Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells19996
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory169.0 B

Variable types

Text9
Numeric4
Categorical6

Dataset

Description음향신호관리번호,지주관리번호,방향 (공통),설치일,교체일,X좌표,Y좌표,제조회사,시설번호,작업구분 (공통),표출구분 (공통),종류,신규정규화ID,상태 (공통),공사관리번호,음향신호관리번호,이력ID,위치정보,공사형태 (공통)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15543/S/1/datasetView.do

Alerts

종류 has constant value ""Constant
공사형태 (공통) is highly overall correlated with 표출구분 (공통) and 1 other fieldsHigh correlation
위치정보 is highly overall correlated with 방향 (공통) and 7 other fieldsHigh correlation
작업구분 (공통) is highly overall correlated with 표출구분 (공통) and 1 other fieldsHigh correlation
표출구분 (공통) is highly overall correlated with 이력ID and 3 other fieldsHigh correlation
상태 (공통) is highly overall correlated with 위치정보High correlation
방향 (공통) is highly overall correlated with 위치정보High correlation
X좌표 is highly overall correlated with 위치정보High correlation
Y좌표 is highly overall correlated with 위치정보High correlation
이력ID is highly overall correlated with 표출구분 (공통) and 1 other fieldsHigh correlation
상태 (공통) is highly imbalanced (97.9%)Imbalance
방향 (공통) has 2647 (26.5%) missing valuesMissing
설치일 has 659 (6.6%) missing valuesMissing
교체일 has 673 (6.7%) missing valuesMissing
X좌표 has 844 (8.4%) missing valuesMissing
Y좌표 has 844 (8.4%) missing valuesMissing
제조회사 has 2618 (26.2%) missing valuesMissing
시설번호 has 8289 (82.9%) missing valuesMissing
신규정규화ID has 2722 (27.2%) missing valuesMissing
공사관리번호 has 700 (7.0%) missing valuesMissing
이력ID has unique valuesUnique
방향 (공통) has 557 (5.6%) zerosZeros

Reproduction

Analysis started2024-05-03 23:22:23.340762
Analysis finished2024-05-03 23:22:32.436641
Duration9.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

음향신호관리번호
Text

Distinct9217
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:22:32.775025image/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

Unique8494 ?
Unique (%)84.9%

Sample

1st row24-0000013716
2nd row24-0000012463
3rd row24-0000005880
4th row24-0000017724
5th row24-0000003845
ValueCountFrequency (%)
24-0000005145 4
 
< 0.1%
24-0000000192 4
 
< 0.1%
24-0000007161 4
 
< 0.1%
24-0000003630 4
 
< 0.1%
24-0000002193 3
 
< 0.1%
24-0000002369 3
 
< 0.1%
24-0000004258 3
 
< 0.1%
24-0000001745 3
 
< 0.1%
24-0000001674 3
 
< 0.1%
24-0000002188 3
 
< 0.1%
Other values (9207) 9966
99.7%
2024-05-03T23:22:33.614786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59775
46.0%
2 14924
 
11.5%
4 14061
 
10.8%
- 10000
 
7.7%
1 8207
 
6.3%
3 3995
 
3.1%
5 3902
 
3.0%
6 3889
 
3.0%
7 3793
 
2.9%
8 3729
 
2.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59775
49.8%
2 14924
 
12.4%
4 14061
 
11.7%
1 8207
 
6.8%
3 3995
 
3.3%
5 3902
 
3.3%
6 3889
 
3.2%
7 3793
 
3.2%
8 3729
 
3.1%
9 3725
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59775
46.0%
2 14924
 
11.5%
4 14061
 
10.8%
- 10000
 
7.7%
1 8207
 
6.3%
3 3995
 
3.1%
5 3902
 
3.0%
6 3889
 
3.0%
7 3793
 
2.9%
8 3729
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59775
46.0%
2 14924
 
11.5%
4 14061
 
10.8%
- 10000
 
7.7%
1 8207
 
6.3%
3 3995
 
3.1%
5 3902
 
3.0%
6 3889
 
3.0%
7 3793
 
2.9%
8 3729
 
2.9%

지주관리번호
Text

Distinct8994
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:22:34.076620image/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

Unique8123 ?
Unique (%)81.2%

Sample

1st row02-0000179139
2nd row02-0000194198
3rd row02-0000149368
4th row02-0000215292
5th row02-0000142572
ValueCountFrequency (%)
02-0000167116 12
 
0.1%
02-0000047895 8
 
0.1%
02-0000112314 7
 
0.1%
02-0000011639 6
 
0.1%
02-0000055892 6
 
0.1%
02-0000151087 5
 
< 0.1%
02-0000133987 5
 
< 0.1%
02-0000057865 4
 
< 0.1%
02-0000077717 4
 
< 0.1%
02-0000143733 4
 
< 0.1%
Other values (8984) 9939
99.4%
2024-05-03T23:22:35.049754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59754
46.0%
2 16294
 
12.5%
1 10115
 
7.8%
- 10000
 
7.7%
6 5317
 
4.1%
7 5248
 
4.0%
9 4791
 
3.7%
5 4784
 
3.7%
8 4741
 
3.6%
3 4684
 
3.6%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59754
49.8%
2 16294
 
13.6%
1 10115
 
8.4%
6 5317
 
4.4%
7 5248
 
4.4%
9 4791
 
4.0%
5 4784
 
4.0%
8 4741
 
4.0%
3 4684
 
3.9%
4 4272
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59754
46.0%
2 16294
 
12.5%
1 10115
 
7.8%
- 10000
 
7.7%
6 5317
 
4.1%
7 5248
 
4.0%
9 4791
 
3.7%
5 4784
 
3.7%
8 4741
 
3.6%
3 4684
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59754
46.0%
2 16294
 
12.5%
1 10115
 
7.8%
- 10000
 
7.7%
6 5317
 
4.1%
7 5248
 
4.0%
9 4791
 
3.7%
5 4784
 
3.7%
8 4741
 
3.6%
3 4684
 
3.6%

방향 (공통)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)0.1%
Missing2647
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean164.74908
Minimum-45
Maximum315
Zeros557
Zeros (%)5.6%
Negative6
Negative (%)0.1%
Memory size166.0 KiB
2024-05-03T23:22:35.435984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-45
5-th percentile0
Q1180
median180
Q3180
95-th percentile180
Maximum315
Range360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation51.894616
Coefficient of variation (CV)0.31499184
Kurtosis5.7137753
Mean164.74908
Median Absolute Deviation (MAD)0
Skewness-2.4797519
Sum1211400
Variance2693.0512
MonotonicityNot monotonic
2024-05-03T23:22:35.830949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
180 6514
65.1%
0 557
 
5.6%
45 92
 
0.9%
90 57
 
0.6%
270 43
 
0.4%
135 38
 
0.4%
315 31
 
0.3%
225 15
 
0.1%
-45 6
 
0.1%
(Missing) 2647
26.5%
ValueCountFrequency (%)
-45 6
 
0.1%
0 557
 
5.6%
45 92
 
0.9%
90 57
 
0.6%
135 38
 
0.4%
180 6514
65.1%
225 15
 
0.1%
270 43
 
0.4%
315 31
 
0.3%
ValueCountFrequency (%)
315 31
 
0.3%
270 43
 
0.4%
225 15
 
0.1%
180 6514
65.1%
135 38
 
0.4%
90 57
 
0.6%
45 92
 
0.9%
0 557
 
5.6%
-45 6
 
0.1%

설치일
Text

MISSING 

Distinct1825
Distinct (%)19.5%
Missing659
Missing (%)6.6%
Memory size156.2 KiB
2024-05-03T23:22:36.428353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters74728
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique650 ?
Unique (%)7.0%

Sample

1st row20181120
2nd row20161009
3rd row20000101
4th row20201210
5th row20070601
ValueCountFrequency (%)
20070601 1640
 
17.6%
00010101 296
 
3.2%
20000101 269
 
2.9%
20080101 187
 
2.0%
20120201 52
 
0.6%
20201123 50
 
0.5%
20071105 49
 
0.5%
20201030 45
 
0.5%
20080806 38
 
0.4%
20161231 34
 
0.4%
Other values (1816) 6682
71.5%
2024-05-03T23:22:37.411791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26744
35.8%
2 16514
22.1%
1 15378
20.6%
6 3283
 
4.4%
7 3035
 
4.1%
3 2641
 
3.5%
9 2094
 
2.8%
8 1882
 
2.5%
4 1581
 
2.1%
5 1573
 
2.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74725
> 99.9%
Other Letter 2
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26744
35.8%
2 16514
22.1%
1 15378
20.6%
6 3283
 
4.4%
7 3035
 
4.1%
3 2641
 
3.5%
9 2094
 
2.8%
8 1882
 
2.5%
4 1581
 
2.1%
5 1573
 
2.1%
Other Letter
ValueCountFrequency (%)
년 1
50.0%
차 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74726
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26744
35.8%
2 16514
22.1%
1 15378
20.6%
6 3283
 
4.4%
7 3035
 
4.1%
3 2641
 
3.5%
9 2094
 
2.8%
8 1882
 
2.5%
4 1581
 
2.1%
5 1573
 
2.1%
Hangul
ValueCountFrequency (%)
년 1
50.0%
차 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74726
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26744
35.8%
2 16514
22.1%
1 15378
20.6%
6 3283
 
4.4%
7 3035
 
4.1%
3 2641
 
3.5%
9 2094
 
2.8%
8 1882
 
2.5%
4 1581
 
2.1%
5 1573
 
2.1%
Hangul
ValueCountFrequency (%)
년 1
50.0%
차 1
50.0%

교체일
Text

MISSING 

Distinct1899
Distinct (%)20.4%
Missing673
Missing (%)6.7%
Memory size156.2 KiB
2024-05-03T23:22:38.057082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters74616
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique686 ?
Unique (%)7.4%

Sample

1st row20181120
2nd row20161009
3rd row20000101
4th row20231105
5th row20070601
ValueCountFrequency (%)
20070601 1192
 
12.8%
00010101 283
 
3.0%
20000101 164
 
1.8%
20080806 68
 
0.7%
20201030 59
 
0.6%
20201123 56
 
0.6%
20161231 45
 
0.5%
20220831 44
 
0.5%
20221001 40
 
0.4%
20080101 38
 
0.4%
Other values (1890) 7339
78.7%
2024-05-03T23:22:39.025926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25398
34.0%
2 18336
24.6%
1 14897
20.0%
6 3057
 
4.1%
3 2907
 
3.9%
7 2716
 
3.6%
9 2281
 
3.1%
8 1955
 
2.6%
5 1541
 
2.1%
4 1525
 
2.0%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74613
> 99.9%
Other Letter 2
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25398
34.0%
2 18336
24.6%
1 14897
20.0%
6 3057
 
4.1%
3 2907
 
3.9%
7 2716
 
3.6%
9 2281
 
3.1%
8 1955
 
2.6%
5 1541
 
2.1%
4 1525
 
2.0%
Other Letter
ValueCountFrequency (%)
년 1
50.0%
차 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74614
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25398
34.0%
2 18336
24.6%
1 14897
20.0%
6 3057
 
4.1%
3 2907
 
3.9%
7 2716
 
3.6%
9 2281
 
3.1%
8 1955
 
2.6%
5 1541
 
2.1%
4 1525
 
2.0%
Hangul
ValueCountFrequency (%)
년 1
50.0%
차 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74614
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25398
34.0%
2 18336
24.6%
1 14897
20.0%
6 3057
 
4.1%
3 2907
 
3.9%
7 2716
 
3.6%
9 2281
 
3.1%
8 1955
 
2.6%
5 1541
 
2.1%
4 1525
 
2.0%
Hangul
ValueCountFrequency (%)
년 1
50.0%
차 1
50.0%

X좌표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8724
Distinct (%)95.3%
Missing844
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean198793.7
Minimum182285.47
Maximum216092.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:22:39.433795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182285.47
5-th percentile185708.01
Q1192203.63
median199239.54
Q3205084.76
95-th percentile211799.6
Maximum216092.87
Range33807.4
Interquartile range (IQR)12881.135

Descriptive statistics

Standard deviation8046.8372
Coefficient of variation (CV)0.040478331
Kurtosis-0.9826653
Mean198793.7
Median Absolute Deviation (MAD)6492.535
Skewness0.0090022373
Sum1.8201551 × 109
Variance64751589
MonotonicityNot monotonic
2024-05-03T23:22:39.794789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199170.29789 12
 
0.1%
192825.228 6
 
0.1%
205062.79671 6
 
0.1%
203648.27651 6
 
0.1%
184847.88219 6
 
0.1%
199064.1975 4
 
< 0.1%
202845.0085 4
 
< 0.1%
198903.72401 4
 
< 0.1%
192233.10924 4
 
< 0.1%
198426.57943 4
 
< 0.1%
Other values (8714) 9100
91.0%
(Missing) 844
 
8.4%
ValueCountFrequency (%)
182285.47074 1
< 0.1%
182286.21848 1
< 0.1%
182299.19246 1
< 0.1%
182300.43016 2
< 0.1%
182303.50248 1
< 0.1%
182535.59229 1
< 0.1%
182538.12122 1
< 0.1%
182622.52461 1
< 0.1%
182632.70772 1
< 0.1%
182663.70772 1
< 0.1%
ValueCountFrequency (%)
216092.87107 1
< 0.1%
216082.71854 1
< 0.1%
216081.5125 1
< 0.1%
216061.59159 1
< 0.1%
216059.18254 1
< 0.1%
216055.82088 1
< 0.1%
216053.87301 1
< 0.1%
216039.85004 1
< 0.1%
216020.03996 1
< 0.1%
215925.51415 1
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8726
Distinct (%)95.3%
Missing844
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean549798.79
Minimum537384.12
Maximum565689.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:22:40.050460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum537384.12
5-th percentile541834.27
Q1544975.96
median549766.27
Q3553217.41
95-th percentile560665.59
Maximum565689.67
Range28305.551
Interquartile range (IQR)8241.4536

Descriptive statistics

Standard deviation5764.3612
Coefficient of variation (CV)0.010484492
Kurtosis-0.53456243
Mean549798.79
Median Absolute Deviation (MAD)4263.3271
Skewness0.38323025
Sum5.0339577 × 109
Variance33227860
MonotonicityNot monotonic
2024-05-03T23:22:40.398208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
551320.86897 12
 
0.1%
551381.75745 6
 
0.1%
559793.07492 6
 
0.1%
548491.6298 6
 
0.1%
551192.66886 6
 
0.1%
551843.52119 4
 
< 0.1%
543164.28003 4
 
< 0.1%
556039.62486 4
 
< 0.1%
551422.68516 4
 
< 0.1%
546524.49695 4
 
< 0.1%
Other values (8716) 9100
91.0%
(Missing) 844
 
8.4%
ValueCountFrequency (%)
537384.11874 1
< 0.1%
537395.61874 1
< 0.1%
537721.55624 1
< 0.1%
537735.76859 2
< 0.1%
537898.91361 1
< 0.1%
537904.57499 1
< 0.1%
538118.39255 1
< 0.1%
538119.17489 1
< 0.1%
538136.06445 1
< 0.1%
538245.99374 1
< 0.1%
ValueCountFrequency (%)
565689.6701 1
< 0.1%
565679.77907 1
< 0.1%
565488.80561 1
< 0.1%
565480.63351 1
< 0.1%
565466.11622 1
< 0.1%
565395.44375 1
< 0.1%
565287.20819 1
< 0.1%
565283.77069 1
< 0.1%
564914.22643 1
< 0.1%
564913.50625 1
< 0.1%

제조회사
Text

MISSING 

Distinct126
Distinct (%)1.7%
Missing2618
Missing (%)26.2%
Memory size156.2 KiB
2024-05-03T23:22:40.966800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.0413167
Min length1

Characters and Unicode

Total characters37215
Distinct characters136
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)0.5%

Sample

1st row대한신호
2nd row대한신호
3rd row대한신호㈜
4th row대한신호㈜
5th row대한신호
ValueCountFrequency (%)
대한신호 2151
29.0%
한길에이치씨 574
 
7.7%
대한신호㈜ 483
 
6.5%
주)대한신호 442
 
6.0%
삼일안전시스템 393
 
5.3%
신흥트래픽 363
 
4.9%
신흥ind 302
 
4.1%
0 268
 
3.6%
대한신호(주 247
 
3.3%
한길hc 218
 
2.9%
Other values (110) 1979
26.7%
2024-05-03T23:22:41.848804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
한 4918
 
13.2%
신 4282
 
11.5%
호 3417
 
9.2%
대 3400
 
9.1%
길 1470
 
4.0%
( 1235
 
3.3%
) 1235
 
3.3%
주 1234
 
3.3%
이 952
 
2.6%
에 926
 
2.5%
Other values (126) 14146
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31543
84.8%
Uppercase Letter 1816
 
4.9%
Open Punctuation 1235
 
3.3%
Close Punctuation 1235
 
3.3%
Other Symbol 880
 
2.4%
Decimal Number 292
 
0.8%
Other Punctuation 151
 
0.4%
Space Separator 55
 
0.1%
Dash Punctuation 5
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 4918
15.6%
신 4282
13.6%
호 3417
 
10.8%
대 3400
 
10.8%
길 1470
 
4.7%
주 1234
 
3.9%
이 952
 
3.0%
에 926
 
2.9%
씨 899
 
2.9%
치 896
 
2.8%
Other values (103) 9149
29.0%
Uppercase Letter
ValueCountFrequency (%)
H 428
23.6%
C 428
23.6%
D 320
17.6%
I 302
16.6%
N 302
16.6%
S 17
 
0.9%
T 17
 
0.9%
L 1
 
0.1%
E 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 272
93.2%
1 12
 
4.1%
2 3
 
1.0%
3 3
 
1.0%
5 1
 
0.3%
6 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 128
84.8%
, 23
 
15.2%
Open Punctuation
ValueCountFrequency (%)
( 1235
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1235
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 880
100.0%
Space Separator
ValueCountFrequency (%)
55
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
q 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32423
87.1%
Common 2973
 
8.0%
Latin 1819
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 4918
15.2%
신 4282
13.2%
호 3417
 
10.5%
대 3400
 
10.5%
길 1470
 
4.5%
주 1234
 
3.8%
이 952
 
2.9%
에 926
 
2.9%
씨 899
 
2.8%
치 896
 
2.8%
Other values (104) 10029
30.9%
Common
ValueCountFrequency (%)
( 1235
41.5%
) 1235
41.5%
0 272
 
9.1%
. 128
 
4.3%
55
 
1.8%
, 23
 
0.8%
1 12
 
0.4%
- 5
 
0.2%
2 3
 
0.1%
3 3
 
0.1%
Other values (2) 2
 
0.1%
Latin
ValueCountFrequency (%)
H 428
23.5%
C 428
23.5%
D 320
17.6%
I 302
16.6%
N 302
16.6%
S 17
 
0.9%
T 17
 
0.9%
q 3
 
0.2%
L 1
 
0.1%
E 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31534
84.7%
ASCII 4792
 
12.9%
None 880
 
2.4%
Compat Jamo 9
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
한 4918
15.6%
신 4282
13.6%
호 3417
 
10.8%
대 3400
 
10.8%
길 1470
 
4.7%
주 1234
 
3.9%
이 952
 
3.0%
에 926
 
2.9%
씨 899
 
2.9%
치 896
 
2.8%
Other values (100) 9140
29.0%
ASCII
ValueCountFrequency (%)
( 1235
25.8%
) 1235
25.8%
H 428
 
8.9%
C 428
 
8.9%
D 320
 
6.7%
I 302
 
6.3%
N 302
 
6.3%
0 272
 
5.7%
. 128
 
2.7%
55
 
1.1%
Other values (12) 87
 
1.8%
None
ValueCountFrequency (%)
㈜ 880
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅎ 7
77.8%
ㄹ 1
 
11.1%
ㅁ 1
 
11.1%

시설번호
Text

MISSING 

Distinct79
Distinct (%)4.6%
Missing8289
Missing (%)82.9%
Memory size156.2 KiB
2024-05-03T23:22:42.321557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length1
Mean length1.509059
Min length1

Characters and Unicode

Total characters2582
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)3.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 1595
93.6%
2 21
 
1.2%
0801-103(수 2
 
0.1%
0801-182(수 2
 
0.1%
0801-052(수 2
 
0.1%
0801-009(수 2
 
0.1%
0801-188(수 2
 
0.1%
0801-105(수 2
 
0.1%
0801-198(수 2
 
0.1%
0801-004(수 2
 
0.1%
Other values (68) 72
 
4.2%
2024-05-03T23:22:43.158576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1833
71.0%
1 142
 
5.5%
8 109
 
4.2%
- 88
 
3.4%
수 84
 
3.3%
) 84
 
3.3%
( 83
 
3.2%
2 44
 
1.7%
9 21
 
0.8%
5 21
 
0.8%
Other values (5) 73
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2236
86.6%
Dash Punctuation 88
 
3.4%
Other Letter 84
 
3.3%
Close Punctuation 84
 
3.3%
Open Punctuation 83
 
3.2%
Space Separator 7
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1833
82.0%
1 142
 
6.4%
8 109
 
4.9%
2 44
 
2.0%
9 21
 
0.9%
5 21
 
0.9%
3 18
 
0.8%
7 17
 
0.8%
6 16
 
0.7%
4 15
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%
Other Letter
ValueCountFrequency (%)
수 84
100.0%
Close Punctuation
ValueCountFrequency (%)
) 84
100.0%
Open Punctuation
ValueCountFrequency (%)
( 83
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2498
96.7%
Hangul 84
 
3.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1833
73.4%
1 142
 
5.7%
8 109
 
4.4%
- 88
 
3.5%
) 84
 
3.4%
( 83
 
3.3%
2 44
 
1.8%
9 21
 
0.8%
5 21
 
0.8%
3 18
 
0.7%
Other values (4) 55
 
2.2%
Hangul
ValueCountFrequency (%)
수 84
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2498
96.7%
Hangul 84
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1833
73.4%
1 142
 
5.7%
8 109
 
4.4%
- 88
 
3.5%
) 84
 
3.4%
( 83
 
3.3%
2 44
 
1.8%
9 21
 
0.8%
5 21
 
0.8%
3 18
 
0.7%
Other values (4) 55
 
2.2%
Hangul
ValueCountFrequency (%)
수 84
100.0%

작업구분 (공통)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7034 
4
1334 
6
993 
2
 
538
3
 
101

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 7034
70.3%
4 1334
 
13.3%
6 993
 
9.9%
2 538
 
5.4%
3 101
 
1.0%

Length

2024-05-03T23:22:43.632597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:22:43.860836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7034
70.3%
4 1334
 
13.3%
6 993
 
9.9%
2 538
 
5.4%
3 101
 
1.0%

표출구분 (공통)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
7184 
1
2816 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 7184
71.8%
1 2816
 
28.2%

Length

2024-05-03T23:22:44.065149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:22:44.269638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 7184
71.8%
1 2816
 
28.2%

종류
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

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 10000
100.0%

Length

2024-05-03T23:22:44.537783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:22:44.823550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

신규정규화ID
Text

MISSING 

Distinct7126
Distinct (%)97.9%
Missing2722
Missing (%)27.2%
Memory size156.2 KiB
2024-05-03T23:22:45.444659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.990382
Min length1

Characters and Unicode

Total characters79988
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

Unique7012 ?
Unique (%)96.3%

Sample

1st row4338645
2nd row4226804
3rd row2347744
4th row4465551
5th row2369832
ValueCountFrequency (%)
24500710 5
 
0.1%
54613010 4
 
0.1%
33903210 4
 
0.1%
02899710 4
 
0.1%
12280510 4
 
0.1%
12323910 4
 
0.1%
21175910 3
 
< 0.1%
32511210 3
 
< 0.1%
51997710 3
 
< 0.1%
53447710 3
 
< 0.1%
Other values (7115) 7234
99.5%
2024-05-03T23:22:46.448590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28501
35.6%
1 8337
 
10.4%
2 8256
 
10.3%
3 6179
 
7.7%
4 5868
 
7.3%
5 5181
 
6.5%
6 3968
 
5.0%
0 3965
 
5.0%
7 3348
 
4.2%
8 3227
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51487
64.4%
Space Separator 28501
35.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8337
16.2%
2 8256
16.0%
3 6179
12.0%
4 5868
11.4%
5 5181
10.1%
6 3968
7.7%
0 3965
7.7%
7 3348
6.5%
8 3227
 
6.3%
9 3158
 
6.1%
Space Separator
ValueCountFrequency (%)
28501
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79988
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
28501
35.6%
1 8337
 
10.4%
2 8256
 
10.3%
3 6179
 
7.7%
4 5868
 
7.3%
5 5181
 
6.5%
6 3968
 
5.0%
0 3965
 
5.0%
7 3348
 
4.2%
8 3227
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28501
35.6%
1 8337
 
10.4%
2 8256
 
10.3%
3 6179
 
7.7%
4 5868
 
7.3%
5 5181
 
6.5%
6 3968
 
5.0%
0 3965
 
5.0%
7 3348
 
4.2%
8 3227
 
4.0%

상태 (공통)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9955 
<NA>
 
43
4
 
1
2
 
1

Length

Max length4
Median length1
Mean length1.0129
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 9955
99.6%
<NA> 43
 
0.4%
4 1
 
< 0.1%
2 1
 
< 0.1%

Length

2024-05-03T23:22:46.879080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:22:47.146851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9955
99.6%
na 43
 
0.4%
4 1
 
< 0.1%
2 1
 
< 0.1%

공사관리번호
Text

MISSING 

Distinct1675
Distinct (%)18.0%
Missing700
Missing (%)7.0%
Memory size156.2 KiB
2024-05-03T23:22:47.652733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters120900
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

Unique553 ?
Unique (%)5.9%

Sample

1st row2018-0101-108
2nd row2016-0201-114
3rd row2000-0000-000
4th row2023-0101-137
5th row2009-1211-001
ValueCountFrequency (%)
2000-0000-000 759
 
8.2%
2009-0111-001 77
 
0.8%
2008-1211-502 67
 
0.7%
2008-0111-877 61
 
0.7%
2012-0101-003 58
 
0.6%
2010-0101-067 53
 
0.6%
2020-0101-020 52
 
0.6%
2011-0101-086 51
 
0.5%
2009-1211-001 50
 
0.5%
2010-0101-018 49
 
0.5%
Other values (1665) 8023
86.3%
2024-05-03T23:22:48.596565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 43559
36.0%
1 25649
21.2%
- 18600
15.4%
2 18213
15.1%
9 2883
 
2.4%
3 2651
 
2.2%
8 2040
 
1.7%
4 1935
 
1.6%
7 1930
 
1.6%
5 1784
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102300
84.6%
Dash Punctuation 18600
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43559
42.6%
1 25649
25.1%
2 18213
17.8%
9 2883
 
2.8%
3 2651
 
2.6%
8 2040
 
2.0%
4 1935
 
1.9%
7 1930
 
1.9%
5 1784
 
1.7%
6 1656
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 18600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43559
36.0%
1 25649
21.2%
- 18600
15.4%
2 18213
15.1%
9 2883
 
2.4%
3 2651
 
2.2%
8 2040
 
1.7%
4 1935
 
1.6%
7 1930
 
1.6%
5 1784
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43559
36.0%
1 25649
21.2%
- 18600
15.4%
2 18213
15.1%
9 2883
 
2.4%
3 2651
 
2.2%
8 2040
 
1.7%
4 1935
 
1.6%
7 1930
 
1.6%
5 1784
 
1.5%

음향신호관리번호.1
Text

Distinct9217
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:22:49.264768image/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

Unique8494 ?
Unique (%)84.9%

Sample

1st row24-013716
2nd row24-012463
3rd row24-005880
4th row24-017724
5th row24-003845
ValueCountFrequency (%)
24-005145 4
 
< 0.1%
24-000192 4
 
< 0.1%
24-007161 4
 
< 0.1%
24-003630 4
 
< 0.1%
24-002193 3
 
< 0.1%
24-002369 3
 
< 0.1%
24-004258 3
 
< 0.1%
24-001745 3
 
< 0.1%
24-001674 3
 
< 0.1%
24-002188 3
 
< 0.1%
Other values (9207) 9966
99.7%
2024-05-03T23:22:50.339165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19775
22.0%
2 14924
16.6%
4 14061
15.6%
- 10000
11.1%
1 8207
9.1%
3 3995
 
4.4%
5 3902
 
4.3%
6 3889
 
4.3%
7 3793
 
4.2%
8 3729
 
4.1%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19775
24.7%
2 14924
18.7%
4 14061
17.6%
1 8207
10.3%
3 3995
 
5.0%
5 3902
 
4.9%
6 3889
 
4.9%
7 3793
 
4.7%
8 3729
 
4.7%
9 3725
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19775
22.0%
2 14924
16.6%
4 14061
15.6%
- 10000
11.1%
1 8207
9.1%
3 3995
 
4.4%
5 3902
 
4.3%
6 3889
 
4.3%
7 3793
 
4.2%
8 3729
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19775
22.0%
2 14924
16.6%
4 14061
15.6%
- 10000
11.1%
1 8207
9.1%
3 3995
 
4.4%
5 3902
 
4.3%
6 3889
 
4.3%
7 3793
 
4.2%
8 3729
 
4.1%

이력ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13528.632
Minimum2
Maximum27073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:22:50.672720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1385.9
Q16914.5
median13473.5
Q320282.25
95-th percentile25525.1
Maximum27073
Range27071
Interquartile range (IQR)13367.75

Descriptive statistics

Standard deviation7758.7643
Coefficient of variation (CV)0.57350695
Kurtosis-1.195576
Mean13528.632
Median Absolute Deviation (MAD)6669
Skewness-0.0086627357
Sum1.3528632 × 108
Variance60198424
MonotonicityNot monotonic
2024-05-03T23:22:51.173356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18444 1
 
< 0.1%
24543 1
 
< 0.1%
22363 1
 
< 0.1%
20689 1
 
< 0.1%
8690 1
 
< 0.1%
23880 1
 
< 0.1%
14715 1
 
< 0.1%
16960 1
 
< 0.1%
1936 1
 
< 0.1%
8063 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
5 1
< 0.1%
10 1
< 0.1%
13 1
< 0.1%
16 1
< 0.1%
24 1
< 0.1%
26 1
< 0.1%
31 1
< 0.1%
32 1
< 0.1%
33 1
< 0.1%
ValueCountFrequency (%)
27073 1
< 0.1%
27069 1
< 0.1%
27067 1
< 0.1%
27065 1
< 0.1%
27062 1
< 0.1%
27059 1
< 0.1%
27052 1
< 0.1%
27051 1
< 0.1%
27050 1
< 0.1%
27049 1
< 0.1%

위치정보
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7353 
<NA>
2647 

Length

Max length4
Median length1
Mean length1.7941
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 7353
73.5%
<NA> 2647
 
26.5%

Length

2024-05-03T23:22:51.601911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:22:51.972325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7353
73.5%
na 2647
 
26.5%

공사형태 (공통)
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
001
4510 
002
3296 
004
930 
<NA>
714 
003
 
327
Other values (3)
 
223

Length

Max length4
Median length3
Mean length3.071
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row002
2nd row001
3rd row<NA>
4th row004
5th row001

Common Values

ValueCountFrequency (%)
001 4510
45.1%
002 3296
33.0%
004 930
 
9.3%
<NA> 714
 
7.1%
003 327
 
3.3%
005 201
 
2.0%
006 20
 
0.2%
2
 
< 0.1%

Length

2024-05-03T23:22:52.203253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:22:52.570473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
001 4510
45.1%
002 3296
33.0%
004 930
 
9.3%
na 714
 
7.1%
003 327
 
3.3%
005 201
 
2.0%
006 20
 
0.2%

Interactions

2024-05-03T23:22:29.882939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:26.344378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:27.602822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:28.915821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:30.168825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:26.643871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:27.931417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:29.136758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:30.443199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:26.955043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:28.233741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:29.360350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:30.802471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:27.267526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:28.506544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:22:29.619260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T23:22:52.824933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향 (공통)X좌표Y좌표시설번호작업구분 (공통)표출구분 (공통)상태 (공통)이력ID공사형태 (공통)
방향 (공통)1.0000.0800.0880.0000.1730.0670.0000.1800.033
X좌표0.0801.0000.6020.3070.2090.1930.0160.4630.169
Y좌표0.0880.6021.0000.3520.1850.0510.0000.4950.103
시설번호0.0000.3070.3521.0000.1330.179NaN0.3360.536
작업구분 (공통)0.1730.2090.1850.1331.0000.6390.0000.6510.351
표출구분 (공통)0.0670.1930.0510.1790.6391.0000.0110.7000.473
상태 (공통)0.0000.0160.000NaN0.0000.0111.0000.0040.046
이력ID0.1800.4630.4950.3360.6510.7000.0041.0000.479
공사형태 (공통)0.0330.1690.1030.5360.3510.4730.0460.4791.000
2024-05-03T23:22:53.147105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사형태 (공통)위치정보작업구분 (공통)표출구분 (공통)상태 (공통)
공사형태 (공통)1.0001.0000.2330.5070.031
위치정보1.0001.0001.0001.0001.000
작업구분 (공통)0.2331.0001.0000.7680.000
표출구분 (공통)0.5071.0000.7681.0000.018
상태 (공통)0.0311.0000.0000.0181.000
2024-05-03T23:22:53.433472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향 (공통)X좌표Y좌표이력ID작업구분 (공통)표출구분 (공통)상태 (공통)위치정보공사형태 (공통)
방향 (공통)1.000-0.060-0.003-0.0810.1000.0670.0001.0000.017
X좌표-0.0601.0000.150-0.0260.0880.1470.0091.0000.086
Y좌표-0.0030.1501.0000.0050.0770.0390.0001.0000.052
이력ID-0.081-0.0260.0051.0000.3260.5460.0021.0000.266
작업구분 (공통)0.1000.0880.0770.3261.0000.7680.0001.0000.233
표출구분 (공통)0.0670.1470.0390.5460.7681.0000.0181.0000.507
상태 (공통)0.0000.0090.0000.0020.0000.0181.0001.0000.031
위치정보1.0001.0001.0001.0001.0001.0001.0001.0001.000
공사형태 (공통)0.0170.0860.0520.2660.2330.5070.0311.0001.000

Missing values

2024-05-03T23:22:31.119980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T23:22:31.594223image/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.
2024-05-03T23:22:32.069872image/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

음향신호관리번호지주관리번호방향 (공통)설치일교체일X좌표Y좌표제조회사시설번호작업구분 (공통)표출구분 (공통)종류신규정규화ID상태 (공통)공사관리번호음향신호관리번호.1이력ID위치정보공사형태 (공통)
1300324-000001371602-00001791391802018112020181120201523.45245555961.14968대한신호<NA>121433864512018-0101-10824-013716184441002
824524-000001246302-000019419802016100920161009201101.67937549764.67354대한신호<NA>121422680412016-0201-11424-012463171911001
58424-000000588002-000014936802000010120000101206585.31139548790.23309<NA>0121<NA>12000-0000-00024-005880112541<NA>
2522124-000001772402-0000215292<NA>2020121020231105192063.7995555499.24219대한신호㈜<NA>121234774412023-0101-13724-01772422452<NA>004
33524-000000384502-00001425721802007060120070601192865.11366540634.16655<NA><NA>411<NA>12009-1211-00124-003845110741001
629424-000000798502-00000904731802012031520120315202960.725559545.39375<NA>0121446555112011-0401-00424-00798575291001
1431824-000000876702-00000115362702012091020181221193088.01007556435.5374대한신호㈜0121236983212018-0101-04424-00876712421004
740124-000000839602-0000060758452012091220120912185817.19685546435.58328대한신호0611<NA>12014-0201-05624-00839651851003
769024-000001242902-000000487702016122620161226195394.7714549306.20243신흥IND<NA>121321541112016-0101-11124-012429171571001
2611524-000001354602-00000600891802023101620231016207460.7849551872.51118한길에이치씨<NA>121535052912023-0101-14224-013546182741002
음향신호관리번호지주관리번호방향 (공통)설치일교체일X좌표Y좌표제조회사시설번호작업구분 (공통)표출구분 (공통)종류신규정규화ID상태 (공통)공사관리번호음향신호관리번호.1이력ID위치정보공사형태 (공통)
682024-000001212702-00000864521802016061420160614205007.25669560646.1336대한신호<NA>121540767112016-0101-00524-012127168551001
2054724-000001048102-00000403951802000010120000101204887.675543470.59374(주)대한신호<NA>12151034481<NA>24-010481152091001
1008024-000000744702-00000855401802012020320120203207723.03421557999.10164대한신호0621546204212013-0201-07624-00744770441004
5424-000000418602-00001074561802007060120070601191667.56864539213.09106.<NA>211<NA>12012-0101-13824-00418689921002
17524-000000598002-000012464500001010100010101198008.11823551749.81365<NA><NA>411<NA>12000-0000-00024-00598099141<NA>
2194624-000001478102-0000031214180<NA><NA>190666.93432545840.59503<NA><NA>111211891101<NA>24-014781195091003
2317324-000001930802-0000215130<NA><NA><NA>185269.23158543868.64122<NA><NA>12111141221<NA>24-01930824036<NA>001
2385224-000002090702-0000061098<NA><NA><NA><NA><NA><NA><NA>12121291961<NA>24-02090725635<NA>001
185824-000000352002-00001034831802007060120070601201077.01984557855.76944<NA><NA>111<NA>12009-0111-00724-00352085171<NA>
2160624-000001951702-0000220301<NA>2022060720220607189403.38585542027.95249대한신호<NA>121119045512022-0201-03524-01951724245<NA>001