Overview

Dataset statistics

Number of variables6
Number of observations34
Missing cells10
Missing cells (%)4.9%
Duplicate rows1
Duplicate rows (%)2.9%
Total size in memory1.7 KiB
Average record size in memory51.9 B

Variable types

Categorical4
Text2

Dataset

Description건설근로자공제회 자산운용내역에 관한 사항에 대한 데이터로 공제부금 운용내역에 대한 정보를 말함(부금운용내역)
URLhttps://www.data.go.kr/data/3074027/fileData.do

Alerts

Dataset has 1 (2.9%) duplicate rowsDuplicates
유형 is highly overall correlated with 대상자산 and 2 other fieldsHigh correlation
선정일 is highly overall correlated with 대상자산 and 2 other fieldsHigh correlation
벤치마크 is highly overall correlated with 대상자산 and 2 other fieldsHigh correlation
대상자산 is highly overall correlated with 유형 and 2 other fieldsHigh correlation
기관명 has 5 (14.7%) missing valuesMissing
주소 has 5 (14.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:10:21.219215
Analysis finished2023-12-12 19:10:21.864397
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대상자산
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
국내주식
10 
국내채권
9 
해외주식
7 
<NA>
5 
해외채권
3 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국내주식 10
29.4%
국내채권 9
26.5%
해외주식 7
20.6%
<NA> 5
14.7%
해외채권 3
 
8.8%

Length

2023-12-13T04:10:21.954336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:10:22.114898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내주식 10
29.4%
국내채권 9
26.5%
해외주식 7
20.6%
na 5
14.7%
해외채권 3
 
8.8%

유형
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size404.0 B
순수주식형
7 
글로벌ETF형
5 
<NA>
5 
회사채형
3 
인덱스형(ETF)
2 
Other values (7)
12 

Length

Max length9
Median length7
Mean length5.6176471
Min length3

Unique

Unique2 ?
Unique (%)5.9%

Sample

1st row인덱스형(ETF)
2nd row인덱스형(ETF)
3rd row중소형주형
4th row순수주식형
5th row순수주식형

Common Values

ValueCountFrequency (%)
순수주식형 7
20.6%
글로벌ETF형 5
14.7%
<NA> 5
14.7%
회사채형 3
8.8%
인덱스형(ETF) 2
 
5.9%
미국주식형 2
 
5.9%
국공채형(중단기) 2
 
5.9%
국공채형(중장기) 2
 
5.9%
매칭형 2
 
5.9%
글로벌채권 2
 
5.9%
Other values (2) 2
 
5.9%

Length

2023-12-13T04:10:22.252443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
순수주식형 7
20.6%
글로벌etf형 5
14.7%
na 5
14.7%
회사채형 3
8.8%
인덱스형(etf 2
 
5.9%
미국주식형 2
 
5.9%
국공채형(중단기 2
 
5.9%
국공채형(중장기 2
 
5.9%
매칭형 2
 
5.9%
글로벌채권 2
 
5.9%
Other values (2) 2
 
5.9%

벤치마크
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size404.0 B
KOSPI지수(95%)+Call(5%)
7 
MSCI ACWI(95%)+Call(5%)
5 
<NA>
5 
KIS회사채(1∼5y)지수
3 
KOSPI200지수
2 
Other values (7)
12 

Length

Max length76
Median length23
Mean length19.735294
Min length4

Unique

Unique2 ?
Unique (%)5.9%

Sample

1st rowKOSPI200지수
2nd rowKOSPI200지수
3rd row중소형지수(KFR)
4th rowKOSPI지수(95%)+Call(5%)
5th rowKOSPI지수(95%)+Call(5%)

Common Values

ValueCountFrequency (%)
KOSPI지수(95%)+Call(5%) 7
20.6%
MSCI ACWI(95%)+Call(5%) 5
14.7%
<NA> 5
14.7%
KIS회사채(1∼5y)지수 3
8.8%
KOSPI200지수 2
 
5.9%
S&P500지수(95%)+Call(5%) 2
 
5.9%
KIS국공채(1∼3y)지수 2
 
5.9%
KIS국공채(3∼5y)지수 2
 
5.9%
별도목표 2
 
5.9%
[Barclays Global Aggregate Index(USD Hedged)+FX Swap Rate(3M)](95%)+Call(5%) 2
 
5.9%
Other values (2) 2
 
5.9%

Length

2023-12-13T04:10:22.428173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kospi지수(95%)+call(5 7
12.5%
msci 5
 
8.9%
acwi(95%)+call(5 5
 
8.9%
na 5
 
8.9%
rate(3m)](95%)+call(5 3
 
5.4%
swap 3
 
5.4%
global 3
 
5.4%
barclays 3
 
5.4%
kis회사채(1∼5y)지수 3
 
5.4%
kis국공채(3∼5y)지수 2
 
3.6%
Other values (10) 17
30.4%

기관명
Text

MISSING 

Distinct16
Distinct (%)55.2%
Missing5
Missing (%)14.7%
Memory size404.0 B
2023-12-13T04:10:22.636951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length8
Min length6

Characters and Unicode

Total characters232
Distinct characters49
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)31.0%

Sample

1st rowKB자산운용
2nd row미래에셋자산운용
3rd row한화자산운용
4th rowNH-Amundi자산운용
5th row마이다스에셋자산운용
ValueCountFrequency (%)
nh-amundi자산운용 4
13.8%
한화자산운용 3
10.3%
미래에셋자산운용 3
10.3%
삼성자산운용 3
10.3%
키움투자자산운용 3
10.3%
교보악사자산운용 2
 
6.9%
한국투자신탁운용 2
 
6.9%
코레이트자산운용 1
 
3.4%
마이다스에셋자산운용 1
 
3.4%
페트라자산운용 1
 
3.4%
Other values (6) 6
20.7%
2023-12-13T04:10:22.973662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
자 32
 
13.8%
운 29
 
12.5%
용 29
 
12.5%
산 27
 
11.6%
한 6
 
2.6%
투 5
 
2.2%
셋 4
 
1.7%
에 4
 
1.7%
H 4
 
1.7%
이 4
 
1.7%
Other values (39) 88
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 194
83.6%
Lowercase Letter 20
 
8.6%
Uppercase Letter 14
 
6.0%
Dash Punctuation 4
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 32
16.5%
운 29
14.9%
용 29
14.9%
산 27
13.9%
한 6
 
3.1%
투 5
 
2.6%
셋 4
 
2.1%
에 4
 
2.1%
이 4
 
2.1%
키 3
 
1.5%
Other values (28) 51
26.3%
Uppercase Letter
ValueCountFrequency (%)
H 4
28.6%
N 4
28.6%
A 4
28.6%
B 1
 
7.1%
K 1
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
i 4
20.0%
d 4
20.0%
n 4
20.0%
u 4
20.0%
m 4
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 194
83.6%
Latin 34
 
14.7%
Common 4
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 32
16.5%
운 29
14.9%
용 29
14.9%
산 27
13.9%
한 6
 
3.1%
투 5
 
2.6%
셋 4
 
2.1%
에 4
 
2.1%
이 4
 
2.1%
키 3
 
1.5%
Other values (28) 51
26.3%
Latin
ValueCountFrequency (%)
H 4
11.8%
N 4
11.8%
i 4
11.8%
d 4
11.8%
n 4
11.8%
u 4
11.8%
m 4
11.8%
A 4
11.8%
B 1
 
2.9%
K 1
 
2.9%
Common
ValueCountFrequency (%)
- 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 194
83.6%
ASCII 38
 
16.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
자 32
16.5%
운 29
14.9%
용 29
14.9%
산 27
13.9%
한 6
 
3.1%
투 5
 
2.6%
셋 4
 
2.1%
에 4
 
2.1%
이 4
 
2.1%
키 3
 
1.5%
Other values (28) 51
26.3%
ASCII
ValueCountFrequency (%)
H 4
10.5%
N 4
10.5%
i 4
10.5%
d 4
10.5%
n 4
10.5%
u 4
10.5%
m 4
10.5%
A 4
10.5%
- 4
10.5%
B 1
 
2.6%

선정일
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
2022-05-18
10 
2022-05-30
9 
2022-05-19
7 
<NA>
5 
2022-05-31
3 

Length

Max length10
Median length10
Mean length9.1176471
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-05-18
2nd row2022-05-18
3rd row2022-05-18
4th row2022-05-18
5th row2022-05-18

Common Values

ValueCountFrequency (%)
2022-05-18 10
29.4%
2022-05-30 9
26.5%
2022-05-19 7
20.6%
<NA> 5
14.7%
2022-05-31 3
 
8.8%

Length

2023-12-13T04:10:23.147378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:10:23.280283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-05-18 10
29.4%
2022-05-30 9
26.5%
2022-05-19 7
20.6%
na 5
14.7%
2022-05-31 3
 
8.8%

주소
Text

MISSING 

Distinct16
Distinct (%)55.2%
Missing5
Missing (%)14.7%
Memory size404.0 B
2023-12-13T04:10:23.507937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length29
Mean length26.206897
Min length15

Characters and Unicode

Total characters760
Distinct characters84
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

Unique9 ?
Unique (%)31.0%

Sample

1st row서울시 영등포구 국제금융로 10 Three IFC 18, 40, 41층
2nd row서울시 종로구 종로 33 그랑서울 tower 1, 13층
3rd row서울시 영등포구 63로 50 한화금융센터 50~52층
4th row서울시 영등포구 국제금융로8길 2, 농협재단빌딩 10층
5th row서울시 종로구 경희궁길 20
ValueCountFrequency (%)
서울시 29
 
17.0%
영등포구 17
 
9.9%
종로구 7
 
4.1%
tower 5
 
2.9%
국제금융로8길 4
 
2.3%
여의대로 4
 
2.3%
18 4
 
2.3%
2 4
 
2.3%
13층 4
 
2.3%
농협재단빌딩 4
 
2.3%
Other values (48) 89
52.0%
2023-12-13T04:10:23.917541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
142
18.7%
1 43
 
5.7%
서 38
 
5.0%
로 35
 
4.6%
울 32
 
4.2%
시 29
 
3.8%
구 29
 
3.8%
층 24
 
3.2%
영 17
 
2.2%
등 17
 
2.2%
Other values (74) 354
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 404
53.2%
Space Separator 142
 
18.7%
Decimal Number 141
 
18.6%
Uppercase Letter 27
 
3.6%
Lowercase Letter 23
 
3.0%
Other Punctuation 14
 
1.8%
Math Symbol 9
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 38
 
9.4%
로 35
 
8.7%
울 32
 
7.9%
시 29
 
7.2%
구 29
 
7.2%
층 24
 
5.9%
영 17
 
4.2%
등 17
 
4.2%
포 17
 
4.2%
종 12
 
3.0%
Other values (45) 154
38.1%
Decimal Number
ValueCountFrequency (%)
1 43
30.5%
0 15
 
10.6%
2 14
 
9.9%
3 14
 
9.9%
8 13
 
9.2%
5 12
 
8.5%
4 10
 
7.1%
6 10
 
7.1%
7 6
 
4.3%
9 4
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
T 5
18.5%
I 4
14.8%
F 4
14.8%
K 3
11.1%
R 2
 
7.4%
C 2
 
7.4%
E 2
 
7.4%
W 2
 
7.4%
O 2
 
7.4%
B 1
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
e 7
30.4%
r 5
21.7%
w 3
13.0%
o 3
13.0%
t 3
13.0%
h 2
 
8.7%
Space Separator
ValueCountFrequency (%)
142
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 404
53.2%
Common 306
40.3%
Latin 50
 
6.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 38
 
9.4%
로 35
 
8.7%
울 32
 
7.9%
시 29
 
7.2%
구 29
 
7.2%
층 24
 
5.9%
영 17
 
4.2%
등 17
 
4.2%
포 17
 
4.2%
종 12
 
3.0%
Other values (45) 154
38.1%
Latin
ValueCountFrequency (%)
e 7
14.0%
T 5
10.0%
r 5
10.0%
I 4
 
8.0%
F 4
 
8.0%
K 3
 
6.0%
w 3
 
6.0%
o 3
 
6.0%
t 3
 
6.0%
R 2
 
4.0%
Other values (6) 11
22.0%
Common
ValueCountFrequency (%)
142
46.4%
1 43
 
14.1%
0 15
 
4.9%
2 14
 
4.6%
3 14
 
4.6%
, 14
 
4.6%
8 13
 
4.2%
5 12
 
3.9%
4 10
 
3.3%
6 10
 
3.3%
Other values (3) 19
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 404
53.2%
ASCII 356
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
142
39.9%
1 43
 
12.1%
0 15
 
4.2%
2 14
 
3.9%
3 14
 
3.9%
, 14
 
3.9%
8 13
 
3.7%
5 12
 
3.4%
4 10
 
2.8%
6 10
 
2.8%
Other values (19) 69
19.4%
Hangul
ValueCountFrequency (%)
서 38
 
9.4%
로 35
 
8.7%
울 32
 
7.9%
시 29
 
7.2%
구 29
 
7.2%
층 24
 
5.9%
영 17
 
4.2%
등 17
 
4.2%
포 17
 
4.2%
종 12
 
3.0%
Other values (45) 154
38.1%

Correlations

2023-12-13T04:10:24.016051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대상자산유형벤치마크기관명선정일주소
대상자산1.0001.0001.0000.0001.0000.000
유형1.0001.0001.0000.0001.0000.000
벤치마크1.0001.0001.0000.0001.0000.000
기관명0.0000.0000.0001.0000.0001.000
선정일1.0001.0001.0000.0001.0000.000
주소0.0000.0000.0001.0000.0001.000
2023-12-13T04:10:24.121242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형선정일벤치마크대상자산
유형1.0000.8491.0000.849
선정일0.8491.0000.8491.000
벤치마크1.0000.8491.0000.849
대상자산0.8491.0000.8491.000
2023-12-13T04:10:24.212072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대상자산유형벤치마크선정일
대상자산1.0000.8490.8491.000
유형0.8491.0001.0000.849
벤치마크0.8491.0001.0000.849
선정일1.0000.8490.8491.000

Missing values

2023-12-13T04:10:21.586157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:10:21.688086image/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.
2023-12-13T04:10:21.787060image/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

대상자산유형벤치마크기관명선정일주소
0국내주식인덱스형(ETF)KOSPI200지수KB자산운용2022-05-18서울시 영등포구 국제금융로 10 Three IFC 18, 40, 41층
1국내주식인덱스형(ETF)KOSPI200지수미래에셋자산운용2022-05-18서울시 종로구 종로 33 그랑서울 tower 1, 13층
2국내주식중소형주형중소형지수(KFR)한화자산운용2022-05-18서울시 영등포구 63로 50 한화금융센터 50~52층
3국내주식순수주식형KOSPI지수(95%)+Call(5%)NH-Amundi자산운용2022-05-18서울시 영등포구 국제금융로8길 2, 농협재단빌딩 10층
4국내주식순수주식형KOSPI지수(95%)+Call(5%)마이다스에셋자산운용2022-05-18서울시 종로구 경희궁길 20
5국내주식순수주식형KOSPI지수(95%)+Call(5%)페트라자산운용2022-05-18서울시 영등포구 은행로 25, 9층
6국내주식순수주식형KOSPI지수(95%)+Call(5%)신한자산운용2022-05-18서울시 영등포구 여의대로 70
7국내주식순수주식형KOSPI지수(95%)+Call(5%)한화자산운용2022-05-18서울시 영등포구 63로 50 한화금융센터 50~52층
8국내주식순수주식형KOSPI지수(95%)+Call(5%)코레이트자산운용2022-05-18서울시 강남구 테헤란로 137, 8~9층
9국내주식순수주식형KOSPI지수(95%)+Call(5%)베어링자산운용2022-05-18서울시 중구 을지로 29, 7층
대상자산유형벤치마크기관명선정일주소
24국내채권매칭형별도목표한국투자신탁운용2022-05-30서울시 영등포구 여의대로 24 FKI TOWER 11~12층
25국내채권매칭형별도목표다올자산운용2022-05-30서울시 영등포구 여의대로 66 KTB빌딩 13층
26해외채권글로벌채권[Barclays Global Aggregate Index(USD Hedged)+FX Swap Rate(3M)](95%)+Call(5%)삼성자산운용2022-05-31서울시 서초구 서초대로74길 11 16~18층
27해외채권글로벌채권[Barclays Global Aggregate Index(USD Hedged)+FX Swap Rate(3M)](95%)+Call(5%)키움투자자산운용2022-05-31서울시 영등포구 여의나루로 4길 18
28해외채권미국채권[Barclays Global US Index+FX Swap Rate(3M)](95%)+Call(5%)미래에셋자산운용2022-05-31서울시 종로구 종로 33 그랑서울 tower 1, 13층
29<NA><NA><NA><NA><NA><NA>
30<NA><NA><NA><NA><NA><NA>
31<NA><NA><NA><NA><NA><NA>
32<NA><NA><NA><NA><NA><NA>
33<NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

대상자산유형벤치마크기관명선정일주소# duplicates
0<NA><NA><NA><NA><NA><NA>5