Overview

Dataset statistics

Number of variables6
Number of observations499
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.0 KiB
Average record size in memory51.3 B

Variable types

Categorical2
Numeric3
DateTime1

Dataset

Description한국주택금융공사 주택연금부 업무 보증번호 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072883/fileData.do

Alerts

REG_ENO is highly overall correlated with BRCDHigh correlation
BRCD is highly overall correlated with REG_ENOHigh correlation
REG_TS has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:07:27.386303
Analysis finished2023-12-12 04:07:29.318548
Duration1.93 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SYS_DVCD
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
R
258 
K
241 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK
2nd rowK
3rd rowR
4th rowK
5th rowR

Common Values

ValueCountFrequency (%)
R 258
51.7%
K 241
48.3%

Length

2023-12-12T13:07:29.430427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:07:29.594564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
r 258
51.7%
k 241
48.3%

BRCD
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
TAA
 
28
TBA
 
28
TNA
 
28
TJA
 
28
TMA
 
28
Other values (23)
359 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTMB
2nd rowTQC
3rd rowTQC
4th rowTOB
5th rowABN

Common Values

ValueCountFrequency (%)
TAA 28
 
5.6%
TBA 28
 
5.6%
TNA 28
 
5.6%
TJA 28
 
5.6%
TMA 28
 
5.6%
THA 28
 
5.6%
QAD 28
 
5.6%
TPA 28
 
5.6%
THO 27
 
5.4%
TRA 26
 
5.2%
Other values (18) 222
44.5%

Length

2023-12-12T13:07:29.741796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
taa 28
 
5.6%
tba 28
 
5.6%
tna 28
 
5.6%
tja 28
 
5.6%
tma 28
 
5.6%
tha 28
 
5.6%
qad 28
 
5.6%
tpa 28
 
5.6%
tho 27
 
5.4%
tra 26
 
5.2%
Other values (18) 222
44.5%

ACPT_YR
Real number (ℝ)

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.5411
Minimum2007
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T13:07:29.899723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2008
Q12011
median2015
Q32018
95-th percentile2020
Maximum2020
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.8843491
Coefficient of variation (CV)0.0019281558
Kurtosis-1.0330498
Mean2014.5411
Median Absolute Deviation (MAD)3
Skewness-0.30155056
Sum1005256
Variance15.088168
MonotonicityNot monotonic
2023-12-12T13:07:30.074640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2020 51
10.2%
2019 50
10.0%
2016 43
8.6%
2018 42
8.4%
2017 41
8.2%
2015 41
8.2%
2013 38
 
7.6%
2014 37
 
7.4%
2012 30
 
6.0%
2010 30
 
6.0%
Other values (4) 96
19.2%
ValueCountFrequency (%)
2007 21
4.2%
2008 22
4.4%
2009 24
4.8%
2010 30
6.0%
2011 29
5.8%
2012 30
6.0%
2013 38
7.6%
2014 37
7.4%
2015 41
8.2%
2016 43
8.6%
ValueCountFrequency (%)
2020 51
10.2%
2019 50
10.0%
2018 42
8.4%
2017 41
8.2%
2016 43
8.6%
2015 41
8.2%
2014 37
7.4%
2013 38
7.6%
2012 30
6.0%
2011 29
5.8%

SEQ
Real number (ℝ)

Distinct317
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.05611
Minimum1
Maximum1249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T13:07:30.239767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q140
median128
Q3351.5
95-th percentile931.6
Maximum1249
Range1248
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation291.09767
Coefficient of variation (CV)1.1548923
Kurtosis1.2770843
Mean252.05611
Median Absolute Deviation (MAD)104
Skewness1.4678679
Sum125776
Variance84737.852
MonotonicityNot monotonic
2023-12-12T13:07:30.525470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
1.8%
6 7
 
1.4%
27 7
 
1.4%
33 7
 
1.4%
2 6
 
1.2%
3 5
 
1.0%
9 5
 
1.0%
21 5
 
1.0%
45 5
 
1.0%
60 5
 
1.0%
Other values (307) 438
87.8%
ValueCountFrequency (%)
1 9
1.8%
2 6
1.2%
3 5
1.0%
4 4
0.8%
6 7
1.4%
7 2
 
0.4%
8 2
 
0.4%
9 5
1.0%
10 3
 
0.6%
12 1
 
0.2%
ValueCountFrequency (%)
1249 1
0.2%
1215 1
0.2%
1214 1
0.2%
1189 1
0.2%
1121 1
0.2%
1113 1
0.2%
1107 1
0.2%
1101 1
0.2%
1088 1
0.2%
1055 1
0.2%

REG_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct179
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5689.3988
Minimum1003
Maximum52046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T13:07:30.766403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile1151.6
Q11660
median7311
Q37510
95-th percentile7567
Maximum52046
Range51043
Interquartile range (IQR)5850

Descriptive statistics

Standard deviation3964.1186
Coefficient of variation (CV)0.69675527
Kurtosis73.004707
Mean5689.3988
Median Absolute Deviation (MAD)256
Skewness6.0975173
Sum2839010
Variance15714236
MonotonicityNot monotonic
2023-12-12T13:07:30.963032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7510 16
 
3.2%
7462 11
 
2.2%
7457 10
 
2.0%
7564 10
 
2.0%
7486 10
 
2.0%
7313 9
 
1.8%
7311 9
 
1.8%
7538 9
 
1.8%
7375 8
 
1.6%
7536 8
 
1.6%
Other values (169) 399
80.0%
ValueCountFrequency (%)
1003 1
0.2%
1009 1
0.2%
1053 1
0.2%
1071 1
0.2%
1088 2
0.4%
1095 1
0.2%
1096 2
0.4%
1104 1
0.2%
1115 1
0.2%
1119 1
0.2%
ValueCountFrequency (%)
52046 1
 
0.2%
51915 1
 
0.2%
7645 1
 
0.2%
7621 2
 
0.4%
7620 1
 
0.2%
7592 2
 
0.4%
7589 5
1.0%
7588 2
 
0.4%
7586 1
 
0.2%
7583 2
 
0.4%

REG_TS
Date

UNIQUE 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2007-07-12 12:53:04
Maximum2020-04-17 09:11:01
2023-12-12T13:07:31.162634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:31.399735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T13:07:28.705040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:27.988199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:28.358533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:28.811856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:28.094331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:28.475233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:28.922022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:28.235202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:28.608560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:07:31.571788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SYS_DVCDBRCDACPT_YRSEQREG_ENO
SYS_DVCD1.0000.0000.0000.4820.000
BRCD0.0001.0000.0000.5220.921
ACPT_YR0.0000.0001.0000.2010.395
SEQ0.4820.5220.2011.0000.319
REG_ENO0.0000.9210.3950.3191.000
2023-12-12T13:07:31.732655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRCDSYS_DVCD
BRCD1.0000.000
SYS_DVCD0.0001.000
2023-12-12T13:07:31.853971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACPT_YRSEQREG_ENOSYS_DVCDBRCD
ACPT_YR1.0000.3880.3580.0000.000
SEQ0.3881.0000.3960.3680.211
REG_ENO0.3580.3961.0000.0000.771
SYS_DVCD0.0000.3680.0001.0000.000
BRCD0.0000.2110.7710.0001.000

Missing values

2023-12-12T13:07:29.104971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:07:29.244770image/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

SYS_DVCDBRCDACPT_YRSEQREG_ENOREG_TS
0KTMB2020315572020/04/17 09:11:01
1KTQC20202619122020/04/13 12:14:04
2RTQC20203819122020/04/13 12:10:04
3KTOB20202461452020/02/04 09:22:12
4RABN2020918252020/02/03 16:51:22
5KTLB20204761042020/01/13 13:15:26
6KTBB20205861362020/01/09 15:51:00
7KTPB202010775102020/01/08 16:11:48
8RTMB20202761832020/01/08 14:30:29
9KTRA20206261062020/01/08 11:11:19
SYS_DVCDBRCDACPT_YRSEQREG_ENOREG_TS
489KTNA2009614342009/04/08 16:17:34
490KTAB201111773132011/01/06 11:18:39
491KTNA20103313292010/01/28 16:04:05
492RQAD201056373132010/01/04 09:56:37
493KTHA20093513752009/02/27 14:00:13
494KTHA20115173832011/01/06 17:49:45
495KTJA20092873082009/01/16 15:17:28
496KTRA2010311852010/04/28 15:49:57
497RTMA20094573442009/01/07 16:32:22
498KQAD20086573132008/01/14 14:45:53