Overview

Dataset statistics

Number of variables5
Number of observations299
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.7 KiB
Average record size in memory43.4 B

Variable types

Numeric1
Categorical2
Text1
DateTime1

Dataset

Description한국주택금융공사 채권관리부 업무 관련 공개 데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072829/fileData.do

Alerts

EXEC_PTTN_SEQ has constant value ""Constant

Reproduction

Analysis started2023-12-12 20:46:35.159383
Analysis finished2023-12-12 20:46:35.612973
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ACPT_PTNO
Real number (ℝ)

Distinct263
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0175651 × 1010
Minimum2.01004 × 1010
Maximum2.02004 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T05:46:35.714331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.01004 × 1010
5-th percentile2.01304 × 1010
Q12.01604 × 1010
median2.01804 × 1010
Q32.01904 × 1010
95-th percentile2.02004 × 1010
Maximum2.02004 × 1010
Range1.0000009 × 108
Interquartile range (IQR)30000046

Descriptive statistics

Standard deviation21430105
Coefficient of variation (CV)0.0010621767
Kurtosis-0.019365519
Mean2.0175651 × 1010
Median Absolute Deviation (MAD)19999967
Skewness-0.77718558
Sum6.0325196 × 1012
Variance4.5924942 × 1014
MonotonicityNot monotonic
2023-12-13T05:46:35.885033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160400021 2
 
0.7%
20190400062 2
 
0.7%
20160400001 2
 
0.7%
20190400069 2
 
0.7%
20200400043 2
 
0.7%
20140400024 2
 
0.7%
20200400041 2
 
0.7%
20160400048 2
 
0.7%
20190400020 2
 
0.7%
20190400078 2
 
0.7%
Other values (253) 279
93.3%
ValueCountFrequency (%)
20100400002 1
0.3%
20110400003 1
0.3%
20120400001 1
0.3%
20120400002 1
0.3%
20120400006 1
0.3%
20130400002 1
0.3%
20130400003 1
0.3%
20130400006 2
0.7%
20130400013 1
0.3%
20130400014 1
0.3%
ValueCountFrequency (%)
20200400095 1
0.3%
20200400093 1
0.3%
20200400087 1
0.3%
20200400086 1
0.3%
20200400085 1
0.3%
20200400081 1
0.3%
20200400078 1
0.3%
20200400077 1
0.3%
20200400076 1
0.3%
20200400075 1
0.3%

EXEC_PTTN_SEQ
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
299 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 299
100.0%

Length

2023-12-13T05:46:36.073058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:46:36.183815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 299
100.0%

GUARNT_NO
Text

Distinct263
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-13T05:46:36.383692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters4186
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)75.9%

Sample

1st rowRTHO2010000012
2nd rowRTPA2013000119
3rd rowRTBA2017000811
4th rowRTAD2015000058
5th rowRTBA2017000897
ValueCountFrequency (%)
rtba2013000018 2
 
0.7%
rtaa2009000027 2
 
0.7%
rtpa2013000234 2
 
0.7%
rtab2011000133 2
 
0.7%
rtab2013000420 2
 
0.7%
rtaa2007000025 2
 
0.7%
rtma2011000031 2
 
0.7%
rqad2010000409 2
 
0.7%
rtqa2014000136 2
 
0.7%
rtpa2018000574 2
 
0.7%
Other values (253) 279
93.3%
2023-12-13T05:46:36.802371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1461
34.9%
2 444
 
10.6%
1 412
 
9.8%
R 299
 
7.1%
A 282
 
6.7%
T 254
 
6.1%
3 146
 
3.5%
4 110
 
2.6%
5 93
 
2.2%
7 88
 
2.1%
Other values (14) 597
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2990
71.4%
Uppercase Letter 1196
 
28.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 299
25.0%
A 282
23.6%
T 254
21.2%
B 87
 
7.3%
H 63
 
5.3%
Q 55
 
4.6%
D 49
 
4.1%
O 48
 
4.0%
P 19
 
1.6%
L 14
 
1.2%
Other values (4) 26
 
2.2%
Decimal Number
ValueCountFrequency (%)
0 1461
48.9%
2 444
 
14.8%
1 412
 
13.8%
3 146
 
4.9%
4 110
 
3.7%
5 93
 
3.1%
7 88
 
2.9%
6 85
 
2.8%
9 81
 
2.7%
8 70
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2990
71.4%
Latin 1196
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 299
25.0%
A 282
23.6%
T 254
21.2%
B 87
 
7.3%
H 63
 
5.3%
Q 55
 
4.6%
D 49
 
4.1%
O 48
 
4.0%
P 19
 
1.6%
L 14
 
1.2%
Other values (4) 26
 
2.2%
Common
ValueCountFrequency (%)
0 1461
48.9%
2 444
 
14.8%
1 412
 
13.8%
3 146
 
4.9%
4 110
 
3.7%
5 93
 
3.1%
7 88
 
2.9%
6 85
 
2.8%
9 81
 
2.7%
8 70
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1461
34.9%
2 444
 
10.6%
1 412
 
9.8%
R 299
 
7.1%
A 282
 
6.7%
T 254
 
6.1%
3 146
 
3.5%
4 110
 
2.6%
5 93
 
2.2%
7 88
 
2.1%
Other values (14) 597
14.3%

INT_CONT_SEQ
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
263 
1
36 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 263
88.0%
1 36
 
12.0%

Length

2023-12-13T05:46:36.957219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:46:37.082409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 263
88.0%
1 36
 
12.0%

REG_TS
Date

Distinct263
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2010-04-05 10:01:47
Maximum2020-10-15 14:37:47
2023-12-13T05:46:37.261330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:46:37.427674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T05:46:35.295222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:46:37.542968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACPT_PTNOINT_CONT_SEQ
ACPT_PTNO1.0000.000
INT_CONT_SEQ0.0001.000
2023-12-13T05:46:37.642940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACPT_PTNOINT_CONT_SEQ
ACPT_PTNO1.0000.000
INT_CONT_SEQ0.0001.000

Missing values

2023-12-13T05:46:35.435856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:46:35.550960image/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

ACPT_PTNOEXEC_PTTN_SEQGUARNT_NOINT_CONT_SEQREG_TS
0202004000950RTHO201000001202020/10/15 14:37:47
1202004000930RTPA201300011902020/10/08 09:28:38
2202004000850RTBA201700081102020/09/07 10:58:51
3202004000860RTAD201500005802020/09/03 14:18:55
4202004000810RTBA201700089702020/09/02 09:58:38
5202004000870RTHA201700035002020/09/10 13:10:44
6202004000740RQAD201600059902020/08/26 13:05:06
7202004000780RTAA201100038402020/08/25 13:25:17
8202004000750RTLA201300002502020/08/20 10:38:59
9202004000760RTLA201400001402020/08/20 10:31:54
ACPT_PTNOEXEC_PTTN_SEQGUARNT_NOINT_CONT_SEQREG_TS
289201304000140RQAD200700029002013/06/19 14:09:26
290201304000130RTPA201200006202013/05/30 11:26:02
291201304000060RTNA200700001302013/03/13 10:46:04
292201304000030RTOA200700001202013/02/07 13:50:42
293201304000020RTOA201000002502013/02/08 13:26:18
294201204000060RTLA201000003602012/10/31 14:00:02
295201204000010RTAA200800008702012/02/15 14:20:17
296201104000030RQAD201000041702011/11/24 11:13:16
297201204000020RQAD200900029702012/06/08 10:27:16
298201004000020RTHO200800003902010/04/05 10:01:47