Overview

Dataset statistics

Number of variables14
Number of observations892
Missing cells1204
Missing cells (%)9.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory106.4 KiB
Average record size in memory122.1 B

Variable types

Numeric7
Categorical5
Text2

Dataset

Description관리번호,구분,관리기관,시설명,위치,출발 X,출발 Y,도착 X,도착 Y,총중량,폭제한,높이제한,길이제한,실제높이
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13668/S/1/datasetView.do

Alerts

폭제한 is highly overall correlated with 관리번호 and 9 other fieldsHigh correlation
관리기관 is highly overall correlated with 관리번호 and 6 other fieldsHigh correlation
높이제한 is highly overall correlated with 실제높이 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
길이제한 is highly overall correlated with 관리번호 and 9 other fieldsHigh correlation
관리번호 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
출발 X is highly overall correlated with 도착 Y and 3 other fieldsHigh correlation
출발 Y is highly overall correlated with 도착 X and 3 other fieldsHigh correlation
도착 X is highly overall correlated with 출발 Y and 3 other fieldsHigh correlation
도착 Y is highly overall correlated with 출발 X and 3 other fieldsHigh correlation
총중량 is highly overall correlated with 폭제한 and 1 other fieldsHigh correlation
실제높이 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
높이제한 is highly imbalanced (98.7%)Imbalance
도착 X has 156 (17.5%) missing valuesMissing
도착 Y has 156 (17.5%) missing valuesMissing
총중량 has 156 (17.5%) missing valuesMissing
실제높이 has 736 (82.5%) missing valuesMissing
관리번호 has unique valuesUnique
총중량 has 16 (1.8%) zerosZeros

Reproduction

Analysis started2024-04-21 16:48:42.322264
Analysis finished2024-04-21 16:48:56.066779
Duration13.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct892
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446.5
Minimum1
Maximum892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2024-04-22T01:48:56.286960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.55
Q1223.75
median446.5
Q3669.25
95-th percentile847.45
Maximum892
Range891
Interquartile range (IQR)445.5

Descriptive statistics

Standard deviation257.64252
Coefficient of variation (CV)0.57702691
Kurtosis-1.2
Mean446.5
Median Absolute Deviation (MAD)223
Skewness0
Sum398278
Variance66379.667
MonotonicityNot monotonic
2024-04-22T01:48:56.715656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 1
 
0.1%
861 1
 
0.1%
850 1
 
0.1%
851 1
 
0.1%
852 1
 
0.1%
853 1
 
0.1%
854 1
 
0.1%
855 1
 
0.1%
856 1
 
0.1%
857 1
 
0.1%
Other values (882) 882
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
892 1
0.1%
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%

구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
교량
461 
육교
156 
지하차도
140 
고가
69 
터널
 
45

Length

Max length4
Median length2
Mean length2.3609865
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
교량 461
51.7%
육교 156
 
17.5%
지하차도 140
 
15.7%
고가 69
 
7.7%
터널 45
 
5.0%
입체교차 21
 
2.4%

Length

2024-04-22T01:48:57.164174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T01:48:57.532601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교량 461
51.7%
육교 156
 
17.5%
지하차도 140
 
15.7%
고가 69
 
7.7%
터널 45
 
5.0%
입체교차 21
 
2.4%

관리기관
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
공단
102 
서부도로사업소
62 
북부도로사업소
62 
강서도로사업소
58 
교량안전과
 
52
Other values (36)
556 

Length

Max length9
Median length2
Mean length3.9674888
Min length2

Unique

Unique9 ?
Unique (%)1.0%

Sample

1st row구로
2nd row구로
3rd row구로
4th row금천
5th row금천

Common Values

ValueCountFrequency (%)
공단 102
 
11.4%
서부도로사업소 62
 
7.0%
북부도로사업소 62
 
7.0%
강서도로사업소 58
 
6.5%
교량안전과 52
 
5.8%
성동도로사업소 46
 
5.2%
서초 41
 
4.6%
남부도로사업소 39
 
4.4%
동부도로사업소 38
 
4.3%
성북 36
 
4.0%
Other values (31) 356
39.9%

Length

2024-04-22T01:48:57.952085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공단 102
 
11.4%
서부도로사업소 62
 
7.0%
북부도로사업소 62
 
7.0%
강서도로사업소 58
 
6.5%
교량안전과 52
 
5.8%
성동도로사업소 46
 
5.2%
서초 41
 
4.6%
남부도로사업소 39
 
4.4%
동부도로사업소 38
 
4.3%
성북 37
 
4.1%
Other values (29) 355
39.8%

시설명
Text

Distinct855
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2024-04-22T01:48:59.227751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length4.3587444
Min length2

Characters and Unicode

Total characters3888
Distinct characters341
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique821 ?
Unique (%)92.0%

Sample

1st row이씨의다리
2nd row구일역앞
3rd row신구로 유수지앞
4th row한신아파트앞
5th row두산초교앞
ValueCountFrequency (%)
화랑교 3
 
0.3%
잠실대교 3
 
0.3%
여의교 3
 
0.3%
여의2교 3
 
0.3%
두모교 3
 
0.3%
반포대교 2
 
0.2%
보도육교 2
 
0.2%
행당 2
 
0.2%
반포대교통과교 2
 
0.2%
군자 2
 
0.2%
Other values (859) 899
97.3%
2024-04-22T01:49:00.977133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 546
 
14.0%
180
 
4.6%
2 75
 
1.9%
동 72
 
1.9%
1 66
 
1.7%
앞 65
 
1.7%
고 63
 
1.6%
대 62
 
1.6%
신 60
 
1.5%
리 57
 
1.5%
Other values (331) 2642
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3394
87.3%
Decimal Number 218
 
5.6%
Space Separator 180
 
4.6%
Uppercase Letter 41
 
1.1%
Open Punctuation 23
 
0.6%
Close Punctuation 22
 
0.6%
Other Punctuation 4
 
0.1%
Math Symbol 3
 
0.1%
Letter Number 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 546
 
16.1%
동 72
 
2.1%
앞 65
 
1.9%
고 63
 
1.9%
대 62
 
1.8%
신 60
 
1.8%
리 57
 
1.7%
가 57
 
1.7%
산 56
 
1.6%
천 53
 
1.6%
Other values (305) 2303
67.9%
Decimal Number
ValueCountFrequency (%)
2 75
34.4%
1 66
30.3%
3 33
15.1%
4 16
 
7.3%
7 9
 
4.1%
5 6
 
2.8%
8 4
 
1.8%
6 4
 
1.8%
0 3
 
1.4%
9 2
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
C 18
43.9%
I 17
41.5%
A 2
 
4.9%
B 2
 
4.9%
R 1
 
2.4%
D 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 3
75.0%
? 1
 
25.0%
Math Symbol
ValueCountFrequency (%)
~ 2
66.7%
> 1
33.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%
Space Separator
ValueCountFrequency (%)
180
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3394
87.3%
Common 451
 
11.6%
Latin 43
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 546
 
16.1%
동 72
 
2.1%
앞 65
 
1.9%
고 63
 
1.9%
대 62
 
1.8%
신 60
 
1.8%
리 57
 
1.7%
가 57
 
1.7%
산 56
 
1.6%
천 53
 
1.6%
Other values (305) 2303
67.9%
Common
ValueCountFrequency (%)
180
39.9%
2 75
16.6%
1 66
 
14.6%
3 33
 
7.3%
( 23
 
5.1%
) 22
 
4.9%
4 16
 
3.5%
7 9
 
2.0%
5 6
 
1.3%
8 4
 
0.9%
Other values (8) 17
 
3.8%
Latin
ValueCountFrequency (%)
C 18
41.9%
I 17
39.5%
A 2
 
4.7%
B 2
 
4.7%
Ⅱ 1
 
2.3%
Ⅰ 1
 
2.3%
R 1
 
2.3%
D 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3394
87.3%
ASCII 492
 
12.7%
Number Forms 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 546
 
16.1%
동 72
 
2.1%
앞 65
 
1.9%
고 63
 
1.9%
대 62
 
1.8%
신 60
 
1.8%
리 57
 
1.7%
가 57
 
1.7%
산 56
 
1.6%
천 53
 
1.6%
Other values (305) 2303
67.9%
ASCII
ValueCountFrequency (%)
180
36.6%
2 75
15.2%
1 66
 
13.4%
3 33
 
6.7%
( 23
 
4.7%
) 22
 
4.5%
C 18
 
3.7%
I 17
 
3.5%
4 16
 
3.3%
7 9
 
1.8%
Other values (14) 33
 
6.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%

위치
Text

Distinct383
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2024-04-22T01:49:02.099524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length7
Mean length9.0123318
Min length6

Characters and Unicode

Total characters8039
Distinct characters192
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique245 ?
Unique (%)27.5%

Sample

1st row구로구 신도림동
2nd row구로구 구로1동
3rd row구로구 구로동
4th row금천구 독산동
5th row금천구 독산동
ValueCountFrequency (%)
178
 
7.9%
서초구 80
 
3.5%
성북구 61
 
2.7%
용산구 56
 
2.5%
구로구 52
 
2.3%
노원구 50
 
2.2%
강남구 48
 
2.1%
영등포구 46
 
2.0%
종로구 46
 
2.0%
도봉구 46
 
2.0%
Other values (323) 1600
70.7%
2024-04-22T01:49:03.765187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1377
17.1%
동 1217
 
15.1%
구 1075
 
13.4%
~ 181
 
2.3%
서 167
 
2.1%
성 140
 
1.7%
로 137
 
1.7%
강 133
 
1.7%
문 127
 
1.6%
포 121
 
1.5%
Other values (182) 3364
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6421
79.9%
Space Separator 1377
 
17.1%
Math Symbol 181
 
2.3%
Decimal Number 59
 
0.7%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1217
 
19.0%
구 1075
 
16.7%
서 167
 
2.6%
성 140
 
2.2%
로 137
 
2.1%
강 133
 
2.1%
문 127
 
2.0%
포 121
 
1.9%
대 105
 
1.6%
신 99
 
1.5%
Other values (172) 3100
48.3%
Decimal Number
ValueCountFrequency (%)
1 16
27.1%
3 14
23.7%
2 10
16.9%
5 8
13.6%
7 5
 
8.5%
4 4
 
6.8%
6 2
 
3.4%
Space Separator
ValueCountFrequency (%)
1377
100.0%
Math Symbol
ValueCountFrequency (%)
~ 181
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6421
79.9%
Common 1618
 
20.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1217
 
19.0%
구 1075
 
16.7%
서 167
 
2.6%
성 140
 
2.2%
로 137
 
2.1%
강 133
 
2.1%
문 127
 
2.0%
포 121
 
1.9%
대 105
 
1.6%
신 99
 
1.5%
Other values (172) 3100
48.3%
Common
ValueCountFrequency (%)
1377
85.1%
~ 181
 
11.2%
1 16
 
1.0%
3 14
 
0.9%
2 10
 
0.6%
5 8
 
0.5%
7 5
 
0.3%
4 4
 
0.2%
6 2
 
0.1%
? 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6421
79.9%
ASCII 1618
 
20.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1377
85.1%
~ 181
 
11.2%
1 16
 
1.0%
3 14
 
0.9%
2 10
 
0.6%
5 8
 
0.5%
7 5
 
0.3%
4 4
 
0.2%
6 2
 
0.1%
? 1
 
0.1%
Hangul
ValueCountFrequency (%)
동 1217
 
19.0%
구 1075
 
16.7%
서 167
 
2.6%
성 140
 
2.2%
로 137
 
2.1%
강 133
 
2.1%
문 127
 
2.0%
포 121
 
1.9%
대 105
 
1.6%
신 99
 
1.5%
Other values (172) 3100
48.3%

출발 X
Real number (ℝ)

HIGH CORRELATION 

Distinct884
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199351.86
Minimum180578.62
Maximum215581.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2024-04-22T01:49:04.168611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum180578.62
5-th percentile187082.43
Q1193518.8
median200141.86
Q3204851.76
95-th percentile210752.68
Maximum215581.33
Range35002.708
Interquartile range (IQR)11332.959

Descriptive statistics

Standard deviation7349.7869
Coefficient of variation (CV)0.036868414
Kurtosis-0.65760129
Mean199351.86
Median Absolute Deviation (MAD)5604.9856
Skewness-0.1369397
Sum1.7782186 × 108
Variance54019368
MonotonicityNot monotonic
2024-04-22T01:49:04.617900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199710.1995 3
 
0.3%
205341.3286 2
 
0.2%
199851.1232 2
 
0.2%
199220.4305 2
 
0.2%
198869.0534 2
 
0.2%
190282.7723 2
 
0.2%
195838.3533 2
 
0.2%
205290.0845 1
 
0.1%
198028.2307 1
 
0.1%
198053.7538 1
 
0.1%
Other values (874) 874
98.0%
ValueCountFrequency (%)
180578.6201 1
0.1%
182160.3511 1
0.1%
182230.1357 1
0.1%
182328.9593 1
0.1%
182589.59 1
0.1%
182733.6344 1
0.1%
182920.7219 1
0.1%
182933.8365 1
0.1%
183229.0405 1
0.1%
183570.4337 1
0.1%
ValueCountFrequency (%)
215581.3285 1
0.1%
215500.4951 1
0.1%
215450.6416 1
0.1%
215374.1504 1
0.1%
215364.829 1
0.1%
215316.782 1
0.1%
215283.7951 1
0.1%
215243.8081 1
0.1%
215237.8421 1
0.1%
215233.5978 1
0.1%

출발 Y
Real number (ℝ)

HIGH CORRELATION 

Distinct886
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean550249.93
Minimum537208.07
Maximum565482.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2024-04-22T01:49:05.028061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum537208.07
5-th percentile540468.16
Q1544976.29
median549906.14
Q3555144.43
95-th percentile561630.34
Maximum565482.77
Range28274.693
Interquartile range (IQR)10168.15

Descriptive statistics

Standard deviation6532.0546
Coefficient of variation (CV)0.011871068
Kurtosis-0.81614605
Mean550249.93
Median Absolute Deviation (MAD)5074.1907
Skewness0.23951546
Sum4.9082293 × 108
Variance42667737
MonotonicityNot monotonic
2024-04-22T01:49:05.449417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
546751.2435 3
 
0.3%
545238.1826 2
 
0.2%
558883.6402 2
 
0.2%
546468.12 2
 
0.2%
545635.0659 2
 
0.2%
545796.1451 1
 
0.1%
557051.8894 1
 
0.1%
539495.497 1
 
0.1%
545060.2505 1
 
0.1%
545022.3305 1
 
0.1%
Other values (876) 876
98.2%
ValueCountFrequency (%)
537208.0741 1
0.1%
537564.9382 1
0.1%
537590.3167 1
0.1%
537701.7729 1
0.1%
538382.0736 1
0.1%
538438.0181 1
0.1%
538555.6991 1
0.1%
538970.6638 1
0.1%
539014.8774 1
0.1%
539343.3194 1
0.1%
ValueCountFrequency (%)
565482.7672 1
0.1%
565172.0322 1
0.1%
565148.6204 1
0.1%
565092.8106 1
0.1%
565032.9872 1
0.1%
564806.5058 1
0.1%
564559.0162 1
0.1%
564537.27 1
0.1%
564514.991 1
0.1%
564456.0839 1
0.1%

도착 X
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct730
Distinct (%)99.2%
Missing156
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean550836.67
Minimum537707.81
Maximum565461.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2024-04-22T01:49:05.842316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum537707.81
5-th percentile540599.66
Q1545502.44
median551066.64
Q3555778.84
95-th percentile561981.46
Maximum565461.55
Range27753.738
Interquartile range (IQR)10276.401

Descriptive statistics

Standard deviation6591.7216
Coefficient of variation (CV)0.011966744
Kurtosis-0.9016662
Mean550836.67
Median Absolute Deviation (MAD)5261.0554
Skewness0.13789008
Sum4.0541579 × 108
Variance43450794
MonotonicityNot monotonic
2024-04-22T01:49:06.261741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
549346.9373 3
 
0.3%
546745.2273 2
 
0.2%
559370.6628 2
 
0.2%
546191.2861 2
 
0.2%
545665.329 2
 
0.2%
540868.4821 1
 
0.1%
539470.0703 1
 
0.1%
544461.6559 1
 
0.1%
542083.3077 1
 
0.1%
542025.43 1
 
0.1%
Other values (720) 720
80.7%
(Missing) 156
 
17.5%
ValueCountFrequency (%)
537707.813 1
0.1%
538146.8529 1
0.1%
538506.0922 1
0.1%
538556.1772 1
0.1%
538988.3524 1
0.1%
539056.7615 1
0.1%
539352.4027 1
0.1%
539398.409 1
0.1%
539420.0528 1
0.1%
539424.5913 1
0.1%
ValueCountFrequency (%)
565461.5509 1
0.1%
565211.3036 1
0.1%
565175.8083 1
0.1%
564839.3579 1
0.1%
564599.9484 1
0.1%
564556.9057 1
0.1%
564554.6401 1
0.1%
564496.1105 1
0.1%
564476.0499 1
0.1%
564468.5327 1
0.1%

도착 Y
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct730
Distinct (%)99.2%
Missing156
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean199708.48
Minimum180612.98
Maximum215553.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2024-04-22T01:49:06.661998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum180612.98
5-th percentile187735.17
Q1193829.84
median200737.18
Q3204911.53
95-th percentile210679.78
Maximum215553.72
Range34940.733
Interquartile range (IQR)11081.685

Descriptive statistics

Standard deviation7187.3022
Coefficient of variation (CV)0.035988968
Kurtosis-0.5856095
Mean199708.48
Median Absolute Deviation (MAD)5423.2444
Skewness-0.19187238
Sum1.4698544 × 108
Variance51657312
MonotonicityNot monotonic
2024-04-22T01:49:07.104156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202494.7639 3
 
0.3%
199688.5961 2
 
0.2%
205625.4594 2
 
0.2%
197597.9851 2
 
0.2%
199900.7108 2
 
0.2%
210940.2308 1
 
0.1%
204665.983 1
 
0.1%
212561.7843 1
 
0.1%
194075.8753 1
 
0.1%
194253.1256 1
 
0.1%
Other values (720) 720
80.7%
(Missing) 156
 
17.5%
ValueCountFrequency (%)
180612.9849 1
0.1%
182191.9747 1
0.1%
182298.248 1
0.1%
182363.6235 1
0.1%
182749.2549 1
0.1%
182762.104 1
0.1%
182837.3929 1
0.1%
183005.8971 1
0.1%
183726.4769 1
0.1%
183869.128 1
0.1%
ValueCountFrequency (%)
215553.7176 1
0.1%
215460.203 1
0.1%
215402.8346 1
0.1%
215393.4118 1
0.1%
215377.4969 1
0.1%
215326.8214 1
0.1%
215274.8317 1
0.1%
215267.5408 1
0.1%
215241.2469 1
0.1%
215205.8697 1
0.1%

총중량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)1.8%
Missing156
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean36.464674
Minimum0
Maximum40
Zeros16
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2024-04-22T01:49:07.486537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q132
median40
Q340
95-th percentile40
Maximum40
Range40
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.5274628
Coefficient of variation (CV)0.20643165
Kurtosis10.878164
Mean36.464674
Median Absolute Deviation (MAD)0
Skewness-3.0856879
Sum26838
Variance56.662696
MonotonicityNot monotonic
2024-04-22T01:49:07.865872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
40 531
59.5%
32 144
 
16.1%
24 22
 
2.5%
0 16
 
1.8%
18 5
 
0.6%
20 4
 
0.4%
23 4
 
0.4%
16 3
 
0.3%
10 2
 
0.2%
38 2
 
0.2%
Other values (3) 3
 
0.3%
(Missing) 156
 
17.5%
ValueCountFrequency (%)
0 16
1.8%
5 1
 
0.1%
10 2
 
0.2%
16 3
 
0.3%
18 5
 
0.6%
20 4
 
0.4%
21 1
 
0.1%
23 4
 
0.4%
24 22
2.5%
30 1
 
0.1%
ValueCountFrequency (%)
40 531
59.5%
38 2
 
0.2%
32 144
 
16.1%
30 1
 
0.1%
24 22
 
2.5%
23 4
 
0.4%
21 1
 
0.1%
20 4
 
0.4%
18 5
 
0.6%
16 3
 
0.3%

폭제한
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2.5
736 
<NA>
156 

Length

Max length4
Median length3
Mean length3.1748879
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
2.5 736
82.5%
<NA> 156
 
17.5%

Length

2024-04-22T01:49:08.285874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T01:49:08.614669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.5 736
82.5%
na 156
 
17.5%

높이제한
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
4
891 
3
 
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 (%)
4 891
99.9%
3 1
 
0.1%

Length

2024-04-22T01:49:08.951335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T01:49:09.258765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 891
99.9%
3 1
 
0.1%

길이제한
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
16.7
736 
<NA>
156 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
16.7 736
82.5%
<NA> 156
 
17.5%

Length

2024-04-22T01:49:09.589014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T01:49:09.897661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
16.7 736
82.5%
na 156
 
17.5%

실제높이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)15.4%
Missing736
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean4.7698718
Minimum0
Maximum23
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2024-04-22T01:49:10.382887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.175
Q14.5
median4.5
Q34.8
95-th percentile6.5
Maximum23
Range23
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation2.0524547
Coefficient of variation (CV)0.43029557
Kurtosis46.597799
Mean4.7698718
Median Absolute Deviation (MAD)0.1
Skewness5.4720286
Sum744.1
Variance4.2125703
MonotonicityNot monotonic
2024-04-22T01:49:10.580739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4.5 76
 
8.5%
5.0 15
 
1.7%
4.8 10
 
1.1%
4.7 10
 
1.1%
4.3 7
 
0.8%
4.2 7
 
0.8%
0.0 5
 
0.6%
4.4 4
 
0.4%
4.6 4
 
0.4%
6.5 2
 
0.2%
Other values (14) 16
 
1.8%
(Missing) 736
82.5%
ValueCountFrequency (%)
0.0 5
 
0.6%
4.0 2
 
0.2%
4.1 1
 
0.1%
4.2 7
 
0.8%
4.3 7
 
0.8%
4.4 4
 
0.4%
4.5 76
8.5%
4.6 4
 
0.4%
4.7 10
 
1.1%
4.8 10
 
1.1%
ValueCountFrequency (%)
23.0 1
0.1%
16.3 1
0.1%
9.0 1
0.1%
8.5 1
0.1%
8.0 1
0.1%
7.0 1
0.1%
6.7 1
0.1%
6.5 2
0.2%
6.2 1
0.1%
6.0 1
0.1%

Interactions

2024-04-22T01:48:52.943088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:43.713223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:45.528668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:47.387985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:49.194969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:50.314394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:51.482390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:53.195345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:43.953894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:45.787644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:47.638668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:49.363015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:50.492935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:51.658671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:53.438901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:44.221226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:46.062877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:47.909860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:49.535065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:50.666419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:51.840086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:53.673656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:44.473080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:46.331246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:48.166368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:49.688479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:50.831349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:52.005586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:53.900087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:44.735183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:46.593323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:48.418073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:49.843338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:50.996007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:52.169680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:54.328044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:45.008811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:46.869265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:48.687421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:50.012793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:51.171193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:52.439723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:54.552992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:45.287892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:47.150889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:48.958869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:50.188135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:51.351797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:48:52.716456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-22T01:49:10.734560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호구분관리기관출발 X출발 Y도착 X도착 Y총중량높이제한실제높이
관리번호1.0000.8780.8960.5120.6440.5050.4070.5970.0080.224
구분0.8781.0000.6810.2630.2620.3450.3620.3750.029NaN
관리기관0.8960.6811.0000.8880.8650.8620.8720.7660.0000.490
출발 X0.5120.2630.8881.0000.6210.6360.9990.2670.0000.283
출발 Y0.6440.2620.8650.6211.0000.9980.6230.2420.0000.342
도착 X0.5050.3450.8620.6360.9981.0000.6290.2330.000NaN
도착 Y0.4070.3620.8720.9990.6230.6291.0000.2730.000NaN
총중량0.5970.3750.7660.2670.2420.2330.2731.0000.000NaN
높이제한0.0080.0290.0000.0000.0000.0000.0000.0001.000NaN
실제높이0.224NaN0.4900.2830.342NaNNaNNaNNaN1.000
2024-04-22T01:49:10.959173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폭제한관리기관높이제한구분길이제한
폭제한1.0001.0001.0001.0001.000
관리기관1.0001.0000.0000.3641.000
높이제한1.0000.0001.0000.0201.000
구분1.0000.3640.0201.0001.000
길이제한1.0001.0001.0001.0001.000
2024-04-22T01:49:11.149072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호출발 X출발 Y도착 X도착 Y총중량실제높이구분관리기관폭제한높이제한길이제한
관리번호1.0000.1390.0960.0100.1310.0040.1000.7120.5671.0000.0051.000
출발 X0.1391.0000.1840.1600.9990.0510.2450.1400.5551.0000.0001.000
출발 Y0.0960.1841.0000.9990.156-0.0740.0280.1400.5101.0000.0001.000
도착 X0.0100.1600.9991.0000.155-0.069NaN0.1490.5031.0000.0001.000
도착 Y0.1310.9990.1560.1551.0000.056NaN0.1570.5341.0000.0001.000
총중량0.0040.051-0.074-0.0690.0561.000NaN0.2400.4131.0000.0001.000
실제높이0.1000.2450.028NaNNaNNaN1.0001.0000.2310.0001.0000.000
구분0.7120.1400.1400.1490.1570.2401.0001.0000.3641.0000.0201.000
관리기관0.5670.5550.5100.5030.5340.4130.2310.3641.0001.0000.0001.000
폭제한1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.000
높이제한0.0050.0000.0000.0000.0000.0001.0000.0200.0001.0001.0001.000
길이제한1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.000

Missing values

2024-04-22T01:48:54.902436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T01:48:55.475057image/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.
2024-04-22T01:48:55.859680image/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

관리번호구분관리기관시설명위치출발 X출발 Y도착 X도착 Y총중량폭제한높이제한길이제한실제높이
086육교구로이씨의다리구로구 신도림동189234.0114545796.1451<NA><NA><NA><NA>4<NA>4.5
187육교구로구일역앞구로구 구로1동188624.7736543985.2152<NA><NA><NA><NA>4<NA>4.5
288육교구로신구로 유수지앞구로구 구로동188736.3833543272.7594<NA><NA><NA><NA>4<NA>4.5
389육교금천한신아파트앞금천구 독산동190388.7101539519.9889<NA><NA><NA><NA>4<NA>4.5
490육교금천두산초교앞금천구 독산동190410.5285540797.2064<NA><NA><NA><NA>4<NA>4.2
591육교금천대륭12차 건물앞금천구 가산동189697.2335541058.1884<NA><NA><NA><NA>4<NA>4.5
692육교금천금천금천구 가산동189935.558540694.8276<NA><NA><NA><NA>4<NA>4.5
793육교금천석수역앞금천구 시흥동191425.9248537208.0741<NA><NA><NA><NA>4<NA>5.0
894육교금천남서울한양 앞보도육교금천구 시흥1동191044.1022538382.0736<NA><NA><NA><NA>4<NA>0.0
995육교금천독산한내금천구 독산동190263.6736540196.3603<NA><NA><NA><NA>4<NA>4.5
관리번호구분관리기관시설명위치출발 X출발 Y도착 X도착 Y총중량폭제한높이제한길이제한실제높이
882478교량성동장안교성동구 송정동204771.3428550212.0722550299.4624204831.0699322.5416.7<NA>
883479교량성동용답교성동구 용답동204929.7767551393.4107551445.5933205066.2059322.5416.7<NA>
884885교량교량안전과비우당교동대문구 용두동 ~ 성동구 상왕십리동202346.4104552339.6432552382.9753202360.8545402.5416.7<NA>
885886교량교량안전과성북천교동대문구 용두동202434.8485552348.8291552361.6894202470.8952402.5416.7<NA>
886887교량교량안전과이화교동대문구 이문동 ~ 중랑구 중화동206295.8418555769.596555773.6504206518.3307402.5416.7<NA>
887888교량교량안전과신정교양천구 신정동 ~ 영등포구 문래동189258.7161546309.7631546402.5091189547.5969402.5416.7<NA>
888889교량교량안전과양평교양천구 목동 ~ 영등포구 양평동189705.2143548771.3317548951.7555190400.5555402.5416.7<NA>
889890교량교량안전과양화교양천구 목동 ~ 영등포구 양화동189405.4371549800.6596549829.0409189507.8124322.5416.7<NA>
890891교량서부도로사업소성산3교마포구 성산동191835.0768551647.723551699.4174191854.3355402.5416.7<NA>
891892교량북부도로사업소하월곡복개교성북구 종암동 ~ 월곡동203556.0354555602.856555686.4794203610.7708402.5416.7<NA>