Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells36055
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory167.0 B

Variable types

Text3
Categorical6
Numeric8
Unsupported1

Dataset

Description안전지대관리번호,상태 (공통),고가 (공통),구경찰서코드 (공통),신경찰서코드 (공통),작업구분 (공통),표출구분 (공통),도로구분 (공통),관할사업소 (공통),구코드 (공통),신규정규화ID,설치일,교체일,공간데이터,이력ID,공사관리번호,구안전지대 관리번호,안전지대종류코드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15533/S/1/datasetView.do

Alerts

구경찰서코드 (공통) is highly overall correlated with 신경찰서코드 (공통) and 1 other fieldsHigh correlation
신경찰서코드 (공통) is highly overall correlated with 구경찰서코드 (공통) and 1 other fieldsHigh correlation
관할사업소 (공통) is highly overall correlated with 구코드 (공통)High correlation
구코드 (공통) is highly overall correlated with 구경찰서코드 (공통) and 2 other fieldsHigh correlation
설치일 is highly overall correlated with 교체일 and 2 other fieldsHigh correlation
교체일 is highly overall correlated with 설치일 and 2 other fieldsHigh correlation
이력ID is highly overall correlated with 표출구분 (공통)High correlation
상태 (공통) is highly overall correlated with 설치일 and 1 other fieldsHigh correlation
작업구분 (공통) is highly overall correlated with 설치일 and 2 other fieldsHigh correlation
표출구분 (공통) is highly overall correlated with 이력ID and 1 other fieldsHigh correlation
상태 (공통) is highly imbalanced (98.3%)Imbalance
고가 (공통) is highly imbalanced (97.0%)Imbalance
도로구분 (공통) is highly imbalanced (60.0%)Imbalance
안전지대종류코드 is highly imbalanced (70.9%)Imbalance
구코드 (공통) has 111 (1.1%) missing valuesMissing
신규정규화ID has 7655 (76.5%) missing valuesMissing
설치일 has 8866 (88.7%) missing valuesMissing
교체일 has 8856 (88.6%) missing valuesMissing
공간데이터 has 10000 (100.0%) missing valuesMissing
공사관리번호 has 353 (3.5%) missing valuesMissing
이력ID has unique valuesUnique
공간데이터 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-03 20:10:18.820427
Analysis finished2024-05-03 20:10:49.388623
Duration30.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct7031
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T20:10:49.850517image/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

Unique5548 ?
Unique (%)55.5%

Sample

1st row14-0000017374
2nd row14-0000012116
3rd row14-0000002357
4th row14-0000012771
5th row14-0000017998
ValueCountFrequency (%)
14-0000002181 74
 
0.7%
14-0000000218 70
 
0.7%
14-0000009600 61
 
0.6%
14-0000007543 51
 
0.5%
14-0000003155 46
 
0.5%
14-0000004095 44
 
0.4%
14-0000004616 33
 
0.3%
14-0000003262 32
 
0.3%
14-0000003452 28
 
0.3%
14-0000008723 27
 
0.3%
Other values (7021) 9534
95.3%
2024-05-03T20:10:51.335510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59685
45.9%
1 18247
 
14.0%
4 14132
 
10.9%
- 10000
 
7.7%
2 5390
 
4.1%
3 4100
 
3.2%
5 3865
 
3.0%
8 3760
 
2.9%
6 3714
 
2.9%
9 3603
 
2.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59685
49.7%
1 18247
 
15.2%
4 14132
 
11.8%
2 5390
 
4.5%
3 4100
 
3.4%
5 3865
 
3.2%
8 3760
 
3.1%
6 3714
 
3.1%
9 3603
 
3.0%
7 3504
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59685
45.9%
1 18247
 
14.0%
4 14132
 
10.9%
- 10000
 
7.7%
2 5390
 
4.1%
3 4100
 
3.2%
5 3865
 
3.0%
8 3760
 
2.9%
6 3714
 
2.9%
9 3603
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59685
45.9%
1 18247
 
14.0%
4 14132
 
10.9%
- 10000
 
7.7%
2 5390
 
4.1%
3 4100
 
3.2%
5 3865
 
3.0%
8 3760
 
2.9%
6 3714
 
2.9%
9 3603
 
2.8%

상태 (공통)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9973 
4
 
26
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0003
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 9973
99.7%
4 26
 
0.3%
<NA> 1
 
< 0.1%

Length

2024-05-03T20:10:51.962217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:10:52.443622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9973
99.7%
4 26
 
0.3%
na 1
 
< 0.1%

고가 (공통)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9936 
2
 
56
3
 
7
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0003
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 9936
99.4%
2 56
 
0.6%
3 7
 
0.1%
<NA> 1
 
< 0.1%

Length

2024-05-03T20:10:53.037926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:10:53.493793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9936
99.4%
2 56
 
0.6%
3 7
 
0.1%
na 1
 
< 0.1%

구경찰서코드 (공통)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing45
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean258.78051
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T20:10:53.992540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile130
Q1190
median250
Q3340
95-th percentile400
Maximum410
Range300
Interquartile range (IQR)150

Descriptive statistics

Standard deviation84.67776
Coefficient of variation (CV)0.32721846
Kurtosis-1.2122756
Mean258.78051
Median Absolute Deviation (MAD)80
Skewness0.093867875
Sum2576160
Variance7170.323
MonotonicityNot monotonic
2024-05-03T20:10:54.601249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
210 699
 
7.0%
340 552
 
5.5%
160 512
 
5.1%
170 493
 
4.9%
360 461
 
4.6%
300 459
 
4.6%
330 446
 
4.5%
230 438
 
4.4%
190 410
 
4.1%
180 403
 
4.0%
Other values (21) 5082
50.8%
ValueCountFrequency (%)
110 127
 
1.3%
120 261
2.6%
130 189
 
1.9%
140 305
3.0%
150 167
 
1.7%
160 512
5.1%
170 493
4.9%
180 403
4.0%
190 410
4.1%
200 358
3.6%
ValueCountFrequency (%)
410 309
3.1%
400 221
2.2%
390 253
2.5%
380 163
 
1.6%
370 228
2.3%
360 461
4.6%
350 307
3.1%
340 552
5.5%
330 446
4.5%
320 213
 
2.1%

신경찰서코드 (공통)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing46
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean256.44063
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T20:10:55.022578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile120
Q1180
median250
Q3340
95-th percentile390
Maximum410
Range300
Interquartile range (IQR)160

Descriptive statistics

Standard deviation86.263397
Coefficient of variation (CV)0.3363874
Kurtosis-1.2883373
Mean256.44063
Median Absolute Deviation (MAD)80
Skewness0.082694195
Sum2552610
Variance7441.3736
MonotonicityNot monotonic
2024-05-03T20:10:55.476731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
210 670
 
6.7%
360 625
 
6.2%
170 572
 
5.7%
160 530
 
5.3%
340 513
 
5.1%
180 483
 
4.8%
330 464
 
4.6%
300 442
 
4.4%
190 429
 
4.3%
200 378
 
3.8%
Other values (21) 4848
48.5%
ValueCountFrequency (%)
110 215
 
2.1%
120 288
2.9%
130 188
 
1.9%
140 318
3.2%
150 105
 
1.1%
160 530
5.3%
170 572
5.7%
180 483
4.8%
190 429
4.3%
200 378
3.8%
ValueCountFrequency (%)
410 252
2.5%
400 153
 
1.5%
390 231
 
2.3%
380 180
 
1.8%
370 313
3.1%
360 625
6.2%
350 316
3.2%
340 513
5.1%
330 464
4.6%
320 168
 
1.7%

작업구분 (공통)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4
4871 
1
3029 
2
1880 
6
 
118
3
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
4 4871
48.7%
1 3029
30.3%
2 1880
 
18.8%
6 118
 
1.2%
3 102
 
1.0%

Length

2024-05-03T20:10:56.063770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:10:56.474219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 4871
48.7%
1 3029
30.3%
2 1880
 
18.8%
6 118
 
1.2%
3 102
 
1.0%

표출구분 (공통)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7597 
2
2403 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 7597
76.0%
2 2403
 
24.0%

Length

2024-05-03T20:10:57.001684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:10:57.351037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7597
76.0%
2 2403
 
24.0%

도로구분 (공통)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8472 
2
1504 
<NA>
 
24

Length

Max length4
Median length1
Mean length1.0072
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 8472
84.7%
2 1504
 
15.0%
<NA> 24
 
0.2%

Length

2024-05-03T20:10:57.793510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:10:58.136961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8472
84.7%
2 1504
 
15.0%
na 24
 
0.2%

관할사업소 (공통)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing99
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean106.58994
Minimum104
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T20:10:58.438328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile104
Q1105
median107
Q3108
95-th percentile109
Maximum109
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6996482
Coefficient of variation (CV)0.015945671
Kurtosis-1.2737056
Mean106.58994
Median Absolute Deviation (MAD)1
Skewness-0.15731368
Sum1055347
Variance2.8888038
MonotonicityNot monotonic
2024-05-03T20:10:58.810855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
108 2292
22.9%
104 1657
16.6%
107 1572
15.7%
106 1487
14.9%
109 1473
14.7%
105 1420
14.2%
(Missing) 99
 
1.0%
ValueCountFrequency (%)
104 1657
16.6%
105 1420
14.2%
106 1487
14.9%
107 1572
15.7%
108 2292
22.9%
109 1473
14.7%
ValueCountFrequency (%)
109 1473
14.7%
108 2292
22.9%
107 1572
15.7%
106 1487
14.9%
105 1420
14.2%
104 1657
16.6%

구코드 (공통)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)0.3%
Missing111
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean426.757
Minimum110
Maximum740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T20:10:59.153064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1230
median440
Q3590
95-th percentile710
Maximum740
Range630
Interquartile range (IQR)360

Descriptive statistics

Standard deviation193.11886
Coefficient of variation (CV)0.45252651
Kurtosis-1.3033982
Mean426.757
Median Absolute Deviation (MAD)180
Skewness-0.032423863
Sum4220200
Variance37294.893
MonotonicityNot monotonic
2024-05-03T20:10:59.847633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
650 684
 
6.8%
440 662
 
6.6%
710 613
 
6.1%
290 598
 
6.0%
560 564
 
5.6%
680 542
 
5.4%
170 530
 
5.3%
200 479
 
4.8%
530 452
 
4.5%
500 442
 
4.4%
Other values (15) 4323
43.2%
ValueCountFrequency (%)
110 392
3.9%
140 409
4.1%
170 530
5.3%
200 479
4.8%
210 328
3.3%
230 381
3.8%
260 242
2.4%
290 598
6.0%
300 153
 
1.5%
320 152
 
1.5%
ValueCountFrequency (%)
740 270
 
2.7%
710 613
6.1%
680 542
5.4%
650 684
6.8%
620 237
 
2.4%
590 307
3.1%
560 564
5.6%
540 139
 
1.4%
530 452
4.5%
500 442
4.4%

신규정규화ID
Real number (ℝ)

MISSING 

Distinct2325
Distinct (%)99.1%
Missing7655
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean5495436.8
Minimum180421
Maximum72265410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T20:11:00.319344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum180421
5-th percentile1154212.6
Q12278431
median4076178
Q35199815
95-th percentile22104230
Maximum72265410
Range72084989
Interquartile range (IQR)2921384

Descriptive statistics

Standard deviation8571414.3
Coefficient of variation (CV)1.559733
Kurtosis20.78668
Mean5495436.8
Median Absolute Deviation (MAD)1369415
Skewness4.4533081
Sum1.2886799 × 1010
Variance7.3469143 × 1013
MonotonicityNot monotonic
2024-05-03T20:11:00.866842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31164110 2
 
< 0.1%
1178802 2
 
< 0.1%
31150810 2
 
< 0.1%
50978710 2
 
< 0.1%
2246725 2
 
< 0.1%
23797710 2
 
< 0.1%
40872910 2
 
< 0.1%
50362010 2
 
< 0.1%
5343032 2
 
< 0.1%
2196965 2
 
< 0.1%
Other values (2315) 2325
 
23.2%
(Missing) 7655
76.5%
ValueCountFrequency (%)
180421 1
< 0.1%
180701 1
< 0.1%
182881 1
< 0.1%
190562 1
< 0.1%
191103 1
< 0.1%
191402 1
< 0.1%
191712 1
< 0.1%
191975 1
< 0.1%
192203 1
< 0.1%
192204 1
< 0.1%
ValueCountFrequency (%)
72265410 1
< 0.1%
62872310 1
< 0.1%
62472610 1
< 0.1%
61690311 1
< 0.1%
61500710 1
< 0.1%
61472611 1
< 0.1%
61095310 1
< 0.1%
60391010 1
< 0.1%
55439310 1
< 0.1%
55410710 1
< 0.1%

설치일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct264
Distinct (%)23.3%
Missing8866
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean20183248
Minimum20150131
Maximum20240331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T20:11:01.305478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150131
5-th percentile20151228
Q120161231
median20180731
Q320200430
95-th percentile20230324
Maximum20240331
Range90200
Interquartile range (IQR)39199

Descriptive statistics

Standard deviation23517.522
Coefficient of variation (CV)0.0011652001
Kurtosis-0.65259192
Mean20183248
Median Absolute Deviation (MAD)19500
Skewness0.63436972
Sum2.2887804 × 1010
Variance5.5307386 × 108
MonotonicityNot monotonic
2024-05-03T20:11:01.816589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20151231 42
 
0.4%
20161230 40
 
0.4%
20171226 39
 
0.4%
20181130 34
 
0.3%
20191220 32
 
0.3%
20161231 31
 
0.3%
20221216 30
 
0.3%
20181231 25
 
0.2%
20181221 24
 
0.2%
20191231 24
 
0.2%
Other values (254) 813
 
8.1%
(Missing) 8866
88.7%
ValueCountFrequency (%)
20150131 4
< 0.1%
20150420 4
< 0.1%
20150531 3
< 0.1%
20150630 1
 
< 0.1%
20150720 6
0.1%
20151020 1
 
< 0.1%
20151030 2
 
< 0.1%
20151031 2
 
< 0.1%
20151118 2
 
< 0.1%
20151130 2
 
< 0.1%
ValueCountFrequency (%)
20240331 1
 
< 0.1%
20240124 6
 
0.1%
20231231 16
0.2%
20231222 2
 
< 0.1%
20231220 2
 
< 0.1%
20231215 1
 
< 0.1%
20231130 8
0.1%
20230817 1
 
< 0.1%
20230731 5
 
0.1%
20230727 1
 
< 0.1%

교체일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct262
Distinct (%)22.9%
Missing8856
Missing (%)88.6%
Infinite0
Infinite (%)0.0%
Mean20186655
Minimum20150131
Maximum20240331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T20:11:02.422885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150131
5-th percentile20151231
Q120170228
median20181130
Q320210917
95-th percentile20230613
Maximum20240331
Range90200
Interquartile range (IQR)40689

Descriptive statistics

Standard deviation24565.053
Coefficient of variation (CV)0.0012168956
Kurtosis-1.0921304
Mean20186655
Median Absolute Deviation (MAD)19900
Skewness0.37067912
Sum2.3093533 × 1010
Variance6.0344181 × 108
MonotonicityNot monotonic
2024-05-03T20:11:02.965450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20221216 44
 
0.4%
20161230 40
 
0.4%
20171226 39
 
0.4%
20151231 36
 
0.4%
20191220 36
 
0.4%
20161231 29
 
0.3%
20181130 25
 
0.2%
20231231 25
 
0.2%
20191231 24
 
0.2%
20181221 23
 
0.2%
Other values (252) 823
 
8.2%
(Missing) 8856
88.6%
ValueCountFrequency (%)
20150131 3
< 0.1%
20150420 4
< 0.1%
20150531 3
< 0.1%
20150630 1
 
< 0.1%
20150720 5
0.1%
20151020 1
 
< 0.1%
20151030 2
 
< 0.1%
20151031 1
 
< 0.1%
20151118 2
 
< 0.1%
20151130 1
 
< 0.1%
ValueCountFrequency (%)
20240331 1
 
< 0.1%
20240124 6
 
0.1%
20231231 25
0.2%
20231222 2
 
< 0.1%
20231220 2
 
< 0.1%
20231215 1
 
< 0.1%
20231130 8
 
0.1%
20230817 1
 
< 0.1%
20230731 5
 
0.1%
20230727 1
 
< 0.1%

공간데이터
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

이력ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25872.734
Minimum4
Maximum53793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T20:11:03.471123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile2518.85
Q112906.5
median25700.5
Q338435.75
95-th percentile51327.1
Maximum53793
Range53789
Interquartile range (IQR)25529.25

Descriptive statistics

Standard deviation15127.971
Coefficient of variation (CV)0.58470709
Kurtosis-1.1169398
Mean25872.734
Median Absolute Deviation (MAD)12766
Skewness0.061331029
Sum2.5872734 × 108
Variance2.2885551 × 108
MonotonicityNot monotonic
2024-05-03T20:11:03.960577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37664 1
 
< 0.1%
41662 1
 
< 0.1%
18190 1
 
< 0.1%
26867 1
 
< 0.1%
10598 1
 
< 0.1%
25695 1
 
< 0.1%
479 1
 
< 0.1%
22500 1
 
< 0.1%
43756 1
 
< 0.1%
24474 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
18 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
29 1
< 0.1%
31 1
< 0.1%
39 1
< 0.1%
ValueCountFrequency (%)
53793 1
< 0.1%
53792 1
< 0.1%
53785 1
< 0.1%
53783 1
< 0.1%
53781 1
< 0.1%
53770 1
< 0.1%
53769 1
< 0.1%
53763 1
< 0.1%
53759 1
< 0.1%
53757 1
< 0.1%

공사관리번호
Text

MISSING 

Distinct1735
Distinct (%)18.0%
Missing353
Missing (%)3.5%
Memory size156.2 KiB
2024-05-03T20:11:04.835448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters125411
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

Unique724 ?
Unique (%)7.5%

Sample

1st row2000-0000-000
2nd row2000-0000-000
3rd row2010-0108-022
4th row2009-1108-158
5th row2012-0708-001
ValueCountFrequency (%)
2000-0000-000 2480
 
25.7%
2010-0108-013 81
 
0.8%
2010-0708-001 76
 
0.8%
2009-1108-157 61
 
0.6%
2005-1108-141 61
 
0.6%
2008-0102-558 57
 
0.6%
2003-1108-210 54
 
0.6%
2007-0108-014 50
 
0.5%
2008-0108-953 49
 
0.5%
2004-0108-110 48
 
0.5%
Other values (1725) 6630
68.7%
2024-05-03T20:11:06.156316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54520
43.5%
- 19294
 
15.4%
1 15726
 
12.5%
2 14116
 
11.3%
8 7269
 
5.8%
7 3197
 
2.5%
9 2611
 
2.1%
4 2491
 
2.0%
5 2410
 
1.9%
3 2047
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106117
84.6%
Dash Punctuation 19294
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54520
51.4%
1 15726
 
14.8%
2 14116
 
13.3%
8 7269
 
6.8%
7 3197
 
3.0%
9 2611
 
2.5%
4 2491
 
2.3%
5 2410
 
2.3%
3 2047
 
1.9%
6 1730
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 19294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125411
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54520
43.5%
- 19294
 
15.4%
1 15726
 
12.5%
2 14116
 
11.3%
8 7269
 
5.8%
7 3197
 
2.5%
9 2611
 
2.1%
4 2491
 
2.0%
5 2410
 
1.9%
3 2047
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54520
43.5%
- 19294
 
15.4%
1 15726
 
12.5%
2 14116
 
11.3%
8 7269
 
5.8%
7 3197
 
2.5%
9 2611
 
2.1%
4 2491
 
2.0%
5 2410
 
1.9%
3 2047
 
1.6%
Distinct7009
Distinct (%)70.3%
Missing24
Missing (%)0.2%
Memory size156.2 KiB
2024-05-03T20:11:07.082769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters89784
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

Unique5527 ?
Unique (%)55.4%

Sample

1st row14-017374
2nd row14-012116
3rd row14-002357
4th row14-012771
5th row14-017998
ValueCountFrequency (%)
14-002181 74
 
0.7%
14-000218 70
 
0.7%
14-009600 61
 
0.6%
14-007543 51
 
0.5%
14-003155 46
 
0.5%
14-004095 44
 
0.4%
14-004616 33
 
0.3%
14-003262 32
 
0.3%
14-003452 28
 
0.3%
14-002386 27
 
0.3%
Other values (6999) 9510
95.3%
2024-05-03T20:11:08.765048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19645
21.9%
1 18202
20.3%
4 14101
15.7%
- 9976
11.1%
2 5370
 
6.0%
3 4088
 
4.6%
5 3858
 
4.3%
8 3748
 
4.2%
6 3707
 
4.1%
9 3594
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79808
88.9%
Dash Punctuation 9976
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19645
24.6%
1 18202
22.8%
4 14101
17.7%
2 5370
 
6.7%
3 4088
 
5.1%
5 3858
 
4.8%
8 3748
 
4.7%
6 3707
 
4.6%
9 3594
 
4.5%
7 3495
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 9976
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89784
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19645
21.9%
1 18202
20.3%
4 14101
15.7%
- 9976
11.1%
2 5370
 
6.0%
3 4088
 
4.6%
5 3858
 
4.3%
8 3748
 
4.2%
6 3707
 
4.1%
9 3594
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19645
21.9%
1 18202
20.3%
4 14101
15.7%
- 9976
11.1%
2 5370
 
6.0%
3 4088
 
4.6%
5 3858
 
4.3%
8 3748
 
4.2%
6 3707
 
4.1%
9 3594
 
4.0%

안전지대종류코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9490 
2
 
510

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9490
94.9%
2 510
 
5.1%

Length

2024-05-03T20:11:09.298879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:11:09.644492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9490
94.9%
2 510
 
5.1%

Interactions

2024-05-03T20:10:44.596288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:23.449690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:26.033012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:28.565221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:31.579461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:34.975698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:37.970045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:41.372587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:44.894339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:23.759619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:26.326319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:28.854976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:31.971310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:35.391035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:38.493645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:41.748471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:45.236876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:24.054357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:26.691866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:29.160434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:32.472591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:35.687320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:38.934047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:42.264680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:45.456884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:24.502019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:27.002595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:29.528393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:32.817334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:35.997500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:39.400619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:42.701593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:45.766702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:24.813736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:27.329498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:30.027599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:33.244385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:36.330565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:39.868155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:43.116234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:46.185134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:25.130225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:27.630731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:30.389790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:33.741219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:36.767377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:40.243955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:43.535211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:46.501926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:25.381344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:27.928826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:30.730544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:34.228921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:37.076966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:40.629829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:43.900038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:46.912876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:25.743208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:28.264499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:31.179928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:34.631434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:37.370309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:41.022483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:10:44.302969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T20:11:09.947463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상태 (공통)고가 (공통)구경찰서코드 (공통)신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)구코드 (공통)신규정규화ID설치일교체일이력ID안전지대종류코드
상태 (공통)1.0000.0000.0300.0260.0420.0380.0000.0570.0460.000NaNNaN0.0700.000
고가 (공통)0.0001.0000.0750.0810.0000.0070.0090.0590.0720.0000.0000.0000.0740.000
구경찰서코드 (공통)0.0300.0751.0000.9960.1550.0610.1440.7550.9250.3150.3810.3970.3380.059
신경찰서코드 (공통)0.0260.0810.9961.0000.1470.0660.1430.7760.9370.3140.3840.4000.3350.039
작업구분 (공통)0.0420.0000.1550.1471.0000.6780.0400.1130.1800.0000.7390.7850.6290.282
표출구분 (공통)0.0380.0070.0610.0660.6781.0000.0440.0910.0880.0000.1610.2020.7000.525
도로구분 (공통)0.0000.0090.1440.1430.0400.0441.0000.0740.1340.0370.3820.3650.1420.099
관할사업소 (공통)0.0570.0590.7550.7760.1130.0910.0741.0000.9450.2630.3680.3950.2660.052
구코드 (공통)0.0460.0720.9250.9370.1800.0880.1340.9451.0000.3960.4740.4940.3420.000
신규정규화ID0.0000.0000.3150.3140.0000.0000.0370.2630.3961.0000.1940.2080.0000.100
설치일NaN0.0000.3810.3840.7390.1610.3820.3680.4740.1941.0000.9980.4610.182
교체일NaN0.0000.3970.4000.7850.2020.3650.3950.4940.2080.9981.0000.4410.160
이력ID0.0700.0740.3380.3350.6290.7000.1420.2660.3420.0000.4610.4411.0000.315
안전지대종류코드0.0000.0000.0590.0390.2820.5250.0990.0520.0000.1000.1820.1600.3151.000
2024-05-03T20:11:10.534101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상태 (공통)작업구분 (공통)안전지대종류코드도로구분 (공통)표출구분 (공통)고가 (공통)
상태 (공통)1.0000.0510.0000.0000.0240.000
작업구분 (공통)0.0511.0000.3450.0490.8110.000
안전지대종류코드0.0000.3451.0000.0630.3520.000
도로구분 (공통)0.0000.0490.0631.0000.0280.015
표출구분 (공통)0.0240.8110.3520.0281.0000.011
고가 (공통)0.0000.0000.0000.0150.0111.000
2024-05-03T20:11:11.072014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구경찰서코드 (공통)신경찰서코드 (공통)관할사업소 (공통)구코드 (공통)신규정규화ID설치일교체일이력ID상태 (공통)고가 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)안전지대종류코드
구경찰서코드 (공통)1.0000.951-0.4180.6200.1580.0130.0180.0590.0230.0440.0650.0470.1100.045
신경찰서코드 (공통)0.9511.000-0.4130.6280.1570.0150.0190.0630.0200.0480.0620.0510.1090.030
관할사업소 (공통)-0.418-0.4131.000-0.6890.4630.0760.124-0.0490.0410.0240.0770.0650.0530.037
구코드 (공통)0.6200.628-0.6891.000-0.045-0.028-0.0550.0880.0350.0430.0760.0670.1030.000
신규정규화ID0.1580.1570.463-0.0451.0000.0900.1250.0540.0000.0000.0000.0000.0290.077
설치일0.0130.0150.076-0.0280.0901.0000.8870.3861.0000.0000.5650.1220.2930.140
교체일0.0180.0190.124-0.0550.1250.8871.0000.3401.0000.0000.6060.1530.2790.123
이력ID0.0590.063-0.0490.0880.0540.3860.3401.0000.0540.0440.3110.5460.1090.242
상태 (공통)0.0230.0200.0410.0350.0001.0001.0000.0541.0000.0000.0510.0240.0000.000
고가 (공통)0.0440.0480.0240.0430.0000.0000.0000.0440.0001.0000.0000.0110.0150.000
작업구분 (공통)0.0650.0620.0770.0760.0000.5650.6060.3110.0510.0001.0000.8110.0490.345
표출구분 (공통)0.0470.0510.0650.0670.0000.1220.1530.5460.0240.0110.8111.0000.0280.352
도로구분 (공통)0.1100.1090.0530.1030.0290.2930.2790.1090.0000.0150.0490.0281.0000.063
안전지대종류코드0.0450.0300.0370.0000.0770.1400.1230.2420.0000.0000.3450.3520.0631.000

Missing values

2024-05-03T20:10:47.411243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T20:10:48.337340image/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.
2024-05-03T20:10:48.986720image/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

안전지대관리번호상태 (공통)고가 (공통)구경찰서코드 (공통)신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)구코드 (공통)신규정규화ID설치일교체일공간데이터이력ID공사관리번호구안전지대 관리번호안전지대종류코드
4199914-000001737411140140121108410<NA><NA><NA><NA>376642000-0000-00014-0173741
29714-000001211611260260412105540<NA><NA><NA><NA>154162000-0000-00014-0121161
967114-0000002357112202201211055903116861<NA><NA><NA>444052010-0108-02214-0023572
280714-000001277111220220411105590<NA><NA><NA><NA>238542009-1108-15814-0127711
4116814-000001799811210210411108440<NA><NA><NA><NA>427492012-0708-00114-0179981
788414-000000145311170170412104560<NA><NA><NA><NA>107532000-0000-00014-0014531
2633414-000000950611350350211104470<NA><NA><NA><NA>443702004-0108-21114-0095061
3787214-000001070611180180411109200<NA><NA><NA><NA>386962009-1004-00214-0107061
1610714-000000452211180180412109200<NA><NA><NA><NA>231212005-1108-23314-0045221
717514-0000001499113803801111056503172621<NA><NA><NA>37742000-0000-00014-0014991
안전지대관리번호상태 (공통)고가 (공통)구경찰서코드 (공통)신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)구코드 (공통)신규정규화ID설치일교체일공간데이터이력ID공사관리번호구안전지대 관리번호안전지대종류코드
1087614-000000303911360360411106710<NA><NA><NA><NA>74612010-1108-10314-0030391
5080314-000000126811330330411104530<NA><NA><NA><NA>348212000-0000-00014-0012681
2481514-000001144711200200412109230<NA><NA><NA><NA>54352007-1112-84214-0114471
3861514-000001565911330330122<NA><NA>1172752<NA><NA><NA>461332000-0000-00014-0156591
1977714-000000611411410360411106710<NA><NA><NA><NA>2682006-1108-10914-0061141
4796814-000001289311210210121108440230347102019081920190819<NA>254992019-0207-06814-0128931
4784514-00000200061334034012110565041083012016103120161031<NA>477882020-9907-00114-0200061
1470814-000000415111200200211109230<NA><NA><NA><NA>3912005-0108-04914-0041511
1193014-000000326211180180412109200<NA><NA><NA><NA>168712005-0108-04014-0032621
2145314-000000739811170170211104560<NA><NA><NA><NA>408632002-0708-03914-0073981