Overview

Dataset statistics

Number of variables4
Number of observations322
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory33.4 B

Variable types

Categorical3
Numeric1

Dataset

Description자치구명,법정동명,업종명,업소수
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-11377/S/1/datasetView.do

Alerts

자치구명 has constant value ""Constant

Reproduction

Analysis started2024-05-11 05:55:44.061690
Analysis finished2024-05-11 05:55:45.794717
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
마포구
322 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마포구 322
100.0%

Length

2024-05-11T14:55:45.919032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:55:46.115539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 322
100.0%

법정동명
Categorical

Distinct26
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
성산동
 
21
동교동
 
21
상암동
 
20
서교동
 
20
망원동
 
19
Other values (21)
221 

Length

Max length4
Median length3
Mean length3.0621118
Min length2

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st row대현동
2nd row아현동
3rd row아현동
4th row아현동
5th row아현동

Common Values

ValueCountFrequency (%)
성산동 21
 
6.5%
동교동 21
 
6.5%
상암동 20
 
6.2%
서교동 20
 
6.2%
망원동 19
 
5.9%
공덕동 18
 
5.6%
합정동 18
 
5.6%
도화동 17
 
5.3%
노고산동 17
 
5.3%
아현동 17
 
5.3%
Other values (16) 134
41.6%

Length

2024-05-11T14:55:46.359585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성산동 21
 
6.5%
동교동 21
 
6.5%
상암동 20
 
6.2%
서교동 20
 
6.2%
망원동 19
 
5.9%
공덕동 18
 
5.6%
합정동 18
 
5.6%
도화동 17
 
5.3%
노고산동 17
 
5.3%
아현동 17
 
5.3%
Other values (16) 134
41.6%

업종명
Categorical

Distinct24
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
미용업
23 
공중이용시설
22 
세탁업
22 
피부미용업
 
21
일반미용업
 
21
Other values (19)
213 

Length

Max length23
Median length16
Mean length8.4968944
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row피부미용업
2nd row숙박업(일반)
3rd row목욕장업
4th row이용업
5th row미용업

Common Values

ValueCountFrequency (%)
미용업 23
 
7.1%
공중이용시설 22
 
6.8%
세탁업 22
 
6.8%
피부미용업 21
 
6.5%
일반미용업 21
 
6.5%
이용업 20
 
6.2%
위생관리용역업 20
 
6.2%
네일미용업 19
 
5.9%
종합미용업 17
 
5.3%
화장ㆍ분장 미용업 15
 
4.7%
Other values (14) 122
37.9%

Length

2024-05-11T14:55:46.642708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 101
19.3%
네일미용업 78
14.9%
화장ㆍ분장 78
14.9%
피부미용업 68
13.0%
일반미용업 68
13.0%
공중이용시설 22
 
4.2%
세탁업 22
 
4.2%
이용업 20
 
3.8%
위생관리용역업 20
 
3.8%
종합미용업 17
 
3.3%
Other values (4) 29
 
5.5%

업소수
Real number (ℝ)

Distinct42
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2608696
Minimum1
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-11T14:55:46.905838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q39
95-th percentile32.85
Maximum165
Range164
Interquartile range (IQR)8

Descriptive statistics

Standard deviation13.690173
Coefficient of variation (CV)1.6572314
Kurtosis55.248469
Mean8.2608696
Median Absolute Deviation (MAD)3
Skewness5.8659655
Sum2660
Variance187.42083
MonotonicityNot monotonic
2024-05-11T14:55:47.205390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 90
28.0%
2 35
 
10.9%
3 29
 
9.0%
5 26
 
8.1%
6 19
 
5.9%
9 14
 
4.3%
4 14
 
4.3%
8 11
 
3.4%
14 9
 
2.8%
7 8
 
2.5%
Other values (32) 67
20.8%
ValueCountFrequency (%)
1 90
28.0%
2 35
 
10.9%
3 29
 
9.0%
4 14
 
4.3%
5 26
 
8.1%
6 19
 
5.9%
7 8
 
2.5%
8 11
 
3.4%
9 14
 
4.3%
10 6
 
1.9%
ValueCountFrequency (%)
165 1
0.3%
64 1
0.3%
58 1
0.3%
56 1
0.3%
55 1
0.3%
51 1
0.3%
45 1
0.3%
44 1
0.3%
43 1
0.3%
41 1
0.3%

Interactions

2024-05-11T14:55:45.345070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:55:47.368761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명업종명업소수
법정동명1.0000.0000.000
업종명0.0001.0000.349
업소수0.0000.3491.000
2024-05-11T14:55:47.513274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명법정동명
업종명1.0000.000
법정동명0.0001.000
2024-05-11T14:55:47.652331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소수법정동명업종명
업소수1.0000.0000.170
법정동명0.0001.0000.000
업종명0.1700.0001.000

Missing values

2024-05-11T14:55:45.587959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:55:45.730779image/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

자치구명법정동명업종명업소수
0마포구대현동피부미용업1
1마포구아현동숙박업(일반)2
2마포구아현동목욕장업1
3마포구아현동이용업5
4마포구아현동미용업11
5마포구아현동세탁업9
6마포구아현동위생관리용역업2
7마포구아현동공중이용시설16
8마포구아현동일반미용업30
9마포구아현동피부미용업5
자치구명법정동명업종명업소수
312마포구상암동네일미용업4
313마포구상암동일반미용업, 피부미용업1
314마포구상암동일반미용업, 네일미용업1
315마포구상암동피부미용업, 네일미용업1
316마포구상암동화장ㆍ분장 미용업4
317마포구상암동일반미용업, 화장ㆍ분장 미용업2
318마포구상암동네일미용업, 화장ㆍ분장 미용업2
319마포구상암동일반미용업, 피부미용업, 화장ㆍ분장 미용업1
320마포구상암동일반미용업, 네일미용업, 화장ㆍ분장 미용업1
321마포구상암동피부미용업, 네일미용업, 화장ㆍ분장 미용업1