Overview

Dataset statistics

Number of variables5
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory44.1 B

Variable types

Numeric1
Categorical1
Text3

Dataset

Description2017년 12월 대구동구금융기관현황
Author대구광역시 동구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3072350&dataSetDetailId=3072350289b7c0628ab2_201909091528&provdMethod=FILE

Alerts

연번 is highly overall correlated with 은행명High correlation
은행명 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2024-04-22 00:11:21.537442
Analysis finished2024-04-22 00:11:22.043803
Duration0.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-22T09:11:22.116445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.05
Q111.25
median21.5
Q331.75
95-th percentile39.95
Maximum42
Range41
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation12.267844
Coefficient of variation (CV)0.5705974
Kurtosis-1.2
Mean21.5
Median Absolute Deviation (MAD)10.5
Skewness0
Sum903
Variance150.5
MonotonicityStrictly increasing
2024-04-22T09:11:22.558860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 1
 
2.4%
33 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
32 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
42 1
2.4%
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%

은행명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
대구은행
18 
국민은행
7 
농협은행
6 
우리은행
4 
KEB하나은행
2 
Other values (3)
5 

Length

Max length7
Median length4
Mean length4.3095238
Min length4

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st rowKEB하나은행
2nd rowKEB하나은행
3rd rowSC제일은행
4th rowSC제일은행
5th row국민은행

Common Values

ValueCountFrequency (%)
대구은행 18
42.9%
국민은행 7
 
16.7%
농협은행 6
 
14.3%
우리은행 4
 
9.5%
KEB하나은행 2
 
4.8%
SC제일은행 2
 
4.8%
기업은행 2
 
4.8%
한국수출입은행 1
 
2.4%

Length

2024-04-22T09:11:22.692554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:11:22.823891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구은행 18
42.9%
국민은행 7
 
16.7%
농협은행 6
 
14.3%
우리은행 4
 
9.5%
keb하나은행 2
 
4.8%
sc제일은행 2
 
4.8%
기업은행 2
 
4.8%
한국수출입은행 1
 
2.4%

지점명
Text

Distinct36
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size468.0 B
2024-04-22T09:11:23.032670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.7619048
Min length2

Characters and Unicode

Total characters200
Distinct characters67
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)76.2%

Sample

1st row가스공사
2nd row대구혁신도시
3rd row대구 신세계점
4th row이마트 반야월점
5th rowK-2(점)
ValueCountFrequency (%)
대구혁신도시 4
 
9.1%
안심 2
 
4.5%
반야월 2
 
4.5%
대구 2
 
4.5%
신세계점 2
 
4.5%
신암동 2
 
4.5%
파티마병원 1
 
2.3%
반야월지점 1
 
2.3%
대구혁신도시금융센터 1
 
2.3%
효목동 1
 
2.3%
Other values (26) 26
59.1%
2024-04-22T09:11:23.398573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 17
 
8.5%
구 12
 
6.0%
대 11
 
5.5%
동 10
 
5.0%
시 9
 
4.5%
점 9
 
4.5%
도 6
 
3.0%
혁 6
 
3.0%
공 4
 
2.0%
반 4
 
2.0%
Other values (57) 112
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 190
95.0%
Space Separator 2
 
1.0%
Open Punctuation 2
 
1.0%
Close Punctuation 2
 
1.0%
Decimal Number 2
 
1.0%
Dash Punctuation 1
 
0.5%
Uppercase Letter 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 17
 
8.9%
구 12
 
6.3%
대 11
 
5.8%
동 10
 
5.3%
시 9
 
4.7%
점 9
 
4.7%
도 6
 
3.2%
혁 6
 
3.2%
공 4
 
2.1%
반 4
 
2.1%
Other values (50) 102
53.7%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 190
95.0%
Common 9
 
4.5%
Latin 1
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 17
 
8.9%
구 12
 
6.3%
대 11
 
5.8%
동 10
 
5.3%
시 9
 
4.7%
점 9
 
4.7%
도 6
 
3.2%
혁 6
 
3.2%
공 4
 
2.1%
반 4
 
2.1%
Other values (50) 102
53.7%
Common
ValueCountFrequency (%)
2
22.2%
( 2
22.2%
) 2
22.2%
2 1
11.1%
- 1
11.1%
4 1
11.1%
Latin
ValueCountFrequency (%)
K 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 190
95.0%
ASCII 10
 
5.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 17
 
8.9%
구 12
 
6.3%
대 11
 
5.8%
동 10
 
5.3%
시 9
 
4.7%
점 9
 
4.7%
도 6
 
3.2%
혁 6
 
3.2%
공 4
 
2.1%
반 4
 
2.1%
Other values (50) 102
53.7%
ASCII
ValueCountFrequency (%)
2
20.0%
( 2
20.0%
) 2
20.0%
2 1
10.0%
- 1
10.0%
K 1
10.0%
4 1
10.0%

주소
Text

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size468.0 B
2024-04-22T09:11:23.624747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length23.5
Mean length17.452381
Min length14

Characters and Unicode

Total characters733
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)95.2%

Sample

1st row대구광역시 동구 첨단로 120 (신서동) 한국가스공사
2nd row대구광역시 동구 이노밸리로 309(신서동)
3rd row대구광역시 동구 신천동 동부로 153
4th row대구광역시 동구 신서동 안심로 389-2
5th row대구광역시 동구 동촌로 223
ValueCountFrequency (%)
대구광역시 42
23.3%
동구 42
23.3%
이노밸리로 5
 
2.8%
첨단로 5
 
2.8%
신서동 5
 
2.8%
아양로 5
 
2.8%
4
 
2.2%
동부로 4
 
2.2%
동촌로 3
 
1.7%
120 3
 
1.7%
Other values (54) 62
34.4%
2024-04-22T09:11:24.014425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138
18.8%
구 85
11.6%
동 62
 
8.5%
대 43
 
5.9%
광 42
 
5.7%
역 42
 
5.7%
시 42
 
5.7%
로 42
 
5.7%
2 30
 
4.1%
3 18
 
2.5%
Other values (50) 189
25.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 468
63.8%
Space Separator 138
 
18.8%
Decimal Number 117
 
16.0%
Other Punctuation 4
 
0.5%
Dash Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%
Open Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 85
18.2%
동 62
13.2%
대 43
9.2%
광 42
9.0%
역 42
9.0%
시 42
9.0%
로 42
9.0%
신 8
 
1.7%
양 6
 
1.3%
아 6
 
1.3%
Other values (35) 90
19.2%
Decimal Number
ValueCountFrequency (%)
2 30
25.6%
3 18
15.4%
1 17
14.5%
0 13
11.1%
9 10
 
8.5%
4 8
 
6.8%
8 7
 
6.0%
7 5
 
4.3%
6 5
 
4.3%
5 4
 
3.4%
Space Separator
ValueCountFrequency (%)
138
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 468
63.8%
Common 265
36.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 85
18.2%
동 62
13.2%
대 43
9.2%
광 42
9.0%
역 42
9.0%
시 42
9.0%
로 42
9.0%
신 8
 
1.7%
양 6
 
1.3%
아 6
 
1.3%
Other values (35) 90
19.2%
Common
ValueCountFrequency (%)
138
52.1%
2 30
 
11.3%
3 18
 
6.8%
1 17
 
6.4%
0 13
 
4.9%
9 10
 
3.8%
4 8
 
3.0%
8 7
 
2.6%
7 5
 
1.9%
6 5
 
1.9%
Other values (5) 14
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 468
63.8%
ASCII 265
36.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
52.1%
2 30
 
11.3%
3 18
 
6.8%
1 17
 
6.4%
0 13
 
4.9%
9 10
 
3.8%
4 8
 
3.0%
8 7
 
2.6%
7 5
 
1.9%
6 5
 
1.9%
Other values (5) 14
 
5.3%
Hangul
ValueCountFrequency (%)
구 85
18.2%
동 62
13.2%
대 43
9.2%
광 42
9.0%
역 42
9.0%
시 42
9.0%
로 42
9.0%
신 8
 
1.7%
양 6
 
1.3%
아 6
 
1.3%
Other values (35) 90
19.2%

전화번호
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2024-04-22T09:11:24.252825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters504
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row053-961-4080
2nd row053-621-1111
3rd row053-759-8651
4th row053-431-7880
5th row053-982-2213
ValueCountFrequency (%)
053-961-4080 1
 
2.4%
053-741-2914 1
 
2.4%
053-964-1899 1
 
2.4%
053-961-3061 1
 
2.4%
053-982-1734 1
 
2.4%
053-741-7145 1
 
2.4%
053-943-6101 1
 
2.4%
053-961-4158 1
 
2.4%
053-742-8590 1
 
2.4%
053-753-1992 1
 
2.4%
Other values (32) 32
76.2%
2024-04-22T09:11:24.602024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 84
16.7%
3 68
13.5%
0 66
13.1%
5 66
13.1%
1 47
9.3%
9 44
8.7%
2 31
 
6.2%
6 28
 
5.6%
8 25
 
5.0%
7 23
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 420
83.3%
Dash Punctuation 84
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 68
16.2%
0 66
15.7%
5 66
15.7%
1 47
11.2%
9 44
10.5%
2 31
7.4%
6 28
6.7%
8 25
 
6.0%
7 23
 
5.5%
4 22
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 504
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 84
16.7%
3 68
13.5%
0 66
13.1%
5 66
13.1%
1 47
9.3%
9 44
8.7%
2 31
 
6.2%
6 28
 
5.6%
8 25
 
5.0%
7 23
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 84
16.7%
3 68
13.5%
0 66
13.1%
5 66
13.1%
1 47
9.3%
9 44
8.7%
2 31
 
6.2%
6 28
 
5.6%
8 25
 
5.0%
7 23
 
4.6%

Interactions

2024-04-22T09:11:21.797391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-22T09:11:24.708093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번은행명지점명주소전화번호
연번1.0000.8620.4160.9401.000
은행명0.8621.0000.7721.0001.000
지점명0.4160.7721.0000.9771.000
주소0.9401.0000.9771.0001.000
전화번호1.0001.0001.0001.0001.000
2024-04-22T09:11:24.798959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번은행명
연번1.0000.627
은행명0.6271.000

Missing values

2024-04-22T09:11:21.900018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T09:11:22.006625image/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

연번은행명지점명주소전화번호
01KEB하나은행가스공사대구광역시 동구 첨단로 120 (신서동) 한국가스공사053-961-4080
12KEB하나은행대구혁신도시대구광역시 동구 이노밸리로 309(신서동)053-621-1111
23SC제일은행대구 신세계점대구광역시 동구 신천동 동부로 153053-759-8651
34SC제일은행이마트 반야월점대구광역시 동구 신서동 안심로 389-2053-431-7880
45국민은행K-2(점)대구광역시 동구 동촌로 223053-982-2213
56국민은행대구이시아폴리스대구광역시 동구 팔공로 249053-982-6352
67국민은행대구혁신도시대구광역시 동구 이노밸리로 322053-963-6516
78국민은행동대구대구광역시 동구 동부로30길 11053-752-7773
89국민은행반야월대구광역시 동구 안심로 368053-964-2256
910국민은행방촌동대구광역시 동구 동촌로 223053-984-8075
연번은행명지점명주소전화번호
3233대구은행율하대구광역시 동구 안심로22길 60053-965-9732
3334대구은행이시아폴리스대구광역시 동구 팔공로 227053-984-0235
3435대구은행파티마병원대구광역시 동구 아양로 99053-951-3852
3536대구은행한국가스공사점대구광역시 동구 첨단로 120053-961-7343
3637대구은행효목동대구광역시 동구 효목로 13053-753-1678
3738우리은행대구혁신도시금융센터대구광역시 동구 신서동 . 이노밸리로 321053-710-1899
3839우리은행반야월지점대구광역시 동구 신서동 . 금강로 3053-962-5361
3940우리은행신암동금융센터대구광역시 동구 신암동 . 아양로 46053-951-3001
4041우리은행신용보증기금지점대구광역시 동구 신서동 . 첨단로 7053-964-1899
4142한국수출입은행대구대구광역시 동구 동대구로 489053-260-4100