Overview

Dataset statistics

Number of variables4
Number of observations164
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory33.8 B

Variable types

Categorical3
Numeric1

Dataset

Description자치구명,법정동명,업종명,업소수
Author동대문구
URLhttps://data.seoul.go.kr/dataList/OA-10376/S/1/datasetView.do

Alerts

자치구명 has constant value ""Constant

Reproduction

Analysis started2024-05-18 07:21:19.743253
Analysis finished2024-05-18 07:21:20.950954
Duration1.21 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
동대문구
164 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동대문구 164
100.0%

Length

2024-05-18T16:21:21.180635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T16:21:21.573683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동대문구 164
100.0%

법정동명
Categorical

Distinct10
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
장안동
21 
제기동
19 
회기동
18 
용두동
17 
전농동
17 
Other values (5)
72 

Length

Max length4
Median length3
Mean length3.1829268
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장안동 21
12.8%
제기동 19
11.6%
회기동 18
11.0%
용두동 17
10.4%
전농동 17
10.4%
답십리동 16
9.8%
이문동 16
9.8%
청량리동 14
8.5%
휘경동 14
8.5%
신설동 12
7.3%

Length

2024-05-18T16:21:22.157858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T16:21:22.650470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장안동 21
12.8%
제기동 19
11.6%
회기동 18
11.0%
용두동 17
10.4%
전농동 17
10.4%
답십리동 16
9.8%
이문동 16
9.8%
청량리동 14
8.5%
휘경동 14
8.5%
신설동 12
7.3%

업종명
Categorical

Distinct25
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
숙박업(일반)
 
10
이용업
 
10
세탁업
 
10
위생관리용역업
 
10
공중이용시설
 
10
Other values (20)
114 

Length

Max length23
Median length16
Mean length8.6097561
Min length3

Unique

Unique4 ?
Unique (%)2.4%

Sample

1st row숙박업(일반)
2nd row목욕장업
3rd row이용업
4th row세탁업
5th row위생관리용역업

Common Values

ValueCountFrequency (%)
숙박업(일반) 10
 
6.1%
이용업 10
 
6.1%
세탁업 10
 
6.1%
위생관리용역업 10
 
6.1%
공중이용시설 10
 
6.1%
일반미용업 10
 
6.1%
피부미용업 10
 
6.1%
종합미용업 10
 
6.1%
목욕장업 10
 
6.1%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 9
 
5.5%
Other values (15) 65
39.6%

Length

2024-05-18T16:21:23.238966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 45
16.9%
네일미용업 41
15.4%
화장ㆍ분장 38
14.3%
일반미용업 35
13.2%
피부미용업 34
12.8%
숙박업(일반 10
 
3.8%
이용업 10
 
3.8%
세탁업 10
 
3.8%
위생관리용역업 10
 
3.8%
공중이용시설 10
 
3.8%
Other values (5) 23
8.6%

업소수
Real number (ℝ)

Distinct42
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.585366
Minimum1
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-18T16:21:23.642869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q314.25
95-th percentile50.1
Maximum177
Range176
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation21.981839
Coefficient of variation (CV)1.746619
Kurtosis25.048569
Mean12.585366
Median Absolute Deviation (MAD)4
Skewness4.3549292
Sum2064
Variance483.20126
MonotonicityNot monotonic
2024-05-18T16:21:24.226930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 38
23.2%
2 22
13.4%
4 10
 
6.1%
6 9
 
5.5%
3 9
 
5.5%
5 7
 
4.3%
10 6
 
3.7%
11 5
 
3.0%
7 4
 
2.4%
8 4
 
2.4%
Other values (32) 50
30.5%
ValueCountFrequency (%)
1 38
23.2%
2 22
13.4%
3 9
 
5.5%
4 10
 
6.1%
5 7
 
4.3%
6 9
 
5.5%
7 4
 
2.4%
8 4
 
2.4%
9 4
 
2.4%
10 6
 
3.7%
ValueCountFrequency (%)
177 1
0.6%
136 1
0.6%
86 1
0.6%
74 1
0.6%
63 1
0.6%
60 1
0.6%
55 1
0.6%
52 1
0.6%
51 1
0.6%
45 1
0.6%

Interactions

2024-05-18T16:21:20.122234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T16:21:24.510739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명업종명업소수
법정동명1.0000.0000.000
업종명0.0001.0000.000
업소수0.0000.0001.000
2024-05-18T16:21:24.757520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명업종명
법정동명1.0000.000
업종명0.0001.000
2024-05-18T16:21:25.031989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소수법정동명업종명
업소수1.0000.0000.000
법정동명0.0001.0000.000
업종명0.0000.0001.000

Missing values

2024-05-18T16:21:20.472409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T16:21:20.782866image/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
0동대문구신설동숙박업(일반)10
1동대문구신설동목욕장업2
2동대문구신설동이용업2
3동대문구신설동세탁업2
4동대문구신설동위생관리용역업11
5동대문구신설동공중이용시설51
6동대문구신설동일반미용업4
7동대문구신설동피부미용업1
8동대문구신설동종합미용업5
9동대문구신설동피부미용업, 네일미용업1
자치구명법정동명업종명업소수
154동대문구이문동일반미용업60
155동대문구이문동피부미용업10
156동대문구이문동종합미용업6
157동대문구이문동네일미용업6
158동대문구이문동일반미용업, 피부미용업2
159동대문구이문동피부미용업, 네일미용업3
160동대문구이문동일반미용업, 화장ㆍ분장 미용업2
161동대문구이문동네일미용업, 화장ㆍ분장 미용업1
162동대문구이문동일반미용업, 피부미용업, 네일미용업1
163동대문구이문동일반미용업, 네일미용업, 화장ㆍ분장 미용업2