Overview

Dataset statistics

Number of variables5
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory46.7 B

Variable types

Categorical3
Numeric2

Dataset

Description햇살론15에 대한 연도별, 소득구간별 대출건수 및 금액 햇살론 15: 대부업 등 고금리 대출 이용이 불가피한 분들이 제도권 금융에서 쉽고 편리하게 이용할 수 있는 정책서민금융 상품
URLhttps://www.data.go.kr/data/15105248/fileData.do

Alerts

상품명 has constant value ""Constant
금액(억 원) is highly overall correlated with 건수(건)High correlation
건수(건) is highly overall correlated with 금액(억 원)High correlation
금액(억 원) has unique valuesUnique
건수(건) has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:31:03.684895
Analysis finished2023-12-12 11:31:04.976016
Duration1.29 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상품명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
햇살론15(17)
36 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row햇살론15(17)
2nd row햇살론15(17)
3rd row햇살론15(17)
4th row햇살론15(17)
5th row햇살론15(17)

Common Values

ValueCountFrequency (%)
햇살론15(17) 36
100.0%

Length

2023-12-12T20:31:05.107204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:31:05.277323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
햇살론15(17 36
100.0%

년도
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
2019
9 
2020
9 
2021
9 
2022
9 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 9
25.0%
2020 9
25.0%
2021 9
25.0%
2022 9
25.0%

Length

2023-12-12T20:31:05.479789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:31:05.671485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 9
25.0%
2020 9
25.0%
2021 9
25.0%
2022 9
25.0%

구분(신용평점별 하위구성비)
Categorical

Distinct9
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
5백만원이하
4 
10백만원이하
4 
15백만원이하
4 
20백만원이하
4 
25백만원이하
4 
Other values (4)
16 

Length

Max length7
Median length7
Mean length6.8888889
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5백만원이하
2nd row10백만원이하
3rd row15백만원이하
4th row20백만원이하
5th row25백만원이하

Common Values

ValueCountFrequency (%)
5백만원이하 4
11.1%
10백만원이하 4
11.1%
15백만원이하 4
11.1%
20백만원이하 4
11.1%
25백만원이하 4
11.1%
30백만원이하 4
11.1%
35백만원이하 4
11.1%
40백만원이하 4
11.1%
45백만원이하 4
11.1%

Length

2023-12-12T20:31:05.855642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:31:06.070999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5백만원이하 4
11.1%
10백만원이하 4
11.1%
15백만원이하 4
11.1%
20백만원이하 4
11.1%
25백만원이하 4
11.1%
30백만원이하 4
11.1%
35백만원이하 4
11.1%
40백만원이하 4
11.1%
45백만원이하 4
11.1%

금액(억 원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1082.2778
Minimum59
Maximum3359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T20:31:06.333037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile138
Q1472
median866
Q31452
95-th percentile2846.5
Maximum3359
Range3300
Interquartile range (IQR)980

Descriptive statistics

Standard deviation845.62571
Coefficient of variation (CV)0.78133888
Kurtosis0.69564095
Mean1082.2778
Median Absolute Deviation (MAD)434
Skewness1.109818
Sum38962
Variance715082.83
MonotonicityNot monotonic
2023-12-12T20:31:06.567089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
59 1
 
2.8%
454 1
 
2.8%
1059 1
 
2.8%
2792 1
 
2.8%
2203 1
 
2.8%
1518 1
 
2.8%
1091 1
 
2.8%
802 1
 
2.8%
152 1
 
2.8%
593 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
59 1
2.8%
111 1
2.8%
147 1
2.8%
152 1
2.8%
226 1
2.8%
250 1
2.8%
354 1
2.8%
431 1
2.8%
454 1
2.8%
478 1
2.8%
ValueCountFrequency (%)
3359 1
2.8%
3010 1
2.8%
2792 1
2.8%
2243 1
2.8%
2203 1
2.8%
2156 1
2.8%
1731 1
2.8%
1661 1
2.8%
1518 1
2.8%
1430 1
2.8%

건수(건)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13553.056
Minimum856
Maximum36789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T20:31:06.802495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum856
5-th percentile1683
Q15909
median11019.5
Q319399.25
95-th percentile33415.25
Maximum36789
Range35933
Interquartile range (IQR)13490.25

Descriptive statistics

Standard deviation10026.572
Coefficient of variation (CV)0.73980156
Kurtosis-0.10064501
Mean13553.056
Median Absolute Deviation (MAD)5505
Skewness0.89205656
Sum487910
Variance1.0053214 × 108
MonotonicityNot monotonic
2023-12-12T20:31:07.027020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
856 1
 
2.8%
5823 1
 
2.8%
13415 1
 
2.8%
36789 1
 
2.8%
29438 1
 
2.8%
20282 1
 
2.8%
14398 1
 
2.8%
10529 1
 
2.8%
1767 1
 
2.8%
5925 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
856 1
2.8%
1431 1
2.8%
1767 1
2.8%
2212 1
2.8%
3125 1
2.8%
3466 1
2.8%
4932 1
2.8%
5823 1
2.8%
5861 1
2.8%
5925 1
2.8%
ValueCountFrequency (%)
36789 1
2.8%
34430 1
2.8%
33077 1
2.8%
31142 1
2.8%
29438 1
2.8%
25458 1
2.8%
22135 1
2.8%
21345 1
2.8%
20282 1
2.8%
19105 1
2.8%

Interactions

2023-12-12T20:31:04.326599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:03.941160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:04.521276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:04.140986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:31:07.191978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도구분(신용평점별 하위구성비)금액(억 원)건수(건)
년도1.0000.0000.0000.000
구분(신용평점별 하위구성비)0.0001.0000.6610.658
금액(억 원)0.0000.6611.0000.880
건수(건)0.0000.6580.8801.000
2023-12-12T20:31:07.350598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도구분(신용평점별 하위구성비)
년도1.0000.000
구분(신용평점별 하위구성비)0.0001.000
2023-12-12T20:31:07.471542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
금액(억 원)건수(건)년도구분(신용평점별 하위구성비)
금액(억 원)1.0000.9840.0000.251
건수(건)0.9841.0000.0000.377
년도0.0000.0001.0000.000
구분(신용평점별 하위구성비)0.2510.3770.0001.000

Missing values

2023-12-12T20:31:04.753802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:31:04.915705image/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

상품명년도구분(신용평점별 하위구성비)금액(억 원)건수(건)
0햇살론15(17)20195백만원이하59856
1햇살론15(17)201910백만원이하2263125
2햇살론15(17)201915백만원이하4315861
3햇살론15(17)201920백만원이하4786516
4햇살론15(17)201925백만원이하83811638
5햇살론15(17)201930백만원이하6639232
6햇살론15(17)201935백만원이하5077056
7햇살론15(17)201940백만원이하3544932
8햇살론15(17)201945백만원이하2503466
9햇살론15(17)20205백만원이하1472212
상품명년도구분(신용평점별 하위구성비)금액(억 원)건수(건)
26햇살론15(17)202145백만원이하80210529
27햇살론15(17)20225백만원이하1521767
28햇살론15(17)202210백만원이하5935925
29햇살론15(17)202215백만원이하9829527
30햇살론15(17)202220백만원이하116211510
31햇살론15(17)202225백만원이하335934430
32햇살론15(17)202230백만원이하301031142
33햇살론15(17)202235백만원이하215622135
34햇살론15(17)202240백만원이하166116833
35햇살론15(17)202245백만원이하123012154