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 시장위험지표 시계열 자료입니다.(2018년 1월에서 2023년 12월까지 최대예상손실을 비율과 금액으로 측정)
Author공무원연금공단
URLhttps://www.data.go.kr/data/15095343/fileData.do

Alerts

순번 has unique valuesUnique
구분 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:54:55.527840
Analysis finished2024-04-29 22:54:57.270177
Duration1.74 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
2024-04-30T07:54:57.347448image/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
2024-04-30T07:54:57.486144image/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
2024-04-30T07:54:57.736080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.791667
Min length18

Characters and Unicode

Total characters2850
Distinct characters29
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(퍼센트)
2nd row2018년 1월 최대예상손실금액(억원)
3rd row2018년 2월최대예상손실(퍼센트)
4th row2018년 2월최대예상손실금액(억원)
5th row2018년 3월최대예상손실(퍼센트)
ValueCountFrequency (%)
최대예상손실금액(억원 39
 
10.7%
최대예상손실 36
 
9.8%
2018년 24
 
6.6%
2019년 24
 
6.6%
2022년 24
 
6.6%
2021년 24
 
6.6%
2020년 24
 
6.6%
2023년 24
 
6.6%
1월 12
 
3.3%
10월 6
 
1.6%
Other values (33) 129
35.2%
2024-04-30T07:54:58.096360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 288
 
10.1%
222
 
7.8%
0 180
 
6.3%
예 144
 
5.1%
) 144
 
5.1%
( 144
 
5.1%
실 144
 
5.1%
손 144
 
5.1%
상 144
 
5.1%
대 144
 
5.1%
Other values (19) 1152
40.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1548
54.3%
Decimal Number 756
26.5%
Space Separator 222
 
7.8%
Close Punctuation 144
 
5.1%
Open Punctuation 144
 
5.1%
Other Punctuation 36
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
예 144
9.3%
실 144
9.3%
손 144
9.3%
상 144
9.3%
대 144
9.3%
최 144
9.3%
월 144
9.3%
년 144
9.3%
억 72
 
4.7%
원 72
 
4.7%
Other values (5) 252
16.3%
Decimal Number
ValueCountFrequency (%)
2 288
38.1%
0 180
23.8%
1 132
17.5%
9 36
 
4.8%
3 36
 
4.8%
8 36
 
4.8%
5 12
 
1.6%
4 12
 
1.6%
6 12
 
1.6%
7 12
 
1.6%
Space Separator
ValueCountFrequency (%)
222
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Other Punctuation
ValueCountFrequency (%)
% 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1548
54.3%
Common 1302
45.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
예 144
9.3%
실 144
9.3%
손 144
9.3%
상 144
9.3%
대 144
9.3%
최 144
9.3%
월 144
9.3%
년 144
9.3%
억 72
 
4.7%
원 72
 
4.7%
Other values (5) 252
16.3%
Common
ValueCountFrequency (%)
2 288
22.1%
222
17.1%
0 180
13.8%
) 144
11.1%
( 144
11.1%
1 132
10.1%
9 36
 
2.8%
3 36
 
2.8%
8 36
 
2.8%
% 36
 
2.8%
Other values (4) 48
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1548
54.3%
ASCII 1302
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 288
22.1%
222
17.1%
0 180
13.8%
) 144
11.1%
( 144
11.1%
1 132
10.1%
9 36
 
2.8%
3 36
 
2.8%
8 36
 
2.8%
% 36
 
2.8%
Other values (4) 48
 
3.7%
Hangul
ValueCountFrequency (%)
예 144
9.3%
실 144
9.3%
손 144
9.3%
상 144
9.3%
대 144
9.3%
최 144
9.3%
월 144
9.3%
년 144
9.3%
억 72
 
4.7%
원 72
 
4.7%
Other values (5) 252
16.3%

해외채권
Real number (ℝ)

Distinct111
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.583819
Minimum0.55
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-30T07:54:58.233983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.55
5-th percentile0.6615
Q11.0325
median14.45
Q365.75
95-th percentile131.55
Maximum280
Range279.45
Interquartile range (IQR)64.7175

Descriptive statistics

Standard deviation51.505221
Coefficient of variation (CV)1.3011685
Kurtosis3.2990032
Mean39.583819
Median Absolute Deviation (MAD)13.795
Skewness1.6346541
Sum5700.07
Variance2652.7878
MonotonicityNot monotonic
2024-04-30T07:54:58.363008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.0 3
 
2.1%
0.72 3
 
2.1%
38.0 3
 
2.1%
0.62 3
 
2.1%
0.75 2
 
1.4%
26.0 2
 
1.4%
0.99 2
 
1.4%
1.21 2
 
1.4%
1.05 2
 
1.4%
79.0 2
 
1.4%
Other values (101) 120
83.3%
ValueCountFrequency (%)
0.55 1
 
0.7%
0.59 1
 
0.7%
0.62 3
2.1%
0.63 1
 
0.7%
0.65 1
 
0.7%
0.66 1
 
0.7%
0.67 1
 
0.7%
0.69 1
 
0.7%
0.7 2
1.4%
0.71 2
1.4%
ValueCountFrequency (%)
280.0 1
0.7%
228.0 1
0.7%
182.0 1
0.7%
140.0 1
0.7%
135.0 1
0.7%
134.0 1
0.7%
132.0 2
1.4%
129.0 1
0.7%
127.0 1
0.7%
126.0 1
0.7%

Interactions

2024-04-30T07:54:56.928935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:54:56.702989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:54:57.013377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:54:56.850281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:54:58.447251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번해외채권
순번1.0000.452
해외채권0.4521.000
2024-04-30T07:54:58.527143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번해외채권
순번1.0000.376
해외채권0.3761.000

Missing values

2024-04-30T07:54:57.145185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:54:57.236119image/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(퍼센트)0.75
122018년 1월 최대예상손실금액(억원)26.0
232018년 2월최대예상손실(퍼센트)0.7
342018년 2월최대예상손실금액(억원)28.0
452018년 3월최대예상손실(퍼센트)0.73
562018년 3월최대예상손실금액(억원)30.0
672018년 4월최대예상손실(퍼센트)0.72
782018년 4월최대예상손실금액(억원)31.0
892018년 5월최대예상손실(퍼센트)0.85
9102018년 5월최대예상손실금액(억원)38.0
순번구분해외채권
1341352023년 8월 최대예상손실(%)2.0
1351362023년 8월 최대예상손실금액(억원)107.0
1361372023년 9월 최대예상손실(%)2.55
1371382023년 9월 최대예상손실금액(억원)134.0
1381392023년 10월 최대예상손실(%)2.7
1391402023년 10월 최대예상손실금액(억원)140.0
1401412023년 11월 최대예상손실(%)2.45
1411422023년 11월 최대예상손실금액(억원)132.0
1421432023년 12월 최대예상손실(%)2.33
1431442023년 12월 최대예상손실금액(억원)129.0