Overview

Dataset statistics

Number of variables8
Number of observations36
Missing cells36
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory72.7 B

Variable types

Categorical2
Numeric4
Boolean1
DateTime1

Dataset

Description한국주택금융공사 채권관리부 업무 관련 공개 데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072923/fileData.do

Alerts

DECIS_DY is highly overall correlated with REG_BRCDHigh correlation
SLIP_PRCSS_DY is highly overall correlated with REG_BRCDHigh correlation
REG_ENO is highly overall correlated with REG_BRCDHigh correlation
REG_BRCD is highly overall correlated with DECIS_DY and 2 other fieldsHigh correlation
DECIS_DY has 22 (61.1%) missing valuesMissing
SLIP_PRCSS_DY has 14 (38.9%) missing valuesMissing
DECIS_NO has 20 (55.6%) zerosZeros

Reproduction

Analysis started2023-12-11 23:51:37.575838
Analysis finished2023-12-11 23:51:39.218833
Duration1.64 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 row2
2nd row1
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

2023-12-12T08:51:39.273844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:51:39.413702image/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%

DECIS_DY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)71.4%
Missing22
Missing (%)61.1%
Infinite0
Infinite (%)0.0%
Mean20080784
Minimum20070123
Maximum20090519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T08:51:39.508964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070123
5-th percentile20070642
Q120080334
median20081070
Q320081230
95-th percentile20090519
Maximum20090519
Range20396
Interquartile range (IQR)895.75

Descriptive statistics

Standard deviation6745.453
Coefficient of variation (CV)0.00033591582
Kurtosis-0.39015202
Mean20080784
Median Absolute Deviation (MAD)706
Skewness-0.11540517
Sum2.8113098 × 108
Variance45501136
MonotonicityNot monotonic
2023-12-12T08:51:39.623129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20081230 3
 
8.3%
20090519 2
 
5.6%
20070921 2
 
5.6%
20081015 1
 
2.8%
20070123 1
 
2.8%
20090406 1
 
2.8%
20081126 1
 
2.8%
20081009 1
 
2.8%
20080304 1
 
2.8%
20080425 1
 
2.8%
(Missing) 22
61.1%
ValueCountFrequency (%)
20070123 1
 
2.8%
20070921 2
5.6%
20080304 1
 
2.8%
20080425 1
 
2.8%
20081009 1
 
2.8%
20081015 1
 
2.8%
20081126 1
 
2.8%
20081230 3
8.3%
20090406 1
 
2.8%
20090519 2
5.6%
ValueCountFrequency (%)
20090519 2
5.6%
20090406 1
 
2.8%
20081230 3
8.3%
20081126 1
 
2.8%
20081015 1
 
2.8%
20081009 1
 
2.8%
20080425 1
 
2.8%
20080304 1
 
2.8%
20070921 2
5.6%
20070123 1
 
2.8%

CANCEL_YN
Boolean

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size168.0 B
False
27 
True
9 
ValueCountFrequency (%)
False 27
75.0%
True 9
 
25.0%
2023-12-12T08:51:39.730147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

DECIS_NO
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4444444
Minimum0
Maximum19
Zeros20
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T08:51:39.817696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.25
95-th percentile17.25
Maximum19
Range19
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation5.2830967
Coefficient of variation (CV)2.1612668
Kurtosis5.0724005
Mean2.4444444
Median Absolute Deviation (MAD)0
Skewness2.5136697
Sum88
Variance27.911111
MonotonicityNot monotonic
2023-12-12T08:51:39.912930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 20
55.6%
1 7
 
19.4%
2 2
 
5.6%
3 2
 
5.6%
4 1
 
2.8%
17 1
 
2.8%
18 1
 
2.8%
13 1
 
2.8%
19 1
 
2.8%
ValueCountFrequency (%)
0 20
55.6%
1 7
 
19.4%
2 2
 
5.6%
3 2
 
5.6%
4 1
 
2.8%
13 1
 
2.8%
17 1
 
2.8%
18 1
 
2.8%
19 1
 
2.8%
ValueCountFrequency (%)
19 1
 
2.8%
18 1
 
2.8%
17 1
 
2.8%
13 1
 
2.8%
4 1
 
2.8%
3 2
 
5.6%
2 2
 
5.6%
1 7
 
19.4%
0 20
55.6%

SLIP_PRCSS_DY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)54.5%
Missing14
Missing (%)38.9%
Infinite0
Infinite (%)0.0%
Mean20079234
Minimum20070315
Maximum20090115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T08:51:40.016904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070315
5-th percentile20070707
Q120080114
median20080365
Q320081204
95-th percentile20089669
Maximum20090115
Range19800
Interquartile range (IQR)1090

Descriptive statistics

Standard deviation5450.5156
Coefficient of variation (CV)0.00027145037
Kurtosis0.37318861
Mean20079234
Median Absolute Deviation (MAD)839
Skewness-0.12550516
Sum4.4174316 × 108
Variance29708120
MonotonicityNot monotonic
2023-12-12T08:51:40.116071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20081204 5
 
13.9%
20080402 4
 
11.1%
20080328 2
 
5.6%
20080114 2
 
5.6%
20090115 2
 
5.6%
20080117 1
 
2.8%
20070706 1
 
2.8%
20071211 1
 
2.8%
20080312 1
 
2.8%
20070731 1
 
2.8%
Other values (2) 2
 
5.6%
(Missing) 14
38.9%
ValueCountFrequency (%)
20070315 1
 
2.8%
20070706 1
 
2.8%
20070731 1
 
2.8%
20071025 1
 
2.8%
20071211 1
 
2.8%
20080114 2
5.6%
20080117 1
 
2.8%
20080312 1
 
2.8%
20080328 2
5.6%
20080402 4
11.1%
ValueCountFrequency (%)
20090115 2
 
5.6%
20081204 5
13.9%
20080402 4
11.1%
20080328 2
 
5.6%
20080312 1
 
2.8%
20080117 1
 
2.8%
20080114 2
 
5.6%
20071211 1
 
2.8%
20071025 1
 
2.8%
20070731 1
 
2.8%

REG_BRCD
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
ACS
12 
BBC
11 
ADD
3 
AAZ
3 
TMA
2 
Other values (4)
5 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique3 ?
Unique (%)8.3%

Sample

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

Common Values

ValueCountFrequency (%)
ACS 12
33.3%
BBC 11
30.6%
ADD 3
 
8.3%
AAZ 3
 
8.3%
TMA 2
 
5.6%
TAA 2
 
5.6%
THO 1
 
2.8%
QAD 1
 
2.8%
THA 1
 
2.8%

Length

2023-12-12T08:51:40.228383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:51:40.330692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
acs 12
33.3%
bbc 11
30.6%
add 3
 
8.3%
aaz 3
 
8.3%
tma 2
 
5.6%
taa 2
 
5.6%
tho 1
 
2.8%
qad 1
 
2.8%
tha 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
2023-12-12T08:51:40.432710image/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
2023-12-12T08:51:40.525162image/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%
1253 3
8.3%
1394 3
8.3%
1365 2
 
5.6%
1251 2
 
5.6%
1254 2
 
5.6%
1095 2
 
5.6%
1235 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

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
Minimum2007-01-23 10:55:42
Maximum2009-05-19 10:35:12
2023-12-12T08:51:40.621387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:40.744338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

Interactions

2023-12-12T08:51:38.658273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:37.802212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.090222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.377380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.739034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:37.886299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.166166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.432435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.815519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:37.961852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.233420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.500551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.899504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.020006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.305847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:51:38.577056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:51:40.838419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPOSIT_SEQDECIS_DYCANCEL_YNDECIS_NOSLIP_PRCSS_DYREG_BRCDREG_ENOREG_DT
DPOSIT_SEQ1.0000.0000.5150.4080.0000.0000.0001.000
DECIS_DY0.0001.0000.0000.468NaN1.0000.7571.000
CANCEL_YN0.5150.0001.0000.2060.3940.0000.2231.000
DECIS_NO0.4080.4680.2061.000NaN0.0000.6471.000
SLIP_PRCSS_DY0.000NaN0.394NaN1.0000.9560.8731.000
REG_BRCD0.0001.0000.0000.0000.9561.0000.8851.000
REG_ENO0.0000.7570.2230.6470.8730.8851.0001.000
REG_DT1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T08:51:40.951742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPOSIT_SEQCANCEL_YNREG_BRCD
DPOSIT_SEQ1.0000.3370.000
CANCEL_YN0.3371.0000.000
REG_BRCD0.0000.0001.000
2023-12-12T08:51:41.034172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DECIS_DYDECIS_NOSLIP_PRCSS_DYREG_ENODPOSIT_SEQCANCEL_YNREG_BRCD
DECIS_DY1.0000.338NaN0.0000.0000.0000.913
DECIS_NO0.3381.000-0.3140.1540.2580.1240.000
SLIP_PRCSS_DYNaN-0.3141.000-0.0950.0000.0000.638
REG_ENO0.0000.154-0.0951.0000.0000.0400.572
DPOSIT_SEQ0.0000.2580.0000.0001.0000.3370.000
CANCEL_YN0.0000.1240.0000.0400.3371.0000.000
REG_BRCD0.9130.0000.6380.5720.0000.0001.000

Missing values

2023-12-12T08:51:38.996140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:51:39.094470image/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.
2023-12-12T08:51:39.178867image/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

DPOSIT_SEQDECIS_DYCANCEL_YNDECIS_NOSLIP_PRCSS_DYREG_BRCDREG_ENOREG_DT
02<NA>N020080328TMA10952008/03/28 17:48:58
11<NA>N120080114TAA13652008/01/14 13:25:22
21<NA>Y020080328TMA10952008/03/28 10:05:11
3120090519N2<NA>ACS12532009/05/19 10:30:37
42<NA>Y020080402BBC11792008/04/02 16:16:19
51<NA>N020080114TAA13652008/01/14 13:26:31
61<NA>N020081204BBC13942008/12/04 17:06:14
72<NA>N020081204BBC11792008/12/04 10:47:35
81<NA>Y020081204BBC11792008/12/04 10:25:16
9220090519N3<NA>ACS12532009/05/19 10:35:12
DPOSIT_SEQDECIS_DYCANCEL_YNDECIS_NOSLIP_PRCSS_DYREG_BRCDREG_ENOREG_DT
262<NA>N020090115BBC12512009/01/15 13:15:50
274<NA>N020080402BBC11792008/04/03 09:51:22
28120080304N1<NA>ACS12532008/03/04 10:17:17
29120070921N3<NA>ADD13372007/09/21 11:13:10
30120080425N1<NA>ACS12542008/04/25 11:13:25
311<NA>Y020090115BBC12512009/01/15 13:11:04
322<NA>N020070731AAZ13372007/07/31 14:05:10
331<NA>Y020070315AAZ13372007/03/15 18:20:16
341<NA>N120071025THA11742007/10/25 11:34:45
35120070921N2<NA>ADD13372007/09/21 11:13:10