Overview

Dataset statistics

Number of variables20
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory174.0 B

Variable types

Unsupported3
Categorical4
Text9
Numeric4

Dataset

Description설치방식,상태 (공통),표지코드,비고,X좌표,Y좌표,설치일,교체일,표지관리번호,지주관리번호,각도 (공통),작업구분 (공통),표출구분 (공통),신규정규화ID,이력ID,공사관리번호,표지관리번호,표지인덱스,위치정보,공사형태
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15540/S/1/datasetView.do

Alerts

작업구분 (공통) is highly overall correlated with 표출구분 (공통)High correlation
표출구분 (공통) is highly overall correlated with 작업구분 (공통)High correlation
표지코드 is highly imbalanced (73.0%)Imbalance
작업구분 (공통) is highly imbalanced (52.5%)Imbalance
공사형태 is highly imbalanced (82.2%)Imbalance
이력ID has unique valuesUnique
설치방식 is an unsupported type, check if it needs cleaning or further analysisUnsupported
상태 (공통) is an unsupported type, check if it needs cleaning or further analysisUnsupported
위치정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported
X좌표 has 296 (3.0%) zerosZeros
각도 (공통) has 8262 (82.6%) zerosZeros

Reproduction

Analysis started2023-12-11 07:04:59.439507
Analysis finished2023-12-11 07:05:04.795294
Duration5.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

설치방식
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

상태 (공통)
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

표지코드
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
002
8577 
003
 
804
004
 
580
000
 
16
 
13
Other values (2)
 
10

Length

Max length3
Median length3
Mean length2.9971
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row002
2nd row002
3rd row002
4th row002
5th row002

Common Values

ValueCountFrequency (%)
002 8577
85.8%
003 804
 
8.0%
004 580
 
5.8%
000 16
 
0.2%
13
 
0.1%
001 7
 
0.1%
00 3
 
< 0.1%

Length

2023-12-11T16:05:04.905087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:05:05.086232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
002 8577
85.9%
003 804
 
8.1%
004 580
 
5.8%
000 16
 
0.2%
001 7
 
0.1%
00 3
 
< 0.1%

비고
Text

Distinct603
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:05:05.447313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length1
Mean length2.1119
Min length1

Characters and Unicode

Total characters21119
Distinct characters312
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique435 ?
Unique (%)4.3%

Sample

1st row
2nd row전방 30m
3rd row
4th row
5th row
ValueCountFrequency (%)
100m 131
 
5.2%
100 113
 
4.5%
주차금지 89
 
3.5%
주차구획선외 82
 
3.3%
유치원앞 71
 
2.8%
일방통행 70
 
2.8%
60 68
 
2.7%
전방 48
 
1.9%
버스전용 41
 
1.6%
사고잦은곳 41
 
1.6%
Other values (632) 1758
70.0%
2023-12-11T16:05:06.011608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9136
43.3%
0 1980
 
9.4%
1 556
 
2.6%
m 441
 
2.1%
387
 
1.8%
: 336
 
1.6%
2 302
 
1.4%
262
 
1.2%
261
 
1.2%
253
 
1.2%
Other values (302) 7205
34.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 9136
43.3%
Other Letter 6879
32.6%
Decimal Number 3629
 
17.2%
Lowercase Letter 553
 
2.6%
Other Punctuation 484
 
2.3%
Uppercase Letter 192
 
0.9%
Math Symbol 150
 
0.7%
Dash Punctuation 43
 
0.2%
Open Punctuation 25
 
0.1%
Close Punctuation 24
 
0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
387
 
5.6%
262
 
3.8%
261
 
3.8%
253
 
3.7%
202
 
2.9%
182
 
2.6%
179
 
2.6%
174
 
2.5%
166
 
2.4%
149
 
2.2%
Other values (240) 4664
67.8%
Uppercase Letter
ValueCountFrequency (%)
M 88
45.8%
O 27
 
14.1%
S 12
 
6.2%
E 9
 
4.7%
N 9
 
4.7%
C 9
 
4.7%
L 8
 
4.2%
Z 8
 
4.2%
H 8
 
4.2%
X 5
 
2.6%
Other values (6) 9
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
m 441
79.7%
t 39
 
7.1%
o 19
 
3.4%
s 8
 
1.4%
c 8
 
1.4%
e 6
 
1.1%
n 6
 
1.1%
z 6
 
1.1%
l 6
 
1.1%
h 6
 
1.1%
Other values (4) 8
 
1.4%
Decimal Number
ValueCountFrequency (%)
0 1980
54.6%
1 556
 
15.3%
2 302
 
8.3%
5 185
 
5.1%
7 176
 
4.8%
4 144
 
4.0%
3 144
 
4.0%
6 90
 
2.5%
8 32
 
0.9%
9 20
 
0.6%
Other Punctuation
ValueCountFrequency (%)
: 336
69.4%
. 124
 
25.6%
, 13
 
2.7%
; 4
 
0.8%
/ 2
 
0.4%
% 2
 
0.4%
1
 
0.2%
' 1
 
0.2%
! 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
~ 132
88.0%
| 10
 
6.7%
4
 
2.7%
> 2
 
1.3%
1
 
0.7%
1
 
0.7%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
9136
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13495
63.9%
Hangul 6879
32.6%
Latin 745
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
387
 
5.6%
262
 
3.8%
261
 
3.8%
253
 
3.7%
202
 
2.9%
182
 
2.6%
179
 
2.6%
174
 
2.5%
166
 
2.4%
149
 
2.2%
Other values (240) 4664
67.8%
Common
ValueCountFrequency (%)
9136
67.7%
0 1980
 
14.7%
1 556
 
4.1%
: 336
 
2.5%
2 302
 
2.2%
5 185
 
1.4%
7 176
 
1.3%
4 144
 
1.1%
3 144
 
1.1%
~ 132
 
1.0%
Other values (22) 404
 
3.0%
Latin
ValueCountFrequency (%)
m 441
59.2%
M 88
 
11.8%
t 39
 
5.2%
O 27
 
3.6%
o 19
 
2.6%
S 12
 
1.6%
E 9
 
1.2%
N 9
 
1.2%
C 9
 
1.2%
s 8
 
1.1%
Other values (20) 84
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14231
67.4%
Hangul 6879
32.6%
Arrows 8
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9136
64.2%
0 1980
 
13.9%
1 556
 
3.9%
m 441
 
3.1%
: 336
 
2.4%
2 302
 
2.1%
5 185
 
1.3%
7 176
 
1.2%
4 144
 
1.0%
3 144
 
1.0%
Other values (46) 831
 
5.8%
Hangul
ValueCountFrequency (%)
387
 
5.6%
262
 
3.8%
261
 
3.8%
253
 
3.7%
202
 
2.9%
182
 
2.6%
179
 
2.6%
174
 
2.5%
166
 
2.4%
149
 
2.2%
Other values (240) 4664
67.8%
Arrows
ValueCountFrequency (%)
4
50.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
1
 
12.5%
None
ValueCountFrequency (%)
1
100.0%

X좌표
Real number (ℝ)

ZEROS 

Distinct9594
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191253.46
Minimum0
Maximum215723.48
Zeros296
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:05:06.184394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186025.33
Q1192161.86
median195252.43
Q3201546.85
95-th percentile209855.92
Maximum215723.48
Range215723.48
Interquartile range (IQR)9384.9978

Descriptive statistics

Standard deviation34020.711
Coefficient of variation (CV)0.17788285
Kurtosis26.596606
Mean191253.46
Median Absolute Deviation (MAD)3883.2319
Skewness-5.2332266
Sum1.9125346 × 109
Variance1.1574088 × 109
MonotonicityNot monotonic
2023-12-11T16:05:06.347983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 296
 
3.0%
194638.23623 3
 
< 0.1%
207564.81413 3
 
< 0.1%
203384.74505 3
 
< 0.1%
208291.70373 3
 
< 0.1%
196061.13973 3
 
< 0.1%
192344.74619 3
 
< 0.1%
204195.67311 3
 
< 0.1%
197690.01408 3
 
< 0.1%
194924.82365 2
 
< 0.1%
Other values (9584) 9678
96.8%
ValueCountFrequency (%)
0.0 296
3.0%
182655.74246 1
 
< 0.1%
182673.74112 1
 
< 0.1%
182763.74338 1
 
< 0.1%
182823.73682 1
 
< 0.1%
182987.31563 1
 
< 0.1%
182999.09942 1
 
< 0.1%
183028.90475 1
 
< 0.1%
183044.74263 1
 
< 0.1%
183122.74287 1
 
< 0.1%
ValueCountFrequency (%)
215723.48181 1
< 0.1%
215224.99095 1
< 0.1%
215135.51289 1
< 0.1%
215124.51365 1
< 0.1%
215123.42468 1
< 0.1%
215089.50735 1
< 0.1%
215063.50491 1
< 0.1%
214881.47448 1
< 0.1%
214870.47417 1
< 0.1%
214779.52736 1
< 0.1%

Y좌표
Real number (ℝ)

Distinct9613
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean534602.5
Minimum100000
Maximum565787.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:05:06.514498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile539781.99
Q1543284.86
median546347.12
Q3551720.8
95-th percentile556651.92
Maximum565787.54
Range465787.54
Interquartile range (IQR)8435.9418

Descriptive statistics

Standard deviation76076.818
Coefficient of variation (CV)0.14230539
Kurtosis28.547736
Mean534602.5
Median Absolute Deviation (MAD)4021.7893
Skewness-5.5124472
Sum5.346025 × 109
Variance5.7876822 × 109
MonotonicityNot monotonic
2023-12-11T16:05:06.674266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000.0 296
 
3.0%
545860.14018 3
 
< 0.1%
545468.47753 3
 
< 0.1%
556765.26575 3
 
< 0.1%
551117.6022 3
 
< 0.1%
554120.76017 3
 
< 0.1%
547004.30953 2
 
< 0.1%
556098.65004 2
 
< 0.1%
543733.14282 2
 
< 0.1%
555903.13517 2
 
< 0.1%
Other values (9603) 9681
96.8%
ValueCountFrequency (%)
100000.0 296
3.0%
537265.60933 1
 
< 0.1%
537294.60862 1
 
< 0.1%
537302.29647 1
 
< 0.1%
537655.2887 1
 
< 0.1%
537665.28868 1
 
< 0.1%
537739.29388 1
 
< 0.1%
537790.29375 1
 
< 0.1%
537851.18203 1
 
< 0.1%
537867.99963 1
 
< 0.1%
ValueCountFrequency (%)
565787.53559 1
< 0.1%
564525.06507 1
< 0.1%
564524.97524 1
< 0.1%
563653.16871 1
< 0.1%
563511.23692 1
< 0.1%
563470.71974 1
< 0.1%
563073.33985 1
< 0.1%
563064.53125 1
< 0.1%
562969.82856 1
< 0.1%
562937.29727 1
< 0.1%
Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:05:06.968542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.1204
Min length1

Characters and Unicode

Total characters11204
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

Unique47 ?
Unique (%)0.5%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
20131010 12
 
7.0%
20121218 11
 
6.4%
20131031 8
 
4.7%
20130607 6
 
3.5%
20141015 6
 
3.5%
20141030 6
 
3.5%
20131119 5
 
2.9%
20140528 4
 
2.3%
20131115 4
 
2.3%
20140830 4
 
2.3%
Other values (72) 106
61.6%
2023-12-11T16:05:07.525789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9828
87.7%
1 395
 
3.5%
0 369
 
3.3%
2 252
 
2.2%
3 95
 
0.8%
8 76
 
0.7%
4 70
 
0.6%
5 35
 
0.3%
7 31
 
0.3%
9 29
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 9828
87.7%
Decimal Number 1376
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 395
28.7%
0 369
26.8%
2 252
18.3%
3 95
 
6.9%
8 76
 
5.5%
4 70
 
5.1%
5 35
 
2.5%
7 31
 
2.3%
9 29
 
2.1%
6 24
 
1.7%
Space Separator
ValueCountFrequency (%)
9828
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11204
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9828
87.7%
1 395
 
3.5%
0 369
 
3.3%
2 252
 
2.2%
3 95
 
0.8%
8 76
 
0.7%
4 70
 
0.6%
5 35
 
0.3%
7 31
 
0.3%
9 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9828
87.7%
1 395
 
3.5%
0 369
 
3.3%
2 252
 
2.2%
3 95
 
0.8%
8 76
 
0.7%
4 70
 
0.6%
5 35
 
0.3%
7 31
 
0.3%
9 29
 
0.3%
Distinct85
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:05:07.886668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.1372
Min length1

Characters and Unicode

Total characters11372
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

Unique48 ?
Unique (%)0.5%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
20141202 15
 
7.7%
20131010 12
 
6.1%
20140528 11
 
5.6%
20121218 11
 
5.6%
20131031 8
 
4.1%
20141015 6
 
3.1%
20141030 6
 
3.1%
20130607 6
 
3.1%
20131119 5
 
2.6%
20140830 4
 
2.0%
Other values (74) 112
57.1%
2023-12-11T16:05:08.530591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9804
86.2%
1 436
 
3.8%
0 418
 
3.7%
2 313
 
2.8%
3 96
 
0.8%
4 92
 
0.8%
8 78
 
0.7%
5 44
 
0.4%
7 35
 
0.3%
9 31
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 9804
86.2%
Decimal Number 1568
 
13.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 436
27.8%
0 418
26.7%
2 313
20.0%
3 96
 
6.1%
4 92
 
5.9%
8 78
 
5.0%
5 44
 
2.8%
7 35
 
2.2%
9 31
 
2.0%
6 25
 
1.6%
Space Separator
ValueCountFrequency (%)
9804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9804
86.2%
1 436
 
3.8%
0 418
 
3.7%
2 313
 
2.8%
3 96
 
0.8%
4 92
 
0.8%
8 78
 
0.7%
5 44
 
0.4%
7 35
 
0.3%
9 31
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9804
86.2%
1 436
 
3.8%
0 418
 
3.7%
2 313
 
2.8%
3 96
 
0.8%
4 92
 
0.8%
8 78
 
0.7%
5 44
 
0.4%
7 35
 
0.3%
9 31
 
0.3%
Distinct9897
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:05:08.857921image/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

Unique9798 ?
Unique (%)98.0%

Sample

1st row04-0000024279
2nd row04-0000019601
3rd row04-0000052131
4th row04-0000016166
5th row04-0000043116
ValueCountFrequency (%)
04-0000026098 3
 
< 0.1%
04-0000072609 3
 
< 0.1%
04-0000173181 3
 
< 0.1%
04-0000064709 3
 
< 0.1%
04-0000064084 2
 
< 0.1%
04-0000251572 2
 
< 0.1%
04-0000252446 2
 
< 0.1%
04-0000015444 2
 
< 0.1%
04-0000015447 2
 
< 0.1%
04-0000050672 2
 
< 0.1%
Other values (9887) 9976
99.8%
2023-12-11T16:05:09.357323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 63158
48.6%
4 15275
 
11.8%
- 10000
 
7.7%
2 6573
 
5.1%
5 6107
 
4.7%
1 5666
 
4.4%
6 5118
 
3.9%
3 5068
 
3.9%
7 4650
 
3.6%
8 4265
 
3.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63158
52.6%
4 15275
 
12.7%
2 6573
 
5.5%
5 6107
 
5.1%
1 5666
 
4.7%
6 5118
 
4.3%
3 5068
 
4.2%
7 4650
 
3.9%
8 4265
 
3.6%
9 4120
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63158
48.6%
4 15275
 
11.8%
- 10000
 
7.7%
2 6573
 
5.1%
5 6107
 
4.7%
1 5666
 
4.4%
6 5118
 
3.9%
3 5068
 
3.9%
7 4650
 
3.6%
8 4265
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63158
48.6%
4 15275
 
11.8%
- 10000
 
7.7%
2 6573
 
5.1%
5 6107
 
4.7%
1 5666
 
4.4%
6 5118
 
3.9%
3 5068
 
3.9%
7 4650
 
3.6%
8 4265
 
3.3%
Distinct9076
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:05:09.685355image/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

Unique8234 ?
Unique (%)82.3%

Sample

1st row02-0000093777
2nd row02-0000000751
3rd row02-0000029874
4th row02-0000104888
5th row02-0000107340
ValueCountFrequency (%)
02-0000014302 5
 
< 0.1%
02-0000042163 5
 
< 0.1%
02-0000036904 4
 
< 0.1%
02-0000044431 4
 
< 0.1%
02-0000003480 4
 
< 0.1%
02-0000036908 4
 
< 0.1%
02-0000095848 4
 
< 0.1%
02-0000107004 4
 
< 0.1%
02-0000036907 4
 
< 0.1%
02-0000154767 3
 
< 0.1%
Other values (9066) 9959
99.6%
2023-12-11T16:05:10.253574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64291
49.5%
2 15579
 
12.0%
- 10000
 
7.7%
1 7852
 
6.0%
3 5417
 
4.2%
5 5098
 
3.9%
9 4702
 
3.6%
4 4688
 
3.6%
7 4258
 
3.3%
8 4155
 
3.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64291
53.6%
2 15579
 
13.0%
1 7852
 
6.5%
3 5417
 
4.5%
5 5098
 
4.2%
9 4702
 
3.9%
4 4688
 
3.9%
7 4258
 
3.5%
8 4155
 
3.5%
6 3960
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64291
49.5%
2 15579
 
12.0%
- 10000
 
7.7%
1 7852
 
6.0%
3 5417
 
4.2%
5 5098
 
3.9%
9 4702
 
3.6%
4 4688
 
3.6%
7 4258
 
3.3%
8 4155
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64291
49.5%
2 15579
 
12.0%
- 10000
 
7.7%
1 7852
 
6.0%
3 5417
 
4.2%
5 5098
 
3.9%
9 4702
 
3.6%
4 4688
 
3.6%
7 4258
 
3.3%
8 4155
 
3.2%

각도 (공통)
Real number (ℝ)

ZEROS 

Distinct351
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.3533
Minimum0
Maximum359
Zeros8262
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:05:10.461723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile253
Maximum359
Range359
Interquartile range (IQR)0

Descriptive statistics

Standard deviation80.580962
Coefficient of variation (CV)2.5700951
Kurtosis5.7546818
Mean31.3533
Median Absolute Deviation (MAD)0
Skewness2.6229346
Sum313533
Variance6493.2914
MonotonicityNot monotonic
2023-12-11T16:05:10.675630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8262
82.6%
180 58
 
0.6%
70 19
 
0.2%
340 17
 
0.2%
159 17
 
0.2%
249 16
 
0.2%
160 15
 
0.1%
273 15
 
0.1%
250 15
 
0.1%
93 11
 
0.1%
Other values (341) 1555
 
15.6%
ValueCountFrequency (%)
0 8262
82.6%
1 5
 
0.1%
2 6
 
0.1%
3 5
 
0.1%
4 6
 
0.1%
5 7
 
0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
359 3
 
< 0.1%
358 4
< 0.1%
357 5
0.1%
356 7
0.1%
355 9
0.1%
354 4
< 0.1%
353 2
 
< 0.1%
352 5
0.1%
351 4
< 0.1%
350 6
0.1%

작업구분 (공통)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7640 
4
1112 
2
1098 
6
 
109
3
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7640
76.4%
4 1112
 
11.1%
2 1098
 
11.0%
6 109
 
1.1%
3 41
 
0.4%

Length

2023-12-11T16:05:10.844556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:05:10.955217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7640
76.4%
4 1112
 
11.1%
2 1098
 
11.0%
6 109
 
1.1%
3 41
 
0.4%

표출구분 (공통)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
6133 
1
3867 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 6133
61.3%
1 3867
38.7%

Length

2023-12-11T16:05:11.060212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:05:11.152481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6133
61.3%
1 3867
38.7%
Distinct107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:05:11.525085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.0636
Min length1

Characters and Unicode

Total characters10636
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

Unique106 ?
Unique (%)1.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
2341091 1
 
0.9%
6018603 1
 
0.9%
4448552 1
 
0.9%
1393443 1
 
0.9%
2298691 1
 
0.9%
5330661 1
 
0.9%
5490751 1
 
0.9%
4317941 1
 
0.9%
2168842 1
 
0.9%
3254243 1
 
0.9%
Other values (96) 96
90.6%
2023-12-11T16:05:12.069028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9894
93.0%
1 135
 
1.3%
3 120
 
1.1%
2 115
 
1.1%
4 75
 
0.7%
5 69
 
0.6%
6 66
 
0.6%
8 49
 
0.5%
0 38
 
0.4%
9 38
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 9894
93.0%
Decimal Number 742
 
7.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 135
18.2%
3 120
16.2%
2 115
15.5%
4 75
10.1%
5 69
9.3%
6 66
8.9%
8 49
 
6.6%
0 38
 
5.1%
9 38
 
5.1%
7 37
 
5.0%
Space Separator
ValueCountFrequency (%)
9894
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10636
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9894
93.0%
1 135
 
1.3%
3 120
 
1.1%
2 115
 
1.1%
4 75
 
0.7%
5 69
 
0.6%
6 66
 
0.6%
8 49
 
0.5%
0 38
 
0.4%
9 38
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9894
93.0%
1 135
 
1.3%
3 120
 
1.1%
2 115
 
1.1%
4 75
 
0.7%
5 69
 
0.6%
6 66
 
0.6%
8 49
 
0.5%
0 38
 
0.4%
9 38
 
0.4%

이력ID
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35324.529
Minimum8
Maximum317589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:05:12.274599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile3558.55
Q116173.75
median31836.5
Q347718.75
95-th percentile62710.1
Maximum317589
Range317581
Interquartile range (IQR)31545

Descriptive statistics

Standard deviation36304.477
Coefficient of variation (CV)1.0277413
Kurtosis38.877029
Mean35324.529
Median Absolute Deviation (MAD)15789
Skewness5.4469432
Sum3.5324529 × 108
Variance1.318015 × 109
MonotonicityNot monotonic
2023-12-11T16:05:12.481662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18905 1
 
< 0.1%
1031 1
 
< 0.1%
34305 1
 
< 0.1%
62052 1
 
< 0.1%
9198 1
 
< 0.1%
15739 1
 
< 0.1%
7455 1
 
< 0.1%
62 1
 
< 0.1%
46714 1
 
< 0.1%
15017 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
8 1
< 0.1%
9 1
< 0.1%
26 1
< 0.1%
41 1
< 0.1%
45 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
56 1
< 0.1%
62 1
< 0.1%
64 1
< 0.1%
ValueCountFrequency (%)
317589 1
< 0.1%
317547 1
< 0.1%
317545 1
< 0.1%
317540 1
< 0.1%
317535 1
< 0.1%
317523 1
< 0.1%
317521 1
< 0.1%
317486 1
< 0.1%
317484 1
< 0.1%
317480 1
< 0.1%
Distinct630
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:05:12.922430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.9928
Min length1

Characters and Unicode

Total characters129928
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique264 ?
Unique (%)2.6%

Sample

1st row2008-0107-634
2nd row2000-0000-000
3rd row2000-0000-000
4th row2000-0000-000
5th row2000-0000-000
ValueCountFrequency (%)
2000-0000-000 7081
70.8%
2007-0507-548 113
 
1.1%
2007-0507-459 51
 
0.5%
2008-0107-824 50
 
0.5%
2008-0107-475 48
 
0.5%
2008-1107-519 45
 
0.5%
2008-0107-903 45
 
0.5%
2007-0507-749 44
 
0.4%
2008-0107-727 42
 
0.4%
2008-0512-002 35
 
0.4%
Other values (621) 2442
 
24.4%
2023-12-11T16:05:13.576424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 83981
64.6%
- 19987
 
15.4%
2 11124
 
8.6%
1 4495
 
3.5%
7 3555
 
2.7%
8 1972
 
1.5%
5 1569
 
1.2%
4 1079
 
0.8%
9 765
 
0.6%
3 744
 
0.6%
Other values (8) 657
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 109927
84.6%
Dash Punctuation 19987
 
15.4%
Space Separator 8
 
< 0.1%
Other Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83981
76.4%
2 11124
 
10.1%
1 4495
 
4.1%
7 3555
 
3.2%
8 1972
 
1.8%
5 1569
 
1.4%
4 1079
 
1.0%
9 765
 
0.7%
3 744
 
0.7%
6 643
 
0.6%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 19987
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129922
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83981
64.6%
- 19987
 
15.4%
2 11124
 
8.6%
1 4495
 
3.5%
7 3555
 
2.7%
8 1972
 
1.5%
5 1569
 
1.2%
4 1079
 
0.8%
9 765
 
0.6%
3 744
 
0.6%
Other values (2) 651
 
0.5%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129922
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83981
64.6%
- 19987
 
15.4%
2 11124
 
8.6%
1 4495
 
3.5%
7 3555
 
2.7%
8 1972
 
1.5%
5 1569
 
1.2%
4 1079
 
0.8%
9 765
 
0.6%
3 744
 
0.6%
Other values (2) 651
 
0.5%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Distinct9897
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:05:14.065846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
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

Unique9798 ?
Unique (%)98.0%

Sample

1st row04-024279
2nd row04-019601
3rd row04-052131
4th row04-016166
5th row04-043116
ValueCountFrequency (%)
04-026098 3
 
< 0.1%
04-072609 3
 
< 0.1%
04-173181 3
 
< 0.1%
04-064709 3
 
< 0.1%
04-064084 2
 
< 0.1%
04-251572 2
 
< 0.1%
04-252446 2
 
< 0.1%
04-015444 2
 
< 0.1%
04-015447 2
 
< 0.1%
04-050672 2
 
< 0.1%
Other values (9887) 9976
99.8%
2023-12-11T16:05:14.699982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23158
25.7%
4 15275
17.0%
- 10000
11.1%
2 6573
 
7.3%
5 6107
 
6.8%
1 5666
 
6.3%
6 5118
 
5.7%
3 5068
 
5.6%
7 4650
 
5.2%
8 4265
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
88.9%
Dash Punctuation 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23158
28.9%
4 15275
19.1%
2 6573
 
8.2%
5 6107
 
7.6%
1 5666
 
7.1%
6 5118
 
6.4%
3 5068
 
6.3%
7 4650
 
5.8%
8 4265
 
5.3%
9 4120
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23158
25.7%
4 15275
17.0%
- 10000
11.1%
2 6573
 
7.3%
5 6107
 
6.8%
1 5666
 
6.3%
6 5118
 
5.7%
3 5068
 
5.6%
7 4650
 
5.2%
8 4265
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23158
25.7%
4 15275
17.0%
- 10000
11.1%
2 6573
 
7.3%
5 6107
 
6.8%
1 5666
 
6.3%
6 5118
 
5.7%
3 5068
 
5.6%
7 4650
 
5.2%
8 4265
 
4.7%
Distinct193
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:05:15.105482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3239
Min length3

Characters and Unicode

Total characters33239
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.4%

Sample

1st row219
2nd row538
3rd row213
4th row127
5th row219
ValueCountFrequency (%)
219 1468
 
14.7%
428 1280
 
12.8%
322 365
 
3.6%
214 333
 
3.3%
213 331
 
3.3%
514-1-100 290
 
2.9%
218 276
 
2.8%
418 259
 
2.6%
326 237
 
2.4%
226 228
 
2.3%
Other values (183) 4933
49.3%
2023-12-11T16:05:16.108289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 9386
28.2%
1 6442
19.4%
3 3337
 
10.0%
4 3291
 
9.9%
8 2196
 
6.6%
9 1819
 
5.5%
0 1782
 
5.4%
5 1676
 
5.0%
- 1267
 
3.8%
6 937
 
2.8%
Other values (2) 1106
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31651
95.2%
Dash Punctuation 1267
 
3.8%
Uppercase Letter 321
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9386
29.7%
1 6442
20.4%
3 3337
 
10.5%
4 3291
 
10.4%
8 2196
 
6.9%
9 1819
 
5.7%
0 1782
 
5.6%
5 1676
 
5.3%
6 937
 
3.0%
7 785
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 1267
100.0%
Uppercase Letter
ValueCountFrequency (%)
Z 321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32918
99.0%
Latin 321
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 9386
28.5%
1 6442
19.6%
3 3337
 
10.1%
4 3291
 
10.0%
8 2196
 
6.7%
9 1819
 
5.5%
0 1782
 
5.4%
5 1676
 
5.1%
- 1267
 
3.8%
6 937
 
2.8%
Latin
ValueCountFrequency (%)
Z 321
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 9386
28.2%
1 6442
19.4%
3 3337
 
10.0%
4 3291
 
9.9%
8 2196
 
6.6%
9 1819
 
5.5%
0 1782
 
5.4%
5 1676
 
5.0%
- 1267
 
3.8%
6 937
 
2.8%
Other values (2) 1106
 
3.3%

위치정보
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

공사형태
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
9156 
003
 
682
001
 
82
002
 
64
004
 
11
Other values (2)
 
5

Length

Max length3
Median length1
Mean length1.1688
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
9156
91.6%
003 682
 
6.8%
001 82
 
0.8%
002 64
 
0.6%
004 11
 
0.1%
006 4
 
< 0.1%
005 1
 
< 0.1%

Length

2023-12-11T16:05:16.294408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:05:16.452570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
003 682
80.8%
001 82
 
9.7%
002 64
 
7.6%
004 11
 
1.3%
006 4
 
0.5%
005 1
 
0.1%

Interactions

2023-12-11T16:05:03.650010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:01.699383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:02.266359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:02.856868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:03.793883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:01.841635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:02.407656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:02.991211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:03.929422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:01.975879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:02.547752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:03.126012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:04.069063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:02.129750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:02.715971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:05:03.510399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:05:16.567915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표지코드X좌표Y좌표설치일교체일각도 (공통)작업구분 (공통)표출구분 (공통)이력ID공사형태
표지코드1.0000.288NaN0.1980.2060.0330.1990.1340.1040.104
X좌표0.2881.000NaN0.0000.0000.1000.4250.1350.1590.094
Y좌표NaNNaN1.000NaNNaNNaNNaNNaNNaNNaN
설치일0.1980.000NaN1.0001.0000.3970.6860.0790.8100.871
교체일0.2060.000NaN1.0001.0000.3700.7190.0900.8900.892
각도 (공통)0.0330.100NaN0.3970.3701.0000.2680.1030.3500.234
작업구분 (공통)0.1990.425NaN0.6860.7190.2681.0000.5550.6860.508
표출구분 (공통)0.1340.135NaN0.0790.0900.1030.5551.0000.0850.323
이력ID0.1040.159NaN0.8100.8900.3500.6860.0851.0000.667
공사형태0.1040.094NaN0.8710.8920.2340.5080.3230.6671.000
2023-12-11T16:05:16.736310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표지코드작업구분 (공통)표출구분 (공통)공사형태
표지코드1.0000.1280.1440.037
작업구분 (공통)0.1281.0000.6710.357
표출구분 (공통)0.1440.6711.0000.346
공사형태0.0370.3570.3461.000
2023-12-11T16:05:16.868277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
X좌표Y좌표각도 (공통)이력ID표지코드작업구분 (공통)표출구분 (공통)공사형태
X좌표1.0000.0780.0620.2210.2020.3560.2230.063
Y좌표0.0781.000-0.071-0.4050.2770.4970.2190.047
각도 (공통)0.062-0.0711.0000.0750.0160.1150.0790.120
이력ID0.221-0.4050.0751.0000.0520.3530.0650.418
표지코드0.2020.2770.0160.0521.0000.1280.1440.037
작업구분 (공통)0.3560.4970.1150.3530.1281.0000.6710.357
표출구분 (공통)0.2230.2190.0790.0650.1440.6711.0000.346
공사형태0.0630.0470.1200.4180.0370.3570.3461.000

Missing values

2023-12-11T16:05:04.268616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:05:04.625896image/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.

Sample

설치방식상태 (공통)표지코드비고X좌표Y좌표설치일교체일표지관리번호지주관리번호각도 (공통)작업구분 (공통)표출구분 (공통)신규정규화ID이력ID공사관리번호표지관리번호.1표지인덱스위치정보공사형태
22952003001002208723.45414556492.4880104-000002427902-0000093777041189052008-0107-63404-0242792199
14233003001002전방 30m196579.58021556049.2281404-000001960102-000000075102192452000-0000-00004-0196015389
3346931002193283.86136544563.2665404-000005213102-00000298749212375112000-0000-00004-0521312139
8713001001002193892.785547813.2487404-000001616602-0000104888011180022000-0000-00004-0161661279
3440131002185242.95593543773.0996304-000004311602-0000107340012397892000-0000-00004-0431162199
20290999001004211357.98427548653.7564304-000016706702-0000036630021175422000-0000-00004-1670674289
1785003001002199911.11035551637.456604-000000235902-000000983804141892000-0000-00004-0023594289
444909991004100198093.169542606.3130104-000015936702-0000017402021460752000-0000-00004-1593674259
14371003001002192528.71685553967.4245104-000000956902-0000008419012165872000-0000-00004-0095692199
16690003001002주차구획선외 주차금지193105.61647555912.1541304-000002826102-0000105938012212402000-0000-00004-0282615139
설치방식상태 (공통)표지코드비고X좌표Y좌표설치일교체일표지관리번호지주관리번호각도 (공통)작업구분 (공통)표출구분 (공통)신규정규화ID이력ID공사관리번호표지관리번호.1표지인덱스위치정보공사형태
7316003001002유치원앞209469.89999556012.4235604-000014186402-000008186202156872007-0507-45904-141864231-Z9
5854431002211145.09049545428.2469404-000007870502-0000045572011615612000-0000-00004-0787054289
3785831003193306.40208557671.8103704-000024084902-0000144026021321592008-0507-00604-2408497129
5178001001002198663.56567546719.0259404-000025225402-000015099501228072007-1107-63804-2522543039
15313003001002190149.68478554072.1074604-000001040502-000000281601293362000-0000-00004-0104053229
3682721002어린이 보호구역 여기서부터 200m192822.95518540456.2961804-000003895302-0000022379021321222000-0000-00004-038953231-Z9
3973231002186320.91861545014.1113504-000004601502-0000097369011401642010-0107-09404-0460154289
32456003001002192060.79672548091.2064804-000004168402-00000238198512391082000-0000-00004-0416843089
3556431002192120.46875542421.062504-000003777902-000010693314212409552000-0000-00004-0377793119
9034001001002200975.8959554483.0286404-000025176202-0000150609012100932008-1112-53504-2517623289