Overview

Dataset statistics

Number of variables14
Number of observations1048
Missing cells72
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory122.9 KiB
Average record size in memory120.1 B

Variable types

Text3
Numeric7
Categorical4

Dataset

Description경상북도 23개 시군의 교량의 노선번호, 소재지, 교장, 총폭, 유효폭, 교고, 경간수, 최대 경간장, 상부형식, 하부형식, 설계하중 준공년도, 관리주체 현황입니다.
Author경상북도
URLhttps://www.data.go.kr/data/15044819/fileData.do

Alerts

교장(미터) is highly overall correlated with 경간수 and 1 other fieldsHigh correlation
총폭(미터) is highly overall correlated with 유효폭(미터) and 2 other fieldsHigh correlation
유효폭(미터) is highly overall correlated with 총폭(미터) and 2 other fieldsHigh correlation
교고(미터) is highly overall correlated with 최대경간장High correlation
경간수 is highly overall correlated with 교장(미터)High correlation
최대경간장 is highly overall correlated with 교장(미터) and 5 other fieldsHigh correlation
준공년도 is highly overall correlated with 총폭(미터) and 3 other fieldsHigh correlation
상부형식 is highly overall correlated with 최대경간장 and 1 other fieldsHigh correlation
하부형식 is highly overall correlated with 관리주체High correlation
설계하중(DB) is highly overall correlated with 준공년도High correlation
관리주체 is highly overall correlated with 상부형식 and 1 other fieldsHigh correlation
유효폭(미터) has 17 (1.6%) missing valuesMissing
교고(미터) has 35 (3.3%) missing valuesMissing

Reproduction

Analysis started2024-03-14 23:49:52.286232
Analysis finished2024-03-14 23:50:09.054159
Duration16.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선번호
Text

Distinct66
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
2024-03-15T08:50:09.909084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.8883588
Min length5

Characters and Unicode

Total characters7219
Distinct characters16
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

Unique4 ?
Unique (%)0.4%

Sample

1st row국지도20호
2nd row국지도20호
3rd row국지도20호
4th row국지도28호
5th row국지도28호
ValueCountFrequency (%)
지방도917호 87
 
8.3%
지방도930호 50
 
4.8%
지방도914호 41
 
3.9%
국지도69호 39
 
3.7%
국지도69호선 37
 
3.5%
지방도901호 36
 
3.4%
국지도68호선 33
 
3.1%
국도28호선 26
 
2.5%
지방도903호선 26
 
2.5%
지방도929호선 25
 
2.4%
Other values (56) 648
61.8%
2024-03-15T08:50:11.267352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 1048
14.5%
호 1047
14.5%
지 910
12.6%
9 887
12.3%
방 656
9.1%
선 411
 
5.7%
국 392
 
5.4%
1 369
 
5.1%
0 264
 
3.7%
2 262
 
3.6%
Other values (6) 973
13.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4464
61.8%
Decimal Number 2755
38.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 887
32.2%
1 369
13.4%
0 264
 
9.6%
2 262
 
9.5%
3 192
 
7.0%
8 190
 
6.9%
7 174
 
6.3%
6 165
 
6.0%
4 145
 
5.3%
5 107
 
3.9%
Other Letter
ValueCountFrequency (%)
도 1048
23.5%
호 1047
23.5%
지 910
20.4%
방 656
14.7%
선 411
 
9.2%
국 392
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4464
61.8%
Common 2755
38.2%

Most frequent character per script

Common
ValueCountFrequency (%)
9 887
32.2%
1 369
13.4%
0 264
 
9.6%
2 262
 
9.5%
3 192
 
7.0%
8 190
 
6.9%
7 174
 
6.3%
6 165
 
6.0%
4 145
 
5.3%
5 107
 
3.9%
Hangul
ValueCountFrequency (%)
도 1048
23.5%
호 1047
23.5%
지 910
20.4%
방 656
14.7%
선 411
 
9.2%
국 392
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4464
61.8%
ASCII 2755
38.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 1048
23.5%
호 1047
23.5%
지 910
20.4%
방 656
14.7%
선 411
 
9.2%
국 392
 
8.8%
ASCII
ValueCountFrequency (%)
9 887
32.2%
1 369
13.4%
0 264
 
9.6%
2 262
 
9.5%
3 192
 
7.0%
8 190
 
6.9%
7 174
 
6.3%
6 165
 
6.0%
4 145
 
5.3%
5 107
 
3.9%

교량명
Text

Distinct1002
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
2024-03-15T08:50:12.726664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length3.7948473
Min length2

Characters and Unicode

Total characters3977
Distinct characters266
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique963 ?
Unique (%)91.9%

Sample

1st row경정교
2nd row오보교
3rd row하저교
4th row금계교(931)
5th row남대교
ValueCountFrequency (%)
신계교 3
 
0.3%
무릉교 3
 
0.3%
79 3
 
0.3%
백운교 3
 
0.3%
구천교 3
 
0.3%
사촌교 3
 
0.3%
덕산교 3
 
0.3%
화산교 3
 
0.3%
대천교 3
 
0.3%
용화교 3
 
0.3%
Other values (997) 1035
97.2%
2024-03-15T08:50:14.439587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 1047
26.3%
1 143
 
3.6%
2 135
 
3.4%
천 91
 
2.3%
신 71
 
1.8%
68
 
1.7%
) 64
 
1.6%
( 64
 
1.6%
곡 58
 
1.5%
산 57
 
1.4%
Other values (256) 2179
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3306
83.1%
Decimal Number 424
 
10.7%
Space Separator 68
 
1.7%
Close Punctuation 64
 
1.6%
Open Punctuation 64
 
1.6%
Uppercase Letter 48
 
1.2%
Dash Punctuation 2
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 1047
31.7%
천 91
 
2.8%
신 71
 
2.1%
곡 58
 
1.8%
산 57
 
1.7%
대 49
 
1.5%
계 43
 
1.3%
전 40
 
1.2%
장 39
 
1.2%
암 38
 
1.1%
Other values (233) 1773
53.6%
Decimal Number
ValueCountFrequency (%)
1 143
33.7%
2 135
31.8%
3 46
 
10.8%
9 28
 
6.6%
4 28
 
6.6%
8 13
 
3.1%
5 11
 
2.6%
7 8
 
1.9%
6 8
 
1.9%
0 3
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
I 19
39.6%
C 19
39.6%
A 3
 
6.2%
P 2
 
4.2%
M 2
 
4.2%
R 2
 
4.2%
B 1
 
2.1%
Space Separator
ValueCountFrequency (%)
68
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3306
83.1%
Common 623
 
15.7%
Latin 48
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 1047
31.7%
천 91
 
2.8%
신 71
 
2.1%
곡 58
 
1.8%
산 57
 
1.7%
대 49
 
1.5%
계 43
 
1.3%
전 40
 
1.2%
장 39
 
1.2%
암 38
 
1.1%
Other values (233) 1773
53.6%
Common
ValueCountFrequency (%)
1 143
23.0%
2 135
21.7%
68
10.9%
) 64
10.3%
( 64
10.3%
3 46
 
7.4%
9 28
 
4.5%
4 28
 
4.5%
8 13
 
2.1%
5 11
 
1.8%
Other values (6) 23
 
3.7%
Latin
ValueCountFrequency (%)
I 19
39.6%
C 19
39.6%
A 3
 
6.2%
P 2
 
4.2%
M 2
 
4.2%
R 2
 
4.2%
B 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3306
83.1%
ASCII 670
 
16.8%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 1047
31.7%
천 91
 
2.8%
신 71
 
2.1%
곡 58
 
1.8%
산 57
 
1.7%
대 49
 
1.5%
계 43
 
1.3%
전 40
 
1.2%
장 39
 
1.2%
암 38
 
1.1%
Other values (233) 1773
53.6%
ASCII
ValueCountFrequency (%)
1 143
21.3%
2 135
20.1%
68
10.1%
) 64
9.6%
( 64
9.6%
3 46
 
6.9%
9 28
 
4.2%
4 28
 
4.2%
I 19
 
2.8%
C 19
 
2.8%
Other values (12) 56
 
8.4%
None
ValueCountFrequency (%)
２ 1
100.0%

소 재 지
Text

Distinct631
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
2024-03-15T08:50:15.928305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.0763359
Min length5

Characters and Unicode

Total characters8464
Distinct characters237
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique411 ?
Unique (%)39.2%

Sample

1st row영덕 축산 경정
2nd row영덕 영덕 오보
3rd row영덕 영덕 하저
4th row영주 풍기 금계
5th row영주 부석 남대
ValueCountFrequency (%)
영양 147
 
4.7%
의성 89
 
2.8%
청송 71
 
2.3%
상주 71
 
2.3%
경주 67
 
2.1%
울진 67
 
2.1%
안동 66
 
2.1%
포항 61
 
1.9%
문경 58
 
1.9%
영덕 53
 
1.7%
Other values (716) 2382
76.1%
2024-03-15T08:50:17.867795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2192
25.9%
영 281
 
3.3%
천 230
 
2.7%
양 208
 
2.5%
주 190
 
2.2%
성 179
 
2.1%
산 178
 
2.1%
경 153
 
1.8%
동 147
 
1.7%
청 145
 
1.7%
Other values (227) 4561
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6270
74.1%
Space Separator 2192
 
25.9%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 281
 
4.5%
천 230
 
3.7%
양 208
 
3.3%
주 190
 
3.0%
성 179
 
2.9%
산 178
 
2.8%
경 153
 
2.4%
동 147
 
2.3%
청 145
 
2.3%
안 137
 
2.2%
Other values (225) 4422
70.5%
Space Separator
ValueCountFrequency (%)
2192
100.0%
Decimal Number
ValueCountFrequency (%)
3 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6270
74.1%
Common 2194
 
25.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 281
 
4.5%
천 230
 
3.7%
양 208
 
3.3%
주 190
 
3.0%
성 179
 
2.9%
산 178
 
2.8%
경 153
 
2.4%
동 147
 
2.3%
청 145
 
2.3%
안 137
 
2.2%
Other values (225) 4422
70.5%
Common
ValueCountFrequency (%)
2192
99.9%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6270
74.1%
ASCII 2194
 
25.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2192
99.9%
3 2
 
0.1%
Hangul
ValueCountFrequency (%)
영 281
 
4.5%
천 230
 
3.7%
양 208
 
3.3%
주 190
 
3.0%
성 179
 
2.9%
산 178
 
2.8%
경 153
 
2.4%
동 147
 
2.3%
청 145
 
2.3%
안 137
 
2.2%
Other values (225) 4422
70.5%

교장(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct281
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.969244
Minimum6
Maximum780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-03-15T08:50:18.303463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q121.9
median35
Q367.125
95-th percentile210
Maximum780
Range774
Interquartile range (IQR)45.225

Descriptive statistics

Standard deviation80.585575
Coefficient of variation (CV)1.2797609
Kurtosis19.413818
Mean62.969244
Median Absolute Deviation (MAD)18.35
Skewness3.7023692
Sum65991.768
Variance6494.0349
MonotonicityNot monotonic
2024-03-15T08:50:18.719872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 57
 
5.4%
25.0 49
 
4.7%
15.0 45
 
4.3%
20.0 42
 
4.0%
60.0 42
 
4.0%
24.0 40
 
3.8%
40.0 33
 
3.1%
12.0 23
 
2.2%
45.0 23
 
2.2%
26.0 21
 
2.0%
Other values (271) 673
64.2%
ValueCountFrequency (%)
6.0 2
 
0.2%
6.6 1
 
0.1%
6.8 1
 
0.1%
7.0 3
 
0.3%
8.0 7
 
0.7%
8.5 1
 
0.1%
9.0 6
 
0.6%
9.6 3
 
0.3%
10.0 18
1.7%
10.5 1
 
0.1%
ValueCountFrequency (%)
780.0 1
0.1%
730.0 1
0.1%
680.0 1
0.1%
612.0 1
0.1%
450.0 1
0.1%
444.0 1
0.1%
440.0 1
0.1%
434.0 1
0.1%
420.0 1
0.1%
400.0 1
0.1%

총폭(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)12.5%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean11.231737
Minimum4.2
Maximum37.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-03-15T08:50:18.981292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile7.4
Q18.5
median9.5
Q311
95-th percentile21.2
Maximum37.5
Range33.3
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation4.9313575
Coefficient of variation (CV)0.43905564
Kurtosis4.4490457
Mean11.231737
Median Absolute Deviation (MAD)1.35
Skewness2.1361514
Sum11703.47
Variance24.318287
MonotonicityNot monotonic
2024-03-15T08:50:19.315638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.0 187
17.8%
10.5 116
 
11.1%
8.0 88
 
8.4%
8.5 60
 
5.7%
11.0 59
 
5.6%
10.0 55
 
5.2%
7.5 46
 
4.4%
9.5 45
 
4.3%
12.0 31
 
3.0%
20.9 24
 
2.3%
Other values (120) 331
31.6%
ValueCountFrequency (%)
4.2 1
 
0.1%
4.5 1
 
0.1%
5.0 6
 
0.6%
5.4 1
 
0.1%
5.5 6
 
0.6%
5.6 1
 
0.1%
5.7 2
 
0.2%
6.0 16
1.5%
6.5 3
 
0.3%
6.6 2
 
0.2%
ValueCountFrequency (%)
37.5 1
0.1%
33.8 1
0.1%
33.0 1
0.1%
32.0 2
0.2%
30.5 2
0.2%
30.0 1
0.1%
29.9 1
0.1%
29.57 1
0.1%
29.0 2
0.2%
28.6 1
0.1%

유효폭(미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct103
Distinct (%)10.0%
Missing17
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean9.7990398
Minimum4
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-03-15T08:50:19.846259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q17.2
median8.1
Q310
95-th percentile20
Maximum33
Range29
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation4.5381646
Coefficient of variation (CV)0.4631234
Kurtosis4.5241632
Mean9.7990398
Median Absolute Deviation (MAD)1.4
Skewness2.1479154
Sum10102.81
Variance20.594938
MonotonicityNot monotonic
2024-03-15T08:50:20.319695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0 171
16.3%
7.0 113
10.8%
9.5 111
10.6%
10.0 75
 
7.2%
7.5 67
 
6.4%
6.0 54
 
5.2%
9.0 52
 
5.0%
8.5 45
 
4.3%
20.0 40
 
3.8%
6.5 33
 
3.1%
Other values (93) 270
25.8%
ValueCountFrequency (%)
4.0 5
 
0.5%
4.1 1
 
0.1%
4.5 3
 
0.3%
4.9 1
 
0.1%
5.0 19
 
1.8%
5.1 2
 
0.2%
5.2 1
 
0.1%
5.5 5
 
0.5%
5.7 1
 
0.1%
6.0 54
5.2%
ValueCountFrequency (%)
33.0 1
 
0.1%
31.5 1
 
0.1%
31.0 1
 
0.1%
29.0 1
 
0.1%
28.98 1
 
0.1%
28.0 1
 
0.1%
27.5 3
0.3%
27.0 3
0.3%
26.9 1
 
0.1%
26.4 1
 
0.1%

교고(미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct138
Distinct (%)13.6%
Missing35
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean5.5749951
Minimum0
Maximum54
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-03-15T08:50:20.757366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q13.3
median4.4
Q36
95-th percentile12.82
Maximum54
Range54
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation4.4813187
Coefficient of variation (CV)0.80382469
Kurtosis30.164223
Mean5.5749951
Median Absolute Deviation (MAD)1.3
Skewness4.4408612
Sum5647.47
Variance20.082217
MonotonicityNot monotonic
2024-03-15T08:50:21.097200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 87
 
8.3%
4.0 83
 
7.9%
5.0 73
 
7.0%
3.5 58
 
5.5%
2.5 44
 
4.2%
6.0 38
 
3.6%
4.5 37
 
3.5%
7.0 24
 
2.3%
3.2 22
 
2.1%
2.0 22
 
2.1%
Other values (128) 525
50.1%
(Missing) 35
 
3.3%
ValueCountFrequency (%)
0.0 2
 
0.2%
1.0 2
 
0.2%
1.5 2
 
0.2%
1.6 1
 
0.1%
1.7 2
 
0.2%
2.0 22
2.1%
2.1 3
 
0.3%
2.2 9
0.9%
2.3 7
 
0.7%
2.4 2
 
0.2%
ValueCountFrequency (%)
54.0 1
0.1%
48.8 1
0.1%
35.0 1
0.1%
31.6 1
0.1%
31.0 1
0.1%
29.0 2
0.2%
28.7 1
0.1%
28.5 1
0.1%
27.8 1
0.1%
26.0 1
0.1%

경간수
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)1.6%
Missing7
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean3.0259366
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-03-15T08:50:21.435396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum18
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4432599
Coefficient of variation (CV)0.80743923
Kurtosis7.1469563
Mean3.0259366
Median Absolute Deviation (MAD)1
Skewness2.3402538
Sum3150
Variance5.969519
MonotonicityNot monotonic
2024-03-15T08:50:21.696966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 327
31.2%
1 264
25.2%
3 170
16.2%
4 92
 
8.8%
5 58
 
5.5%
6 50
 
4.8%
7 25
 
2.4%
8 14
 
1.3%
9 13
 
1.2%
12 10
 
1.0%
Other values (7) 18
 
1.7%
(Missing) 7
 
0.7%
ValueCountFrequency (%)
1 264
25.2%
2 327
31.2%
3 170
16.2%
4 92
 
8.8%
5 58
 
5.5%
6 50
 
4.8%
7 25
 
2.4%
8 14
 
1.3%
9 13
 
1.2%
10 4
 
0.4%
ValueCountFrequency (%)
18 1
 
0.1%
16 1
 
0.1%
15 6
0.6%
14 2
 
0.2%
13 2
 
0.2%
12 10
1.0%
11 2
 
0.2%
10 4
 
0.4%
9 13
1.2%
8 14
1.3%

최대경간장
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)15.3%
Missing7
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean19.666418
Minimum4.3
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-03-15T08:50:22.195768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile8.5
Q112
median15
Q327.5
95-th percentile45
Maximum150
Range145.7
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation12.467309
Coefficient of variation (CV)0.63393899
Kurtosis13.432832
Mean19.666418
Median Absolute Deviation (MAD)4
Skewness2.4569632
Sum20472.741
Variance155.4338
MonotonicityNot monotonic
2024-03-15T08:50:22.619527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0 135
 
12.9%
30.0 96
 
9.2%
12.0 85
 
8.1%
10.0 68
 
6.5%
13.0 56
 
5.3%
12.5 56
 
5.3%
14.0 53
 
5.1%
11.0 37
 
3.5%
25.0 31
 
3.0%
8.0 29
 
2.8%
Other values (149) 395
37.7%
ValueCountFrequency (%)
4.3 1
 
0.1%
4.5 2
 
0.2%
5.3 1
 
0.1%
5.7 1
 
0.1%
6.0 3
0.3%
6.1 2
 
0.2%
6.3 1
 
0.1%
6.6 1
 
0.1%
6.8 2
 
0.2%
7.0 7
0.7%
ValueCountFrequency (%)
150.0 1
 
0.1%
100.0 1
 
0.1%
72.5 1
 
0.1%
65.0 1
 
0.1%
60.0 3
 
0.3%
56.0 1
 
0.1%
55.0 8
0.8%
54.0 1
 
0.1%
53.0 1
 
0.1%
52.5 1
 
0.1%

상부형식
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
RC슬래브교
339 
RSC
127 
라멘교
98 
PSCI
91 
RA
90 
Other values (41)
303 

Length

Max length11
Median length8
Mean length4.4141221
Min length2

Unique

Unique14 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
RC슬래브교 339
32.3%
RSC 127
 
12.1%
라멘교 98
 
9.4%
PSCI 91
 
8.7%
RA 90
 
8.6%
RCS 65
 
6.2%
PSC I형교 52
 
5.0%
STB 29
 
2.8%
PF 26
 
2.5%
RCT 16
 
1.5%
Other values (36) 115
 
11.0%

Length

2024-03-15T08:50:23.053761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rc슬래브교 339
30.1%
rsc 127
 
11.3%
라멘교 98
 
8.7%
psci 91
 
8.1%
ra 90
 
8.0%
psc 69
 
6.1%
rcs 65
 
5.8%
i형교 52
 
4.6%
stb 29
 
2.6%
pf 26
 
2.3%
Other values (40) 142
12.6%

하부형식
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
벽식
139 
T형 교각식
100 
RTA
93 
중력식
75 
TP
74 
Other values (34)
567 

Length

Max length7
Median length6
Mean length3.2996183
Min length2

Unique

Unique9 ?
Unique (%)0.9%

Sample

1st row라멘식
2nd row중력식
3rd row라멘식
4th row벽식
5th row벽식

Common Values

ValueCountFrequency (%)
벽식 139
13.3%
T형 교각식 100
 
9.5%
RTA 93
 
8.9%
중력식 75
 
7.2%
TP 74
 
7.1%
라멘식 71
 
6.8%
기타 68
 
6.5%
RAP 59
 
5.6%
GP 53
 
5.1%
역T형식 교대 41
 
3.9%
Other values (29) 275
26.2%

Length

2024-03-15T08:50:23.558555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
벽식 140
11.2%
중력식 104
 
8.3%
교각식 101
 
8.1%
t형 101
 
8.1%
교대 96
 
7.7%
rta 93
 
7.5%
라멘식 80
 
6.4%
tp 74
 
5.9%
기타 68
 
5.4%
rap 59
 
4.7%
Other values (25) 332
26.6%

설계하중(DB)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
24.0
696 
18.0
287 
14.0
 
48
13.5
 
17

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18.0
2nd row18.0
3rd row18.0
4th row14.0
5th row24.0

Common Values

ValueCountFrequency (%)
24.0 696
66.4%
18.0 287
27.4%
14.0 48
 
4.6%
13.5 17
 
1.6%

Length

2024-03-15T08:50:24.044004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:50:24.492830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
24.0 696
66.4%
18.0 287
27.4%
14.0 48
 
4.6%
13.5 17
 
1.6%

준공년도
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.4427
Minimum1967
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-03-15T08:50:24.851084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1967
5-th percentile1981
Q11992
median1998
Q32008
95-th percentile2017
Maximum2022
Range55
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.801083
Coefficient of variation (CV)0.0054020465
Kurtosis-0.332224
Mean1999.4427
Median Absolute Deviation (MAD)7
Skewness-0.14668729
Sum2095416
Variance116.66339
MonotonicityNot monotonic
2024-03-15T08:50:25.461331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1995 69
 
6.6%
1994 57
 
5.4%
1992 57
 
5.4%
2002 37
 
3.5%
1999 36
 
3.4%
1991 35
 
3.3%
2011 33
 
3.1%
2004 33
 
3.1%
1998 32
 
3.1%
2001 32
 
3.1%
Other values (45) 627
59.8%
ValueCountFrequency (%)
1967 1
 
0.1%
1968 1
 
0.1%
1970 5
0.5%
1971 4
0.4%
1972 2
 
0.2%
1973 1
 
0.1%
1974 2
 
0.2%
1975 2
 
0.2%
1976 8
0.8%
1977 7
0.7%
ValueCountFrequency (%)
2022 1
 
0.1%
2021 3
 
0.3%
2020 1
 
0.1%
2019 16
1.5%
2018 12
 
1.1%
2017 30
2.9%
2016 29
2.8%
2015 26
2.5%
2014 10
 
1.0%
2013 26
2.5%

관리주체
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
경상북도 북부건설사업소
637 
경상북도 남부건설사업소
411 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도 북부건설사업소
2nd row경상북도 북부건설사업소
3rd row경상북도 북부건설사업소
4th row경상북도 북부건설사업소
5th row경상북도 북부건설사업소

Common Values

ValueCountFrequency (%)
경상북도 북부건설사업소 637
60.8%
경상북도 남부건설사업소 411
39.2%

Length

2024-03-15T08:50:25.963518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:50:26.268546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 1048
50.0%
북부건설사업소 637
30.4%
남부건설사업소 411
 
19.6%

Interactions

2024-03-15T08:50:04.954494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:54.315887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:56.123811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:57.719747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:59.550885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:01.220190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:03.081472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:05.290565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:54.601478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:56.446739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:57.968128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:59.808720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:01.526888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:03.355300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:05.716891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:54.878731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:56.665066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:58.234552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:00.082283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:01.833545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:03.626140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:06.064411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:55.159460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:56.852341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:58.490389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:00.244957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:02.128260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:03.891517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:06.355309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:55.430511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:57.048217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:58.755489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:00.418713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:02.389381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:04.162739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:06.726738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:55.708288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:57.221880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:59.027781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:00.589805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:02.560874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:04.443736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:07.077314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:55.949463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:57.468853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:49:59.304151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:00.759293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:02.853103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:50:04.639773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:50:26.482787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호교장(미터)총폭(미터)유효폭(미터)교고(미터)경간수최대경간장상부형식하부형식설계하중(DB)준공년도관리주체
노선번호1.0000.6840.6780.6930.4400.5000.4940.8200.8720.7720.7121.000
교장(미터)0.6841.0000.1740.1540.5040.7080.5610.8340.6390.1560.1310.136
총폭(미터)0.6780.1741.0000.9700.2710.0000.4190.7010.5890.6060.6450.519
유효폭(미터)0.6930.1540.9701.0000.2290.0000.3700.7190.5730.5370.6310.521
교고(미터)0.4400.5040.2710.2291.0000.3460.4670.7480.6900.3130.3010.130
경간수0.5000.7080.0000.0000.3461.0000.2020.6000.4410.0000.1240.100
최대경간장0.4940.5610.4190.3700.4670.2021.0000.9280.7040.3250.4600.176
상부형식0.8200.8340.7010.7190.7480.6000.9281.0000.9380.4950.6360.954
하부형식0.8720.6390.5890.5730.6900.4410.7040.9381.0000.6890.6610.976
설계하중(DB)0.7720.1560.6060.5370.3130.0000.3250.4950.6891.0000.7710.380
준공년도0.7120.1310.6450.6310.3010.1240.4600.6360.6610.7711.0000.337
관리주체1.0000.1360.5190.5210.1300.1000.1760.9540.9760.3800.3371.000
2024-03-15T08:50:26.850647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리주체상부형식하부형식설계하중(DB)
관리주체1.0000.8720.8730.254
상부형식0.8721.0000.4490.266
하부형식0.8730.4491.0000.419
설계하중(DB)0.2540.2660.4191.000
2024-03-15T08:50:27.134857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교장(미터)총폭(미터)유효폭(미터)교고(미터)경간수최대경간장준공년도상부형식하부형식설계하중(DB)관리주체
교장(미터)1.0000.2850.2630.4970.8040.6420.2520.4450.2890.1000.135
총폭(미터)0.2851.0000.9470.386-0.0600.5880.6890.3130.2440.4080.398
유효폭(미터)0.2630.9471.0000.354-0.0590.5560.6820.3300.2370.3510.401
교고(미터)0.4970.3860.3541.0000.2380.5620.3930.3870.3450.1480.106
경간수0.804-0.060-0.0590.2381.0000.135-0.1380.2430.1660.0000.076
최대경간장0.6420.5880.5560.5620.1351.0000.6040.6920.3720.2280.188
준공년도0.2520.6890.6820.393-0.1380.6041.0000.2660.2930.5870.259
상부형식0.4450.3130.3300.3870.2430.6920.2661.0000.4490.2660.872
하부형식0.2890.2440.2370.3450.1660.3720.2930.4491.0000.4190.873
설계하중(DB)0.1000.4080.3510.1480.0000.2280.5870.2660.4191.0000.254
관리주체0.1350.3980.4010.1060.0760.1880.2590.8720.8730.2541.000

Missing values

2024-03-15T08:50:07.537102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:50:08.350047image/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.
2024-03-15T08:50:08.816807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

노선번호교량명소 재 지교장(미터)총폭(미터)유효폭(미터)교고(미터)경간수최대경간장상부형식하부형식설계하중(DB)준공년도관리주체
0국지도20호경정교영덕 축산 경정73.29.07.55.0612.2RC슬래브교라멘식18.01995경상북도 북부건설사업소
1국지도20호오보교영덕 영덕 오보36.010.08.53.9312.0RC슬래브교중력식18.01988경상북도 북부건설사업소
2국지도20호하저교영덕 영덕 하저40.08.57.52.0313.4RC슬래브교라멘식18.01991경상북도 북부건설사업소
3국지도28호금계교(931)영주 풍기 금계40.09.07.54.3410.0RC슬래브교벽식14.01985경상북도 북부건설사업소
4국지도28호남대교영주 부석 남대26.07.06.04.7213.0RC슬래브교벽식24.01996경상북도 북부건설사업소
5국지도28호남원대교영주 풍기 금계120.020.015.06.0430.0IPC거더교기타24.02016경상북도 북부건설사업소
6국지도28호단곡천교영주 단산 단곡20.013.012.03.0130.3PSC빔교기타24.02016경상북도 북부건설사업소
7국지도28호두봉교영주 부석 소천32.09.08.03.5216.0RC슬래브교벽식24.01998경상북도 북부건설사업소
8국지도28호부석교영주 부석 소천39.07.76.23.0313.0RC슬래브교벽식18.02014경상북도 북부건설사업소
9국지도28호옥대교영주 단산 옥대90.013.010.05.0330.0PSC빔교기타24.02016경상북도 북부건설사업소
노선번호교량명소 재 지교장(미터)총폭(미터)유효폭(미터)교고(미터)경간수최대경간장상부형식하부형식설계하중(DB)준공년도관리주체
1038국도59호선제2이곡교구미 옥성 농소11.011.010.04.0111.0RAETC24.02006경상북도 남부건설사업소
1039국도59호선제2구봉교구미 옥성 구봉30.012.011.03.028.0RAETC24.01994경상북도 남부건설사업소
1040국도59호선제1구봉교구미 옥성 구봉16.013.011.03.0216.0RAETC24.02005경상북도 남부건설사업소
1041국도67호선왜관과선교칠곡 왜관 왜관366.021.120.04.7765.0STBTP24.02012경상북도 남부건설사업소
1042국도67호선중지교칠곡 석적 중지226.020.920.04.5545.2PFTP24.02012경상북도 남부건설사업소
1043국도67호선포남교칠곡 석적 포남15.026.419.02.6115.0RARAA24.02012경상북도 남부건설사업소
1044국도67호선광암교칠곡 석적 중38.025.017.03.0219.0RCSRAP24.01992경상북도 남부건설사업소
1045국도67호선성수천교구미 산동 성수55.023.923.06.3155.0STBRAA24.02014경상북도 남부건설사업소
1046국도67호선오로교구미 장천 오로30.08.47.06.5215.0RA기타18.01990경상북도 남부건설사업소
1047국도67호선수서교군위 군위 수서150.011.510.58.0437.5PSCI역T형24.02013경상북도 남부건설사업소