Overview

Dataset statistics

Number of variables3
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory682.0 B
Average record size in memory31.0 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description한국자산관리공사 공사채권 신용정보사 보유현황 데이터
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15069736/fileData.do

Reproduction

Analysis started2023-12-12 20:52:25.242992
Analysis finished2023-12-12 20:52:25.587669
Duration0.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준월
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
2016-11
11 
2016-12
11 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-11
2nd row2016-11
3rd row2016-11
4th row2016-11
5th row2016-11

Common Values

ValueCountFrequency (%)
2016-11 11
50.0%
2016-12 11
50.0%

Length

2023-12-13T05:52:25.678524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:52:25.833060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-11 11
50.0%
2016-12 11
50.0%

수탁회사명
Text

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T05:52:26.035251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.7272727
Min length6

Characters and Unicode

Total characters148
Distinct characters28
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIBK신용정보
2nd rowSGI신용정보
3rd row고려신용정보
4th row나이스신용정보
5th row미래신용정보
ValueCountFrequency (%)
ibk신용정보 2
9.1%
sgi신용정보 2
9.1%
고려신용정보 2
9.1%
나이스신용정보 2
9.1%
미래신용정보 2
9.1%
신한신용정보 2
9.1%
에스엠신용정보 2
9.1%
에이앤디신용정보 2
9.1%
우리신용정보 2
9.1%
중앙신용정보 2
9.1%
2023-12-13T05:52:26.400078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 24
16.2%
용 22
14.9%
정 22
14.9%
보 22
14.9%
이 6
 
4.1%
I 4
 
2.7%
에 4
 
2.7%
스 4
 
2.7%
우 2
 
1.4%
앤 2
 
1.4%
Other values (18) 36
24.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 136
91.9%
Uppercase Letter 12
 
8.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 24
17.6%
용 22
16.2%
정 22
16.2%
보 22
16.2%
이 6
 
4.4%
에 4
 
2.9%
스 4
 
2.9%
우 2
 
1.5%
앤 2
 
1.5%
디 2
 
1.5%
Other values (13) 26
19.1%
Uppercase Letter
ValueCountFrequency (%)
I 4
33.3%
B 2
16.7%
G 2
16.7%
S 2
16.7%
K 2
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 136
91.9%
Latin 12
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 24
17.6%
용 22
16.2%
정 22
16.2%
보 22
16.2%
이 6
 
4.4%
에 4
 
2.9%
스 4
 
2.9%
우 2
 
1.5%
앤 2
 
1.5%
디 2
 
1.5%
Other values (13) 26
19.1%
Latin
ValueCountFrequency (%)
I 4
33.3%
B 2
16.7%
G 2
16.7%
S 2
16.7%
K 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 136
91.9%
ASCII 12
 
8.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 24
17.6%
용 22
16.2%
정 22
16.2%
보 22
16.2%
이 6
 
4.4%
에 4
 
2.9%
스 4
 
2.9%
우 2
 
1.5%
앤 2
 
1.5%
디 2
 
1.5%
Other values (13) 26
19.1%
ASCII
ValueCountFrequency (%)
I 4
33.3%
B 2
16.7%
G 2
16.7%
S 2
16.7%
K 2
16.7%

차주수
Real number (ℝ)

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23786.818
Minimum22956
Maximum24487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T05:52:26.558640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22956
5-th percentile23087.2
Q123515.5
median23750
Q324025.25
95-th percentile24483.65
Maximum24487
Range1531
Interquartile range (IQR)509.75

Descriptive statistics

Standard deviation425.23524
Coefficient of variation (CV)0.017876928
Kurtosis-0.40552073
Mean23786.818
Median Absolute Deviation (MAD)250
Skewness0.070713267
Sum523310
Variance180825.01
MonotonicityNot monotonic
2023-12-13T05:52:26.718626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
24487 2
 
9.1%
23587 2
 
9.1%
23939 1
 
4.5%
23456 1
 
4.5%
23964 1
 
4.5%
24398 1
 
4.5%
22956 1
 
4.5%
23708 1
 
4.5%
23887 1
 
4.5%
23452 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
22956 1
4.5%
23068 1
4.5%
23452 1
4.5%
23456 1
4.5%
23489 1
4.5%
23511 1
4.5%
23529 1
4.5%
23533 1
4.5%
23587 2
9.1%
23708 1
4.5%
ValueCountFrequency (%)
24487 2
9.1%
24420 1
4.5%
24398 1
4.5%
24049 1
4.5%
24045 1
4.5%
23966 1
4.5%
23964 1
4.5%
23939 1
4.5%
23887 1
4.5%
23792 1
4.5%

Interactions

2023-12-13T05:52:25.342358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:52:26.811558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준월수탁회사명차주수
기준월1.0000.0000.000
수탁회사명0.0001.0000.839
차주수0.0000.8391.000
2023-12-13T05:52:26.918589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차주수기준월
차주수1.0000.000
기준월0.0001.000

Missing values

2023-12-13T05:52:25.478259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:52:25.553925image/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

기준월수탁회사명차주수
02016-11IBK신용정보24487
12016-11SGI신용정보23587
22016-11고려신용정보24045
32016-11나이스신용정보23587
42016-11미래신용정보23529
52016-11신한신용정보23966
62016-11에스엠신용정보23792
72016-11에이앤디신용정보23068
82016-11우리신용정보24487
92016-11중앙신용정보24049
기준월수탁회사명차주수
122016-12SGI신용정보23511
132016-12고려신용정보23939
142016-12나이스신용정보23489
152016-12미래신용정보23452
162016-12신한신용정보23887
172016-12에스엠신용정보23708
182016-12에이앤디신용정보22956
192016-12우리신용정보24398
202016-12중앙신용정보23964
212016-12케이티비신용정보23456