Overview

Dataset statistics

Number of variables5
Number of observations25
Missing cells1
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory46.3 B

Variable types

Numeric1
Categorical1
Text3

Dataset

Description인천광역시 부평구 더베스트 우수 숙박업소 현황 데이터는 우수 숙박업소 명, 소재지, 소재지 전화번호 대한 데이터를 제공합니다
URLhttps://www.data.go.kr/data/15103346/fileData.do

Alerts

업종 has constant value ""Constant
소재지전화번호 has 1 (4.0%) missing valuesMissing
순번 has unique valuesUnique
업소명 has unique valuesUnique
소재지(도로명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:39:49.413722
Analysis finished2023-12-12 22:39:49.829699
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T07:39:49.901229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q17
median13
Q319
95-th percentile23.8
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.56613852
Kurtosis-1.2
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum325
Variance54.166667
MonotonicityStrictly increasing
2023-12-13T07:39:50.046371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1
 
4.0%
2 1
 
4.0%
25 1
 
4.0%
24 1
 
4.0%
23 1
 
4.0%
22 1
 
4.0%
21 1
 
4.0%
20 1
 
4.0%
19 1
 
4.0%
18 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
5 1
4.0%
6 1
4.0%
7 1
4.0%
8 1
4.0%
9 1
4.0%
10 1
4.0%
ValueCountFrequency (%)
25 1
4.0%
24 1
4.0%
23 1
4.0%
22 1
4.0%
21 1
4.0%
20 1
4.0%
19 1
4.0%
18 1
4.0%
17 1
4.0%
16 1
4.0%

업종
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
숙박업(일반)
25 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
숙박업(일반) 25
100.0%

Length

2023-12-13T07:39:50.144109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:50.215092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박업(일반 25
100.0%

업소명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-13T07:39:50.360489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length6.44
Min length3

Characters and Unicode

Total characters161
Distinct characters78
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row갤러리아호텔
2nd row고추잠자리호텔
3rd row샴푸호텔
4th row호텔러브스
5th row이오스호텔
ValueCountFrequency (%)
갤러리아호텔 1
 
3.8%
고추잠자리호텔 1
 
3.8%
모텔린 1
 
3.8%
굿타임호텔 1
 
3.8%
리치모텔 1
 
3.8%
호텔코고라 1
 
3.8%
1 1
 
3.8%
테이크원(take 1
 
3.8%
호텔마루 1
 
3.8%
아이러브유(i.love.you 1
 
3.8%
Other values (16) 16
61.5%
2023-12-13T07:39:50.641918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 20
 
12.4%
호 17
 
10.6%
( 6
 
3.7%
) 6
 
3.7%
이 5
 
3.1%
인 3
 
1.9%
코 3
 
1.9%
러 3
 
1.9%
스 3
 
1.9%
모 3
 
1.9%
Other values (68) 92
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127
78.9%
Uppercase Letter 17
 
10.6%
Open Punctuation 6
 
3.7%
Close Punctuation 6
 
3.7%
Other Punctuation 2
 
1.2%
Space Separator 1
 
0.6%
Decimal Number 1
 
0.6%
Lowercase Letter 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 20
 
15.7%
호 17
 
13.4%
이 5
 
3.9%
인 3
 
2.4%
코 3
 
2.4%
러 3
 
2.4%
스 3
 
2.4%
모 3
 
2.4%
부 3
 
2.4%
평 3
 
2.4%
Other values (49) 64
50.4%
Uppercase Letter
ValueCountFrequency (%)
E 2
11.8%
O 2
11.8%
I 2
11.8%
T 2
11.8%
L 1
 
5.9%
V 1
 
5.9%
Y 1
 
5.9%
U 1
 
5.9%
A 1
 
5.9%
K 1
 
5.9%
Other values (3) 3
17.6%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127
78.9%
Latin 18
 
11.2%
Common 16
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 20
 
15.7%
호 17
 
13.4%
이 5
 
3.9%
인 3
 
2.4%
코 3
 
2.4%
러 3
 
2.4%
스 3
 
2.4%
모 3
 
2.4%
부 3
 
2.4%
평 3
 
2.4%
Other values (49) 64
50.4%
Latin
ValueCountFrequency (%)
E 2
11.1%
O 2
11.1%
I 2
11.1%
T 2
11.1%
L 1
 
5.6%
V 1
 
5.6%
Y 1
 
5.6%
U 1
 
5.6%
A 1
 
5.6%
K 1
 
5.6%
Other values (4) 4
22.2%
Common
ValueCountFrequency (%)
( 6
37.5%
) 6
37.5%
. 2
 
12.5%
1
 
6.2%
1 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127
78.9%
ASCII 34
 
21.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 20
 
15.7%
호 17
 
13.4%
이 5
 
3.9%
인 3
 
2.4%
코 3
 
2.4%
러 3
 
2.4%
스 3
 
2.4%
모 3
 
2.4%
부 3
 
2.4%
평 3
 
2.4%
Other values (49) 64
50.4%
ASCII
ValueCountFrequency (%)
( 6
17.6%
) 6
17.6%
E 2
 
5.9%
O 2
 
5.9%
I 2
 
5.9%
. 2
 
5.9%
T 2
 
5.9%
L 1
 
2.9%
V 1
 
2.9%
Y 1
 
2.9%
Other values (9) 9
26.5%

소재지(도로명)
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-13T07:39:50.832297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length26.84
Min length22

Characters and Unicode

Total characters671
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 대정로90번길 24 (부평동)
2nd row인천광역시 부평구 동암광장로4번길 12 (십정동)
3rd row인천광역시 부평구 광장로4번길 23 (부평동)
4th row인천광역시 부평구 경원대로1417번길 23 (부평동)
5th row인천광역시 부평구 대정로82번길 25 (부평동)
ValueCountFrequency (%)
인천광역시 25
21.6%
부평구 25
21.6%
부평동 10
 
8.6%
경원대로 3
 
2.6%
1417번길 2
 
1.7%
시장로12번길 2
 
1.7%
경원대로1377번길 2
 
1.7%
25 2
 
1.7%
대정로82번길 2
 
1.7%
경원대로1417번길 2
 
1.7%
Other values (37) 41
35.3%
2023-12-13T07:39:51.151741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91
 
13.6%
부 45
 
6.7%
평 45
 
6.7%
광 31
 
4.6%
1 29
 
4.3%
동 29
 
4.3%
시 27
 
4.0%
천 27
 
4.0%
인 25
 
3.7%
( 25
 
3.7%
Other values (32) 297
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 413
61.5%
Decimal Number 109
 
16.2%
Space Separator 91
 
13.6%
Open Punctuation 25
 
3.7%
Close Punctuation 25
 
3.7%
Dash Punctuation 8
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 45
10.9%
평 45
10.9%
광 31
 
7.5%
동 29
 
7.0%
시 27
 
6.5%
천 27
 
6.5%
인 25
 
6.1%
역 25
 
6.1%
구 25
 
6.1%
로 25
 
6.1%
Other values (18) 109
26.4%
Decimal Number
ValueCountFrequency (%)
1 29
26.6%
7 16
14.7%
2 15
13.8%
4 13
11.9%
3 10
 
9.2%
8 8
 
7.3%
9 6
 
5.5%
0 5
 
4.6%
6 4
 
3.7%
5 3
 
2.8%
Space Separator
ValueCountFrequency (%)
91
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 413
61.5%
Common 258
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 45
10.9%
평 45
10.9%
광 31
 
7.5%
동 29
 
7.0%
시 27
 
6.5%
천 27
 
6.5%
인 25
 
6.1%
역 25
 
6.1%
구 25
 
6.1%
로 25
 
6.1%
Other values (18) 109
26.4%
Common
ValueCountFrequency (%)
91
35.3%
1 29
 
11.2%
( 25
 
9.7%
) 25
 
9.7%
7 16
 
6.2%
2 15
 
5.8%
4 13
 
5.0%
3 10
 
3.9%
- 8
 
3.1%
8 8
 
3.1%
Other values (4) 18
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 413
61.5%
ASCII 258
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
91
35.3%
1 29
 
11.2%
( 25
 
9.7%
) 25
 
9.7%
7 16
 
6.2%
2 15
 
5.8%
4 13
 
5.0%
3 10
 
3.9%
- 8
 
3.1%
8 8
 
3.1%
Other values (4) 18
 
7.0%
Hangul
ValueCountFrequency (%)
부 45
10.9%
평 45
10.9%
광 31
 
7.5%
동 29
 
7.0%
시 27
 
6.5%
천 27
 
6.5%
인 25
 
6.1%
역 25
 
6.1%
구 25
 
6.1%
로 25
 
6.1%
Other values (18) 109
26.4%

소재지전화번호
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing1
Missing (%)4.0%
Memory size332.0 B
2023-12-13T07:39:51.331044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters288
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

Unique24 ?
Unique (%)100.0%

Sample

1st row032-503-4848
2nd row032-421-0381
3rd row032-522-5855
4th row032-523-7135
5th row032-512-2253
ValueCountFrequency (%)
032-503-4848 1
 
4.2%
032-421-0381 1
 
4.2%
032-424-2247 1
 
4.2%
032-422-0180 1
 
4.2%
032-330-1131 1
 
4.2%
032-526-5533 1
 
4.2%
032-511-0178 1
 
4.2%
032-519-3100 1
 
4.2%
032-527-1045 1
 
4.2%
032-521-1154 1
 
4.2%
Other values (14) 14
58.3%
2023-12-13T07:39:51.624773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 49
17.0%
- 48
16.7%
3 44
15.3%
0 42
14.6%
5 37
12.8%
1 24
8.3%
4 16
 
5.6%
8 9
 
3.1%
6 9
 
3.1%
7 6
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240
83.3%
Dash Punctuation 48
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 49
20.4%
3 44
18.3%
0 42
17.5%
5 37
15.4%
1 24
10.0%
4 16
 
6.7%
8 9
 
3.8%
6 9
 
3.8%
7 6
 
2.5%
9 4
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49
17.0%
- 48
16.7%
3 44
15.3%
0 42
14.6%
5 37
12.8%
1 24
8.3%
4 16
 
5.6%
8 9
 
3.1%
6 9
 
3.1%
7 6
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49
17.0%
- 48
16.7%
3 44
15.3%
0 42
14.6%
5 37
12.8%
1 24
8.3%
4 16
 
5.6%
8 9
 
3.1%
6 9
 
3.1%
7 6
 
2.1%

Interactions

2023-12-13T07:39:49.590584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:39:51.719852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업소명소재지(도로명)소재지전화번호
순번1.0001.0001.0001.000
업소명1.0001.0001.0001.000
소재지(도로명)1.0001.0001.0001.000
소재지전화번호1.0001.0001.0001.000

Missing values

2023-12-13T07:39:49.696551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:39:49.788768image/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

순번업종업소명소재지(도로명)소재지전화번호
01숙박업(일반)갤러리아호텔인천광역시 부평구 대정로90번길 24 (부평동)032-503-4848
12숙박업(일반)고추잠자리호텔인천광역시 부평구 동암광장로4번길 12 (십정동)032-421-0381
23숙박업(일반)샴푸호텔인천광역시 부평구 광장로4번길 23 (부평동)032-522-5855
34숙박업(일반)호텔러브스인천광역시 부평구 경원대로1417번길 23 (부평동)032-523-7135
45숙박업(일반)이오스호텔인천광역시 부평구 대정로82번길 25 (부평동)032-512-2253
56숙박업(일반)라임호텔인천광역시 부평구 부평문화로79번길 40 (부평동)032-526-5590
67숙박업(일반)호텔민트인천광역시 부평구 동암광장로4번길 7 (십정동)032-437-8136
78숙박업(일반)더블유(W)인천광역시 부평구 경원대로1377번길 17-5(부평동)032-527-4565
89숙박업(일반)호텔두루와(본관)인천광역시 부평구 시장로12번길 31(부평동)032-516-1006
910숙박업(일반)Rg호텔(알지)인천광역시 부평구 원길로11번길 7(산곡동)032-502-5553
순번업종업소명소재지(도로명)소재지전화번호
1516숙박업(일반)호텔주노인천광역시 부평구 대정로36번길 7-1 (부평동)032-521-1154
1617숙박업(일반)토요코인인천부평인천광역시 부평구 광장로 10 (부평동)032-527-1045
1718숙박업(일반)아이러브유(I.LOVE.YOU)인천광역시 부평구 시장로12번길 25 (부평동)032-519-3100
1819숙박업(일반)호텔마루인천광역시 부평구 대정로90번길 27 (부평동)032-511-0178
1920숙박업(일반)테이크원(TAKE 1)인천광역시 부평구 경원대로1417번길 18-1 (부평동)032-526-5533
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