Overview

Dataset statistics

Number of variables12
Number of observations4701
Missing cells5073
Missing cells (%)9.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory473.0 KiB
Average record size in memory103.0 B

Variable types

Numeric6
Categorical2
Text3
DateTime1

Dataset

Description경상남도의 하자관리 현황데이터입니다. (하자기간, 시작일, 마감일, 보증율, 보증금액, 납부방법, 보험회사등의 데이터를 포함하고있습니다.)
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15049511

Alerts

부서코드 has constant value ""Constant
하자기간 is highly overall correlated with 공사구분High correlation
공사구분 is highly overall correlated with 하자기간High correlation
공사구분 is highly imbalanced (62.6%)Imbalance
하자기간 has 810 (17.2%) missing valuesMissing
시작일 has 482 (10.3%) missing valuesMissing
마감일 has 496 (10.6%) missing valuesMissing
보증율 has 819 (17.4%) missing valuesMissing
보증금액 has 149 (3.2%) missing valuesMissing
납부방법 has 219 (4.7%) missing valuesMissing
보험회사 has 2098 (44.6%) missing valuesMissing
보증금액 is highly skewed (γ1 = 23.88702285)Skewed
보증율 has 80 (1.7%) zerosZeros
보증금액 has 257 (5.5%) zerosZeros

Reproduction

Analysis started2023-12-11 00:14:11.881674
Analysis finished2023-12-11 00:14:16.339387
Duration4.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공사년도
Real number (ℝ)

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.5775
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-12-11T09:14:16.398074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1992
Q12001
median2008
Q32012
95-th percentile2018
Maximum2019
Range29
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.726536
Coefficient of variation (CV)0.0038506043
Kurtosis-0.73039807
Mean2006.5775
Median Absolute Deviation (MAD)5
Skewness-0.42426956
Sum9432921
Variance59.699359
MonotonicityNot monotonic
2023-12-11T09:14:16.501573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2010 433
 
9.2%
2011 247
 
5.3%
2009 243
 
5.2%
2012 241
 
5.1%
2003 230
 
4.9%
2004 224
 
4.8%
2018 215
 
4.6%
2016 215
 
4.6%
2017 208
 
4.4%
2008 192
 
4.1%
Other values (20) 2253
47.9%
ValueCountFrequency (%)
1990 88
1.9%
1991 119
2.5%
1992 82
1.7%
1993 82
1.7%
1994 70
1.5%
1995 99
2.1%
1996 106
2.3%
1997 103
2.2%
1998 105
2.2%
1999 104
2.2%
ValueCountFrequency (%)
2019 51
 
1.1%
2018 215
4.6%
2017 208
4.4%
2016 215
4.6%
2015 158
 
3.4%
2014 127
 
2.7%
2013 118
 
2.5%
2012 241
5.1%
2011 247
5.3%
2010 433
9.2%

공사구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
공사
3931 
용역
672 
기타
 
79
구매
 
19

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공사 3931
83.6%
용역 672
 
14.3%
기타 79
 
1.7%
구매 19
 
0.4%

Length

2023-12-11T09:14:16.615989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:14:16.714896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사 3931
83.6%
용역 672
 
14.3%
기타 79
 
1.7%
구매 19
 
0.4%

공사번호
Real number (ℝ)

Distinct461
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.12912
Minimum1
Maximum623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-12-11T09:14:16.835395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q143
median83
Q3138
95-th percentile427
Maximum623
Range622
Interquartile range (IQR)95

Descriptive statistics

Standard deviation114.35561
Coefficient of variation (CV)0.9847281
Kurtosis3.6473887
Mean116.12912
Median Absolute Deviation (MAD)45
Skewness1.9342421
Sum545923
Variance13077.205
MonotonicityNot monotonic
2023-12-11T09:14:16.965577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
445 104
 
2.2%
103 73
 
1.6%
105 72
 
1.5%
109 64
 
1.4%
70 61
 
1.3%
79 54
 
1.1%
36 46
 
1.0%
64 46
 
1.0%
2 36
 
0.8%
51 35
 
0.7%
Other values (451) 4110
87.4%
ValueCountFrequency (%)
1 24
0.5%
2 36
0.8%
3 28
0.6%
4 24
0.5%
5 33
0.7%
6 25
0.5%
7 28
0.6%
8 28
0.6%
9 27
0.6%
10 32
0.7%
ValueCountFrequency (%)
623 1
< 0.1%
620 1
< 0.1%
619 1
< 0.1%
617 1
< 0.1%
616 1
< 0.1%
615 1
< 0.1%
607 1
< 0.1%
604 1
< 0.1%
601 2
< 0.1%
596 1
< 0.1%

부서코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
1
4701 

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 4701
100.0%

Length

2023-12-11T09:14:17.079184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:14:17.178789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4701
100.0%

순번
Real number (ℝ)

Distinct104
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6426292
Minimum1
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-12-11T09:14:17.297376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile17
Maximum104
Range103
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.529489
Coefficient of variation (CV)2.8906287
Kurtosis37.093793
Mean3.6426292
Median Absolute Deviation (MAD)0
Skewness5.7210525
Sum17124
Variance110.87013
MonotonicityNot monotonic
2023-12-11T09:14:17.465675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3620
77.0%
2 404
 
8.6%
3 149
 
3.2%
4 97
 
2.1%
5 44
 
0.9%
6 32
 
0.7%
7 22
 
0.5%
8 19
 
0.4%
9 14
 
0.3%
10 12
 
0.3%
Other values (94) 288
 
6.1%
ValueCountFrequency (%)
1 3620
77.0%
2 404
 
8.6%
3 149
 
3.2%
4 97
 
2.1%
5 44
 
0.9%
6 32
 
0.7%
7 22
 
0.5%
8 19
 
0.4%
9 14
 
0.3%
10 12
 
0.3%
ValueCountFrequency (%)
104 1
< 0.1%
103 1
< 0.1%
102 1
< 0.1%
101 1
< 0.1%
100 1
< 0.1%
99 1
< 0.1%
98 1
< 0.1%
97 1
< 0.1%
96 1
< 0.1%
95 1
< 0.1%

하자기간
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)0.8%
Missing810
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean171.58976
Minimum1
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-12-11T09:14:17.604716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q35
95-th percentile2009
Maximum2017
Range2016
Interquartile range (IQR)3

Descriptive statistics

Standard deviation556.66321
Coefficient of variation (CV)3.2441516
Kurtosis7.0012144
Mean171.58976
Median Absolute Deviation (MAD)1
Skewness2.9995643
Sum667655.76
Variance309873.92
MonotonicityNot monotonic
2023-12-11T09:14:17.736335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.0 1087
23.1%
1.0 887
18.9%
3.0 808
17.2%
7.0 331
 
7.0%
5.0 240
 
5.1%
10.0 124
 
2.6%
2009.0 69
 
1.5%
4.0 62
 
1.3%
2008.0 45
 
1.0%
2010.0 43
 
0.9%
Other values (20) 195
 
4.1%
(Missing) 810
17.2%
ValueCountFrequency (%)
1.0 887
18.9%
1.6 1
 
< 0.1%
2.0 1087
23.1%
3.0 808
17.2%
4.0 62
 
1.3%
5.0 240
 
5.1%
6.0 12
 
0.3%
6.16 1
 
< 0.1%
7.0 331
 
7.0%
8.0 5
 
0.1%
ValueCountFrequency (%)
2017.0 1
 
< 0.1%
2015.0 19
 
0.4%
2014.0 32
0.7%
2013.0 1
 
< 0.1%
2012.0 3
 
0.1%
2011.0 36
0.8%
2010.0 43
0.9%
2009.0 69
1.5%
2008.0 45
1.0%
2007.0 5
 
0.1%

시작일
Text

MISSING 

Distinct2225
Distinct (%)52.7%
Missing482
Missing (%)10.3%
Memory size36.9 KiB
2023-12-11T09:14:18.050963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters42190
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

Unique1452 ?
Unique (%)34.4%

Sample

1st row1990-05-21
2nd row1990-12-22
3rd row1990-12-22
4th row1990-12-26
5th row1991-06-05
ValueCountFrequency (%)
2010-12-14 69
 
1.6%
2018-03-01 60
 
1.4%
2010-12-09 53
 
1.3%
2010-03-09 37
 
0.9%
2019-04-01 30
 
0.7%
2010-05-10 24
 
0.6%
2002-05-09 24
 
0.6%
2009-12-28 22
 
0.5%
2005-06-27 19
 
0.5%
2004-06-09 17
 
0.4%
Other values (2215) 3864
91.6%
2023-12-11T09:14:18.803126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10033
23.8%
- 8438
20.0%
1 6992
16.6%
2 6660
15.8%
9 2922
 
6.9%
8 1300
 
3.1%
3 1237
 
2.9%
6 1234
 
2.9%
7 1196
 
2.8%
4 1133
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33752
80.0%
Dash Punctuation 8438
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10033
29.7%
1 6992
20.7%
2 6660
19.7%
9 2922
 
8.7%
8 1300
 
3.9%
3 1237
 
3.7%
6 1234
 
3.7%
7 1196
 
3.5%
4 1133
 
3.4%
5 1045
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 8438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42190
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10033
23.8%
- 8438
20.0%
1 6992
16.6%
2 6660
15.8%
9 2922
 
6.9%
8 1300
 
3.1%
3 1237
 
2.9%
6 1234
 
2.9%
7 1196
 
2.8%
4 1133
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10033
23.8%
- 8438
20.0%
1 6992
16.6%
2 6660
15.8%
9 2922
 
6.9%
8 1300
 
3.1%
3 1237
 
2.9%
6 1234
 
2.9%
7 1196
 
2.8%
4 1133
 
2.7%

마감일
Date

MISSING 

Distinct2649
Distinct (%)63.0%
Missing496
Missing (%)10.6%
Memory size36.9 KiB
Minimum1991-07-08 00:00:00
Maximum2029-03-31 00:00:00
2023-12-11T09:14:18.963966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:19.123601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

보증율
Real number (ℝ)

MISSING  ZEROS 

Distinct477
Distinct (%)12.3%
Missing819
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean13.835219
Minimum0
Maximum983.45
Zeros80
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-12-11T09:14:19.298626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q12
median3
Q34
95-th percentile63.14
Maximum983.45
Range983.45
Interquartile range (IQR)2

Descriptive statistics

Standard deviation59.853241
Coefficient of variation (CV)4.3261506
Kurtosis89.428222
Mean13.835219
Median Absolute Deviation (MAD)1
Skewness8.6146465
Sum53708.32
Variance3582.4105
MonotonicityNot monotonic
2023-12-11T09:14:19.460141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 1593
33.9%
2.0 602
 
12.8%
5.0 372
 
7.9%
4.0 190
 
4.0%
0.0 80
 
1.7%
0.02 32
 
0.7%
7.0 28
 
0.6%
2.98 24
 
0.5%
100.0 24
 
0.5%
0.01 20
 
0.4%
Other values (467) 917
19.5%
(Missing) 819
17.4%
ValueCountFrequency (%)
0.0 80
1.7%
0.01 20
 
0.4%
0.02 32
 
0.7%
0.03 20
 
0.4%
0.04 18
 
0.4%
0.05 16
 
0.3%
0.06 13
 
0.3%
0.07 14
 
0.3%
0.08 8
 
0.2%
0.09 6
 
0.1%
ValueCountFrequency (%)
983.45 2
< 0.1%
723.45 2
< 0.1%
681.74 1
 
< 0.1%
649.82 2
< 0.1%
626.0 2
< 0.1%
609.18 3
0.1%
527.98 3
0.1%
522.19 3
0.1%
509.97 1
 
< 0.1%
504.61 2
< 0.1%

보증금액
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3833
Distinct (%)84.2%
Missing149
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean1.1693024 × 108
Minimum0
Maximum2.485911 × 1010
Zeros257
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-12-11T09:14:19.608131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11996521
median12915935
Q352123691
95-th percentile5.6353961 × 108
Maximum2.485911 × 1010
Range2.485911 × 1010
Interquartile range (IQR)50127170

Descriptive statistics

Standard deviation5.4730927 × 108
Coefficient of variation (CV)4.6806478
Kurtosis949.77864
Mean1.1693024 × 108
Median Absolute Deviation (MAD)12633035
Skewness23.887023
Sum5.3226645 × 1011
Variance2.9954743 × 1017
MonotonicityNot monotonic
2023-12-11T09:14:19.750836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 257
 
5.5%
190000 10
 
0.2%
6973679 8
 
0.2%
15606800 8
 
0.2%
13956000 7
 
0.1%
270000 7
 
0.1%
150000 6
 
0.1%
1992479 6
 
0.1%
10958638 6
 
0.1%
198000 6
 
0.1%
Other values (3823) 4231
90.0%
(Missing) 149
 
3.2%
ValueCountFrequency (%)
0 257
5.5%
1 3
 
0.1%
10 1
 
< 0.1%
2590 1
 
< 0.1%
12945 1
 
< 0.1%
15000 1
 
< 0.1%
17654 1
 
< 0.1%
18992 1
 
< 0.1%
19080 1
 
< 0.1%
20400 1
 
< 0.1%
ValueCountFrequency (%)
24859110000 1
< 0.1%
8400629900 1
< 0.1%
6937668480 1
< 0.1%
5823423080 1
< 0.1%
5767279000 1
< 0.1%
5197329461 1
< 0.1%
4992975800 1
< 0.1%
4787329458 1
< 0.1%
4771312729 1
< 0.1%
4656217900 1
< 0.1%

납부방법
Text

MISSING 

Distinct54
Distinct (%)1.2%
Missing219
Missing (%)4.7%
Memory size36.9 KiB
2023-12-11T09:14:19.902012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length4
Mean length3.2045962
Min length1

Characters and Unicode

Total characters14363
Distinct characters78
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

Unique33 ?
Unique (%)0.7%

Sample

1st row기타
2nd row하천
3rd row하천
4th row하천
5th row하천
ValueCountFrequency (%)
공제조합 2030
45.2%
각서 777
 
17.3%
보증보험 464
 
10.3%
도로 423
 
9.4%
하천 251
 
5.6%
기타 146
 
3.3%
교량 94
 
2.1%
인공어초 69
 
1.5%
방파제 58
 
1.3%
전공종 46
 
1.0%
Other values (42) 130
 
2.9%
2023-12-11T09:14:20.165352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 2151
15.0%
제 2092
14.6%
조 2034
14.2%
합 2030
14.1%
보 929
6.5%
각 778
 
5.4%
서 778
 
5.4%
증 481
 
3.3%
험 464
 
3.2%
로 428
 
3.0%
Other values (68) 2198
15.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14330
99.8%
Other Punctuation 12
 
0.1%
Decimal Number 9
 
0.1%
Space Separator 6
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 2151
15.0%
제 2092
14.6%
조 2034
14.2%
합 2030
14.2%
보 929
6.5%
각 778
 
5.4%
서 778
 
5.4%
증 481
 
3.4%
험 464
 
3.2%
로 428
 
3.0%
Other values (60) 2165
15.1%
Decimal Number
ValueCountFrequency (%)
0 4
44.4%
3 3
33.3%
2 2
22.2%
Other Punctuation
ValueCountFrequency (%)
, 11
91.7%
' 1
 
8.3%
Space Separator
ValueCountFrequency (%)
6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14330
99.8%
Common 33
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 2151
15.0%
제 2092
14.6%
조 2034
14.2%
합 2030
14.2%
보 929
6.5%
각 778
 
5.4%
서 778
 
5.4%
증 481
 
3.4%
험 464
 
3.2%
로 428
 
3.0%
Other values (60) 2165
15.1%
Common
ValueCountFrequency (%)
, 11
33.3%
6
18.2%
0 4
 
12.1%
3 3
 
9.1%
( 3
 
9.1%
) 3
 
9.1%
2 2
 
6.1%
' 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14330
99.8%
ASCII 33
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 2151
15.0%
제 2092
14.6%
조 2034
14.2%
합 2030
14.2%
보 929
6.5%
각 778
 
5.4%
서 778
 
5.4%
증 481
 
3.4%
험 464
 
3.2%
로 428
 
3.0%
Other values (60) 2165
15.1%
ASCII
ValueCountFrequency (%)
, 11
33.3%
6
18.2%
0 4
 
12.1%
3 3
 
9.1%
( 3
 
9.1%
) 3
 
9.1%
2 2
 
6.1%
' 1
 
3.0%

보험회사
Text

MISSING 

Distinct83
Distinct (%)3.2%
Missing2098
Missing (%)44.6%
Memory size36.9 KiB
2023-12-11T09:14:20.367824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length5.6142912
Min length2

Characters and Unicode

Total characters14614
Distinct characters102
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)1.5%

Sample

1st row보증보험증권
2nd row건설공제
3rd row건설공제
4th row건설공제
5th row건설공제
ValueCountFrequency (%)
건설공제조합 682
26.1%
건설 336
12.9%
엔지니어링공제조합 260
 
10.0%
서울보증보험 218
 
8.4%
엔지니어링 195
 
7.5%
건설공제 151
 
5.8%
서울 134
 
5.1%
전문건설공제조합 111
 
4.3%
전기공사공제조합 81
 
3.1%
정보통신공제조합 72
 
2.8%
Other values (73) 369
14.1%
2023-12-11T09:14:20.692004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 1578
 
10.8%
제 1459
 
10.0%
설 1377
 
9.4%
건 1365
 
9.3%
조 1290
 
8.8%
합 1285
 
8.8%
보 696
 
4.8%
서 497
 
3.4%
지 491
 
3.4%
어 485
 
3.3%
Other values (92) 4091
28.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14554
99.6%
Close Punctuation 19
 
0.1%
Open Punctuation 18
 
0.1%
Decimal Number 10
 
0.1%
Space Separator 6
 
< 0.1%
Math Symbol 3
 
< 0.1%
Uppercase Letter 3
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 1578
 
10.8%
제 1459
 
10.0%
설 1377
 
9.5%
건 1365
 
9.4%
조 1290
 
8.9%
합 1285
 
8.8%
보 696
 
4.8%
서 497
 
3.4%
지 491
 
3.4%
어 485
 
3.3%
Other values (77) 4031
27.7%
Decimal Number
ValueCountFrequency (%)
1 3
30.0%
2 2
20.0%
9 2
20.0%
5 2
20.0%
0 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
33.3%
E 1
33.3%
M 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 18
94.7%
] 1
 
5.3%
Math Symbol
ValueCountFrequency (%)
~ 2
66.7%
+ 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14554
99.6%
Common 57
 
0.4%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 1578
 
10.8%
제 1459
 
10.0%
설 1377
 
9.5%
건 1365
 
9.4%
조 1290
 
8.9%
합 1285
 
8.8%
보 696
 
4.8%
서 497
 
3.4%
지 491
 
3.4%
어 485
 
3.3%
Other values (77) 4031
27.7%
Common
ValueCountFrequency (%)
) 18
31.6%
( 18
31.6%
6
 
10.5%
1 3
 
5.3%
2 2
 
3.5%
9 2
 
3.5%
5 2
 
3.5%
~ 2
 
3.5%
. 1
 
1.8%
0 1
 
1.8%
Other values (2) 2
 
3.5%
Latin
ValueCountFrequency (%)
N 1
33.3%
E 1
33.3%
M 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14554
99.6%
ASCII 60
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 1578
 
10.8%
제 1459
 
10.0%
설 1377
 
9.5%
건 1365
 
9.4%
조 1290
 
8.9%
합 1285
 
8.8%
보 696
 
4.8%
서 497
 
3.4%
지 491
 
3.4%
어 485
 
3.3%
Other values (77) 4031
27.7%
ASCII
ValueCountFrequency (%)
) 18
30.0%
( 18
30.0%
6
 
10.0%
1 3
 
5.0%
2 2
 
3.3%
9 2
 
3.3%
5 2
 
3.3%
~ 2
 
3.3%
. 1
 
1.7%
0 1
 
1.7%
Other values (5) 5
 
8.3%

Interactions

2023-12-11T09:14:15.387717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:12.631809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.115616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.733612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.181770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.808199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:15.470027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:12.702515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.188279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.803737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.261519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.892113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:15.564561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:12.791963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.259805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.875822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.342899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.985808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:15.644092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:12.873532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.522684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.954617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.426288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:15.089287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:15.731109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:12.971881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.596746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.044255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.517240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:15.190450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:15.819975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.042713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:13.662045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.112463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:14.699340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:14:15.287923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:14:20.780722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사구분공사번호순번하자기간보증율보증금액납부방법보험회사
공사년도1.0000.4120.6740.3440.5060.1760.0560.7530.737
공사구분0.4121.0000.4860.0780.7850.4130.0920.4390.867
공사번호0.6740.4861.0000.5630.4960.3640.1760.4300.710
순번0.3440.0780.5631.0000.0730.0000.0000.0000.147
하자기간0.5060.7850.4960.0731.0000.3150.0570.2660.655
보증율0.1760.4130.3640.0000.3151.0000.6650.0000.467
보증금액0.0560.0920.1760.0000.0570.6651.0000.0000.000
납부방법0.7530.4390.4300.0000.2660.0000.0001.0000.960
보험회사0.7370.8670.7100.1470.6550.4670.0000.9601.000
2023-12-11T09:14:20.883472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사번호순번하자기간보증율보증금액공사구분
공사년도1.0000.3770.207-0.164-0.144-0.1850.260
공사번호0.3771.0000.228-0.054-0.088-0.0480.311
순번0.2070.2281.0000.103-0.2060.2630.047
하자기간-0.164-0.0540.1031.0000.3600.3810.577
보증율-0.144-0.088-0.2060.3601.0000.4470.276
보증금액-0.185-0.0480.2630.3810.4471.0000.076
공사구분0.2600.3110.0470.5770.2760.0761.000

Missing values

2023-12-11T09:14:15.953183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:14:16.131552image/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.
2023-12-11T09:14:16.263944image/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

공사년도공사구분공사번호부서코드순번하자기간시작일마감일보증율보증금액납부방법보험회사
01990공사5112.01990-05-211992-07-203.02475000기타<NA>
11990공사2113.01990-12-221994-02-213.014193960하천<NA>
21990공사1113.01990-12-221994-02-213.010686000하천<NA>
31990공사4113.01990-12-261994-02-253.010980000하천<NA>
41990공사3113.01991-06-051994-08-033.046936620하천<NA>
51990공사6112.01990-06-261992-08-242.04423320기타<NA>
61990공사7113.01991-03-121994-05-103.021173100하천<NA>
71990공사8113.01991-10-101994-10-093.017334900하천<NA>
81990공사10112.01991-07-051993-09-033.043330000기타<NA>
91990공사9113.01990-11-021994-01-013.04650000하천<NA>
공사년도공사구분공사번호부서코드순번하자기간시작일마감일보증율보증금액납부방법보험회사
46912018공사36192.02017-09-102019-09-096.7236371500공제조합건설공제조합
46922018공사361107.02019-05-102026-05-090.134706800공제조합건설공제조합
46932018공사361112.02019-05-102021-05-090.051814250공제조합건설공제조합
46942018공사361122.02019-05-102021-05-093.33117451470공제조합건설공제조합
46952018공사361137.02019-05-102026-05-090.134477200공제조합건설공제조합
46962018공사361142.02019-05-102021-05-090.051725750공제조합건설공제조합
46972018공사361152.02019-05-102021-05-093.17111722130공제조합건설공제조합
46982018공사361167.02019-05-102026-05-090.072296000공제조합건설공제조합
46992018공사361172.02019-05-102021-05-090.03885000공제조합건설공제조합
47002012공사181112.02013-03-162015-03-152.02695033공제조합정보통신공제조합