Overview

Dataset statistics

Number of variables8
Number of observations733
Missing cells392
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.4 KiB
Average record size in memory66.2 B

Variable types

Numeric2
Categorical2
Text4

Dataset

Description대구광역시 수성구_카페현황_20200511
Author대구광역시 수성구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15038007&dataSetDetailId=150380071fcbdc7d5a83d&provdMethod=FILE

Alerts

업종명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
업태명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
소재지전화 has 383 (52.3%) missing valuesMissing
영업장면적 has 18 (2.5%) zerosZeros

Reproduction

Analysis started2023-12-10 19:12:56.273275
Analysis finished2023-12-10 19:12:58.549217
Duration2.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION 

Distinct726
Distinct (%)100.0%
Missing7
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean363.5
Minimum1
Maximum726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T04:12:58.651414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37.25
Q1182.25
median363.5
Q3544.75
95-th percentile689.75
Maximum726
Range725
Interquartile range (IQR)362.5

Descriptive statistics

Standard deviation209.72244
Coefficient of variation (CV)0.57695306
Kurtosis-1.2
Mean363.5
Median Absolute Deviation (MAD)181.5
Skewness0
Sum263901
Variance43983.5
MonotonicityStrictly increasing
2023-12-11T04:12:58.835744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
479 1
 
0.1%
481 1
 
0.1%
482 1
 
0.1%
483 1
 
0.1%
484 1
 
0.1%
485 1
 
0.1%
486 1
 
0.1%
487 1
 
0.1%
488 1
 
0.1%
489 1
 
0.1%
Other values (716) 716
97.7%
(Missing) 7
 
1.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
726 1
0.1%
725 1
0.1%
724 1
0.1%
723 1
0.1%
722 1
0.1%
721 1
0.1%
720 1
0.1%
719 1
0.1%
718 1
0.1%
717 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
휴게음식점
596 
일반음식점
137 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반음식점
2nd row휴게음식점
3rd row휴게음식점
4th row휴게음식점
5th row휴게음식점

Common Values

ValueCountFrequency (%)
휴게음식점 596
81.3%
일반음식점 137
 
18.7%

Length

2023-12-11T04:12:59.022911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T04:12:59.144161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
휴게음식점 596
81.3%
일반음식점 137
 
18.7%

업소명
Text

Distinct720
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2023-12-11T04:12:59.390028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length7.3396999
Min length1

Characters and Unicode

Total characters5380
Distinct characters555
Distinct categories9 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique707 ?
Unique (%)96.5%

Sample

1st row올패랑
2nd row끌레임
3rd row다빈치커피
4th row더블유카페
5th row(주)스타벅스동아수성점
ValueCountFrequency (%)
coffee 13
 
1.5%
투썸플레이스 6
 
0.7%
cafe 4
 
0.5%
커피 4
 
0.5%
시지점 4
 
0.5%
스타벅스 3
 
0.4%
수성점 3
 
0.4%
더 3
 
0.4%
주식회사 3
 
0.4%
스타벅스커피 3
 
0.4%
Other values (782) 804
94.6%
2023-12-11T04:12:59.804965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 176
 
3.3%
스 172
 
3.2%
피 154
 
2.9%
커 151
 
2.8%
( 149
 
2.8%
) 149
 
2.8%
페 123
 
2.3%
카 121
 
2.2%
117
 
2.2%
이 107
 
2.0%
Other values (545) 3961
73.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4036
75.0%
Lowercase Letter 490
 
9.1%
Uppercase Letter 360
 
6.7%
Open Punctuation 149
 
2.8%
Close Punctuation 149
 
2.8%
Space Separator 117
 
2.2%
Decimal Number 61
 
1.1%
Other Punctuation 17
 
0.3%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 176
 
4.4%
스 172
 
4.3%
피 154
 
3.8%
커 151
 
3.7%
페 123
 
3.0%
카 121
 
3.0%
이 107
 
2.7%
구 85
 
2.1%
대 78
 
1.9%
리 77
 
1.9%
Other values (476) 2792
69.2%
Uppercase Letter
ValueCountFrequency (%)
C 39
 
10.8%
E 38
 
10.6%
O 34
 
9.4%
F 27
 
7.5%
T 20
 
5.6%
M 20
 
5.6%
D 20
 
5.6%
S 20
 
5.6%
A 19
 
5.3%
R 19
 
5.3%
Other values (15) 104
28.9%
Lowercase Letter
ValueCountFrequency (%)
e 86
17.6%
o 67
13.7%
f 46
9.4%
a 45
9.2%
n 26
 
5.3%
r 24
 
4.9%
c 24
 
4.9%
l 22
 
4.5%
u 22
 
4.5%
i 20
 
4.1%
Other values (14) 108
22.0%
Decimal Number
ValueCountFrequency (%)
1 14
23.0%
2 11
18.0%
9 7
11.5%
4 7
11.5%
5 7
11.5%
0 6
9.8%
3 6
9.8%
7 1
 
1.6%
8 1
 
1.6%
6 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 6
35.3%
& 5
29.4%
, 2
 
11.8%
' 2
 
11.8%
! 1
 
5.9%
; 1
 
5.9%
Open Punctuation
ValueCountFrequency (%)
( 149
100.0%
Close Punctuation
ValueCountFrequency (%)
) 149
100.0%
Space Separator
ValueCountFrequency (%)
117
100.0%
Other Symbol
ValueCountFrequency (%)
℃ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4031
74.9%
Latin 849
 
15.8%
Common 494
 
9.2%
Han 5
 
0.1%
Cyrillic 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 176
 
4.4%
스 172
 
4.3%
피 154
 
3.8%
커 151
 
3.7%
페 123
 
3.1%
카 121
 
3.0%
이 107
 
2.7%
구 85
 
2.1%
대 78
 
1.9%
리 77
 
1.9%
Other values (471) 2787
69.1%
Latin
ValueCountFrequency (%)
e 86
 
10.1%
o 67
 
7.9%
f 46
 
5.4%
a 45
 
5.3%
C 39
 
4.6%
E 38
 
4.5%
O 34
 
4.0%
F 27
 
3.2%
n 26
 
3.1%
r 24
 
2.8%
Other values (38) 417
49.1%
Common
ValueCountFrequency (%)
( 149
30.2%
) 149
30.2%
117
23.7%
1 14
 
2.8%
2 11
 
2.2%
9 7
 
1.4%
4 7
 
1.4%
5 7
 
1.4%
. 6
 
1.2%
0 6
 
1.2%
Other values (10) 21
 
4.3%
Han
ValueCountFrequency (%)
休 1
20.0%
房 1
20.0%
名 1
20.0%
品 1
20.0%
茶 1
20.0%
Cyrillic
ValueCountFrequency (%)
Ё 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4031
74.9%
ASCII 1342
 
24.9%
CJK 5
 
0.1%
Letterlike Symbols 1
 
< 0.1%
Cyrillic 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 176
 
4.4%
스 172
 
4.3%
피 154
 
3.8%
커 151
 
3.7%
페 123
 
3.1%
카 121
 
3.0%
이 107
 
2.7%
구 85
 
2.1%
대 78
 
1.9%
리 77
 
1.9%
Other values (471) 2787
69.1%
ASCII
ValueCountFrequency (%)
( 149
 
11.1%
) 149
 
11.1%
117
 
8.7%
e 86
 
6.4%
o 67
 
5.0%
f 46
 
3.4%
a 45
 
3.4%
C 39
 
2.9%
E 38
 
2.8%
O 34
 
2.5%
Other values (57) 572
42.6%
Letterlike Symbols
ValueCountFrequency (%)
℃ 1
100.0%
CJK
ValueCountFrequency (%)
休 1
20.0%
房 1
20.0%
名 1
20.0%
品 1
20.0%
茶 1
20.0%
Cyrillic
ValueCountFrequency (%)
Ё 1
100.0%

소재지(도로명)
Text

Distinct720
Distinct (%)98.5%
Missing2
Missing (%)0.3%
Memory size5.9 KiB
2023-12-11T04:13:00.171770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length48
Mean length29.380301
Min length21

Characters and Unicode

Total characters21477
Distinct characters223
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique712 ?
Unique (%)97.4%

Sample

1st row대구광역시 수성구 시지로3길 66 (시지동)
2nd row대구광역시 수성구 수성로 308 (수성동2가)
3rd row대구광역시 수성구 동원로 110, 322동 106호 (만촌동,메트로팔레스상가)
4th row대구광역시 수성구 화랑로 58 (만촌동)
5th row대구광역시 수성구 지범로 191 (범물동,동아수성점(1층, 2층))
ValueCountFrequency (%)
대구광역시 731
 
17.2%
수성구 731
 
17.2%
1층 226
 
5.3%
범어동 156
 
3.7%
만촌동 78
 
1.8%
지산동 71
 
1.7%
달구벌대로 67
 
1.6%
두산동 59
 
1.4%
신매동 55
 
1.3%
2층 45
 
1.1%
Other values (842) 2026
47.7%
2023-12-11T04:13:00.728562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3514
 
16.4%
구 1696
 
7.9%
대 979
 
4.6%
1 923
 
4.3%
수 912
 
4.2%
동 892
 
4.2%
성 883
 
4.1%
시 797
 
3.7%
) 754
 
3.5%
( 754
 
3.5%
Other values (213) 9373
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12332
57.4%
Space Separator 3514
 
16.4%
Decimal Number 3452
 
16.1%
Close Punctuation 754
 
3.5%
Open Punctuation 754
 
3.5%
Other Punctuation 546
 
2.5%
Dash Punctuation 107
 
0.5%
Uppercase Letter 16
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1696
13.8%
대 979
 
7.9%
수 912
 
7.4%
동 892
 
7.2%
성 883
 
7.2%
시 797
 
6.5%
역 735
 
6.0%
광 734
 
6.0%
로 725
 
5.9%
층 366
 
3.0%
Other values (188) 3613
29.3%
Decimal Number
ValueCountFrequency (%)
1 923
26.7%
2 541
15.7%
4 334
 
9.7%
3 332
 
9.6%
0 299
 
8.7%
5 243
 
7.0%
6 235
 
6.8%
9 196
 
5.7%
7 175
 
5.1%
8 174
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
B 5
31.2%
S 3
18.8%
K 2
 
12.5%
M 1
 
6.2%
J 1
 
6.2%
C 1
 
6.2%
N 1
 
6.2%
L 1
 
6.2%
A 1
 
6.2%
Space Separator
ValueCountFrequency (%)
3514
100.0%
Close Punctuation
ValueCountFrequency (%)
) 754
100.0%
Open Punctuation
ValueCountFrequency (%)
( 754
100.0%
Other Punctuation
ValueCountFrequency (%)
, 546
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 107
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12332
57.4%
Common 9129
42.5%
Latin 16
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1696
13.8%
대 979
 
7.9%
수 912
 
7.4%
동 892
 
7.2%
성 883
 
7.2%
시 797
 
6.5%
역 735
 
6.0%
광 734
 
6.0%
로 725
 
5.9%
층 366
 
3.0%
Other values (188) 3613
29.3%
Common
ValueCountFrequency (%)
3514
38.5%
1 923
 
10.1%
) 754
 
8.3%
( 754
 
8.3%
, 546
 
6.0%
2 541
 
5.9%
4 334
 
3.7%
3 332
 
3.6%
0 299
 
3.3%
5 243
 
2.7%
Other values (6) 889
 
9.7%
Latin
ValueCountFrequency (%)
B 5
31.2%
S 3
18.8%
K 2
 
12.5%
M 1
 
6.2%
J 1
 
6.2%
C 1
 
6.2%
N 1
 
6.2%
L 1
 
6.2%
A 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12332
57.4%
ASCII 9145
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3514
38.4%
1 923
 
10.1%
) 754
 
8.2%
( 754
 
8.2%
, 546
 
6.0%
2 541
 
5.9%
4 334
 
3.7%
3 332
 
3.6%
0 299
 
3.3%
5 243
 
2.7%
Other values (15) 905
 
9.9%
Hangul
ValueCountFrequency (%)
구 1696
13.8%
대 979
 
7.9%
수 912
 
7.4%
동 892
 
7.2%
성 883
 
7.2%
시 797
 
6.5%
역 735
 
6.0%
광 734
 
6.0%
로 725
 
5.9%
층 366
 
3.0%
Other values (188) 3613
29.3%

소재지(지번)
Text

Distinct714
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2023-12-11T04:13:01.090542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length49
Mean length26.249659
Min length4

Characters and Unicode

Total characters19241
Distinct characters189
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique698 ?
Unique (%)95.2%

Sample

1st row대구광역시 수성구 시지동 381번지 16호
2nd row대구광역시 수성구 수성동2가 273번지 6호
3rd row대구광역시 수성구 만촌동 1429번지 메트로팔레스상가 322동 106호
4th row대구광역시 수성구 만촌동 1356번지 17호
5th row대구광역시 수성구 범물동 1273번지 동아수성점(1층, 2층)
ValueCountFrequency (%)
대구광역시 732
18.5%
수성구 732
18.5%
범어동 160
 
4.0%
1층 139
 
3.5%
1호 83
 
2.1%
만촌동 82
 
2.1%
지산동 73
 
1.8%
3호 70
 
1.8%
두산동 66
 
1.7%
신매동 60
 
1.5%
Other values (721) 1767
44.6%
2023-12-11T04:13:01.682822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4366
22.7%
구 1473
 
7.7%
1 939
 
4.9%
지 870
 
4.5%
성 824
 
4.3%
수 818
 
4.3%
동 784
 
4.1%
시 767
 
4.0%
대 755
 
3.9%
광 735
 
3.8%
Other values (179) 6910
35.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11080
57.6%
Space Separator 4366
 
22.7%
Decimal Number 3680
 
19.1%
Other Punctuation 40
 
0.2%
Open Punctuation 29
 
0.2%
Close Punctuation 29
 
0.2%
Uppercase Letter 11
 
0.1%
Dash Punctuation 3
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1473
13.3%
지 870
 
7.9%
성 824
 
7.4%
수 818
 
7.4%
동 784
 
7.1%
시 767
 
6.9%
대 755
 
6.8%
광 735
 
6.6%
역 733
 
6.6%
번 732
 
6.6%
Other values (156) 2589
23.4%
Decimal Number
ValueCountFrequency (%)
1 939
25.5%
2 470
12.8%
3 352
 
9.6%
5 326
 
8.9%
0 302
 
8.2%
7 299
 
8.1%
6 277
 
7.5%
4 272
 
7.4%
8 227
 
6.2%
9 216
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 3
27.3%
S 2
18.2%
K 2
18.2%
M 1
 
9.1%
J 1
 
9.1%
A 1
 
9.1%
C 1
 
9.1%
Space Separator
ValueCountFrequency (%)
4366
100.0%
Other Punctuation
ValueCountFrequency (%)
, 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11080
57.6%
Common 8150
42.4%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1473
13.3%
지 870
 
7.9%
성 824
 
7.4%
수 818
 
7.4%
동 784
 
7.1%
시 767
 
6.9%
대 755
 
6.8%
광 735
 
6.6%
역 733
 
6.6%
번 732
 
6.6%
Other values (156) 2589
23.4%
Common
ValueCountFrequency (%)
4366
53.6%
1 939
 
11.5%
2 470
 
5.8%
3 352
 
4.3%
5 326
 
4.0%
0 302
 
3.7%
7 299
 
3.7%
6 277
 
3.4%
4 272
 
3.3%
8 227
 
2.8%
Other values (6) 320
 
3.9%
Latin
ValueCountFrequency (%)
B 3
27.3%
S 2
18.2%
K 2
18.2%
M 1
 
9.1%
J 1
 
9.1%
A 1
 
9.1%
C 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11080
57.6%
ASCII 8161
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4366
53.5%
1 939
 
11.5%
2 470
 
5.8%
3 352
 
4.3%
5 326
 
4.0%
0 302
 
3.7%
7 299
 
3.7%
6 277
 
3.4%
4 272
 
3.3%
8 227
 
2.8%
Other values (13) 331
 
4.1%
Hangul
ValueCountFrequency (%)
구 1473
13.3%
지 870
 
7.9%
성 824
 
7.4%
수 818
 
7.4%
동 784
 
7.1%
시 767
 
6.9%
대 755
 
6.8%
광 735
 
6.6%
역 733
 
6.6%
번 732
 
6.6%
Other values (156) 2589
23.4%

영업장면적
Real number (ℝ)

ZEROS 

Distinct650
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.246385
Minimum0
Maximum830.4
Zeros18
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T04:13:01.878320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.606
Q134.34
median60.76
Q3111.02
95-th percentile295.788
Maximum830.4
Range830.4
Interquartile range (IQR)76.68

Descriptive statistics

Standard deviation100.19655
Coefficient of variation (CV)1.0745355
Kurtosis11.562597
Mean93.246385
Median Absolute Deviation (MAD)31.53
Skewness2.8841849
Sum68349.6
Variance10039.349
MonotonicityNot monotonic
2023-12-11T04:13:02.034276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 18
 
2.5%
33.0 8
 
1.1%
45.0 6
 
0.8%
66.0 4
 
0.5%
30.0 4
 
0.5%
36.0 4
 
0.5%
60.0 4
 
0.5%
50.0 4
 
0.5%
49.5 3
 
0.4%
33.6 3
 
0.4%
Other values (640) 675
92.1%
ValueCountFrequency (%)
0.0 18
2.5%
3.3 1
 
0.1%
7.29 1
 
0.1%
7.54 1
 
0.1%
8.32 1
 
0.1%
8.97 1
 
0.1%
9.0 1
 
0.1%
9.47 1
 
0.1%
9.9 2
 
0.3%
10.0 2
 
0.3%
ValueCountFrequency (%)
830.4 1
0.1%
788.63 1
0.1%
618.0 1
0.1%
560.09 1
0.1%
552.19 1
0.1%
550.68 1
0.1%
540.09 1
0.1%
533.4 1
0.1%
515.02 1
0.1%
473.01 1
0.1%

소재지전화
Text

MISSING 

Distinct346
Distinct (%)98.9%
Missing383
Missing (%)52.3%
Memory size5.9 KiB
2023-12-11T04:13:02.350050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.042857
Min length12

Characters and Unicode

Total characters4215
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

Unique342 ?
Unique (%)97.7%

Sample

1st row053-745-2234
2nd row053-217-3883
3rd row053-741-7469
4th row053-757-2000
5th row053-782-1875
ValueCountFrequency (%)
053-754-7617 2
 
0.6%
053-745-2234 2
 
0.6%
053-784-6080 2
 
0.6%
053-754-0910 2
 
0.6%
053-216-3814 1
 
0.3%
053-638-0803 1
 
0.3%
053-767-9616 1
 
0.3%
053-249-0408 1
 
0.3%
053-667-8989 1
 
0.3%
053-744-0002 1
 
0.3%
Other values (336) 336
96.0%
2023-12-11T04:13:02.844889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 700
16.6%
0 579
13.7%
5 572
13.6%
3 525
12.5%
7 456
10.8%
2 260
 
6.2%
6 247
 
5.9%
4 241
 
5.7%
8 224
 
5.3%
1 222
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3515
83.4%
Dash Punctuation 700
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 579
16.5%
5 572
16.3%
3 525
14.9%
7 456
13.0%
2 260
7.4%
6 247
7.0%
4 241
6.9%
8 224
 
6.4%
1 222
 
6.3%
9 189
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4215
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 700
16.6%
0 579
13.7%
5 572
13.6%
3 525
12.5%
7 456
10.8%
2 260
 
6.2%
6 247
 
5.9%
4 241
 
5.7%
8 224
 
5.3%
1 222
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 700
16.6%
0 579
13.7%
5 572
13.6%
3 525
12.5%
7 456
10.8%
2 260
 
6.2%
6 247
 
5.9%
4 241
 
5.7%
8 224
 
5.3%
1 222
 
5.3%

업태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
커피숍
597 
까페
136 

Length

Max length3
Median length3
Mean length2.8144611
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
커피숍 597
81.4%
까페 136
 
18.6%

Length

2023-12-11T04:13:03.048213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T04:13:03.190958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
커피숍 597
81.4%
까페 136
 
18.6%

Interactions

2023-12-11T04:12:57.812063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:12:57.199722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:12:57.963993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:12:57.347224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T04:13:03.277930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명영업장면적업태명
연번1.0000.9940.2440.995
업종명0.9941.0000.3291.000
영업장면적0.2440.3291.0000.324
업태명0.9951.0000.3241.000
2023-12-11T04:13:03.390399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.991
업태명0.9911.000
2023-12-11T04:13:03.493370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번영업장면적업종명업태명
연번1.0000.0560.9290.934
영업장면적0.0561.0000.3270.322
업종명0.9290.3271.0000.991
업태명0.9340.3220.9911.000

Missing values

2023-12-11T04:12:58.147946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T04:12:58.315234image/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.
2023-12-11T04:12:58.467905image/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

연번업종명업소명소재지(도로명)소재지(지번)영업장면적소재지전화업태명
01일반음식점올패랑대구광역시 수성구 시지로3길 66 (시지동)대구광역시 수성구 시지동 381번지 16호85.34053-745-2234커피숍
12휴게음식점끌레임대구광역시 수성구 수성로 308 (수성동2가)대구광역시 수성구 수성동2가 273번지 6호70.35053-217-3883커피숍
23휴게음식점다빈치커피대구광역시 수성구 동원로 110, 322동 106호 (만촌동,메트로팔레스상가)대구광역시 수성구 만촌동 1429번지 메트로팔레스상가 322동 106호25.7053-741-7469커피숍
34휴게음식점더블유카페대구광역시 수성구 화랑로 58 (만촌동)대구광역시 수성구 만촌동 1356번지 17호30.8053-757-2000커피숍
45휴게음식점(주)스타벅스동아수성점대구광역시 수성구 지범로 191 (범물동,동아수성점(1층, 2층))대구광역시 수성구 범물동 1273번지 동아수성점(1층, 2층)89.5053-782-1875커피숍
56휴게음식점마시그래이대구법원점대구광역시 수성구 동대구로 371, 1층 (범어동)대구광역시 수성구 범어동 13번지 10호18.27053-741-5611커피숍
67휴게음식점말카커피대구광역시 수성구 지범로46길 19 (범물동)대구광역시 수성구 범물동 484번지76.72053-782-0456커피숍
78휴게음식점수대구광역시 수성구 용학로 316 (범물동,범물영남115동(상가) 101호-3)대구광역시 수성구 범물동 1269번지 범물영남115동(상가) 101호-352.8053-783-2723커피숍
89휴게음식점커피명가대구광역시 수성구 지범로 167 (지산동)대구광역시 수성구 지산동 1276번지 5호173.4053-784-0892커피숍
910휴게음식점카페관심 롯데캐슬황금점대구광역시 수성구 청수로 261, 종합상가동 126호 (황금동,캐슬골드파크)대구광역시 수성구 황금동 368번지 캐슬골드파크 종합상가동 126호29.1053-765-5150커피숍
연번업종명업소명소재지(도로명)소재지(지번)영업장면적소재지전화업태명
723724일반음식점안녕내사람Hello,my dear대구광역시 수성구 명덕로75길 34 (수성동1가, 주2)대구광역시 수성구 수성동1가 260번지 10호 주215.3<NA>까페
724725일반음식점아담브릿지대구광역시 수성구 수성로15길 46 (상동)대구광역시 수성구 상동 221번지173.51053-292-0000까페
725726일반음식점커피스미스대구수성레이크점대구광역시 수성구 용학로 82 (두산동)대구광역시 수성구 두산동 899번지 9호409.8<NA>까페
726<NA>일반음식점펫타임대구수성레이크점대구광역시 수성구 용학로 82, 1층 (두산동)대구광역시 수성구 두산동 899번지 9호118.8053-766-8506까페
727<NA>일반음식점이에스커피(ES커피)대구광역시 수성구 청수로 26 (중동)대구광역시 수성구 중동 412번지 1호58.41053-752-0851까페
728<NA>일반음식점아미대구광역시 수성구 지범로 289 (범물동)대구광역시 수성구 범물동 1306번지 12호39.78<NA>까페
729<NA>일반음식점수고했어,오늘도대구광역시 수성구 달구벌대로480길 22 (범어동)대구광역시 수성구 범어동 560번지 1호19.83<NA>까페
730<NA>일반음식점(주)텀트리대구광역시 수성구 용학로 138, 2층 (두산동)대구광역시 수성구 두산동 746번지618.0053-944-3912까페
731<NA>일반음식점문웨이(Moon Way)대구광역시 수성구 상화로3길 50-3 (상동)대구광역시 수성구 상동 154번지 18호53.96053-768-1185까페
732<NA>일반음식점레이카페(Ray cafe)대구광역시 수성구 무학로35길 25, 1층 (지산동)대구광역시 수성구 지산동 1071번지 2호57.6<NA>까페