Overview

Dataset statistics

Number of variables5
Number of observations24
Missing cells5
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory46.5 B

Variable types

Numeric1
Text3
Categorical1

Dataset

Description인천광역시 부평구 시장 현황 데이터는 시장의 명칭, 시장의 유형, 시장의 위치, 시장의 연락처에 대한 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15104143/fileData.do

Alerts

연번 is highly overall correlated with 유형High correlation
유형 is highly overall correlated with 연번High correlation
연락처 has 5 (20.8%) missing valuesMissing
연번 has unique valuesUnique
명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:25:35.808580
Analysis finished2023-12-12 00:25:36.307267
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T09:25:36.385474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2023-12-12T09:25:36.511871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

명칭
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T09:25:36.754925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length6.9583333
Min length4

Characters and Unicode

Total characters167
Distinct characters64
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

Unique24 ?
Unique (%)100.0%

Sample

1st row진흥종합시장
2nd row일신시장
3rd row열우물전통시장
4th row부평종합시장
5th row부평문화의거리
ValueCountFrequency (%)
롯데마트 3
 
10.3%
부평점 3
 
10.3%
진흥종합시장 1
 
3.4%
부평시장로타리지하상가 1
 
3.4%
경남종합상가 1
 
3.4%
부평역점 1
 
3.4%
삼산점 1
 
3.4%
모다백화점 1
 
3.4%
부평역사쇼핑몰 1
 
3.4%
아이즈빌아울렛 1
 
3.4%
Other values (15) 15
51.7%
2023-12-12T09:25:37.095702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 16
 
9.6%
평 15
 
9.0%
시 9
 
5.4%
장 9
 
5.4%
가 6
 
3.6%
점 6
 
3.6%
상 5
 
3.0%
5
 
3.0%
지 4
 
2.4%
종 4
 
2.4%
Other values (54) 88
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 158
94.6%
Space Separator 5
 
3.0%
Decimal Number 4
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 16
 
10.1%
평 15
 
9.5%
시 9
 
5.7%
장 9
 
5.7%
가 6
 
3.8%
점 6
 
3.8%
상 5
 
3.2%
지 4
 
2.5%
종 4
 
2.5%
합 4
 
2.5%
Other values (50) 80
50.6%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
2 1
25.0%
1 1
25.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 158
94.6%
Common 9
 
5.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 16
 
10.1%
평 15
 
9.5%
시 9
 
5.7%
장 9
 
5.7%
가 6
 
3.8%
점 6
 
3.8%
상 5
 
3.2%
지 4
 
2.5%
종 4
 
2.5%
합 4
 
2.5%
Other values (50) 80
50.6%
Common
ValueCountFrequency (%)
5
55.6%
0 2
 
22.2%
2 1
 
11.1%
1 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 158
94.6%
ASCII 9
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 16
 
10.1%
평 15
 
9.5%
시 9
 
5.7%
장 9
 
5.7%
가 6
 
3.8%
점 6
 
3.8%
상 5
 
3.2%
지 4
 
2.5%
종 4
 
2.5%
합 4
 
2.5%
Other values (50) 80
50.6%
ASCII
ValueCountFrequency (%)
5
55.6%
0 2
 
22.2%
2 1
 
11.1%
1 1
 
11.1%

유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
전통시장
8 
대규모점포
8 
상점가
4 
지하상가
4 

Length

Max length5
Median length4.5
Mean length4.1666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전통시장 8
33.3%
대규모점포 8
33.3%
상점가 4
16.7%
지하상가 4
16.7%

Length

2023-12-12T09:25:37.271417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:37.398996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전통시장 8
33.3%
대규모점포 8
33.3%
상점가 4
16.7%
지하상가 4
16.7%

위치
Text

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T09:25:37.607709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length19.458333
Min length16

Characters and Unicode

Total characters467
Distinct characters43
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

Unique22 ?
Unique (%)91.7%

Sample

1st row인천광역시 부평구 부흥로304번길 27
2nd row인천광역시 부평구 경인로 1106
3rd row인천광역시 부평구 배곶남로21번길 18
4th row인천광역시 부평구 주부토로22번길 29-10
5th row인천광역시 부평구 부평문화로80번길 6-7
ValueCountFrequency (%)
인천광역시 24
25.3%
부평구 24
25.3%
마장로 2
 
2.1%
부평대로지하 2
 
2.1%
시장로지하 2
 
2.1%
27 2
 
2.1%
7 2
 
2.1%
16 2
 
2.1%
광장로 2
 
2.1%
18 1
 
1.1%
Other values (32) 32
33.7%
2023-12-12T09:25:38.178889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
15.4%
부 34
 
7.3%
평 30
 
6.4%
시 27
 
5.8%
광 26
 
5.6%
인 25
 
5.4%
구 24
 
5.1%
천 24
 
5.1%
로 24
 
5.1%
역 24
 
5.1%
Other values (33) 157
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 307
65.7%
Decimal Number 83
 
17.8%
Space Separator 72
 
15.4%
Dash Punctuation 5
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 34
11.1%
평 30
9.8%
시 27
8.8%
광 26
8.5%
인 25
8.1%
구 24
7.8%
천 24
7.8%
로 24
7.8%
역 24
7.8%
길 11
 
3.6%
Other values (21) 58
18.9%
Decimal Number
ValueCountFrequency (%)
2 19
22.9%
1 13
15.7%
6 10
12.0%
3 9
10.8%
7 9
10.8%
0 8
9.6%
8 5
 
6.0%
9 4
 
4.8%
4 4
 
4.8%
5 2
 
2.4%
Space Separator
ValueCountFrequency (%)
72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 307
65.7%
Common 160
34.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 34
11.1%
평 30
9.8%
시 27
8.8%
광 26
8.5%
인 25
8.1%
구 24
7.8%
천 24
7.8%
로 24
7.8%
역 24
7.8%
길 11
 
3.6%
Other values (21) 58
18.9%
Common
ValueCountFrequency (%)
72
45.0%
2 19
 
11.9%
1 13
 
8.1%
6 10
 
6.2%
3 9
 
5.6%
7 9
 
5.6%
0 8
 
5.0%
- 5
 
3.1%
8 5
 
3.1%
9 4
 
2.5%
Other values (2) 6
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 307
65.7%
ASCII 160
34.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
45.0%
2 19
 
11.9%
1 13
 
8.1%
6 10
 
6.2%
3 9
 
5.6%
7 9
 
5.6%
0 8
 
5.0%
- 5
 
3.1%
8 5
 
3.1%
9 4
 
2.5%
Other values (2) 6
 
3.8%
Hangul
ValueCountFrequency (%)
부 34
11.1%
평 30
9.8%
시 27
8.8%
광 26
8.5%
인 25
8.1%
구 24
7.8%
천 24
7.8%
로 24
7.8%
역 24
7.8%
길 11
 
3.6%
Other values (21) 58
18.9%

연락처
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing5
Missing (%)20.8%
Memory size324.0 B
2023-12-12T09:25:38.476095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.052632
Min length12

Characters and Unicode

Total characters229
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row032-505-9432
2nd row032-505-5732
3rd row032-421-6202
4th row032-516-0655
5th row032-511-6890
ValueCountFrequency (%)
032-505-9432 1
 
5.3%
032-270-2000 1
 
5.3%
032-523-9991 1
 
5.3%
032-505-6884 1
 
5.3%
032-454-2500 1
 
5.3%
032-509-2500 1
 
5.3%
032-363-2500 1
 
5.3%
032-280-8887 1
 
5.3%
032-515-0151 1
 
5.3%
032-523-9992 1
 
5.3%
Other values (9) 9
47.4%
2023-12-12T09:25:39.042366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 46
20.1%
2 38
16.6%
- 38
16.6%
3 28
12.2%
5 25
10.9%
9 13
 
5.7%
1 11
 
4.8%
7 9
 
3.9%
8 8
 
3.5%
6 7
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 191
83.4%
Dash Punctuation 38
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46
24.1%
2 38
19.9%
3 28
14.7%
5 25
13.1%
9 13
 
6.8%
1 11
 
5.8%
7 9
 
4.7%
8 8
 
4.2%
6 7
 
3.7%
4 6
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46
20.1%
2 38
16.6%
- 38
16.6%
3 28
12.2%
5 25
10.9%
9 13
 
5.7%
1 11
 
4.8%
7 9
 
3.9%
8 8
 
3.5%
6 7
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46
20.1%
2 38
16.6%
- 38
16.6%
3 28
12.2%
5 25
10.9%
9 13
 
5.7%
1 11
 
4.8%
7 9
 
3.9%
8 8
 
3.5%
6 7
 
3.1%

Interactions

2023-12-12T09:25:36.030807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:25:39.180871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번명칭유형위치연락처
연번1.0001.0000.9400.9191.000
명칭1.0001.0001.0001.0001.000
유형0.9401.0001.0001.0001.000
위치0.9191.0001.0001.0001.000
연락처1.0001.0001.0001.0001.000
2023-12-12T09:25:39.347366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번유형
연번1.0000.713
유형0.7131.000

Missing values

2023-12-12T09:25:36.150860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:25:36.268210image/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

연번명칭유형위치연락처
01진흥종합시장전통시장인천광역시 부평구 부흥로304번길 27032-505-9432
12일신시장전통시장인천광역시 부평구 경인로 1106032-505-5732
23열우물전통시장전통시장인천광역시 부평구 배곶남로21번길 18032-421-6202
34부평종합시장전통시장인천광역시 부평구 주부토로22번길 29-10032-516-0655
45부평문화의거리전통시장인천광역시 부평구 부평문화로80번길 6-7032-511-6890
56부평깡시장전통시장인천광역시 부평구 시장로79번길 36-3032-502-7180
67삼산시장전통시장인천광역시 부평구 후정동로25번길 8<NA>
78갈산시장전통시장인천광역시 부평구 주부토로262번길 13<NA>
89부평테마의거리상점가인천광역시 부평구 경원대로403번길 20-1070-7333-9291
910부평일번가상점가인천광역시 부평구 부평문화로66번길 7<NA>
연번명칭유형위치연락처
1415부평중앙지하상가지하상가인천광역시 부평구 시장로지하 10032-523-9992
1516부평역지하상가지하상가인천광역시 부평구 부평대로지하 7032-523-9991
1617아이즈빌아울렛대규모점포인천광역시 부평구 마장로 489032-270-2000
1718부평역사쇼핑몰대규모점포인천광역시 부평구 광장로 16032-515-0151
1819모다백화점 부평점대규모점포인천광역시 부평구 부평문화로 35032-280-8887
1920롯데마트 삼산점대규모점포인천광역시 부평구 길주로 623032-363-2500
2021롯데마트 부평점대규모점포인천광역시 부평구 마장로 296032-509-2500
2122롯데마트 부평역점대규모점포인천광역시 부평구 광장로 16032-454-2500
2223경남종합상가대규모점포인천광역시 부평구 안남로222번길 27032-505-6884
23242001아울렛 부평점대규모점포인천광역시 부평구 경원대로 1277032-573-2001