Overview

Dataset statistics

Number of variables5
Number of observations81
Missing cells18
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory42.6 B

Variable types

Numeric1
Text3
Categorical1

Dataset

Description인천광역시 남동구에 위치한 철물점 현황에 대한 데이터로 연번, 업체명, 소재지, 전화번호, 데이터기준일자 항목을 제공합니다.
Author인천광역시 남동구
URLhttps://www.data.go.kr/data/15091182/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
전화번호 has 18 (22.2%) missing valuesMissing
연번 has unique valuesUnique
업체명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:05:18.095062
Analysis finished2023-12-12 03:05:18.767128
Duration0.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T12:05:19.214068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q121
median41
Q361
95-th percentile77
Maximum81
Range80
Interquartile range (IQR)40

Descriptive statistics

Standard deviation23.526581
Coefficient of variation (CV)0.57381904
Kurtosis-1.2
Mean41
Median Absolute Deviation (MAD)20
Skewness0
Sum3321
Variance553.5
MonotonicityStrictly increasing
2023-12-12T12:05:19.384208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
62 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
53 1
 
1.2%
Other values (71) 71
87.7%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%

업체명
Text

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-12T12:05:19.756505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.654321
Min length3

Characters and Unicode

Total characters458
Distinct characters123
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)100.0%

Sample

1st row철물박사 공구점
2nd row논현철물건재
3rd row철물백화점 논현점
4th row철물박사 구월점
5th row현대철물공구
ValueCountFrequency (%)
철물박사 2
 
2.3%
하나종합상사 1
 
1.2%
빗자루손잡이 1
 
1.2%
이지쓸 1
 
1.2%
동해설비철물 1
 
1.2%
믿음철물설비 1
 
1.2%
국제설비철물 1
 
1.2%
장수철물 1
 
1.2%
서우철망 1
 
1.2%
마이홈공구철물 1
 
1.2%
Other values (75) 75
87.2%
2023-12-12T12:05:20.290788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
철 54
 
11.8%
물 54
 
11.8%
전 17
 
3.7%
기 15
 
3.3%
사 14
 
3.1%
성 13
 
2.8%
건 13
 
2.8%
재 12
 
2.6%
공 10
 
2.2%
리 9
 
2.0%
Other values (113) 247
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 451
98.5%
Space Separator 5
 
1.1%
Uppercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
철 54
 
12.0%
물 54
 
12.0%
전 17
 
3.8%
기 15
 
3.3%
사 14
 
3.1%
성 13
 
2.9%
건 13
 
2.9%
재 12
 
2.7%
공 10
 
2.2%
리 9
 
2.0%
Other values (110) 240
53.2%
Uppercase Letter
ValueCountFrequency (%)
D 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 451
98.5%
Common 5
 
1.1%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
철 54
 
12.0%
물 54
 
12.0%
전 17
 
3.8%
기 15
 
3.3%
사 14
 
3.1%
성 13
 
2.9%
건 13
 
2.9%
재 12
 
2.7%
공 10
 
2.2%
리 9
 
2.0%
Other values (110) 240
53.2%
Latin
ValueCountFrequency (%)
D 1
50.0%
C 1
50.0%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 451
98.5%
ASCII 7
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
철 54
 
12.0%
물 54
 
12.0%
전 17
 
3.8%
기 15
 
3.3%
사 14
 
3.1%
성 13
 
2.9%
건 13
 
2.9%
재 12
 
2.7%
공 10
 
2.2%
리 9
 
2.0%
Other values (110) 240
53.2%
ASCII
ValueCountFrequency (%)
5
71.4%
D 1
 
14.3%
C 1
 
14.3%

소재지
Text

Distinct78
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-12T12:05:20.744767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length27
Mean length20.358025
Min length15

Characters and Unicode

Total characters1649
Distinct characters97
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)93.8%

Sample

1st row인천광역시 남동구 인주대로 803
2nd row인천광역시 남동구 소래역서로 5
3rd row인천광역시 남동구 논현로 165
4th row인천광역시 남동구 인주대로 801
5th row인천광역시 남동구 문화서로61번길 13 석경베스트빌 101호
ValueCountFrequency (%)
인천광역시 81
23.5%
남동구 81
23.5%
은청로 9
 
2.6%
4-7 5
 
1.4%
석산로 4
 
1.2%
구월말로 3
 
0.9%
1 3
 
0.9%
33 3
 
0.9%
56 3
 
0.9%
만수로75번길 2
 
0.6%
Other values (130) 151
43.8%
2023-12-12T12:05:21.406888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
264
16.0%
구 95
 
5.8%
동 91
 
5.5%
인 90
 
5.5%
남 90
 
5.5%
천 84
 
5.1%
역 83
 
5.0%
광 81
 
4.9%
시 81
 
4.9%
로 81
 
4.9%
Other values (87) 609
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1071
64.9%
Decimal Number 293
 
17.8%
Space Separator 264
 
16.0%
Dash Punctuation 16
 
1.0%
Uppercase Letter 4
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 95
 
8.9%
동 91
 
8.5%
인 90
 
8.4%
남 90
 
8.4%
천 84
 
7.8%
역 83
 
7.7%
광 81
 
7.6%
시 81
 
7.6%
로 81
 
7.6%
길 34
 
3.2%
Other values (71) 261
24.4%
Decimal Number
ValueCountFrequency (%)
1 61
20.8%
3 37
12.6%
2 36
12.3%
7 29
9.9%
4 28
9.6%
6 25
8.5%
5 25
8.5%
0 23
 
7.8%
8 20
 
6.8%
9 9
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
B 1
25.0%
D 1
25.0%
Space Separator
ValueCountFrequency (%)
264
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1071
64.9%
Common 574
34.8%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 95
 
8.9%
동 91
 
8.5%
인 90
 
8.4%
남 90
 
8.4%
천 84
 
7.8%
역 83
 
7.7%
광 81
 
7.6%
시 81
 
7.6%
로 81
 
7.6%
길 34
 
3.2%
Other values (71) 261
24.4%
Common
ValueCountFrequency (%)
264
46.0%
1 61
 
10.6%
3 37
 
6.4%
2 36
 
6.3%
7 29
 
5.1%
4 28
 
4.9%
6 25
 
4.4%
5 25
 
4.4%
0 23
 
4.0%
8 20
 
3.5%
Other values (3) 26
 
4.5%
Latin
ValueCountFrequency (%)
A 2
50.0%
B 1
25.0%
D 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1071
64.9%
ASCII 578
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
264
45.7%
1 61
 
10.6%
3 37
 
6.4%
2 36
 
6.2%
7 29
 
5.0%
4 28
 
4.8%
6 25
 
4.3%
5 25
 
4.3%
0 23
 
4.0%
8 20
 
3.5%
Other values (6) 30
 
5.2%
Hangul
ValueCountFrequency (%)
구 95
 
8.9%
동 91
 
8.5%
인 90
 
8.4%
남 90
 
8.4%
천 84
 
7.8%
역 83
 
7.7%
광 81
 
7.6%
시 81
 
7.6%
로 81
 
7.6%
길 34
 
3.2%
Other values (71) 261
24.4%

전화번호
Text

MISSING 

Distinct63
Distinct (%)100.0%
Missing18
Missing (%)22.2%
Memory size780.0 B
2023-12-12T12:05:21.793337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.031746
Min length12

Characters and Unicode

Total characters758
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

Unique63 ?
Unique (%)100.0%

Sample

1st row032-467-3910
2nd row032-442-4535
3rd row032-442-0303
4th row032-223-3935
5th row032-429-0909
ValueCountFrequency (%)
032-467-3910 1
 
1.6%
032-463-5891 1
 
1.6%
032-435-8805 1
 
1.6%
032-461-7205 1
 
1.6%
032-471-3830 1
 
1.6%
032-462-1013 1
 
1.6%
032-468-7744 1
 
1.6%
032-461-6349 1
 
1.6%
032-469-3950 1
 
1.6%
032-464-9030 1
 
1.6%
Other values (53) 53
84.1%
2023-12-12T12:05:22.328800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 126
16.6%
0 113
14.9%
2 105
13.9%
3 94
12.4%
4 87
11.5%
8 51
6.7%
6 46
 
6.1%
1 45
 
5.9%
9 35
 
4.6%
7 29
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 632
83.4%
Dash Punctuation 126
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 113
17.9%
2 105
16.6%
3 94
14.9%
4 87
13.8%
8 51
8.1%
6 46
7.3%
1 45
 
7.1%
9 35
 
5.5%
7 29
 
4.6%
5 27
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 126
16.6%
0 113
14.9%
2 105
13.9%
3 94
12.4%
4 87
11.5%
8 51
6.7%
6 46
 
6.1%
1 45
 
5.9%
9 35
 
4.6%
7 29
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 126
16.6%
0 113
14.9%
2 105
13.9%
3 94
12.4%
4 87
11.5%
8 51
6.7%
6 46
 
6.1%
1 45
 
5.9%
9 35
 
4.6%
7 29
 
3.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-09-08
81 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-08
2nd row2023-09-08
3rd row2023-09-08
4th row2023-09-08
5th row2023-09-08

Common Values

ValueCountFrequency (%)
2023-09-08 81
100.0%

Length

2023-12-12T12:05:22.519021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:05:22.687139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-08 81
100.0%

Interactions

2023-12-12T12:05:18.462178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:05:22.778653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업체명소재지전화번호
연번1.0001.0000.9361.000
업체명1.0001.0001.0001.000
소재지0.9361.0001.0001.000
전화번호1.0001.0001.0001.000

Missing values

2023-12-12T12:05:18.602508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:05:18.722419image/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

연번업체명소재지전화번호데이터기준일자
01철물박사 공구점인천광역시 남동구 인주대로 803032-467-39102023-09-08
12논현철물건재인천광역시 남동구 소래역서로 5032-442-45352023-09-08
23철물백화점 논현점인천광역시 남동구 논현로 165032-442-03032023-09-08
34철물박사 구월점인천광역시 남동구 인주대로 801032-223-39352023-09-08
45현대철물공구인천광역시 남동구 문화서로61번길 13 석경베스트빌 101호032-429-09092023-09-08
56성진철물인천광역시 남동구 장아산로 133<NA>2023-09-08
67남동철물인테리어인천광역시 남동구 포구로 75032-815-34842023-09-08
78부성볼트인천광역시 남동구 은청로 4032-812-20112023-09-08
89용인인테리어철물건재인천광역시 남동구 만수로 76 용인인테리어철물건재032-462-53002023-09-08
910뉴서울부수센터인천광역시 남동구 담방로21번길 6102-2108-10002023-09-08
연번업체명소재지전화번호데이터기준일자
7172은혜철물인천광역시 남동구 구월말로 100032-467-74742023-09-08
7273오소리철부자인천광역시 남동구 복개서로35번길 17-7032-463-66302023-09-08
7374구월만물인천광역시 남동구 하촌서로 33032-464-31902023-09-08
7475만수철물인천광역시 남동구 구월말로58번길 56032-465-88782023-09-08
7576철물바다인천광역시 남동구 복개서로 31 2층 철물바다<NA>2023-09-08
7677명성상사인천광역시 남동구 은청로 4-8032-821-27142023-09-08
7778종합전기철물인천광역시 남동구 구월말로 114032-464-20542023-09-08
7879부평상사인천광역시 남동구 은청로 16032-818-54682023-09-08
7980진영종합상사인천광역시 남동구 은청로 4-7032-816-41142023-09-08
8081곰소니금손인천광역시 남동구 만경로8번길 41070-7607-15562023-09-08