Overview

Dataset statistics

Number of variables4
Number of observations46
Missing cells2
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory34.9 B

Variable types

Categorical2
Text1
Unsupported1

Dataset

Description보증공급시 금리현화을 제공
Author울산신용보증재단
URLhttps://www.data.go.kr/data/3076318/fileData.do

Alerts

Unnamed: 3 is highly overall correlated with <은행별 보증금리 현황표>High correlation
<은행별 보증금리 현황표> is highly overall correlated with Unnamed: 3High correlation
Unnamed: 3 is highly imbalanced (74.2%)Imbalance
Unnamed: 1 has 1 (2.2%) missing valuesMissing
Unnamed: 2 has 1 (2.2%) missing valuesMissing
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 22:59:10.313939
Analysis finished2023-12-12 22:59:10.629477
Duration0.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

<은행별 보증금리 현황표>
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size500.0 B
경남은행
11 
농협은행
7 
신한은행
6 
국민은행
5 
기업은행
4 
Other values (7)
13 

Length

Max length9
Median length4
Mean length4.326087
Min length2

Unique

Unique3 ?
Unique (%)6.5%

Sample

1st row<NA>
2nd row은행
3rd row경남은행
4th row경남은행
5th row경남은행

Common Values

ValueCountFrequency (%)
경남은행 11
23.9%
농협은행 7
15.2%
신한은행 6
13.0%
국민은행 5
10.9%
기업은행 4
 
8.7%
우리은행 3
 
6.5%
한국스탠다드차타드 3
 
6.5%
부산은행 2
 
4.3%
하나은행 2
 
4.3%
<NA> 1
 
2.2%
Other values (2) 2
 
4.3%

Length

2023-12-13T07:59:10.695375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경남은행 11
23.9%
농협은행 7
15.2%
신한은행 6
13.0%
국민은행 5
10.9%
기업은행 4
 
8.7%
우리은행 3
 
6.5%
한국스탠다드차타드 3
 
6.5%
부산은행 2
 
4.3%
하나은행 2
 
4.3%
na 1
 
2.2%
Other values (2) 2
 
4.3%

Unnamed: 1
Text

MISSING 

Distinct29
Distinct (%)64.4%
Missing1
Missing (%)2.2%
Memory size500.0 B
2023-12-13T07:59:10.861932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.8666667
Min length2

Characters and Unicode

Total characters219
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)46.7%

Sample

1st row지점
2nd row삼호지점
3rd row신복지점
4th row달동지점
5th row성남동지점
ValueCountFrequency (%)
울산지점 5
 
11.1%
무거지점 5
 
11.1%
울산중앙지점 3
 
6.7%
동울산지점 3
 
6.7%
전하동지점 2
 
4.4%
동평지점 2
 
4.4%
울산남지점 2
 
4.4%
달동지점 2
 
4.4%
삼호지점 1
 
2.2%
울산남구청지점 1
 
2.2%
Other values (19) 19
42.2%
2023-12-13T07:59:11.127358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 44
20.1%
점 44
20.1%
산 23
10.5%
울 22
10.0%
동 14
 
6.4%
남 6
 
2.7%
거 5
 
2.3%
무 5
 
2.3%
중 4
 
1.8%
앙 4
 
1.8%
Other values (32) 48
21.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 219
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 44
20.1%
점 44
20.1%
산 23
10.5%
울 22
10.0%
동 14
 
6.4%
남 6
 
2.7%
거 5
 
2.3%
무 5
 
2.3%
중 4
 
1.8%
앙 4
 
1.8%
Other values (32) 48
21.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 219
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 44
20.1%
점 44
20.1%
산 23
10.5%
울 22
10.0%
동 14
 
6.4%
남 6
 
2.7%
거 5
 
2.3%
무 5
 
2.3%
중 4
 
1.8%
앙 4
 
1.8%
Other values (32) 48
21.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 219
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 44
20.1%
점 44
20.1%
산 23
10.5%
울 22
10.0%
동 14
 
6.4%
남 6
 
2.7%
거 5
 
2.3%
무 5
 
2.3%
중 4
 
1.8%
앙 4
 
1.8%
Other values (32) 48
21.9%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.2%
Memory size500.0 B

Unnamed: 3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
%
44 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.1304348
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row%
4th row%
5th row%

Common Values

ValueCountFrequency (%)
% 44
95.7%
<NA> 2
 
4.3%

Length

2023-12-13T07:59:11.236096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:59:11.315464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44
95.7%
na 2
 
4.3%

Correlations

2023-12-13T07:59:11.362755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
<은행별 보증금리 현황표>Unnamed: 1
<은행별 보증금리 현황표>1.0000.000
Unnamed: 10.0001.000
2023-12-13T07:59:11.431051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 3<은행별 보증금리 현황표>
Unnamed: 31.0001.000
<은행별 보증금리 현황표>1.0001.000
2023-12-13T07:59:11.501456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
<은행별 보증금리 현황표>Unnamed: 3
<은행별 보증금리 현황표>1.0001.000
Unnamed: 31.0001.000

Missing values

2023-12-13T07:59:10.419275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:59:10.488867image/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.
2023-12-13T07:59:10.576424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

<은행별 보증금리 현황표>Unnamed: 1Unnamed: 2Unnamed: 3
0<NA><NA>NaN<NA>
1은행지점금리<NA>
2경남은행삼호지점4.03%
3경남은행신복지점4.45%
4경남은행달동지점3.51%
5경남은행성남동지점3.86%
6경남은행전하동지점3.64%
7경남은행동울산지점3.69%
8경남은행대송지점4.89%
9경남은행화봉동지점4%
<은행별 보증금리 현황표>Unnamed: 1Unnamed: 2Unnamed: 3
36신한은행울산남지점2.99%
37우리은행무거지점3.53%
38우리은행동평지점3.69%
39우리은행동울산지점4.98%
40하나은행무거지점3.69%
41하나은행울산지점4.56%
42한국스탠다드차타드울산지점3.71%
43한국스탠다드차타드울산중앙지점3.84%
44한국스탠다드차타드울산남지점3.89%
45한국씨티은행울산지점3.53%