Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.8 KiB
Average record size in memory75.3 B

Variable types

Categorical3
Text2
Numeric2
Boolean2

Dataset

Description해당 파일 데이터는 신용보증기금의 보증고객 창업기업 중단 정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093262/fileData.do

Alerts

업무구분코드 has constant value ""Constant
삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
중단사유해당여부 is highly overall correlated with 중단일자High correlation
중단일자 is highly overall correlated with 중단사유해당여부High correlation

Reproduction

Analysis started2023-12-12 22:15:09.799545
Analysis finished2023-12-12 22:15:10.671277
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업무구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
G
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
G 500
100.0%

Length

2023-12-13T07:15:10.723798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:15:10.799619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

원장번호
Text

Distinct62
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T07:15:10.972786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6000
Distinct characters31
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

Unique16 ?
Unique (%)3.2%

Sample

1st rowTIE200800236
2nd rowTQA201000562
3rd rowTQA201200218
4th rowTHW200900484
5th rowTHW200900484
ValueCountFrequency (%)
tho200800144 12
 
2.4%
ihc200900173 12
 
2.4%
thn201000749 12
 
2.4%
tah201000227 12
 
2.4%
tbk200800497 12
 
2.4%
thg201001365 12
 
2.4%
ioc200900093 12
 
2.4%
tam200800121 12
 
2.4%
tav201001607 12
 
2.4%
tmf200800370 12
 
2.4%
Other values (52) 380
76.0%
2023-12-13T07:15:11.310089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2032
33.9%
2 697
 
11.6%
1 516
 
8.6%
T 413
 
6.9%
8 229
 
3.8%
3 186
 
3.1%
7 185
 
3.1%
5 180
 
3.0%
9 176
 
2.9%
6 165
 
2.8%
Other values (21) 1221
20.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4500
75.0%
Uppercase Letter 1500
 
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 413
27.5%
I 160
 
10.7%
A 142
 
9.5%
H 127
 
8.5%
B 102
 
6.8%
M 81
 
5.4%
C 72
 
4.8%
D 65
 
4.3%
P 55
 
3.7%
N 50
 
3.3%
Other values (11) 233
15.5%
Decimal Number
ValueCountFrequency (%)
0 2032
45.2%
2 697
 
15.5%
1 516
 
11.5%
8 229
 
5.1%
3 186
 
4.1%
7 185
 
4.1%
5 180
 
4.0%
9 176
 
3.9%
6 165
 
3.7%
4 134
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4500
75.0%
Latin 1500
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 413
27.5%
I 160
 
10.7%
A 142
 
9.5%
H 127
 
8.5%
B 102
 
6.8%
M 81
 
5.4%
C 72
 
4.8%
D 65
 
4.3%
P 55
 
3.7%
N 50
 
3.3%
Other values (11) 233
15.5%
Common
ValueCountFrequency (%)
0 2032
45.2%
2 697
 
15.5%
1 516
 
11.5%
8 229
 
5.1%
3 186
 
4.1%
7 185
 
4.1%
5 180
 
4.0%
9 176
 
3.9%
6 165
 
3.7%
4 134
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2032
33.9%
2 697
 
11.6%
1 516
 
8.6%
T 413
 
6.9%
8 229
 
3.8%
3 186
 
3.1%
7 185
 
3.1%
5 180
 
3.0%
9 176
 
2.9%
6 165
 
2.8%
Other values (21) 1221
20.3%

창업기업프로그램중단사유코드
Real number (ℝ)

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.998
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T07:15:11.419812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median10
Q314
95-th percentile16
Maximum16
Range15
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation5.0811053
Coefficient of variation (CV)0.56469274
Kurtosis-1.38209
Mean8.998
Median Absolute Deviation (MAD)5
Skewness-0.23156684
Sum4499
Variance25.817631
MonotonicityNot monotonic
2023-12-13T07:15:11.507626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
12 49
9.8%
16 44
8.8%
13 43
8.6%
5 43
8.6%
14 43
8.6%
1 42
8.4%
3 42
8.4%
2 41
8.2%
15 39
7.8%
8 36
7.2%
Other values (3) 78
15.6%
ValueCountFrequency (%)
1 42
8.4%
2 41
8.2%
3 42
8.4%
4 6
 
1.2%
5 43
8.6%
8 36
7.2%
9 36
7.2%
10 36
7.2%
12 49
9.8%
13 43
8.6%
ValueCountFrequency (%)
16 44
8.8%
15 39
7.8%
14 43
8.6%
13 43
8.6%
12 49
9.8%
10 36
7.2%
9 36
7.2%
8 36
7.2%
5 43
8.6%
4 6
 
1.2%

중단사유해당여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
433 
True
67 
ValueCountFrequency (%)
False 433
86.6%
True 67
 
13.4%
2023-12-13T07:15:11.599063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

중단일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
433 
00:00.0
67 

Length

Max length26
Median length26
Mean length23.454
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row0001-01-01 00:00:00.000000
5th row00:00.0

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 433
86.6%
00:00.0 67
 
13.4%

Length

2023-12-13T07:15:11.700842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:15:11.792498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 433
46.4%
00:00:00.000000 433
46.4%
00:00.0 67
 
7.2%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-13T07:15:11.858146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 500
100.0%

Length

2023-12-13T07:15:11.936540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:15:12.018003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

처리시각
Text

Distinct62
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T07:15:12.204257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)3.2%

Sample

1st row40:54.7
2nd row32:10.4
3rd row42:36.9
4th row08:58.9
5th row08:58.9
ValueCountFrequency (%)
30:32.8 12
 
2.4%
23:27.6 12
 
2.4%
22:24.5 12
 
2.4%
17:51.4 12
 
2.4%
52:47.9 12
 
2.4%
14:18.4 12
 
2.4%
04:57.4 12
 
2.4%
44:02.7 12
 
2.4%
38:25.4 12
 
2.4%
37:03.7 12
 
2.4%
Other values (52) 380
76.0%
2023-12-13T07:15:12.534269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 362
10.3%
2 352
10.1%
3 311
8.9%
5 306
8.7%
0 304
8.7%
1 253
7.2%
7 234
6.7%
6 163
 
4.7%
Other values (2) 215
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 362
14.5%
2 352
14.1%
3 311
12.4%
5 306
12.2%
0 304
12.2%
1 253
10.1%
7 234
9.4%
6 163
6.5%
8 127
 
5.1%
9 88
 
3.5%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 362
10.3%
2 352
10.1%
3 311
8.9%
5 306
8.7%
0 304
8.7%
1 253
7.2%
7 234
6.7%
6 163
 
4.7%
Other values (2) 215
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 362
10.3%
2 352
10.1%
3 311
8.9%
5 306
8.7%
0 304
8.7%
1 253
7.2%
7 234
6.7%
6 163
 
4.7%
Other values (2) 215
6.1%

처리직원번호
Real number (ℝ)

Distinct53
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4085.48
Minimum3200
Maximum5037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T07:15:12.681878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3200
5-th percentile3291.4
Q13676
median4104
Q34435.75
95-th percentile4931
Maximum5037
Range1837
Interquartile range (IQR)759.75

Descriptive statistics

Standard deviation515.38405
Coefficient of variation (CV)0.12615018
Kurtosis-0.97315354
Mean4085.48
Median Absolute Deviation (MAD)401
Skewness-0.015085757
Sum2042740
Variance265620.72
MonotonicityNot monotonic
2023-12-13T07:15:12.829524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3676 25
 
5.0%
4231 24
 
4.8%
3903 22
 
4.4%
4679 22
 
4.4%
3992 17
 
3.4%
4024 13
 
2.6%
3242 12
 
2.4%
4505 12
 
2.4%
3200 12
 
2.4%
4931 12
 
2.4%
Other values (43) 329
65.8%
ValueCountFrequency (%)
3200 12
2.4%
3239 1
 
0.2%
3242 12
2.4%
3294 12
2.4%
3303 12
2.4%
3330 12
2.4%
3348 1
 
0.2%
3352 12
2.4%
3361 1
 
0.2%
3415 1
 
0.2%
ValueCountFrequency (%)
5037 10
2.0%
4993 2
 
0.4%
4984 1
 
0.2%
4963 10
2.0%
4931 12
2.4%
4851 12
2.4%
4813 1
 
0.2%
4792 2
 
0.4%
4753 12
2.4%
4720 10
2.0%

Interactions

2023-12-13T07:15:10.244454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:10.066718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:10.353565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:10.151561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:15:12.928959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원장번호창업기업프로그램중단사유코드중단사유해당여부중단일자처리시각처리직원번호
원장번호1.0000.0000.5180.5181.0001.000
창업기업프로그램중단사유코드0.0001.0000.2750.2750.0000.000
중단사유해당여부0.5180.2751.0001.0000.5180.000
중단일자0.5180.2751.0001.0000.5180.000
처리시각1.0000.0000.5180.5181.0001.000
처리직원번호1.0000.0000.0000.0001.0001.000
2023-12-13T07:15:13.022307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중단사유해당여부중단일자
중단사유해당여부1.0000.991
중단일자0.9911.000
2023-12-13T07:15:13.092600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
창업기업프로그램중단사유코드처리직원번호중단사유해당여부중단일자
창업기업프로그램중단사유코드1.000-0.0010.2510.251
처리직원번호-0.0011.0000.0000.000
중단사유해당여부0.2510.0001.0000.991
중단일자0.2510.0000.9911.000

Missing values

2023-12-13T07:15:10.479922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:15:10.625357image/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

업무구분코드원장번호창업기업프로그램중단사유코드중단사유해당여부중단일자삭제여부최종수정수처리시각처리직원번호
0GTIE20080023612Y00:00.0N140:54.74813
1GTQA20100056212Y00:00.0N132:10.44792
2GTQA20120021812Y00:00.0N142:36.94792
3GTHW20090048413N0001-01-01 00:00:00.000000N108:58.94670
4GTHW20090048412Y00:00.0N108:58.94670
5GTHW20120021512Y00:00.0N108:20.14670
6GTHW20120021513N0001-01-01 00:00:00.000000N108:20.14670
7GIAT2009000371N0001-01-01 00:00:00.000000N117:57.63992
8GIAT2009000372N0001-01-01 00:00:00.000000N117:57.63992
9GIAT2009000373N0001-01-01 00:00:00.000000N117:57.63992
업무구분코드원장번호창업기업프로그램중단사유코드중단사유해당여부중단일자삭제여부최종수정수처리시각처리직원번호
490GICA20080053013Y00:00.0N128:55.93676
491GICA20080053014N0001-01-01 00:00:00.000000N128:55.93676
492GICA20080053015N0001-01-01 00:00:00.000000N128:55.93676
493GTND20100010416Y00:00.0N144:00.94984
494GTPD20080064314Y00:00.0N141:33.14131
495GTPE2007002661N0001-01-01 00:00:00.000000N144:16.53992
496GTPE2007002665N0001-01-01 00:00:00.000000N144:16.53992
497GTPE2007002664N0001-01-01 00:00:00.000000N144:16.53992
498GTPE2007002663N0001-01-01 00:00:00.000000N144:16.53992
499GTPE2007002662N0001-01-01 00:00:00.000000N144:16.53992