Overview

Dataset statistics

Number of variables16
Number of observations455
Missing cells511
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.8 KiB
Average record size in memory132.3 B

Variable types

Numeric4
Text5
Categorical1
Boolean6

Dataset

Description대구광역시 북구_팔달신시장 상점정보 데이터_20221212
Author대구광역시 북구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15109919&dataSetDetailId=15109919186dc74cb5ddd&provdMethod=FILE

Alerts

상점 홈페이지 주소 has constant value ""Constant
상점코드 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 상점코드High correlation
전통시장 상가번영회 가입유무 is highly overall correlated with 온누리 상품권 사용유무High correlation
온누리 상품권 사용유무 is highly overall correlated with 전통시장 상가번영회 가입유무High correlation
문화상품권 사용유무 is highly imbalanced (91.3%)Imbalance
전자상품권 사용유무 is highly imbalanced (61.7%)Imbalance
대표품목 has 57 (12.5%) missing valuesMissing
상점 홈페이지 주소 has 454 (99.8%) missing valuesMissing
상점코드 has unique valuesUnique

Reproduction

Analysis started2024-04-17 19:15:02.623645
Analysis finished2024-04-17 19:15:04.918955
Duration2.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상점코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct455
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.94945
Minimum1
Maximum523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-18T04:15:04.973175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23.7
Q1144.5
median272
Q3403.5
95-th percentile500.3
Maximum523
Range522
Interquartile range (IQR)259

Descriptive statistics

Standard deviation151.1168
Coefficient of variation (CV)0.56187806
Kurtosis-1.1638882
Mean268.94945
Median Absolute Deviation (MAD)130
Skewness-0.028984071
Sum122372
Variance22836.286
MonotonicityStrictly increasing
2024-04-18T04:15:05.077362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
364 1
 
0.2%
362 1
 
0.2%
361 1
 
0.2%
360 1
 
0.2%
359 1
 
0.2%
358 1
 
0.2%
357 1
 
0.2%
356 1
 
0.2%
355 1
 
0.2%
Other values (445) 445
97.8%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
523 1
0.2%
522 1
0.2%
521 1
0.2%
520 1
0.2%
519 1
0.2%
518 1
0.2%
517 1
0.2%
516 1
0.2%
515 1
0.2%
514 1
0.2%

상점명
Text

Distinct428
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-18T04:15:05.322676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length4
Mean length4.7098901
Min length2

Characters and Unicode

Total characters2143
Distinct characters317
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique404 ?
Unique (%)88.8%

Sample

1st row부산 영진어묵
2nd row내일을 만들어 가는 사람들
3rd row부일상회
4th row두원상회
5th row안동상회
ValueCountFrequency (%)
성주상회 4
 
0.8%
의성상회 3
 
0.6%
대성상회 3
 
0.6%
부일상회 3
 
0.6%
부용상회 2
 
0.4%
축산물 2
 
0.4%
남지상회 2
 
0.4%
농산 2
 
0.4%
이름없음 2
 
0.4%
팔달 2
 
0.4%
Other values (449) 469
94.9%
2024-04-18T04:15:05.677765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
상 193
 
9.0%
회 182
 
8.5%
산 56
 
2.6%
대 49
 
2.3%
40
 
1.9%
동 39
 
1.8%
점 38
 
1.8%
성 36
 
1.7%
식 34
 
1.6%
노 33
 
1.5%
Other values (307) 1443
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2022
94.4%
Decimal Number 71
 
3.3%
Space Separator 40
 
1.9%
Lowercase Letter 5
 
0.2%
Other Punctuation 3
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 193
 
9.5%
회 182
 
9.0%
산 56
 
2.8%
대 49
 
2.4%
동 39
 
1.9%
점 38
 
1.9%
성 36
 
1.8%
식 34
 
1.7%
노 33
 
1.6%
농 30
 
1.5%
Other values (287) 1332
65.9%
Decimal Number
ValueCountFrequency (%)
2 20
28.2%
1 19
26.8%
3 5
 
7.0%
8 5
 
7.0%
0 5
 
7.0%
9 4
 
5.6%
4 4
 
5.6%
6 3
 
4.2%
7 3
 
4.2%
5 3
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
o 1
20.0%
w 1
20.0%
r 1
20.0%
l 1
20.0%
d 1
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
66.7%
• 1
33.3%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2022
94.4%
Common 116
 
5.4%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 193
 
9.5%
회 182
 
9.0%
산 56
 
2.8%
대 49
 
2.4%
동 39
 
1.9%
점 38
 
1.9%
성 36
 
1.8%
식 34
 
1.7%
노 33
 
1.6%
농 30
 
1.5%
Other values (287) 1332
65.9%
Common
ValueCountFrequency (%)
40
34.5%
2 20
17.2%
1 19
16.4%
3 5
 
4.3%
8 5
 
4.3%
0 5
 
4.3%
9 4
 
3.4%
4 4
 
3.4%
6 3
 
2.6%
7 3
 
2.6%
Other values (5) 8
 
6.9%
Latin
ValueCountFrequency (%)
o 1
20.0%
w 1
20.0%
r 1
20.0%
l 1
20.0%
d 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2022
94.4%
ASCII 120
 
5.6%
Punctuation 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
상 193
 
9.5%
회 182
 
9.0%
산 56
 
2.8%
대 49
 
2.4%
동 39
 
1.9%
점 38
 
1.9%
성 36
 
1.8%
식 34
 
1.7%
노 33
 
1.6%
농 30
 
1.5%
Other values (287) 1332
65.9%
ASCII
ValueCountFrequency (%)
40
33.3%
2 20
16.7%
1 19
15.8%
3 5
 
4.2%
8 5
 
4.2%
0 5
 
4.2%
9 4
 
3.3%
4 4
 
3.3%
6 3
 
2.5%
7 3
 
2.5%
Other values (9) 12
 
10.0%
Punctuation
ValueCountFrequency (%)
• 1
100.0%

소재지 도로명 주소
Text

Distinct135
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-18T04:15:05.851514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length17.927473
Min length14

Characters and Unicode

Total characters8157
Distinct characters27
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

Unique52 ?
Unique (%)11.4%

Sample

1st row대구광역시 북구 팔달로33길 42
2nd row대구광역시 북구 팔달로33길 42
3rd row대구광역시 북구 팔달로33길 42
4th row대구광역시 북구 팔달로33길 46
5th row대구광역시 북구 팔달로33길 46
ValueCountFrequency (%)
대구광역시 455
25.6%
북구 455
25.6%
팔달로33길 234
13.2%
팔달로27길 144
 
8.1%
팔달로 43
 
2.4%
노원로 29
 
1.6%
45 26
 
1.5%
59 20
 
1.1%
20 19
 
1.1%
27 13
 
0.7%
Other values (109) 340
19.1%
2024-04-18T04:15:06.107985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1335
16.4%
구 910
11.2%
3 567
 
7.0%
북 458
 
5.6%
대 455
 
5.6%
로 455
 
5.6%
시 455
 
5.6%
역 455
 
5.6%
광 455
 
5.6%
팔 424
 
5.2%
Other values (17) 2188
26.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4935
60.5%
Decimal Number 1764
 
21.6%
Space Separator 1335
 
16.4%
Dash Punctuation 119
 
1.5%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 910
18.4%
북 458
9.3%
대 455
9.2%
로 455
9.2%
시 455
9.2%
역 455
9.2%
광 455
9.2%
팔 424
8.6%
달 424
8.6%
길 380
7.7%
Other values (3) 64
 
1.3%
Decimal Number
ValueCountFrequency (%)
3 567
32.1%
2 299
17.0%
1 261
14.8%
7 227
12.9%
4 135
 
7.7%
5 81
 
4.6%
0 68
 
3.9%
9 60
 
3.4%
6 42
 
2.4%
8 24
 
1.4%
Space Separator
ValueCountFrequency (%)
1335
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 119
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4935
60.5%
Common 3222
39.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1335
41.4%
3 567
17.6%
2 299
 
9.3%
1 261
 
8.1%
7 227
 
7.0%
4 135
 
4.2%
- 119
 
3.7%
5 81
 
2.5%
0 68
 
2.1%
9 60
 
1.9%
Other values (4) 70
 
2.2%
Hangul
ValueCountFrequency (%)
구 910
18.4%
북 458
9.3%
대 455
9.2%
로 455
9.2%
시 455
9.2%
역 455
9.2%
광 455
9.2%
팔 424
8.6%
달 424
8.6%
길 380
7.7%
Other values (3) 64
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4935
60.5%
ASCII 3222
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1335
41.4%
3 567
17.6%
2 299
 
9.3%
1 261
 
8.1%
7 227
 
7.0%
4 135
 
4.2%
- 119
 
3.7%
5 81
 
2.5%
0 68
 
2.1%
9 60
 
1.9%
Other values (4) 70
 
2.2%
Hangul
ValueCountFrequency (%)
구 910
18.4%
북 458
9.3%
대 455
9.2%
로 455
9.2%
시 455
9.2%
역 455
9.2%
광 455
9.2%
팔 424
8.6%
달 424
8.6%
길 380
7.7%
Other values (3) 64
 
1.3%

소재지 지번주소
Text

Distinct128
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-18T04:15:06.338053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length19.487912
Min length18

Characters and Unicode

Total characters8867
Distinct characters22
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

Unique46 ?
Unique (%)10.1%

Sample

1st row대구광역시 북구 노원동3가 876-9
2nd row대구광역시 북구 노원동3가 876-9
3rd row대구광역시 북구 노원동3가 876-9
4th row대구광역시 북구 노원동3가 876-10
5th row대구광역시 북구 노원동3가 876-10
ValueCountFrequency (%)
대구광역시 455
25.0%
북구 455
25.0%
노원동3가 455
25.0%
750 46
 
2.5%
252-3 30
 
1.6%
750-10 20
 
1.1%
728 13
 
0.7%
742 11
 
0.6%
879-2 11
 
0.6%
747-1 9
 
0.5%
Other values (121) 315
17.3%
2024-04-18T04:15:06.678349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1365
15.4%
구 910
 
10.3%
3 556
 
6.3%
7 489
 
5.5%
대 455
 
5.1%
원 455
 
5.1%
동 455
 
5.1%
가 455
 
5.1%
노 455
 
5.1%
북 455
 
5.1%
Other values (12) 2817
31.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5005
56.4%
Decimal Number 2181
24.6%
Space Separator 1365
 
15.4%
Dash Punctuation 316
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 910
18.2%
대 455
9.1%
원 455
9.1%
동 455
9.1%
가 455
9.1%
노 455
9.1%
북 455
9.1%
시 455
9.1%
역 455
9.1%
광 455
9.1%
Decimal Number
ValueCountFrequency (%)
3 556
25.5%
7 489
22.4%
2 200
 
9.2%
5 197
 
9.0%
8 155
 
7.1%
1 142
 
6.5%
0 121
 
5.5%
6 120
 
5.5%
4 115
 
5.3%
9 86
 
3.9%
Space Separator
ValueCountFrequency (%)
1365
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 316
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5005
56.4%
Common 3862
43.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1365
35.3%
3 556
14.4%
7 489
 
12.7%
- 316
 
8.2%
2 200
 
5.2%
5 197
 
5.1%
8 155
 
4.0%
1 142
 
3.7%
0 121
 
3.1%
6 120
 
3.1%
Other values (2) 201
 
5.2%
Hangul
ValueCountFrequency (%)
구 910
18.2%
대 455
9.1%
원 455
9.1%
동 455
9.1%
가 455
9.1%
노 455
9.1%
북 455
9.1%
시 455
9.1%
역 455
9.1%
광 455
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5005
56.4%
ASCII 3862
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1365
35.3%
3 556
14.4%
7 489
 
12.7%
- 316
 
8.2%
2 200
 
5.2%
5 197
 
5.1%
8 155
 
4.0%
1 142
 
3.7%
0 121
 
3.1%
6 120
 
3.1%
Other values (2) 201
 
5.2%
Hangul
ValueCountFrequency (%)
구 910
18.2%
대 455
9.1%
원 455
9.1%
동 455
9.1%
가 455
9.1%
노 455
9.1%
북 455
9.1%
시 455
9.1%
역 455
9.1%
광 455
9.1%

상점업종분류
Categorical

Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
기타
279 
쇼핑시설
133 
음식점
37 
숙박업소
 
4
카페
 
2

Length

Max length4
Median length2
Mean length2.6835165
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 279
61.3%
쇼핑시설 133
29.2%
음식점 37
 
8.1%
숙박업소 4
 
0.9%
카페 2
 
0.4%

Length

2024-04-18T04:15:06.796154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:15:06.879972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 279
61.3%
쇼핑시설 133
29.2%
음식점 37
 
8.1%
숙박업소 4
 
0.9%
카페 2
 
0.4%

대표품목
Text

MISSING 

Distinct161
Distinct (%)40.5%
Missing57
Missing (%)12.5%
Memory size3.7 KiB
2024-04-18T04:15:07.137416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length2
Mean length3.1331658
Min length1

Characters and Unicode

Total characters1247
Distinct characters181
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)26.9%

Sample

1st row어묵
2nd row생필품
3rd row고추가루, 참기름
4th row콩나물
5th row마늘, 대파 등
ValueCountFrequency (%)
야채 40
 
8.5%
농산물 37
 
7.9%
채소 32
 
6.8%
식품 13
 
2.8%
마늘 13
 
2.8%
등 12
 
2.5%
과일 10
 
2.1%
양파 9
 
1.9%
고추 9
 
1.9%
생선 9
 
1.9%
Other values (143) 287
60.9%
2024-04-18T04:15:07.509927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
채 81
 
6.5%
74
 
5.9%
물 70
 
5.6%
, 59
 
4.7%
산 55
 
4.4%
야 42
 
3.4%
소 42
 
3.4%
품 39
 
3.1%
농 38
 
3.0%
고 28
 
2.2%
Other values (171) 719
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1110
89.0%
Space Separator 74
 
5.9%
Other Punctuation 59
 
4.7%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
채 81
 
7.3%
물 70
 
6.3%
산 55
 
5.0%
야 42
 
3.8%
소 42
 
3.8%
품 39
 
3.5%
농 38
 
3.4%
고 28
 
2.5%
식 26
 
2.3%
생 23
 
2.1%
Other values (167) 666
60.0%
Space Separator
ValueCountFrequency (%)
74
100.0%
Other Punctuation
ValueCountFrequency (%)
, 59
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1110
89.0%
Common 137
 
11.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
채 81
 
7.3%
물 70
 
6.3%
산 55
 
5.0%
야 42
 
3.8%
소 42
 
3.8%
품 39
 
3.5%
농 38
 
3.4%
고 28
 
2.5%
식 26
 
2.3%
생 23
 
2.1%
Other values (167) 666
60.0%
Common
ValueCountFrequency (%)
74
54.0%
, 59
43.1%
( 2
 
1.5%
) 2
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1110
89.0%
ASCII 137
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
채 81
 
7.3%
물 70
 
6.3%
산 55
 
5.0%
야 42
 
3.8%
소 42
 
3.8%
품 39
 
3.5%
농 38
 
3.4%
고 28
 
2.5%
식 26
 
2.3%
생 23
 
2.1%
Other values (167) 666
60.0%
ASCII
ValueCountFrequency (%)
74
54.0%
, 59
43.1%
( 2
 
1.5%
) 2
 
1.5%

전통시장 상가번영회 가입유무
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size587.0 B
True
402 
False
53 
ValueCountFrequency (%)
True 402
88.4%
False 53
 
11.6%
2024-04-18T04:15:07.595588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

온누리 상품권 사용유무
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size587.0 B
True
386 
False
69 
ValueCountFrequency (%)
True 386
84.8%
False 69
 
15.2%
2024-04-18T04:15:07.652319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

문화상품권 사용유무
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size587.0 B
False
450 
True
 
5
ValueCountFrequency (%)
False 450
98.9%
True 5
 
1.1%
2024-04-18T04:15:07.711092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

전자상품권 사용유무
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size587.0 B
False
421 
True
 
34
ValueCountFrequency (%)
False 421
92.5%
True 34
 
7.5%
2024-04-18T04:15:07.765882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

카드단말기 유무
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size587.0 B
False
282 
True
173 
ValueCountFrequency (%)
False 282
62.0%
True 173
38.0%
2024-04-18T04:15:07.821581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

택배서비스 유무
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size587.0 B
False
311 
True
144 
ValueCountFrequency (%)
False 311
68.4%
True 144
31.6%
2024-04-18T04:15:07.880903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

종업원 수
Real number (ℝ)

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3736264
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-18T04:15:07.944094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.771867
Coefficient of variation (CV)0.56191918
Kurtosis35.612751
Mean1.3736264
Median Absolute Deviation (MAD)0
Skewness4.2965985
Sum625
Variance0.59577867
MonotonicityNot monotonic
2024-04-18T04:15:08.019303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 331
72.7%
2 92
 
20.2%
3 25
 
5.5%
4 5
 
1.1%
5 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
1 331
72.7%
2 92
 
20.2%
3 25
 
5.5%
4 5
 
1.1%
5 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.2%
5 1
 
0.2%
4 5
 
1.1%
3 25
 
5.5%
2 92
 
20.2%
1 331
72.7%

상점 홈페이지 주소
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing454
Missing (%)99.8%
Memory size3.7 KiB
2024-04-18T04:15:08.129628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length28
Mean length28
Min length28

Characters and Unicode

Total characters28
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowsmartstore.naver.com/dhj0713
ValueCountFrequency (%)
smartstore.naver.com/dhj0713 1
100.0%
2024-04-18T04:15:08.333160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 3
 
10.7%
s 2
 
7.1%
a 2
 
7.1%
t 2
 
7.1%
o 2
 
7.1%
e 2
 
7.1%
. 2
 
7.1%
m 2
 
7.1%
h 1
 
3.6%
1 1
 
3.6%
Other values (9) 9
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21
75.0%
Decimal Number 4
 
14.3%
Other Punctuation 3
 
10.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 3
14.3%
s 2
9.5%
a 2
9.5%
t 2
9.5%
o 2
9.5%
e 2
9.5%
m 2
9.5%
h 1
 
4.8%
j 1
 
4.8%
v 1
 
4.8%
Other values (3) 3
14.3%
Decimal Number
ValueCountFrequency (%)
1 1
25.0%
7 1
25.0%
0 1
25.0%
3 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
/ 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 21
75.0%
Common 7
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 3
14.3%
s 2
9.5%
a 2
9.5%
t 2
9.5%
o 2
9.5%
e 2
9.5%
m 2
9.5%
h 1
 
4.8%
j 1
 
4.8%
v 1
 
4.8%
Other values (3) 3
14.3%
Common
ValueCountFrequency (%)
. 2
28.6%
1 1
14.3%
7 1
14.3%
0 1
14.3%
/ 1
14.3%
3 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 3
 
10.7%
s 2
 
7.1%
a 2
 
7.1%
t 2
 
7.1%
o 2
 
7.1%
e 2
 
7.1%
. 2
 
7.1%
m 2
 
7.1%
h 1
 
3.6%
1 1
 
3.6%
Other values (9) 9
32.1%

위도
Real number (ℝ)

Distinct128
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.890546
Minimum35.876231
Maximum35.89516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-18T04:15:08.434506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.876231
5-th percentile35.889268
Q135.889829
median35.890346
Q335.890773
95-th percentile35.89516
Maximum35.89516
Range0.018929
Interquartile range (IQR)0.0009445

Descriptive statistics

Standard deviation0.0015150219
Coefficient of variation (CV)4.2212285 × 10-5
Kurtosis20.668613
Mean35.890546
Median Absolute Deviation (MAD)0.000449
Skewness-0.067875412
Sum16330.198
Variance2.2952915 × 10-6
MonotonicityNot monotonic
2024-04-18T04:15:08.570204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.89068 46
 
10.1%
35.89516 30
 
6.6%
35.890255 20
 
4.4%
35.891051 13
 
2.9%
35.889514 11
 
2.4%
35.891199 11
 
2.4%
35.88995 11
 
2.4%
35.890773 9
 
2.0%
35.89047 9
 
2.0%
35.890878 8
 
1.8%
Other values (118) 287
63.1%
ValueCountFrequency (%)
35.876231 1
 
0.2%
35.888977 4
0.9%
35.88901 4
0.9%
35.889076 1
 
0.2%
35.889091 3
0.7%
35.88911 1
 
0.2%
35.889166 1
 
0.2%
35.889199 3
0.7%
35.88921 1
 
0.2%
35.889222 3
0.7%
ValueCountFrequency (%)
35.89516 30
6.6%
35.893304 2
 
0.4%
35.891508 3
 
0.7%
35.891265 1
 
0.2%
35.891199 11
 
2.4%
35.891187 6
 
1.3%
35.891051 13
2.9%
35.891017 4
 
0.9%
35.890991 5
 
1.1%
35.89096 5
 
1.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.5667
Minimum128.56392
Maximum128.60569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-18T04:15:08.698135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.56392
5-th percentile128.5649
Q1128.56557
median128.5662
Q3128.5671
95-th percentile128.57183
Maximum128.60569
Range0.041769
Interquartile range (IQR)0.00153

Descriptive statistics

Standard deviation0.002471054
Coefficient of variation (CV)1.9220016 × 10-5
Kurtosis136.71531
Mean128.5667
Median Absolute Deviation (MAD)0.000684
Skewness9.2231314
Sum58497.847
Variance6.1061078 × 10-6
MonotonicityNot monotonic
2024-04-18T04:15:08.835717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.56599 46
 
10.1%
128.571829 30
 
6.6%
128.566637 20
 
4.4%
128.565226 13
 
2.9%
128.565342 11
 
2.4%
128.565573 11
 
2.4%
128.565599 9
 
2.0%
128.56644 9
 
2.0%
128.566339 8
 
1.8%
128.566713 8
 
1.8%
Other values (120) 290
63.7%
ValueCountFrequency (%)
128.563923 3
0.7%
128.564301 1
 
0.2%
128.564354 4
0.9%
128.564469 1
 
0.2%
128.564482 1
 
0.2%
128.564586 1
 
0.2%
128.56459 2
0.4%
128.564614 1
 
0.2%
128.564656 2
0.4%
128.564672 3
0.7%
ValueCountFrequency (%)
128.605692 1
 
0.2%
128.571829 30
6.6%
128.568469 4
 
0.9%
128.5684 2
 
0.4%
128.568391 1
 
0.2%
128.568339 1
 
0.2%
128.568282 3
 
0.7%
128.568206 2
 
0.4%
128.568123 3
 
0.7%
128.568083 4
 
0.9%

Interactions

2024-04-18T04:15:04.344134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:03.299489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:03.557510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:04.069764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:04.404538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:03.362105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:03.620161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:04.136713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:04.465041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:03.423851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:03.694408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:04.203448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:04.534508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:03.496526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:03.782861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:15:04.278281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T04:15:08.916839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상점코드상점업종분류전통시장 상가번영회 가입유무온누리 상품권 사용유무문화상품권 사용유무전자상품권 사용유무카드단말기 유무택배서비스 유무종업원 수위도경도
상점코드1.0000.7420.4800.3270.0000.3730.5020.4950.2280.7790.619
상점업종분류0.7421.0000.2370.2030.0000.0000.2500.1000.0000.3360.028
전통시장 상가번영회 가입유무0.4800.2371.0000.7720.0600.0000.4110.1760.1840.2570.089
온누리 상품권 사용유무0.3270.2030.7721.0000.0000.1120.0000.3240.1920.1150.049
문화상품권 사용유무0.0000.0000.0600.0001.0000.2550.0800.0230.0000.0000.000
전자상품권 사용유무0.3730.0000.0000.1120.2551.0000.4030.0760.0700.0140.043
카드단말기 유무0.5020.2500.4110.0000.0800.4031.0000.0000.2780.2630.046
택배서비스 유무0.4950.1000.1760.3240.0230.0760.0001.0000.4660.2070.118
종업원 수0.2280.0000.1840.1920.0000.0700.2780.4661.0000.2670.000
위도0.7790.3360.2570.1150.0000.0140.2630.2070.2671.0000.848
경도0.6190.0280.0890.0490.0000.0430.0460.1180.0000.8481.000
2024-04-18T04:15:09.022614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드단말기 유무전자상품권 사용유무문화상품권 사용유무전통시장 상가번영회 가입유무상점업종분류택배서비스 유무온누리 상품권 사용유무
카드단말기 유무1.0000.2640.0510.2690.3050.0000.000
전자상품권 사용유무0.2641.0000.1640.0000.0000.0480.071
문화상품권 사용유무0.0510.1641.0000.0380.0000.0140.000
전통시장 상가번영회 가입유무0.2690.0000.0381.0000.2890.1130.561
상점업종분류0.3050.0000.0000.2891.0000.1220.247
택배서비스 유무0.0000.0480.0140.1130.1221.0000.210
온누리 상품권 사용유무0.0000.0710.0000.5610.2470.2101.000
2024-04-18T04:15:09.121540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상점코드종업원 수위도경도상점업종분류전통시장 상가번영회 가입유무온누리 상품권 사용유무문화상품권 사용유무전자상품권 사용유무카드단말기 유무택배서비스 유무
상점코드1.0000.093-0.4650.6040.3970.3660.2480.0000.2840.3830.378
종업원 수0.0931.000-0.1070.0140.0000.1320.1370.0000.0500.1990.334
위도-0.465-0.1071.000-0.1310.1310.3130.1400.0000.0160.3200.252
경도0.6040.014-0.1311.0000.0160.1480.0790.0000.0700.0760.197
상점업종분류0.3970.0000.1310.0161.0000.2890.2470.0000.0000.3050.122
전통시장 상가번영회 가입유무0.3660.1320.3130.1480.2891.0000.5610.0380.0000.2690.113
온누리 상품권 사용유무0.2480.1370.1400.0790.2470.5611.0000.0000.0710.0000.210
문화상품권 사용유무0.0000.0000.0000.0000.0000.0380.0001.0000.1640.0510.014
전자상품권 사용유무0.2840.0500.0160.0700.0000.0000.0710.1641.0000.2640.048
카드단말기 유무0.3830.1990.3200.0760.3050.2690.0000.0510.2641.0000.000
택배서비스 유무0.3780.3340.2520.1970.1220.1130.2100.0140.0480.0001.000

Missing values

2024-04-18T04:15:04.627167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T04:15:04.781997image/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.
2024-04-18T04:15:04.878832image/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

상점코드상점명소재지 도로명 주소소재지 지번주소상점업종분류대표품목전통시장 상가번영회 가입유무온누리 상품권 사용유무문화상품권 사용유무전자상품권 사용유무카드단말기 유무택배서비스 유무종업원 수상점 홈페이지 주소위도경도
01부산 영진어묵대구광역시 북구 팔달로33길 42대구광역시 북구 노원동3가 876-9쇼핑시설어묵YYNNYN1<NA>35.890457128.566943
12내일을 만들어 가는 사람들대구광역시 북구 팔달로33길 42대구광역시 북구 노원동3가 876-9쇼핑시설생필품YYNNYN1<NA>35.890457128.566943
23부일상회대구광역시 북구 팔달로33길 42대구광역시 북구 노원동3가 876-9쇼핑시설고추가루, 참기름YYNNNN1<NA>35.890457128.566943
34두원상회대구광역시 북구 팔달로33길 46대구광역시 북구 노원동3가 876-10쇼핑시설콩나물YYNNNN1<NA>35.890495128.566882
45안동상회대구광역시 북구 팔달로33길 46대구광역시 북구 노원동3가 876-10쇼핑시설마늘, 대파 등YYNNNN1<NA>35.890495128.566882
56광평농산대구광역시 북구 팔달로33길 48대구광역시 북구 노원동3가 876-8쇼핑시설고추YYNNNN1<NA>35.890533128.566822
67약목상회대구광역시 북구 팔달로33길 48대구광역시 북구 노원동3가 876-8쇼핑시설생필품YYNNNN1<NA>35.890533128.566822
78벽진상회대구광역시 북구 팔달로33길 50대구광역시 북구 노원동3가 876-7쇼핑시설생필품YYNNNN1<NA>35.8906128.566713
89칠곡상회대구광역시 북구 팔달로33길 50대구광역시 북구 노원동3가 876-7쇼핑시설감자, 고구마YYNNNN1<NA>35.8906128.566713
910정희상회대구광역시 북구 팔달로33길 50대구광역시 북구 노원동3가 876-7쇼핑시설마늘YNNNNN1<NA>35.8906128.566713
상점코드상점명소재지 도로명 주소소재지 지번주소상점업종분류대표품목전통시장 상가번영회 가입유무온누리 상품권 사용유무문화상품권 사용유무전자상품권 사용유무카드단말기 유무택배서비스 유무종업원 수상점 홈페이지 주소위도경도
445514노점22대구광역시 북구 팔달로33길대구광역시 북구 노원동3가 252-3기타채소YYNNNN1<NA>35.89516128.571829
446515노점23대구광역시 북구 팔달로33길대구광역시 북구 노원동3가 252-3기타채소YYNNNN1<NA>35.89516128.571829
447516노점24대구광역시 북구 팔달로33길대구광역시 북구 노원동3가 252-3기타뻥튀기YYNNNN1<NA>35.89516128.571829
448517노점25대구광역시 북구 팔달로33길대구광역시 북구 노원동3가 252-3기타옷YYNNNN1<NA>35.89516128.571829
449518노점26대구광역시 북구 팔달로33길대구광역시 북구 노원동3가 252-3기타채소YYNNNN1<NA>35.89516128.571829
450519노점27대구광역시 북구 팔달로33길대구광역시 북구 노원동3가 252-3기타화장품YYNNNN1<NA>35.89516128.571829
451520노점28대구광역시 북구 팔달로33길대구광역시 북구 노원동3가 252-3기타식료품YYNNNN1<NA>35.89516128.571829
452521노점29대구광역시 북구 팔달로33길대구광역시 북구 노원동3가 252-3기타감주YYNNNN1<NA>35.89516128.571829
453522노점30대구광역시 북구 팔달로33길대구광역시 북구 노원동3가 252-3기타과일YYNNNN1<NA>35.89516128.571829
454523대신상회창고대구광역시 북구 팔달북로 9대구광역시 북구 노원동3가 725기타야채YYNNNY3<NA>35.891508128.563923