Overview

Dataset statistics

Number of variables4
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory948.0 B
Average record size in memory39.5 B

Variable types

Numeric2
Text1
Categorical1

Dataset

Description공무원연금 자금운용사업 중 국내 주식 자산의 월별 신용위험지표 시계열 자료입니다. (2017년 1월에서 2017년 12월까지 최대예상손실을 비율과 금액으로 측정)
URLhttps://www.data.go.kr/data/15095348/fileData.do

Alerts

기준일 has constant value ""Constant
순번 has unique valuesUnique
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:29:07.862898
Analysis finished2023-12-12 22:29:08.433756
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T07:29:08.498707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2023-12-13T07:29:08.612328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

구분
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T07:29:08.795477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20.5
Mean length19.833333
Min length19

Characters and Unicode

Total characters476
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
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 (%)
2017년 24
48.0%
1월 2
 
4.0%
12월최대예상손실(퍼센트 1
 
2.0%
11월최대예상손실금액(억원 1
 
2.0%
11월최대예상손실(퍼센트 1
 
2.0%
10월최대예상손실금액(억원 1
 
2.0%
10월최대예상손실(퍼센트 1
 
2.0%
9월최대예상손실금액(억원 1
 
2.0%
9월최대예상손실(퍼센트 1
 
2.0%
8월최대예상손실금액(억원 1
 
2.0%
Other values (16) 16
32.0%
2023-12-13T07:29:09.156080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 34
 
7.1%
2 28
 
5.9%
7 26
 
5.5%
0 26
 
5.5%
26
 
5.5%
상 24
 
5.0%
( 24
 
5.0%
실 24
 
5.0%
손 24
 
5.0%
예 24
 
5.0%
Other values (18) 216
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 276
58.0%
Decimal Number 126
26.5%
Space Separator 26
 
5.5%
Open Punctuation 24
 
5.0%
Close Punctuation 24
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 24
 
8.7%
실 24
 
8.7%
손 24
 
8.7%
예 24
 
8.7%
대 24
 
8.7%
최 24
 
8.7%
월 24
 
8.7%
년 24
 
8.7%
액 12
 
4.3%
원 12
 
4.3%
Other values (5) 60
21.7%
Decimal Number
ValueCountFrequency (%)
1 34
27.0%
2 28
22.2%
7 26
20.6%
0 26
20.6%
3 2
 
1.6%
4 2
 
1.6%
5 2
 
1.6%
6 2
 
1.6%
8 2
 
1.6%
9 2
 
1.6%
Space Separator
ValueCountFrequency (%)
26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 276
58.0%
Common 200
42.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 24
 
8.7%
실 24
 
8.7%
손 24
 
8.7%
예 24
 
8.7%
대 24
 
8.7%
최 24
 
8.7%
월 24
 
8.7%
년 24
 
8.7%
액 12
 
4.3%
원 12
 
4.3%
Other values (5) 60
21.7%
Common
ValueCountFrequency (%)
1 34
17.0%
2 28
14.0%
7 26
13.0%
0 26
13.0%
26
13.0%
( 24
12.0%
) 24
12.0%
3 2
 
1.0%
4 2
 
1.0%
5 2
 
1.0%
Other values (3) 6
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 276
58.0%
ASCII 200
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 34
17.0%
2 28
14.0%
7 26
13.0%
0 26
13.0%
26
13.0%
( 24
12.0%
) 24
12.0%
3 2
 
1.0%
4 2
 
1.0%
5 2
 
1.0%
Other values (3) 6
 
3.0%
Hangul
ValueCountFrequency (%)
상 24
 
8.7%
실 24
 
8.7%
손 24
 
8.7%
예 24
 
8.7%
대 24
 
8.7%
최 24
 
8.7%
월 24
 
8.7%
년 24
 
8.7%
액 12
 
4.3%
원 12
 
4.3%
Other values (5) 60
21.7%

채권
Real number (ℝ)

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.474167
Minimum0.16
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T07:29:09.269643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile0.19
Q10.32
median16.695
Q373.5
95-th percentile87.4
Maximum96
Range95.84
Interquartile range (IQR)73.18

Descriptive statistics

Standard deviation37.044366
Coefficient of variation (CV)1.1066554
Kurtosis-1.5459492
Mean33.474167
Median Absolute Deviation (MAD)16.52
Skewness0.45336368
Sum803.38
Variance1372.2851
MonotonicityNot monotonic
2023-12-13T07:29:09.384853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.32 2
 
8.3%
0.2 2
 
8.3%
47.0 2
 
8.3%
0.19 2
 
8.3%
45.0 2
 
8.3%
0.39 1
 
4.2%
0.35 1
 
4.2%
96.0 1
 
4.2%
0.37 1
 
4.2%
82.0 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
0.16 1
4.2%
0.19 2
8.3%
0.2 2
8.3%
0.32 2
8.3%
0.33 1
4.2%
0.35 1
4.2%
0.36 1
4.2%
0.37 1
4.2%
0.39 1
4.2%
33.0 1
4.2%
ValueCountFrequency (%)
96.0 1
4.2%
88.0 1
4.2%
84.0 1
4.2%
83.0 1
4.2%
82.0 1
4.2%
78.0 1
4.2%
72.0 1
4.2%
47.0 2
8.3%
45.0 2
8.3%
33.0 1
4.2%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2017-12-31
24 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-12-31
2nd row2017-12-31
3rd row2017-12-31
4th row2017-12-31
5th row2017-12-31

Common Values

ValueCountFrequency (%)
2017-12-31 24
100.0%

Length

2023-12-13T07:29:09.503710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:29:09.595267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-12-31 24
100.0%

Interactions

2023-12-13T07:29:08.139109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:07.963464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:08.213847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:08.061168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:29:09.652543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분채권
순번1.0001.0000.000
구분1.0001.0001.000
채권0.0001.0001.000
2023-12-13T07:29:09.742949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번채권
순번1.0000.085
채권0.0851.000

Missing values

2023-12-13T07:29:08.310370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:29:08.397656image/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

순번구분채권기준일
012017년 1월 최대예상손실(퍼센트)0.392017-12-31
122017년 1월 최대예상손실금액(억원)88.02017-12-31
232017년 2월최대예상손실(퍼센트)0.362017-12-31
342017년 2월최대예상손실금액(억원)83.02017-12-31
452017년 3월최대예상손실(퍼센트)0.322017-12-31
562017년 3월최대예상손실금액(억원)72.02017-12-31
672017년 4월최대예상손실(퍼센트)0.22017-12-31
782017년 4월최대예상손실금액(억원)47.02017-12-31
892017년 5월최대예상손실(퍼센트)0.22017-12-31
9102017년 5월최대예상손실금액(억원)47.02017-12-31
순번구분채권기준일
14152017년 8월최대예상손실(퍼센트)0.192017-12-31
15162017년 8월최대예상손실금액(억원)45.02017-12-31
16172017년 9월최대예상손실(퍼센트)0.322017-12-31
17182017년 9월최대예상손실금액(억원)78.02017-12-31
18192017년 10월최대예상손실(퍼센트)0.352017-12-31
19202017년 10월최대예상손실금액(억원)84.02017-12-31
20212017년 11월최대예상손실(퍼센트)0.332017-12-31
21222017년 11월최대예상손실금액(억원)82.02017-12-31
22232017년 12월최대예상손실(퍼센트)0.372017-12-31
23242017년 12월최대예상손실금액(억원)96.02017-12-31