Overview

Dataset statistics

Number of variables3
Number of observations144
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory26.9 B

Variable types

Numeric2
Text1

Dataset

Description공무원연금 자금운용사업 중 해외 채권 자산의 월별 신용위험지표 시계열 자료입니다.(2018년 1월에서 2023년 12월까지 최대예상손실을 비율과 금액으로 측정)
Author공무원연금공단
URLhttps://www.data.go.kr/data/15095350/fileData.do

Alerts

순번 has unique valuesUnique
구분 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:55:28.303632
Analysis finished2024-04-29 22:55:30.001071
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.5
Minimum1
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-30T07:55:30.067797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.15
Q136.75
median72.5
Q3108.25
95-th percentile136.85
Maximum144
Range143
Interquartile range (IQR)71.5

Descriptive statistics

Standard deviation41.713307
Coefficient of variation (CV)0.57535596
Kurtosis-1.2
Mean72.5
Median Absolute Deviation (MAD)36
Skewness0
Sum10440
Variance1740
MonotonicityStrictly increasing
2024-04-30T07:55:30.201643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
74 1
 
0.7%
94 1
 
0.7%
95 1
 
0.7%
96 1
 
0.7%
97 1
 
0.7%
98 1
 
0.7%
99 1
 
0.7%
100 1
 
0.7%
101 1
 
0.7%
Other values (134) 134
93.1%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
144 1
0.7%
143 1
0.7%
142 1
0.7%
141 1
0.7%
140 1
0.7%
139 1
0.7%
138 1
0.7%
137 1
0.7%
136 1
0.7%
135 1
0.7%

구분
Text

UNIQUE 

Distinct144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-04-30T07:55:30.412512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.4375
Min length17

Characters and Unicode

Total characters2799
Distinct characters26
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)100.0%

Sample

1st row2018년 1월 최대예상손실(%)
2nd row2018년 1월 최대예상손실금액(억원)
3rd row2018년 2월최대예상손실(%)
4th row2018년 2월최대예상손실금액(억원)
5th row2018년 3월최대예상손실(%)
ValueCountFrequency (%)
최대예상손실 50
12.9%
최대예상손실금액(억원 49
 
12.7%
2018년 24
 
6.2%
2019년 24
 
6.2%
2022년 24
 
6.2%
2021년 24
 
6.2%
2020년 24
 
6.2%
2023년 24
 
6.2%
1월 12
 
3.1%
7월 8
 
2.1%
Other values (32) 124
32.0%
2024-04-30T07:55:30.737381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 288
 
10.3%
243
 
8.7%
0 180
 
6.4%
예 144
 
5.1%
) 144
 
5.1%
실 144
 
5.1%
손 144
 
5.1%
상 144
 
5.1%
( 144
 
5.1%
대 144
 
5.1%
Other values (16) 1080
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1440
51.4%
Decimal Number 756
27.0%
Space Separator 243
 
8.7%
Close Punctuation 144
 
5.1%
Open Punctuation 144
 
5.1%
Other Punctuation 72
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
예 144
10.0%
실 144
10.0%
손 144
10.0%
상 144
10.0%
대 144
10.0%
최 144
10.0%
월 144
10.0%
년 144
10.0%
원 72
5.0%
억 72
5.0%
Other values (2) 144
10.0%
Decimal Number
ValueCountFrequency (%)
2 288
38.1%
0 180
23.8%
1 132
17.5%
8 36
 
4.8%
3 36
 
4.8%
9 36
 
4.8%
5 12
 
1.6%
4 12
 
1.6%
6 12
 
1.6%
7 12
 
1.6%
Space Separator
ValueCountFrequency (%)
243
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Other Punctuation
ValueCountFrequency (%)
% 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1440
51.4%
Common 1359
48.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 288
21.2%
243
17.9%
0 180
13.2%
) 144
10.6%
( 144
10.6%
1 132
9.7%
% 72
 
5.3%
8 36
 
2.6%
3 36
 
2.6%
9 36
 
2.6%
Other values (4) 48
 
3.5%
Hangul
ValueCountFrequency (%)
예 144
10.0%
실 144
10.0%
손 144
10.0%
상 144
10.0%
대 144
10.0%
최 144
10.0%
월 144
10.0%
년 144
10.0%
원 72
5.0%
억 72
5.0%
Other values (2) 144
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1440
51.4%
ASCII 1359
48.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 288
21.2%
243
17.9%
0 180
13.2%
) 144
10.6%
( 144
10.6%
1 132
9.7%
% 72
 
5.3%
8 36
 
2.6%
3 36
 
2.6%
9 36
 
2.6%
Other values (4) 48
 
3.5%
Hangul
ValueCountFrequency (%)
예 144
10.0%
실 144
10.0%
손 144
10.0%
상 144
10.0%
대 144
10.0%
최 144
10.0%
월 144
10.0%
년 144
10.0%
원 72
5.0%
억 72
5.0%
Other values (2) 144
10.0%

해외채권
Real number (ℝ)

Distinct63
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3709722
Minimum0.15
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-30T07:55:30.871426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.22
Q10.35
median2.89
Q316
95-th percentile30
Maximum43
Range42.85
Interquartile range (IQR)15.65

Descriptive statistics

Standard deviation10.792049
Coefficient of variation (CV)1.1516467
Kurtosis-0.068218575
Mean9.3709722
Median Absolute Deviation (MAD)2.735
Skewness0.93724216
Sum1349.42
Variance116.46832
MonotonicityNot monotonic
2024-04-30T07:55:30.996459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.0 8
 
5.6%
10.0 7
 
4.9%
0.23 6
 
4.2%
14.0 5
 
3.5%
30.0 4
 
2.8%
0.47 4
 
2.8%
0.72 4
 
2.8%
12.0 4
 
2.8%
0.26 4
 
2.8%
21.0 4
 
2.8%
Other values (53) 94
65.3%
ValueCountFrequency (%)
0.15 1
 
0.7%
0.16 2
 
1.4%
0.17 1
 
0.7%
0.18 2
 
1.4%
0.19 1
 
0.7%
0.22 2
 
1.4%
0.23 6
4.2%
0.24 3
2.1%
0.25 3
2.1%
0.26 4
2.8%
ValueCountFrequency (%)
43.0 1
 
0.7%
42.0 1
 
0.7%
34.0 1
 
0.7%
31.0 2
1.4%
30.0 4
2.8%
29.0 1
 
0.7%
28.0 1
 
0.7%
27.0 1
 
0.7%
26.0 3
2.1%
25.0 3
2.1%

Interactions

2024-04-30T07:55:29.724970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:29.498187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:29.800002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:29.643843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:55:31.072330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번해외채권
순번1.0000.360
해외채권0.3601.000
2024-04-30T07:55:31.150711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번해외채권
순번1.0000.163
해외채권0.1631.000

Missing values

2024-04-30T07:55:29.893876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:55:29.971252image/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

순번구분해외채권
012018년 1월 최대예상손실(%)0.16
122018년 1월 최대예상손실금액(억원)5.0
232018년 2월최대예상손실(%)0.18
342018년 2월최대예상손실금액(억원)6.0
452018년 3월최대예상손실(%)0.25
562018년 3월최대예상손실금액(억원)9.0
672018년 4월최대예상손실(%)0.31
782018년 4월최대예상손실금액(억원)12.0
892018년 5월최대예상손실(%)0.24
9102018년 5월최대예상손실금액(억원)10.0
순번구분해외채권
1341352023년 8월 최대예상손실(%)0.22
1351362023년 8월 최대예상손실금액(억원)12.0
1361372023년 9월 최대예상손실(%)0.37
1371382023년 9월 최대예상손실금액(억원)20.0
1381392023년 10월 최대예상손실(%)0.42
1391402023년 10월 최대예상손실금액(억원)23.0
1401412023년 11월 최대예상손실(%)0.48
1411422023년 11월 최대예상손실금액(억원)27.0
1421432023년 12월 최대예상손실(%)0.51
1431442023년 12월 최대예상손실금액(억원)29.0