Overview

Dataset statistics

Number of variables5
Number of observations84
Missing cells47
Missing cells (%)11.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory42.6 B

Variable types

Numeric1
Text4

Dataset

Description인천광역시 미추홀구의 옷수선업체에 대한 데이터이며 상호명, 지번주소, 도로명주소, 전화번호 등의 항목을 제공하고 있습니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15087016&srcSe=7661IVAWM27C61E190

Alerts

도로명주소 has 6 (7.1%) missing valuesMissing
전화번호 has 41 (48.8%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 12:06:52.018165
Analysis finished2024-01-28 12:06:52.888840
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.5
Minimum1
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-01-28T21:06:52.958799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.15
Q121.75
median42.5
Q363.25
95-th percentile79.85
Maximum84
Range83
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation24.392622
Coefficient of variation (CV)0.57394404
Kurtosis-1.2
Mean42.5
Median Absolute Deviation (MAD)21
Skewness0
Sum3570
Variance595
MonotonicityStrictly increasing
2024-01-28T21:06:53.073165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
55 1
 
1.2%
63 1
 
1.2%
62 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
Other values (74) 74
88.1%
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 (%)
84 1
1.2%
83 1
1.2%
82 1
1.2%
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%

상호명
Text

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
2024-01-28T21:06:53.315394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length5.8809524
Min length2

Characters and Unicode

Total characters494
Distinct characters161
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)97.6%

Sample

1st row수선실
2nd row용남옷수선
3rd row의류수선실 롯데백화점인천점
4th row명품수선
5th row수선집
ValueCountFrequency (%)
옷수선 3
 
3.2%
명동옷수선 2
 
2.1%
의류수선실 2
 
2.1%
수선실 2
 
2.1%
유미수선 1
 
1.1%
영진이네옷수선 1
 
1.1%
다솜의상실 1
 
1.1%
왕이네 1
 
1.1%
의류수선 1
 
1.1%
우연옷방 1
 
1.1%
Other values (79) 79
84.0%
2024-01-28T21:06:53.672833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 61
 
12.3%
선 58
 
11.7%
옷 34
 
6.9%
실 15
 
3.0%
의 13
 
2.6%
상 11
 
2.2%
이 11
 
2.2%
10
 
2.0%
네 8
 
1.6%
지 7
 
1.4%
Other values (151) 266
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 479
97.0%
Space Separator 10
 
2.0%
Uppercase Letter 2
 
0.4%
Lowercase Letter 2
 
0.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 61
 
12.7%
선 58
 
12.1%
옷 34
 
7.1%
실 15
 
3.1%
의 13
 
2.7%
상 11
 
2.3%
이 11
 
2.3%
네 8
 
1.7%
지 7
 
1.5%
점 7
 
1.5%
Other values (145) 254
53.0%
Uppercase Letter
ValueCountFrequency (%)
O 1
50.0%
K 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
y 1
50.0%
b 1
50.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 479
97.0%
Common 11
 
2.2%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 61
 
12.7%
선 58
 
12.1%
옷 34
 
7.1%
실 15
 
3.1%
의 13
 
2.7%
상 11
 
2.3%
이 11
 
2.3%
네 8
 
1.7%
지 7
 
1.5%
점 7
 
1.5%
Other values (145) 254
53.0%
Latin
ValueCountFrequency (%)
O 1
25.0%
K 1
25.0%
y 1
25.0%
b 1
25.0%
Common
ValueCountFrequency (%)
10
90.9%
& 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 479
97.0%
ASCII 15
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 61
 
12.7%
선 58
 
12.1%
옷 34
 
7.1%
실 15
 
3.1%
의 13
 
2.7%
상 11
 
2.3%
이 11
 
2.3%
네 8
 
1.7%
지 7
 
1.5%
점 7
 
1.5%
Other values (145) 254
53.0%
ASCII
ValueCountFrequency (%)
10
66.7%
O 1
 
6.7%
K 1
 
6.7%
y 1
 
6.7%
b 1
 
6.7%
& 1
 
6.7%

지번주소
Text

Distinct77
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size804.0 B
2024-01-28T21:06:53.941746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length33
Mean length21.607143
Min length17

Characters and Unicode

Total characters1815
Distinct characters69
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)88.1%

Sample

1st row인천광역시 미추홀구 용현동 648 홈에버인하점 1층
2nd row인천광역시 미추홀구 용현동 94-48
3rd row인천광역시 미추홀구 관교동 15 3층
4th row인천광역시 미추홀구 도화동 104-23 4호 명품수선
5th row인천광역시 미추홀구 도화동 545-20
ValueCountFrequency (%)
인천광역시 84
23.3%
미추홀구 84
23.3%
주안동 36
 
10.0%
용현동 15
 
4.2%
도화동 12
 
3.3%
관교동 7
 
1.9%
학익동 7
 
1.9%
숭의동 6
 
1.7%
188-5 5
 
1.4%
1층 3
 
0.8%
Other values (94) 101
28.1%
2024-01-28T21:06:54.303969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
284
 
15.6%
인 85
 
4.7%
동 85
 
4.7%
추 84
 
4.6%
구 84
 
4.6%
홀 84
 
4.6%
천 84
 
4.6%
미 84
 
4.6%
시 84
 
4.6%
역 84
 
4.6%
Other values (59) 773
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1069
58.9%
Decimal Number 389
 
21.4%
Space Separator 284
 
15.6%
Dash Punctuation 73
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 85
 
8.0%
동 85
 
8.0%
추 84
 
7.9%
구 84
 
7.9%
홀 84
 
7.9%
천 84
 
7.9%
미 84
 
7.9%
시 84
 
7.9%
역 84
 
7.9%
광 84
 
7.9%
Other values (47) 227
21.2%
Decimal Number
ValueCountFrequency (%)
1 83
21.3%
2 46
11.8%
5 44
11.3%
4 44
11.3%
3 37
9.5%
8 35
9.0%
6 33
 
8.5%
9 31
 
8.0%
0 23
 
5.9%
7 13
 
3.3%
Space Separator
ValueCountFrequency (%)
284
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1069
58.9%
Common 746
41.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 85
 
8.0%
동 85
 
8.0%
추 84
 
7.9%
구 84
 
7.9%
홀 84
 
7.9%
천 84
 
7.9%
미 84
 
7.9%
시 84
 
7.9%
역 84
 
7.9%
광 84
 
7.9%
Other values (47) 227
21.2%
Common
ValueCountFrequency (%)
284
38.1%
1 83
 
11.1%
- 73
 
9.8%
2 46
 
6.2%
5 44
 
5.9%
4 44
 
5.9%
3 37
 
5.0%
8 35
 
4.7%
6 33
 
4.4%
9 31
 
4.2%
Other values (2) 36
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1069
58.9%
ASCII 746
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
284
38.1%
1 83
 
11.1%
- 73
 
9.8%
2 46
 
6.2%
5 44
 
5.9%
4 44
 
5.9%
3 37
 
5.0%
8 35
 
4.7%
6 33
 
4.4%
9 31
 
4.2%
Other values (2) 36
 
4.8%
Hangul
ValueCountFrequency (%)
인 85
 
8.0%
동 85
 
8.0%
추 84
 
7.9%
구 84
 
7.9%
홀 84
 
7.9%
천 84
 
7.9%
미 84
 
7.9%
시 84
 
7.9%
역 84
 
7.9%
광 84
 
7.9%
Other values (47) 227
21.2%

도로명주소
Text

MISSING 

Distinct75
Distinct (%)96.2%
Missing6
Missing (%)7.1%
Memory size804.0 B
2024-01-28T21:06:54.545964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length22.320513
Min length17

Characters and Unicode

Total characters1741
Distinct characters107
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)93.6%

Sample

1st row인천광역시 미추홀구 소성로 6 홈에버인하점 1층
2nd row인천광역시 미추홀구 인하로119번길 13
3rd row인천광역시 미추홀구 연남로 35 3층
4th row인천광역시 미추홀구 석정로279번길 7 4호 명품수선
5th row인천광역시 미추홀구 경인로252번길 24-8
ValueCountFrequency (%)
인천광역시 78
22.5%
미추홀구 78
22.5%
경인로 5
 
1.4%
소성로 5
 
1.4%
1층 5
 
1.4%
7 4
 
1.2%
경원대로 3
 
0.9%
343 3
 
0.9%
주승로 3
 
0.9%
35 3
 
0.9%
Other values (139) 160
46.1%
2024-01-28T21:06:54.900867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
269
 
15.5%
인 101
 
5.8%
미 82
 
4.7%
추 82
 
4.7%
홀 81
 
4.7%
광 78
 
4.5%
역 78
 
4.5%
시 78
 
4.5%
구 78
 
4.5%
천 78
 
4.5%
Other values (97) 736
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1146
65.8%
Decimal Number 315
 
18.1%
Space Separator 269
 
15.5%
Dash Punctuation 10
 
0.6%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 101
 
8.8%
미 82
 
7.2%
추 82
 
7.2%
홀 81
 
7.1%
광 78
 
6.8%
역 78
 
6.8%
시 78
 
6.8%
구 78
 
6.8%
천 78
 
6.8%
로 73
 
6.4%
Other values (84) 337
29.4%
Decimal Number
ValueCountFrequency (%)
1 53
16.8%
2 45
14.3%
4 42
13.3%
3 41
13.0%
5 26
8.3%
6 25
7.9%
7 25
7.9%
8 22
7.0%
0 22
7.0%
9 14
 
4.4%
Space Separator
ValueCountFrequency (%)
269
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1146
65.8%
Common 595
34.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 101
 
8.8%
미 82
 
7.2%
추 82
 
7.2%
홀 81
 
7.1%
광 78
 
6.8%
역 78
 
6.8%
시 78
 
6.8%
구 78
 
6.8%
천 78
 
6.8%
로 73
 
6.4%
Other values (84) 337
29.4%
Common
ValueCountFrequency (%)
269
45.2%
1 53
 
8.9%
2 45
 
7.6%
4 42
 
7.1%
3 41
 
6.9%
5 26
 
4.4%
6 25
 
4.2%
7 25
 
4.2%
8 22
 
3.7%
0 22
 
3.7%
Other values (3) 25
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1146
65.8%
ASCII 595
34.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
269
45.2%
1 53
 
8.9%
2 45
 
7.6%
4 42
 
7.1%
3 41
 
6.9%
5 26
 
4.4%
6 25
 
4.2%
7 25
 
4.2%
8 22
 
3.7%
0 22
 
3.7%
Other values (3) 25
 
4.2%
Hangul
ValueCountFrequency (%)
인 101
 
8.8%
미 82
 
7.2%
추 82
 
7.2%
홀 81
 
7.1%
광 78
 
6.8%
역 78
 
6.8%
시 78
 
6.8%
구 78
 
6.8%
천 78
 
6.8%
로 73
 
6.4%
Other values (84) 337
29.4%

전화번호
Text

MISSING 

Distinct43
Distinct (%)100.0%
Missing41
Missing (%)48.8%
Memory size804.0 B
2024-01-28T21:06:55.099548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.651163
Min length12

Characters and Unicode

Total characters544
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

Unique43 ?
Unique (%)100.0%

Sample

1st row0507-1485-6014
2nd row032-875-4252
3rd row032-242-2994
4th row0507-1392-4157
5th row0507-1433-5015
ValueCountFrequency (%)
0507-1485-6014 1
 
2.3%
032-867-3173 1
 
2.3%
032-434-7905 1
 
2.3%
032-865-8652 1
 
2.3%
032-430-1191 1
 
2.3%
032-865-0429 1
 
2.3%
032-424-8881 1
 
2.3%
032-863-3124 1
 
2.3%
032-438-8900 1
 
2.3%
032-433-3407 1
 
2.3%
Other values (33) 33
76.7%
2024-01-28T21:06:55.431251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 86
15.8%
0 79
14.5%
3 74
13.6%
2 54
9.9%
4 44
8.1%
5 43
7.9%
8 41
7.5%
7 40
7.4%
1 37
6.8%
9 26
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 458
84.2%
Dash Punctuation 86
 
15.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79
17.2%
3 74
16.2%
2 54
11.8%
4 44
9.6%
5 43
9.4%
8 41
9.0%
7 40
8.7%
1 37
8.1%
9 26
 
5.7%
6 20
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 86
15.8%
0 79
14.5%
3 74
13.6%
2 54
9.9%
4 44
8.1%
5 43
7.9%
8 41
7.5%
7 40
7.4%
1 37
6.8%
9 26
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 86
15.8%
0 79
14.5%
3 74
13.6%
2 54
9.9%
4 44
8.1%
5 43
7.9%
8 41
7.5%
7 40
7.4%
1 37
6.8%
9 26
 
4.8%

Interactions

2024-01-28T21:06:52.614627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T21:06:55.521016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호명지번주소도로명주소전화번호
연번1.0000.9390.3020.7721.000
상호명0.9391.0000.9950.9951.000
지번주소0.3020.9951.0001.0001.000
도로명주소0.7720.9951.0001.0001.000
전화번호1.0001.0001.0001.0001.000

Missing values

2024-01-28T21:06:52.695747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T21:06:52.774968image/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.
2024-01-28T21:06:52.846659image/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

연번상호명지번주소도로명주소전화번호
01수선실인천광역시 미추홀구 용현동 648 홈에버인하점 1층인천광역시 미추홀구 소성로 6 홈에버인하점 1층0507-1485-6014
12용남옷수선인천광역시 미추홀구 용현동 94-48인천광역시 미추홀구 인하로119번길 13032-875-4252
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