Overview

Dataset statistics

Number of variables21
Number of observations10000
Missing cells39824
Missing cells (%)19.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory194.0 B

Variable types

Categorical6
Text3
Numeric11
Unsupported1

Dataset

Description상태 (공통),차선관리번호,차선코드대분류,고가 (공통),구경찰서코드 (공통),구코드 (공통),신경찰서코드 (공통),작업구분 (공통),표출구분 (공통),도로구분 (공통),관할사업소 (공통),신규정규화ID,설치일,교체일,공간데이터,이력ID,공사관리번호,구차선관리번호,차선코드중분류,공사형태 (공통),길이
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15537/S/1/datasetView.do

Alerts

상태 (공통) is highly imbalanced (98.7%)Imbalance
고가 (공통) is highly imbalanced (95.8%)Imbalance
차선코드중분류 is highly imbalanced (57.9%)Imbalance
신규정규화ID has 7684 (76.8%) missing valuesMissing
설치일 has 9680 (96.8%) missing valuesMissing
교체일 has 9680 (96.8%) missing valuesMissing
공간데이터 has 10000 (100.0%) missing valuesMissing
공사형태 (공통) has 2662 (26.6%) missing valuesMissing
이력ID has unique valuesUnique
공간데이터 is an unsupported type, check if it needs cleaning or further analysisUnsupported
길이 has 367 (3.7%) zerosZeros

Reproduction

Analysis started2024-05-04 05:41:41.907933
Analysis finished2024-05-04 05:41:42.874185
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상태 (공통)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9982 
3
 
13
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9982
99.8%
3 13
 
0.1%
4 5
 
0.1%

Length

2024-05-04T05:41:43.053780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:41:43.368543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9982
99.8%
3 13
 
0.1%
4 5
 
< 0.1%

차선관리번호
Text

Distinct9877
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T05:41:43.959651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters130000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9766 ?
Unique (%)97.7%

Sample

1st row11-0000015047
2nd row11-0000261359
3rd row11-0000011963
4th row11-0000123991
5th row11-0000108242
ValueCountFrequency (%)
11-0000135154 5
 
< 0.1%
11-0000228934 4
 
< 0.1%
11-0000136361 3
 
< 0.1%
11-0000049427 3
 
< 0.1%
11-0000292496 3
 
< 0.1%
11-0000285979 3
 
< 0.1%
11-0000228931 3
 
< 0.1%
11-0000037804 3
 
< 0.1%
11-0000229005 3
 
< 0.1%
11-0000232121 2
 
< 0.1%
Other values (9867) 9968
99.7%
2024-05-04T05:41:45.440810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 49329
37.9%
1 28862
22.2%
- 10000
 
7.7%
2 6718
 
5.2%
9 5250
 
4.0%
3 5194
 
4.0%
4 5161
 
4.0%
8 4954
 
3.8%
6 4932
 
3.8%
5 4837
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120000
92.3%
Dash Punctuation 10000
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49329
41.1%
1 28862
24.1%
2 6718
 
5.6%
9 5250
 
4.4%
3 5194
 
4.3%
4 5161
 
4.3%
8 4954
 
4.1%
6 4932
 
4.1%
5 4837
 
4.0%
7 4763
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49329
37.9%
1 28862
22.2%
- 10000
 
7.7%
2 6718
 
5.2%
9 5250
 
4.0%
3 5194
 
4.0%
4 5161
 
4.0%
8 4954
 
3.8%
6 4932
 
3.8%
5 4837
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49329
37.9%
1 28862
22.2%
- 10000
 
7.7%
2 6718
 
5.2%
9 5250
 
4.0%
3 5194
 
4.0%
4 5161
 
4.0%
8 4954
 
3.8%
6 4932
 
3.8%
5 4837
 
3.7%

차선코드대분류
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5386
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:41:45.827515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median9
Q310
95-th percentile11
Maximum18
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2056727
Coefficient of variation (CV)0.42523449
Kurtosis-0.28704115
Mean7.5386
Median Absolute Deviation (MAD)2
Skewness-0.45283537
Sum75386
Variance10.276338
MonotonicityNot monotonic
2024-05-04T05:41:46.173801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
9 2344
23.4%
10 1768
17.7%
6 1641
16.4%
4 1332
13.3%
11 1087
10.9%
1 895
 
8.9%
8 332
 
3.3%
7 148
 
1.5%
12 147
 
1.5%
3 75
 
0.8%
Other values (7) 231
 
2.3%
ValueCountFrequency (%)
1 895
 
8.9%
2 1
 
< 0.1%
3 75
 
0.8%
4 1332
13.3%
5 56
 
0.6%
6 1641
16.4%
7 148
 
1.5%
8 332
 
3.3%
9 2344
23.4%
10 1768
17.7%
ValueCountFrequency (%)
18 2
 
< 0.1%
17 50
 
0.5%
15 49
 
0.5%
14 51
 
0.5%
13 22
 
0.2%
12 147
 
1.5%
11 1087
10.9%
10 1768
17.7%
9 2344
23.4%
8 332
 
3.3%

고가 (공통)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9931 
2
 
45
3
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9931
99.3%
2 45
 
0.4%
3 24
 
0.2%

Length

2024-05-04T05:41:46.651746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:41:46.978972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9931
99.3%
2 45
 
0.4%
3 24
 
0.2%

구경찰서코드 (공통)
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing7
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean275.713
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:41:47.483686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1210
median280
Q3340
95-th percentile400
Maximum410
Range300
Interquartile range (IQR)130

Descriptive statistics

Standard deviation80.686199
Coefficient of variation (CV)0.29264561
Kurtosis-0.91080549
Mean275.713
Median Absolute Deviation (MAD)60
Skewness-0.20560925
Sum2755200
Variance6510.2627
MonotonicityNot monotonic
2024-05-04T05:41:47.989441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
280 875
 
8.8%
360 596
 
6.0%
300 560
 
5.6%
260 497
 
5.0%
340 457
 
4.6%
350 433
 
4.3%
230 424
 
4.2%
140 421
 
4.2%
410 415
 
4.2%
310 396
 
4.0%
Other values (21) 4919
49.2%
ValueCountFrequency (%)
110 113
 
1.1%
120 211
2.1%
130 54
 
0.5%
140 421
4.2%
150 107
 
1.1%
160 233
2.3%
170 370
3.7%
180 199
2.0%
190 250
2.5%
200 274
2.7%
ValueCountFrequency (%)
410 415
4.2%
400 351
3.5%
390 153
 
1.5%
380 173
 
1.7%
370 293
2.9%
360 596
6.0%
350 433
4.3%
340 457
4.6%
330 324
3.2%
320 214
 
2.1%

구코드 (공통)
Real number (ℝ)

Distinct25
Distinct (%)0.3%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean469.63953
Minimum110
Maximum740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:41:48.490376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1300
median500
Q3650
95-th percentile710
Maximum740
Range630
Interquartile range (IQR)350

Descriptive statistics

Standard deviation188.5261
Coefficient of variation (CV)0.40142723
Kurtosis-1.1725537
Mean469.63953
Median Absolute Deviation (MAD)180
Skewness-0.27978616
Sum4690290
Variance35542.089
MonotonicityNot monotonic
2024-05-04T05:41:48.960574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
680 1133
 
11.3%
710 740
 
7.4%
650 590
 
5.9%
500 557
 
5.6%
410 541
 
5.4%
560 498
 
5.0%
290 443
 
4.4%
470 437
 
4.4%
540 401
 
4.0%
740 387
 
3.9%
Other values (15) 4260
42.6%
ValueCountFrequency (%)
110 325
3.2%
140 243
2.4%
170 237
2.4%
200 248
2.5%
210 336
3.4%
230 289
2.9%
260 225
2.2%
290 443
4.4%
300 280
2.8%
320 310
3.1%
ValueCountFrequency (%)
740 387
 
3.9%
710 740
7.4%
680 1133
11.3%
650 590
5.9%
620 378
 
3.8%
590 200
 
2.0%
560 498
5.0%
540 401
 
4.0%
530 309
 
3.1%
500 557
5.6%

신경찰서코드 (공통)
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.074
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:41:49.554211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1200
median280
Q3350
95-th percentile410
Maximum410
Range300
Interquartile range (IQR)150

Descriptive statistics

Standard deviation85.852196
Coefficient of variation (CV)0.31324458
Kurtosis-1.0960031
Mean274.074
Median Absolute Deviation (MAD)70
Skewness-0.20473071
Sum2740740
Variance7370.5996
MonotonicityNot monotonic
2024-05-04T05:41:50.118062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
360 742
 
7.4%
280 615
 
6.2%
300 557
 
5.6%
140 541
 
5.4%
410 519
 
5.2%
170 498
 
5.0%
350 437
 
4.4%
260 432
 
4.3%
340 424
 
4.2%
310 389
 
3.9%
Other values (21) 4846
48.5%
ValueCountFrequency (%)
110 163
 
1.6%
120 241
2.4%
130 80
 
0.8%
140 541
5.4%
150 84
 
0.8%
160 237
2.4%
170 498
5.0%
180 248
2.5%
190 286
2.9%
200 289
2.9%
ValueCountFrequency (%)
410 519
5.2%
400 312
3.1%
390 151
 
1.5%
380 168
 
1.7%
370 359
3.6%
360 742
7.4%
350 437
4.4%
340 424
4.2%
330 309
3.1%
320 157
 
1.6%

작업구분 (공통)
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5671 
4
3852 
2
 
245
3
 
182
6
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row1
5th row4

Common Values

ValueCountFrequency (%)
1 5671
56.7%
4 3852
38.5%
2 245
 
2.5%
3 182
 
1.8%
6 50
 
0.5%

Length

2024-05-04T05:41:50.499141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:41:50.824821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5671
56.7%
4 3852
38.5%
2 245
 
2.5%
3 182
 
1.8%
6 50
 
0.5%

표출구분 (공통)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5661 
2
4339 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 5661
56.6%
2 4339
43.4%

Length

2024-05-04T05:41:51.277719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:41:51.627404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5661
56.6%
2 4339
43.4%

도로구분 (공통)
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5949 
1
4050 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0003
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 5949
59.5%
1 4050
40.5%
<NA> 1
 
< 0.1%

Length

2024-05-04T05:41:52.004883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:41:52.299884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5949
59.5%
1 4050
40.5%
na 1
 
< 0.1%

관할사업소 (공통)
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing18
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean106.31707
Minimum104
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:41:52.658429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile104
Q1105
median106
Q3108
95-th percentile109
Maximum109
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5795847
Coefficient of variation (CV)0.014857301
Kurtosis-1.0496779
Mean106.31707
Median Absolute Deviation (MAD)1
Skewness0.12973357
Sum1061257
Variance2.4950878
MonotonicityNot monotonic
2024-05-04T05:41:53.180165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
106 2309
23.1%
107 1716
17.2%
105 1709
17.1%
104 1609
16.1%
108 1541
15.4%
109 1098
11.0%
(Missing) 18
 
0.2%
ValueCountFrequency (%)
104 1609
16.1%
105 1709
17.1%
106 2309
23.1%
107 1716
17.2%
108 1541
15.4%
109 1098
11.0%
ValueCountFrequency (%)
109 1098
11.0%
108 1541
15.4%
107 1716
17.2%
106 2309
23.1%
105 1709
17.1%
104 1609
16.1%

신규정규화ID
Real number (ℝ)

MISSING 

Distinct2245
Distinct (%)96.9%
Missing7684
Missing (%)76.8%
Infinite0
Infinite (%)0.0%
Mean12963412
Minimum192561
Maximum72087910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:41:53.620158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum192561
5-th percentile1194533.5
Q12372041.2
median4456401
Q312773410
95-th percentile54357035
Maximum72087910
Range71895349
Interquartile range (IQR)10401369

Descriptive statistics

Standard deviation17501797
Coefficient of variation (CV)1.3500918
Kurtosis1.3858837
Mean12963412
Median Absolute Deviation (MAD)2103079.5
Skewness1.665216
Sum3.0023263 × 1010
Variance3.0631291 × 1014
MonotonicityNot monotonic
2024-05-04T05:41:54.304461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54413210 3
 
< 0.1%
11836610 3
 
< 0.1%
23339010 3
 
< 0.1%
23721210 3
 
< 0.1%
23722310 3
 
< 0.1%
51132810 3
 
< 0.1%
33905210 3
 
< 0.1%
21317810 3
 
< 0.1%
55420510 2
 
< 0.1%
53469210 2
 
< 0.1%
Other values (2235) 2288
 
22.9%
(Missing) 7684
76.8%
ValueCountFrequency (%)
192561 1
< 0.1%
192576 1
< 0.1%
193389 1
< 0.1%
193669 1
< 0.1%
259843 1
< 0.1%
268891 1
< 0.1%
269182 1
< 0.1%
269321 1
< 0.1%
269428 1
< 0.1%
276782 1
< 0.1%
ValueCountFrequency (%)
72087910 1
< 0.1%
72051610 2
< 0.1%
63174810 1
< 0.1%
63070810 2
< 0.1%
62978410 1
< 0.1%
62952710 1
< 0.1%
62851810 1
< 0.1%
62793010 2
< 0.1%
62783910 1
< 0.1%
62777710 1
< 0.1%

설치일
Real number (ℝ)

MISSING 

Distinct105
Distinct (%)32.8%
Missing9680
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean20185216
Minimum20150513
Maximum20240331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:41:54.925781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150513
5-th percentile20151228
Q120170595
median20181231
Q320201231
95-th percentile20221134
Maximum20240331
Range89818
Interquartile range (IQR)30636.25

Descriptive statistics

Standard deviation23314.461
Coefficient of variation (CV)0.0011550266
Kurtosis-0.86808803
Mean20185216
Median Absolute Deviation (MAD)19590.5
Skewness0.34358864
Sum6.4592691 × 109
Variance5.4356407 × 108
MonotonicityNot monotonic
2024-05-04T05:41:55.678547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191231 27
 
0.3%
20151231 17
 
0.2%
20191220 14
 
0.1%
20221130 14
 
0.1%
20181231 13
 
0.1%
20201231 11
 
0.1%
20170630 10
 
0.1%
20171208 9
 
0.1%
20171226 9
 
0.1%
20171231 8
 
0.1%
Other values (95) 188
 
1.9%
(Missing) 9680
96.8%
ValueCountFrequency (%)
20150513 1
 
< 0.1%
20151031 3
 
< 0.1%
20151118 3
 
< 0.1%
20151130 1
 
< 0.1%
20151207 1
 
< 0.1%
20151222 6
 
0.1%
20151228 2
 
< 0.1%
20151231 17
0.2%
20160130 1
 
< 0.1%
20160229 1
 
< 0.1%
ValueCountFrequency (%)
20240331 3
< 0.1%
20231222 2
< 0.1%
20231220 1
 
< 0.1%
20231210 2
< 0.1%
20230731 1
 
< 0.1%
20230501 1
 
< 0.1%
20230410 1
 
< 0.1%
20221231 1
 
< 0.1%
20221223 1
 
< 0.1%
20221220 2
< 0.1%

교체일
Real number (ℝ)

MISSING 

Distinct107
Distinct (%)33.4%
Missing9680
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean20185619
Minimum20150513
Maximum20240331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:41:56.141710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150513
5-th percentile20151228
Q120170616
median20181231
Q320201231
95-th percentile20221134
Maximum20240331
Range89818
Interquartile range (IQR)30615

Descriptive statistics

Standard deviation23375.018
Coefficient of variation (CV)0.0011580035
Kurtosis-0.90706319
Mean20185619
Median Absolute Deviation (MAD)19700
Skewness0.30312761
Sum6.459398 × 109
Variance5.4639148 × 108
MonotonicityNot monotonic
2024-05-04T05:41:56.625147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191231 27
 
0.3%
20151231 17
 
0.2%
20191220 14
 
0.1%
20221130 14
 
0.1%
20181231 13
 
0.1%
20201231 11
 
0.1%
20171208 9
 
0.1%
20170630 9
 
0.1%
20171226 9
 
0.1%
20171231 8
 
0.1%
Other values (97) 189
 
1.9%
(Missing) 9680
96.8%
ValueCountFrequency (%)
20150513 1
 
< 0.1%
20151031 3
 
< 0.1%
20151118 3
 
< 0.1%
20151130 1
 
< 0.1%
20151207 1
 
< 0.1%
20151222 6
 
0.1%
20151228 2
 
< 0.1%
20151231 17
0.2%
20160130 1
 
< 0.1%
20160229 1
 
< 0.1%
ValueCountFrequency (%)
20240331 3
< 0.1%
20231222 2
< 0.1%
20231220 1
 
< 0.1%
20231210 2
< 0.1%
20230731 1
 
< 0.1%
20230501 1
 
< 0.1%
20230410 1
 
< 0.1%
20221231 2
< 0.1%
20221220 2
< 0.1%
20221201 1
 
< 0.1%

공간데이터
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

이력ID
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197193.82
Minimum9
Maximum8291536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:41:57.233872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile6721.6
Q133589
median63143.5
Q397735.5
95-th percentile360493.35
Maximum8291536
Range8291527
Interquartile range (IQR)64146.5

Descriptive statistics

Standard deviation830884.81
Coefficient of variation (CV)4.2135439
Kurtosis87.551755
Mean197193.82
Median Absolute Deviation (MAD)32204.5
Skewness9.3277318
Sum1.9719382 × 109
Variance6.9036957 × 1011
MonotonicityNot monotonic
2024-05-04T05:41:57.824153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7672 1
 
< 0.1%
36698 1
 
< 0.1%
12246 1
 
< 0.1%
15588 1
 
< 0.1%
89720 1
 
< 0.1%
14272 1
 
< 0.1%
98335 1
 
< 0.1%
3636 1
 
< 0.1%
46190 1
 
< 0.1%
77026 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
9 1
< 0.1%
21 1
< 0.1%
25 1
< 0.1%
26 1
< 0.1%
34 1
< 0.1%
48 1
< 0.1%
51 1
< 0.1%
62 1
< 0.1%
253 1
< 0.1%
262 1
< 0.1%
ValueCountFrequency (%)
8291536 1
< 0.1%
8290454 1
< 0.1%
8290451 1
< 0.1%
8290426 1
< 0.1%
8288216 1
< 0.1%
8287769 1
< 0.1%
8286625 1
< 0.1%
8283730 1
< 0.1%
8283034 1
< 0.1%
8282963 1
< 0.1%

공사관리번호
Text

Distinct1048
Distinct (%)10.5%
Missing33
Missing (%)0.3%
Memory size156.2 KiB
2024-05-04T05:41:58.725663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters129571
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique361 ?
Unique (%)3.6%

Sample

1st row2008-0102-558
2nd row2010-0108-048
3rd row2010-1004-003
4th row2012-1008-026
5th row2011-1112-019
ValueCountFrequency (%)
2000-0000-000 4203
42.2%
2009-1002-001 185
 
1.9%
2008-0102-558 135
 
1.4%
2009-0108-012 124
 
1.2%
2008-0108-019 96
 
1.0%
2009-1008-002 92
 
0.9%
2008-1008-970 86
 
0.9%
2009-0108-018 81
 
0.8%
2012-0112-003 60
 
0.6%
2008-0108-593 59
 
0.6%
Other values (1038) 4846
48.6%
2024-05-04T05:42:00.109506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 67537
52.1%
- 19934
 
15.4%
2 13524
 
10.4%
1 13034
 
10.1%
8 6793
 
5.2%
9 2589
 
2.0%
5 1392
 
1.1%
4 1318
 
1.0%
7 1312
 
1.0%
6 1082
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 109637
84.6%
Dash Punctuation 19934
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67537
61.6%
2 13524
 
12.3%
1 13034
 
11.9%
8 6793
 
6.2%
9 2589
 
2.4%
5 1392
 
1.3%
4 1318
 
1.2%
7 1312
 
1.2%
6 1082
 
1.0%
3 1056
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 19934
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129571
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67537
52.1%
- 19934
 
15.4%
2 13524
 
10.4%
1 13034
 
10.1%
8 6793
 
5.2%
9 2589
 
2.0%
5 1392
 
1.1%
4 1318
 
1.0%
7 1312
 
1.0%
6 1082
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67537
52.1%
- 19934
 
15.4%
2 13524
 
10.4%
1 13034
 
10.1%
8 6793
 
5.2%
9 2589
 
2.0%
5 1392
 
1.1%
4 1318
 
1.0%
7 1312
 
1.0%
6 1082
 
0.8%

구차선관리번호
Text

Distinct9830
Distinct (%)98.8%
Missing47
Missing (%)0.5%
Memory size156.2 KiB
2024-05-04T05:42:00.984025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters89577
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9719 ?
Unique (%)97.6%

Sample

1st row11-015047
2nd row11-261359
3rd row11-011963
4th row11-123991
5th row11-108242
ValueCountFrequency (%)
11-135154 5
 
0.1%
11-228934 4
 
< 0.1%
11-049427 3
 
< 0.1%
11-136361 3
 
< 0.1%
11-037804 3
 
< 0.1%
11-292496 3
 
< 0.1%
11-285979 3
 
< 0.1%
11-229005 3
 
< 0.1%
11-228931 3
 
< 0.1%
11-021893 2
 
< 0.1%
Other values (9820) 9921
99.7%
2024-05-04T05:42:02.464936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 28728
32.1%
- 9953
 
11.1%
0 9296
 
10.4%
2 6683
 
7.5%
9 5230
 
5.8%
3 5169
 
5.8%
4 5134
 
5.7%
8 4931
 
5.5%
6 4912
 
5.5%
5 4800
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79624
88.9%
Dash Punctuation 9953
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28728
36.1%
0 9296
 
11.7%
2 6683
 
8.4%
9 5230
 
6.6%
3 5169
 
6.5%
4 5134
 
6.4%
8 4931
 
6.2%
6 4912
 
6.2%
5 4800
 
6.0%
7 4741
 
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 9953
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28728
32.1%
- 9953
 
11.1%
0 9296
 
10.4%
2 6683
 
7.5%
9 5230
 
5.8%
3 5169
 
5.8%
4 5134
 
5.7%
8 4931
 
5.5%
6 4912
 
5.5%
5 4800
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28728
32.1%
- 9953
 
11.1%
0 9296
 
10.4%
2 6683
 
7.5%
9 5230
 
5.8%
3 5169
 
5.8%
4 5134
 
5.7%
8 4931
 
5.5%
6 4912
 
5.5%
5 4800
 
5.4%

차선코드중분류
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8134 
2
1447 
3
 
413
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 8134
81.3%
2 1447
 
14.5%
3 413
 
4.1%
4 6
 
0.1%

Length

2024-05-04T05:42:02.985470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:42:03.507865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8134
81.3%
2 1447
 
14.5%
3 413
 
4.1%
4 6
 
0.1%

공사형태 (공통)
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)0.1%
Missing2662
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean4.4348596
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:42:04.162101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8040647
Coefficient of variation (CV)0.40679184
Kurtosis3.259466
Mean4.4348596
Median Absolute Deviation (MAD)1
Skewness0.90681949
Sum32543
Variance3.2546494
MonotonicityNot monotonic
2024-05-04T05:42:04.775068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 3651
36.5%
4 1789
17.9%
3 788
 
7.9%
1 701
 
7.0%
10 398
 
4.0%
9 7
 
0.1%
6 4
 
< 0.1%
(Missing) 2662
26.6%
ValueCountFrequency (%)
1 701
 
7.0%
3 788
 
7.9%
4 1789
17.9%
5 3651
36.5%
6 4
 
< 0.1%
9 7
 
0.1%
10 398
 
4.0%
ValueCountFrequency (%)
10 398
 
4.0%
9 7
 
0.1%
6 4
 
< 0.1%
5 3651
36.5%
4 1789
17.9%
3 788
 
7.9%
1 701
 
7.0%

길이
Real number (ℝ)

ZEROS 

Distinct404
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.2182
Minimum0
Maximum1844
Zeros367
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:42:05.240855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median24
Q364
95-th percentile185
Maximum1844
Range1844
Interquartile range (IQR)57

Descriptive statistics

Standard deviation79.569555
Coefficient of variation (CV)1.5535406
Kurtosis71.192693
Mean51.2182
Median Absolute Deviation (MAD)20
Skewness5.6759138
Sum512182
Variance6331.3141
MonotonicityNot monotonic
2024-05-04T05:42:05.855680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 464
 
4.6%
0 367
 
3.7%
4 363
 
3.6%
5 333
 
3.3%
6 294
 
2.9%
3 288
 
2.9%
7 273
 
2.7%
8 218
 
2.2%
9 214
 
2.1%
10 202
 
2.0%
Other values (394) 6984
69.8%
ValueCountFrequency (%)
0 367
3.7%
1 143
 
1.4%
2 464
4.6%
3 288
2.9%
4 363
3.6%
5 333
3.3%
6 294
2.9%
7 273
2.7%
8 218
2.2%
9 214
2.1%
ValueCountFrequency (%)
1844 1
< 0.1%
1657 1
< 0.1%
1400 1
< 0.1%
1352 1
< 0.1%
953 1
< 0.1%
857 2
< 0.1%
834 1
< 0.1%
767 1
< 0.1%
704 1
< 0.1%
698 1
< 0.1%

Sample

상태 (공통)차선관리번호차선코드대분류고가 (공통)구경찰서코드 (공통)구코드 (공통)신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)신규정규화ID설치일교체일공간데이터이력ID공사관리번호구차선관리번호차선코드중분류공사형태 (공통)길이
4710111-000001504791140110120411107<NA><NA><NA><NA>76722008-0102-55811-0150471537
41048111-000026135991390380390412108<NA><NA><NA><NA>720352010-0108-04811-2613591527
60337111-000001196341290620290412105<NA><NA><NA><NA>3475252010-1004-00311-011963156
41383111-0000123991111802001801121094247045<NA><NA><NA>717982012-1008-02611-1239912321
69659111-000010824211310740310412106<NA><NA><NA><NA>3659932011-1112-01911-108242310108
9738111-00000245451012104402101121083204623<NA><NA><NA>179072000-0000-00011-0245451342
22448111-000024282861370350370411107<NA><NA><NA><NA>396922010-1108-04111-2428282439
68281111-000020571641250300250112107<NA><NA><NA><NA>3550262009-0108-01811-205716153
16458111-000006321461220590220411105<NA><NA><NA><NA>295242012-1108-05011-0632142177
22716111-000008346711280680280122106<NA><NA><NA><NA>414902000-0000-00011-0834671<NA>8
상태 (공통)차선관리번호차선코드대분류고가 (공통)구경찰서코드 (공통)구코드 (공통)신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)신규정규화ID설치일교체일공간데이터이력ID공사관리번호구차선관리번호차선코드중분류공사형태 (공통)길이
21422111-000009009211280680280211106<NA><NA><NA><NA>384372012-0108-00311-0900921<NA>96
10122111-0000024597101310740310122106<NA><NA><NA><NA>176702000-0000-00011-0245971<NA>30
9290111-000003284411380650380112105<NA><NA><NA><NA>157522012-1008-04211-0328442545
11163111-000003521361290620290411105<NA><NA><NA><NA>204832010-0108-02111-03521325259
26058111-00000805931013607103601221065137745<NA><NA><NA>6733552013-0107-15711-08059315164
8150111-0000029358101340650340122105<NA><NA><NA><NA>144722000-0000-00011-0293581<NA>32
8169111-00000300701113305303301121041193405<NA><NA><NA>149032000-0000-00011-030070132
66012111-000018615141270260270411109<NA><NA><NA><NA>3586022012-0108-11911-1861511511
6886111-00000261751111404101401221082393052<NA><NA><NA>128072000-0000-00011-0261751413
43457111-000014911411300500300411104<NA><NA><NA><NA>769532010-0108-03811-1491143554