Overview

Dataset statistics

Number of variables16
Number of observations28
Missing cells22
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory136.7 B

Variable types

Text5
Categorical5
Numeric4
Boolean2

Dataset

Description대구광역시_북구_전통시장_20190610
Author대구광역시 북구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15005373&dataSetDetailId=1500537328665a4139564_201909181408&provdMethod=FILE

Alerts

시장유형 has constant value ""Constant
시장개설주기 has constant value ""Constant
사용가능상품권 has constant value ""Constant
데이터기준일자 has constant value ""Constant
공중화장실보유여부 is highly overall correlated with 주차장보유여부High correlation
주차장보유여부 is highly overall correlated with 공중화장실보유여부High correlation
홈페이지주소 has 20 (71.4%) missing valuesMissing
전화번호 has 2 (7.1%) missing valuesMissing
시장명 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
소재지지번주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-04-17 19:22:24.515210
Analysis finished2024-04-17 19:22:26.297724
Duration1.78 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시장명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-18T04:22:26.409678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.9642857
Min length3

Characters and Unicode

Total characters139
Distinct characters51
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

Unique28 ?
Unique (%)100.0%

Sample

1st row칠성시장
2nd row삼성시장
3rd row경명시장
4th row팔달시장
5th row팔달신시장
ValueCountFrequency (%)
칠성시장 1
 
3.6%
삼성시장 1
 
3.6%
전기조명관 1
 
3.6%
전기재료관 1
 
3.6%
산업용재관 1
 
3.6%
전자관 1
 
3.6%
전자상가 1
 
3.6%
섬유제품관 1
 
3.6%
칠성전자주방시장 1
 
3.6%
칠성진시장 1
 
3.6%
Other values (18) 18
64.3%
2024-04-18T04:22:26.664170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 22
15.8%
장 22
15.8%
성 7
 
5.0%
칠 6
 
4.3%
전 6
 
4.3%
관 6
 
4.3%
대 5
 
3.6%
구 5
 
3.6%
자 3
 
2.2%
신 3
 
2.2%
Other values (41) 54
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 22
15.8%
장 22
15.8%
성 7
 
5.0%
칠 6
 
4.3%
전 6
 
4.3%
관 6
 
4.3%
대 5
 
3.6%
구 5
 
3.6%
자 3
 
2.2%
신 3
 
2.2%
Other values (41) 54
38.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 139
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 22
15.8%
장 22
15.8%
성 7
 
5.0%
칠 6
 
4.3%
전 6
 
4.3%
관 6
 
4.3%
대 5
 
3.6%
구 5
 
3.6%
자 3
 
2.2%
신 3
 
2.2%
Other values (41) 54
38.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 139
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 22
15.8%
장 22
15.8%
성 7
 
5.0%
칠 6
 
4.3%
전 6
 
4.3%
관 6
 
4.3%
대 5
 
3.6%
구 5
 
3.6%
자 3
 
2.2%
신 3
 
2.2%
Other values (41) 54
38.8%

시장유형
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
상설장
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상설장 28
100.0%

Length

2024-04-18T04:22:26.762750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:22:26.843364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상설장 28
100.0%

소재지도로명주소
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-18T04:22:26.994720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length17.857143
Min length15

Characters and Unicode

Total characters500
Distinct characters46
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

Unique28 ?
Unique (%)100.0%

Sample

1st row대구광역시 북구 칠성시장로 26
2nd row대구광역시 북구 칠성시장로 42
3rd row대구광역시 북구 칠성시장로3길 10-22
4th row대구광역시 북구 팔달로37길 11
5th row대구광역시 북구 팔달로33길 59
ValueCountFrequency (%)
대구광역시 28
25.2%
북구 28
25.2%
유통단지로 6
 
5.4%
칠성시장로 6
 
5.4%
34 2
 
1.8%
경진로1길 2
 
1.8%
45 1
 
0.9%
25 1
 
0.9%
50 1
 
0.9%
28-4 1
 
0.9%
Other values (35) 35
31.5%
2024-04-18T04:22:27.258664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83
16.6%
구 56
 
11.2%
시 36
 
7.2%
대 32
 
6.4%
역 28
 
5.6%
광 28
 
5.6%
로 28
 
5.6%
북 28
 
5.6%
1 19
 
3.8%
길 12
 
2.4%
Other values (36) 150
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 328
65.6%
Decimal Number 84
 
16.8%
Space Separator 83
 
16.6%
Dash Punctuation 5
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 56
17.1%
시 36
11.0%
대 32
9.8%
역 28
8.5%
광 28
8.5%
로 28
8.5%
북 28
8.5%
길 12
 
3.7%
칠 10
 
3.0%
성 9
 
2.7%
Other values (24) 61
18.6%
Decimal Number
ValueCountFrequency (%)
1 19
22.6%
2 11
13.1%
3 11
13.1%
5 9
10.7%
4 7
 
8.3%
8 6
 
7.1%
6 6
 
7.1%
0 6
 
7.1%
7 5
 
6.0%
9 4
 
4.8%
Space Separator
ValueCountFrequency (%)
83
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 328
65.6%
Common 172
34.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 56
17.1%
시 36
11.0%
대 32
9.8%
역 28
8.5%
광 28
8.5%
로 28
8.5%
북 28
8.5%
길 12
 
3.7%
칠 10
 
3.0%
성 9
 
2.7%
Other values (24) 61
18.6%
Common
ValueCountFrequency (%)
83
48.3%
1 19
 
11.0%
2 11
 
6.4%
3 11
 
6.4%
5 9
 
5.2%
4 7
 
4.1%
8 6
 
3.5%
6 6
 
3.5%
0 6
 
3.5%
7 5
 
2.9%
Other values (2) 9
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 328
65.6%
ASCII 172
34.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
83
48.3%
1 19
 
11.0%
2 11
 
6.4%
3 11
 
6.4%
5 9
 
5.2%
4 7
 
4.1%
8 6
 
3.5%
6 6
 
3.5%
0 6
 
3.5%
7 5
 
2.9%
Other values (2) 9
 
5.2%
Hangul
ValueCountFrequency (%)
구 56
17.1%
시 36
11.0%
대 32
9.8%
역 28
8.5%
광 28
8.5%
로 28
8.5%
북 28
8.5%
길 12
 
3.7%
칠 10
 
3.0%
성 9
 
2.7%
Other values (24) 61
18.6%

소재지지번주소
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-18T04:22:27.421905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length18.607143
Min length16

Characters and Unicode

Total characters521
Distinct characters41
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

Unique28 ?
Unique (%)100.0%

Sample

1st row대구광역시 북구 칠성동1가 276-110
2nd row대구광역시 북구 칠성동1가 276-108
3rd row대구광역시 북구 칠성동1가 99-5
4th row대구광역시 북구 노원동2가 375
5th row대구광역시 북구 노원동3가 750
ValueCountFrequency (%)
대구광역시 28
24.8%
북구 28
24.8%
칠성동1가 7
 
6.2%
산격동 7
 
6.2%
칠성동2가 3
 
2.7%
대현동 2
 
1.8%
복현동 2
 
1.8%
1626 1
 
0.9%
1665 1
 
0.9%
1746-3 1
 
0.9%
Other values (33) 33
29.2%
2024-04-18T04:22:27.668000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
16.3%
구 57
 
10.9%
1 31
 
6.0%
대 30
 
5.8%
동 28
 
5.4%
광 28
 
5.4%
역 28
 
5.4%
시 28
 
5.4%
북 28
 
5.4%
2 19
 
3.6%
Other values (31) 159
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 294
56.4%
Decimal Number 127
24.4%
Space Separator 85
 
16.3%
Dash Punctuation 13
 
2.5%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 57
19.4%
대 30
10.2%
동 28
9.5%
광 28
9.5%
역 28
9.5%
시 28
9.5%
북 28
9.5%
가 12
 
4.1%
성 10
 
3.4%
칠 10
 
3.4%
Other values (17) 35
11.9%
Decimal Number
ValueCountFrequency (%)
1 31
24.4%
2 19
15.0%
6 18
14.2%
7 11
 
8.7%
0 10
 
7.9%
9 9
 
7.1%
5 8
 
6.3%
3 8
 
6.3%
4 7
 
5.5%
8 6
 
4.7%
Space Separator
ValueCountFrequency (%)
85
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 294
56.4%
Common 227
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 57
19.4%
대 30
10.2%
동 28
9.5%
광 28
9.5%
역 28
9.5%
시 28
9.5%
북 28
9.5%
가 12
 
4.1%
성 10
 
3.4%
칠 10
 
3.4%
Other values (17) 35
11.9%
Common
ValueCountFrequency (%)
85
37.4%
1 31
 
13.7%
2 19
 
8.4%
6 18
 
7.9%
- 13
 
5.7%
7 11
 
4.8%
0 10
 
4.4%
9 9
 
4.0%
5 8
 
3.5%
3 8
 
3.5%
Other values (4) 15
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 294
56.4%
ASCII 227
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
37.4%
1 31
 
13.7%
2 19
 
8.4%
6 18
 
7.9%
- 13
 
5.7%
7 11
 
4.8%
0 10
 
4.4%
9 9
 
4.0%
5 8
 
3.5%
3 8
 
3.5%
Other values (4) 15
 
6.6%
Hangul
ValueCountFrequency (%)
구 57
19.4%
대 30
10.2%
동 28
9.5%
광 28
9.5%
역 28
9.5%
시 28
9.5%
북 28
9.5%
가 12
 
4.1%
성 10
 
3.4%
칠 10
 
3.4%
Other values (17) 35
11.9%

시장개설주기
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
매일
28 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
매일 28
100.0%

Length

2024-04-18T04:22:27.767125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:22:27.833153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매일 28
100.0%

위도
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.89613
Minimum35.875204
Maximum35.944911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-18T04:22:27.902996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.875204
5-th percentile35.87543
Q135.877614
median35.891636
Q335.906121
95-th percentile35.936854
Maximum35.944911
Range0.069707
Interquartile range (IQR)0.028506225

Descriptive statistics

Standard deviation0.020560744
Coefficient of variation (CV)0.00057278443
Kurtosis0.10882855
Mean35.89613
Median Absolute Deviation (MAD)0.0144153
Skewness0.94362037
Sum1005.0916
Variance0.00042274421
MonotonicityNot monotonic
2024-04-18T04:22:28.017667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
35.8765691 1
 
3.6%
35.9449107 1
 
3.6%
35.882294 1
 
3.6%
35.9056884 1
 
3.6%
35.9053688 1
 
3.6%
35.9039863 1
 
3.6%
35.9071846 1
 
3.6%
35.906071 1
 
3.6%
35.906269 1
 
3.6%
35.8802268 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
35.8752037 1
3.6%
35.8752635 1
3.6%
35.8757382 1
3.6%
35.8761622 1
3.6%
35.8765691 1
3.6%
35.8769713 1
3.6%
35.8772404 1
3.6%
35.8777389 1
3.6%
35.8802268 1
3.6%
35.8811819 1
3.6%
ValueCountFrequency (%)
35.9449107 1
3.6%
35.9423746 1
3.6%
35.9266003 1
3.6%
35.9216539 1
3.6%
35.9213543 1
3.6%
35.9071846 1
3.6%
35.906269 1
3.6%
35.906071 1
3.6%
35.9056884 1
3.6%
35.9053688 1
3.6%

경도
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.59544
Minimum128.5449
Maximum128.61994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-18T04:22:28.117932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.5449
5-th percentile128.54859
Q1128.5996
median128.60402
Q3128.60772
95-th percentile128.61616
Maximum128.61994
Range0.0750404
Interquartile range (IQR)0.00811155

Descriptive statistics

Standard deviation0.022153648
Coefficient of variation (CV)0.00017227398
Kurtosis0.62963597
Mean128.59544
Median Absolute Deviation (MAD)0.00387385
Skewness-1.418738
Sum3600.6723
Variance0.00049078412
MonotonicityNot monotonic
2024-04-18T04:22:28.226726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
128.6042994 1
 
3.6%
128.5472256 1
 
3.6%
128.600739 1
 
3.6%
128.608262 1
 
3.6%
128.607534 1
 
3.6%
128.6058815 1
 
3.6%
128.6070281 1
 
3.6%
128.605496 1
 
3.6%
128.609657 1
 
3.6%
128.601538 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
128.544899 1
3.6%
128.5472256 1
3.6%
128.5511118 1
3.6%
128.5541561 1
3.6%
128.5659881 1
3.6%
128.570852 1
3.6%
128.5962008 1
3.6%
128.600739 1
3.6%
128.6009707 1
3.6%
128.601538 1
3.6%
ValueCountFrequency (%)
128.6199394 1
3.6%
128.6191994 1
3.6%
128.6105282 1
3.6%
128.6103725 1
3.6%
128.609657 1
3.6%
128.6089517 1
3.6%
128.608262 1
3.6%
128.607534 1
3.6%
128.6070281 1
3.6%
128.6058815 1
3.6%

점포수
Real number (ℝ)

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.03571
Minimum19
Maximum1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-18T04:22:28.358179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile32.1
Q160.75
median106
Q3228.25
95-th percentile795.85
Maximum1011
Range992
Interquartile range (IQR)167.5

Descriptive statistics

Standard deviation264.63805
Coefficient of variation (CV)1.23642
Kurtosis2.9226575
Mean214.03571
Median Absolute Deviation (MAD)49.5
Skewness1.9475236
Sum5993
Variance70033.295
MonotonicityNot monotonic
2024-04-18T04:22:28.450344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
120 2
 
7.1%
338 1
 
3.6%
52 1
 
3.6%
75 1
 
3.6%
83 1
 
3.6%
848 1
 
3.6%
1011 1
 
3.6%
369 1
 
3.6%
699 1
 
3.6%
71 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
19 1
3.6%
30 1
3.6%
36 1
3.6%
47 1
3.6%
52 1
3.6%
56 1
3.6%
57 1
3.6%
62 1
3.6%
70 1
3.6%
71 1
3.6%
ValueCountFrequency (%)
1011 1
3.6%
848 1
3.6%
699 1
3.6%
650 1
3.6%
369 1
3.6%
338 1
3.6%
265 1
3.6%
216 1
3.6%
145 1
3.6%
139 1
3.6%

취급품목
Categorical

Distinct13
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
농산물, 생필품, 수산물, 잡화 등
14 
의류,잡화
2 
농산물
2 
생선류
 
1
잡화
 
1
Other values (8)
8 

Length

Max length25
Median length24
Mean length16.035714
Min length2

Unique

Unique10 ?
Unique (%)35.7%

Sample

1st row농산물, 생필품, 수산물, 잡화 등
2nd row농산물, 생필품, 수산물, 잡화 등
3rd row생선류
4th row농산물, 생필품, 수산물, 잡화 등
5th row농산물, 생필품, 수산물, 잡화 등

Common Values

ValueCountFrequency (%)
농산물, 생필품, 수산물, 잡화 등 14
50.0%
의류,잡화 2
 
7.1%
농산물 2
 
7.1%
생선류 1
 
3.6%
잡화 1
 
3.6%
전자기기, 주방기기 등 1
 
3.6%
커튼, 침구, 그릇, 귀금속, 직물, 가구 등 1
 
3.6%
가전제품, 음향기기, 컴퓨터 소모품 등 1
 
3.6%
TV, 디지털카메라, 에어컨 등 종합가전 1
 
3.6%
기계공구, 농업용 기자재, 자동차 부품 등 1
 
3.6%
Other values (3) 3
 
10.7%

Length

2024-04-18T04:22:28.547932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등 22
19.0%
농산물 16
13.8%
잡화 16
13.8%
생필품 15
12.9%
수산물 14
12.1%
전기재료 2
 
1.7%
의류,잡화 2
 
1.7%
기계공구 1
 
0.9%
농업용 1
 
0.9%
기자재 1
 
0.9%
Other values (26) 26
22.4%

사용가능상품권
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
온누리상품권
28 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
온누리상품권 28
100.0%

Length

2024-04-18T04:22:28.634266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:22:28.700924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
온누리상품권 28
100.0%

홈페이지주소
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing20
Missing (%)71.4%
Memory size356.0 B
2024-04-18T04:22:28.817183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length22
Mean length22
Min length16

Characters and Unicode

Total characters176
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st rowhttp://www.chilseongmarket.co.kr
2nd rowhttp://www.paldal.org
3rd rowhttp://www.texvile.com
4th rowhttp://www.eshop.or.kr
5th rowhttp://www.ezone.or.kr
ValueCountFrequency (%)
http://www.chilseongmarket.co.kr 1
12.5%
http://www.paldal.org 1
12.5%
http://www.texvile.com 1
12.5%
http://www.eshop.or.kr 1
12.5%
http://www.ezone.or.kr 1
12.5%
http://www.dgtool.com 1
12.5%
http://www.dgemc.com 1
12.5%
http://dgelc.com 1
12.5%
2024-04-18T04:22:29.066326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 21
11.9%
t 19
10.8%
. 18
10.2%
/ 16
 
9.1%
o 13
 
7.4%
h 10
 
5.7%
p 10
 
5.7%
e 9
 
5.1%
c 8
 
4.5%
: 8
 
4.5%
Other values (13) 44
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 134
76.1%
Other Punctuation 42
 
23.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 21
15.7%
t 19
14.2%
o 13
9.7%
h 10
 
7.5%
p 10
 
7.5%
e 9
 
6.7%
c 8
 
6.0%
r 7
 
5.2%
l 6
 
4.5%
m 6
 
4.5%
Other values (10) 25
18.7%
Other Punctuation
ValueCountFrequency (%)
. 18
42.9%
/ 16
38.1%
: 8
19.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 134
76.1%
Common 42
 
23.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 21
15.7%
t 19
14.2%
o 13
9.7%
h 10
 
7.5%
p 10
 
7.5%
e 9
 
6.7%
c 8
 
6.0%
r 7
 
5.2%
l 6
 
4.5%
m 6
 
4.5%
Other values (10) 25
18.7%
Common
ValueCountFrequency (%)
. 18
42.9%
/ 16
38.1%
: 8
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 21
11.9%
t 19
10.8%
. 18
10.2%
/ 16
 
9.1%
o 13
 
7.4%
h 10
 
5.7%
p 10
 
5.7%
e 9
 
5.1%
c 8
 
4.5%
: 8
 
4.5%
Other values (13) 44
25.0%

공중화장실보유여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size160.0 B
True
20 
False
8 
ValueCountFrequency (%)
True 20
71.4%
False 8
 
28.6%
2024-04-18T04:22:29.147878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

주차장보유여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size160.0 B
True
15 
False
13 
ValueCountFrequency (%)
True 15
53.6%
False 13
46.4%
2024-04-18T04:22:29.207377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

개설년도
Real number (ℝ)

Distinct16
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2000.4286
Minimum1969
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-18T04:22:29.528957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1969
5-th percentile1970.05
Q11979.75
median2009
Q32015.5
95-th percentile2017
Maximum2017
Range48
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation18.270455
Coefficient of variation (CV)0.0091332703
Kurtosis-1.2603555
Mean2000.4286
Median Absolute Deviation (MAD)8
Skewness-0.75597727
Sum56012
Variance333.80952
MonotonicityNot monotonic
2024-04-18T04:22:29.607544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2017 7
25.0%
1976 3
10.7%
2009 2
 
7.1%
1969 2
 
7.1%
2010 2
 
7.1%
2007 2
 
7.1%
1974 1
 
3.6%
2015 1
 
3.6%
2012 1
 
3.6%
2011 1
 
3.6%
Other values (6) 6
21.4%
ValueCountFrequency (%)
1969 2
7.1%
1972 1
 
3.6%
1974 1
 
3.6%
1976 3
10.7%
1981 1
 
3.6%
1983 1
 
3.6%
2005 1
 
3.6%
2007 2
7.1%
2008 1
 
3.6%
2009 2
7.1%
ValueCountFrequency (%)
2017 7
25.0%
2015 1
 
3.6%
2014 1
 
3.6%
2012 1
 
3.6%
2011 1
 
3.6%
2010 2
 
7.1%
2009 2
 
7.1%
2008 1
 
3.6%
2007 2
 
7.1%
2005 1
 
3.6%

전화번호
Text

MISSING 

Distinct25
Distinct (%)96.2%
Missing2
Missing (%)7.1%
Memory size356.0 B
2024-04-18T04:22:29.757367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters312
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

Unique24 ?
Unique (%)92.3%

Sample

1st row053-423-3480
2nd row053-425-4073
3rd row053-257-9558
4th row053-357-8715
5th row053-357-4953
ValueCountFrequency (%)
053-257-9558 2
 
7.7%
053-322-8260 1
 
3.8%
053-423-3480 1
 
3.8%
053-953-0599 1
 
3.8%
053-604-6114 1
 
3.8%
053-604-4114 1
 
3.8%
053-604-0700 1
 
3.8%
053-604-2000 1
 
3.8%
053-604-2011 1
 
3.8%
053-601-1500 1
 
3.8%
Other values (15) 15
57.7%
2024-04-18T04:22:30.000286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 53
17.0%
- 52
16.7%
5 50
16.0%
3 44
14.1%
4 25
8.0%
2 22
7.1%
1 18
 
5.8%
7 15
 
4.8%
9 12
 
3.8%
6 11
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260
83.3%
Dash Punctuation 52
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53
20.4%
5 50
19.2%
3 44
16.9%
4 25
9.6%
2 22
8.5%
1 18
 
6.9%
7 15
 
5.8%
9 12
 
4.6%
6 11
 
4.2%
8 10
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53
17.0%
- 52
16.7%
5 50
16.0%
3 44
14.1%
4 25
8.0%
2 22
7.1%
1 18
 
5.8%
7 15
 
4.8%
9 12
 
3.8%
6 11
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53
17.0%
- 52
16.7%
5 50
16.0%
3 44
14.1%
4 25
8.0%
2 22
7.1%
1 18
 
5.8%
7 15
 
4.8%
9 12
 
3.8%
6 11
 
3.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2019-06-10
28 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-06-10
2nd row2019-06-10
3rd row2019-06-10
4th row2019-06-10
5th row2019-06-10

Common Values

ValueCountFrequency (%)
2019-06-10 28
100.0%

Length

2024-04-18T04:22:30.108051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:22:30.176724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-06-10 28
100.0%

Interactions

2024-04-18T04:22:25.715988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:24.916547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.180495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.457134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.780287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:24.984013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.248809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.521928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.845434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.054818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.329183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.598382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.903964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.118089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.395767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:22:25.657297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T04:22:30.228203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장명소재지도로명주소소재지지번주소위도경도점포수취급품목홈페이지주소공중화장실보유여부주차장보유여부개설년도전화번호
시장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0000.9850.0000.0001.0000.1750.5280.4961.000
경도1.0001.0001.0000.9851.0000.0000.0001.0000.0000.6470.0001.000
점포수1.0001.0001.0000.0000.0001.0000.7361.0000.0000.2540.0000.960
취급품목1.0001.0001.0000.0000.0000.7361.0001.0000.1340.0000.4520.931
홈페이지주소1.0001.0001.0001.0001.0001.0001.0001.000NaNNaN1.0001.000
공중화장실보유여부1.0001.0001.0000.1750.0000.0000.134NaN1.0000.7900.5610.000
주차장보유여부1.0001.0001.0000.5280.6470.2540.000NaN0.7901.0000.7730.000
개설년도1.0001.0001.0000.4960.0000.0000.4521.0000.5610.7731.0000.000
전화번호1.0001.0001.0001.0001.0000.9600.9311.0000.0000.0000.0001.000
2024-04-18T04:22:30.332384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주차장보유여부취급품목공중화장실보유여부
주차장보유여부1.0000.0000.579
취급품목0.0001.0000.000
공중화장실보유여부0.5790.0001.000
2024-04-18T04:22:30.399749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도점포수개설년도취급품목공중화장실보유여부주차장보유여부
위도1.000-0.164-0.0560.4740.0000.0610.338
경도-0.1641.0000.4520.1570.0000.0000.424
점포수-0.0560.4521.0000.2250.3650.0000.228
개설년도0.4740.1570.2251.0000.0000.4090.479
취급품목0.0000.0000.3650.0001.0000.0000.000
공중화장실보유여부0.0610.0000.0000.4090.0001.0000.579
주차장보유여부0.3380.4240.2280.4790.0000.5791.000

Missing values

2024-04-18T04:22:25.994534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T04:22:26.163682image/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.
2024-04-18T04:22:26.260182image/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

시장명시장유형소재지도로명주소소재지지번주소시장개설주기위도경도점포수취급품목사용가능상품권홈페이지주소공중화장실보유여부주차장보유여부개설년도전화번호데이터기준일자
0칠성시장상설장대구광역시 북구 칠성시장로 26대구광역시 북구 칠성동1가 276-110매일35.876569128.604299338농산물, 생필품, 수산물, 잡화 등온누리상품권http://www.chilseongmarket.co.krYY1974053-423-34802019-06-10
1삼성시장상설장대구광역시 북구 칠성시장로 42대구광역시 북구 칠성동1가 276-108매일35.877739128.60360670농산물, 생필품, 수산물, 잡화 등온누리상품권<NA>YY1976053-425-40732019-06-10
2경명시장상설장대구광역시 북구 칠성시장로3길 10-22대구광역시 북구 칠성동1가 99-5매일35.875204128.60426747생선류온누리상품권<NA>YY1972053-257-95582019-06-10
3팔달시장상설장대구광역시 북구 팔달로37길 11대구광역시 북구 노원동2가 375매일35.889035128.570852120농산물, 생필품, 수산물, 잡화 등온누리상품권<NA>YN1969053-357-87152019-06-10
4팔달신시장상설장대구광역시 북구 팔달로33길 59대구광역시 북구 노원동3가 750매일35.890704128.565988650농산물, 생필품, 수산물, 잡화 등온누리상품권http://www.paldal.orgYY1976053-357-49532019-06-10
5대성시장상설장대구광역시 북구 공평로135대구광역시 북구 칠성동2가 302-95매일35.876162128.60097119잡화온누리상품권<NA>NN1976053-425-85442019-06-10
6복현종합시장상설장대구광역시 북구 경진로1길 78대구광역시 북구 복현동 462매일35.892568128.619939265농산물, 생필품, 수산물, 잡화 등온누리상품권<NA>YN1983<NA>2019-06-10
7산격종합시장상설장대구광역시 북구 대동로1길 34대구광역시 북구 산격동 1258매일35.898421128.610528216농산물, 생필품, 수산물, 잡화 등온누리상품권<NA>YY1981053-959-85962019-06-10
8동대구시장상설장대구광역시 북구 대현로20길 30대구광역시 북구 대현동 390매일35.881182128.60895273의류,잡화온누리상품권<NA>YY1969053-952-12232019-06-10
9칠곡시장상설장대구광역시 북구 칠곡중앙대로 526대구광역시 북구 읍내동 1201-1(외 3필)매일35.942375128.55111262농산물, 생필품, 수산물, 잡화 등온누리상품권<NA>YY2014053-321-80702019-06-10
시장명시장유형소재지도로명주소소재지지번주소시장개설주기위도경도점포수취급품목사용가능상품권홈페이지주소공중화장실보유여부주차장보유여부개설년도전화번호데이터기준일자
18복현전통신시장상설장대구광역시 북구 경진로1길 68대구광역시 북구 복현동 457-8매일35.89327128.61919956농산물, 생필품, 수산물, 잡화 등온누리상품권<NA>NN2011<NA>2019-06-10
19칠성진시장상설장대구광역시 북구 칠성시장로 19대구광역시 북구 칠성동1가 89매일35.875738128.604084130농산물, 생필품, 수산물, 잡화 등온누리상품권<NA>NN2012053-257-95582019-06-10
20칠성전자주방시장상설장대구광역시 북구 칠성시장로 73-1대구광역시 북구 칠성동2가 409-711매일35.880227128.60153871전자기기, 주방기기 등온누리상품권<NA>NN2017053-424-45722019-06-10
21섬유제품관상설장대구광역시 북구 유통단지로 60대구광역시 북구 산격동 1667매일35.906269128.609657699커튼, 침구, 그릇, 귀금속, 직물, 가구 등온누리상품권http://www.texvile.comYY2017053-601-15002019-06-10
22전자상가상설장대구광역시 북구 유통단지로 25대구광역시 북구 산격동 1626매일35.906071128.605496369가전제품, 음향기기, 컴퓨터 소모품 등온누리상품권http://www.eshop.or.krYY2017053-604-20112019-06-10
23전자관상설장대구광역시 북구 유통단지로 45대구광역시 북구 산격동 1621매일35.907185128.6070281011TV, 디지털카메라, 에어컨 등 종합가전온누리상품권http://www.ezone.or.krYY2017053-604-20002019-06-10
24산업용재관상설장대구광역시 북구 유통단지로 16대구광역시 북구 산격동 1629매일35.903986128.605882848기계공구, 농업용 기자재, 자동차 부품 등온누리상품권http://www.dgtool.comYY2017053-604-07002019-06-10
25전기재료관상설장대구광역시 북구 유통단지로 38대구광역시 북구 산격동 1665매일35.905369128.607534120전기재료, 조명기구, 반도체, 전자부품 등온누리상품권http://www.dgemc.comYY2017053-604-41142019-06-10
26전기조명관상설장대구광역시 북구 유통단지로 50대구광역시 북구 산격동 1666매일35.905688128.60826283전기조명, 특수조명, 관련 전기재료 등온누리상품권http://dgelc.comYY2017053-604-61142019-06-10
27칠성본시장상설장대구광역시 북구 칠성시장로 97-1대구광역시 북구 칠성동2가 400-17매일35.882294128.60073975농수산물, 생필품, 잡화 등온누리상품권<NA>NN2015053-253-77052019-06-10