Overview

Dataset statistics

Number of variables4
Number of observations190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory33.7 B

Variable types

Categorical3
Numeric1

Dataset

Description자치구명,법정동명,업종명,업소수
Author서대문구
URLhttps://data.seoul.go.kr/dataList/OA-2377/S/1/datasetView.do

Alerts

자치구명 has constant value ""Constant

Reproduction

Analysis started2024-05-11 07:04:33.201305
Analysis finished2024-05-11 07:04:34.138572
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
서대문구
190 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서대문구 190
100.0%

Length

2024-05-11T07:04:34.365126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:04:34.661980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서대문구 190
100.0%

법정동명
Categorical

Distinct20
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
홍제동
19 
창천동
18 
북가좌동
17 
홍은동
16 
대현동
16 
Other values (15)
104 

Length

Max length5
Median length3
Mean length3.3842105
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row충정로2가
2nd row충정로2가
3rd row충정로2가
4th row충정로2가
5th row충정로2가

Common Values

ValueCountFrequency (%)
홍제동 19
10.0%
창천동 18
9.5%
북가좌동 17
 
8.9%
홍은동 16
 
8.4%
대현동 16
 
8.4%
북아현동 15
 
7.9%
남가좌동 15
 
7.9%
연희동 14
 
7.4%
냉천동 9
 
4.7%
충정로2가 8
 
4.2%
Other values (10) 43
22.6%

Length

2024-05-11T07:04:35.023095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
홍제동 19
10.0%
창천동 18
9.5%
북가좌동 17
 
8.9%
홍은동 16
 
8.4%
대현동 16
 
8.4%
북아현동 15
 
7.9%
남가좌동 15
 
7.9%
연희동 14
 
7.4%
냉천동 9
 
4.7%
충정로2가 8
 
4.2%
Other values (10) 43
22.6%

업종명
Categorical

Distinct26
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
일반미용업
17 
세탁업
15 
피부미용업
15 
위생관리용역업
14 
네일미용업
14 
Other values (21)
115 

Length

Max length23
Median length16
Mean length7.6473684
Min length3

Unique

Unique4 ?
Unique (%)2.1%

Sample

1st row목욕장업
2nd row위생관리용역업
3rd row공중이용시설
4th row일반미용업
5th row피부미용업

Common Values

ValueCountFrequency (%)
일반미용업 17
 
8.9%
세탁업 15
 
7.9%
피부미용업 15
 
7.9%
위생관리용역업 14
 
7.4%
네일미용업 14
 
7.4%
공중이용시설 13
 
6.8%
이용업 13
 
6.8%
목욕장업 12
 
6.3%
종합미용업 10
 
5.3%
숙박업(일반) 10
 
5.3%
Other values (16) 57
30.0%

Length

2024-05-11T07:04:35.500789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
네일미용업 42
15.3%
일반미용업 39
14.2%
피부미용업 38
13.8%
미용업 33
12.0%
화장ㆍ분장 30
10.9%
세탁업 15
 
5.5%
위생관리용역업 14
 
5.1%
공중이용시설 13
 
4.7%
이용업 13
 
4.7%
목욕장업 12
 
4.4%
Other values (6) 26
9.5%

업소수
Real number (ℝ)

Distinct28
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8789474
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-11T07:04:35.922689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q37
95-th percentile25.05
Maximum92
Range91
Interquartile range (IQR)6

Descriptive statistics

Standard deviation13.37656
Coefficient of variation (CV)1.944565
Kurtosis19.168488
Mean6.8789474
Median Absolute Deviation (MAD)1
Skewness4.1707835
Sum1307
Variance178.93236
MonotonicityNot monotonic
2024-05-11T07:04:36.291356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 71
37.4%
2 29
15.3%
3 14
 
7.4%
4 11
 
5.8%
5 9
 
4.7%
6 7
 
3.7%
8 6
 
3.2%
7 6
 
3.2%
10 5
 
2.6%
11 4
 
2.1%
Other values (18) 28
 
14.7%
ValueCountFrequency (%)
1 71
37.4%
2 29
15.3%
3 14
 
7.4%
4 11
 
5.8%
5 9
 
4.7%
6 7
 
3.7%
7 6
 
3.2%
8 6
 
3.2%
9 4
 
2.1%
10 5
 
2.6%
ValueCountFrequency (%)
92 1
0.5%
82 1
0.5%
75 1
0.5%
63 1
0.5%
53 1
0.5%
51 1
0.5%
50 1
0.5%
43 1
0.5%
33 1
0.5%
30 1
0.5%

Interactions

2024-05-11T07:04:33.417395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T07:04:36.499929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명업종명업소수
법정동명1.0000.0000.000
업종명0.0001.0000.000
업소수0.0000.0001.000
2024-05-11T07:04:36.684824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명법정동명
업종명1.0000.000
법정동명0.0001.000
2024-05-11T07:04:36.876797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소수법정동명업종명
업소수1.0000.0000.000
법정동명0.0001.0000.000
업종명0.0000.0001.000

Missing values

2024-05-11T07:04:33.748029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T07:04:34.030587image/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
0서대문구충정로2가목욕장업1
1서대문구충정로2가위생관리용역업3
2서대문구충정로2가공중이용시설11
3서대문구충정로2가일반미용업5
4서대문구충정로2가피부미용업1
5서대문구충정로2가종합미용업2
6서대문구충정로2가네일미용업2
7서대문구충정로2가피부미용업, 네일미용업, 화장ㆍ분장 미용업1
8서대문구충정로3가이용업1
9서대문구충정로3가세탁업2
자치구명법정동명업종명업소수
180서대문구남가좌동공중이용시설5
181서대문구남가좌동일반미용업75
182서대문구남가좌동피부미용업10
183서대문구남가좌동종합미용업4
184서대문구남가좌동네일미용업6
185서대문구남가좌동피부미용업, 네일미용업3
186서대문구남가좌동화장ㆍ분장 미용업1
187서대문구남가좌동일반미용업, 화장ㆍ분장 미용업1
188서대문구남가좌동일반미용업, 네일미용업, 화장ㆍ분장 미용업4
189서대문구남가좌동피부미용업, 네일미용업, 화장ㆍ분장 미용업1