Overview

Dataset statistics

Number of variables3
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory27.1 B

Variable types

Numeric2
Text1

Dataset

Description공무원연금 자금운용사업 중 단기금융 자산의 월별 신용위험지표 시계열 자료입니다.(2017년 1월부터 2023년 12월까지 매해 월별 최대손실, 최대손실액을 측정)
Author공무원연금공단
URLhttps://www.data.go.kr/data/15095352/fileData.do

Alerts

순번 is highly overall correlated with 단기자금High correlation
단기자금 is highly overall correlated with 순번High correlation
순번 has unique valuesUnique
구분 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:55:40.962181
Analysis finished2024-04-29 22:55:42.682548
Duration1.72 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.5
Minimum1
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T07:55:42.764642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q130.75
median60.5
Q390.25
95-th percentile114.05
Maximum120
Range119
Interquartile range (IQR)59.5

Descriptive statistics

Standard deviation34.785054
Coefficient of variation (CV)0.57495957
Kurtosis-1.2
Mean60.5
Median Absolute Deviation (MAD)30
Skewness0
Sum7260
Variance1210
MonotonicityStrictly increasing
2024-04-30T07:55:42.894164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
62 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
83 1
 
0.8%
Other values (110) 110
91.7%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
112 1
0.8%
111 1
0.8%

구분
Text

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-30T07:55:43.092268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length18.508333
Min length17

Characters and Unicode

Total characters2221
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

Unique120 ?
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
 
14.1%
2017년 24
 
8.5%
2018년 24
 
8.5%
2019년 24
 
8.5%
2022년 12
 
4.2%
2021년 12
 
4.2%
2020년 12
 
4.2%
2023년 12
 
4.2%
1월 10
 
3.5%
4월최대예상손실 4
 
1.4%
Other values (33) 109
38.5%
2024-04-30T07:55:43.422441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 200
 
9.0%
163
 
7.3%
0 142
 
6.4%
1 134
 
6.0%
예 120
 
5.4%
) 120
 
5.4%
실 120
 
5.4%
손 120
 
5.4%
상 120
 
5.4%
( 120
 
5.4%
Other values (16) 862
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1104
49.7%
Decimal Number 630
28.4%
Space Separator 163
 
7.3%
Close Punctuation 120
 
5.4%
Open Punctuation 120
 
5.4%
Other Punctuation 84
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
예 120
10.9%
실 120
10.9%
손 120
10.9%
상 120
10.9%
대 120
10.9%
최 120
10.9%
월 120
10.9%
년 120
10.9%
금 36
 
3.3%
액 36
 
3.3%
Other values (2) 72
6.5%
Decimal Number
ValueCountFrequency (%)
2 200
31.7%
0 142
22.5%
1 134
21.3%
7 34
 
5.4%
8 34
 
5.4%
9 34
 
5.4%
3 22
 
3.5%
4 10
 
1.6%
5 10
 
1.6%
6 10
 
1.6%
Space Separator
ValueCountFrequency (%)
163
100.0%
Close Punctuation
ValueCountFrequency (%)
) 120
100.0%
Open Punctuation
ValueCountFrequency (%)
( 120
100.0%
Other Punctuation
ValueCountFrequency (%)
% 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1117
50.3%
Hangul 1104
49.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 200
17.9%
163
14.6%
0 142
12.7%
1 134
12.0%
) 120
10.7%
( 120
10.7%
% 84
7.5%
7 34
 
3.0%
8 34
 
3.0%
9 34
 
3.0%
Other values (4) 52
 
4.7%
Hangul
ValueCountFrequency (%)
예 120
10.9%
실 120
10.9%
손 120
10.9%
상 120
10.9%
대 120
10.9%
최 120
10.9%
월 120
10.9%
년 120
10.9%
금 36
 
3.3%
액 36
 
3.3%
Other values (2) 72
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1117
50.3%
Hangul 1104
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 200
17.9%
163
14.6%
0 142
12.7%
1 134
12.0%
) 120
10.7%
( 120
10.7%
% 84
7.5%
7 34
 
3.0%
8 34
 
3.0%
9 34
 
3.0%
Other values (4) 52
 
4.7%
Hangul
ValueCountFrequency (%)
예 120
10.9%
실 120
10.9%
손 120
10.9%
상 120
10.9%
대 120
10.9%
최 120
10.9%
월 120
10.9%
년 120
10.9%
금 36
 
3.3%
액 36
 
3.3%
Other values (2) 72
6.5%

단기자금
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.370333
Minimum0.01
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T07:55:43.585084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.24
Q10.32
median0.43
Q338.75
95-th percentile112.05
Maximum181
Range180.99
Interquartile range (IQR)38.43

Descriptive statistics

Standard deviation39.510663
Coefficient of variation (CV)1.6906333
Kurtosis1.7794667
Mean23.370333
Median Absolute Deviation (MAD)0.16
Skewness1.614179
Sum2804.44
Variance1561.0925
MonotonicityNot monotonic
2024-04-30T07:55:43.743948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33 5
 
4.2%
0.42 4
 
3.3%
0.32 4
 
3.3%
0.27 4
 
3.3%
0.29 4
 
3.3%
0.24 4
 
3.3%
0.35 4
 
3.3%
0.45 4
 
3.3%
0.44 3
 
2.5%
0.3 3
 
2.5%
Other values (61) 81
67.5%
ValueCountFrequency (%)
0.01 1
 
0.8%
0.17 2
1.7%
0.22 1
 
0.8%
0.23 1
 
0.8%
0.24 4
3.3%
0.25 2
1.7%
0.26 3
2.5%
0.27 4
3.3%
0.28 2
1.7%
0.29 4
3.3%
ValueCountFrequency (%)
181.0 1
0.8%
123.0 1
0.8%
119.0 1
0.8%
117.0 1
0.8%
114.0 1
0.8%
113.0 1
0.8%
112.0 1
0.8%
104.0 1
0.8%
99.0 1
0.8%
96.0 1
0.8%

Interactions

2024-04-30T07:55:42.388173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:42.118144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:42.474577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:55:42.284332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:55:43.835022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번단기자금
순번1.0000.513
단기자금0.5131.000
2024-04-30T07:55:43.915699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번단기자금
순번1.000-0.566
단기자금-0.5661.000

Missing values

2024-04-30T07:55:42.575101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:55:42.649431image/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

순번구분단기자금
012017년 1월 최대예상손실(%)0.51
122017년 1월 최대예상손실금액(억원)96.0
232017년 2월최대예상손실(%)0.41
342017년 2월최대예상손실금액(억원)99.0
452017년 3월최대예상손실(%)0.44
562017년 3월최대예상손실금액(억원)64.0
672017년 4월최대예상손실(%)0.45
782017년 4월최대예상손실금액(억원)82.0
892017년 5월최대예상손실(%)0.42
9102017년 5월최대예상손실금액(억원)87.0
순번구분단기자금
1101112023년 3월 최대예상손실(%)3.15
1111122023년 4월 최대예상손실(%)3.15
1121132023년 5월 최대예상손실(%)3.3
1131142023년 6월 최대예상손실(%)0.17
1141152023년 7월 최대예상손실(%)0.26
1151162023년 8월 최대예상손실(%)3.06
1161172023년 9월 최대예상손실(%)0.01
1171182023년 10월 최대예상손실(%)0.27
1181192023년 11월 최대예상손실(%)1.32
1191202023년 12월 최대예상손실(%)0.43