Overview

Dataset statistics

Number of variables4
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1016.0 B
Average record size in memory39.1 B

Variable types

Numeric2
Text2

Dataset

Description광주광역시에서 지정한 우수숙박업소(크린호텔)에 대한 현황자료입니다.연번,업소명,소재지,객실수 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15055846/fileData.do

Alerts

연번 has unique valuesUnique
업소명 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:12:28.781763
Analysis finished2023-12-12 09:12:29.438906
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:12:29.523545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q17.25
median13.5
Q319.75
95-th percentile24.75
Maximum26
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.6485293
Coefficient of variation (CV)0.56655772
Kurtosis-1.2
Mean13.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum351
Variance58.5
MonotonicityStrictly increasing
2023-12-12T18:12:29.652962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1
 
3.8%
15 1
 
3.8%
26 1
 
3.8%
25 1
 
3.8%
24 1
 
3.8%
23 1
 
3.8%
22 1
 
3.8%
21 1
 
3.8%
20 1
 
3.8%
19 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1 1
3.8%
2 1
3.8%
3 1
3.8%
4 1
3.8%
5 1
3.8%
6 1
3.8%
7 1
3.8%
8 1
3.8%
9 1
3.8%
10 1
3.8%
ValueCountFrequency (%)
26 1
3.8%
25 1
3.8%
24 1
3.8%
23 1
3.8%
22 1
3.8%
21 1
3.8%
20 1
3.8%
19 1
3.8%
18 1
3.8%
17 1
3.8%

업소명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T18:12:29.854062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8.5
Mean length5.6538462
Min length3

Characters and Unicode

Total characters147
Distinct characters66
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

Unique26 ?
Unique (%)100.0%

Sample

1st row게스트145
2nd row더메이
3rd row에스호텔
4th row에프엔티호텔
5th row퀸호텔
ValueCountFrequency (%)
게스트145 1
 
3.6%
더메이 1
 
3.6%
hotel 1
 
3.6%
sol 1
 
3.6%
the 1
 
3.6%
더존호텔 1
 
3.6%
아우라호텔 1
 
3.6%
마드리드호텔 1
 
3.6%
바나나호텔 1
 
3.6%
비엔날레호텔 1
 
3.6%
Other values (18) 18
64.3%
2023-12-12T18:12:30.216484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
호 21
 
14.3%
텔 21
 
14.3%
스 6
 
4.1%
더 4
 
2.7%
지 3
 
2.0%
바 3
 
2.0%
티 3
 
2.0%
리 3
 
2.0%
비 3
 
2.0%
나 3
 
2.0%
Other values (56) 77
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128
87.1%
Lowercase Letter 8
 
5.4%
Decimal Number 5
 
3.4%
Uppercase Letter 4
 
2.7%
Space Separator 2
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 21
 
16.4%
텔 21
 
16.4%
스 6
 
4.7%
더 4
 
3.1%
지 3
 
2.3%
바 3
 
2.3%
티 3
 
2.3%
리 3
 
2.3%
비 3
 
2.3%
나 3
 
2.3%
Other values (42) 58
45.3%
Lowercase Letter
ValueCountFrequency (%)
e 2
25.0%
o 2
25.0%
l 2
25.0%
h 1
12.5%
t 1
12.5%
Decimal Number
ValueCountFrequency (%)
5 2
40.0%
3 1
20.0%
1 1
20.0%
4 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
25.0%
H 1
25.0%
T 1
25.0%
B 1
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128
87.1%
Latin 12
 
8.2%
Common 7
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 21
 
16.4%
텔 21
 
16.4%
스 6
 
4.7%
더 4
 
3.1%
지 3
 
2.3%
바 3
 
2.3%
티 3
 
2.3%
리 3
 
2.3%
비 3
 
2.3%
나 3
 
2.3%
Other values (42) 58
45.3%
Latin
ValueCountFrequency (%)
e 2
16.7%
o 2
16.7%
l 2
16.7%
h 1
8.3%
S 1
8.3%
H 1
8.3%
t 1
8.3%
T 1
8.3%
B 1
8.3%
Common
ValueCountFrequency (%)
2
28.6%
5 2
28.6%
3 1
14.3%
1 1
14.3%
4 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128
87.1%
ASCII 19
 
12.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
호 21
 
16.4%
텔 21
 
16.4%
스 6
 
4.7%
더 4
 
3.1%
지 3
 
2.3%
바 3
 
2.3%
티 3
 
2.3%
리 3
 
2.3%
비 3
 
2.3%
나 3
 
2.3%
Other values (42) 58
45.3%
ASCII
ValueCountFrequency (%)
e 2
10.5%
2
10.5%
o 2
10.5%
5 2
10.5%
l 2
10.5%
3 1
 
5.3%
h 1
 
5.3%
S 1
 
5.3%
H 1
 
5.3%
t 1
 
5.3%
Other values (4) 4
21.1%

소재지
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T18:12:30.416758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length13.307692
Min length9

Characters and Unicode

Total characters346
Distinct characters49
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

Unique26 ?
Unique (%)100.0%

Sample

1st row동구 구성로194번길 19
2nd row서구 상무번영로 51
3rd row서구 상무평화로 103
4th row서구 상무연하로 31
5th row서구 상무평화로 97-10
ValueCountFrequency (%)
서구 16
20.8%
광산구 6
 
7.8%
상무평화로 4
 
5.2%
북구 3
 
3.9%
20 2
 
2.6%
상무연하로 2
 
2.6%
6 2
 
2.6%
상무중앙로38번길 2
 
2.6%
11 2
 
2.6%
상무번영로 2
 
2.6%
Other values (36) 36
46.8%
2023-12-12T18:12:30.724037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51
 
14.7%
구 27
 
7.8%
로 26
 
7.5%
1 22
 
6.4%
서 16
 
4.6%
번 15
 
4.3%
길 13
 
3.8%
상 12
 
3.5%
무 12
 
3.5%
3 11
 
3.2%
Other values (39) 141
40.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 193
55.8%
Decimal Number 95
27.5%
Space Separator 51
 
14.7%
Dash Punctuation 7
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 27
14.0%
로 26
13.5%
서 16
 
8.3%
번 15
 
7.8%
길 13
 
6.7%
상 12
 
6.2%
무 12
 
6.2%
광 7
 
3.6%
대 7
 
3.6%
산 7
 
3.6%
Other values (27) 51
26.4%
Decimal Number
ValueCountFrequency (%)
1 22
23.2%
3 11
11.6%
0 10
10.5%
2 10
10.5%
8 10
10.5%
5 8
 
8.4%
7 7
 
7.4%
6 7
 
7.4%
4 6
 
6.3%
9 4
 
4.2%
Space Separator
ValueCountFrequency (%)
51
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 193
55.8%
Common 153
44.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 27
14.0%
로 26
13.5%
서 16
 
8.3%
번 15
 
7.8%
길 13
 
6.7%
상 12
 
6.2%
무 12
 
6.2%
광 7
 
3.6%
대 7
 
3.6%
산 7
 
3.6%
Other values (27) 51
26.4%
Common
ValueCountFrequency (%)
51
33.3%
1 22
14.4%
3 11
 
7.2%
0 10
 
6.5%
2 10
 
6.5%
8 10
 
6.5%
5 8
 
5.2%
- 7
 
4.6%
7 7
 
4.6%
6 7
 
4.6%
Other values (2) 10
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 193
55.8%
ASCII 153
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51
33.3%
1 22
14.4%
3 11
 
7.2%
0 10
 
6.5%
2 10
 
6.5%
8 10
 
6.5%
5 8
 
5.2%
- 7
 
4.6%
7 7
 
4.6%
6 7
 
4.6%
Other values (2) 10
 
6.5%
Hangul
ValueCountFrequency (%)
구 27
14.0%
로 26
13.5%
서 16
 
8.3%
번 15
 
7.8%
길 13
 
6.7%
상 12
 
6.2%
무 12
 
6.2%
광 7
 
3.6%
대 7
 
3.6%
산 7
 
3.6%
Other values (27) 51
26.4%

객실수
Real number (ℝ)

Distinct20
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.884615
Minimum28
Maximum448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:12:30.849658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile30
Q135
median42.5
Q358.5
95-th percentile80
Maximum448
Range420
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation80.13455
Coefficient of variation (CV)1.3161708
Kurtosis24.316317
Mean60.884615
Median Absolute Deviation (MAD)9
Skewness4.8643126
Sum1583
Variance6421.5462
MonotonicityNot monotonic
2023-12-12T18:12:30.966365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
35 5
19.2%
60 2
 
7.7%
30 2
 
7.7%
44 1
 
3.8%
53 1
 
3.8%
48 1
 
3.8%
59 1
 
3.8%
36 1
 
3.8%
28 1
 
3.8%
39 1
 
3.8%
Other values (10) 10
38.5%
ValueCountFrequency (%)
28 1
 
3.8%
30 2
 
7.7%
32 1
 
3.8%
35 5
19.2%
36 1
 
3.8%
39 1
 
3.8%
40 1
 
3.8%
41 1
 
3.8%
44 1
 
3.8%
45 1
 
3.8%
ValueCountFrequency (%)
448 1
3.8%
85 1
3.8%
65 1
3.8%
61 1
3.8%
60 2
7.7%
59 1
3.8%
57 1
3.8%
53 1
3.8%
48 1
3.8%
47 1
3.8%

Interactions

2023-12-12T18:12:29.108869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:12:28.943898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:12:29.186963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:12:29.037525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:12:31.064236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업소명소재지객실수
연번1.0001.0001.0000.404
업소명1.0001.0001.0001.000
소재지1.0001.0001.0001.000
객실수0.4041.0001.0001.000
2023-12-12T18:12:31.186119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번객실수
연번1.0000.205
객실수0.2051.000

Missing values

2023-12-12T18:12:29.292109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:12:29.389022image/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

연번업소명소재지객실수
01게스트145동구 구성로194번길 1944
12더메이서구 상무번영로 5135
23에스호텔서구 상무평화로 10340
34에프엔티호텔서구 상무연하로 3145
45퀸호텔서구 상무평화로 97-1032
56하바나호텔서구 상무중앙로 78번길 2035
67부티끄호텔와인서구 상무평화로 14030
78벤틀리비지니스호텔서구 상무평화로 12447
89벤처비지니스호텔서구 상무중앙로38번길 5-657
910두바이호텔서구 상무번영로 4785
연번업소명소재지객실수
1617유탑부티크호텔서구 시청로 53448
1718세종호텔북구 경양로165번길 3035
1819호텔더스팟북구 앰코로 3239
1920비엔날레호텔북구 대자로 10428
2021바나나호텔광산구 임방울대로825번길22-1336
2122마드리드호텔광산구 광산로19번길 1159
2223아우라호텔광산구 송정로1번길 2860
2324더존호텔광산구 임방울대로826번길 60-3735
2425The Sol Hotel광산구 어등대로 71160
2526탑클라우드광산구 임방울대로825번길57-148