Overview

Dataset statistics

Number of variables4
Number of observations604
Missing cells3
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.6 KiB
Average record size in memory33.2 B

Variable types

Categorical2
Text1
Numeric1

Dataset

Description자치구명,법정동명,업태명,업소수
Author서대문구
URLhttps://data.seoul.go.kr/dataList/OA-2365/S/1/datasetView.do

Alerts

자치구명 has constant value ""Constant

Reproduction

Analysis started2024-05-18 02:13:41.257219
Analysis finished2024-05-18 02:13:42.559355
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
서대문구
604 

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 604
100.0%

Length

2024-05-18T11:13:42.767055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:13:43.085937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서대문구 604
100.0%

법정동명
Categorical

Distinct20
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
창천동
55 
홍은동
52 
홍제동
51 
연희동
48 
남가좌동
47 
Other values (15)
351 

Length

Max length5
Median length3
Mean length3.3940397
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
창천동 55
 
9.1%
홍은동 52
 
8.6%
홍제동 51
 
8.4%
연희동 48
 
7.9%
남가좌동 47
 
7.8%
북가좌동 44
 
7.3%
북아현동 38
 
6.3%
대현동 37
 
6.1%
충정로3가 33
 
5.5%
충정로2가 30
 
5.0%
Other values (10) 169
28.0%

Length

2024-05-18T11:13:43.514234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창천동 55
 
9.1%
홍은동 52
 
8.6%
홍제동 51
 
8.4%
연희동 48
 
7.9%
남가좌동 47
 
7.8%
북가좌동 44
 
7.3%
북아현동 38
 
6.3%
대현동 37
 
6.1%
충정로3가 33
 
5.5%
미근동 30
 
5.0%
Other values (10) 169
28.0%

업태명
Text

Distinct71
Distinct (%)11.8%
Missing3
Missing (%)0.5%
Memory size4.8 KiB
2024-05-18T11:13:43.973098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length5.4825291
Min length2

Characters and Unicode

Total characters3295
Distinct characters151
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

Unique8 ?
Unique (%)1.3%

Sample

1st row한식
2nd row중국식
3rd row경양식
4th row일식
5th row분식
ValueCountFrequency (%)
기타 47
 
7.2%
한식 20
 
3.1%
휴게음식점 18
 
2.8%
편의점 18
 
2.8%
경양식 17
 
2.6%
분식 17
 
2.6%
전자상거래(통신판매업 17
 
2.6%
즉석판매제조가공업 17
 
2.6%
커피숍 17
 
2.6%
제과점영업 16
 
2.4%
Other values (63) 450
68.8%
2024-05-18T11:13:44.862601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 227
 
6.9%
업 192
 
5.8%
매 143
 
4.3%
판 140
 
4.2%
기 104
 
3.2%
품 84
 
2.5%
점 76
 
2.3%
통 73
 
2.2%
) 71
 
2.2%
( 71
 
2.2%
Other values (141) 2114
64.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3046
92.4%
Close Punctuation 71
 
2.2%
Open Punctuation 71
 
2.2%
Other Punctuation 54
 
1.6%
Space Separator 53
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 227
 
7.5%
업 192
 
6.3%
매 143
 
4.7%
판 140
 
4.6%
기 104
 
3.4%
품 84
 
2.8%
점 76
 
2.5%
통 73
 
2.4%
타 63
 
2.1%
영 62
 
2.0%
Other values (136) 1882
61.8%
Other Punctuation
ValueCountFrequency (%)
/ 45
83.3%
, 9
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 71
100.0%
Open Punctuation
ValueCountFrequency (%)
( 71
100.0%
Space Separator
ValueCountFrequency (%)
53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3046
92.4%
Common 249
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 227
 
7.5%
업 192
 
6.3%
매 143
 
4.7%
판 140
 
4.6%
기 104
 
3.4%
품 84
 
2.8%
점 76
 
2.5%
통 73
 
2.4%
타 63
 
2.1%
영 62
 
2.0%
Other values (136) 1882
61.8%
Common
ValueCountFrequency (%)
) 71
28.5%
( 71
28.5%
53
21.3%
/ 45
18.1%
, 9
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3046
92.4%
ASCII 249
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 227
 
7.5%
업 192
 
6.3%
매 143
 
4.7%
판 140
 
4.6%
기 104
 
3.4%
품 84
 
2.8%
점 76
 
2.5%
통 73
 
2.4%
타 63
 
2.1%
영 62
 
2.0%
Other values (136) 1882
61.8%
ASCII
ValueCountFrequency (%)
) 71
28.5%
( 71
28.5%
53
21.3%
/ 45
18.1%
, 9
 
3.6%

업소수
Real number (ℝ)

Distinct66
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.09106
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-05-18T11:13:45.172267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310
95-th percentile45.85
Maximum289
Range288
Interquartile range (IQR)9

Descriptive statistics

Standard deviation24.167258
Coefficient of variation (CV)2.1789855
Kurtosis48.036652
Mean11.09106
Median Absolute Deviation (MAD)2
Skewness5.8773013
Sum6699
Variance584.05637
MonotonicityNot monotonic
2024-05-18T11:13:45.561176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 192
31.8%
2 88
14.6%
3 48
 
7.9%
4 33
 
5.5%
5 27
 
4.5%
8 19
 
3.1%
6 17
 
2.8%
7 14
 
2.3%
10 12
 
2.0%
9 12
 
2.0%
Other values (56) 142
23.5%
ValueCountFrequency (%)
1 192
31.8%
2 88
14.6%
3 48
 
7.9%
4 33
 
5.5%
5 27
 
4.5%
6 17
 
2.8%
7 14
 
2.3%
8 19
 
3.1%
9 12
 
2.0%
10 12
 
2.0%
ValueCountFrequency (%)
289 1
0.2%
221 1
0.2%
198 1
0.2%
148 1
0.2%
133 1
0.2%
128 1
0.2%
119 1
0.2%
107 1
0.2%
92 1
0.2%
90 1
0.2%

Interactions

2024-05-18T11:13:41.694856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T11:13:45.821386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명업태명업소수
법정동명1.0000.0000.000
업태명0.0001.0000.000
업소수0.0000.0001.000
2024-05-18T11:13:46.041883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소수법정동명
업소수1.0000.000
법정동명0.0001.000

Missing values

2024-05-18T11:13:42.130713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T11:13:42.453271image/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
0서대문구충정로2가한식48
1서대문구충정로2가중국식2
2서대문구충정로2가경양식6
3서대문구충정로2가일식7
4서대문구충정로2가분식10
5서대문구충정로2가정종/대포집/소주방3
6서대문구충정로2가호프/통닭11
7서대문구충정로2가통닭(치킨)1
8서대문구충정로2가식육(숯불구이)1
9서대문구충정로2가외국음식전문점(인도,태국등)1
자치구명법정동명업태명업소수
594서대문구남가좌동식품자동판매기영업14
595서대문구남가좌동유통전문판매업9
596서대문구남가좌동기타식품판매업5
597서대문구남가좌동위탁급식영업1
598서대문구남가좌동제과점영업17
599서대문구남가좌동영업장판매22
600서대문구남가좌동방문판매2
601서대문구남가좌동전자상거래(통신판매업)38
602서대문구남가좌동기타(복합 등)1
603서대문구남가좌동건강기능식품유통전문판매업1