Overview

Dataset statistics

Number of variables7
Number of observations37
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory64.6 B

Variable types

Categorical2
Numeric3
DateTime2

Dataset

Description한국주택금융공사 주택연금부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터) 수납구분코드,적용시작일자,등록사번,수정사번 칼럼과 관련값이 포함되어있습니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072844/fileData.do

Alerts

APPLY_END_DY has constant value ""Constant
APPLY_STRT_DY is highly overall correlated with REG_ENO and 2 other fieldsHigh correlation
REG_ENO is highly overall correlated with APPLY_STRT_DY and 2 other fieldsHigh correlation
UPDT_ENO is highly overall correlated with APPLY_STRT_DY and 1 other fieldsHigh correlation
RECEIPT_DVCD is highly overall correlated with APPLY_STRT_DY and 1 other fieldsHigh correlation
REG_TS has unique valuesUnique
UPDT_TS has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:35:18.861775
Analysis finished2023-12-12 22:35:19.958361
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RECEIPT_DVCD
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size428.0 B
1
18 
3
13 
2
6 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 18
48.6%
3 13
35.1%
2 6
 
16.2%

Length

2023-12-13T07:35:20.052148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:35:20.191272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 18
48.6%
3 13
35.1%
2 6
 
16.2%

APPLY_STRT_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20130669
Minimum20070720
Maximum20190625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-13T07:35:20.282467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070720
5-th percentile20070720
Q120090714
median20140708
Q320170210
95-th percentile20190159
Maximum20190625
Range119905
Interquartile range (IQR)79496

Descriptive statistics

Standard deviation42908.473
Coefficient of variation (CV)0.0021314977
Kurtosis-1.5033191
Mean20130669
Median Absolute Deviation (MAD)30009
Skewness-0.21338245
Sum7.4483474 × 108
Variance1.8411371 × 109
MonotonicityNot monotonic
2023-12-13T07:35:20.396104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20070720 8
21.6%
20160712 3
 
8.1%
20160727 2
 
5.4%
20160719 2
 
5.4%
20190329 1
 
2.7%
20170717 1
 
2.7%
20100929 1
 
2.7%
20120604 1
 
2.7%
20130117 1
 
2.7%
20140925 1
 
2.7%
Other values (16) 16
43.2%
ValueCountFrequency (%)
20070720 8
21.6%
20090629 1
 
2.7%
20090714 1
 
2.7%
20090922 1
 
2.7%
20100614 1
 
2.7%
20100929 1
 
2.7%
20111010 1
 
2.7%
20120110 1
 
2.7%
20120604 1
 
2.7%
20120608 1
 
2.7%
ValueCountFrequency (%)
20190625 1
2.7%
20190329 1
2.7%
20190117 1
2.7%
20181214 1
2.7%
20181203 1
2.7%
20170717 1
2.7%
20170628 1
2.7%
20170608 1
2.7%
20170411 1
2.7%
20170210 1
2.7%

APPLY_END_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size428.0 B
99991231
37 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99991231
2nd row99991231
3rd row99991231
4th row99991231
5th row99991231

Common Values

ValueCountFrequency (%)
99991231 37
100.0%

Length

2023-12-13T07:35:20.501216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:35:20.580688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99991231 37
100.0%

REG_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1379.2162
Minimum1200
Maximum1704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-13T07:35:20.649467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1200
Q11202
median1398
Q31518
95-th percentile1704
Maximum1704
Range504
Interquartile range (IQR)316

Descriptive statistics

Standard deviation165.28765
Coefficient of variation (CV)0.11984173
Kurtosis-0.79032084
Mean1379.2162
Median Absolute Deviation (MAD)135
Skewness0.42485313
Sum51031
Variance27320.008
MonotonicityNot monotonic
2023-12-13T07:35:20.751498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1419 8
21.6%
1202 8
21.6%
1200 6
16.2%
1533 4
10.8%
1704 3
 
8.1%
1377 3
 
8.1%
1518 2
 
5.4%
1398 2
 
5.4%
1656 1
 
2.7%
ValueCountFrequency (%)
1200 6
16.2%
1202 8
21.6%
1377 3
 
8.1%
1398 2
 
5.4%
1419 8
21.6%
1518 2
 
5.4%
1533 4
10.8%
1656 1
 
2.7%
1704 3
 
8.1%
ValueCountFrequency (%)
1704 3
 
8.1%
1656 1
 
2.7%
1533 4
10.8%
1518 2
 
5.4%
1419 8
21.6%
1398 2
 
5.4%
1377 3
 
8.1%
1202 8
21.6%
1200 6
16.2%

REG_TS
Date

UNIQUE 

Distinct37
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size428.0 B
Minimum2007-07-20 10:42:16
Maximum2019-03-28 13:32:25
2023-12-13T07:35:20.844350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:20.959876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)

UPDT_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1405.7568
Minimum1200
Maximum1770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-13T07:35:21.075833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1200
Q11202
median1419
Q31519
95-th percentile1704
Maximum1770
Range570
Interquartile range (IQR)317

Descriptive statistics

Standard deviation173.33853
Coefficient of variation (CV)0.1233062
Kurtosis-0.80483354
Mean1405.7568
Median Absolute Deviation (MAD)114
Skewness0.30419173
Sum52013
Variance30046.245
MonotonicityNot monotonic
2023-12-13T07:35:21.171638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1419 7
18.9%
1202 7
18.9%
1200 5
13.5%
1533 4
10.8%
1704 3
8.1%
1518 3
8.1%
1377 3
8.1%
1398 2
 
5.4%
1519 1
 
2.7%
1770 1
 
2.7%
ValueCountFrequency (%)
1200 5
13.5%
1202 7
18.9%
1377 3
8.1%
1398 2
 
5.4%
1419 7
18.9%
1518 3
8.1%
1519 1
 
2.7%
1533 4
10.8%
1652 1
 
2.7%
1704 3
8.1%
ValueCountFrequency (%)
1770 1
 
2.7%
1704 3
8.1%
1652 1
 
2.7%
1533 4
10.8%
1519 1
 
2.7%
1518 3
8.1%
1419 7
18.9%
1398 2
 
5.4%
1377 3
8.1%
1202 7
18.9%

UPDT_TS
Date

UNIQUE 

Distinct37
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size428.0 B
Minimum2007-07-20 10:42:16
Maximum2019-06-25 13:17:46
2023-12-13T07:35:21.273503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:21.378092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)

Interactions

2023-12-13T07:35:19.559106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:19.066281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:19.317446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:19.635157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:19.140446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:19.410854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:19.708489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:19.219362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:19.487409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:35:21.455227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RECEIPT_DVCDAPPLY_STRT_DYREG_ENOREG_TSUPDT_ENOUPDT_TS
RECEIPT_DVCD1.0000.7620.6811.0000.8931.000
APPLY_STRT_DY0.7621.0000.9501.0000.6641.000
REG_ENO0.6810.9501.0001.0000.9691.000
REG_TS1.0001.0001.0001.0001.0001.000
UPDT_ENO0.8930.6640.9691.0001.0001.000
UPDT_TS1.0001.0001.0001.0001.0001.000
2023-12-13T07:35:21.541848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
APPLY_STRT_DYREG_ENOUPDT_ENORECEIPT_DVCD
APPLY_STRT_DY1.0000.6880.7780.661
REG_ENO0.6881.0000.8270.611
UPDT_ENO0.7780.8271.0000.447
RECEIPT_DVCD0.6610.6110.4471.000

Missing values

2023-12-13T07:35:19.804071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:35:19.917100image/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

RECEIPT_DVCDAPPLY_STRT_DYAPPLY_END_DYREG_ENOREG_TSUPDT_ENOUPDT_TS
01201903299999123115332019/03/28 13:32:2515332019/03/28 13:32:25
13201901179999123115332019/01/17 17:36:2415332019/01/17 17:36:24
23201812149999123115332018/12/14 09:23:2215332018/12/14 09:23:22
31201812039999123115332018/11/28 15:14:3315332018/11/28 15:14:33
42201706289999123116562017/06/28 15:01:5715192018/05/17 10:54:41
53201706089999123117042017/06/08 09:42:3417042017/06/08 09:42:34
63201704119999123117042017/04/11 09:34:5717042017/04/11 09:34:57
73201702109999123117042017/02/10 14:05:2817042017/02/10 14:05:28
83201607279999123114192016/07/27 15:20:3914192016/07/27 15:20:39
93201607279999123114192016/07/27 14:47:3114192016/07/27 14:47:31
RECEIPT_DVCDAPPLY_STRT_DYAPPLY_END_DYREG_ENOREG_TSUPDT_ENOUPDT_TS
271200707209999123112022007/07/20 10:45:4612022007/07/20 10:45:46
281200707209999123112022007/07/20 10:46:2612022007/07/20 10:46:26
291200707209999123112022007/07/20 10:42:1612022007/07/20 10:42:16
301200707209999123112022007/07/20 10:47:1212022007/07/20 10:47:12
311200707209999123112022007/07/20 10:44:5812022007/07/20 10:44:58
321200707209999123112022007/07/20 10:47:4915182014/06/02 17:41:30
331201409259999123115182014/10/15 16:41:3115182014/10/15 16:41:31
341201301179999123112002013/01/17 15:21:4512002013/01/17 15:21:45
352201206049999123112002008/09/19 10:46:2112002012/06/04 16:19:08
361201009299999123113982010/09/15 16:49:3913982010/09/15 16:49:39