Overview

Dataset statistics

Number of variables3
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory707.0 B
Average record size in memory30.7 B

Variable types

DateTime1
Text1
Numeric1

Dataset

Description한국자산관리공사 행복기금 위임사 연체 정보 데이터
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15069806/fileData.do

Alerts

기준일자 has constant value ""Constant
위임사 has unique valuesUnique
연체건수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:00:25.655750
Analysis finished2023-12-12 07:00:25.965297
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
Minimum2018-12-31 00:00:00
Maximum2018-12-31 00:00:00
2023-12-12T16:00:26.009705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:00:26.089874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

위임사
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T16:00:26.253987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8
Min length6

Characters and Unicode

Total characters184
Distinct characters59
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row고려신용정보
2nd rowIBK신용정보(주)
3rd row케이티비신용정보
4th row대구신용정보
5th row미래신용정보
ValueCountFrequency (%)
고려신용정보 1
 
4.3%
kb신용정보(주 1
 
4.3%
bs신용정보(주 1
 
4.3%
nice신용정보(주 1
 
4.3%
mg신용정보(주 1
 
4.3%
에프앤유신용정보(주 1
 
4.3%
진흥신용정보 1
 
4.3%
예스신용정보(주 1
 
4.3%
농협자산관리회사 1
 
4.3%
a&d신용정보 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T16:00:26.573451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 23
12.5%
용 22
 
12.0%
정 22
 
12.0%
보 22
 
12.0%
( 10
 
5.4%
주 10
 
5.4%
) 10
 
5.4%
스 3
 
1.6%
에 3
 
1.6%
B 3
 
1.6%
Other values (49) 56
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148
80.4%
Uppercase Letter 15
 
8.2%
Open Punctuation 10
 
5.4%
Close Punctuation 10
 
5.4%
Other Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 23
15.5%
용 22
14.9%
정 22
14.9%
보 22
14.9%
주 10
 
6.8%
스 3
 
2.0%
에 3
 
2.0%
한 3
 
2.0%
리 2
 
1.4%
산 2
 
1.4%
Other values (35) 36
24.3%
Uppercase Letter
ValueCountFrequency (%)
B 3
20.0%
K 2
13.3%
I 2
13.3%
G 1
 
6.7%
M 1
 
6.7%
D 1
 
6.7%
N 1
 
6.7%
C 1
 
6.7%
E 1
 
6.7%
S 1
 
6.7%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 148
80.4%
Common 21
 
11.4%
Latin 15
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 23
15.5%
용 22
14.9%
정 22
14.9%
보 22
14.9%
주 10
 
6.8%
스 3
 
2.0%
에 3
 
2.0%
한 3
 
2.0%
리 2
 
1.4%
산 2
 
1.4%
Other values (35) 36
24.3%
Latin
ValueCountFrequency (%)
B 3
20.0%
K 2
13.3%
I 2
13.3%
G 1
 
6.7%
M 1
 
6.7%
D 1
 
6.7%
N 1
 
6.7%
C 1
 
6.7%
E 1
 
6.7%
S 1
 
6.7%
Common
ValueCountFrequency (%)
( 10
47.6%
) 10
47.6%
& 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 148
80.4%
ASCII 36
 
19.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 23
15.5%
용 22
14.9%
정 22
14.9%
보 22
14.9%
주 10
 
6.8%
스 3
 
2.0%
에 3
 
2.0%
한 3
 
2.0%
리 2
 
1.4%
산 2
 
1.4%
Other values (35) 36
24.3%
ASCII
ValueCountFrequency (%)
( 10
27.8%
) 10
27.8%
B 3
 
8.3%
K 2
 
5.6%
I 2
 
5.6%
G 1
 
2.8%
M 1
 
2.8%
D 1
 
2.8%
N 1
 
2.8%
C 1
 
2.8%
Other values (4) 4
 
11.1%

연체건수
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2083
Minimum6
Maximum6845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T16:00:26.719491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11.3
Q142
median794
Q34535.5
95-th percentile6185.8
Maximum6845
Range6839
Interquartile range (IQR)4493.5

Descriptive statistics

Standard deviation2597.2363
Coefficient of variation (CV)1.2468729
Kurtosis-1.0428048
Mean2083
Median Absolute Deviation (MAD)778
Skewness0.87371287
Sum47909
Variance6745636.6
MonotonicityNot monotonic
2023-12-12T16:00:26.849166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4532 1
 
4.3%
2272 1
 
4.3%
11 1
 
4.3%
16 1
 
4.3%
1261 1
 
4.3%
14 1
 
4.3%
19 1
 
4.3%
195 1
 
4.3%
38 1
 
4.3%
232 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
6 1
4.3%
11 1
4.3%
14 1
4.3%
16 1
4.3%
19 1
4.3%
38 1
4.3%
46 1
4.3%
64 1
4.3%
171 1
4.3%
195 1
4.3%
ValueCountFrequency (%)
6845 1
4.3%
6186 1
4.3%
6184 1
4.3%
6174 1
4.3%
5937 1
4.3%
4539 1
4.3%
4532 1
4.3%
2272 1
4.3%
1261 1
4.3%
1200 1
4.3%

Interactions

2023-12-12T16:00:25.746006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:00:26.934232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위임사연체건수
위임사1.0001.000
연체건수1.0001.000

Missing values

2023-12-12T16:00:25.863175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:00:25.935336image/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

기준일자위임사연체건수
02018-12-31고려신용정보4532
12018-12-31IBK신용정보(주)2272
22018-12-31케이티비신용정보6174
32018-12-31대구신용정보64
42018-12-31미래신용정보6186
52018-12-31부산신용정보6
62018-12-31새한신용정보171
72018-12-31에스엠신용정보(주)6184
82018-12-31신한신용정보6845
92018-12-31에스지아이신용정보1200
기준일자위임사연체건수
132018-12-31한국신용평가정보(주)46
142018-12-31A&D신용정보794
152018-12-31농협자산관리회사232
162018-12-31예스신용정보(주)38
172018-12-31진흥신용정보195
182018-12-31에프앤유신용정보(주)19
192018-12-31MG신용정보(주)14
202018-12-31NICE신용정보(주)1261
212018-12-31BS신용정보(주)16
222018-12-31세일신용정보(주)11