Overview

Dataset statistics

Number of variables4
Number of observations463
Missing cells8
Missing cells (%)0.4%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory15.0 KiB
Average record size in memory33.3 B

Variable types

Categorical2
Text1
Numeric1

Dataset

Description자치구명,법정동명,업태명,업소수
Author서초구
URLhttps://data.seoul.go.kr/dataList/OA-11068/S/1/datasetView.do

Alerts

자치구명 has constant value ""Constant
Dataset has 1 (0.2%) duplicate rowsDuplicates
업태명 has 8 (1.7%) missing valuesMissing

Reproduction

Analysis started2024-05-11 02:25:31.310735
Analysis finished2024-05-11 02:25:33.254580
Duration1.94 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
서초구
463 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서초구 463
100.0%

Length

2024-05-11T02:25:33.431411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T02:25:33.738896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서초구 463
100.0%

법정동명
Categorical

Distinct10
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
서초동
78 
방배동
68 
양재동
61 
반포동
59 
잠원동
58 
Other values (5)
139 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서초동 78
16.8%
방배동 68
14.7%
양재동 61
13.2%
반포동 59
12.7%
잠원동 58
12.5%
우면동 45
9.7%
내곡동 27
 
5.8%
신원동 27
 
5.8%
염곡동 21
 
4.5%
원지동 19
 
4.1%

Length

2024-05-11T02:25:34.055720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T02:25:34.413651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서초동 78
16.8%
방배동 68
14.7%
양재동 61
13.2%
반포동 59
12.7%
잠원동 58
12.5%
우면동 45
9.7%
내곡동 27
 
5.8%
신원동 27
 
5.8%
염곡동 21
 
4.5%
원지동 19
 
4.1%

업태명
Text

MISSING 

Distinct77
Distinct (%)16.9%
Missing8
Missing (%)1.7%
Memory size3.7 KiB
2024-05-11T02:25:35.088567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length5.6505495
Min length2

Characters and Unicode

Total characters2571
Distinct characters155
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)1.5%

Sample

1st row한식
2nd row중국식
3rd row경양식
4th row일식
5th row분식
ValueCountFrequency (%)
기타 38
 
7.6%
패스트푸드 12
 
2.4%
커피숍 10
 
2.0%
즉석판매제조가공업 10
 
2.0%
식품등 10
 
2.0%
수입판매업 10
 
2.0%
일반조리판매 10
 
2.0%
한식 10
 
2.0%
건강기능식품유통전문판매업 10
 
2.0%
휴게음식점 10
 
2.0%
Other values (68) 371
74.1%
2024-05-11T02:25:36.226224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 167
 
6.5%
업 150
 
5.8%
매 120
 
4.7%
판 115
 
4.5%
기 92
 
3.6%
품 79
 
3.1%
통 53
 
2.1%
타 50
 
1.9%
점 47
 
1.8%
46
 
1.8%
Other values (145) 1652
64.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2404
93.5%
Space Separator 46
 
1.8%
Open Punctuation 46
 
1.8%
Close Punctuation 46
 
1.8%
Other Punctuation 29
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 167
 
6.9%
업 150
 
6.2%
매 120
 
5.0%
판 115
 
4.8%
기 92
 
3.8%
품 79
 
3.3%
통 53
 
2.2%
타 50
 
2.1%
점 47
 
2.0%
전 44
 
1.8%
Other values (139) 1487
61.9%
Other Punctuation
ValueCountFrequency (%)
/ 20
69.0%
, 6
 
20.7%
. 3
 
10.3%
Space Separator
ValueCountFrequency (%)
46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2404
93.5%
Common 167
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 167
 
6.9%
업 150
 
6.2%
매 120
 
5.0%
판 115
 
4.8%
기 92
 
3.8%
품 79
 
3.3%
통 53
 
2.2%
타 50
 
2.1%
점 47
 
2.0%
전 44
 
1.8%
Other values (139) 1487
61.9%
Common
ValueCountFrequency (%)
46
27.5%
( 46
27.5%
) 46
27.5%
/ 20
12.0%
, 6
 
3.6%
. 3
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2404
93.5%
ASCII 167
 
6.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 167
 
6.9%
업 150
 
6.2%
매 120
 
5.0%
판 115
 
4.8%
기 92
 
3.8%
품 79
 
3.3%
통 53
 
2.2%
타 50
 
2.1%
점 47
 
2.0%
전 44
 
1.8%
Other values (139) 1487
61.9%
ASCII
ValueCountFrequency (%)
46
27.5%
( 46
27.5%
) 46
27.5%
/ 20
12.0%
, 6
 
3.6%
. 3
 
1.8%

업소수
Real number (ℝ)

Distinct105
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.604752
Minimum1
Maximum1005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-05-11T02:25:36.791781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q323
95-th percentile149.8
Maximum1005
Range1004
Interquartile range (IQR)21

Descriptive statistics

Standard deviation83.779584
Coefficient of variation (CV)2.5695514
Kurtosis50.929857
Mean32.604752
Median Absolute Deviation (MAD)5
Skewness6.0710153
Sum15096
Variance7019.0188
MonotonicityNot monotonic
2024-05-11T02:25:37.345786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 95
20.5%
2 57
 
12.3%
4 29
 
6.3%
6 27
 
5.8%
5 25
 
5.4%
3 23
 
5.0%
7 12
 
2.6%
8 11
 
2.4%
9 8
 
1.7%
10 8
 
1.7%
Other values (95) 168
36.3%
ValueCountFrequency (%)
1 95
20.5%
2 57
12.3%
3 23
 
5.0%
4 29
 
6.3%
5 25
 
5.4%
6 27
 
5.8%
7 12
 
2.6%
8 11
 
2.4%
9 8
 
1.7%
10 8
 
1.7%
ValueCountFrequency (%)
1005 1
0.2%
606 1
0.2%
526 1
0.2%
525 1
0.2%
469 1
0.2%
369 1
0.2%
348 1
0.2%
345 1
0.2%
302 1
0.2%
281 1
0.2%

Interactions

2024-05-11T02:25:31.888281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:25:37.618264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명업태명업소수
법정동명1.0000.0000.000
업태명0.0001.0000.000
업소수0.0000.0001.000
2024-05-11T02:25:37.807419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소수법정동명
업소수1.0000.000
법정동명0.0001.000

Missing values

2024-05-11T02:25:32.540347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:25:33.042215image/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

자치구명법정동명업태명업소수
0서초구방배동한식525
1서초구방배동중국식50
2서초구방배동경양식97
3서초구방배동일식70
4서초구방배동분식74
5서초구방배동뷔페식2
6서초구방배동정종/대포집/소주방7
7서초구방배동출장조리1
8서초구방배동패스트푸드2
9서초구방배동호프/통닭79
자치구명법정동명업태명업소수
453서초구신원동기타 집단급식소1
454서초구신원동즉석판매제조가공업5
455서초구신원동식품소분업2
456서초구신원동식품등 수입판매업5
457서초구신원동유통전문판매업6
458서초구신원동제과점영업1
459서초구신원동영업장판매4
460서초구신원동전자상거래(통신판매업)15
461서초구신원동다단계판매2
462서초구신원동건강기능식품유통전문판매업2

Duplicate rows

Most frequently occurring

자치구명법정동명업태명업소수# duplicates
0서초구방배동키즈카페12