Overview

Dataset statistics

Number of variables3
Number of observations1761
Missing cells3
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.1 KiB
Average record size in memory25.1 B

Variable types

Text1
Numeric1
Categorical1

Dataset

Description전국의 전자상품권 가맹점에 대한 데이터로 전자상품권 가맹점이 속한 시장 명과 가맹점 수, 전자 여부를 항목으로 제공합니다.
Author소상공인시장진흥공단
URLhttps://www.data.go.kr/data/15090976/fileData.do

Alerts

전자 여부 has constant value ""Constant
가맹점수 is highly skewed (γ1 = 24.84560852)Skewed

Reproduction

Analysis started2023-12-12 16:11:14.891808
Analysis finished2023-12-12 16:11:15.268505
Duration0.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시장명
Text

Distinct1738
Distinct (%)98.9%
Missing3
Missing (%)0.2%
Memory size13.9 KiB
2023-12-13T01:11:15.374163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length6.7605233
Min length3

Characters and Unicode

Total characters11885
Distinct characters429
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

Unique1721 ?
Unique (%)97.9%

Sample

1st row남대문시장
2nd row삼익패션타운
3rd row서울중부시장
4th row신중부시장
5th row평화시장
ValueCountFrequency (%)
상점가 5
 
0.3%
동문시장 3
 
0.2%
역전시장 3
 
0.2%
한솔동 3
 
0.2%
제일시장 3
 
0.2%
고투몰(강남터미널지하도상점가 3
 
0.2%
구암시장 2
 
0.1%
덕산시장 2
 
0.1%
가야시장 2
 
0.1%
충주자유시장 2
 
0.1%
Other values (1799) 1814
98.5%
2023-12-13T01:11:15.736488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 1526
 
12.8%
시 1475
 
12.4%
상 430
 
3.6%
가 409
 
3.4%
동 247
 
2.1%
산 212
 
1.8%
전 202
 
1.7%
중 166
 
1.4%
점 165
 
1.4%
구 158
 
1.3%
Other values (419) 6895
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11418
96.1%
Open Punctuation 136
 
1.1%
Close Punctuation 136
 
1.1%
Decimal Number 90
 
0.8%
Space Separator 85
 
0.7%
Other Punctuation 7
 
0.1%
Uppercase Letter 7
 
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 1526
 
13.4%
시 1475
 
12.9%
상 430
 
3.8%
가 409
 
3.6%
동 247
 
2.2%
산 212
 
1.9%
전 202
 
1.8%
중 166
 
1.5%
점 165
 
1.4%
구 158
 
1.4%
Other values (396) 6428
56.3%
Decimal Number
ValueCountFrequency (%)
5 34
37.8%
1 20
22.2%
2 14
15.6%
3 9
 
10.0%
4 5
 
5.6%
7 3
 
3.3%
6 3
 
3.3%
9 1
 
1.1%
0 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
A 2
28.6%
S 1
14.3%
C 1
14.3%
D 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 6
85.7%
! 1
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
s 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 136
100.0%
Close Punctuation
ValueCountFrequency (%)
) 136
100.0%
Space Separator
ValueCountFrequency (%)
85
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11418
96.1%
Common 458
 
3.9%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 1526
 
13.4%
시 1475
 
12.9%
상 430
 
3.8%
가 409
 
3.6%
동 247
 
2.2%
산 212
 
1.9%
전 202
 
1.8%
중 166
 
1.5%
점 165
 
1.4%
구 158
 
1.4%
Other values (396) 6428
56.3%
Common
ValueCountFrequency (%)
( 136
29.7%
) 136
29.7%
85
18.6%
5 34
 
7.4%
1 20
 
4.4%
2 14
 
3.1%
3 9
 
2.0%
, 6
 
1.3%
4 5
 
1.1%
~ 3
 
0.7%
Other values (6) 10
 
2.2%
Latin
ValueCountFrequency (%)
B 2
22.2%
A 2
22.2%
S 1
11.1%
C 1
11.1%
D 1
11.1%
k 1
11.1%
s 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11418
96.1%
ASCII 467
 
3.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 1526
 
13.4%
시 1475
 
12.9%
상 430
 
3.8%
가 409
 
3.6%
동 247
 
2.2%
산 212
 
1.9%
전 202
 
1.8%
중 166
 
1.5%
점 165
 
1.4%
구 158
 
1.4%
Other values (396) 6428
56.3%
ASCII
ValueCountFrequency (%)
( 136
29.1%
) 136
29.1%
85
18.2%
5 34
 
7.3%
1 20
 
4.3%
2 14
 
3.0%
3 9
 
1.9%
, 6
 
1.3%
4 5
 
1.1%
~ 3
 
0.6%
Other values (13) 19
 
4.1%

가맹점수
Real number (ℝ)

SKEWED 

Distinct260
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.39523
Minimum1
Maximum5859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2023-12-13T01:11:15.873221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median34
Q376
95-th percentile197
Maximum5859
Range5858
Interquartile range (IQR)66

Descriptive statistics

Standard deviation167.37723
Coefficient of variation (CV)2.640218
Kurtosis825.97627
Mean63.39523
Median Absolute Deviation (MAD)28
Skewness24.845609
Sum111639
Variance28015.137
MonotonicityNot monotonic
2023-12-13T01:11:16.005057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 90
 
5.1%
2 71
 
4.0%
4 52
 
3.0%
3 50
 
2.8%
5 45
 
2.6%
6 37
 
2.1%
19 30
 
1.7%
7 29
 
1.6%
11 28
 
1.6%
8 28
 
1.6%
Other values (250) 1301
73.9%
ValueCountFrequency (%)
1 90
5.1%
2 71
4.0%
3 50
2.8%
4 52
3.0%
5 45
2.6%
6 37
2.1%
7 29
 
1.6%
8 28
 
1.6%
9 25
 
1.4%
10 26
 
1.5%
ValueCountFrequency (%)
5859 1
0.1%
1829 1
0.1%
1162 1
0.1%
802 1
0.1%
770 1
0.1%
748 1
0.1%
717 1
0.1%
701 1
0.1%
689 1
0.1%
609 1
0.1%

전자 여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
O
1761 

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 1761
100.0%

Length

2023-12-13T01:11:16.123502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:11:16.212424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 1761
100.0%

Interactions

2023-12-13T01:11:15.079579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-13T01:11:15.179182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:11:15.240970image/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

시장명가맹점수전자 여부
0남대문시장1829O
1삼익패션타운609O
2서울중부시장218O
3신중부시장152O
4평화시장748O
5서울중앙시장84O
6남평화시장80O
7제일평화시장260O
8벨포스트(구 에리어식스)24O
9광희패션물106O
시장명가맹점수전자 여부
1751그린종합시장10O
1752영일대북부시장(구 북부시장)61O
1753환호시장2O
1754장량성도시장16O
1755양학시장24O
1756포항서부시장19O
1757장성종합시장10O
1758해변종합큰상가1O
1759죽도중앙시장1O
1760일신시장5859O