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 국내채권 자산의 월별 신용위험지표 시계열 자료입니다.(2018년 1월에서 2023년 12월까지 최대예상손실을 비율과 금액으로 측정)
Author공무원연금공단
URLhttps://www.data.go.kr/data/15095349/fileData.do

Alerts

순번 has unique valuesUnique
구분 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:55:21.633583
Analysis finished2024-04-29 22:55:23.326008
Duration1.69 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
2024-04-30T07:55:23.423084image/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
2024-04-30T07:55:23.594352image/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
2024-04-30T07:55:23.814663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.291667
Min length17

Characters and Unicode

Total characters2778
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 39
 
10.7%
최대예상손실금액(억원 39
 
10.7%
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%
9월 6
 
1.6%
Other values (32) 126
34.4%
2024-04-30T07:55:24.168332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 288
 
10.4%
222
 
8.0%
0 180
 
6.5%
예 144
 
5.2%
) 144
 
5.2%
실 144
 
5.2%
손 144
 
5.2%
상 144
 
5.2%
( 144
 
5.2%
대 144
 
5.2%
Other values (16) 1080
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1440
51.8%
Decimal Number 756
27.2%
Space Separator 222
 
8.0%
Close Punctuation 144
 
5.2%
Open Punctuation 144
 
5.2%
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 (%)
222
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
51.8%
Common 1338
48.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 288
21.5%
222
16.6%
0 180
13.5%
) 144
10.8%
( 144
10.8%
1 132
9.9%
% 72
 
5.4%
8 36
 
2.7%
3 36
 
2.7%
9 36
 
2.7%
Other values (4) 48
 
3.6%
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
51.8%
ASCII 1338
48.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 288
21.5%
222
16.6%
0 180
13.5%
) 144
10.8%
( 144
10.8%
1 132
9.9%
% 72
 
5.4%
8 36
 
2.7%
3 36
 
2.7%
9 36
 
2.7%
Other values (4) 48
 
3.6%
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%

국내채권
Real number (ℝ)

Distinct83
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.849444
Minimum0.08
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-30T07:55:24.299289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.183
Q10.34
median4.35
Q358.75
95-th percentile97.7
Maximum141
Range140.92
Interquartile range (IQR)58.41

Descriptive statistics

Standard deviation38.377194
Coefficient of variation (CV)1.2049565
Kurtosis-0.19759788
Mean31.849444
Median Absolute Deviation (MAD)4.27
Skewness0.90036918
Sum4586.32
Variance1472.809
MonotonicityNot monotonic
2024-04-30T07:55:24.445615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35 6
 
4.2%
0.32 5
 
3.5%
0.38 4
 
2.8%
0.34 4
 
2.8%
57.0 4
 
2.8%
0.28 4
 
2.8%
0.3 3
 
2.1%
0.31 3
 
2.1%
0.42 3
 
2.1%
0.36 3
 
2.1%
Other values (73) 105
72.9%
ValueCountFrequency (%)
0.08 3
2.1%
0.09 2
1.4%
0.1 1
 
0.7%
0.11 1
 
0.7%
0.18 1
 
0.7%
0.2 2
1.4%
0.21 1
 
0.7%
0.25 1
 
0.7%
0.26 2
1.4%
0.27 1
 
0.7%
ValueCountFrequency (%)
141.0 1
0.7%
139.0 1
0.7%
138.0 1
0.7%
136.0 1
0.7%
106.0 1
0.7%
100.0 1
0.7%
98.0 2
1.4%
96.0 1
0.7%
95.0 2
1.4%
91.0 1
0.7%

Interactions

2024-04-30T07:55:23.037653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:22.779180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:23.120087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:22.934242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:55:24.543333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번국내채권
순번1.0000.623
국내채권0.6231.000
2024-04-30T07:55:24.636475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번국내채권
순번1.000-0.184
국내채권-0.1841.000

Missing values

2024-04-30T07:55:23.228151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:55:23.294067image/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

순번구분국내채권
012018년 1월 최대예상손실(%)0.2
122018년 1월 최대예상손실금액(억원)44.0
232018년 2월최대예상손실(%)0.21
342018년 2월최대예상손실금액(억원)48.0
452018년 3월최대예상손실(%)0.2
562018년 3월최대예상손실금액(억원)47.0
672018년 4월최대예상손실(%)0.25
782018년 4월최대예상손실금액(억원)54.0
892018년 5월최대예상손실(%)0.26
9102018년 5월최대예상손실금액(억원)61.0
순번구분국내채권
1341352023년 8월 최대예상손실(%)0.08
1351362023년 8월 최대예상손실금액(억원)8.0
1361372023년 9월 최대예상손실(%)0.08
1371382023년 9월 최대예상손실금액(억원)8.0
1381392023년 10월 최대예상손실(%)0.08
1391402023년 10월 최대예상손실금액(억원)9.0
1401412023년 11월 최대예상손실(%)0.09
1411422023년 11월 최대예상손실금액(억원)9.0
1421432023년 12월 최대예상손실(%)0.09
1431442023년 12월 최대예상손실금액(억원)10.0