Overview

Dataset statistics

Number of variables22
Number of observations10000
Missing cells40247
Missing cells (%)18.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory202.0 B

Variable types

Categorical6
Text4
Numeric11
Unsupported1

Dataset

Description상태 (공통),문자표시 관리번호,방향,길이,내용,고가 (공통),구경찰서코드 (공통),구코드 (공통),신경찰서코드 (공통),작업구분 (공통),표출구분 (공통),도로구분 (공통),관할사업소 (공통),신규정규화ID,설치일,교체일,공간데이터,이력ID,공사관리번호,문자표시 관리번호,문자표시 종류코드,공사형태 (공통)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15535/S/1/datasetView.do

Alerts

상태 (공통) is highly imbalanced (99.7%)Imbalance
고가 (공통) is highly imbalanced (89.5%)Imbalance
신규정규화ID has 9014 (90.1%) missing valuesMissing
설치일 has 9923 (99.2%) missing valuesMissing
교체일 has 9923 (99.2%) missing valuesMissing
공간데이터 has 10000 (100.0%) missing valuesMissing
공사형태 (공통) has 1158 (11.6%) missing valuesMissing
길이 is highly skewed (γ1 = 42.43042218)Skewed
이력ID has unique valuesUnique
공간데이터 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-04 05:47:14.030518
Analysis finished2024-05-04 05:47:15.559399
Duration1.53 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상태 (공통)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9995 
3
 
3
<NA>
 
1
4
 
1

Length

Max length4
Median length1
Mean length1.0003
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 9995
> 99.9%
3 3
 
< 0.1%
<NA> 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-05-04T05:47:15.763760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:47:16.153751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9995
> 99.9%
3 3
 
< 0.1%
na 1
 
< 0.1%
4 1
 
< 0.1%

문자표시 관리번호
Text

Distinct9906
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T05:47:16.600073image/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

Unique9814 ?
Unique (%)98.1%

Sample

1st row07-0000023531
2nd row07-0000069968
3rd row07-0000038121
4th row07-0000034216
5th row07-0000049305
ValueCountFrequency (%)
07-0000011247 4
 
< 0.1%
07-0000066164 2
 
< 0.1%
07-0000004404 2
 
< 0.1%
07-0000021115 2
 
< 0.1%
07-0000106728 2
 
< 0.1%
07-0000072509 2
 
< 0.1%
07-0000032069 2
 
< 0.1%
07-0000076298 2
 
< 0.1%
07-0000001591 2
 
< 0.1%
07-0000065840 2
 
< 0.1%
Other values (9896) 9978
99.8%
2024-05-04T05:47:17.501353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64932
49.9%
7 15334
 
11.8%
- 10000
 
7.7%
1 5508
 
4.2%
3 5226
 
4.0%
4 5129
 
3.9%
2 5065
 
3.9%
5 5002
 
3.8%
6 4976
 
3.8%
8 4582
 
3.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64932
54.1%
7 15334
 
12.8%
1 5508
 
4.6%
3 5226
 
4.4%
4 5129
 
4.3%
2 5065
 
4.2%
5 5002
 
4.2%
6 4976
 
4.1%
8 4582
 
3.8%
9 4246
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64932
49.9%
7 15334
 
11.8%
- 10000
 
7.7%
1 5508
 
4.2%
3 5226
 
4.0%
4 5129
 
3.9%
2 5065
 
3.9%
5 5002
 
3.8%
6 4976
 
3.8%
8 4582
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64932
49.9%
7 15334
 
11.8%
- 10000
 
7.7%
1 5508
 
4.2%
3 5226
 
4.0%
4 5129
 
3.9%
2 5065
 
3.9%
5 5002
 
3.8%
6 4976
 
3.8%
8 4582
 
3.5%

방향
Real number (ℝ)

Distinct361
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.7439
Minimum0
Maximum360
Zeros51
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:17.915122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q188
median178
Q3270
95-th percentile340
Maximum360
Range360
Interquartile range (IQR)182

Descriptive statistics

Standard deviation104.416
Coefficient of variation (CV)0.59077567
Kurtosis-1.2153124
Mean176.7439
Median Absolute Deviation (MAD)91
Skewness0.017176635
Sum1767439
Variance10902.7
MonotonicityNot monotonic
2024-05-04T05:47:18.412081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
270 73
 
0.7%
180 69
 
0.7%
291 58
 
0.6%
110 56
 
0.6%
90 56
 
0.6%
292 54
 
0.5%
21 51
 
0.5%
0 51
 
0.5%
111 49
 
0.5%
88 48
 
0.5%
Other values (351) 9435
94.3%
ValueCountFrequency (%)
0 51
0.5%
1 31
0.3%
2 36
0.4%
3 20
 
0.2%
4 30
0.3%
5 21
0.2%
6 27
0.3%
7 23
0.2%
8 36
0.4%
9 40
0.4%
ValueCountFrequency (%)
360 6
 
0.1%
359 29
0.3%
358 34
0.3%
357 20
0.2%
356 16
0.2%
355 24
0.2%
354 30
0.3%
353 36
0.4%
352 21
0.2%
351 21
0.2%

길이
Real number (ℝ)

SKEWED 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0455
Minimum0
Maximum56
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:18.843202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q13
median3
Q33
95-th percentile3
Maximum56
Range56
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.90780624
Coefficient of variation (CV)0.29808118
Kurtosis2076.7101
Mean3.0455
Median Absolute Deviation (MAD)0
Skewness42.430422
Sum30455
Variance0.82411216
MonotonicityNot monotonic
2024-05-04T05:47:19.175805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 9857
98.6%
5 130
 
1.3%
0 5
 
0.1%
32 3
 
< 0.1%
16 2
 
< 0.1%
2 1
 
< 0.1%
48 1
 
< 0.1%
56 1
 
< 0.1%
ValueCountFrequency (%)
0 5
 
0.1%
2 1
 
< 0.1%
3 9857
98.6%
5 130
 
1.3%
16 2
 
< 0.1%
32 3
 
< 0.1%
48 1
 
< 0.1%
56 1
 
< 0.1%
ValueCountFrequency (%)
56 1
 
< 0.1%
48 1
 
< 0.1%
32 3
 
< 0.1%
16 2
 
< 0.1%
5 130
 
1.3%
3 9857
98.6%
2 1
 
< 0.1%
0 5
 
0.1%

내용
Text

Distinct918
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T05:47:19.799601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length3.0148
Min length1

Characters and Unicode

Total characters30148
Distinct characters324
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)4.2%

Sample

1st row일방통행
2nd row어린이
3rd row일방통행
4th row07:00
5th row학교앞
ValueCountFrequency (%)
천천히 1285
 
12.7%
일방통행 1106
 
10.9%
학교앞 944
 
9.3%
진입금지 388
 
3.8%
60 380
 
3.8%
30 369
 
3.6%
버 218
 
2.2%
통행 215
 
2.1%
유치원앞 197
 
1.9%
스 182
 
1.8%
Other values (897) 4831
47.8%
2024-05-04T05:47:20.844261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 2660
 
8.8%
0 1715
 
5.7%
행 1381
 
4.6%
방 1350
 
4.5%
통 1350
 
4.5%
일 1334
 
4.4%
교 1319
 
4.4%
히 1292
 
4.3%
앞 1155
 
3.8%
학 961
 
3.2%
Other values (314) 15631
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26062
86.4%
Decimal Number 3346
 
11.1%
Other Punctuation 266
 
0.9%
Uppercase Letter 215
 
0.7%
Space Separator 127
 
0.4%
Math Symbol 108
 
0.4%
Lowercase Letter 17
 
0.1%
Other Symbol 5
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 2660
 
10.2%
행 1381
 
5.3%
방 1350
 
5.2%
통 1350
 
5.2%
일 1334
 
5.1%
교 1319
 
5.1%
히 1292
 
5.0%
앞 1155
 
4.4%
학 961
 
3.7%
지 740
 
2.8%
Other values (277) 12520
48.0%
Uppercase Letter
ValueCountFrequency (%)
C 97
45.1%
I 87
40.5%
T 7
 
3.3%
M 5
 
2.3%
V 5
 
2.3%
B 3
 
1.4%
S 3
 
1.4%
U 2
 
0.9%
A 1
 
0.5%
X 1
 
0.5%
Other values (4) 4
 
1.9%
Decimal Number
ValueCountFrequency (%)
0 1715
51.3%
3 398
 
11.9%
6 389
 
11.6%
7 265
 
7.9%
8 159
 
4.8%
1 155
 
4.6%
2 141
 
4.2%
4 88
 
2.6%
5 30
 
0.9%
9 6
 
0.2%
Other Punctuation
ValueCountFrequency (%)
: 229
86.1%
. 35
 
13.2%
; 1
 
0.4%
？ 1
 
0.4%
Other Symbol
ValueCountFrequency (%)
▲ 3
60.0%
△ 1
 
20.0%
▽ 1
 
20.0%
Lowercase Letter
ValueCountFrequency (%)
t 12
70.6%
m 5
29.4%
Space Separator
ValueCountFrequency (%)
127
100.0%
Math Symbol
ValueCountFrequency (%)
~ 108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26062
86.4%
Common 3854
 
12.8%
Latin 232
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 2660
 
10.2%
행 1381
 
5.3%
방 1350
 
5.2%
통 1350
 
5.2%
일 1334
 
5.1%
교 1319
 
5.1%
히 1292
 
5.0%
앞 1155
 
4.4%
학 961
 
3.7%
지 740
 
2.8%
Other values (277) 12520
48.0%
Common
ValueCountFrequency (%)
0 1715
44.5%
3 398
 
10.3%
6 389
 
10.1%
7 265
 
6.9%
: 229
 
5.9%
8 159
 
4.1%
1 155
 
4.0%
2 141
 
3.7%
127
 
3.3%
~ 108
 
2.8%
Other values (11) 168
 
4.4%
Latin
ValueCountFrequency (%)
C 97
41.8%
I 87
37.5%
t 12
 
5.2%
T 7
 
3.0%
m 5
 
2.2%
M 5
 
2.2%
V 5
 
2.2%
B 3
 
1.3%
S 3
 
1.3%
U 2
 
0.9%
Other values (6) 6
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26062
86.4%
ASCII 4080
 
13.5%
Geometric Shapes 5
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 2660
 
10.2%
행 1381
 
5.3%
방 1350
 
5.2%
통 1350
 
5.2%
일 1334
 
5.1%
교 1319
 
5.1%
히 1292
 
5.0%
앞 1155
 
4.4%
학 961
 
3.7%
지 740
 
2.8%
Other values (277) 12520
48.0%
ASCII
ValueCountFrequency (%)
0 1715
42.0%
3 398
 
9.8%
6 389
 
9.5%
7 265
 
6.5%
: 229
 
5.6%
8 159
 
3.9%
1 155
 
3.8%
2 141
 
3.5%
127
 
3.1%
~ 108
 
2.6%
Other values (23) 394
 
9.7%
Geometric Shapes
ValueCountFrequency (%)
▲ 3
60.0%
△ 1
 
20.0%
▽ 1
 
20.0%
None
ValueCountFrequency (%)
？ 1
100.0%

고가 (공통)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9756 
2
 
242
3
 
2

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 9756
97.6%
2 242
 
2.4%
3 2
 
< 0.1%

Length

2024-05-04T05:47:21.097322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:47:21.268374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9756
97.6%
2 242
 
2.4%
3 2
 
< 0.1%

구경찰서코드 (공통)
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing46
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean266.69178
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:21.484075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile130
Q1210
median270
Q3330
95-th percentile400
Maximum410
Range300
Interquartile range (IQR)120

Descriptive statistics

Standard deviation80.673818
Coefficient of variation (CV)0.30249833
Kurtosis-0.97197544
Mean266.69178
Median Absolute Deviation (MAD)60
Skewness-0.056168496
Sum2654650
Variance6508.2649
MonotonicityNot monotonic
2024-05-04T05:47:21.762707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
300 714
 
7.1%
230 638
 
6.4%
280 569
 
5.7%
210 531
 
5.3%
260 471
 
4.7%
360 437
 
4.4%
160 419
 
4.2%
350 404
 
4.0%
170 388
 
3.9%
330 378
 
3.8%
Other values (21) 5005
50.0%
ValueCountFrequency (%)
110 197
2.0%
120 132
 
1.3%
130 196
2.0%
140 210
2.1%
150 146
 
1.5%
160 419
4.2%
170 388
3.9%
180 277
2.8%
190 221
2.2%
200 257
2.6%
ValueCountFrequency (%)
410 346
3.5%
400 276
2.8%
390 158
 
1.6%
380 179
1.8%
370 254
2.5%
360 437
4.4%
350 404
4.0%
340 345
3.5%
330 378
3.8%
320 201
2.0%

구코드 (공통)
Real number (ℝ)

Distinct25
Distinct (%)0.3%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean445.18813
Minimum110
Maximum740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:22.045235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1290
median470
Q3620
95-th percentile710
Maximum740
Range630
Interquartile range (IQR)330

Descriptive statistics

Standard deviation191.64807
Coefficient of variation (CV)0.43048782
Kurtosis-1.259771
Mean445.18813
Median Absolute Deviation (MAD)180
Skewness-0.16334145
Sum4425170
Variance36728.982
MonotonicityNot monotonic
2024-05-04T05:47:22.411200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
680 737
 
7.4%
500 700
 
7.0%
710 630
 
6.3%
560 604
 
6.0%
140 488
 
4.9%
650 481
 
4.8%
210 470
 
4.7%
440 456
 
4.6%
470 407
 
4.1%
620 406
 
4.1%
Other values (15) 4561
45.6%
ValueCountFrequency (%)
110 258
2.6%
140 488
4.9%
170 401
4.0%
200 360
3.6%
210 470
4.7%
230 269
2.7%
260 218
2.2%
290 378
3.8%
300 262
2.6%
320 208
2.1%
ValueCountFrequency (%)
740 359
3.6%
710 630
6.3%
680 737
7.4%
650 481
4.8%
620 406
4.1%
590 255
 
2.5%
560 604
6.0%
540 336
3.4%
530 348
3.5%
500 700
7.0%

신경찰서코드 (공통)
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing36
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean264.94882
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:22.778035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile130
Q1190
median270
Q3340
95-th percentile400
Maximum410
Range300
Interquartile range (IQR)150

Descriptive statistics

Standard deviation84.485485
Coefficient of variation (CV)0.31887474
Kurtosis-1.1412534
Mean264.94882
Median Absolute Deviation (MAD)70
Skewness-0.069353595
Sum2639950
Variance7137.7972
MonotonicityNot monotonic
2024-05-04T05:47:23.155268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
300 710
 
7.1%
360 630
 
6.3%
170 606
 
6.1%
230 470
 
4.7%
210 458
 
4.6%
350 411
 
4.1%
160 400
 
4.0%
370 393
 
3.9%
280 378
 
3.8%
260 376
 
3.8%
Other values (21) 5132
51.3%
ValueCountFrequency (%)
110 252
2.5%
120 154
 
1.5%
130 236
 
2.4%
140 257
2.6%
150 104
 
1.0%
160 400
4.0%
170 606
6.1%
180 360
3.6%
190 252
2.5%
200 269
2.7%
ValueCountFrequency (%)
410 360
3.6%
400 208
 
2.1%
390 156
 
1.6%
380 160
 
1.6%
370 393
3.9%
360 630
6.3%
350 411
4.1%
340 321
3.2%
330 348
3.5%
320 126
 
1.3%

작업구분 (공통)
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4
5167 
1
3360 
2
1365 
6
 
84
3
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
4 5167
51.7%
1 3360
33.6%
2 1365
 
13.7%
6 84
 
0.8%
3 24
 
0.2%

Length

2024-05-04T05:47:23.552008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:47:23.861512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 5167
51.7%
1 3360
33.6%
2 1365
 
13.7%
6 84
 
0.8%
3 24
 
0.2%

표출구분 (공통)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8115 
2
1885 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 8115
81.2%
2 1885
 
18.9%

Length

2024-05-04T05:47:24.217989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:47:24.507660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8115
81.2%
2 1885
 
18.9%

도로구분 (공통)
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5012 
1
4979 
<NA>
 
9

Length

Max length4
Median length1
Mean length1.0027
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 5012
50.1%
1 4979
49.8%
<NA> 9
 
0.1%

Length

2024-05-04T05:47:24.838735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:47:25.162640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5012
50.1%
1 4979
49.8%
na 9
 
0.1%

관할사업소 (공통)
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing44
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean106.38148
Minimum104
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:25.459478image/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 deviation1.6955386
Coefficient of variation (CV)0.015938288
Kurtosis-1.2709595
Mean106.38148
Median Absolute Deviation (MAD)2
Skewness0.045027764
Sum1059134
Variance2.8748513
MonotonicityNot monotonic
2024-05-04T05:47:25.828378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
104 1893
18.9%
108 1859
18.6%
106 1776
17.8%
105 1610
16.1%
107 1489
14.9%
109 1329
13.3%
(Missing) 44
 
0.4%
ValueCountFrequency (%)
104 1893
18.9%
105 1610
16.1%
106 1776
17.8%
107 1489
14.9%
108 1859
18.6%
109 1329
13.3%
ValueCountFrequency (%)
109 1329
13.3%
108 1859
18.6%
107 1489
14.9%
106 1776
17.8%
105 1610
16.1%
104 1893
18.9%

신규정규화ID
Real number (ℝ)

MISSING 

Distinct966
Distinct (%)98.0%
Missing9014
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean9475163.6
Minimum194184
Maximum62766110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:26.210343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum194184
5-th percentile1196294.5
Q12295461.8
median4391045
Q36108635.2
95-th percentile44693510
Maximum62766110
Range62571926
Interquartile range (IQR)3813173.5

Descriptive statistics

Standard deviation14253872
Coefficient of variation (CV)1.5043405
Kurtosis4.0679878
Mean9475163.6
Median Absolute Deviation (MAD)1774078.5
Skewness2.2955627
Sum9.3425113 × 109
Variance2.0317287 × 1014
MonotonicityNot monotonic
2024-05-04T05:47:26.563635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43206110 3
 
< 0.1%
51821410 3
 
< 0.1%
32387410 3
 
< 0.1%
32480710 2
 
< 0.1%
42095310 2
 
< 0.1%
32577910 2
 
< 0.1%
32389610 2
 
< 0.1%
41531610 2
 
< 0.1%
52335010 2
 
< 0.1%
45625610 2
 
< 0.1%
Other values (956) 963
 
9.6%
(Missing) 9014
90.1%
ValueCountFrequency (%)
194184 1
< 0.1%
194198 1
< 0.1%
194512 1
< 0.1%
194743 1
< 0.1%
277691 1
< 0.1%
278323 1
< 0.1%
284406 1
< 0.1%
284407 1
< 0.1%
284412 1
< 0.1%
284517 1
< 0.1%
ValueCountFrequency (%)
62766110 1
< 0.1%
62682010 1
< 0.1%
62463010 1
< 0.1%
62313610 2
< 0.1%
62312614 1
< 0.1%
62312611 1
< 0.1%
61750810 1
< 0.1%
61745310 1
< 0.1%
61659810 1
< 0.1%
61564510 1
< 0.1%

설치일
Real number (ℝ)

MISSING 

Distinct32
Distinct (%)41.6%
Missing9923
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean20186781
Minimum20141231
Maximum20240331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:27.037549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20141231
5-th percentile20151210
Q120161109
median20181231
Q320221130
95-th percentile20231231
Maximum20240331
Range99100
Interquartile range (IQR)60021

Descriptive statistics

Standard deviation29069.056
Coefficient of variation (CV)0.0014400045
Kurtosis-1.1909416
Mean20186781
Median Absolute Deviation (MAD)20311
Skewness0.4670518
Sum1.5543821 × 109
Variance8.4501002 × 108
MonotonicityNot monotonic
2024-05-04T05:47:27.449060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20181231 9
 
0.1%
20191231 7
 
0.1%
20221130 5
 
0.1%
20161231 5
 
0.1%
20160720 4
 
< 0.1%
20231231 4
 
< 0.1%
20160831 4
 
< 0.1%
20171210 3
 
< 0.1%
20151210 3
 
< 0.1%
20200812 2
 
< 0.1%
Other values (22) 31
 
0.3%
(Missing) 9923
99.2%
ValueCountFrequency (%)
20141231 1
 
< 0.1%
20151007 1
 
< 0.1%
20151207 1
 
< 0.1%
20151210 3
< 0.1%
20151231 2
< 0.1%
20160720 4
< 0.1%
20160831 4
< 0.1%
20160920 2
< 0.1%
20161109 2
< 0.1%
20161130 1
 
< 0.1%
ValueCountFrequency (%)
20240331 2
 
< 0.1%
20240124 1
 
< 0.1%
20231231 4
< 0.1%
20231220 2
 
< 0.1%
20231210 1
 
< 0.1%
20230410 2
 
< 0.1%
20221231 2
 
< 0.1%
20221216 1
 
< 0.1%
20221130 5
0.1%
20220722 1
 
< 0.1%

교체일
Real number (ℝ)

MISSING 

Distinct32
Distinct (%)41.6%
Missing9923
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean20186781
Minimum20141231
Maximum20240331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:27.877543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20141231
5-th percentile20151210
Q120161109
median20181231
Q320221130
95-th percentile20231231
Maximum20240331
Range99100
Interquartile range (IQR)60021

Descriptive statistics

Standard deviation29069.056
Coefficient of variation (CV)0.0014400045
Kurtosis-1.1909416
Mean20186781
Median Absolute Deviation (MAD)20311
Skewness0.4670518
Sum1.5543821 × 109
Variance8.4501002 × 108
MonotonicityNot monotonic
2024-05-04T05:47:28.377331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20181231 9
 
0.1%
20191231 7
 
0.1%
20221130 5
 
0.1%
20161231 5
 
0.1%
20160720 4
 
< 0.1%
20231231 4
 
< 0.1%
20160831 4
 
< 0.1%
20171210 3
 
< 0.1%
20151210 3
 
< 0.1%
20200812 2
 
< 0.1%
Other values (22) 31
 
0.3%
(Missing) 9923
99.2%
ValueCountFrequency (%)
20141231 1
 
< 0.1%
20151007 1
 
< 0.1%
20151207 1
 
< 0.1%
20151210 3
< 0.1%
20151231 2
< 0.1%
20160720 4
< 0.1%
20160831 4
< 0.1%
20160920 2
< 0.1%
20161109 2
< 0.1%
20161130 1
 
< 0.1%
ValueCountFrequency (%)
20240331 2
 
< 0.1%
20240124 1
 
< 0.1%
20231231 4
< 0.1%
20231220 2
 
< 0.1%
20231210 1
 
< 0.1%
20230410 2
 
< 0.1%
20221231 2
 
< 0.1%
20221216 1
 
< 0.1%
20221130 5
0.1%
20220722 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%
Mean45103.88
Minimum14
Maximum410459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:28.977477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile3969.05
Q122193.75
median41333
Q359911.25
95-th percentile76165.15
Maximum410459
Range410445
Interquartile range (IQR)37717.5

Descriptive statistics

Standard deviation40765.689
Coefficient of variation (CV)0.90381778
Kurtosis24.751192
Mean45103.88
Median Absolute Deviation (MAD)18869.5
Skewness4.2128954
Sum4.510388 × 108
Variance1.6618414 × 109
MonotonicityNot monotonic
2024-05-04T05:47:29.421986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43353 1
 
< 0.1%
48498 1
 
< 0.1%
37415 1
 
< 0.1%
59248 1
 
< 0.1%
936 1
 
< 0.1%
58475 1
 
< 0.1%
32284 1
 
< 0.1%
22425 1
 
< 0.1%
19440 1
 
< 0.1%
23381 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
14 1
< 0.1%
25 1
< 0.1%
28 1
< 0.1%
38 1
< 0.1%
39 1
< 0.1%
64 1
< 0.1%
65 1
< 0.1%
68 1
< 0.1%
86 1
< 0.1%
93 1
< 0.1%
ValueCountFrequency (%)
410459 1
< 0.1%
375795 1
< 0.1%
371405 1
< 0.1%
369853 1
< 0.1%
369352 1
< 0.1%
366925 1
< 0.1%
366922 1
< 0.1%
366381 1
< 0.1%
364419 1
< 0.1%
362443 1
< 0.1%

공사관리번호
Text

Distinct1664
Distinct (%)16.6%
Missing6
Missing (%)0.1%
Memory size156.2 KiB
2024-05-04T05:47:30.032756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters129922
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

Unique499 ?
Unique (%)5.0%

Sample

1st row2005-0108-008
2nd row2008-1112-571
3rd row2002-0108-045
4th row2010-0108-018
5th row2005-0108-015
ValueCountFrequency (%)
2000-0000-000 1402
 
14.0%
2007-0508-468 111
 
1.1%
2003-0208-018 98
 
1.0%
2008-0108-019 84
 
0.8%
2012-0504-001 71
 
0.7%
2008-1208-536 59
 
0.6%
2003-0108-036 58
 
0.6%
2005-0108-143 54
 
0.5%
2007-0108-056 53
 
0.5%
2008-0108-453 52
 
0.5%
Other values (1654) 7952
79.6%
2024-05-04T05:47:31.105806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 52257
40.2%
- 19988
 
15.4%
1 17065
 
13.1%
2 14549
 
11.2%
8 10047
 
7.7%
5 3626
 
2.8%
4 3360
 
2.6%
3 2620
 
2.0%
6 2253
 
1.7%
7 2099
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 109934
84.6%
Dash Punctuation 19988
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52257
47.5%
1 17065
 
15.5%
2 14549
 
13.2%
8 10047
 
9.1%
5 3626
 
3.3%
4 3360
 
3.1%
3 2620
 
2.4%
6 2253
 
2.0%
7 2099
 
1.9%
9 2058
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 19988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52257
40.2%
- 19988
 
15.4%
1 17065
 
13.1%
2 14549
 
11.2%
8 10047
 
7.7%
5 3626
 
2.8%
4 3360
 
2.6%
3 2620
 
2.0%
6 2253
 
1.7%
7 2099
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52257
40.2%
- 19988
 
15.4%
1 17065
 
13.1%
2 14549
 
11.2%
8 10047
 
7.7%
5 3626
 
2.8%
4 3360
 
2.6%
3 2620
 
2.0%
6 2253
 
1.7%
7 2099
 
1.6%

문자표시 관리번호.1
Text

Distinct9869
Distinct (%)99.1%
Missing37
Missing (%)0.4%
Memory size156.2 KiB
2024-05-04T05:47:31.684555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters89667
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

Unique9777 ?
Unique (%)98.1%

Sample

1st row07-069968
2nd row07-038121
3rd row07-034216
4th row07-049305
5th row07-000452
ValueCountFrequency (%)
07-011247 4
 
< 0.1%
07-066164 2
 
< 0.1%
07-045971 2
 
< 0.1%
07-011786 2
 
< 0.1%
07-072509 2
 
< 0.1%
07-032069 2
 
< 0.1%
07-076298 2
 
< 0.1%
07-097677 2
 
< 0.1%
07-106378 2
 
< 0.1%
07-077578 2
 
< 0.1%
Other values (9859) 9941
99.8%
2024-05-04T05:47:32.426968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24850
27.7%
7 15272
17.0%
- 9963
11.1%
1 5477
 
6.1%
3 5214
 
5.8%
4 5108
 
5.7%
2 5043
 
5.6%
5 4989
 
5.6%
6 4962
 
5.5%
8 4561
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79704
88.9%
Dash Punctuation 9963
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24850
31.2%
7 15272
19.2%
1 5477
 
6.9%
3 5214
 
6.5%
4 5108
 
6.4%
2 5043
 
6.3%
5 4989
 
6.3%
6 4962
 
6.2%
8 4561
 
5.7%
9 4228
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 9963
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89667
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24850
27.7%
7 15272
17.0%
- 9963
11.1%
1 5477
 
6.1%
3 5214
 
5.8%
4 5108
 
5.7%
2 5043
 
5.6%
5 4989
 
5.6%
6 4962
 
5.5%
8 4561
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24850
27.7%
7 15272
17.0%
- 9963
11.1%
1 5477
 
6.1%
3 5214
 
5.8%
4 5108
 
5.7%
2 5043
 
5.6%
5 4989
 
5.6%
6 4962
 
5.5%
8 4561
 
5.1%

문자표시 종류코드
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8029 
1
1971 

Length

Max length4
Median length4
Mean length3.4087
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 8029
80.3%
1 1971
 
19.7%

Length

2024-05-04T05:47:32.841010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:47:33.038586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8029
80.3%
1 1971
 
19.7%

공사형태 (공통)
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)0.1%
Missing1158
Missing (%)11.6%
Infinite0
Infinite (%)0.0%
Mean4.234223
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:47:33.368180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4300629
Coefficient of variation (CV)0.57390999
Kurtosis0.63871403
Mean4.234223
Median Absolute Deviation (MAD)1
Skewness0.68008985
Sum37439
Variance5.9052058
MonotonicityNot monotonic
2024-05-04T05:47:33.662040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 4206
42.1%
1 2045
20.4%
4 893
 
8.9%
3 880
 
8.8%
10 806
 
8.1%
9 6
 
0.1%
6 5
 
0.1%
8 1
 
< 0.1%
(Missing) 1158
 
11.6%
ValueCountFrequency (%)
1 2045
20.4%
3 880
 
8.8%
4 893
 
8.9%
5 4206
42.1%
6 5
 
0.1%
8 1
 
< 0.1%
9 6
 
0.1%
10 806
 
8.1%
ValueCountFrequency (%)
10 806
 
8.1%
9 6
 
0.1%
8 1
 
< 0.1%
6 5
 
0.1%
5 4206
42.1%
4 893
 
8.9%
3 880
 
8.8%
1 2045
20.4%

Sample

상태 (공통)문자표시 관리번호방향길이내용고가 (공통)구경찰서코드 (공통)구코드 (공통)신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)신규정규화ID설치일교체일공간데이터이력ID공사관리번호문자표시 관리번호.1문자표시 종류코드공사형태 (공통)
38866107-00000235312403일방통행13607103601221066175091<NA><NA><NA>433532005-0108-008<NA>15
21246107-00000699682203어린이1330530330412104<NA><NA><NA><NA>247512008-1112-57107-069968<NA>10
16627107-00000381213153일방통행1170560170412104<NA><NA><NA><NA>224292002-0108-04507-038121<NA>4
32986107-0000034216256307:001300500300111104<NA><NA><NA><NA>379922010-0108-01807-034216<NA>4
50425107-000004930523학교앞1310740310411106<NA><NA><NA><NA>569192005-0108-01507-049305<NA>5
28618107-00000004521503천천히1390380390412108<NA><NA><NA><NA>28702011-1008-00207-000452<NA>10
48246107-00000538541383천천히1360710360122106<NA><NA><NA><NA>526502003-0108-03607-05385415
64216107-00000253812683일방1310740310122106<NA><NA><NA><NA>456282000-0000-00007-0253811<NA>
13185107-0000035616583일방1300500300212104<NA><NA><NA><NA>187912004-1108-38807-035616<NA>5
13789107-00001034531713천천히12302102301121095277269<NA><NA><NA>198322007-0108-01907-103453<NA>3
상태 (공통)문자표시 관리번호방향길이내용고가 (공통)구경찰서코드 (공통)구코드 (공통)신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)신규정규화ID설치일교체일공간데이터이력ID공사관리번호문자표시 관리번호.1문자표시 종류코드공사형태 (공통)
44954107-000005715583일방통행13005003001221041242203<NA><NA><NA>507722003-1108-22207-05715514
24574107-00000303832333일방통행11601701601221083241043<NA><NA><NA>282702004-0108-17507-03038315
37292107-00000377051033인천1300500300212104<NA><NA><NA><NA>427682000-0000-00007-037705<NA><NA>
63279107-0000077482933을지로1110140110411108<NA><NA><NA><NA>736782010-1008-01307-077482<NA>10
15373107-00000687572383301230210230122109<NA><NA><NA><NA>194552004-0108-15407-06875715
11515107-0000014686193학교앞1220590220122105<NA><NA><NA><NA>172692000-0000-00007-0146861<NA>
29772107-0000056245813중계동14003204001111075407366<NA><NA><NA>343202003-0208-01707-056245<NA>3
44391107-00000657193553하남시1280680280411106<NA><NA><NA><NA>515232011-0408-00107-065719<NA>5
57665107-00000576571963진입금지1210440210412108<NA><NA><NA><NA>663042012-0108-05707-057657<NA>5
66107-0000173571175버111014011012110843206110<NA><NA><NA>2965342009-0207-00307-17357111