Overview

Dataset statistics

Number of variables21
Number of observations10000
Missing cells40659
Missing cells (%)19.4%
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),방향표시관리번호,각도,길이,방향코드,고가 (공통),구경찰서코드 (공통),구코드 (공통),신경찰서코드 (공통),작업구분 (공통),표출구분 (공통),도로구분 (공통),관할사업소 (공통),신규정규화ID,설치일,교체일,공간데이터,이력ID,공사관리번호,방향표시관리번호,공사형태 (공통)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15536/S/1/datasetView.do

Alerts

상태 (공통) is highly imbalanced (96.3%)Imbalance
고가 (공통) is highly imbalanced (92.7%)Imbalance
도로구분 (공통) is highly imbalanced (57.9%)Imbalance
구경찰서코드 (공통) has 1640 (16.4%) missing valuesMissing
구코드 (공통) has 1726 (17.3%) missing valuesMissing
신경찰서코드 (공통) has 1629 (16.3%) missing valuesMissing
관할사업소 (공통) has 111 (1.1%) missing valuesMissing
신규정규화ID has 6419 (64.2%) missing valuesMissing
설치일 has 8798 (88.0%) missing valuesMissing
교체일 has 8787 (87.9%) missing valuesMissing
공간데이터 has 10000 (100.0%) missing valuesMissing
공사관리번호 has 142 (1.4%) missing valuesMissing
공사형태 (공통) has 1381 (13.8%) missing valuesMissing
이력ID has unique valuesUnique
공간데이터 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-04 00:35:45.713715
Analysis finished2024-05-04 00:35:47.058530
Duration1.34 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
9918 
4
 
74
<NA>
 
6
3
 
2

Length

Max length4
Median length1
Mean length1.0018
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9918
99.2%
4 74
 
0.7%
<NA> 6
 
0.1%
3 2
 
< 0.1%

Length

2024-05-04T00:35:47.274403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:35:47.628302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9918
99.2%
4 74
 
0.7%
na 6
 
0.1%
3 2
 
< 0.1%

방향표시관리번호
Text

Distinct9751
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T00:35:48.329364image/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

Unique9508 ?
Unique (%)95.1%

Sample

1st row05-0000133102
2nd row05-0000149304
3rd row05-0000190931
4th row05-0000054470
5th row05-0000035014
ValueCountFrequency (%)
05-0000104777 4
 
< 0.1%
05-0000148727 3
 
< 0.1%
05-0000171560 3
 
< 0.1%
05-0000171941 3
 
< 0.1%
05-0000173647 3
 
< 0.1%
05-0000178473 2
 
< 0.1%
05-0000019336 2
 
< 0.1%
05-0000100634 2
 
< 0.1%
05-0000177378 2
 
< 0.1%
05-0000176214 2
 
< 0.1%
Other values (9741) 9974
99.7%
2024-05-04T00:35:49.481795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59302
45.6%
5 14708
 
11.3%
1 10346
 
8.0%
- 10000
 
7.7%
7 5753
 
4.4%
8 5532
 
4.3%
2 5147
 
4.0%
4 4885
 
3.8%
3 4865
 
3.7%
9 4750
 
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 59302
49.4%
5 14708
 
12.3%
1 10346
 
8.6%
7 5753
 
4.8%
8 5532
 
4.6%
2 5147
 
4.3%
4 4885
 
4.1%
3 4865
 
4.1%
9 4750
 
4.0%
6 4712
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59302
45.6%
5 14708
 
11.3%
1 10346
 
8.0%
- 10000
 
7.7%
7 5753
 
4.4%
8 5532
 
4.3%
2 5147
 
4.0%
4 4885
 
3.8%
3 4865
 
3.7%
9 4750
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59302
45.6%
5 14708
 
11.3%
1 10346
 
8.0%
- 10000
 
7.7%
7 5753
 
4.4%
8 5532
 
4.3%
2 5147
 
4.0%
4 4885
 
3.8%
3 4865
 
3.7%
9 4750
 
3.7%

각도
Real number (ℝ)

Distinct361
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.258
Minimum0
Maximum360
Zeros32
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:35:50.047427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q190
median178
Q3273
95-th percentile339
Maximum360
Range360
Interquartile range (IQR)183

Descriptive statistics

Standard deviation104.04351
Coefficient of variation (CV)0.5836681
Kurtosis-1.2420434
Mean178.258
Median Absolute Deviation (MAD)91
Skewness0.0087134503
Sum1782580
Variance10825.052
MonotonicityNot monotonic
2024-05-04T00:35:50.838509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291 84
 
0.8%
112 80
 
0.8%
290 79
 
0.8%
292 70
 
0.7%
90 70
 
0.7%
111 67
 
0.7%
293 66
 
0.7%
109 65
 
0.7%
201 65
 
0.7%
180 60
 
0.6%
Other values (351) 9294
92.9%
ValueCountFrequency (%)
0 32
0.3%
1 35
0.4%
2 25
0.2%
3 15
 
0.1%
4 28
0.3%
5 25
0.2%
6 24
0.2%
7 17
 
0.2%
8 35
0.4%
9 45
0.4%
ValueCountFrequency (%)
360 9
 
0.1%
359 27
0.3%
358 29
0.3%
357 29
0.3%
356 21
0.2%
355 21
0.2%
354 24
0.2%
353 25
0.2%
352 32
0.3%
351 24
0.2%

길이
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
5
6066 
0
3934 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row0
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 6066
60.7%
0 3934
39.3%

Length

2024-05-04T00:35:51.494503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:35:52.003685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 6066
60.7%
0 3934
39.3%

방향코드
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1021
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:35:52.462139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median7
Q37
95-th percentile11
Maximum23
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.2976299
Coefficient of variation (CV)0.54040902
Kurtosis2.6589802
Mean6.1021
Median Absolute Deviation (MAD)1
Skewness0.48184929
Sum61021
Variance10.874363
MonotonicityNot monotonic
2024-05-04T00:35:53.186779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
7 4269
42.7%
1 1842
18.4%
5 923
 
9.2%
9 836
 
8.4%
6 566
 
5.7%
2 393
 
3.9%
11 315
 
3.1%
8 300
 
3.0%
12 211
 
2.1%
10 165
 
1.7%
Other values (11) 180
 
1.8%
ValueCountFrequency (%)
1 1842
18.4%
2 393
 
3.9%
3 1
 
< 0.1%
4 19
 
0.2%
5 923
 
9.2%
6 566
 
5.7%
7 4269
42.7%
8 300
 
3.0%
9 836
 
8.4%
10 165
 
1.7%
ValueCountFrequency (%)
23 20
 
0.2%
22 22
 
0.2%
21 9
 
0.1%
19 34
 
0.3%
18 1
 
< 0.1%
17 1
 
< 0.1%
15 36
 
0.4%
14 14
 
0.1%
13 23
 
0.2%
12 211
2.1%

고가 (공통)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9850 
2
 
144
3
 
6

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 9850
98.5%
2 144
 
1.4%
3 6
 
0.1%

Length

2024-05-04T00:35:53.861657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:35:54.339364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9850
98.5%
2 144
 
1.4%
3 6
 
0.1%

구경찰서코드 (공통)
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)0.4%
Missing1640
Missing (%)16.4%
Infinite0
Infinite (%)0.0%
Mean269.28469
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:35:54.652002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile130
Q1200
median280
Q3340
95-th percentile400
Maximum410
Range300
Interquartile range (IQR)140

Descriptive statistics

Standard deviation83.717693
Coefficient of variation (CV)0.31088917
Kurtosis-1.0757026
Mean269.28469
Median Absolute Deviation (MAD)70
Skewness-0.15214432
Sum2251220
Variance7008.652
MonotonicityNot monotonic
2024-05-04T00:35:55.090928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
280 594
 
5.9%
340 503
 
5.0%
210 458
 
4.6%
360 437
 
4.4%
170 397
 
4.0%
350 394
 
3.9%
300 390
 
3.9%
310 375
 
3.8%
230 321
 
3.2%
160 314
 
3.1%
Other values (21) 4177
41.8%
(Missing) 1640
 
16.4%
ValueCountFrequency (%)
110 186
1.9%
120 135
 
1.4%
130 154
 
1.5%
140 271
2.7%
150 141
 
1.4%
160 314
3.1%
170 397
4.0%
180 160
1.6%
190 132
 
1.3%
200 210
2.1%
ValueCountFrequency (%)
410 293
2.9%
400 205
2.1%
390 217
2.2%
380 155
 
1.6%
370 195
 
1.9%
360 437
4.4%
350 394
3.9%
340 503
5.0%
330 286
2.9%
320 131
 
1.3%

구코드 (공통)
Real number (ℝ)

MISSING 

Distinct25
Distinct (%)0.3%
Missing1726
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean461.38264
Minimum110
Maximum740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:35:55.470197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1290
median470
Q3650
95-th percentile710
Maximum740
Range630
Interquartile range (IQR)360

Descriptive statistics

Standard deviation193.99793
Coefficient of variation (CV)0.4204708
Kurtosis-1.2016042
Mean461.38264
Median Absolute Deviation (MAD)180
Skewness-0.27848492
Sum3817480
Variance37635.197
MonotonicityNot monotonic
2024-05-04T00:35:56.015743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
680 791
 
7.9%
650 656
 
6.6%
710 524
 
5.2%
560 514
 
5.1%
440 427
 
4.3%
500 392
 
3.9%
470 385
 
3.9%
140 373
 
3.7%
740 368
 
3.7%
380 337
 
3.4%
Other values (15) 3507
35.1%
(Missing) 1726
17.3%
ValueCountFrequency (%)
110 283
2.8%
140 373
3.7%
170 307
3.1%
200 231
2.3%
210 223
2.2%
230 220
2.2%
260 246
2.5%
290 255
2.5%
300 158
1.6%
320 126
 
1.3%
ValueCountFrequency (%)
740 368
3.7%
710 524
5.2%
680 791
7.9%
650 656
6.6%
620 225
 
2.2%
590 230
 
2.3%
560 514
5.1%
540 172
 
1.7%
530 258
 
2.6%
500 392
3.9%

신경찰서코드 (공통)
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)0.4%
Missing1629
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean268.25111
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:35:56.421572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile130
Q1190
median280
Q3340
95-th percentile400
Maximum410
Range300
Interquartile range (IQR)150

Descriptive statistics

Standard deviation86.458529
Coefficient of variation (CV)0.32230446
Kurtosis-1.1925273
Mean268.25111
Median Absolute Deviation (MAD)70
Skewness-0.1458838
Sum2245530
Variance7475.0772
MonotonicityNot monotonic
2024-05-04T00:35:56.887909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
360 524
 
5.2%
170 514
 
5.1%
340 495
 
5.0%
280 445
 
4.5%
210 433
 
4.3%
300 394
 
3.9%
350 386
 
3.9%
310 376
 
3.8%
410 349
 
3.5%
160 307
 
3.1%
Other values (21) 4148
41.5%
(Missing) 1629
 
16.3%
ValueCountFrequency (%)
110 199
 
2.0%
120 156
 
1.6%
130 174
 
1.7%
140 299
3.0%
150 127
 
1.3%
160 307
3.1%
170 514
5.1%
180 231
2.3%
190 140
 
1.4%
200 220
2.2%
ValueCountFrequency (%)
410 349
3.5%
400 132
 
1.3%
390 205
 
2.1%
380 162
 
1.6%
370 279
2.8%
360 524
5.2%
350 386
3.9%
340 495
5.0%
330 281
2.8%
320 115
 
1.1%

작업구분 (공통)
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
4988 
2
2341 
4
1581 
6
650 
3
 
440

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 4988
49.9%
2 2341
23.4%
4 1581
 
15.8%
6 650
 
6.5%
3 440
 
4.4%

Length

2024-05-04T00:35:57.469501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:35:57.809464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4988
49.9%
2 2341
23.4%
4 1581
 
15.8%
6 650
 
6.5%
3 440
 
4.4%

표출구분 (공통)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5041 
2
4959 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 5041
50.4%
2 4959
49.6%

Length

2024-05-04T00:35:58.295404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:35:58.629526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5041
50.4%
2 4959
49.6%

도로구분 (공통)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8327 
2
1652 
<NA>
 
21

Length

Max length4
Median length1
Mean length1.0063
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 8327
83.3%
2 1652
 
16.5%
<NA> 21
 
0.2%

Length

2024-05-04T00:35:58.966086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:35:59.341673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8327
83.3%
2 1652
 
16.5%
na 21
 
0.2%

관할사업소 (공통)
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)0.1%
Missing111
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean106.38325
Minimum104
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:35:59.611515image/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 deviation1.6398983
Coefficient of variation (CV)0.015415004
Kurtosis-1.2223635
Mean106.38325
Median Absolute Deviation (MAD)1
Skewness0.040906078
Sum1052024
Variance2.6892665
MonotonicityNot monotonic
2024-05-04T00:36:00.008327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
108 2099
21.0%
106 2097
21.0%
104 1689
16.9%
105 1628
16.3%
107 1265
12.7%
109 1111
11.1%
(Missing) 111
 
1.1%
ValueCountFrequency (%)
104 1689
16.9%
105 1628
16.3%
106 2097
21.0%
107 1265
12.7%
108 2099
21.0%
109 1111
11.1%
ValueCountFrequency (%)
109 1111
11.1%
108 2099
21.0%
107 1265
12.7%
106 2097
21.0%
105 1628
16.3%
104 1689
16.9%

신규정규화ID
Real number (ℝ)

MISSING 

Distinct3430
Distinct (%)95.8%
Missing6419
Missing (%)64.2%
Infinite0
Infinite (%)0.0%
Mean13110464
Minimum182462
Maximum3.2226513 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:36:00.389216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182462
5-th percentile1177973
Q13104781
median4435329
Q321071325
95-th percentile52484310
Maximum3.2226513 × 108
Range3.2208267 × 108
Interquartile range (IQR)17966544

Descriptive statistics

Standard deviation17885758
Coefficient of variation (CV)1.3642354
Kurtosis25.498112
Mean13110464
Median Absolute Deviation (MAD)2075445
Skewness2.8213237
Sum4.694857 × 1010
Variance3.1990035 × 1014
MonotonicityNot monotonic
2024-05-04T00:36:00.823730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44657510 4
 
< 0.1%
42276410 4
 
< 0.1%
43426210 4
 
< 0.1%
40871910 4
 
< 0.1%
30875410 3
 
< 0.1%
12108310 3
 
< 0.1%
55437310 3
 
< 0.1%
61381110 3
 
< 0.1%
42306510 3
 
< 0.1%
43203810 3
 
< 0.1%
Other values (3420) 3547
35.5%
(Missing) 6419
64.2%
ValueCountFrequency (%)
182462 1
< 0.1%
182679 1
< 0.1%
182782 1
< 0.1%
182784 1
< 0.1%
191559 1
< 0.1%
191843 1
< 0.1%
192208 1
< 0.1%
193697 1
< 0.1%
193722 1
< 0.1%
264893 1
< 0.1%
ValueCountFrequency (%)
322265129 1
< 0.1%
72362510 1
< 0.1%
72360410 1
< 0.1%
72171310 1
< 0.1%
72166110 1
< 0.1%
72165012 1
< 0.1%
72152833 1
< 0.1%
72152810 1
< 0.1%
72089310 1
< 0.1%
72088710 2
< 0.1%

설치일
Real number (ℝ)

MISSING 

Distinct169
Distinct (%)14.1%
Missing8798
Missing (%)88.0%
Infinite0
Infinite (%)0.0%
Mean20172672
Minimum20130710
Maximum20240331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:36:01.418923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20130710
5-th percentile20151031
Q120160630
median20170628
Q320180930
95-th percentile20221216
Maximum20240331
Range109621
Interquartile range (IQR)20300

Descriptive statistics

Standard deviation21677.51
Coefficient of variation (CV)0.0010745979
Kurtosis1.2157968
Mean20172672
Median Absolute Deviation (MAD)10110.5
Skewness1.3471828
Sum2.4247552 × 1010
Variance4.6991446 × 108
MonotonicityNot monotonic
2024-05-04T00:36:01.992682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20151231 136
 
1.4%
20161231 98
 
1.0%
20171226 76
 
0.8%
20161016 34
 
0.3%
20181130 26
 
0.3%
20181221 23
 
0.2%
20221216 23
 
0.2%
20151228 22
 
0.2%
20160831 21
 
0.2%
20161230 21
 
0.2%
Other values (159) 722
 
7.2%
(Missing) 8798
88.0%
ValueCountFrequency (%)
20130710 1
 
< 0.1%
20131126 1
 
< 0.1%
20150420 10
0.1%
20150513 2
 
< 0.1%
20150531 12
0.1%
20150720 17
0.2%
20150730 1
 
< 0.1%
20150930 1
 
< 0.1%
20151020 2
 
< 0.1%
20151030 5
 
0.1%
ValueCountFrequency (%)
20240331 5
0.1%
20240124 8
0.1%
20231231 11
0.1%
20231222 2
 
< 0.1%
20231212 3
 
< 0.1%
20231130 8
0.1%
20230731 1
 
< 0.1%
20230727 1
 
< 0.1%
20230324 5
0.1%
20221231 6
0.1%

교체일
Real number (ℝ)

MISSING 

Distinct171
Distinct (%)14.1%
Missing8787
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean20176139
Minimum20130710
Maximum20240331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:36:02.502264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20130710
5-th percentile20151130
Q120160731
median20170910
Q320190620
95-th percentile20221216
Maximum20240331
Range109621
Interquartile range (IQR)29889

Descriptive statistics

Standard deviation24105.688
Coefficient of variation (CV)0.0011947622
Kurtosis-0.11725132
Mean20176139
Median Absolute Deviation (MAD)10311
Skewness0.99594432
Sum2.4473656 × 1010
Variance5.8108419 × 108
MonotonicityNot monotonic
2024-05-04T00:36:03.074640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20151231 134
 
1.3%
20161231 93
 
0.9%
20171226 74
 
0.7%
20221216 47
 
0.5%
20161016 34
 
0.3%
20210917 28
 
0.3%
20181221 23
 
0.2%
20181130 22
 
0.2%
20160831 21
 
0.2%
20151228 21
 
0.2%
Other values (161) 716
 
7.2%
(Missing) 8787
87.9%
ValueCountFrequency (%)
20130710 1
 
< 0.1%
20131126 1
 
< 0.1%
20150420 4
 
< 0.1%
20150513 2
 
< 0.1%
20150531 11
0.1%
20150720 16
0.2%
20150730 1
 
< 0.1%
20151020 2
 
< 0.1%
20151030 5
 
0.1%
20151031 8
0.1%
ValueCountFrequency (%)
20240331 5
 
0.1%
20240124 8
0.1%
20231231 13
0.1%
20231222 2
 
< 0.1%
20231212 3
 
< 0.1%
20231130 8
0.1%
20231129 1
 
< 0.1%
20230731 1
 
< 0.1%
20230727 1
 
< 0.1%
20230324 5
 
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%
Mean285222.56
Minimum20
Maximum433626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:36:03.629920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile95096.95
Q1279660.75
median302024.5
Q3325614.75
95-th percentile346492.9
Maximum433626
Range433606
Interquartile range (IQR)45954

Descriptive statistics

Standard deviation74602.714
Coefficient of variation (CV)0.26155966
Kurtosis4.7621218
Mean285222.56
Median Absolute Deviation (MAD)22935
Skewness-2.0863736
Sum2.8522256 × 109
Variance5.5655649 × 109
MonotonicityNot monotonic
2024-05-04T00:36:04.034833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
335091 1
 
< 0.1%
324231 1
 
< 0.1%
287026 1
 
< 0.1%
305631 1
 
< 0.1%
661 1
 
< 0.1%
338122 1
 
< 0.1%
277395 1
 
< 0.1%
350122 1
 
< 0.1%
309086 1
 
< 0.1%
236815 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
20 1
< 0.1%
21 1
< 0.1%
36 1
< 0.1%
43 1
< 0.1%
54 1
< 0.1%
72 1
< 0.1%
74 1
< 0.1%
99 1
< 0.1%
101 1
< 0.1%
125 1
< 0.1%
ValueCountFrequency (%)
433626 1
< 0.1%
433609 1
< 0.1%
433587 1
< 0.1%
433584 1
< 0.1%
433579 1
< 0.1%
433577 1
< 0.1%
433570 1
< 0.1%
433519 1
< 0.1%
433480 1
< 0.1%
433402 1
< 0.1%

공사관리번호
Text

MISSING 

Distinct1787
Distinct (%)18.1%
Missing142
Missing (%)1.4%
Memory size156.2 KiB
2024-05-04T00:36:04.869490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters128154
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

Unique668 ?
Unique (%)6.8%

Sample

1st row2012-1108-117
2nd row2008-1008-006
3rd row2014-0207-044
4th row2018-0107-037
5th row2005-1108-015
ValueCountFrequency (%)
2000-0000-000 1677
 
17.0%
2010-0108-013 82
 
0.8%
2008-0102-558 78
 
0.8%
2012-0108-042 67
 
0.7%
2012-1008-067 60
 
0.6%
2012-0902-001 59
 
0.6%
2009-0108-049 57
 
0.6%
2011-0108-175 49
 
0.5%
2012-1108-118 43
 
0.4%
2008-0108-593 42
 
0.4%
Other values (1777) 7644
77.5%
2024-05-04T00:36:06.704818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 50168
39.1%
- 19716
 
15.4%
1 18821
 
14.7%
2 15880
 
12.4%
8 7618
 
5.9%
7 3726
 
2.9%
4 2986
 
2.3%
3 2747
 
2.1%
5 2457
 
1.9%
6 2019
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 108438
84.6%
Dash Punctuation 19716
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50168
46.3%
1 18821
 
17.4%
2 15880
 
14.6%
8 7618
 
7.0%
7 3726
 
3.4%
4 2986
 
2.8%
3 2747
 
2.5%
5 2457
 
2.3%
6 2019
 
1.9%
9 2016
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 19716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 128154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 50168
39.1%
- 19716
 
15.4%
1 18821
 
14.7%
2 15880
 
12.4%
8 7618
 
5.9%
7 3726
 
2.9%
4 2986
 
2.3%
3 2747
 
2.1%
5 2457
 
1.9%
6 2019
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 50168
39.1%
- 19716
 
15.4%
1 18821
 
14.7%
2 15880
 
12.4%
8 7618
 
5.9%
7 3726
 
2.9%
4 2986
 
2.3%
3 2747
 
2.1%
5 2457
 
1.9%
6 2019
 
1.6%

방향표시관리번호.1
Text

Distinct9725
Distinct (%)97.5%
Missing26
Missing (%)0.3%
Memory size156.2 KiB
2024-05-04T00:36:07.634423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters89766
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

Unique9482 ?
Unique (%)95.1%

Sample

1st row05-133102
2nd row05-149304
3rd row05-190931
4th row05-054470
5th row05-035014
ValueCountFrequency (%)
05-104777 4
 
< 0.1%
05-171560 3
 
< 0.1%
05-148727 3
 
< 0.1%
05-171941 3
 
< 0.1%
05-173647 3
 
< 0.1%
05-006937 2
 
< 0.1%
05-178075 2
 
< 0.1%
05-177378 2
 
< 0.1%
05-086227 2
 
< 0.1%
05-176214 2
 
< 0.1%
Other values (9715) 9948
99.7%
2024-05-04T00:36:08.852552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19253
21.4%
5 14674
16.3%
1 10318
11.5%
- 9974
11.1%
7 5742
 
6.4%
8 5504
 
6.1%
2 5137
 
5.7%
4 4871
 
5.4%
3 4851
 
5.4%
9 4740
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79792
88.9%
Dash Punctuation 9974
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19253
24.1%
5 14674
18.4%
1 10318
12.9%
7 5742
 
7.2%
8 5504
 
6.9%
2 5137
 
6.4%
4 4871
 
6.1%
3 4851
 
6.1%
9 4740
 
5.9%
6 4702
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 9974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19253
21.4%
5 14674
16.3%
1 10318
11.5%
- 9974
11.1%
7 5742
 
6.4%
8 5504
 
6.1%
2 5137
 
5.7%
4 4871
 
5.4%
3 4851
 
5.4%
9 4740
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19253
21.4%
5 14674
16.3%
1 10318
11.5%
- 9974
11.1%
7 5742
 
6.4%
8 5504
 
6.1%
2 5137
 
5.7%
4 4871
 
5.4%
3 4851
 
5.4%
9 4740
 
5.3%

공사형태 (공통)
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)0.1%
Missing1381
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean4.1139343
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:36:09.312907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q35
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9076533
Coefficient of variation (CV)0.46370532
Kurtosis2.034047
Mean4.1139343
Median Absolute Deviation (MAD)1
Skewness0.46754125
Sum35458
Variance3.6391409
MonotonicityNot monotonic
2024-05-04T00:36:09.663115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 4137
41.4%
4 1897
19.0%
1 1537
 
15.4%
3 686
 
6.9%
10 354
 
3.5%
6 5
 
0.1%
9 2
 
< 0.1%
2 1
 
< 0.1%
(Missing) 1381
 
13.8%
ValueCountFrequency (%)
1 1537
 
15.4%
2 1
 
< 0.1%
3 686
 
6.9%
4 1897
19.0%
5 4137
41.4%
6 5
 
0.1%
9 2
 
< 0.1%
10 354
 
3.5%
ValueCountFrequency (%)
10 354
 
3.5%
9 2
 
< 0.1%
6 5
 
0.1%
5 4137
41.4%
4 1897
19.0%
3 686
 
6.9%
2 1
 
< 0.1%
1 1537
 
15.4%

Sample

상태 (공통)방향표시관리번호각도길이방향코드고가 (공통)구경찰서코드 (공통)구코드 (공통)신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)신규정규화ID설치일교체일공간데이터이력ID공사관리번호방향표시관리번호.1공사형태 (공통)
48958105-000013310242591210440210111108<NA><NA><NA><NA>3350912012-1108-11705-1331024
20102105-000014930432507124038024012110823230810<NA><NA><NA>2974702008-1008-00605-1493044
66772105-00001909315157121044021011210822364910<NA><NA><NA>3466262014-0207-04405-1909313
65397105-00000544702857116017016012110832334192018113020181130<NA>698952018-0107-03705-0544705
14569105-0000035014293571280680280211106<NA><NA><NA><NA>2887362005-1108-01505-0350141
64578105-00000887232165131330530330412104<NA><NA><NA><NA>1040132009-1112-05705-0887231
15989105-0000177549233571230210230121109<NA><NA><NA><NA>2945212000-0000-00005-177549<NA>
6603105-0000068283281511<NA><NA><NA>211104<NA><NA><NA><NA>2834422003-0208-00505-0682835
13155105-0000016479126071220590220411105<NA><NA><NA><NA>2901022012-1108-08005-0164795
34645105-000005681465571230210230121109<NA><NA><NA><NA>3173722005-1108-24405-0568141
상태 (공통)방향표시관리번호각도길이방향코드고가 (공통)구경찰서코드 (공통)구코드 (공통)신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)신규정규화ID설치일교체일공간데이터이력ID공사관리번호방향표시관리번호.1공사형태 (공통)
31621105-0000020117182011<NA><NA><NA>211104<NA><NA><NA><NA>3125602010-0108-06205-0201175
59967105-0000022823292561330530330411104<NA><NA><NA><NA>147262009-0108-04905-0228231
51891105-00001773621700913504703501211041280891<NA><NA><NA>3369722012-0108-11805-1773625
41300105-000017202264511<NA><NA><NA>211106<NA><NA><NA><NA>3253522000-0000-00005-172022<NA>
29314105-0000053605202061170560170411104<NA><NA><NA><NA>3064502008-0108-59305-0536055
29460105-0000178188122571240380240211108<NA><NA><NA><NA>3117082000-0000-00005-178188<NA>
6439105-0000050193330071310740310122106<NA><NA><NA><NA>2776722012-0108-01405-0501935
59716105-0000097963242511<NA><NA><NA>211108<NA><NA><NA><NA>2714712003-0108-03705-0979635
826105-0000160314114071410680410322106<NA><NA><NA><NA>2730822010-0112-00905-1603145
21896105-00001773083585713107403101221067218125<NA><NA><NA>2997732000-0000-00005-1773084