Overview

Dataset statistics

Number of variables6
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory52.7 B

Variable types

Numeric2
Text1
Boolean2
Categorical1

Dataset

Description한국콘텐츠진흥원의 광화문 CKL기업지원센터에서의 시설 예약정보에 관한 내용을 나타내고 있는 데이터 정보를 제공하고 있습니다.
Author한국콘텐츠진흥원
URLhttps://www.data.go.kr/data/15041601/fileData.do

Alerts

사용여부 is highly imbalanced (61.2%)Imbalance
예약가능시간 is highly imbalanced (56.8%)Imbalance
예약시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:42:36.066211
Analysis finished2023-12-11 23:42:36.980639
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

예약층
Real number (ℝ)

Distinct10
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.594937
Minimum8
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-12T08:42:37.035474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q19
median11
Q314
95-th percentile17
Maximum17
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9850868
Coefficient of variation (CV)0.25744744
Kurtosis-0.98894497
Mean11.594937
Median Absolute Deviation (MAD)2
Skewness0.65959752
Sum916
Variance8.9107433
MonotonicityNot monotonic
2023-12-12T08:42:37.165742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 22
27.8%
11 19
24.1%
16 10
12.7%
17 7
 
8.9%
8 7
 
8.9%
10 4
 
5.1%
14 4
 
5.1%
13 3
 
3.8%
12 2
 
2.5%
15 1
 
1.3%
ValueCountFrequency (%)
8 7
 
8.9%
9 22
27.8%
10 4
 
5.1%
11 19
24.1%
12 2
 
2.5%
13 3
 
3.8%
14 4
 
5.1%
15 1
 
1.3%
16 10
12.7%
17 7
 
8.9%
ValueCountFrequency (%)
17 7
 
8.9%
16 10
12.7%
15 1
 
1.3%
14 4
 
5.1%
13 3
 
3.8%
12 2
 
2.5%
11 19
24.1%
10 4
 
5.1%
9 22
27.8%
8 7
 
8.9%

예약시설명
Text

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
2023-12-12T08:42:37.456204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length16
Mean length9.3670886
Min length3

Characters and Unicode

Total characters740
Distinct characters72
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)100.0%

Sample

1st row회의실(1703-1)
2nd row회의실(1703-2)
3rd row1608(8인)
4th row1108(12인)
5th row1109(40인)
ValueCountFrequency (%)
회의실(1703-1 1
 
1.3%
회의실(1611 1
 
1.3%
라운지(1703 1
 
1.3%
컨퍼런스룸 1
 
1.3%
1510(12인 1
 
1.3%
1411(6인 1
 
1.3%
1412(8인 1
 
1.3%
1411(8인 1
 
1.3%
1412(6인 1
 
1.3%
1309(8인 1
 
1.3%
Other values (69) 69
87.3%
2023-12-12T08:42:37.906393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 94
 
12.7%
( 62
 
8.4%
) 62
 
8.4%
인 34
 
4.6%
0 32
 
4.3%
2 22
 
3.0%
8 21
 
2.8%
6 20
 
2.7%
실 19
 
2.6%
P 17
 
2.3%
Other values (62) 357
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 271
36.6%
Decimal Number 240
32.4%
Open Punctuation 62
 
8.4%
Close Punctuation 62
 
8.4%
Uppercase Letter 55
 
7.4%
Lowercase Letter 39
 
5.3%
Dash Punctuation 11
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 34
 
12.5%
실 19
 
7.0%
룸 17
 
6.3%
영 16
 
5.9%
상 16
 
5.9%
편 16
 
5.9%
집 16
 
5.9%
스 10
 
3.7%
의 9
 
3.3%
회 9
 
3.3%
Other values (30) 109
40.2%
Uppercase Letter
ValueCountFrequency (%)
P 17
30.9%
C 16
29.1%
A 4
 
7.3%
M 4
 
7.3%
B 3
 
5.5%
S 3
 
5.5%
W 3
 
5.5%
D 2
 
3.6%
I 1
 
1.8%
E 1
 
1.8%
Decimal Number
ValueCountFrequency (%)
1 94
39.2%
0 32
 
13.3%
2 22
 
9.2%
8 21
 
8.8%
6 20
 
8.3%
4 15
 
6.2%
3 12
 
5.0%
7 11
 
4.6%
5 9
 
3.8%
9 4
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
o 9
23.1%
r 6
15.4%
a 6
15.4%
t 6
15.4%
i 3
 
7.7%
k 3
 
7.7%
c 3
 
7.7%
n 3
 
7.7%
Open Punctuation
ValueCountFrequency (%)
( 62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 375
50.7%
Hangul 271
36.6%
Latin 94
 
12.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 34
 
12.5%
실 19
 
7.0%
룸 17
 
6.3%
영 16
 
5.9%
상 16
 
5.9%
편 16
 
5.9%
집 16
 
5.9%
스 10
 
3.7%
의 9
 
3.3%
회 9
 
3.3%
Other values (30) 109
40.2%
Latin
ValueCountFrequency (%)
P 17
18.1%
C 16
17.0%
o 9
9.6%
r 6
 
6.4%
a 6
 
6.4%
t 6
 
6.4%
A 4
 
4.3%
M 4
 
4.3%
B 3
 
3.2%
i 3
 
3.2%
Other values (9) 20
21.3%
Common
ValueCountFrequency (%)
1 94
25.1%
( 62
16.5%
) 62
16.5%
0 32
 
8.5%
2 22
 
5.9%
8 21
 
5.6%
6 20
 
5.3%
4 15
 
4.0%
3 12
 
3.2%
- 11
 
2.9%
Other values (3) 24
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 469
63.4%
Hangul 271
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 94
20.0%
( 62
13.2%
) 62
13.2%
0 32
 
6.8%
2 22
 
4.7%
8 21
 
4.5%
6 20
 
4.3%
P 17
 
3.6%
C 16
 
3.4%
4 15
 
3.2%
Other values (22) 108
23.0%
Hangul
ValueCountFrequency (%)
인 34
 
12.5%
실 19
 
7.0%
룸 17
 
6.3%
영 16
 
5.9%
상 16
 
5.9%
편 16
 
5.9%
집 16
 
5.9%
스 10
 
3.7%
의 9
 
3.3%
회 9
 
3.3%
Other values (30) 109
40.2%

정렬번호
Real number (ℝ)

Distinct9
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4050633
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-12T08:42:38.085638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0663295
Coefficient of variation (CV)0.60684026
Kurtosis0.0091793653
Mean3.4050633
Median Absolute Deviation (MAD)1
Skewness0.79619792
Sum269
Variance4.2697176
MonotonicityNot monotonic
2023-12-12T08:42:38.231585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 16
20.3%
1 16
20.3%
3 14
17.7%
4 12
15.2%
5 8
10.1%
6 5
 
6.3%
7 5
 
6.3%
9 2
 
2.5%
8 1
 
1.3%
ValueCountFrequency (%)
1 16
20.3%
2 16
20.3%
3 14
17.7%
4 12
15.2%
5 8
10.1%
6 5
 
6.3%
7 5
 
6.3%
8 1
 
1.3%
9 2
 
2.5%
ValueCountFrequency (%)
9 2
 
2.5%
8 1
 
1.3%
7 5
 
6.3%
6 5
 
6.3%
5 8
10.1%
4 12
15.2%
3 14
17.7%
2 16
20.3%
1 16
20.3%

노출여부
Boolean

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size211.0 B
False
41 
True
38 
ValueCountFrequency (%)
False 41
51.9%
True 38
48.1%
2023-12-12T08:42:38.397177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

사용여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size211.0 B
True
73 
False
 
6
ValueCountFrequency (%)
True 73
92.4%
False 6
 
7.6%
2023-12-12T08:42:38.513495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

예약가능시간
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size764.0 B
1
72 
2
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 72
91.1%
2 7
 
8.9%

Length

2023-12-12T08:42:38.655912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:42:39.046207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 72
91.1%
2 7
 
8.9%

Interactions

2023-12-12T08:42:36.515478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:36.328130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:36.609771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:36.414187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:42:39.127726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예약층예약시설명정렬번호노출여부사용여부예약가능시간
예약층1.0001.0000.0000.5850.5040.512
예약시설명1.0001.0001.0001.0001.0001.000
정렬번호0.0001.0001.0000.0000.0000.000
노출여부0.5851.0000.0001.0000.1110.240
사용여부0.5041.0000.0000.1111.0000.000
예약가능시간0.5121.0000.0000.2400.0001.000
2023-12-12T08:42:39.271872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예약가능시간사용여부노출여부
예약가능시간1.0000.0000.154
사용여부0.0001.0000.069
노출여부0.1540.0691.000
2023-12-12T08:42:39.379365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예약층정렬번호노출여부사용여부예약가능시간
예약층1.000-0.2530.4270.3660.372
정렬번호-0.2531.0000.0000.0000.000
노출여부0.4270.0001.0000.0690.154
사용여부0.3660.0000.0691.0000.000
예약가능시간0.3720.0000.1540.0001.000

Missing values

2023-12-12T08:42:36.773178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:42:36.930468image/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

예약층예약시설명정렬번호노출여부사용여부예약가능시간
017회의실(1703-1)2YY1
117회의실(1703-2)3NY1
2161608(8인)5NY1
3111108(12인)1NY1
4111109(40인)2NY1
5111110(6인)3NY1
6111111(6인)4NY1
7111112(6인)5NY1
8111113(6인)6NY1
9131312(8인)3YY1
예약층예약시설명정렬번호노출여부사용여부예약가능시간
699영상편집실PC1-3(MacPro)3YY2
709영상편집실PC1-4(MacPro)4NY2
719영상편집실PC2-1(WorkStation)5YY2
729영상편집실PC2-2(WorkStation)6YY2
739영상편집실PC2-3(WorkStation)7YY2
7411스튜디오(B)7YY1
7511스튜디오(A)8YY1
7617회의실(1701)2YY1
7717회의실(1702)3YY1
7811포토스튜디오9YY1