Overview

Dataset statistics

Number of variables8
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory73.0 B

Variable types

Categorical3
Text2
Numeric3

Dataset

Description제주특별자치도 내에 소재하고 있는 국제회의시설(호텔, 컨벤션, 리조트 등)의 구분, 업체명, 객실, 수용가능인원 정보를 제공합니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15043700/fileData.do

Alerts

담당부서 has constant value ""Constant
데이터기준일자 has constant value ""Constant
회의장수 is highly overall correlated with 구분High correlation
최대 수용가능인원(명) is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 회의장수 and 1 other fieldsHigh correlation
구분 is highly imbalanced (73.3%)Imbalance
업체명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:53:35.555459
Analysis finished2023-12-12 18:53:38.032689
Duration2.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
준회의시설
21 
전문회의시설
 
1

Length

Max length6
Median length5
Mean length5.0454545
Min length5

Unique

Unique1 ?
Unique (%)4.5%

Sample

1st row전문회의시설
2nd row준회의시설
3rd row준회의시설
4th row준회의시설
5th row준회의시설

Common Values

ValueCountFrequency (%)
준회의시설 21
95.5%
전문회의시설 1
 
4.5%

Length

2023-12-13T03:53:38.151521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:38.329011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
준회의시설 21
95.5%
전문회의시설 1
 
4.5%

업체명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T03:53:38.592802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length8.2727273
Min length6

Characters and Unicode

Total characters182
Distinct characters77
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row제주국제컨벤션센터
2nd row제주신화월드 랜팅컨벤션센터
3rd row해비치호텔&리조트
4th row라마다프라자제주
5th row부영호텔앤리조트
ValueCountFrequency (%)
제주 4
 
13.8%
제주국제컨벤션센터 1
 
3.4%
제주한라대학교 1
 
3.4%
오션스위츠제주호텔 1
 
3.4%
kal호텔 1
 
3.4%
서귀포 1
 
3.4%
그랜드하얏트제주 1
 
3.4%
켄싱턴리조트서귀포 1
 
3.4%
금호리조트제주 1
 
3.4%
그랜드조선제주 1
 
3.4%
Other values (16) 16
55.2%
2023-12-13T03:53:39.095197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 19
 
10.4%
주 18
 
9.9%
호 11
 
6.0%
텔 10
 
5.5%
7
 
3.8%
트 5
 
2.7%
조 5
 
2.7%
리 5
 
2.7%
라 5
 
2.7%
드 4
 
2.2%
Other values (67) 93
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 171
94.0%
Space Separator 7
 
3.8%
Uppercase Letter 3
 
1.6%
Other Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 19
 
11.1%
주 18
 
10.5%
호 11
 
6.4%
텔 10
 
5.8%
트 5
 
2.9%
조 5
 
2.9%
리 5
 
2.9%
라 5
 
2.9%
드 4
 
2.3%
션 4
 
2.3%
Other values (62) 85
49.7%
Uppercase Letter
ValueCountFrequency (%)
L 1
33.3%
A 1
33.3%
K 1
33.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 171
94.0%
Common 8
 
4.4%
Latin 3
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 19
 
11.1%
주 18
 
10.5%
호 11
 
6.4%
텔 10
 
5.8%
트 5
 
2.9%
조 5
 
2.9%
리 5
 
2.9%
라 5
 
2.9%
드 4
 
2.3%
션 4
 
2.3%
Other values (62) 85
49.7%
Latin
ValueCountFrequency (%)
L 1
33.3%
A 1
33.3%
K 1
33.3%
Common
ValueCountFrequency (%)
7
87.5%
& 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 171
94.0%
ASCII 11
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 19
 
11.1%
주 18
 
10.5%
호 11
 
6.4%
텔 10
 
5.8%
트 5
 
2.9%
조 5
 
2.9%
리 5
 
2.9%
라 5
 
2.9%
드 4
 
2.3%
션 4
 
2.3%
Other values (62) 85
49.7%
ASCII
ValueCountFrequency (%)
7
63.6%
L 1
 
9.1%
A 1
 
9.1%
K 1
 
9.1%
& 1
 
9.1%

객실정보
Text

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T03:53:39.359897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.0909091
Min length4

Characters and Unicode

Total characters178
Distinct characters21
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

Unique20 ?
Unique (%)90.9%

Sample

1st row객실없음
2nd row특1 / 2062실
3rd row특1 / 호텔 288실
4th row특1 / 400실
5th row특1 / 449실
ValueCountFrequency (%)
14
25.0%
특1 12
21.4%
콘도 5
 
8.9%
객실없음 2
 
3.6%
특2 2
 
3.6%
350실 1
 
1.8%
225실 1
 
1.8%
1600실 1
 
1.8%
214실 1
 
1.8%
324실 1
 
1.8%
Other values (16) 16
28.6%
2023-12-13T03:53:39.889375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34
19.1%
실 22
12.4%
1 17
9.6%
특 14
7.9%
/ 14
7.9%
2 14
7.9%
0 11
 
6.2%
3 10
 
5.6%
4 8
 
4.5%
9 5
 
2.8%
Other values (11) 29
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76
42.7%
Other Letter 54
30.3%
Space Separator 34
19.1%
Other Punctuation 14
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
22.4%
2 14
18.4%
0 11
14.5%
3 10
13.2%
4 8
10.5%
9 5
 
6.6%
7 3
 
3.9%
5 3
 
3.9%
8 3
 
3.9%
6 2
 
2.6%
Other Letter
ValueCountFrequency (%)
실 22
40.7%
특 14
25.9%
도 5
 
9.3%
콘 5
 
9.3%
객 2
 
3.7%
음 2
 
3.7%
없 2
 
3.7%
텔 1
 
1.9%
호 1
 
1.9%
Space Separator
ValueCountFrequency (%)
34
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 124
69.7%
Hangul 54
30.3%

Most frequent character per script

Common
ValueCountFrequency (%)
34
27.4%
1 17
13.7%
/ 14
11.3%
2 14
11.3%
0 11
 
8.9%
3 10
 
8.1%
4 8
 
6.5%
9 5
 
4.0%
7 3
 
2.4%
5 3
 
2.4%
Other values (2) 5
 
4.0%
Hangul
ValueCountFrequency (%)
실 22
40.7%
특 14
25.9%
도 5
 
9.3%
콘 5
 
9.3%
객 2
 
3.7%
음 2
 
3.7%
없 2
 
3.7%
텔 1
 
1.9%
호 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124
69.7%
Hangul 54
30.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
27.4%
1 17
13.7%
/ 14
11.3%
2 14
11.3%
0 11
 
8.9%
3 10
 
8.1%
4 8
 
6.5%
9 5
 
4.0%
7 3
 
2.4%
5 3
 
2.4%
Other values (2) 5
 
4.0%
Hangul
ValueCountFrequency (%)
실 22
40.7%
특 14
25.9%
도 5
 
9.3%
콘 5
 
9.3%
객 2
 
3.7%
음 2
 
3.7%
없 2
 
3.7%
텔 1
 
1.9%
호 1
 
1.9%

회의장수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8636364
Minimum3
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T03:53:40.079346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median5
Q38.5
95-th percentile10
Maximum31
Range28
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation5.8983085
Coefficient of variation (CV)0.85935621
Kurtosis14.378398
Mean6.8636364
Median Absolute Deviation (MAD)2
Skewness3.5170216
Sum151
Variance34.790043
MonotonicityNot monotonic
2023-12-13T03:53:40.251618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 5
22.7%
5 4
18.2%
3 4
18.2%
9 3
13.6%
7 3
13.6%
10 2
 
9.1%
31 1
 
4.5%
ValueCountFrequency (%)
3 4
18.2%
4 5
22.7%
5 4
18.2%
7 3
13.6%
9 3
13.6%
10 2
 
9.1%
31 1
 
4.5%
ValueCountFrequency (%)
31 1
 
4.5%
10 2
 
9.1%
9 3
13.6%
7 3
13.6%
5 4
18.2%
4 5
22.7%
3 4
18.2%

최소 수용가능인원(명)
Real number (ℝ)

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.045455
Minimum10
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T03:53:40.442339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15.05
Q120
median35
Q360
95-th percentile99
Maximum150
Range140
Interquartile range (IQR)40

Descriptive statistics

Standard deviation34.479086
Coefficient of variation (CV)0.73288878
Kurtosis2.3684083
Mean47.045455
Median Absolute Deviation (MAD)17
Skewness1.4480101
Sum1035
Variance1188.8074
MonotonicityNot monotonic
2023-12-13T03:53:40.631878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20 4
18.2%
60 3
13.6%
30 3
13.6%
80 3
13.6%
40 2
9.1%
100 1
 
4.5%
150 1
 
4.5%
10 1
 
4.5%
15 1
 
4.5%
24 1
 
4.5%
Other values (2) 2
9.1%
ValueCountFrequency (%)
10 1
 
4.5%
15 1
 
4.5%
16 1
 
4.5%
20 4
18.2%
24 1
 
4.5%
30 3
13.6%
40 2
9.1%
50 1
 
4.5%
60 3
13.6%
80 3
13.6%
ValueCountFrequency (%)
150 1
 
4.5%
100 1
 
4.5%
80 3
13.6%
60 3
13.6%
50 1
 
4.5%
40 2
9.1%
30 3
13.6%
24 1
 
4.5%
20 4
18.2%
16 1
 
4.5%

최대 수용가능인원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean907.36364
Minimum180
Maximum4300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T03:53:40.851696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile180
Q1362.5
median500
Q31450
95-th percentile1982.15
Maximum4300
Range4120
Interquartile range (IQR)1087.5

Descriptive statistics

Standard deviation939.8982
Coefficient of variation (CV)1.0358561
Kurtosis7.579344
Mean907.36364
Median Absolute Deviation (MAD)300
Skewness2.4596263
Sum19962
Variance883408.62
MonotonicityNot monotonic
2023-12-13T03:53:41.049407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1500 3
13.6%
500 3
13.6%
180 3
13.6%
350 2
 
9.1%
4300 1
 
4.5%
2000 1
 
4.5%
1643 1
 
4.5%
1300 1
 
4.5%
800 1
 
4.5%
400 1
 
4.5%
Other values (5) 5
22.7%
ValueCountFrequency (%)
180 3
13.6%
200 1
 
4.5%
350 2
9.1%
400 1
 
4.5%
441 1
 
4.5%
450 1
 
4.5%
500 3
13.6%
588 1
 
4.5%
600 1
 
4.5%
800 1
 
4.5%
ValueCountFrequency (%)
4300 1
 
4.5%
2000 1
 
4.5%
1643 1
 
4.5%
1500 3
13.6%
1300 1
 
4.5%
800 1
 
4.5%
600 1
 
4.5%
588 1
 
4.5%
500 3
13.6%
450 1
 
4.5%

담당부서
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
관광정책과
22 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관광정책과
2nd row관광정책과
3rd row관광정책과
4th row관광정책과
5th row관광정책과

Common Values

ValueCountFrequency (%)
관광정책과 22
100.0%

Length

2023-12-13T03:53:41.232815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:41.379992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광정책과 22
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2022-12-31
22 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 22
100.0%

Length

2023-12-13T03:53:41.562992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:41.733424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 22
100.0%

Interactions

2023-12-13T03:53:36.784757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:35.958768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:36.373457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:36.933890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:36.097435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:36.510936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:37.066240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:36.234666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:36.636962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:53:41.852274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분업체명객실정보회의장수최소 수용가능인원(명)최대 수용가능인원(명)
구분1.0001.0000.0001.0000.1861.000
업체명1.0001.0001.0001.0001.0001.000
객실정보0.0001.0001.0000.0000.9680.000
회의장수1.0001.0000.0001.0000.4010.886
최소 수용가능인원(명)0.1861.0000.9680.4011.0000.276
최대 수용가능인원(명)1.0001.0000.0000.8860.2761.000
2023-12-13T03:53:42.034916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회의장수최소 수용가능인원(명)최대 수용가능인원(명)구분
회의장수1.000-0.1120.2210.949
최소 수용가능인원(명)-0.1121.0000.2220.129
최대 수용가능인원(명)0.2210.2221.0000.894
구분0.9490.1290.8941.000

Missing values

2023-12-13T03:53:37.715147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:53:37.932953image/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

구분업체명객실정보회의장수최소 수용가능인원(명)최대 수용가능인원(명)담당부서데이터기준일자
0전문회의시설제주국제컨벤션센터객실없음31604300관광정책과2022-12-31
1준회의시설제주신화월드 랜팅컨벤션센터특1 / 2062실10302000관광정책과2022-12-31
2준회의시설해비치호텔&리조트특1 / 호텔 288실51001643관광정책과2022-12-31
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