

Distinct717
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean890.35602
Minimum54
Maximum10965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-12-12T09:57:41.749383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile140
Q1343.25
median602
Q31048
95-th percentile2606.85
Maximum10965
Range10911
Interquartile range (IQR)704.75

Descriptive statistics

Standard deviation1018.1087
Coefficient of variation (CV)1.143485
Kurtosis29.16877
Mean890.35602
Median Absolute Deviation (MAD)303.5
Skewness4.3850117
Sum952680.94
Variance1036545.4
MonotonicityNot monotonic
2023-12-12T09:57:41.900561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
380.0 9
 
0.8%
140.0 7
 
0.7%
210.0 7
 
0.7%
212.0 7
 
0.7%
760.0 7
 
0.7%
250.0 6
 
0.6%
680.0 6
 
0.6%
550.0 6
 
0.6%
475.0 6
 
0.6%
520.0 6
 
0.6%
Other values (707) 1003
93.7%
ValueCountFrequency (%)
54.0 1
 
0.1%
65.0 1
 
0.1%
66.0 1
 
0.1%
80.0 2
0.2%
84.0 1
 
0.1%
100.0 4
0.4%
104.0 1
 
0.1%
105.0 1
 
0.1%
107.99 1
 
0.1%
110.0 1
 
0.1%
ValueCountFrequency (%)
10965.0 1
0.1%
10962.0 1
0.1%
7982.0 1
0.1%
7912.0 1
0.1%
7543.0 1
0.1%
7540.0 1
0.1%
7142.0 1
0.1%
7087.0 1
0.1%
6463.83 1
0.1%
6455.0 1
0.1%

터널 폭
Real number (ℝ)

HIGH CORRELATION 

Distinct147
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.843278
Minimum7.6
Maximum47.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-12-12T09:57:42.049997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile9.98
Q110.8125
median11.5
Q312.55
95-th percentile13.9
Maximum47.3
Range39.7
Interquartile range (IQR)1.7375

Descriptive statistics

Standard deviation1.8984983
Coefficient of variation (CV)0.16030176
Kurtosis119.21904
Mean11.843278
Median Absolute Deviation (MAD)0.8
Skewness7.5499703
Sum12672.307
Variance3.6042959
MonotonicityNot monotonic
2023-12-12T09:57:42.227217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.5 184
17.2%
10.7 126
 
11.8%
9.98 104
 
9.7%
12.55 65
 
6.1%
11.6 43
 
4.0%
11.4 29
 
2.7%
12.6 28
 
2.6%
13.6 24
 
2.2%
11.57 21
 
2.0%
11.7 20
 
1.9%
Other values (137) 426
39.8%
ValueCountFrequency (%)
7.6 1
 
0.1%
8.15 1
 
0.1%
9.2 4
 
0.4%
9.57 2
 
0.2%
9.575 6
 
0.6%
9.62 1
 
0.1%
9.623 3
 
0.3%
9.8 1
 
0.1%
9.98 104
9.7%
10.0 8
 
0.7%
ValueCountFrequency (%)
47.3 1
 
0.1%
25.6 1
 
0.1%
23.77 1
 
0.1%
20.4 4
0.4%
18.63 4
0.4%
18.36 2
0.2%
18.3 1
 
0.1%
17.94 3
0.3%
17.93432 1
 
0.1%
17.6 3
0.3%

높이
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3294701
Minimum5
Maximum10.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-12-12T09:57:42.422272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.7
Q17.26
median7.3
Q37.334
95-th percentile8.3665
Maximum10.1
Range5.1
Interquartile range (IQR)0.074

Descriptive statistics

Standard deviation0.55642969
Coefficient of variation (CV)0.075916769
Kurtosis4.6456628
Mean7.3294701
Median Absolute Deviation (MAD)0.037
Skewness0.88613556
Sum7842.533
Variance0.309614
MonotonicityNot monotonic
2023-12-12T09:57:42.581861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.3 227
21.2%
7.29 165
15.4%
6.7 97
 
9.1%
7.33 62
 
5.8%
8.3 38
 
3.6%
7.4 28
 
2.6%
6.55 18
 
1.7%
7.26 18
 
1.7%
7.295 16
 
1.5%
7.334 14
 
1.3%
Other values (113) 387
36.2%
ValueCountFrequency (%)
5.0 2
0.2%
5.2 1
0.1%
5.264 1
0.1%
5.3 1
0.1%
5.45 2
0.2%
5.5 2
0.2%
5.7 1
0.1%
5.907 1
0.1%
6.04 1
0.1%
6.15 2
0.2%
ValueCountFrequency (%)
10.1 1
 
0.1%
10.0 1
 
0.1%
9.8 2
 
0.2%
9.78 2
 
0.2%
9.6 1
 
0.1%
9.3 8
0.7%
9.11 2
 
0.2%
8.8 1
 
0.1%
8.75 1
 
0.1%
8.7 2
 
0.2%

차로수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2
949 
3
 
94
4
 
22
1
 
4
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
2 949
88.7%
3 94
 
8.8%
4 22
 
2.1%
1 4
 
0.4%
8 1
 
0.1%

Length

2023-12-12T09:57:42.711399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:42.809403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 949
88.7%
3 94
 
8.8%
4 22
 
2.1%
1 4
 
0.4%
8 1
 
0.1%

터널준공년도
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.5682
Minimum1969
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-12-12T09:57:42.936845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1969
5-th percentile1995
Q12004
median2009
Q32015
95-th percentile2017
Maximum2020
Range51
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.5167739
Coefficient of variation (CV)0.0037423543
Kurtosis1.4035382
Mean2008.5682
Median Absolute Deviation (MAD)6
Skewness-0.92264912
Sum2149168
Variance56.501889
MonotonicityNot monotonic
2023-12-12T09:57:43.092853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2015 114
 
10.7%
2017 99
 
9.3%
2016 94
 
8.8%
2007 88
 
8.2%
2012 84
 
7.9%
2009 84
 
7.9%
2001 75
 
7.0%
2004 58
 
5.4%
2010 48
 
4.5%
2011 35
 
3.3%
Other values (23) 291
27.2%
ValueCountFrequency (%)
1969 2
 
0.2%
1973 1
 
0.1%
1977 1
 
0.1%
1986 1
 
0.1%
1987 8
 
0.7%
1991 6
 
0.6%
1992 8
 
0.7%
1994 7
 
0.7%
1995 28
2.6%
1996 20
1.9%
ValueCountFrequency (%)
2020 33
 
3.1%
2018 1
 
0.1%
2017 99
9.3%
2016 94
8.8%
2015 114
10.7%
2014 16
 
1.5%
2013 5
 
0.5%
2012 84
7.9%
2011 35
 
3.3%
2010 48
4.5%

관리본부명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
강원본부
211 
광주전남본부
202 
부산경남본부
174 
대구경북본부
156 
충북본부
124 
Other values (3)
203 

Length

Max length6
Median length6
Mean length5.1588785
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수도권본부
2nd row수도권본부
3rd row수도권본부
4th row수도권본부
5th row수도권본부

Common Values

ValueCountFrequency (%)
강원본부 211
19.7%
광주전남본부 202
18.9%
부산경남본부 174
16.3%
대구경북본부 156
14.6%
충북본부 124
11.6%
전북본부 84
 
7.9%
수도권본부 62
 
5.8%
대전충남본부 57
 
5.3%

Length

2023-12-12T09:57:43.245340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:43.376816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원본부 211
19.7%
광주전남본부 202
18.9%
부산경남본부 174
16.3%
대구경북본부 156
14.6%
충북본부 124
11.6%
전북본부 84
 
7.9%
수도권본부 62
 
5.8%
대전충남본부 57
 
5.3%

관리지사명
Text

Distinct53
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2023-12-12T09:57:43.602967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.0869159
Min length4

Characters and Unicode

Total characters4373
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천지사
2nd row인천지사
3rd row시흥지사
4th row시흥지사
5th row시흥지사
ValueCountFrequency (%)
청송지사 74
 
6.9%
춘천지사 70
 
6.5%
보성지사 65
 
6.1%
진안지사 58
 
5.4%
양양지사 56
 
5.2%
경주지사 42
 
3.9%
구례지사 40
 
3.7%
함평지사 38
 
3.6%
엄정지사 37
 
3.5%
서울산지사 33
 
3.1%
Other values (43) 557
52.1%
2023-12-12T09:57:43.988162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 1070
24.5%
사 1070
24.5%
주 167
 
3.8%
천 153
 
3.5%
양 152
 
3.5%
성 109
 
2.5%
보 97
 
2.2%
산 87
 
2.0%
청 86
 
2.0%
송 74
 
1.7%
Other values (48) 1308
29.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4373
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 1070
24.5%
사 1070
24.5%
주 167
 
3.8%
천 153
 
3.5%
양 152
 
3.5%
성 109
 
2.5%
보 97
 
2.2%
산 87
 
2.0%
청 86
 
2.0%
송 74
 
1.7%
Other values (48) 1308
29.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4373
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 1070
24.5%
사 1070
24.5%
주 167
 
3.8%
천 153
 
3.5%
양 152
 
3.5%
성 109
 
2.5%
보 97
 
2.2%
산 87
 
2.0%
청 86
 
2.0%
송 74
 
1.7%
Other values (48) 1308
29.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4373
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 1070
24.5%
사 1070
24.5%
주 167
 
3.8%
천 153
 
3.5%
양 152
 
3.5%
성 109
 
2.5%
보 97
 
2.2%
산 87
 
2.0%
청 86
 
2.0%
송 74
 
1.7%
Other values (48) 1308
29.9%

Interactions

2023-12-12T09:57:36.845254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:35.504323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:35.986353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:36.424444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:36.946914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:35.633508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:36.101389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:36.531775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:37.065472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:35.745608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:36.221345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:36.640032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:37.178584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:35.868755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:36.321613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:36.737987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:57:44.138343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로노선명시설물종별구분터널 형식시점 형식시점 상세형식종점 형식종점 상세형식터널연장터널 폭높이차로수터널준공년도관리본부명관리지사명
도로노선명1.0000.4380.6800.5090.7780.4970.7490.4460.7990.8410.8120.8870.9720.995
시설물종별구분0.4381.0000.1530.2150.1610.2340.1870.8270.7130.5170.5340.2890.1440.531
터널 형식0.6800.1531.0000.4560.7300.4430.7290.5400.3920.7810.6330.1600.2440.774
시점 형식0.5090.2150.4561.0001.0000.9500.8430.2170.2840.4460.1290.0600.2630.565
시점 상세형식0.7780.1610.7301.0001.0000.8310.9680.1250.3270.7220.3780.5060.4530.888
종점 형식0.4970.2340.4430.9500.8311.0001.0000.2600.2750.4390.1140.1530.2650.555
종점 상세형식0.7490.1870.7290.8430.9681.0001.0000.1710.3350.7140.3730.4740.4640.876
터널연장0.4460.8270.5400.2170.1250.2600.1711.0000.3070.1380.0000.0000.1880.479
터널 폭0.7990.7130.3920.2840.3270.2750.3350.3071.0000.7420.8280.2840.3210.807
높이0.8410.5170.7810.4460.7220.4390.7140.1380.7421.0000.9180.4830.4270.849
차로수0.8120.5340.6330.1290.3780.1140.3730.0000.8280.9181.0000.2620.3850.770
터널준공년도0.8870.2890.1600.0600.5060.1530.4740.0000.2840.4830.2621.0000.5300.926
관리본부명0.9720.1440.2440.2630.4530.2650.4640.1880.3210.4270.3850.5301.0001.000
관리지사명0.9950.5310.7740.5650.8880.5550.8760.4790.8070.8490.7700.9261.0001.000
2023-12-12T09:57:44.326793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로노선명종점 상세형식시점 형식터널 형식관리본부명시설물종별구분도로종류시점 상세형식종점 형식차로수
도로노선명1.0000.3620.2930.3620.8320.2411.0000.3920.2840.495
종점 상세형식0.3621.0000.7540.3870.2430.1121.0000.6900.9970.164
시점 형식0.2930.7541.0000.3880.1720.0681.0000.9970.7250.097
터널 형식0.3620.3870.3881.0000.1510.1161.0000.3880.3740.282
관리본부명0.8320.2430.1720.1511.0000.0911.0000.2350.1730.248
시설물종별구분0.2410.1120.0680.1160.0911.0001.0000.0970.0750.474
도로종류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시점 상세형식0.3920.6900.9970.3880.2350.0971.0001.0000.7360.166
종점 형식0.2840.9970.7250.3740.1730.0751.0000.7361.0000.085
차로수0.4950.1640.0970.2820.2480.4741.0000.1660.0851.000
2023-12-12T09:57:44.472761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
터널연장터널 폭높이터널준공년도도로종류도로노선명시설물종별구분터널 형식시점 형식시점 상세형식종점 형식종점 상세형식차로수관리본부명
터널연장1.0000.2860.1970.0931.0000.1770.5360.3480.0970.0570.1180.0780.0000.093
터널 폭0.2861.0000.4990.1791.0000.4900.4140.2790.1230.1740.1200.1760.7290.180
높이0.1970.4991.0000.0961.0000.4720.3610.4360.2990.2980.2930.2920.6200.219
터널준공년도0.0930.1790.0961.0001.0000.5730.1320.1510.0150.2580.0630.2370.1550.293
도로종류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로노선명0.1770.4900.4720.5731.0001.0000.2410.3620.2930.3920.2840.3620.4950.832
시설물종별구분0.5360.4140.3610.1321.0000.2411.0000.1160.0680.0970.0750.1120.4740.091
터널 형식0.3480.2790.4360.1511.0000.3620.1161.0000.3880.3880.3740.3870.2820.151
시점 형식0.0970.1230.2990.0151.0000.2930.0680.3881.0000.9970.7250.7540.0970.172
시점 상세형식0.0570.1740.2980.2581.0000.3920.0970.3880.9971.0000.7360.6900.1660.235
종점 형식0.1180.1200.2930.0631.0000.2840.0750.3740.7250.7361.0000.9970.0850.173
종점 상세형식0.0780.1760.2920.2371.0000.3620.1120.3870.7540.6900.9971.0000.1640.243
차로수0.0000.7290.6200.1551.0000.4950.4740.2820.0970.1660.0850.1641.0000.248
관리본부명0.0930.1800.2190.2931.0000.8320.0910.1510.1720.2350.1730.2430.2481.000

Missing values

2023-12-12T09:57:37.323378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:57:37.880536image/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

터널명도로종류도로노선명소재지지번주소시설물종별구분터널 형식시점 형식시점 상세형식종점 형식종점 상세형식터널연장터널 폭높이차로수터널준공년도관리본부명관리지사명
0소래터널(판교)1수도권제1순환선경기도 시흥시 대야동1종NATM면벽형반중력식면벽형반중력식421.020.46.9241999수도권본부인천지사
1소래터널(일산)1수도권제1순환선경기도 시흥시 대야동1종NATM면벽형반중력식면벽형반중력식446.020.46.9241999수도권본부인천지사
2수암터널(판교)1수도권제1순환선경기도 안산시 상록구 수암동1종NATM돌출형벨마우스식돌출형벨마우스식1254.017.948.6541999수도권본부시흥지사
3수암터널(일산)1수도권제1순환선경기도 안양시 만안구 안양동1종NATM돌출형벨마우스식돌출형벨마우스식1294.017.934328.641999수도권본부시흥지사
4수리터널(판교)1수도권제1순환선경기도 안양시 만안구 안양동1종NATM면벽형반중력식면벽형반중력식1865.017.948.6541999수도권본부시흥지사
5수리터널(일산)1수도권제1순환선경기도 군포시 산본동1종NATM면벽형반중력식면벽형반중력식1882.017.948.641996수도권본부시흥지사
6안양터널(일산)1수도권제1순환선경기도 안양시 동안구 호계동1종개착식기타형BOX식기타형BOX식390.020.48.041996수도권본부시흥지사
7안양터널(판교)1수도권제1순환선경기도 안양시 동안구 호계동1종개착식기타형BOX식기타형BOX식390.020.48.041995수도권본부시흥지사
8광명터널(성남)1제2경인선(인천안양)경기도 광명시 노온사동1종NATM면벽형반중력식돌출형원통절개식905.013.818.331995수도권본부시흥지사
9광명터널(인천)1제2경인선(인천안양)경기도 광명시 소하동1종NATM면벽형반중력식돌출형원통절개식929.013.68.332001수도권본부시흥지사
터널명도로종류도로노선명소재지지번주소시설물종별구분터널 형식시점 형식시점 상세형식종점 형식종점 상세형식터널연장터널 폭높이차로수터널준공년도관리본부명관리지사명
1060치악2터널(부산)<NA>중앙선강원도 원주시 판부면 금대리2종NATM면벽형반중력식면벽형반중력식298.09.986.721995충북본부제천지사
1061치악2터널(춘천)<NA>중앙선강원도 원주시 판부면 금대리2종NATM면벽형반중력식면벽형반중력식179.09.986.721995충북본부제천지사
1062치악3터널(춘천)<NA>중앙선강원도 원주시 판부면 금대리2종NATM면벽형반중력식돌출형원통절개식298.09.986.721999충북본부제천지사
1063치악3터널(부산)<NA>중앙선강원도 원주시 판부면 금대리2종NATM돌출형돌출식돌출형원통절개식320.09.986.721995충북본부제천지사
1064치악4터널(춘천)<NA>중앙선강원도 원주시 판부면 금대리2종NATM면벽형반중력식면벽형반중력식147.09.986.721999충북본부제천지사
1065치악4터널(부산)<NA>중앙선강원도 원주시 판부면 금대리2종NATM면벽형반중력식면벽형반중력식151.09.986.721995충북본부제천지사
1066금대1터널(춘천)<NA>중앙선강원도 원주시 판부면 금대리2종NATM면벽형반중력식면벽형반중력식324.09.986.721999충북본부제천지사
1067금대1터널(부산)<NA>중앙선강원도 원주시 판부면 금대리2종NATM면벽형반중력식면벽형반중력식317.09.986.721995충북본부제천지사
1068금대2터널(춘천)<NA>중앙선강원도 원주시 판부면 금대리2종NATM면벽형반중력식돌출형원통절개식228.09.986.722000충북본부제천지사
1069금대2터널(부산)<NA>중앙선강원도 원주시 판부면 금대리2종NATM돌출형원통절개식면벽형반중력식213.09.986.722000충북본부제천지사