Overview

Dataset statistics

Number of variables16
Number of observations500
Missing cells1511
Missing cells (%)18.9%
Duplicate rows4
Duplicate rows (%)0.8%
Total size in memory65.1 KiB
Average record size in memory133.3 B

Variable types

Text6
DateTime1
Categorical6
Numeric2
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 보증 투자 사후관리 마스터 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093217/fileData.do

Alerts

검토일자 has constant value ""Constant
상장추진경과내용 has constant value ""Constant
삭제여부 has constant value ""Constant
Dataset has 4 (0.8%) duplicate rowsDuplicates
최종수정수 is highly overall correlated with #N/AHigh correlation
#N/A is highly overall correlated with 사후관리입력직원번호 and 6 other fieldsHigh correlation
투자사후관리방법코드 is highly overall correlated with #N/AHigh correlation
서면동의검토항목코드 is highly overall correlated with #N/AHigh correlation
투자사후관리등급코드 is highly overall correlated with 사후관리유형코드 and 1 other fieldsHigh correlation
사후관리유형코드 is highly overall correlated with 투자사후관리등급코드 and 1 other fieldsHigh correlation
사후관리입력직원번호 is highly overall correlated with 처리직원번호 and 1 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 사후관리입력직원번호 and 1 other fieldsHigh correlation
서면동의검토항목코드 is highly imbalanced (71.9%)Imbalance
투자사후관리등급코드 is highly imbalanced (64.9%)Imbalance
최종수정수 is highly imbalanced (80.4%)Imbalance
#N/A is highly imbalanced (87.0%)Imbalance
상장추진경과내용 has 499 (99.8%) missing valuesMissing
주주회의안건내용 has 375 (75.0%) missing valuesMissing
서면동의검토내용 has 407 (81.4%) missing valuesMissing
사후관리분석의견내용 has 230 (46.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:09:13.297766
Analysis finished2023-12-12 19:09:15.757056
Duration2.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기업고객ID
Text

Distinct290
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T04:09:16.054179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)27.8%

Sample

1st row9bZtmSe7mu
2nd row9cHE55NANA
3rd row9b4wR1gwYt
4th row9clBHoQytU
5th row9cRhC5l1Zz
ValueCountFrequency (%)
9cppibxcke 6
 
1.2%
9cnkbflwkz 6
 
1.2%
9cmdx9twwf 5
 
1.0%
9cr3rmc6vh 5
 
1.0%
9cugakduoz 5
 
1.0%
9cpyulsggs 5
 
1.0%
9c0sm6bdn9 5
 
1.0%
9ch0nl3gyz 4
 
0.8%
9czdszysan 4
 
0.8%
9cuhafnhrk 4
 
0.8%
Other values (280) 451
90.2%
2023-12-13T04:09:16.595511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 566
 
11.3%
c 474
 
9.5%
b 125
 
2.5%
a 95
 
1.9%
x 86
 
1.7%
U 85
 
1.7%
Z 85
 
1.7%
H 82
 
1.6%
N 82
 
1.6%
l 82
 
1.6%
Other values (52) 3238
64.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2182
43.6%
Uppercase Letter 1684
33.7%
Decimal Number 1134
22.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 474
21.7%
b 125
 
5.7%
a 95
 
4.4%
x 86
 
3.9%
l 82
 
3.8%
s 79
 
3.6%
p 78
 
3.6%
d 73
 
3.3%
m 73
 
3.3%
y 72
 
3.3%
Other values (16) 945
43.3%
Uppercase Letter
ValueCountFrequency (%)
U 85
 
5.0%
Z 85
 
5.0%
H 82
 
4.9%
N 82
 
4.9%
R 80
 
4.8%
K 79
 
4.7%
I 76
 
4.5%
A 74
 
4.4%
M 72
 
4.3%
G 72
 
4.3%
Other values (16) 897
53.3%
Decimal Number
ValueCountFrequency (%)
9 566
49.9%
7 81
 
7.1%
1 66
 
5.8%
5 66
 
5.8%
6 65
 
5.7%
0 64
 
5.6%
8 63
 
5.6%
3 63
 
5.6%
4 61
 
5.4%
2 39
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 3866
77.3%
Common 1134
 
22.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 474
 
12.3%
b 125
 
3.2%
a 95
 
2.5%
x 86
 
2.2%
U 85
 
2.2%
Z 85
 
2.2%
H 82
 
2.1%
N 82
 
2.1%
l 82
 
2.1%
R 80
 
2.1%
Other values (42) 2590
67.0%
Common
ValueCountFrequency (%)
9 566
49.9%
7 81
 
7.1%
1 66
 
5.8%
5 66
 
5.8%
6 65
 
5.7%
0 64
 
5.6%
8 63
 
5.6%
3 63
 
5.6%
4 61
 
5.4%
2 39
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 566
 
11.3%
c 474
 
9.5%
b 125
 
2.5%
a 95
 
1.9%
x 86
 
1.7%
U 85
 
1.7%
Z 85
 
1.7%
H 82
 
1.6%
N 82
 
1.6%
l 82
 
1.6%
Other values (52) 3238
64.8%

검토일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T04:09:16.727239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:16.834223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

사후관리유형코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4
271 
2
125 
3
93 
8
 
9
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 271
54.2%
2 125
25.0%
3 93
 
18.6%
8 9
 
1.8%
1 2
 
0.4%

Length

2023-12-13T04:09:16.992025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:09:17.114665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 271
54.2%
2 125
25.0%
3 93
 
18.6%
8 9
 
1.8%
1 2
 
0.4%

상장추진경과내용
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing499
Missing (%)99.8%
Memory size4.0 KiB
2023-12-13T04:09:17.292715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters31
Distinct characters25
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowKB증권과 상장주관사계약('21.08.02. 체결 예정)
ValueCountFrequency (%)
kb증권과 1
25.0%
상장주관사계약('21.08.02 1
25.0%
체결 1
25.0%
예정 1
25.0%
2023-12-13T04:09:17.634670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3
 
9.7%
. 3
 
9.7%
0 2
 
6.5%
2 2
 
6.5%
K 1
 
3.2%
( 1
 
3.2%
정 1
 
3.2%
예 1
 
3.2%
결 1
 
3.2%
체 1
 
3.2%
Other values (15) 15
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14
45.2%
Decimal Number 6
19.4%
Other Punctuation 4
 
12.9%
Space Separator 3
 
9.7%
Uppercase Letter 2
 
6.5%
Open Punctuation 1
 
3.2%
Close Punctuation 1
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 1
 
7.1%
예 1
 
7.1%
결 1
 
7.1%
체 1
 
7.1%
약 1
 
7.1%
계 1
 
7.1%
사 1
 
7.1%
관 1
 
7.1%
주 1
 
7.1%
장 1
 
7.1%
Other values (4) 4
28.6%
Decimal Number
ValueCountFrequency (%)
0 2
33.3%
2 2
33.3%
8 1
16.7%
1 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
' 1
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
48.4%
Hangul 14
45.2%
Latin 2
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 1
 
7.1%
예 1
 
7.1%
결 1
 
7.1%
체 1
 
7.1%
약 1
 
7.1%
계 1
 
7.1%
사 1
 
7.1%
관 1
 
7.1%
주 1
 
7.1%
장 1
 
7.1%
Other values (4) 4
28.6%
Common
ValueCountFrequency (%)
3
20.0%
. 3
20.0%
0 2
13.3%
2 2
13.3%
( 1
 
6.7%
8 1
 
6.7%
1 1
 
6.7%
' 1
 
6.7%
) 1
 
6.7%
Latin
ValueCountFrequency (%)
K 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
54.8%
Hangul 14
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3
17.6%
. 3
17.6%
0 2
11.8%
2 2
11.8%
K 1
 
5.9%
( 1
 
5.9%
8 1
 
5.9%
1 1
 
5.9%
' 1
 
5.9%
B 1
 
5.9%
Hangul
ValueCountFrequency (%)
정 1
 
7.1%
예 1
 
7.1%
결 1
 
7.1%
체 1
 
7.1%
약 1
 
7.1%
계 1
 
7.1%
사 1
 
7.1%
관 1
 
7.1%
주 1
 
7.1%
장 1
 
7.1%
Other values (4) 4
28.6%

주주회의안건내용
Text

MISSING 

Distinct66
Distinct (%)52.8%
Missing375
Missing (%)75.0%
Memory size4.0 KiB
2023-12-13T04:09:17.952447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length58
Mean length18.656
Min length7

Characters and Unicode

Total characters2332
Distinct characters102
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)45.6%

Sample

1st row2020년도 재무제표 승인의 건 외
2nd row2020년 재무제표 동의 건 등
3rd row20년도 재무제표 승인 외
4th row제5기(2020.4 - 2021.3) 결산 및 주총보고
5th row1호 : 2020년 결산보고서 승인건
ValueCountFrequency (%)
재무제표 62
 
10.4%
건 54
 
9.0%
승인의 47
 
7.9%
외 43
 
7.2%
20년 36
 
6.0%
20결산 28
 
4.7%
1분기 28
 
4.7%
보고서 28
 
4.7%
주총결의 28
 
4.7%
승인 23
 
3.8%
Other values (102) 221
37.0%
2023-12-13T04:09:18.525766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
475
20.4%
2 146
 
6.3%
0 133
 
5.7%
의 99
 
4.2%
인 87
 
3.7%
결 86
 
3.7%
승 84
 
3.6%
제 80
 
3.4%
재 73
 
3.1%
, 73
 
3.1%
Other values (92) 996
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1396
59.9%
Space Separator 475
 
20.4%
Decimal Number 347
 
14.9%
Other Punctuation 99
 
4.2%
Close Punctuation 5
 
0.2%
Dash Punctuation 4
 
0.2%
Open Punctuation 4
 
0.2%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 99
 
7.1%
인 87
 
6.2%
결 86
 
6.2%
승 84
 
6.0%
제 80
 
5.7%
재 73
 
5.2%
표 71
 
5.1%
년 70
 
5.0%
건 70
 
5.0%
무 69
 
4.9%
Other values (71) 607
43.5%
Decimal Number
ValueCountFrequency (%)
2 146
42.1%
0 133
38.3%
1 44
 
12.7%
3 10
 
2.9%
4 6
 
1.7%
5 3
 
0.9%
7 2
 
0.6%
6 2
 
0.6%
8 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
, 73
73.7%
: 15
 
15.2%
' 7
 
7.1%
/ 2
 
2.0%
. 2
 
2.0%
Open Punctuation
ValueCountFrequency (%)
( 3
75.0%
[ 1
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
50.0%
Y 1
50.0%
Space Separator
ValueCountFrequency (%)
475
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1396
59.9%
Common 934
40.1%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 99
 
7.1%
인 87
 
6.2%
결 86
 
6.2%
승 84
 
6.0%
제 80
 
5.7%
재 73
 
5.2%
표 71
 
5.1%
년 70
 
5.0%
건 70
 
5.0%
무 69
 
4.9%
Other values (71) 607
43.5%
Common
ValueCountFrequency (%)
475
50.9%
2 146
 
15.6%
0 133
 
14.2%
, 73
 
7.8%
1 44
 
4.7%
: 15
 
1.6%
3 10
 
1.1%
' 7
 
0.7%
4 6
 
0.6%
) 5
 
0.5%
Other values (9) 20
 
2.1%
Latin
ValueCountFrequency (%)
F 1
50.0%
Y 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1396
59.9%
ASCII 936
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
475
50.7%
2 146
 
15.6%
0 133
 
14.2%
, 73
 
7.8%
1 44
 
4.7%
: 15
 
1.6%
3 10
 
1.1%
' 7
 
0.7%
4 6
 
0.6%
) 5
 
0.5%
Other values (11) 22
 
2.4%
Hangul
ValueCountFrequency (%)
의 99
 
7.1%
인 87
 
6.2%
결 86
 
6.2%
승 84
 
6.0%
제 80
 
5.7%
재 73
 
5.2%
표 71
 
5.1%
년 70
 
5.0%
건 70
 
5.0%
무 69
 
4.9%
Other values (71) 607
43.5%

서면동의검토항목코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
407 
1
65 
4
 
12
17
 
4
21
 
2
Other values (7)
 
10

Length

Max length2
Median length1
Mean length1.024
Min length1

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st row1
2nd row21
3rd row8
4th row17
5th row17

Common Values

ValueCountFrequency (%)
407
81.4%
1 65
 
13.0%
4 12
 
2.4%
17 4
 
0.8%
21 2
 
0.4%
8 2
 
0.4%
24 2
 
0.4%
99 2
 
0.4%
18 1
 
0.2%
5 1
 
0.2%
Other values (2) 2
 
0.4%

Length

2023-12-13T04:09:18.717206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 65
69.9%
4 12
 
12.9%
17 4
 
4.3%
21 2
 
2.2%
8 2
 
2.2%
24 2
 
2.2%
99 2
 
2.2%
18 1
 
1.1%
5 1
 
1.1%
2 1
 
1.1%

서면동의검토내용
Text

MISSING 

Distinct61
Distinct (%)65.6%
Missing407
Missing (%)81.4%
Memory size4.0 KiB
2023-12-13T04:09:18.971750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length31
Mean length15.064516
Min length4

Characters and Unicode

Total characters1401
Distinct characters167
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)50.5%

Sample

1st row후속투자 사전동의
2nd row자회사 설립
3rd row경영권 양도계약(구주인수, 신주발행 등 과반수 이상) 동의회신
4th row연구소기업 설립 사후동의
5th row인도 현지 자회사 설립
ValueCountFrequency (%)
유상증자 37
 
11.7%
서면동의 29
 
9.2%
동의 19
 
6.0%
후속투자 15
 
4.7%
검토 15
 
4.7%
주식매수선택권 12
 
3.8%
요청 11
 
3.5%
신주발행 10
 
3.2%
부여 9
 
2.8%
사전 8
 
2.5%
Other values (99) 151
47.8%
2023-12-13T04:09:19.415189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
230
 
16.4%
자 76
 
5.4%
의 61
 
4.4%
동 58
 
4.1%
상 51
 
3.6%
증 50
 
3.6%
유 49
 
3.5%
서 40
 
2.9%
면 38
 
2.7%
주 35
 
2.5%
Other values (157) 713
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1109
79.2%
Space Separator 230
 
16.4%
Decimal Number 16
 
1.1%
Other Punctuation 13
 
0.9%
Close Punctuation 13
 
0.9%
Open Punctuation 13
 
0.9%
Dash Punctuation 2
 
0.1%
Math Symbol 2
 
0.1%
Uppercase Letter 2
 
0.1%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 76
 
6.9%
의 61
 
5.5%
동 58
 
5.2%
상 51
 
4.6%
증 50
 
4.5%
유 49
 
4.4%
서 40
 
3.6%
면 38
 
3.4%
주 35
 
3.2%
사 26
 
2.3%
Other values (137) 625
56.4%
Decimal Number
ValueCountFrequency (%)
1 4
25.0%
4 4
25.0%
0 2
12.5%
5 1
 
6.2%
9 1
 
6.2%
8 1
 
6.2%
2 1
 
6.2%
3 1
 
6.2%
7 1
 
6.2%
Close Punctuation
ValueCountFrequency (%)
) 11
84.6%
] 2
 
15.4%
Open Punctuation
ValueCountFrequency (%)
( 11
84.6%
[ 2
 
15.4%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
230
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
> 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1109
79.2%
Common 290
 
20.7%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 76
 
6.9%
의 61
 
5.5%
동 58
 
5.2%
상 51
 
4.6%
증 50
 
4.5%
유 49
 
4.4%
서 40
 
3.6%
면 38
 
3.4%
주 35
 
3.2%
사 26
 
2.3%
Other values (137) 625
56.4%
Common
ValueCountFrequency (%)
230
79.3%
, 13
 
4.5%
) 11
 
3.8%
( 11
 
3.8%
1 4
 
1.4%
4 4
 
1.4%
- 2
 
0.7%
0 2
 
0.7%
[ 2
 
0.7%
] 2
 
0.7%
Other values (8) 9
 
3.1%
Latin
ValueCountFrequency (%)
B 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1109
79.2%
ASCII 292
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
230
78.8%
, 13
 
4.5%
) 11
 
3.8%
( 11
 
3.8%
1 4
 
1.4%
4 4
 
1.4%
- 2
 
0.7%
0 2
 
0.7%
[ 2
 
0.7%
] 2
 
0.7%
Other values (10) 11
 
3.8%
Hangul
ValueCountFrequency (%)
자 76
 
6.9%
의 61
 
5.5%
동 58
 
5.2%
상 51
 
4.6%
증 50
 
4.5%
유 49
 
4.4%
서 40
 
3.6%
면 38
 
3.4%
주 35
 
3.2%
사 26
 
2.3%
Other values (137) 625
56.4%

투자사후관리등급코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
429 
6
 
39
5
 
19
3
 
9
7
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
429
85.8%
6 39
 
7.8%
5 19
 
3.8%
3 9
 
1.8%
7 4
 
0.8%

Length

2023-12-13T04:09:19.608062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:09:19.733628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 39
54.9%
5 19
26.8%
3 9
 
12.7%
7 4
 
5.6%

사후관리분석의견내용
Text

MISSING 

Distinct190
Distinct (%)70.4%
Missing230
Missing (%)46.0%
Memory size4.0 KiB
2023-12-13T04:09:20.027037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length51
Mean length25.640741
Min length3

Characters and Unicode

Total characters6923
Distinct characters381
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)67.0%

Sample

1st row소부장 스타트업 100대기업 선정에 이어 올해 K-유니콘 프로젝트 '아기유니콘 200대 기업'에서 최고점 기업으로 선정됨.
2nd row2020년 635백만원의 매출시현하였으며 정보조회상 특이사항 없음.
3rd row후행투자 유치등으로 활발한 영업활동중이며 정보조회상 특이사항 없음.
4th row엘지화학으로부터 21억 투자유치받았으며 본격 제품 출지중으로 매출신장 기대됨.
5th row후속투자를 유치하였으며 게임개발에 따른 마케팅,영업활동을 본격화 하고 있음.
ValueCountFrequency (%)
대비 52
 
3.4%
동기 43
 
2.8%
영업실적 36
 
2.4%
금년 35
 
2.3%
상반기 35
 
2.3%
매출 32
 
2.1%
참조 32
 
2.1%
2분기 32
 
2.1%
31
 
2.0%
첨부파일참조 30
 
2.0%
Other values (736) 1167
76.5%
2023-12-13T04:09:21.007668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1272
 
18.4%
기 201
 
2.9%
2 124
 
1.8%
상 116
 
1.7%
년 110
 
1.6%
업 109
 
1.6%
매 105
 
1.5%
출 102
 
1.5%
1 100
 
1.4%
대 94
 
1.4%
Other values (371) 4590
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4531
65.4%
Space Separator 1272
 
18.4%
Decimal Number 586
 
8.5%
Other Punctuation 271
 
3.9%
Uppercase Letter 106
 
1.5%
Open Punctuation 67
 
1.0%
Close Punctuation 66
 
1.0%
Lowercase Letter 15
 
0.2%
Dash Punctuation 6
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 201
 
4.4%
상 116
 
2.6%
년 110
 
2.4%
업 109
 
2.4%
매 105
 
2.3%
출 102
 
2.3%
대 94
 
2.1%
동 91
 
2.0%
원 87
 
1.9%
부 85
 
1.9%
Other values (318) 3431
75.7%
Uppercase Letter
ValueCountFrequency (%)
C 26
24.5%
B 12
11.3%
O 12
11.3%
A 11
10.4%
P 8
 
7.5%
I 6
 
5.7%
D 6
 
5.7%
T 4
 
3.8%
K 4
 
3.8%
S 3
 
2.8%
Other values (8) 14
13.2%
Lowercase Letter
ValueCountFrequency (%)
i 2
13.3%
k 2
13.3%
l 2
13.3%
o 2
13.3%
t 1
6.7%
n 1
6.7%
m 1
6.7%
r 1
6.7%
s 1
6.7%
q 1
6.7%
Decimal Number
ValueCountFrequency (%)
2 124
21.2%
1 100
17.1%
3 66
11.3%
0 56
9.6%
4 49
 
8.4%
7 44
 
7.5%
6 43
 
7.3%
5 39
 
6.7%
8 34
 
5.8%
9 31
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 85
31.4%
. 57
21.0%
% 52
19.2%
/ 41
15.1%
' 18
 
6.6%
: 14
 
5.2%
; 3
 
1.1%
" 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
> 2
66.7%
= 1
33.3%
Space Separator
ValueCountFrequency (%)
1272
100.0%
Open Punctuation
ValueCountFrequency (%)
( 67
100.0%
Close Punctuation
ValueCountFrequency (%)
) 66
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4531
65.4%
Common 2271
32.8%
Latin 121
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 201
 
4.4%
상 116
 
2.6%
년 110
 
2.4%
업 109
 
2.4%
매 105
 
2.3%
출 102
 
2.3%
대 94
 
2.1%
동 91
 
2.0%
원 87
 
1.9%
부 85
 
1.9%
Other values (318) 3431
75.7%
Latin
ValueCountFrequency (%)
C 26
21.5%
B 12
9.9%
O 12
9.9%
A 11
 
9.1%
P 8
 
6.6%
I 6
 
5.0%
D 6
 
5.0%
T 4
 
3.3%
K 4
 
3.3%
S 3
 
2.5%
Other values (19) 29
24.0%
Common
ValueCountFrequency (%)
1272
56.0%
2 124
 
5.5%
1 100
 
4.4%
, 85
 
3.7%
( 67
 
3.0%
3 66
 
2.9%
) 66
 
2.9%
. 57
 
2.5%
0 56
 
2.5%
% 52
 
2.3%
Other values (14) 326
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4531
65.4%
ASCII 2392
34.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1272
53.2%
2 124
 
5.2%
1 100
 
4.2%
, 85
 
3.6%
( 67
 
2.8%
3 66
 
2.8%
) 66
 
2.8%
. 57
 
2.4%
0 56
 
2.3%
% 52
 
2.2%
Other values (43) 447
 
18.7%
Hangul
ValueCountFrequency (%)
기 201
 
4.4%
상 116
 
2.6%
년 110
 
2.4%
업 109
 
2.4%
매 105
 
2.3%
출 102
 
2.3%
대 94
 
2.1%
동 91
 
2.0%
원 87
 
1.9%
부 85
 
1.9%
Other values (318) 3431
75.7%

사후관리입력직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3411.458
Minimum2130
Maximum5859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T04:09:21.163795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2130
5-th percentile2130
Q12552
median2868
Q33983
95-th percentile5616
Maximum5859
Range3729
Interquartile range (IQR)1431

Descriptive statistics

Standard deviation1186.4936
Coefficient of variation (CV)0.34779664
Kurtosis-0.79687465
Mean3411.458
Median Absolute Deviation (MAD)623
Skewness0.84527475
Sum1705729
Variance1407767.2
MonotonicityNot monotonic
2023-12-13T04:09:21.320754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3844 62
12.4%
2868 56
11.2%
2577 48
 
9.6%
2245 40
 
8.0%
2552 34
 
6.8%
2610 32
 
6.4%
2550 30
 
6.0%
2130 29
 
5.8%
2953 23
 
4.6%
5337 19
 
3.8%
Other values (12) 127
25.4%
ValueCountFrequency (%)
2130 29
5.8%
2245 40
8.0%
2550 30
6.0%
2552 34
6.8%
2577 48
9.6%
2602 17
 
3.4%
2610 32
6.4%
2868 56
11.2%
2953 23
 
4.6%
3844 62
12.4%
ValueCountFrequency (%)
5859 7
 
1.4%
5682 13
2.6%
5616 13
2.6%
5580 19
3.8%
5337 19
3.8%
5327 11
2.2%
5091 10
2.0%
5007 17
3.4%
4534 1
 
0.2%
4376 6
 
1.2%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-13T04:09:21.460956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
465 
2
 
33
3
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 465
93.0%
2 33
 
6.6%
3 1
 
0.2%
5 1
 
0.2%

Length

2023-12-13T04:09:21.576976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:09:21.695672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 465
93.0%
2 33
 
6.6%
3 1
 
0.2%
5 1
 
0.2%

처리시각
Text

Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T04:09:22.068241image/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

Unique470 ?
Unique (%)94.0%

Sample

1st row57:15.2
2nd row01:24.0
3rd row11:17.5
4th row47:44.6
5th row17:59.9
ValueCountFrequency (%)
46:48.5 3
 
0.6%
32:39.7 3
 
0.6%
45:11.8 2
 
0.4%
01:02.8 2
 
0.4%
51:11.5 2
 
0.4%
16:35.4 2
 
0.4%
14:09.1 2
 
0.4%
06:40.4 2
 
0.4%
56:34.3 2
 
0.4%
38:44.9 2
 
0.4%
Other values (474) 478
95.6%
2023-12-13T04:09:22.649558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 335
9.6%
4 326
9.3%
5 307
8.8%
2 307
8.8%
1 307
8.8%
0 296
8.5%
8 170
 
4.9%
7 169
 
4.8%
Other values (2) 283
8.1%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 335
13.4%
4 326
13.0%
5 307
12.3%
2 307
12.3%
1 307
12.3%
0 296
11.8%
8 170
6.8%
7 169
6.8%
9 142
5.7%
6 141
5.6%
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%
3 335
9.6%
4 326
9.3%
5 307
8.8%
2 307
8.8%
1 307
8.8%
0 296
8.5%
8 170
 
4.9%
7 169
 
4.8%
Other values (2) 283
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 335
9.6%
4 326
9.3%
5 307
8.8%
2 307
8.8%
1 307
8.8%
0 296
8.5%
8 170
 
4.9%
7 169
 
4.8%
Other values (2) 283
8.1%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3414.886
Minimum2130
Maximum5859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T04:09:22.806650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2130
5-th percentile2130
Q12552
median2868
Q33983
95-th percentile5616
Maximum5859
Range3729
Interquartile range (IQR)1431

Descriptive statistics

Standard deviation1185.2592
Coefficient of variation (CV)0.34708604
Kurtosis-0.8002105
Mean3414.886
Median Absolute Deviation (MAD)623
Skewness0.8418476
Sum1707443
Variance1404839.5
MonotonicityNot monotonic
2023-12-13T04:09:22.941807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3844 63
12.6%
2868 56
11.2%
2577 48
 
9.6%
2245 40
 
8.0%
2552 34
 
6.8%
2610 32
 
6.4%
2550 30
 
6.0%
2130 28
 
5.6%
2953 23
 
4.6%
5337 19
 
3.8%
Other values (12) 127
25.4%
ValueCountFrequency (%)
2130 28
5.6%
2245 40
8.0%
2550 30
6.0%
2552 34
6.8%
2577 48
9.6%
2602 17
 
3.4%
2610 32
6.4%
2868 56
11.2%
2953 23
 
4.6%
3844 63
12.6%
ValueCountFrequency (%)
5859 7
 
1.4%
5682 13
2.6%
5616 13
2.6%
5580 19
3.8%
5337 19
3.8%
5327 11
2.2%
5091 10
2.0%
5007 17
3.4%
4534 1
 
0.2%
4376 6
 
1.2%

투자사후관리방법코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3
242 
230 
2
 
18
1
 
9
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
3 242
48.4%
230
46.0%
2 18
 
3.6%
1 9
 
1.8%
5 1
 
0.2%

Length

2023-12-13T04:09:23.081168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:09:23.187593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 242
89.6%
2 18
 
6.7%
1 9
 
3.3%
5 1
 
0.4%

#N/A
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
491 
3983
 
9

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 491
98.2%
3983 9
 
1.8%

Length

2023-12-13T04:09:23.303994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:09:23.398646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 491
98.2%
3983 9
 
1.8%

Interactions

2023-12-13T04:09:14.723399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:14.415997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:14.863313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:14.577139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:09:23.482464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사후관리유형코드주주회의안건내용서면동의검토항목코드서면동의검토내용투자사후관리등급코드사후관리입력직원번호최종수정수처리직원번호투자사후관리방법코드
사후관리유형코드1.000NaN0.700NaN0.8740.6760.0000.6750.854
주주회의안건내용NaN1.000NaNNaNNaN0.9961.0000.996NaN
서면동의검토항목코드0.700NaN1.0001.0000.0000.6470.0000.6470.370
서면동의검토내용NaNNaN1.0001.000NaN0.9791.0000.979NaN
투자사후관리등급코드0.874NaN0.000NaN1.0000.6070.0000.6030.727
사후관리입력직원번호0.6760.9960.6470.9790.6071.0000.2601.0000.568
최종수정수0.0001.0000.0001.0000.0000.2601.0000.2510.000
처리직원번호0.6750.9960.6470.9790.6031.0000.2511.0000.566
투자사후관리방법코드0.854NaN0.370NaN0.7270.5680.0000.5661.000
2023-12-13T04:09:23.629960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수#N/A투자사후관리방법코드서면동의검토항목코드투자사후관리등급코드사후관리유형코드
최종수정수1.0001.0000.0000.0000.0000.000
#N/A1.0001.0001.0001.0001.0001.000
투자사후관리방법코드0.0001.0001.0000.2130.3520.492
서면동의검토항목코드0.0001.0000.2131.0000.0000.479
투자사후관리등급코드0.0001.0000.3520.0001.0000.523
사후관리유형코드0.0001.0000.4920.4790.5231.000
2023-12-13T04:09:23.763024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사후관리입력직원번호처리직원번호사후관리유형코드서면동의검토항목코드투자사후관리등급코드최종수정수투자사후관리방법코드#N/A
사후관리입력직원번호1.0000.9950.4810.3430.3380.1670.3541.000
처리직원번호0.9951.0000.4810.3430.3330.1680.3541.000
사후관리유형코드0.4810.4811.0000.4790.5230.0000.4921.000
서면동의검토항목코드0.3430.3430.4791.0000.0000.0000.2131.000
투자사후관리등급코드0.3380.3330.5230.0001.0000.0000.3521.000
최종수정수0.1670.1680.0000.0000.0001.0000.0001.000
투자사후관리방법코드0.3540.3540.4920.2130.3520.0001.0001.000
#N/A1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T04:09:15.080662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:09:15.394394image/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.
2023-12-13T04:09:15.645175image/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

기업고객ID검토일자사후관리유형코드상장추진경과내용주주회의안건내용서면동의검토항목코드서면동의검토내용투자사후관리등급코드사후관리분석의견내용사후관리입력직원번호삭제여부최종수정수처리시각처리직원번호투자사후관리방법코드#N/A
09bZtmSe7mu00:00.03<NA><NA>1후속투자 사전동의<NA>5007N157:15.25007<NA>
19cHE55NANA00:00.03<NA><NA>21자회사 설립<NA>5007N101:24.05007<NA>
29b4wR1gwYt00:00.03<NA><NA>8경영권 양도계약(구주인수, 신주발행 등 과반수 이상) 동의회신<NA>5327N111:17.55327<NA>
39clBHoQytU00:00.03<NA><NA>17연구소기업 설립 사후동의<NA>5859N147:44.65859<NA>
49cRhC5l1Zz00:00.03<NA><NA>17인도 현지 자회사 설립<NA>5337N117:59.95337<NA>
59cPPIbxcKe00:00.03<NA><NA>1유상증자 및 정관변경 서면동의<NA>5337N138:39.55337<NA>
69djcABokIZ00:00.04<NA><NA><NA><NA>5580N147:41.755805<NA>
79c5Bu69iRv00:00.03<NA><NA>1유상증자[스카이투자조합]<NA>4376N142:46.04376<NA>
89cAdftBsMg00:00.03<NA><NA>1유상증자 및 주식매수선택권 부여에 따른 서면동의 요청<NA>5859N153:50.95859<NA>
99c94ovobD400:00.04<NA><NA><NA>소부장 스타트업 100대기업 선정에 이어 올해 K-유니콘 프로젝트 '아기유니콘 200대 기업'에서 최고점 기업으로 선정됨.2953N146:30.629533<NA>
기업고객ID검토일자사후관리유형코드상장추진경과내용주주회의안건내용서면동의검토항목코드서면동의검토내용투자사후관리등급코드사후관리분석의견내용사후관리입력직원번호삭제여부최종수정수처리시각처리직원번호투자사후관리방법코드#N/A
4909b4nDB52y300:00.02<NA>제13기 재무제표 승인의건 외3<NA><NA>2868N156:54.82868<NA>
4919bnY3BHRzo00:00.02<NA>제13기 재무제표 승인의건 외4<NA><NA>2868N155:02.02868<NA>
4929c2sRvF7Tk00:00.02<NA>20년도 회계결산 처리의 건<NA><NA>2868N152:25.72868<NA>
4939cyz96H5kQ00:00.02<NA>20년도 결산보고서 승인의건<NA><NA>2868N150:23.22868<NA>
4949cqanorOsq00:00.02<NA>결산승인, 임원보수한도 승인<NA><NA>2868N148:02.52868<NA>
4959c4bsvxlDf00:00.02<NA>2020년 재무제표 승인의 건 외<NA><NA>2577N105:05.82577<NA>
4969bVCVJgxLz00:00.02<NA>2020년도 결산보고서 승인의 건<NA><NA>2577N234:45.92577<NA>
4979cx5T1Eo2K00:00.02<NA>2020년 재무제표 승인 외<NA><NA>2577N105:51.22577<NA>
4989c5Vxo8nnS00:00.02<NA>재무재표 승인의 건<NA><NA>2577N151:13.02577<NA>
4999cp50FYROo00:00.02<NA>재무제표 승인<NA><NA>2550N133:38.32550<NA>

Duplicate rows

Most frequently occurring

기업고객ID검토일자사후관리유형코드상장추진경과내용주주회의안건내용서면동의검토항목코드서면동의검토내용투자사후관리등급코드사후관리분석의견내용사후관리입력직원번호삭제여부최종수정수처리시각처리직원번호투자사후관리방법코드#N/A# duplicates
19cNKbflwKz00:00.03<NA><NA>1신주발행 유상증자 동의 검토<NA>5091N146:48.55091<NA>3
09bvUuNupaD00:00.04<NA><NA><NA>6상환청구행사 하여 매년 상환중인 업체5616N114:09.156162<NA>2
29cR3Rmc6Vh00:00.03<NA><NA>1유상증자<NA>5580N132:39.75580<NA>2
39cZDSZysAn00:00.03<NA><NA>1유상증자 관련 사전 서면 동의<NA>5580N115:21.75580<NA>2