Overview

Dataset statistics

Number of variables9
Number of observations36
Missing cells36
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory79.7 B

Variable types

Categorical5
Unsupported1
Numeric2
DateTime1

Dataset

Description한국주택금융공사의 공탁계좌에 대한 데이터로, 공탁순번과 유동화계획코드에 대한 정보를 포함하고 있습니다. 공공데이터 개방 정책에 따라 공개됩니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073228/fileData.do

Alerts

HOLD_CD is highly overall correlated with REG_ENO and 2 other fieldsHigh correlation
LIQD_PLAN_CD is highly overall correlated with REG_ENO and 2 other fieldsHigh correlation
SLIP_NO is highly overall correlated with REG_BRCDHigh correlation
REG_ENO is highly overall correlated with LIQD_PLAN_CD and 2 other fieldsHigh correlation
TREAT_ORG_CD is highly overall correlated with LIQD_PLAN_CD and 1 other fieldsHigh correlation
REG_BRCD is highly overall correlated with SLIP_NO and 1 other fieldsHigh correlation
DEPOSIT_AMT_CD has 36 (100.0%) missing valuesMissing
REG_DT has unique valuesUnique
DEPOSIT_AMT_CD is an unsupported type, check if it needs cleaning or further analysisUnsupported
SLIP_NO has 14 (38.9%) zerosZeros

Reproduction

Analysis started2023-12-12 10:19:32.124356
Analysis finished2023-12-12 10:19:33.204240
Duration1.08 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DPOSIT_SEQ
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
1
25 
2
7 
3
 
2
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 25
69.4%
2 7
 
19.4%
3 2
 
5.6%
4 2
 
5.6%

Length

2023-12-12T19:19:33.299097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:19:33.445663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 25
69.4%
2 7
 
19.4%
3 2
 
5.6%
4 2
 
5.6%

LIQD_PLAN_CD
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
KHFCMB2005S-06
6 
KHFCMB2004S-07
5 
KHFCMB2005S-05
4 
KHFCMB2005S-03
3 
KHFCMB2006S-01
3 
Other values (9)
15 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique3 ?
Unique (%)8.3%

Sample

1st rowKHFCMB2005S-06
2nd rowKHFCMB2005S-03
3rd rowKHFCMB2005S-03
4th rowKHFCMB2006S-01
5th rowKHFCMB2005S-05

Common Values

ValueCountFrequency (%)
KHFCMB2005S-06 6
16.7%
KHFCMB2004S-07 5
13.9%
KHFCMB2005S-05 4
11.1%
KHFCMB2005S-03 3
8.3%
KHFCMB2006S-01 3
8.3%
KHFCMB2005S-09 2
 
5.6%
KHFCMB2005S-07 2
 
5.6%
KHFCMB2004S-01 2
 
5.6%
KHFCMB2007S-04 2
 
5.6%
KHFCMB2005S-01 2
 
5.6%
Other values (4) 5
13.9%

Length

2023-12-12T19:19:33.614977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
khfcmb2005s-06 6
16.7%
khfcmb2004s-07 5
13.9%
khfcmb2005s-05 4
11.1%
khfcmb2005s-03 3
8.3%
khfcmb2006s-01 3
8.3%
khfcmb2005s-09 2
 
5.6%
khfcmb2005s-07 2
 
5.6%
khfcmb2004s-01 2
 
5.6%
khfcmb2007s-04 2
 
5.6%
khfcmb2005s-01 2
 
5.6%
Other values (4) 5
13.9%

HOLD_CD
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size420.0 B
B005-2005-0005
6 
B005-2004-0005
5 
B005-2005-0004
4 
B005-2005-0003
3 
B005-2005-0009
2 
Other values (11)
16 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique6 ?
Unique (%)16.7%

Sample

1st rowB005-2005-0005
2nd rowB005-2005-0003
3rd rowB005-2005-0003
4th rowB021-2005-0005
5th rowB005-2005-0004

Common Values

ValueCountFrequency (%)
B005-2005-0005 6
16.7%
B005-2004-0005 5
13.9%
B005-2005-0004 4
11.1%
B005-2005-0003 3
8.3%
B005-2005-0009 2
 
5.6%
B005-2005-0010 2
 
5.6%
B081-2004-0001 2
 
5.6%
B020-2007-0006 2
 
5.6%
B005-2005-0001 2
 
5.6%
C001-2006-0001 2
 
5.6%
Other values (6) 6
16.7%

Length

2023-12-12T19:19:33.755167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b005-2005-0005 6
16.7%
b005-2004-0005 5
13.9%
b005-2005-0004 4
11.1%
b005-2005-0003 3
8.3%
b005-2005-0009 2
 
5.6%
b005-2005-0010 2
 
5.6%
b081-2004-0001 2
 
5.6%
b020-2007-0006 2
 
5.6%
b005-2005-0001 2
 
5.6%
c001-2006-0001 2
 
5.6%
Other values (6) 6
16.7%

TREAT_ORG_CD
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size420.0 B
B005
25 
C001
4 
B081
3 
B020
 
2
B088
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)5.6%

Sample

1st rowB005
2nd rowB005
3rd rowB005
4th rowB088
5th rowB005

Common Values

ValueCountFrequency (%)
B005 25
69.4%
C001 4
 
11.1%
B081 3
 
8.3%
B020 2
 
5.6%
B088 1
 
2.8%
B004 1
 
2.8%

Length

2023-12-12T19:19:33.902007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:19:34.086043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b005 25
69.4%
c001 4
 
11.1%
b081 3
 
8.3%
b020 2
 
5.6%
b088 1
 
2.8%
b004 1
 
2.8%

DEPOSIT_AMT_CD
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

SLIP_NO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.72222
Minimum0
Maximum503
Zeros14
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T19:19:34.271541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median57.5
Q3271.75
95-th percentile430.25
Maximum503
Range503
Interquartile range (IQR)271.75

Descriptive statistics

Standard deviation163.50266
Coefficient of variation (CV)1.2227037
Kurtosis-0.81695579
Mean133.72222
Median Absolute Deviation (MAD)57.5
Skewness0.84301412
Sum4814
Variance26733.121
MonotonicityNot monotonic
2023-12-12T19:19:34.445316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 14
38.9%
369 1
 
2.8%
76 1
 
2.8%
368 1
 
2.8%
260 1
 
2.8%
327 1
 
2.8%
42 1
 
2.8%
12 1
 
2.8%
431 1
 
2.8%
348 1
 
2.8%
Other values (13) 13
36.1%
ValueCountFrequency (%)
0 14
38.9%
6 1
 
2.8%
12 1
 
2.8%
13 1
 
2.8%
42 1
 
2.8%
73 1
 
2.8%
75 1
 
2.8%
76 1
 
2.8%
83 1
 
2.8%
84 1
 
2.8%
ValueCountFrequency (%)
503 1
2.8%
431 1
2.8%
430 1
2.8%
369 1
2.8%
368 1
2.8%
348 1
2.8%
328 1
2.8%
327 1
2.8%
307 1
2.8%
260 1
2.8%

REG_BRCD
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
ACS
12 
BBC
11 
AAZ
6 
TAA
2 
TMA
2 
Other values (3)
3 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique3 ?
Unique (%)8.3%

Sample

1st rowTAA
2nd rowTMA
3rd rowBBC
4th rowACS
5th rowBBC

Common Values

ValueCountFrequency (%)
ACS 12
33.3%
BBC 11
30.6%
AAZ 6
16.7%
TAA 2
 
5.6%
TMA 2
 
5.6%
THA 1
 
2.8%
THO 1
 
2.8%
QAD 1
 
2.8%

Length

2023-12-12T19:19:34.624468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:19:34.784232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
acs 12
33.3%
bbc 11
30.6%
aaz 6
16.7%
taa 2
 
5.6%
tma 2
 
5.6%
tha 1
 
2.8%
tho 1
 
2.8%
qad 1
 
2.8%

REG_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1273.25
Minimum1095
Maximum1444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T19:19:34.958189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1095
5-th percentile1114.5
Q11179
median1253.5
Q31343
95-th percentile1401.5
Maximum1444
Range349
Interquartile range (IQR)164

Descriptive statistics

Standard deviation94.50907
Coefficient of variation (CV)0.07422664
Kurtosis-0.94850688
Mean1273.25
Median Absolute Deviation (MAD)83.5
Skewness-0.12303289
Sum45837
Variance8931.9643
MonotonicityNot monotonic
2023-12-12T19:19:35.123141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1179 6
16.7%
1337 5
13.9%
1343 4
11.1%
1394 3
8.3%
1253 3
8.3%
1365 2
 
5.6%
1251 2
 
5.6%
1254 2
 
5.6%
1095 2
 
5.6%
1121 1
 
2.8%
Other values (6) 6
16.7%
ValueCountFrequency (%)
1095 2
 
5.6%
1121 1
 
2.8%
1174 1
 
2.8%
1179 6
16.7%
1212 1
 
2.8%
1225 1
 
2.8%
1235 1
 
2.8%
1251 2
 
5.6%
1253 3
8.3%
1254 2
 
5.6%
ValueCountFrequency (%)
1444 1
 
2.8%
1424 1
 
2.8%
1394 3
8.3%
1365 2
 
5.6%
1343 4
11.1%
1337 5
13.9%
1254 2
 
5.6%
1253 3
8.3%
1251 2
 
5.6%
1235 1
 
2.8%

REG_DT
Date

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
Minimum2007-01-23 10:54:38
Maximum2009-05-19 10:35:12
2023-12-12T19:19:35.288930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:35.460036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)

Interactions

2023-12-12T19:19:32.692621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:32.516990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:32.834984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:32.601352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:19:35.603647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPOSIT_SEQLIQD_PLAN_CDHOLD_CDTREAT_ORG_CDSLIP_NOREG_BRCDREG_ENOREG_DT
DPOSIT_SEQ1.0000.0000.0000.0000.4890.0000.0001.000
LIQD_PLAN_CD0.0001.0001.0000.9510.0000.7300.9241.000
HOLD_CD0.0001.0001.0001.0000.2850.8780.9821.000
TREAT_ORG_CD0.0000.9511.0001.0000.0000.0000.6361.000
SLIP_NO0.4890.0000.2850.0001.0000.9390.6271.000
REG_BRCD0.0000.7300.8780.0000.9391.0000.7811.000
REG_ENO0.0000.9240.9820.6360.6270.7811.0001.000
REG_DT1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T19:19:36.061526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HOLD_CDDPOSIT_SEQLIQD_PLAN_CDTREAT_ORG_CDREG_BRCD
HOLD_CD1.0000.0000.9530.8160.427
DPOSIT_SEQ0.0001.0000.0000.0000.000
LIQD_PLAN_CD0.9530.0001.0000.7270.369
TREAT_ORG_CD0.8160.0000.7271.0000.000
REG_BRCD0.4270.0000.3690.0001.000
2023-12-12T19:19:36.181443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SLIP_NOREG_ENODPOSIT_SEQLIQD_PLAN_CDHOLD_CDTREAT_ORG_CDREG_BRCD
SLIP_NO1.000-0.0520.2070.0000.0000.0000.611
REG_ENO-0.0521.0000.0000.6060.6100.3590.594
DPOSIT_SEQ0.2070.0001.0000.0000.0000.0000.000
LIQD_PLAN_CD0.0000.6060.0001.0000.9530.7270.369
HOLD_CD0.0000.6100.0000.9531.0000.8160.427
TREAT_ORG_CD0.0000.3590.0000.7270.8161.0000.000
REG_BRCD0.6110.5940.0000.3690.4270.0001.000

Missing values

2023-12-12T19:19:32.970156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:19:33.132879image/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

DPOSIT_SEQLIQD_PLAN_CDHOLD_CDTREAT_ORG_CDDEPOSIT_AMT_CDSLIP_NOREG_BRCDREG_ENOREG_DT
01KHFCMB2005S-06B005-2005-0005B005<NA>369TAA13652008/01/14 13:26:31
12KHFCMB2005S-03B005-2005-0003B005<NA>430TMA10952008/03/28 17:48:58
21KHFCMB2005S-03B005-2005-0003B005<NA>83BBC13942008/12/04 16:55:48
31KHFCMB2006S-01B021-2005-0005B088<NA>0ACS14442008/11/26 10:25:53
42KHFCMB2005S-05B005-2005-0004B005<NA>328BBC12512009/01/15 13:15:50
51KHFCMB2005S-09B005-2005-0009B005<NA>222THA11742007/10/25 11:34:45
61KHFCMB2005S-03B005-2005-0003B005<NA>6TMA10952008/03/28 10:05:12
72KHFCMB2005S-06B005-2005-0005B005<NA>75BBC11792008/04/02 16:16:19
81KHFCMB2004S-02B004-2004-0001B004<NA>84BBC13942008/12/04 17:06:14
92KHFCMB2006S-01B005-2005-0010B005<NA>13BBC11792008/12/04 10:47:35
DPOSIT_SEQLIQD_PLAN_CDHOLD_CDTREAT_ORG_CDDEPOSIT_AMT_CDSLIP_NOREG_BRCDREG_ENOREG_DT
263KHFCMB2004S-07B005-2004-0005B005<NA>0ACS13432008/12/30 17:19:36
272KHFCMB2004S-07B005-2004-0005B005<NA>0ACS13432008/12/30 17:07:00
281KHFCMB2005S-01B005-2005-0001B005<NA>42BBC13942008/12/04 16:39:01
291KHFCMB2005S-05B005-2005-0004B005<NA>0AAZ13372007/09/21 10:23:45
301KHFCMB2006S-02C001-2006-0001C001<NA>0ACS12532008/03/04 10:17:16
311KHFCMB2005S-05B005-2005-0004B005<NA>327BBC12512009/01/15 13:11:04
321KHFCMB2006S-04C001-2006-0003C001<NA>0ACS12542008/04/25 11:13:24
334KHFCMB2005S-06B005-2005-0005B005<NA>260BBC11792008/04/03 09:51:22
341KHFCMB2004S-07B005-2004-0005B005<NA>368TAA13652008/01/14 13:25:22
352KHFCMB2007S-04B020-2007-0006B020<NA>0ACS12532009/05/19 10:35:12