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 국내 주식 자산의 월별 시장위험지표 시계열 자료입니다.(2017년 1월에서 2023년 12월까지 최대예상손실을 비율과 금액으로 측정)
Author공무원연금공단
URLhttps://www.data.go.kr/data/15095344/fileData.do

Alerts

순번 has unique valuesUnique
구분 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:55:01.986795
Analysis finished2024-04-29 22:55:03.842213
Duration1.86 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
2024-04-30T07:55:03.922295image/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
2024-04-30T07:55:04.062572image/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
2024-04-30T07:55:04.298304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.797619
Min length18

Characters and Unicode

Total characters3326
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

Unique168 ?
Unique (%)100.0%

Sample

1st row2017년 1월 최대예상손실(퍼센트)
2nd row2017년 1월 최대예상손실금액(억원)
3rd row2017년 2월최대예상손실(퍼센트)
4th row2017년 2월최대예상손실금액(억원)
5th row2017년 3월최대예상손실(퍼센트)
ValueCountFrequency (%)
최대예상손실금액(억원 40
 
9.6%
최대예상손실 36
 
8.7%
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 (34) 158
38.0%
2024-04-30T07:55:04.662785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 316
 
9.5%
248
 
7.5%
0 206
 
6.2%
대 168
 
5.1%
( 168
 
5.1%
실 168
 
5.1%
손 168
 
5.1%
상 168
 
5.1%
예 168
 
5.1%
최 168
 
5.1%
Other values (19) 1380
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1824
54.8%
Decimal Number 882
26.5%
Space Separator 248
 
7.5%
Open Punctuation 168
 
5.1%
Close Punctuation 168
 
5.1%
Other Punctuation 36
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 168
9.2%
실 168
9.2%
손 168
9.2%
상 168
9.2%
예 168
9.2%
최 168
9.2%
월 168
9.2%
년 168
9.2%
금 84
 
4.6%
원 84
 
4.6%
Other values (5) 312
17.1%
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%
Open Punctuation
ValueCountFrequency (%)
( 168
100.0%
Close Punctuation
ValueCountFrequency (%)
) 168
100.0%
Other Punctuation
ValueCountFrequency (%)
% 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1824
54.8%
Common 1502
45.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 168
9.2%
실 168
9.2%
손 168
9.2%
상 168
9.2%
예 168
9.2%
최 168
9.2%
월 168
9.2%
년 168
9.2%
금 84
 
4.6%
원 84
 
4.6%
Other values (5) 312
17.1%
Common
ValueCountFrequency (%)
2 316
21.0%
248
16.5%
0 206
13.7%
( 168
11.2%
) 168
11.2%
1 166
11.1%
7 38
 
2.5%
3 38
 
2.5%
8 38
 
2.5%
9 38
 
2.5%
Other values (4) 78
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1824
54.8%
ASCII 1502
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 316
21.0%
248
16.5%
0 206
13.7%
( 168
11.2%
) 168
11.2%
1 166
11.1%
7 38
 
2.5%
3 38
 
2.5%
8 38
 
2.5%
9 38
 
2.5%
Other values (4) 78
 
5.2%
Hangul
ValueCountFrequency (%)
대 168
9.2%
실 168
9.2%
손 168
9.2%
상 168
9.2%
예 168
9.2%
최 168
9.2%
월 168
9.2%
년 168
9.2%
금 84
 
4.6%
원 84
 
4.6%
Other values (5) 312
17.1%

국내주식
Real number (ℝ)

Distinct161
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean571.97673
Minimum3.87
Maximum3520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-30T07:55:04.798777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.87
5-th percentile4.6415
Q16.82
median233.975
Q31008.5
95-th percentile1803.75
Maximum3520
Range3516.13
Interquartile range (IQR)1001.68

Descriptive statistics

Standard deviation696.95804
Coefficient of variation (CV)1.2185077
Kurtosis2.2949506
Mean571.97673
Median Absolute Deviation (MAD)230.105
Skewness1.3734062
Sum96092.09
Variance485750.51
MonotonicityNot monotonic
2024-04-30T07:55:04.946569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.87 3
 
1.8%
597.0 2
 
1.2%
1006.0 2
 
1.2%
729.0 2
 
1.2%
6.63 2
 
1.2%
9.52 2
 
1.2%
4.52 1
 
0.6%
7.24 1
 
0.6%
6.64 1
 
0.6%
1060.0 1
 
0.6%
Other values (151) 151
89.9%
ValueCountFrequency (%)
3.87 3
1.8%
4.21 1
 
0.6%
4.44 1
 
0.6%
4.49 1
 
0.6%
4.52 1
 
0.6%
4.57 1
 
0.6%
4.61 1
 
0.6%
4.7 1
 
0.6%
4.76 1
 
0.6%
4.91 1
 
0.6%
ValueCountFrequency (%)
3520.0 1
0.6%
3164.0 1
0.6%
2681.0 1
0.6%
2645.0 1
0.6%
2147.0 1
0.6%
2090.0 1
0.6%
2070.0 1
0.6%
2002.0 1
0.6%
1837.0 1
0.6%
1742.0 1
0.6%

Interactions

2024-04-30T07:55:03.484382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:03.245551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:03.599618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:03.391232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:55:05.063939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번국내주식
순번1.0000.640
국내주식0.6401.000
2024-04-30T07:55:05.156325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번국내주식
순번1.0000.070
국내주식0.0701.000

Missing values

2024-04-30T07:55:03.719106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:55:03.799483image/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

순번구분국내주식
012017년 1월 최대예상손실(퍼센트)4.52
122017년 1월 최대예상손실금액(억원)682.0
232017년 2월최대예상손실(퍼센트)3.87
342017년 2월최대예상손실금액(억원)597.0
452017년 3월최대예상손실(퍼센트)3.87
562017년 3월최대예상손실금액(억원)620.0
672017년 4월최대예상손실(퍼센트)3.87
782017년 4월최대예상손실금액(억원)607.0
892017년 5월최대예상손실(퍼센트)4.49
9102017년 5월최대예상손실금액(억원)729.0
순번구분국내주식
1581592023년 8월 최대예상손실(%)5.92
1591602023년 8월 최대예상손실금액(억원)528.0
1601612023년 9월 최대예상손실(%)6.09
1611622023년 9월 최대예상손실금액(억원)543.0
1621632023년 10월 최대예상손실(%)7.52
1631642023년 10월 최대예상손실금액(억원)646.0
1641652023년 11월 최대예상손실(%)8.45
1651662023년 11월 최대예상손실금액(억원)813.0
1661672023년 12월 최대예상손실(%)7.39
1671682023년 12월 최대예상손실금액(억원)760.0