Overview

Dataset statistics

Number of variables7
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory68.0 B

Variable types

Text1
Numeric4
Categorical2

Dataset

Description중소벤처기업진흥공단에서 운영하는 해외 글로벌비즈니스센터 입주 현황에 대한 자료로서 지역, 규모, 입주기업수, 공실 등에 대한 내용입니다.
URLhttps://www.data.go.kr/data/3060399/fileData.do

Alerts

규모(개실) is highly overall correlated with 입주중(개실)High correlation
입주중(개실) is highly overall correlated with 규모(개실) and 1 other fieldsHigh correlation
예비(건수) is highly overall correlated with 입주중(개실) and 1 other fieldsHigh correlation
신청(건수) is highly overall correlated with 예비(건수)High correlation
예비(건수) is highly imbalanced (60.5%)Imbalance
지역 has unique valuesUnique
입주중(개실) has 1 (4.5%) zerosZeros
입주준비중(개실) has 3 (13.6%) zerosZeros
공실(개실) has 14 (63.6%) zerosZeros

Reproduction

Analysis started2023-12-12 19:59:39.126103
Analysis finished2023-12-12 19:59:41.289900
Duration2.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T04:59:41.424855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.0454545
Min length2

Characters and Unicode

Total characters67
Distinct characters51
Distinct categories2 ?
Distinct scripts2 ?
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 row시애틀
2nd row뉴델리
3rd row싱가포르
4th row베이징
5th row상하이
ValueCountFrequency (%)
시애틀 1
 
4.5%
뉴델리 1
 
4.5%
방콕 1
 
4.5%
산티아고 1
 
4.5%
알마티 1
 
4.5%
두바이 1
 
4.5%
모스크바 1
 
4.5%
멕시코시티 1
 
4.5%
하노이 1
 
4.5%
호치민 1
 
4.5%
Other values (12) 12
54.5%
2023-12-13T04:59:41.799295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 4
 
6.0%
이 4
 
6.0%
티 3
 
4.5%
르 2
 
3.0%
바 2
 
3.0%
크 2
 
3.0%
하 2
 
3.0%
고 2
 
3.0%
싱 2
 
3.0%
리 2
 
3.0%
Other values (41) 42
62.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
97.0%
Uppercase Letter 2
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 4
 
6.2%
이 4
 
6.2%
티 3
 
4.6%
르 2
 
3.1%
바 2
 
3.1%
크 2
 
3.1%
하 2
 
3.1%
고 2
 
3.1%
싱 2
 
3.1%
리 2
 
3.1%
Other values (39) 40
61.5%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
A 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
97.0%
Latin 2
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 4
 
6.2%
이 4
 
6.2%
티 3
 
4.6%
르 2
 
3.1%
바 2
 
3.1%
크 2
 
3.1%
하 2
 
3.1%
고 2
 
3.1%
싱 2
 
3.1%
리 2
 
3.1%
Other values (39) 40
61.5%
Latin
ValueCountFrequency (%)
L 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
97.0%
ASCII 2
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 4
 
6.2%
이 4
 
6.2%
티 3
 
4.6%
르 2
 
3.1%
바 2
 
3.1%
크 2
 
3.1%
하 2
 
3.1%
고 2
 
3.1%
싱 2
 
3.1%
리 2
 
3.1%
Other values (39) 40
61.5%
ASCII
ValueCountFrequency (%)
L 1
50.0%
A 1
50.0%

규모(개실)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.090909
Minimum7
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T04:59:41.925771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.05
Q19
median12.5
Q316.75
95-th percentile20
Maximum22
Range15
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation4.7198577
Coefficient of variation (CV)0.36054468
Kurtosis-1.2336096
Mean13.090909
Median Absolute Deviation (MAD)3.5
Skewness0.34394214
Sum288
Variance22.277056
MonotonicityNot monotonic
2023-12-13T04:59:42.039126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 3
13.6%
9 3
13.6%
15 3
13.6%
7 2
9.1%
18 2
9.1%
8 2
9.1%
20 2
9.1%
14 1
 
4.5%
17 1
 
4.5%
22 1
 
4.5%
Other values (2) 2
9.1%
ValueCountFrequency (%)
7 2
9.1%
8 2
9.1%
9 3
13.6%
10 3
13.6%
11 1
 
4.5%
14 1
 
4.5%
15 3
13.6%
16 1
 
4.5%
17 1
 
4.5%
18 2
9.1%
ValueCountFrequency (%)
22 1
 
4.5%
20 2
9.1%
18 2
9.1%
17 1
 
4.5%
16 1
 
4.5%
15 3
13.6%
14 1
 
4.5%
11 1
 
4.5%
10 3
13.6%
9 3
13.6%

입주중(개실)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7727273
Minimum0
Maximum21
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T04:59:42.162381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.15
Q16.25
median9
Q313
95-th percentile17.95
Maximum21
Range21
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation5.1909428
Coefficient of variation (CV)0.53116624
Kurtosis-0.11225085
Mean9.7727273
Median Absolute Deviation (MAD)3
Skewness0.33369416
Sum215
Variance26.945887
MonotonicityNot monotonic
2023-12-13T04:59:42.276668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6 3
13.6%
7 3
13.6%
13 2
9.1%
10 2
9.1%
8 2
9.1%
11 2
9.1%
2 1
 
4.5%
14 1
 
4.5%
17 1
 
4.5%
15 1
 
4.5%
Other values (4) 4
18.2%
ValueCountFrequency (%)
0 1
 
4.5%
2 1
 
4.5%
5 1
 
4.5%
6 3
13.6%
7 3
13.6%
8 2
9.1%
10 2
9.1%
11 2
9.1%
13 2
9.1%
14 1
 
4.5%
ValueCountFrequency (%)
21 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
15 1
 
4.5%
14 1
 
4.5%
13 2
9.1%
11 2
9.1%
10 2
9.1%
8 2
9.1%
7 3
13.6%

입주준비중(개실)
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5454545
Minimum0
Maximum8
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T04:59:42.386813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1.5
Q34
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4049012
Coefficient of variation (CV)0.94478261
Kurtosis0.10933615
Mean2.5454545
Median Absolute Deviation (MAD)1
Skewness1.0683646
Sum56
Variance5.7835498
MonotonicityNot monotonic
2023-12-13T04:59:42.515509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 8
36.4%
4 3
 
13.6%
2 3
 
13.6%
0 3
 
13.6%
7 2
 
9.1%
5 1
 
4.5%
3 1
 
4.5%
8 1
 
4.5%
ValueCountFrequency (%)
0 3
 
13.6%
1 8
36.4%
2 3
 
13.6%
3 1
 
4.5%
4 3
 
13.6%
5 1
 
4.5%
7 2
 
9.1%
8 1
 
4.5%
ValueCountFrequency (%)
8 1
 
4.5%
7 2
 
9.1%
5 1
 
4.5%
4 3
 
13.6%
3 1
 
4.5%
2 3
 
13.6%
1 8
36.4%
0 3
 
13.6%

공실(개실)
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9090909
Minimum0
Maximum19
Zeros14
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T04:59:42.630834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile9.65
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.428362
Coefficient of variation (CV)2.3196182
Kurtosis11.324361
Mean1.9090909
Median Absolute Deviation (MAD)0
Skewness3.2730324
Sum42
Variance19.61039
MonotonicityNot monotonic
2023-12-13T04:59:42.766964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 14
63.6%
2 3
 
13.6%
3 2
 
9.1%
10 1
 
4.5%
1 1
 
4.5%
19 1
 
4.5%
ValueCountFrequency (%)
0 14
63.6%
1 1
 
4.5%
2 3
 
13.6%
3 2
 
9.1%
10 1
 
4.5%
19 1
 
4.5%
ValueCountFrequency (%)
19 1
 
4.5%
10 1
 
4.5%
3 2
 
9.1%
2 3
 
13.6%
1 1
 
4.5%
0 14
63.6%

예비(건수)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
19 
4
 
1
6
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)13.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
86.4%
4 1
 
4.5%
6 1
 
4.5%
1 1
 
4.5%

Length

2023-12-13T04:59:42.910409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:59:43.035454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
86.4%
4 1
 
4.5%
6 1
 
4.5%
1 1
 
4.5%

신청(건수)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
12 
1
7 
2
 
1
4
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)13.6%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12
54.5%
1 7
31.8%
2 1
 
4.5%
4 1
 
4.5%
3 1
 
4.5%

Length

2023-12-13T04:59:43.173377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:59:43.295041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12
54.5%
1 7
31.8%
2 1
 
4.5%
4 1
 
4.5%
3 1
 
4.5%

Interactions

2023-12-13T04:59:40.580940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:39.383278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:39.752697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:40.092154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:40.695923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:39.475737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:39.841262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:40.218691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:40.795744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:39.557463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:39.912153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:40.307405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:40.918702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:39.661562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:40.003911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:59:40.438220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:59:43.396452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역규모(개실)입주중(개실)입주준비중(개실)공실(개실)예비(건수)신청(건수)
지역1.0001.0001.0001.0001.0001.0001.000
규모(개실)1.0001.0000.7780.0000.7540.4250.000
입주중(개실)1.0000.7781.0000.0000.0000.8200.756
입주준비중(개실)1.0000.0000.0001.0000.0000.0000.000
공실(개실)1.0000.7540.0000.0001.0000.0000.000
예비(건수)1.0000.4250.8200.0000.0001.0000.826
신청(건수)1.0000.0000.7560.0000.0000.8261.000
2023-12-13T04:59:43.527247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예비(건수)신청(건수)
예비(건수)1.0000.770
신청(건수)0.7701.000
2023-12-13T04:59:43.621502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규모(개실)입주중(개실)입주준비중(개실)공실(개실)예비(건수)신청(건수)
규모(개실)1.0000.5980.0030.2110.0760.000
입주중(개실)0.5981.000-0.031-0.4130.5770.481
입주준비중(개실)0.003-0.0311.000-0.2960.0000.000
공실(개실)0.211-0.413-0.2961.0000.0000.000
예비(건수)0.0760.5770.0000.0001.0000.770
신청(건수)0.0000.4810.0000.0000.7701.000

Missing values

2023-12-13T04:59:41.096066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:59:41.239707image/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

지역규모(개실)입주중(개실)입주준비중(개실)공실(개실)예비(건수)신청(건수)
0시애틀727000
1뉴델리14131001
2싱가포르18141001
3베이징17105200
4상하이22841000
5선전1061300
6충칭863000
7뉴욕987000
8LA20174042
9워싱턴1074000
지역규모(개실)입주중(개실)입주준비중(개실)공실(개실)예비(건수)신청(건수)
12프랑크푸르트15132000
13호치민15111300
14하노이15182011
15멕시코시티970201
16모스크바861101
17두바이11118001
18알마티752000
19산티아고970200
20방콕10100003
21파리20011900