Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells91
Missing cells (%)11.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory66.3 B

Variable types

Categorical4
Numeric1
Boolean1
Text1
DateTime1

Dataset

Description주택금융공사의 안심전환 기관별 승인번호_se4122(기관코드, 전문수신일, 전문관리번호, 거절사유코드, 등록일시 등을 제공합니다.)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15049762/fileData.do

Alerts

전문수신일 has constant value ""Constant
승인여부 is highly overall correlated with 승인신청/취소구분코드 and 1 other fieldsHigh correlation
승인신청/취소구분코드 is highly overall correlated with 승인여부High correlation
승인일자 is highly overall correlated with 승인여부High correlation
전문관리번호 is highly overall correlated with 기관코드High correlation
기관코드 is highly overall correlated with 전문관리번호High correlation
승인신청/취소구분코드 is highly imbalanced (53.0%)Imbalance
승인여부 is highly imbalanced (78.5%)Imbalance
승인일자 is highly imbalanced (56.6%)Imbalance
승인여부 has 12 (12.0%) missing valuesMissing
공사승인번호 has 79 (79.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 10:02:34.134354
Analysis finished2023-12-12 10:02:34.879464
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관코드
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
기업은행
45 
우리은행
26 
SC은행
13 
농협
11 
신한은행
 
3

Length

Max length4
Median length4
Mean length3.78
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기업은행
2nd row하나은행
3rd row신한은행
4th rowSC은행
5th row기업은행

Common Values

ValueCountFrequency (%)
기업은행 45
45.0%
우리은행 26
26.0%
SC은행 13
 
13.0%
농협 11
 
11.0%
신한은행 3
 
3.0%
하나은행 2
 
2.0%

Length

2023-12-12T19:02:34.949565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:02:35.086763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기업은행 45
45.0%
우리은행 26
26.0%
sc은행 13
 
13.0%
농협 11
 
11.0%
신한은행 3
 
3.0%
하나은행 2
 
2.0%

전문수신일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-01-03
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-01-03
2nd row2020-01-03
3rd row2020-01-03
4th row2020-01-03
5th row2020-01-03

Common Values

ValueCountFrequency (%)
2020-01-03 100
100.0%

Length

2023-12-12T19:02:35.231350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:02:35.367598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-01-03 100
100.0%

전문관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76194.93
Minimum21
Maximum587804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T19:02:35.800428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile24.95
Q171.75
median2099.5
Q3121855
95-th percentile499576.6
Maximum587804
Range587783
Interquartile range (IQR)121783.25

Descriptive statistics

Standard deviation156236.71
Coefficient of variation (CV)2.050487
Kurtosis4.0546812
Mean76194.93
Median Absolute Deviation (MAD)2031
Skewness2.2928685
Sum7619493
Variance2.4409911 × 1010
MonotonicityNot monotonic
2023-12-12T19:02:35.956088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 2
 
2.0%
2325 1
 
1.0%
130324 1
 
1.0%
64 1
 
1.0%
1903 1
 
1.0%
1962 1
 
1.0%
1968 1
 
1.0%
1971 1
 
1.0%
130210 1
 
1.0%
1977 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
21 1
1.0%
22 1
1.0%
23 2
2.0%
24 1
1.0%
25 1
1.0%
27 1
1.0%
29 1
1.0%
31 1
1.0%
33 1
1.0%
35 1
1.0%
ValueCountFrequency (%)
587804 1
1.0%
587800 1
1.0%
500372 1
1.0%
500175 1
1.0%
499854 1
1.0%
499562 1
1.0%
499461 1
1.0%
499426 1
1.0%
499424 1
1.0%
499416 1
1.0%

승인신청/취소구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
신청
84 
재신청
13 
취소
 
3

Length

Max length3
Median length2
Mean length2.13
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신청 84
84.0%
재신청 13
 
13.0%
취소 3
 
3.0%

Length

2023-12-12T19:02:36.098984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:02:36.215533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신청 84
84.0%
재신청 13
 
13.0%
취소 3
 
3.0%

승인여부
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)2.3%
Missing12
Missing (%)12.0%
Memory size332.0 B
True
85 
False
 
3
(Missing)
12 
ValueCountFrequency (%)
True 85
85.0%
False 3
 
3.0%
(Missing) 12
 
12.0%
2023-12-12T19:02:36.320688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

승인일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-01-03
84 
<NA>
15 
2019-12-27
 
1

Length

Max length10
Median length10
Mean length9.1
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2020-01-03
2nd row2020-01-03
3rd row2020-01-03
4th row2020-01-03
5th row2020-01-03

Common Values

ValueCountFrequency (%)
2020-01-03 84
84.0%
<NA> 15
 
15.0%
2019-12-27 1
 
1.0%

Length

2023-12-12T19:02:36.438890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:02:36.556719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-01-03 84
84.0%
na 15
 
15.0%
2019-12-27 1
 
1.0%

공사승인번호
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing79
Missing (%)79.0%
Memory size932.0 B
2023-12-12T19:02:36.757413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.714286
Min length10

Characters and Unicode

Total characters246
Distinct characters13
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row20B081107474
2nd row20B088102098
3rd row20B081107473
4th row20농협106426
5th row20B023107159
ValueCountFrequency (%)
20b081107474 1
 
4.8%
20b020104282 1
 
4.8%
20b023107154 1
 
4.8%
20b003102819 1
 
4.8%
20b003102823 1
 
4.8%
20b020104281 1
 
4.8%
20b023107155 1
 
4.8%
20b023107156 1
 
4.8%
20농협106424 1
 
4.8%
20b003102824 1
 
4.8%
Other values (11) 11
52.4%
2023-12-12T19:02:37.180066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 70
28.5%
2 47
19.1%
1 33
13.4%
B 18
 
7.3%
8 14
 
5.7%
4 14
 
5.7%
3 13
 
5.3%
7 12
 
4.9%
5 9
 
3.7%
6 6
 
2.4%
Other values (3) 10
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 222
90.2%
Uppercase Letter 18
 
7.3%
Other Letter 6
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 70
31.5%
2 47
21.2%
1 33
14.9%
8 14
 
6.3%
4 14
 
6.3%
3 13
 
5.9%
7 12
 
5.4%
5 9
 
4.1%
6 6
 
2.7%
9 4
 
1.8%
Other Letter
ValueCountFrequency (%)
농 3
50.0%
협 3
50.0%
Uppercase Letter
ValueCountFrequency (%)
B 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 222
90.2%
Latin 18
 
7.3%
Hangul 6
 
2.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 70
31.5%
2 47
21.2%
1 33
14.9%
8 14
 
6.3%
4 14
 
6.3%
3 13
 
5.9%
7 12
 
5.4%
5 9
 
4.1%
6 6
 
2.7%
9 4
 
1.8%
Hangul
ValueCountFrequency (%)
농 3
50.0%
협 3
50.0%
Latin
ValueCountFrequency (%)
B 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 240
97.6%
Hangul 6
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70
29.2%
2 47
19.6%
1 33
13.8%
B 18
 
7.5%
8 14
 
5.8%
4 14
 
5.8%
3 13
 
5.4%
7 12
 
5.0%
5 9
 
3.8%
6 6
 
2.5%
Hangul
ValueCountFrequency (%)
농 3
50.0%
협 3
50.0%

등록일시
Date

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-01-03 12:18:00
Maximum2020-01-03 14:29:00
2023-12-12T19:02:37.375327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:37.560476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T19:02:34.454865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:02:37.747759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관코드전문관리번호승인신청/취소구분코드승인여부승인일자공사승인번호등록일시
기관코드1.0000.9170.6530.0000.0001.0000.868
전문관리번호0.9171.0000.4050.0000.6591.0000.683
승인신청/취소구분코드0.6530.4051.0001.0000.0471.0000.677
승인여부0.0000.0001.0001.000NaN1.0000.756
승인일자0.0000.6590.047NaN1.0001.0000.000
공사승인번호1.0001.0001.0001.0001.0001.0001.000
등록일시0.8680.6830.6770.7560.0001.0001.000
2023-12-12T19:02:37.926849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승인여부승인신청/취소구분코드기관코드승인일자
승인여부1.0000.9940.0001.000
승인신청/취소구분코드0.9941.0000.3380.027
기관코드0.0000.3381.0000.000
승인일자1.0000.0270.0001.000
2023-12-12T19:02:38.074549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전문관리번호기관코드승인신청/취소구분코드승인여부승인일자
전문관리번호1.0000.7970.3940.0000.456
기관코드0.7971.0000.3380.0000.000
승인신청/취소구분코드0.3940.3381.0000.9940.027
승인여부0.0000.0000.9941.0001.000
승인일자0.4560.0000.0271.0001.000

Missing values

2023-12-12T19:02:34.585789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:02:34.718799image/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.
2023-12-12T19:02:34.825366image/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

기관코드전문수신일전문관리번호승인신청/취소구분코드승인여부승인일자공사승인번호등록일시
0기업은행2020-01-032325신청Y2020-01-03<NA>2020-01-03 14:29
1하나은행2020-01-03587804신청Y2020-01-0320B0811074742020-01-03 14:28
2신한은행2020-01-0324신청Y2020-01-0320B0881020982020-01-03 14:26
3SC은행2020-01-0373신청Y2020-01-03<NA>2020-01-03 14:25
4기업은행2020-01-032303신청Y2020-01-03<NA>2020-01-03 14:24
5하나은행2020-01-03587800신청Y2020-01-0320B0811074732020-01-03 14:24
6기업은행2020-01-032301취소N<NA><NA>2020-01-03 14:24
7농협2020-01-0341신청Y2020-01-0320농협1064262020-01-03 14:23
8기업은행2020-01-032297신청Y2020-01-03<NA>2020-01-03 14:23
9우리은행2020-01-03142241신청Y2020-01-03<NA>2020-01-03 14:22
기관코드전문수신일전문관리번호승인신청/취소구분코드승인여부승인일자공사승인번호등록일시
90우리은행2020-01-03122309재신청Y2020-01-03<NA>2020-01-03 12:23
91SC은행2020-01-0361신청Y2020-01-0320B0231071542020-01-03 12:21
92기업은행2020-01-031790신청Y2020-01-03<NA>2020-01-03 12:20
93기업은행2020-01-031786신청Y2020-01-03<NA>2020-01-03 12:19
94기업은행2020-01-031780신청Y2020-01-03<NA>2020-01-03 12:19
95우리은행2020-01-03121900재신청Y2020-01-03<NA>2020-01-03 12:19
96우리은행2020-01-03121840재신청Y2020-01-03<NA>2020-01-03 12:18
97우리은행2020-01-03499461신청Y2020-01-0320B0201042802020-01-03 12:18
98우리은행2020-01-03121819신청Y2020-01-03<NA>2020-01-03 12:18
99우리은행2020-01-03121812재신청Y2020-01-03<NA>2020-01-03 12:18