Overview

Dataset statistics

Number of variables3
Number of observations168
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory26.8 B

Variable types

Numeric2
Text1

Dataset

Description공무원연금 자금운용사업 중 대체투자 자산의 월별 신용위험지표 시계열 자료입니다.(2017년 1월에서 2023년 12월까지 최대예상손실을 비율과 금액으로 측정)
Author공무원연금공단
URLhttps://www.data.go.kr/data/15095351/fileData.do

Alerts

순번 has unique valuesUnique
구분 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:55:34.532872
Analysis finished2024-04-29 22:55:36.263132
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct168
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.5
Minimum1
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-30T07:55:36.332396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.35
Q142.75
median84.5
Q3126.25
95-th percentile159.65
Maximum168
Range167
Interquartile range (IQR)83.5

Descriptive statistics

Standard deviation48.641546
Coefficient of variation (CV)0.5756396
Kurtosis-1.2
Mean84.5
Median Absolute Deviation (MAD)42
Skewness0
Sum14196
Variance2366
MonotonicityStrictly increasing
2024-04-30T07:55:36.460351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
117 1
 
0.6%
109 1
 
0.6%
110 1
 
0.6%
111 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
Other values (158) 158
94.0%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%
164 1
0.6%
163 1
0.6%
162 1
0.6%
161 1
0.6%
160 1
0.6%
159 1
0.6%

구분
Text

UNIQUE 

Distinct168
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-30T07:55:36.701813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.22619
Min length17

Characters and Unicode

Total characters3230
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

Unique168 ?
Unique (%)100.0%

Sample

1st row2017년 1월 최대예상손실(%)
2nd row2017년 1월 최대예상손실금액(억원)
3rd row2017년 2월최대예상손실(%)
4th row2017년 2월최대예상손실금액(억원)
5th row2017년 3월최대예상손실(%)
ValueCountFrequency (%)
최대예상손실 40
 
9.6%
최대예상손실금액(억원 40
 
9.6%
2017년 24
 
5.8%
2021년 24
 
5.8%
2022년 24
 
5.8%
2019년 24
 
5.8%
2020년 24
 
5.8%
2018년 24
 
5.8%
2023년 24
 
5.8%
1월 14
 
3.4%
Other values (33) 154
37.0%
2024-04-30T07:55:37.063118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 316
 
9.8%
248
 
7.7%
0 206
 
6.4%
예 168
 
5.2%
) 168
 
5.2%
실 168
 
5.2%
손 168
 
5.2%
상 168
 
5.2%
( 168
 
5.2%
대 168
 
5.2%
Other values (16) 1284
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1680
52.0%
Decimal Number 882
27.3%
Space Separator 248
 
7.7%
Close Punctuation 168
 
5.2%
Open Punctuation 168
 
5.2%
Other Punctuation 84
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
예 168
10.0%
실 168
10.0%
손 168
10.0%
상 168
10.0%
대 168
10.0%
최 168
10.0%
월 168
10.0%
년 168
10.0%
원 84
5.0%
억 84
5.0%
Other values (2) 168
10.0%
Decimal Number
ValueCountFrequency (%)
2 316
35.8%
0 206
23.4%
1 166
18.8%
7 38
 
4.3%
3 38
 
4.3%
8 38
 
4.3%
9 38
 
4.3%
5 14
 
1.6%
4 14
 
1.6%
6 14
 
1.6%
Space Separator
ValueCountFrequency (%)
248
100.0%
Close Punctuation
ValueCountFrequency (%)
) 168
100.0%
Open Punctuation
ValueCountFrequency (%)
( 168
100.0%
Other Punctuation
ValueCountFrequency (%)
% 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1680
52.0%
Common 1550
48.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 316
20.4%
248
16.0%
0 206
13.3%
) 168
10.8%
( 168
10.8%
1 166
10.7%
% 84
 
5.4%
7 38
 
2.5%
3 38
 
2.5%
8 38
 
2.5%
Other values (4) 80
 
5.2%
Hangul
ValueCountFrequency (%)
예 168
10.0%
실 168
10.0%
손 168
10.0%
상 168
10.0%
대 168
10.0%
최 168
10.0%
월 168
10.0%
년 168
10.0%
원 84
5.0%
억 84
5.0%
Other values (2) 168
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1680
52.0%
ASCII 1550
48.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 316
20.4%
248
16.0%
0 206
13.3%
) 168
10.8%
( 168
10.8%
1 166
10.7%
% 84
 
5.4%
7 38
 
2.5%
3 38
 
2.5%
8 38
 
2.5%
Other values (4) 80
 
5.2%
Hangul
ValueCountFrequency (%)
예 168
10.0%
실 168
10.0%
손 168
10.0%
상 168
10.0%
대 168
10.0%
최 168
10.0%
월 168
10.0%
년 168
10.0%
원 84
5.0%
억 84
5.0%
Other values (2) 168
10.0%

대체투자
Real number (ℝ)

Distinct149
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.88905
Minimum1.5
Maximum642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-30T07:55:37.191328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.6835
Q12.575
median95.885
Q3409
95-th percentile548.6
Maximum642
Range640.5
Interquartile range (IQR)406.425

Descriptive statistics

Standard deviation217.34612
Coefficient of variation (CV)1.045491
Kurtosis-1.5648942
Mean207.88905
Median Absolute Deviation (MAD)94.38
Skewness0.30403875
Sum34925.36
Variance47239.337
MonotonicityNot monotonic
2024-04-30T07:55:37.327570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.37 3
 
1.8%
302.0 3
 
1.8%
3.65 2
 
1.2%
2.14 2
 
1.2%
1.56 2
 
1.2%
2.71 2
 
1.2%
2.01 2
 
1.2%
2.31 2
 
1.2%
3.16 2
 
1.2%
4.1 2
 
1.2%
Other values (139) 146
86.9%
ValueCountFrequency (%)
1.5 1
0.6%
1.51 1
0.6%
1.56 2
1.2%
1.59 1
0.6%
1.65 1
0.6%
1.66 1
0.6%
1.67 1
0.6%
1.68 1
0.6%
1.69 1
0.6%
1.73 1
0.6%
ValueCountFrequency (%)
642.0 1
0.6%
640.0 1
0.6%
618.0 1
0.6%
615.0 1
0.6%
570.0 1
0.6%
565.0 1
0.6%
560.0 1
0.6%
558.0 1
0.6%
550.0 1
0.6%
546.0 1
0.6%

Interactions

2024-04-30T07:55:35.973323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:35.733594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:36.057761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:35.874887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:55:37.412912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번대체투자
순번1.0000.559
대체투자0.5591.000
2024-04-30T07:55:37.491925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번대체투자
순번1.000-0.127
대체투자-0.1271.000

Missing values

2024-04-30T07:55:36.159570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:55:36.227632image/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

순번구분대체투자
012017년 1월 최대예상손실(%)4.37
122017년 1월 최대예상손실금액(억원)387.0
232017년 2월최대예상손실(%)4.77
342017년 2월최대예상손실금액(억원)425.0
452017년 3월최대예상손실(%)4.55
562017년 3월최대예상손실금액(억원)407.0
672017년 4월최대예상손실(%)4.69
782017년 4월최대예상손실금액(억원)427.0
892017년 5월최대예상손실(%)4.68
9102017년 5월최대예상손실금액(억원)451.0
순번구분대체투자
1581592023년 8월 최대예상손실(%)2.3
1591602023년 8월 최대예상손실금액(억원)465.0
1601612023년 9월 최대예상손실(%)2.31
1611622023년 9월 최대예상손실금액(억원)469.0
1621632023년 10월 최대예상손실(%)2.31
1631642023년 10월 최대예상손실금액(억원)472.0
1641652023년 11월 최대예상손실(%)2.45
1651662023년 11월 최대예상손실금액(억원)487.0
1661672023년 12월 최대예상손실(%)2.4
1671682023년 12월 최대예상손실금액(억원)481.0