Overview

Dataset statistics

Number of variables17
Number of observations56
Missing cells40
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory140.4 B

Variable types

Categorical8
Text5
Numeric2
Unsupported2

Alerts

데이터기준일자 has constant value ""Constant
시군 is highly overall correlated with 경도(WGS84좌표) and 6 other fieldsHigh correlation
사용가능상품권 is highly overall correlated with 시군 and 3 other fieldsHigh correlation
경도(WGS84좌표) is highly overall correlated with 시군 and 1 other fieldsHigh correlation
위도(WGS84좌표) is highly overall correlated with 시군 and 1 other fieldsHigh correlation
시장유형 is highly overall correlated with 시군 and 1 other fieldsHigh correlation
시장개설주기 is highly overall correlated with 시장유형High correlation
홈페이지주소 is highly overall correlated with 경도(WGS84좌표) and 4 other fieldsHigh correlation
공중화장실 보유여부 is highly overall correlated with 시군 and 3 other fieldsHigh correlation
주차장 보유여부 is highly overall correlated with 시군 and 2 other fieldsHigh correlation
경도(WGS84좌표) has 10 (17.9%) missing valuesMissing
위도(WGS84좌표) has 10 (17.9%) missing valuesMissing
개설년도 has 3 (5.4%) missing valuesMissing
연락처 has 17 (30.4%) missing valuesMissing
소재지도로명주소 has unique valuesUnique
소재지지번주소 has unique valuesUnique
점포수 is an unsupported type, check if it needs cleaning or further analysisUnsupported
개설년도 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-13 23:51:17.450882
Analysis finished2024-03-13 23:51:18.851814
Duration1.4 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size580.0 B
군산시
11 
전주시
10 
익산시
10 
고창군
6 
임실군
5 
Other values (6)
14 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
군산시 11
19.6%
전주시 10
17.9%
익산시 10
17.9%
고창군 6
10.7%
임실군 5
8.9%
무주군 4
 
7.1%
남원시 3
 
5.4%
장수군 3
 
5.4%
부안군 2
 
3.6%
진안군 1
 
1.8%

Length

2024-03-14T08:51:18.926869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
군산시 11
19.6%
전주시 10
17.9%
익산시 10
17.9%
고창군 6
10.7%
임실군 5
8.9%
무주군 4
 
7.1%
남원시 3
 
5.4%
장수군 3
 
5.4%
부안군 2
 
3.6%
진안군 1
 
1.8%

시장명
Text

Distinct54
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-03-14T08:51:19.112390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.8928571
Min length3

Characters and Unicode

Total characters274
Distinct characters81
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

Unique52 ?
Unique (%)92.9%

Sample

1st row남부시장
2nd row중앙상가
3rd row모래내시장
4th row신중앙시장
5th row동부시장
ValueCountFrequency (%)
남부시장 2
 
3.6%
동부시장 2
 
3.6%
장계시장 1
 
1.8%
부안상설시장 1
 
1.8%
관촌시장 1
 
1.8%
남원용남시장 1
 
1.8%
인월공설시장 1
 
1.8%
진안시장 1
 
1.8%
무주시장 1
 
1.8%
무풍시장 1
 
1.8%
Other values (44) 44
78.6%
2024-03-14T08:51:19.453059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 53
19.3%
시 49
17.9%
전 11
 
4.0%
상 9
 
3.3%
산 8
 
2.9%
부 7
 
2.6%
통 7
 
2.6%
남 6
 
2.2%
설 6
 
2.2%
가 6
 
2.2%
Other values (71) 112
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 274
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 53
19.3%
시 49
17.9%
전 11
 
4.0%
상 9
 
3.3%
산 8
 
2.9%
부 7
 
2.6%
통 7
 
2.6%
남 6
 
2.2%
설 6
 
2.2%
가 6
 
2.2%
Other values (71) 112
40.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 274
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 53
19.3%
시 49
17.9%
전 11
 
4.0%
상 9
 
3.3%
산 8
 
2.9%
부 7
 
2.6%
통 7
 
2.6%
남 6
 
2.2%
설 6
 
2.2%
가 6
 
2.2%
Other values (71) 112
40.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 274
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 53
19.3%
시 49
17.9%
전 11
 
4.0%
상 9
 
3.3%
산 8
 
2.9%
부 7
 
2.6%
통 7
 
2.6%
남 6
 
2.2%
설 6
 
2.2%
가 6
 
2.2%
Other values (71) 112
40.9%

시장유형
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size580.0 B
오일장
18 
상설시장
10 
상설
9 
인정시장
6 
사설시장
4 
Other values (6)
9 

Length

Max length10
Median length5
Mean length3.5714286
Min length2

Unique

Unique3 ?
Unique (%)5.4%

Sample

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

Common Values

ValueCountFrequency (%)
오일장 18
32.1%
상설시장 10
17.9%
상설 9
16.1%
인정시장 6
 
10.7%
사설시장 4
 
7.1%
등록시장 2
 
3.6%
공설시장 2
 
3.6%
공설시장 2
 
3.6%
정기 1
 
1.8%
공설시장, 상설시장 1
 
1.8%

Length

2024-03-14T08:51:19.565647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
오일장 19
32.2%
상설시장 11
18.6%
상설 9
15.3%
인정시장 6
 
10.2%
공설시장 5
 
8.5%
사설시장 4
 
6.8%
등록시장 2
 
3.4%
정기 1
 
1.7%
상설장 1
 
1.7%
및 1
 
1.7%

소재지도로명주소
Text

UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-03-14T08:51:19.812173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length15
Min length9

Characters and Unicode

Total characters840
Distinct characters118
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 풍남문1길 19-3
2nd row전주시 완산구 태평3길 70
3rd row전주시 덕진구 모래내4길 8-8
4th row전주시 완산구 태평5길 33
5th row전주시 완산구 충경로 109
ValueCountFrequency (%)
군산시 11
 
5.3%
전주시 10
 
4.9%
익산시 10
 
4.9%
완산구 7
 
3.4%
전북 6
 
2.9%
고창군 6
 
2.9%
임실군 5
 
2.4%
무주군 4
 
1.9%
장수군 3
 
1.5%
중앙로3길 3
 
1.5%
Other values (131) 141
68.4%
2024-03-14T08:51:20.198161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
152
 
18.1%
1 45
 
5.4%
시 40
 
4.8%
군 33
 
3.9%
산 32
 
3.8%
길 31
 
3.7%
로 30
 
3.6%
3 28
 
3.3%
2 23
 
2.7%
면 17
 
2.0%
Other values (108) 409
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 497
59.2%
Decimal Number 164
 
19.5%
Space Separator 152
 
18.1%
Dash Punctuation 13
 
1.5%
Close Punctuation 7
 
0.8%
Open Punctuation 7
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 40
 
8.0%
군 33
 
6.6%
산 32
 
6.4%
길 31
 
6.2%
로 30
 
6.0%
면 17
 
3.4%
전 16
 
3.2%
동 15
 
3.0%
주 15
 
3.0%
장 12
 
2.4%
Other values (94) 256
51.5%
Decimal Number
ValueCountFrequency (%)
1 45
27.4%
3 28
17.1%
2 23
14.0%
5 13
 
7.9%
8 13
 
7.9%
7 10
 
6.1%
0 9
 
5.5%
9 8
 
4.9%
6 8
 
4.9%
4 7
 
4.3%
Space Separator
ValueCountFrequency (%)
152
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 497
59.2%
Common 343
40.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 40
 
8.0%
군 33
 
6.6%
산 32
 
6.4%
길 31
 
6.2%
로 30
 
6.0%
면 17
 
3.4%
전 16
 
3.2%
동 15
 
3.0%
주 15
 
3.0%
장 12
 
2.4%
Other values (94) 256
51.5%
Common
ValueCountFrequency (%)
152
44.3%
1 45
 
13.1%
3 28
 
8.2%
2 23
 
6.7%
- 13
 
3.8%
5 13
 
3.8%
8 13
 
3.8%
7 10
 
2.9%
0 9
 
2.6%
9 8
 
2.3%
Other values (4) 29
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 497
59.2%
ASCII 343
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
152
44.3%
1 45
 
13.1%
3 28
 
8.2%
2 23
 
6.7%
- 13
 
3.8%
5 13
 
3.8%
8 13
 
3.8%
7 10
 
2.9%
0 9
 
2.6%
9 8
 
2.3%
Other values (4) 29
 
8.5%
Hangul
ValueCountFrequency (%)
시 40
 
8.0%
군 33
 
6.6%
산 32
 
6.4%
길 31
 
6.2%
로 30
 
6.0%
면 17
 
3.4%
전 16
 
3.2%
동 15
 
3.0%
주 15
 
3.0%
장 12
 
2.4%
Other values (94) 256
51.5%

소재지지번주소
Text

UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-03-14T08:51:20.461346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length15.928571
Min length11

Characters and Unicode

Total characters892
Distinct characters97
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 전동 295-4
2nd row전주시 완산구 태평동 36-1
3rd row전주시 덕진구 인후동2가 203-10
4th row전주시 완산구 태평동 43
5th row전주시 완산구 경원동3가 90-13
ValueCountFrequency (%)
군산시 11
 
5.2%
전주시 10
 
4.7%
익산시 10
 
4.7%
완산구 7
 
3.3%
고창군 6
 
2.8%
전북 6
 
2.8%
임실군 5
 
2.4%
무주군 4
 
1.9%
남원시 3
 
1.4%
장수군 3
 
1.4%
Other values (134) 146
69.2%
2024-03-14T08:51:20.811550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
156
 
17.5%
1 54
 
6.1%
- 46
 
5.2%
시 34
 
3.8%
군 34
 
3.8%
산 33
 
3.7%
동 31
 
3.5%
리 28
 
3.1%
2 28
 
3.1%
3 26
 
2.9%
Other values (87) 422
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 465
52.1%
Decimal Number 225
25.2%
Space Separator 156
 
17.5%
Dash Punctuation 46
 
5.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 34
 
7.3%
군 34
 
7.3%
산 33
 
7.1%
동 31
 
6.7%
리 28
 
6.0%
면 19
 
4.1%
전 18
 
3.9%
주 15
 
3.2%
창 12
 
2.6%
장 11
 
2.4%
Other values (75) 230
49.5%
Decimal Number
ValueCountFrequency (%)
1 54
24.0%
2 28
12.4%
3 26
11.6%
5 21
 
9.3%
4 19
 
8.4%
6 18
 
8.0%
8 17
 
7.6%
9 16
 
7.1%
0 15
 
6.7%
7 11
 
4.9%
Space Separator
ValueCountFrequency (%)
156
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 465
52.1%
Common 427
47.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 34
 
7.3%
군 34
 
7.3%
산 33
 
7.1%
동 31
 
6.7%
리 28
 
6.0%
면 19
 
4.1%
전 18
 
3.9%
주 15
 
3.2%
창 12
 
2.6%
장 11
 
2.4%
Other values (75) 230
49.5%
Common
ValueCountFrequency (%)
156
36.5%
1 54
 
12.6%
- 46
 
10.8%
2 28
 
6.6%
3 26
 
6.1%
5 21
 
4.9%
4 19
 
4.4%
6 18
 
4.2%
8 17
 
4.0%
9 16
 
3.7%
Other values (2) 26
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 465
52.1%
ASCII 427
47.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
156
36.5%
1 54
 
12.6%
- 46
 
10.8%
2 28
 
6.6%
3 26
 
6.1%
5 21
 
4.9%
4 19
 
4.4%
6 18
 
4.2%
8 17
 
4.0%
9 16
 
3.7%
Other values (2) 26
 
6.1%
Hangul
ValueCountFrequency (%)
시 34
 
7.3%
군 34
 
7.3%
산 33
 
7.1%
동 31
 
6.7%
리 28
 
6.0%
면 19
 
4.1%
전 18
 
3.9%
주 15
 
3.2%
창 12
 
2.6%
장 11
 
2.4%
Other values (75) 230
49.5%

시장개설주기
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
상설
20 
매일
9 
2일, 7일
5 
4일, 9일
4 
5일, 10일
4 
Other values (10)
14 

Length

Max length11
Median length2
Mean length4.375
Min length2

Unique

Unique7 ?
Unique (%)12.5%

Sample

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

Common Values

ValueCountFrequency (%)
상설 20
35.7%
매일 9
16.1%
2일, 7일 5
 
8.9%
4일, 9일 4
 
7.1%
5일, 10일 4
 
7.1%
1일, 6일 3
 
5.4%
3일, 8일 2
 
3.6%
정기(5일,10일) 2
 
3.6%
정기(5일) 및 상설 1
 
1.8%
5일장 1
 
1.8%
Other values (5) 5
 
8.9%

Length

2024-03-14T08:51:20.930356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상설 21
27.3%
매일 9
11.7%
2일 5
 
6.5%
7일 5
 
6.5%
4일 4
 
5.2%
9일 4
 
5.2%
5일 4
 
5.2%
10일 4
 
5.2%
1일 3
 
3.9%
6일 3
 
3.9%
Other values (11) 15
19.5%

경도(WGS84좌표)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)100.0%
Missing10
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean119.39545
Minimum35.866121
Maximum136.43453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2024-03-14T08:51:21.111011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.866121
5-th percentile36.005954
Q1126.44499
median126.95063
Q3127.14372
95-th percentile127.88231
Maximum136.43453
Range100.56841
Interquartile range (IQR)0.69872987

Descriptive statistics

Standard deviation26.108499
Coefficient of variation (CV)0.21867248
Kurtosis7.3979565
Mean119.39545
Median Absolute Deviation (MAD)0.25126785
Skewness-2.9976285
Sum5492.1908
Variance681.65374
MonotonicityNot monotonic
2024-03-14T08:51:21.236938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
36.0045903 1
 
1.8%
126.9496753 1
 
1.8%
126.9584983 1
 
1.8%
126.9452773 1
 
1.8%
127.0537813 1
 
1.8%
126.9569344 1
 
1.8%
127.22 1
 
1.8%
127.18 1
 
1.8%
127.36 1
 
1.8%
128.05641 1
 
1.8%
Other values (36) 36
64.3%
(Missing) 10
 
17.9%
ValueCountFrequency (%)
35.8661205 1
1.8%
35.9694058 1
1.8%
36.0045903 1
1.8%
36.0100434 1
1.8%
126.414618 1
1.8%
126.415461 1
1.8%
126.423482 1
1.8%
126.42455 1
1.8%
126.42551 1
1.8%
126.431325 1
1.8%
ValueCountFrequency (%)
136.434533 1
1.8%
136.42174 1
1.8%
128.05641 1
1.8%
127.36 1
1.8%
127.22 1
1.8%
127.18 1
1.8%
127.1510817 1
1.8%
127.1510318 1
1.8%
127.1475776 1
1.8%
127.146978 1
1.8%

위도(WGS84좌표)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)97.8%
Missing10
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean43.766528
Minimum35.340025
Maximum127.84903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2024-03-14T08:51:21.349102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.340025
5-th percentile35.418982
Q135.583715
median35.821911
Q335.951689
95-th percentile127.65393
Maximum127.84903
Range92.509
Interquartile range (IQR)0.36797427

Descriptive statistics

Standard deviation26.201087
Coefficient of variation (CV)0.59865583
Kurtosis7.5135642
Mean43.766528
Median Absolute Deviation (MAD)0.2351165
Skewness3.0310641
Sum2013.2603
Variance686.49698
MonotonicityNot monotonic
2024-03-14T08:51:21.459227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
36.18 2
 
3.6%
127.6540485 1
 
1.8%
35.9425513 1
 
1.8%
35.9403894 1
 
1.8%
36.0810472 1
 
1.8%
36.0059938 1
 
1.8%
35.9916469 1
 
1.8%
35.9517135 1
 
1.8%
36.21 1
 
1.8%
36.430122 1
 
1.8%
Other values (35) 35
62.5%
(Missing) 10
 
17.9%
ValueCountFrequency (%)
35.340025 1
1.8%
35.371816 1
1.8%
35.414459 1
1.8%
35.43255 1
1.8%
35.444213 1
1.8%
35.458485 1
1.8%
35.520626 1
1.8%
35.564043 1
1.8%
35.5808 1
1.8%
35.582319 1
1.8%
ValueCountFrequency (%)
127.8490255 1
1.8%
127.7882957 1
1.8%
127.6540485 1
1.8%
127.6535573 1
1.8%
36.430122 1
1.8%
36.21 1
1.8%
36.18 2
3.6%
36.0810472 1
1.8%
36.0059938 1
1.8%
35.9916469 1
1.8%

점포수
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size580.0 B

취급품목
Text

Distinct33
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-03-14T08:51:21.610626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length11.589286
Min length2

Characters and Unicode

Total characters649
Distinct characters65
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)46.4%

Sample

1st row가구 주단 식당
2nd row의류 폐백 귀금속
3rd row농축산물
4th row농축산물
5th row생필품
ValueCountFrequency (%)
등 38
20.4%
잡화 17
 
9.1%
농수산물 14
 
7.5%
생선 14
 
7.5%
야채 9
 
4.8%
의류 9
 
4.8%
수산물 8
 
4.3%
식당 7
 
3.8%
생필품 7
 
3.8%
채소 6
 
3.2%
Other values (33) 57
30.6%
2024-03-14T08:51:21.858034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
131
20.2%
, 89
13.7%
등 41
 
6.3%
물 40
 
6.2%
산 33
 
5.1%
생 26
 
4.0%
농 25
 
3.9%
수 22
 
3.4%
잡 18
 
2.8%
화 18
 
2.8%
Other values (55) 206
31.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 429
66.1%
Space Separator 131
 
20.2%
Other Punctuation 89
 
13.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
등 41
 
9.6%
물 40
 
9.3%
산 33
 
7.7%
생 26
 
6.1%
농 25
 
5.8%
수 22
 
5.1%
잡 18
 
4.2%
화 18
 
4.2%
류 16
 
3.7%
채 15
 
3.5%
Other values (53) 175
40.8%
Space Separator
ValueCountFrequency (%)
131
100.0%
Other Punctuation
ValueCountFrequency (%)
, 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 429
66.1%
Common 220
33.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
등 41
 
9.6%
물 40
 
9.3%
산 33
 
7.7%
생 26
 
6.1%
농 25
 
5.8%
수 22
 
5.1%
잡 18
 
4.2%
화 18
 
4.2%
류 16
 
3.7%
채 15
 
3.5%
Other values (53) 175
40.8%
Common
ValueCountFrequency (%)
131
59.5%
, 89
40.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 429
66.1%
ASCII 220
33.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131
59.5%
, 89
40.5%
Hangul
ValueCountFrequency (%)
등 41
 
9.6%
물 40
 
9.3%
산 33
 
7.7%
생 26
 
6.1%
농 25
 
5.8%
수 22
 
5.1%
잡 18
 
4.2%
화 18
 
4.2%
류 16
 
3.7%
채 15
 
3.5%
Other values (53) 175
40.8%

사용가능상품권
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size580.0 B
온누리상품권
36 
온누리 상품권
15 
장수사랑상품권, 온누리상품권
 
3
<NA>
 
2

Length

Max length15
Median length6
Mean length6.6785714
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
온누리상품권 36
64.3%
온누리 상품권 15
26.8%
장수사랑상품권, 온누리상품권 3
 
5.4%
<NA> 2
 
3.6%

Length

2024-03-14T08:51:21.967481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:51:22.047867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
온누리상품권 39
52.7%
온누리 15
 
20.3%
상품권 15
 
20.3%
장수사랑상품권 3
 
4.1%
na 2
 
2.7%

홈페이지주소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
<NA>
26 
없음
11 
-
6 
n
3 
http://jbsj.kr/?m_code=jjnm
 
1
Other values (9)
9 

Length

Max length48
Median length45
Mean length7.9821429
Min length1

Unique

Unique10 ?
Unique (%)17.9%

Sample

1st rowhttp://jbsj.kr/?m_code=jjnm
2nd rowhttp://jbsj.kr/?m_code=jjja
3rd rowhttp://jbsj.kr/?m_code=jjmrn
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 26
46.4%
없음 11
19.6%
- 6
 
10.7%
n 3
 
5.4%
http://jbsj.kr/?m_code=jjnm 1
 
1.8%
http://jbsj.kr/?m_code=jjja 1
 
1.8%
http://jbsj.kr/?m_code=jjmrn 1
 
1.8%
http://jbsj.kr/?m_code=jjpnm 1
 
1.8%
http://jbsj.kr/?m_code=jjdm 1
 
1.8%
http://jbsj.kr/?m_code=jjkrr 1
 
1.8%
Other values (4) 4
 
7.1%

Length

2024-03-14T08:51:22.157984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 26
46.4%
없음 11
19.6%
6
 
10.7%
n 3
 
5.4%
http://jbsj.kr/?m_code=jjnm 1
 
1.8%
http://jbsj.kr/?m_code=jjja 1
 
1.8%
http://jbsj.kr/?m_code=jjmrn 1
 
1.8%
http://jbsj.kr/?m_code=jjpnm 1
 
1.8%
http://jbsj.kr/?m_code=jjdm 1
 
1.8%
http://jbsj.kr/?m_code=jjkrr 1
 
1.8%
Other values (4) 4
 
7.1%

공중화장실 보유여부
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size580.0 B
Y
26 
여
25 
y
3 
부
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Y 26
46.4%
여 25
44.6%
y 3
 
5.4%
부 2
 
3.6%

Length

2024-03-14T08:51:22.245560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:51:22.320671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 29
51.8%
여 25
44.6%
부 2
 
3.6%

주차장 보유여부
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size580.0 B
Y
22 
여
21 
부
6 
N
4 
y
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
Y 22
39.3%
여 21
37.5%
부 6
 
10.7%
N 4
 
7.1%
y 2
 
3.6%
n 1
 
1.8%

Length

2024-03-14T08:51:22.666972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:51:22.752679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 24
42.9%
여 21
37.5%
부 6
 
10.7%
n 5
 
8.9%

개설년도
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)5.4%
Memory size580.0 B

연락처
Text

MISSING 

Distinct33
Distinct (%)84.6%
Missing17
Missing (%)30.4%
Memory size580.0 B
2024-03-14T08:51:22.911186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.410256
Min length2

Characters and Unicode

Total characters406
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)79.5%

Sample

1st row063-284-1344
2nd row063-253-6535
3rd row063-278-5802
4th row063-274-4535
5th row063-288-6602
ValueCountFrequency (%)
없음 5
 
12.8%
063-640-2405 3
 
7.7%
625-1498 1
 
2.6%
063-855-1522 1
 
2.6%
063-855-9240 1
 
2.6%
063-857-2661 1
 
2.6%
625-4010 1
 
2.6%
632-5133 1
 
2.6%
063-284-1344 1
 
2.6%
063-430-2951 1
 
2.6%
Other values (23) 23
59.0%
2024-03-14T08:51:23.217663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 65
16.0%
0 55
13.5%
6 48
11.8%
3 48
11.8%
4 39
9.6%
2 38
9.4%
5 37
9.1%
8 24
 
5.9%
7 18
 
4.4%
1 13
 
3.2%
Other values (3) 21
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 331
81.5%
Dash Punctuation 65
 
16.0%
Other Letter 10
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55
16.6%
6 48
14.5%
3 48
14.5%
4 39
11.8%
2 38
11.5%
5 37
11.2%
8 24
7.3%
7 18
 
5.4%
1 13
 
3.9%
9 11
 
3.3%
Other Letter
ValueCountFrequency (%)
없 5
50.0%
음 5
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 396
97.5%
Hangul 10
 
2.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 65
16.4%
0 55
13.9%
6 48
12.1%
3 48
12.1%
4 39
9.8%
2 38
9.6%
5 37
9.3%
8 24
 
6.1%
7 18
 
4.5%
1 13
 
3.3%
Hangul
ValueCountFrequency (%)
없 5
50.0%
음 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396
97.5%
Hangul 10
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 65
16.4%
0 55
13.9%
6 48
12.1%
3 48
12.1%
4 39
9.8%
2 38
9.6%
5 37
9.3%
8 24
 
6.1%
7 18
 
4.5%
1 13
 
3.3%
Hangul
ValueCountFrequency (%)
없 5
50.0%
음 5
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
2015.09.30
56 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015.09.30
2nd row2015.09.30
3rd row2015.09.30
4th row2015.09.30
5th row2015.09.30

Common Values

ValueCountFrequency (%)
2015.09.30 56
100.0%

Length

2024-03-14T08:51:23.374798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:51:23.482931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015.09.30 56
100.0%

Interactions

2024-03-14T08:51:18.236004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:51:18.104987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:51:18.326981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:51:18.165825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T08:51:23.541891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군시장명시장유형소재지도로명주소소재지지번주소시장개설주기경도(WGS84좌표)위도(WGS84좌표)취급품목사용가능상품권홈페이지주소공중화장실 보유여부주차장 보유여부연락처
시군1.0000.9740.9471.0001.0000.7911.0001.0000.9951.0000.9610.9050.7891.000
시장명0.9741.0000.9481.0001.0000.9961.0001.0000.9490.9190.9010.9140.9740.972
시장유형0.9470.9481.0001.0001.0000.9340.1670.1670.9900.6500.2900.4860.0000.983
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시장개설주기0.7910.9960.9341.0001.0001.0000.6860.6860.8670.3460.0000.0000.2160.000
경도(WGS84좌표)1.0001.0000.1671.0001.0000.6861.0000.9761.0000.0001.0000.1300.3741.000
위도(WGS84좌표)1.0001.0000.1671.0001.0000.6860.9761.0001.0000.0001.0000.1300.3741.000
취급품목0.9950.9490.9901.0001.0000.8671.0001.0001.0001.0000.9720.8140.9730.981
사용가능상품권1.0000.9190.6501.0001.0000.3460.0000.0001.0001.0001.0000.6740.9321.000
홈페이지주소0.9610.9010.2901.0001.0000.0001.0001.0000.9721.0001.0000.8350.7261.000
공중화장실 보유여부0.9050.9140.4861.0001.0000.0000.1300.1300.8140.6740.8351.0000.9531.000
주차장 보유여부0.7890.9740.0001.0001.0000.2160.3740.3740.9730.9320.7260.9531.0000.289
연락처1.0000.9720.9831.0001.0000.0001.0001.0000.9811.0001.0001.0000.2891.000
2024-03-14T08:51:23.679210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공중화장실 보유여부주차장 보유여부시군사용가능상품권시장개설주기시장유형홈페이지주소
공중화장실 보유여부1.0000.8410.7490.6960.0000.2880.531
주차장 보유여부0.8411.0000.5290.6630.0700.0000.377
시군0.7490.5291.0000.9180.4470.5980.764
사용가능상품권0.6960.6630.9181.0000.1680.4430.793
시장개설주기0.0000.0700.4470.1681.0000.7110.000
시장유형0.2880.0000.5980.4430.7111.0000.046
홈페이지주소0.5310.3770.7640.7930.0000.0461.000
2024-03-14T08:51:23.788327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도(WGS84좌표)위도(WGS84좌표)시군시장유형시장개설주기사용가능상품권홈페이지주소공중화장실 보유여부주차장 보유여부
경도(WGS84좌표)1.0000.1420.9290.1400.4790.0000.7910.2090.242
위도(WGS84좌표)0.1421.0000.9290.1400.4790.0000.7910.2090.242
시군0.9290.9291.0000.5980.4470.9180.7640.7490.529
시장유형0.1400.1400.5981.0000.7110.4430.0460.2880.000
시장개설주기0.4790.4790.4470.7111.0000.1680.0000.0000.070
사용가능상품권0.0000.0000.9180.4430.1681.0000.7930.6960.663
홈페이지주소0.7910.7910.7640.0460.0000.7931.0000.5310.377
공중화장실 보유여부0.2090.2090.7490.2880.0000.6960.5311.0000.841
주차장 보유여부0.2420.2420.5290.0000.0700.6630.3770.8411.000

Missing values

2024-03-14T08:51:18.425885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T08:51:18.590211image/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.
2024-03-14T08:51:18.727875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군시장명시장유형소재지도로명주소소재지지번주소시장개설주기경도(WGS84좌표)위도(WGS84좌표)점포수취급품목사용가능상품권홈페이지주소공중화장실 보유여부주차장 보유여부개설년도연락처데이터기준일자
0전주시남부시장상설시장전주시 완산구 풍남문1길 19-3전주시 완산구 전동 295-4상설127.14757835.812765350가구 주단 식당온누리상품권http://jbsj.kr/?m_code=jjnmYY1977063-284-13442015.09.30
1전주시중앙상가상설시장전주시 완산구 태평3길 70전주시 완산구 태평동 36-1상설127.14247835.825202440의류 폐백 귀금속온누리상품권http://jbsj.kr/?m_code=jjjaYY1990063-253-65352015.09.30
2전주시모래내시장상설시장전주시 덕진구 모래내4길 8-8전주시 덕진구 인후동2가 203-10상설127.14388735.833543220농축산물온누리상품권http://jbsj.kr/?m_code=jjmrnYY1975063-278-58022015.09.30
3전주시신중앙시장상설시장전주시 완산구 태평5길 33전주시 완산구 태평동 43상설127.14320235.823841236농축산물온누리상품권<NA>YY2012063-274-45352015.09.30
4전주시동부시장상설시장전주시 완산구 충경로 109전주시 완산구 경원동3가 90-13상설127.15103235.8199816생필품온누리상품권<NA>YY1982<NA>2015.09.30
5전주시서부시장상점가상설시장전주시 완산구 효동2길 18전주시 완산구 효자동1가 201-2상설127.12664835.804892177생필품온누리상품권<NA>YY1982<NA>2015.09.30
6전주시풍남문상점가상설시장전주시 완산구 풍남문3길 19전주시 완산구 전동 149-2상설127.14697835.814612270주단 잡화온누리상품권http://jbsj.kr/?m_code=jjpnmYY2007063-288-66022015.09.30
7전주시동문상점가상설시장전주시 완산구 동문길 50전주시 완산구 풍남동1가 26-1상설127.15108235.81883165잡화온누리상품권http://jbsj.kr/?m_code=jjdmYN2007063-288-52882015.09.30
8전주시전주전자상가상설시장전주시 기린대로 317전주시 덕진구 진북동 320-28상설127.13855535.83230980전자 컴퓨터 음향기기온누리상품권http://jbsj.kr/?m_code=jjkrrYN2008063-254-55022015.09.30
9전주시전북대대학로상점가상설시장전주시 덕진구 권삼득로 285전주시 덕진구 금암동 664-55상설127.12971835.841966360식당 주류온누리상품권<NA>YN2015<NA>2015.09.30
시군시장명시장유형소재지도로명주소소재지지번주소시장개설주기경도(WGS84좌표)위도(WGS84좌표)점포수취급품목사용가능상품권홈페이지주소공중화장실 보유여부주차장 보유여부개설년도연락처데이터기준일자
46임실군오수시장사설시장임실군 오수면 오수로 159임실군 오수면 오수리 351-1정기(5일,10일)<NA><NA>23채소, 의류 등온누리상품권<NA>여여19846442--20072015.09.30
47순창군순창전통시장오일장순창군 순창읍 남계로 58순창군 순창읍 남계리 8001일, 6일127.14505535.371816188농수산물, 의류잡화, 생필품, 순대 등온누리상품권<NA>YY1923063-650-13222015.09.30
48고창군고창전통시장상설장 및 오일장전북 고창군 고창읍 동리로 62-11전북 고창군 고창읍 읍내리 367매일(3일, 8일)126.69859735.4325572수산물, 의류 등온누리 상품권-YY1965063-564-30972015.09.30
49고창군무장전통시장오일장전북 고창군 무장면 왕제산로 723전북 고창군 무장면 무장리 85-15일, 10일126.56040535.414459-건어물, 생선 등온누리 상품권-YY1965없음2015.09.30
50고창군흥덕전통시장오일장전북 고창군 흥덕면 흥덕시장길 3전북 고창군 흥덕면 흥덕리 239-14일, 9일126.7001235.520626-건어물, 생선 등온누리 상품권-YY1965없음2015.09.30
51고창군해리전통시장오일장전북 고창군 해리면 남시길 4전북 고창군 해리면 하련리 129-14일, 9일126.54209835.458485-건어물, 생선 등온누리 상품권-YN1965없음2015.09.30
52고창군대산전통시장오일장전북 고창군 공음대산로 935전북 고창군 대산면 매산리 200-132일, 7일126.59916135.340025-건어물, 생선 등온누리 상품권-YY1967없음2015.09.30
53고창군상하전통시장오일장전북 고창군 상하면 명동1길 3전북 고창군 상하면 하장리 936-41일, 6일126.49125135.444213-건어물, 생선 등온누리 상품권-YY1974없음2015.09.30
54부안군부안상설시장상설부안군 번영로 115-10부안군 부안읍 서외리 47매일<NA><NA>141농산물,생필품, 수산물, 잡화 등온누리상품권http://www.basj.kr/public/index.asp?mtid=buanYY1965<NA>2015.09.30
55부안군줄포상설시장상설부안군 부안로 865부안군 줄포면 줄포리 728-3매일<NA><NA>30농산물,생필품, 수산물, 잡화 등온누리상품권<NA>YY1965<NA>2015.09.30