Overview

Dataset statistics

Number of variables7
Number of observations1811
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory104.5 KiB
Average record size in memory59.1 B

Variable types

Numeric1
Categorical4
Text2

Dataset

Description전국의 시장 정보에 대한 데이터로 시장의 주소와 해당 시장에서의 배송 서비스와 장보기 서비스 여부 등을 항목으로 제공합니다.
Author소상공인시장진흥공단
URLhttps://www.data.go.kr/data/15090612/fileData.do

Alerts

순번 is highly overall correlated with 시장-상점가 구분 and 1 other fieldsHigh correlation
시장-상점가 구분 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
인정여부 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
시장-상점가 구분 is highly imbalanced (51.8%)Imbalance
실시여부 - 1) 배송서비스 is highly imbalanced (63.6%)Imbalance
실시여부 - 2) 장보기서비스 is highly imbalanced (84.4%)Imbalance
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:33:19.742733
Analysis finished2023-12-12 16:33:20.530884
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1811
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean906
Minimum1
Maximum1811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2023-12-13T01:33:20.604325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile91.5
Q1453.5
median906
Q31358.5
95-th percentile1720.5
Maximum1811
Range1810
Interquartile range (IQR)905

Descriptive statistics

Standard deviation522.93499
Coefficient of variation (CV)0.57719093
Kurtosis-1.2
Mean906
Median Absolute Deviation (MAD)453
Skewness0
Sum1640766
Variance273461
MonotonicityStrictly increasing
2023-12-13T01:33:20.781642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1191 1
 
0.1%
1217 1
 
0.1%
1216 1
 
0.1%
1215 1
 
0.1%
1214 1
 
0.1%
1213 1
 
0.1%
1212 1
 
0.1%
1211 1
 
0.1%
1210 1
 
0.1%
Other values (1801) 1801
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1811 1
0.1%
1810 1
0.1%
1809 1
0.1%
1808 1
0.1%
1807 1
0.1%
1806 1
0.1%
1805 1
0.1%
1804 1
0.1%
1803 1
0.1%
1802 1
0.1%

시장-상점가 구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
전통시장
1519 
상점가
224 
지하도상점가
 
68

Length

Max length6
Median length4
Mean length3.9514081
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전통시장 1519
83.9%
상점가 224
 
12.4%
지하도상점가 68
 
3.8%

Length

2023-12-13T01:33:20.969925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:33:21.112229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전통시장 1519
83.9%
상점가 224
 
12.4%
지하도상점가 68
 
3.8%

인정여부
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
인정시장
1449 
상점가
256 
미인정시장
 
106

Length

Max length5
Median length4
Mean length3.9171728
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인정시장 1449
80.0%
상점가 256
 
14.1%
미인정시장 106
 
5.9%

Length

2023-12-13T01:33:21.291643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:33:21.438271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인정시장 1449
80.0%
상점가 256
 
14.1%
미인정시장 106
 
5.9%

지자체
Text

Distinct227
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2023-12-13T01:33:21.742908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8978465
Min length4

Characters and Unicode

Total characters8870
Distinct characters132
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.8%

Sample

1st row서울강남구
2nd row서울강남구
3rd row서울강남구
4th row서울강남구
5th row서울강동구
ValueCountFrequency (%)
경남창원시 76
 
4.2%
서울중구 39
 
2.2%
경북포항시 38
 
2.1%
경기성남시 28
 
1.5%
부산부산진구 28
 
1.5%
서울종로구 27
 
1.5%
대구달서구 27
 
1.5%
대구중구 25
 
1.4%
경기수원시 22
 
1.2%
경기부천시 21
 
1.2%
Other values (217) 1480
81.7%
2023-12-13T01:33:22.352828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 934
 
10.5%
시 719
 
8.1%
경 586
 
6.6%
남 477
 
5.4%
서 415
 
4.7%
울 374
 
4.2%
산 364
 
4.1%
북 363
 
4.1%
군 319
 
3.6%
부 267
 
3.0%
Other values (122) 4052
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8870
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 934
 
10.5%
시 719
 
8.1%
경 586
 
6.6%
남 477
 
5.4%
서 415
 
4.7%
울 374
 
4.2%
산 364
 
4.1%
북 363
 
4.1%
군 319
 
3.6%
부 267
 
3.0%
Other values (122) 4052
45.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8870
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 934
 
10.5%
시 719
 
8.1%
경 586
 
6.6%
남 477
 
5.4%
서 415
 
4.7%
울 374
 
4.2%
산 364
 
4.1%
북 363
 
4.1%
군 319
 
3.6%
부 267
 
3.0%
Other values (122) 4052
45.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8870
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 934
 
10.5%
시 719
 
8.1%
경 586
 
6.6%
남 477
 
5.4%
서 415
 
4.7%
울 374
 
4.2%
산 364
 
4.1%
북 363
 
4.1%
군 319
 
3.6%
부 267
 
3.0%
Other values (122) 4052
45.7%

시장-상점가명
Text

Distinct1748
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2023-12-13T01:33:22.671197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length6.3821093
Min length2

Characters and Unicode

Total characters11558
Distinct characters423
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1711 ?
Unique (%)94.5%

Sample

1st row강남시장
2nd row신사상가
3rd row영동전통시장
4th row청담삼익시장
5th row고분다리전통시장
ValueCountFrequency (%)
상점가 23
 
1.2%
중앙시장 12
 
0.6%
역전시장 5
 
0.3%
동부시장 5
 
0.3%
제일시장 4
 
0.2%
신흥시장 4
 
0.2%
서동시장 3
 
0.2%
강남시장 3
 
0.2%
신중앙시장 3
 
0.2%
현대시장 3
 
0.2%
Other values (1773) 1813
96.5%
2023-12-13T01:33:23.129430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 1552
 
13.4%
시 1494
 
12.9%
가 409
 
3.5%
상 401
 
3.5%
동 239
 
2.1%
전 218
 
1.9%
산 185
 
1.6%
통 173
 
1.5%
점 149
 
1.3%
중 144
 
1.2%
Other values (413) 6594
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11121
96.2%
Decimal Number 123
 
1.1%
Open Punctuation 111
 
1.0%
Close Punctuation 111
 
1.0%
Space Separator 70
 
0.6%
Uppercase Letter 13
 
0.1%
Lowercase Letter 5
 
< 0.1%
Other Symbol 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 1552
 
14.0%
시 1494
 
13.4%
가 409
 
3.7%
상 401
 
3.6%
동 239
 
2.1%
전 218
 
2.0%
산 185
 
1.7%
통 173
 
1.6%
점 149
 
1.3%
중 144
 
1.3%
Other values (387) 6157
55.4%
Decimal Number
ValueCountFrequency (%)
5 65
52.8%
1 25
 
20.3%
2 14
 
11.4%
3 9
 
7.3%
4 5
 
4.1%
7 2
 
1.6%
0 1
 
0.8%
6 1
 
0.8%
9 1
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
A 3
23.1%
B 3
23.1%
S 2
15.4%
D 2
15.4%
T 1
 
7.7%
K 1
 
7.7%
C 1
 
7.7%
Lowercase Letter
ValueCountFrequency (%)
a 2
40.0%
m 1
20.0%
e 1
20.0%
b 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 111
100.0%
Close Punctuation
ValueCountFrequency (%)
) 111
100.0%
Space Separator
ValueCountFrequency (%)
70
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
! 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11123
96.2%
Common 417
 
3.6%
Latin 18
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 1552
 
14.0%
시 1494
 
13.4%
가 409
 
3.7%
상 401
 
3.6%
동 239
 
2.1%
전 218
 
2.0%
산 185
 
1.7%
통 173
 
1.6%
점 149
 
1.3%
중 144
 
1.3%
Other values (388) 6159
55.4%
Common
ValueCountFrequency (%)
( 111
26.6%
) 111
26.6%
70
16.8%
5 65
15.6%
1 25
 
6.0%
2 14
 
3.4%
3 9
 
2.2%
4 5
 
1.2%
7 2
 
0.5%
0 1
 
0.2%
Other values (4) 4
 
1.0%
Latin
ValueCountFrequency (%)
A 3
16.7%
B 3
16.7%
S 2
11.1%
a 2
11.1%
D 2
11.1%
m 1
 
5.6%
e 1
 
5.6%
T 1
 
5.6%
K 1
 
5.6%
b 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11121
96.2%
ASCII 435
 
3.8%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 1552
 
14.0%
시 1494
 
13.4%
가 409
 
3.7%
상 401
 
3.6%
동 239
 
2.1%
전 218
 
2.0%
산 185
 
1.7%
통 173
 
1.6%
점 149
 
1.3%
중 144
 
1.3%
Other values (387) 6157
55.4%
ASCII
ValueCountFrequency (%)
( 111
25.5%
) 111
25.5%
70
16.1%
5 65
14.9%
1 25
 
5.7%
2 14
 
3.2%
3 9
 
2.1%
4 5
 
1.1%
A 3
 
0.7%
B 3
 
0.7%
Other values (15) 19
 
4.4%
None
ValueCountFrequency (%)
㈜ 2
100.0%

실시여부 - 1) 배송서비스
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2
1685 
1
 
126

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 1685
93.0%
1 126
 
7.0%

Length

2023-12-13T01:33:23.600670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:33:23.700355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1685
93.0%
1 126
 
7.0%

실시여부 - 2) 장보기서비스
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2
1770 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1770
97.7%
1 41
 
2.3%

Length

2023-12-13T01:33:23.838528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:33:23.966527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1770
97.7%
1 41
 
2.3%

Interactions

2023-12-13T01:33:20.226599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:33:24.065520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시장-상점가 구분인정여부실시여부 - 1) 배송서비스실시여부 - 2) 장보기서비스
순번1.0000.7180.6540.2560.138
시장-상점가 구분0.7181.0000.9310.0510.035
인정여부0.6540.9311.0000.0560.041
실시여부 - 1) 배송서비스0.2560.0510.0561.0000.554
실시여부 - 2) 장보기서비스0.1380.0350.0410.5541.000
2023-12-13T01:33:24.222010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인정여부실시여부 - 2) 장보기서비스시장-상점가 구분실시여부 - 1) 배송서비스
인정여부1.0000.0680.6790.093
실시여부 - 2) 장보기서비스0.0681.0000.0580.374
시장-상점가 구분0.6790.0581.0000.085
실시여부 - 1) 배송서비스0.0930.3740.0851.000
2023-12-13T01:33:24.322091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시장-상점가 구분인정여부실시여부 - 1) 배송서비스실시여부 - 2) 장보기서비스
순번1.0000.5780.5010.1960.105
시장-상점가 구분0.5781.0000.6790.0850.058
인정여부0.5010.6791.0000.0930.068
실시여부 - 1) 배송서비스0.1960.0850.0931.0000.374
실시여부 - 2) 장보기서비스0.1050.0580.0680.3741.000

Missing values

2023-12-13T01:33:20.349229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:33:20.485990image/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

순번시장-상점가 구분인정여부지자체시장-상점가명실시여부 - 1) 배송서비스실시여부 - 2) 장보기서비스
01전통시장인정시장서울강남구강남시장22
12전통시장인정시장서울강남구신사상가22
23전통시장인정시장서울강남구영동전통시장12
34전통시장인정시장서울강남구청담삼익시장22
45전통시장인정시장서울강동구고분다리전통시장12
56전통시장인정시장서울강동구길동복조리시장12
67전통시장인정시장서울강동구둔촌역전통시장11
78전통시장인정시장서울강동구명일전통시장22
89전통시장인정시장서울강동구성내전통시장22
910전통시장인정시장서울강동구암사종합시장11
순번시장-상점가 구분인정여부지자체시장-상점가명실시여부 - 1) 배송서비스실시여부 - 2) 장보기서비스
18011802지하도상점가상점가서울서초구엔터식스 강남점22
18021803상점가상점가경기과천시중앙동상점가22
18031804상점가상점가경기남양주시덕소상점가22
18041805상점가상점가경기남양주시맷돌모루상점가22
18051806상점가상점가경기여주시강변상점가22
18061807상점가상점가강원태백시중앙로 상점가22
18071808지하도상점가상점가서울종로구대일상가(낙원지하상가)22
18081809전통시장인정시장서울중구Team20422
18091810상점가상점가경기여주시터미널상점가22
18101811상점가상점가전남여수시흥국상가22