Overview

Dataset statistics

Number of variables3
Number of observations1598
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.1 KiB
Average record size in memory25.1 B

Variable types

Text1
Numeric1
Categorical1

Dataset

Description전국의 모바일상품권 가맹점에 대한 데이터로 모바일상품권 가맹점이 속한 시장 명과 가맹점 수, 모바일 여부를 항목으로 제공합니다.
Author소상공인시장진흥공단
URLhttps://www.data.go.kr/data/15090978/fileData.do

Alerts

모바일 여부 has constant value ""Constant

Reproduction

Analysis started2023-12-12 03:12:31.582491
Analysis finished2023-12-12 03:12:32.299106
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시장명
Text

Distinct1582
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
2023-12-12T12:12:32.598262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length6.8285357
Min length3

Characters and Unicode

Total characters10912
Distinct characters416
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1568 ?
Unique (%)98.1%

Sample

1st row남대문시장
2nd row삼익패션타운
3rd row서울중부시장
4th row신중부시장
5th row평화시장
ValueCountFrequency (%)
상점가 6
 
0.4%
제일시장 3
 
0.2%
고투몰(강남터미널지하도상점가 3
 
0.2%
한솔동 3
 
0.2%
동문시장 3
 
0.2%
평화시장 2
 
0.1%
대현프리몰 2
 
0.1%
서문시장 2
 
0.1%
충주자유시장 2
 
0.1%
덕산시장 2
 
0.1%
Other values (1641) 1654
98.3%
2023-12-12T12:12:33.199209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 1383
 
12.7%
시 1342
 
12.3%
상 395
 
3.6%
가 379
 
3.5%
동 229
 
2.1%
전 185
 
1.7%
산 185
 
1.7%
점 158
 
1.4%
중 154
 
1.4%
구 151
 
1.4%
Other values (406) 6351
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10475
96.0%
Open Punctuation 127
 
1.2%
Close Punctuation 127
 
1.2%
Space Separator 85
 
0.8%
Decimal Number 81
 
0.7%
Other Punctuation 7
 
0.1%
Uppercase Letter 4
 
< 0.1%
Math Symbol 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 1383
 
13.2%
시 1342
 
12.8%
상 395
 
3.8%
가 379
 
3.6%
동 229
 
2.2%
전 185
 
1.8%
산 185
 
1.8%
점 158
 
1.5%
중 154
 
1.5%
구 151
 
1.4%
Other values (384) 5914
56.5%
Decimal Number
ValueCountFrequency (%)
5 26
32.1%
1 20
24.7%
2 15
18.5%
3 9
 
11.1%
4 4
 
4.9%
6 3
 
3.7%
7 2
 
2.5%
9 1
 
1.2%
0 1
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 1
25.0%
S 1
25.0%
A 1
25.0%
D 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 6
85.7%
! 1
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
s 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 127
100.0%
Space Separator
ValueCountFrequency (%)
85
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10475
96.0%
Common 431
 
3.9%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 1383
 
13.2%
시 1342
 
12.8%
상 395
 
3.8%
가 379
 
3.6%
동 229
 
2.2%
전 185
 
1.8%
산 185
 
1.8%
점 158
 
1.5%
중 154
 
1.5%
구 151
 
1.4%
Other values (384) 5914
56.5%
Common
ValueCountFrequency (%)
( 127
29.5%
) 127
29.5%
85
19.7%
5 26
 
6.0%
1 20
 
4.6%
2 15
 
3.5%
3 9
 
2.1%
, 6
 
1.4%
4 4
 
0.9%
6 3
 
0.7%
Other values (6) 9
 
2.1%
Latin
ValueCountFrequency (%)
B 1
16.7%
S 1
16.7%
A 1
16.7%
D 1
16.7%
k 1
16.7%
s 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10475
96.0%
ASCII 437
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 1383
 
13.2%
시 1342
 
12.8%
상 395
 
3.8%
가 379
 
3.6%
동 229
 
2.2%
전 185
 
1.8%
산 185
 
1.8%
점 158
 
1.5%
중 154
 
1.5%
구 151
 
1.4%
Other values (384) 5914
56.5%
ASCII
ValueCountFrequency (%)
( 127
29.1%
) 127
29.1%
85
19.5%
5 26
 
5.9%
1 20
 
4.6%
2 15
 
3.4%
3 9
 
2.1%
, 6
 
1.4%
4 4
 
0.9%
6 3
 
0.7%
Other values (12) 15
 
3.4%

가맹점수
Real number (ℝ)

Distinct196
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.498123
Minimum1
Maximum724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2023-12-12T12:12:33.419455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16.25
median24
Q358
95-th percentile140.15
Maximum724
Range723
Interquartile range (IQR)51.75

Descriptive statistics

Standard deviation56.100163
Coefficient of variation (CV)1.3200621
Kurtosis24.650193
Mean42.498123
Median Absolute Deviation (MAD)21
Skewness3.6222771
Sum67912
Variance3147.2282
MonotonicityNot monotonic
2023-12-12T12:12:33.677505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 145
 
9.1%
3 67
 
4.2%
2 62
 
3.9%
4 50
 
3.1%
7 49
 
3.1%
5 44
 
2.8%
12 34
 
2.1%
6 32
 
2.0%
8 32
 
2.0%
9 31
 
1.9%
Other values (186) 1052
65.8%
ValueCountFrequency (%)
1 145
9.1%
2 62
3.9%
3 67
4.2%
4 50
 
3.1%
5 44
 
2.8%
6 32
 
2.0%
7 49
 
3.1%
8 32
 
2.0%
9 31
 
1.9%
10 30
 
1.9%
ValueCountFrequency (%)
724 1
0.1%
545 1
0.1%
483 1
0.1%
383 1
0.1%
380 1
0.1%
363 1
0.1%
349 1
0.1%
333 1
0.1%
331 1
0.1%
320 2
0.1%

모바일 여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
O
1598 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
O 1598
100.0%

Length

2023-12-12T12:12:33.885952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:12:34.030379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 1598
100.0%

Interactions

2023-12-12T12:12:31.956505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T12:12:32.150832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:12:32.249899image/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

시장명가맹점수모바일 여부
0남대문시장724O
1삼익패션타운383O
2서울중부시장151O
3신중부시장138O
4평화시장229O
5서울중앙시장64O
6남평화시장13O
7청평화시장1O
8제일평화시장70O
9벨포스트(구 에리어식스)39O
시장명가맹점수모바일 여부
1588포항죽도농산물시장102O
1589두호1시장3O
1590죽도어시장36O
1591그린종합시장1O
1592영일대북부시장(구 북부시장)23O
1593장량성도시장36O
1594양학시장10O
1595포항서부시장2O
1596청하시장3O
1597장성종합시장1O