Overview

Dataset statistics

Number of variables4
Number of observations515
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.7 KiB
Average record size in memory35.3 B

Variable types

Numeric3
Text1

Dataset

Description국민연금의 연도말 기준 국내채권 투자 발행기관별 평가액, 자산군 내 비중 등 투자 현황에 대한 정보 (단위: 억 원, %)
Author국민연금공단
URLhttps://www.data.go.kr/data/15071589/fileData.do

Alerts

번호 is highly overall correlated with 평가액(억 원) and 1 other fieldsHigh correlation
평가액(억 원) is highly overall correlated with 번호 and 1 other fieldsHigh correlation
비중(퍼센트) is highly overall correlated with 번호 and 1 other fieldsHigh correlation
평가액(억 원) is highly skewed (γ1 = 20.90679055)Skewed
비중(퍼센트) is highly skewed (γ1 = 20.90660725)Skewed
번호 has unique valuesUnique
발행기관명 has unique valuesUnique
비중(퍼센트) has 63 (12.2%) zerosZeros

Reproduction

Analysis started2023-12-12 18:08:33.649487
Analysis finished2023-12-12 18:08:35.282041
Duration1.63 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct515
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258
Minimum1
Maximum515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-13T03:08:35.366782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.7
Q1129.5
median258
Q3386.5
95-th percentile489.3
Maximum515
Range514
Interquartile range (IQR)257

Descriptive statistics

Standard deviation148.81196
Coefficient of variation (CV)0.57679055
Kurtosis-1.2
Mean258
Median Absolute Deviation (MAD)129
Skewness0
Sum132870
Variance22145
MonotonicityStrictly increasing
2023-12-13T03:08:35.540698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
324 1
 
0.2%
354 1
 
0.2%
353 1
 
0.2%
352 1
 
0.2%
351 1
 
0.2%
350 1
 
0.2%
349 1
 
0.2%
348 1
 
0.2%
347 1
 
0.2%
Other values (505) 505
98.1%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
515 1
0.2%
514 1
0.2%
513 1
0.2%
512 1
0.2%
511 1
0.2%
510 1
0.2%
509 1
0.2%
508 1
0.2%
507 1
0.2%
506 1
0.2%

발행기관명
Text

UNIQUE 

Distinct515
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-13T03:08:35.768911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length10.798058
Min length2

Characters and Unicode

Total characters5561
Distinct characters300
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

Unique515 ?
Unique (%)100.0%

Sample

1st row국채
2nd row한국주택금융공사
3rd row한국전력공사
4th row한국은행
5th row한국토지주택공사
ValueCountFrequency (%)
유한회사 175
 
25.3%
gs 2
 
0.3%
국채 1
 
0.1%
코리아세븐 1
 
0.1%
신보2021제15차유동화전문 1
 
0.1%
롯데오토리스 1
 
0.1%
폭스바겐파이낸셜서비스코리아 1
 
0.1%
키움증권 1
 
0.1%
신보2020제12차유동화전문 1
 
0.1%
삼성물산 1
 
0.1%
Other values (506) 506
73.2%
2023-12-13T03:08:36.189220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 405
 
7.3%
사 259
 
4.7%
한 243
 
4.4%
전 204
 
3.7%
화 200
 
3.6%
동 196
 
3.5%
회 193
 
3.5%
문 192
 
3.5%
제 190
 
3.4%
176
 
3.2%
Other values (290) 3303
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4942
88.9%
Decimal Number 344
 
6.2%
Space Separator 176
 
3.2%
Uppercase Letter 96
 
1.7%
Dash Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 405
 
8.2%
사 259
 
5.2%
한 243
 
4.9%
전 204
 
4.1%
화 200
 
4.0%
동 196
 
4.0%
회 193
 
3.9%
문 192
 
3.9%
제 190
 
3.8%
차 175
 
3.5%
Other values (260) 2685
54.3%
Uppercase Letter
ValueCountFrequency (%)
S 22
22.9%
K 16
16.7%
G 12
12.5%
L 9
9.4%
B 7
 
7.3%
C 6
 
6.2%
J 4
 
4.2%
I 3
 
3.1%
N 3
 
3.1%
E 3
 
3.1%
Other values (6) 11
11.5%
Decimal Number
ValueCountFrequency (%)
2 161
46.8%
0 77
22.4%
1 61
 
17.7%
4 10
 
2.9%
5 8
 
2.3%
3 7
 
2.0%
8 6
 
1.7%
6 6
 
1.7%
7 5
 
1.5%
9 3
 
0.9%
Space Separator
ValueCountFrequency (%)
176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4942
88.9%
Common 523
 
9.4%
Latin 96
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 405
 
8.2%
사 259
 
5.2%
한 243
 
4.9%
전 204
 
4.1%
화 200
 
4.0%
동 196
 
4.0%
회 193
 
3.9%
문 192
 
3.9%
제 190
 
3.8%
차 175
 
3.5%
Other values (260) 2685
54.3%
Latin
ValueCountFrequency (%)
S 22
22.9%
K 16
16.7%
G 12
12.5%
L 9
9.4%
B 7
 
7.3%
C 6
 
6.2%
J 4
 
4.2%
I 3
 
3.1%
N 3
 
3.1%
E 3
 
3.1%
Other values (6) 11
11.5%
Common
ValueCountFrequency (%)
176
33.7%
2 161
30.8%
0 77
14.7%
1 61
 
11.7%
4 10
 
1.9%
5 8
 
1.5%
3 7
 
1.3%
8 6
 
1.1%
6 6
 
1.1%
7 5
 
1.0%
Other values (4) 6
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4942
88.9%
ASCII 619
 
11.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 405
 
8.2%
사 259
 
5.2%
한 243
 
4.9%
전 204
 
4.1%
화 200
 
4.0%
동 196
 
4.0%
회 193
 
3.9%
문 192
 
3.9%
제 190
 
3.8%
차 175
 
3.5%
Other values (260) 2685
54.3%
ASCII
ValueCountFrequency (%)
176
28.4%
2 161
26.0%
0 77
12.4%
1 61
 
9.9%
S 22
 
3.6%
K 16
 
2.6%
G 12
 
1.9%
4 10
 
1.6%
L 9
 
1.5%
5 8
 
1.3%
Other values (20) 67
 
10.8%

평가액(억 원)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct434
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5763.2097
Minimum1
Maximum1296198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-13T03:08:36.363606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile66.4
Q1296
median776
Q32021
95-th percentile11071.2
Maximum1296198
Range1296197
Interquartile range (IQR)1725

Descriptive statistics

Standard deviation58717.561
Coefficient of variation (CV)10.188344
Kurtosis456.80167
Mean5763.2097
Median Absolute Deviation (MAD)581
Skewness20.906791
Sum2968053
Variance3.447752 × 109
MonotonicityDecreasing
2023-12-13T03:08:36.524837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 14
 
2.7%
1 13
 
2.5%
99 5
 
1.0%
191 5
 
1.0%
573 4
 
0.8%
95 3
 
0.6%
292 3
 
0.6%
289 3
 
0.6%
181 3
 
0.6%
287 3
 
0.6%
Other values (424) 459
89.1%
ValueCountFrequency (%)
1 13
2.5%
11 1
 
0.2%
26 1
 
0.2%
39 1
 
0.2%
45 1
 
0.2%
49 1
 
0.2%
50 2
 
0.4%
55 1
 
0.2%
59 1
 
0.2%
60 1
 
0.2%
ValueCountFrequency (%)
1296198 1
0.2%
246484 1
0.2%
134049 1
0.2%
131974 1
0.2%
49864 1
0.2%
31482 1
0.2%
29338 1
0.2%
26287 1
0.2%
21965 1
0.2%
21803 1
0.2%

비중(퍼센트)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct56
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19413592
Minimum0
Maximum43.67
Zeros63
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-13T03:08:36.702263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.03
Q30.07
95-th percentile0.37
Maximum43.67
Range43.67
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation1.9782479
Coefficient of variation (CV)10.190015
Kurtosis456.79851
Mean0.19413592
Median Absolute Deviation (MAD)0.02
Skewness20.906607
Sum99.98
Variance3.9134648
MonotonicityDecreasing
2023-12-13T03:08:36.842766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 103
20.0%
0.02 83
16.1%
0.0 63
12.2%
0.03 60
11.7%
0.04 34
 
6.6%
0.05 21
 
4.1%
0.06 17
 
3.3%
0.07 10
 
1.9%
0.09 9
 
1.7%
0.11 9
 
1.7%
Other values (46) 106
20.6%
ValueCountFrequency (%)
0.0 63
12.2%
0.01 103
20.0%
0.02 83
16.1%
0.03 60
11.7%
0.04 34
 
6.6%
0.05 21
 
4.1%
0.06 17
 
3.3%
0.07 10
 
1.9%
0.08 7
 
1.4%
0.09 9
 
1.7%
ValueCountFrequency (%)
43.67 1
0.2%
8.3 1
0.2%
4.52 1
0.2%
4.45 1
0.2%
1.68 1
0.2%
1.06 1
0.2%
0.99 1
0.2%
0.89 1
0.2%
0.74 1
0.2%
0.73 1
0.2%

Interactions

2023-12-13T03:08:34.805623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.872585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.496732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.915952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.300151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.592965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:35.021605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.406475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.694688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:08:36.933750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호평가액(억 원)비중(퍼센트)
번호1.0000.2180.218
평가액(억 원)0.2181.0001.000
비중(퍼센트)0.2181.0001.000
2023-12-13T03:08:37.031531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호평가액(억 원)비중(퍼센트)
번호1.000-1.000-0.992
평가액(억 원)-1.0001.0000.992
비중(퍼센트)-0.9920.9921.000

Missing values

2023-12-13T03:08:35.157577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:08:35.246159image/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

번호발행기관명평가액(억 원)비중(퍼센트)
01국채129619843.67
12한국주택금융공사2464848.3
23한국전력공사1340494.52
34한국은행1319744.45
45한국토지주택공사498641.68
56서울특별시314821.06
67농협중앙회293380.99
78한국산업은행262870.89
89중소기업은행219650.74
910농협은행218030.73
번호발행기관명평가액(억 원)비중(퍼센트)
505506티월드제사십일차유동화전문 유한회사10.0
506507유플러스파이브지제사십칠차유동화전문 유한회사10.0
507508티월드제사십삼차유동화전문 유한회사10.0
508509티월드제사십사차유동화전문 유한회사10.0
509510티월드제사십육차유동화전문 유한회사10.0
510511티월드제사십칠차유동화전문 유한회사10.0
511512티월드제사십구차유동화전문 유한회사10.0
512513티월드제오십차유동화전문 유한회사10.0
513514티월드제오십이차유동화전문 유한회사10.0
514515유플러스파이브지제사십팔차유동화전문 유한회사10.0