Overview

Dataset statistics

Number of variables9
Number of observations831
Missing cells129
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.2 KiB
Average record size in memory74.2 B

Variable types

Categorical1
Text4
Numeric2
DateTime2

Dataset

Description경상남도 거제시 이미용업 등록현황(업종명, 업소명, 주소, 위도, 경도, 전화번호, 영업자시작일자, 기준일자)에 대한 정보를 제공합니다.
Author경상남도 거제시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3079236

Alerts

기준일자 has constant value ""Constant
소재지전화번호 has 129 (15.5%) missing valuesMissing

Reproduction

Analysis started2023-12-10 23:24:29.789926
Analysis finished2023-12-10 23:24:30.882436
Duration1.09 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct16
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
일반미용업
330 
미용업
173 
피부미용업
95 
이용업
69 
네일미용업
53 
Other values (11)
111 

Length

Max length23
Median length5
Mean length5.4476534
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 330
39.7%
미용업 173
20.8%
피부미용업 95
 
11.4%
이용업 69
 
8.3%
네일미용업 53
 
6.4%
종합미용업 20
 
2.4%
피부미용업, 네일미용업 16
 
1.9%
네일미용업, 화장ㆍ분장 미용업 16
 
1.9%
피부미용업, 화장ㆍ분장 미용업 15
 
1.8%
화장ㆍ분장 미용업 14
 
1.7%
Other values (6) 30
 
3.6%

Length

2023-12-11T08:24:31.173569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 353
36.1%
미용업 233
23.8%
피부미용업 137
 
14.0%
네일미용업 107
 
10.9%
이용업 69
 
7.0%
화장ㆍ분장 60
 
6.1%
종합미용업 20
 
2.0%

업소명
Text

Distinct806
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
2023-12-11T08:24:31.390878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length23
Mean length6.4055355
Min length1

Characters and Unicode

Total characters5323
Distinct characters497
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique784 ?
Unique (%)94.3%

Sample

1st row현대이용원
2nd row제일이용원
3rd row공신이용원
4th row신신이용원
5th row일신이용원
ValueCountFrequency (%)
hair 10
 
1.1%
nail 7
 
0.7%
헤어 5
 
0.5%
백조미용실 4
 
0.4%
미용실 4
 
0.4%
네일 4
 
0.4%
뷰티 3
 
0.3%
미녀뷰티 3
 
0.3%
에스테틱 3
 
0.3%
헤어샵 3
 
0.3%
Other values (864) 904
95.2%
2023-12-11T08:24:31.751956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 329
 
6.2%
헤 314
 
5.9%
미 147
 
2.8%
이 143
 
2.7%
용 138
 
2.6%
119
 
2.2%
스 110
 
2.1%
) 105
 
2.0%
( 105
 
2.0%
리 97
 
1.8%
Other values (487) 3716
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4244
79.7%
Lowercase Letter 401
 
7.5%
Uppercase Letter 269
 
5.1%
Space Separator 119
 
2.2%
Close Punctuation 105
 
2.0%
Open Punctuation 105
 
2.0%
Other Punctuation 45
 
0.8%
Decimal Number 27
 
0.5%
Dash Punctuation 6
 
0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 329
 
7.8%
헤 314
 
7.4%
미 147
 
3.5%
이 143
 
3.4%
용 138
 
3.3%
스 110
 
2.6%
리 97
 
2.3%
실 92
 
2.2%
일 90
 
2.1%
네 84
 
2.0%
Other values (422) 2700
63.6%
Uppercase Letter
ValueCountFrequency (%)
N 31
 
11.5%
I 28
 
10.4%
A 26
 
9.7%
H 22
 
8.2%
S 21
 
7.8%
M 15
 
5.6%
L 14
 
5.2%
B 13
 
4.8%
E 11
 
4.1%
T 10
 
3.7%
Other values (14) 78
29.0%
Lowercase Letter
ValueCountFrequency (%)
a 59
14.7%
i 50
12.5%
e 36
9.0%
o 32
8.0%
s 30
7.5%
l 30
7.5%
n 27
 
6.7%
r 27
 
6.7%
h 23
 
5.7%
m 16
 
4.0%
Other values (13) 71
17.7%
Other Punctuation
ValueCountFrequency (%)
# 14
31.1%
& 11
24.4%
. 6
13.3%
' 5
 
11.1%
· 4
 
8.9%
: 3
 
6.7%
, 2
 
4.4%
Decimal Number
ValueCountFrequency (%)
0 10
37.0%
1 9
33.3%
2 3
 
11.1%
9 2
 
7.4%
3 2
 
7.4%
8 1
 
3.7%
Space Separator
ValueCountFrequency (%)
119
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4237
79.6%
Latin 672
 
12.6%
Common 407
 
7.6%
Han 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 329
 
7.8%
헤 314
 
7.4%
미 147
 
3.5%
이 143
 
3.4%
용 138
 
3.3%
스 110
 
2.6%
리 97
 
2.3%
실 92
 
2.2%
일 90
 
2.1%
네 84
 
2.0%
Other values (419) 2693
63.6%
Latin
ValueCountFrequency (%)
a 59
 
8.8%
i 50
 
7.4%
e 36
 
5.4%
o 32
 
4.8%
N 31
 
4.6%
s 30
 
4.5%
l 30
 
4.5%
I 28
 
4.2%
n 27
 
4.0%
r 27
 
4.0%
Other values (38) 322
47.9%
Common
ValueCountFrequency (%)
119
29.2%
) 105
25.8%
( 105
25.8%
# 14
 
3.4%
& 11
 
2.7%
0 10
 
2.5%
1 9
 
2.2%
- 6
 
1.5%
. 6
 
1.5%
' 5
 
1.2%
Other values (7) 17
 
4.2%
Han
ValueCountFrequency (%)
美 4
57.1%
愛 2
28.6%
珍 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4237
79.6%
ASCII 1073
 
20.2%
CJK 7
 
0.1%
None 4
 
0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 329
 
7.8%
헤 314
 
7.4%
미 147
 
3.5%
이 143
 
3.4%
용 138
 
3.3%
스 110
 
2.6%
리 97
 
2.3%
실 92
 
2.2%
일 90
 
2.1%
네 84
 
2.0%
Other values (419) 2693
63.6%
ASCII
ValueCountFrequency (%)
119
 
11.1%
) 105
 
9.8%
( 105
 
9.8%
a 59
 
5.5%
i 50
 
4.7%
e 36
 
3.4%
o 32
 
3.0%
N 31
 
2.9%
s 30
 
2.8%
l 30
 
2.8%
Other values (53) 476
44.4%
None
ValueCountFrequency (%)
· 4
100.0%
CJK
ValueCountFrequency (%)
美 4
57.1%
愛 2
28.6%
珍 1
 
14.3%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%

영업소 주소(도로명)
Text

Distinct810
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
2023-12-11T08:24:32.038592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length52
Mean length30.389892
Min length19

Characters and Unicode

Total characters25254
Distinct characters238
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique792 ?
Unique (%)95.3%

Sample

1st row경상남도 거제시 거제중앙로11길 2 (고현동)
2nd row경상남도 거제시 장목면 거제북로 1170, 1층
3rd row경상남도 거제시 하청면 연하해안로 1707
4th row경상남도 거제시 하청면 하청로 13
5th row경상남도 거제시 옥포로10길 24-1 (옥포동)
ValueCountFrequency (%)
경상남도 831
 
15.7%
거제시 831
 
15.7%
1층 352
 
6.7%
고현동 219
 
4.1%
옥포동 154
 
2.9%
2층 104
 
2.0%
장평동 72
 
1.4%
아주동 66
 
1.2%
거제중앙로 58
 
1.1%
상동동 55
 
1.0%
Other values (876) 2549
48.2%
2023-12-11T08:24:32.516333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4460
 
17.7%
1 1442
 
5.7%
동 1048
 
4.1%
제 1039
 
4.1%
상 1038
 
4.1%
거 1025
 
4.1%
시 849
 
3.4%
남 841
 
3.3%
도 835
 
3.3%
경 833
 
3.3%
Other values (228) 11844
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13885
55.0%
Space Separator 4460
 
17.7%
Decimal Number 4275
 
16.9%
Close Punctuation 818
 
3.2%
Open Punctuation 818
 
3.2%
Other Punctuation 768
 
3.0%
Dash Punctuation 178
 
0.7%
Uppercase Letter 33
 
0.1%
Lowercase Letter 11
 
< 0.1%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1048
 
7.5%
제 1039
 
7.5%
상 1038
 
7.5%
거 1025
 
7.4%
시 849
 
6.1%
남 841
 
6.1%
도 835
 
6.0%
경 833
 
6.0%
로 756
 
5.4%
층 544
 
3.9%
Other values (199) 5077
36.6%
Decimal Number
ValueCountFrequency (%)
1 1442
33.7%
2 659
15.4%
0 457
 
10.7%
3 420
 
9.8%
4 306
 
7.2%
5 269
 
6.3%
7 202
 
4.7%
6 195
 
4.6%
8 174
 
4.1%
9 151
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
B 21
63.6%
A 5
 
15.2%
P 1
 
3.0%
I 1
 
3.0%
K 1
 
3.0%
R 1
 
3.0%
C 1
 
3.0%
J 1
 
3.0%
G 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 766
99.7%
/ 1
 
0.1%
@ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
4460
100.0%
Close Punctuation
ValueCountFrequency (%)
) 818
100.0%
Open Punctuation
ValueCountFrequency (%)
( 818
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 178
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 11
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13885
55.0%
Common 11324
44.8%
Latin 45
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1048
 
7.5%
제 1039
 
7.5%
상 1038
 
7.5%
거 1025
 
7.4%
시 849
 
6.1%
남 841
 
6.1%
도 835
 
6.0%
경 833
 
6.0%
로 756
 
5.4%
층 544
 
3.9%
Other values (199) 5077
36.6%
Common
ValueCountFrequency (%)
4460
39.4%
1 1442
 
12.7%
) 818
 
7.2%
( 818
 
7.2%
, 766
 
6.8%
2 659
 
5.8%
0 457
 
4.0%
3 420
 
3.7%
4 306
 
2.7%
5 269
 
2.4%
Other values (8) 909
 
8.0%
Latin
ValueCountFrequency (%)
B 21
46.7%
e 11
24.4%
A 5
 
11.1%
P 1
 
2.2%
I 1
 
2.2%
K 1
 
2.2%
R 1
 
2.2%
C 1
 
2.2%
J 1
 
2.2%
Ⅱ 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13885
55.0%
ASCII 11368
45.0%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4460
39.2%
1 1442
 
12.7%
) 818
 
7.2%
( 818
 
7.2%
, 766
 
6.7%
2 659
 
5.8%
0 457
 
4.0%
3 420
 
3.7%
4 306
 
2.7%
5 269
 
2.4%
Other values (18) 953
 
8.4%
Hangul
ValueCountFrequency (%)
동 1048
 
7.5%
제 1039
 
7.5%
상 1038
 
7.5%
거 1025
 
7.4%
시 849
 
6.1%
남 841
 
6.1%
도 835
 
6.0%
경 833
 
6.0%
로 756
 
5.4%
층 544
 
3.9%
Other values (199) 5077
36.6%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

영업소 주소(지번)
Text

Distinct775
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
2023-12-11T08:24:32.815202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length41
Mean length22.56799
Min length15

Characters and Unicode

Total characters18754
Distinct characters231
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique728 ?
Unique (%)87.6%

Sample

1st row경상남도 거제시 고현동 173
2nd row경상남도 거제시 장목면 241-2
3rd row경상남도 거제시 하청면 하청리 704-4
4th row경상남도 거제시 하청면 하청리 659-7
5th row경상남도 거제시 옥포동 420-2
ValueCountFrequency (%)
거제시 831
20.4%
경상남도 830
20.3%
고현동 241
 
5.9%
옥포동 173
 
4.2%
1층 134
 
3.3%
장평동 83
 
2.0%
아주동 67
 
1.6%
상동동 57
 
1.4%
능포동 39
 
1.0%
수월동 32
 
0.8%
Other values (960) 1593
39.0%
2023-12-11T08:24:33.305132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3249
17.3%
1 1114
 
5.9%
상 979
 
5.2%
동 929
 
5.0%
제 895
 
4.8%
거 889
 
4.7%
시 851
 
4.5%
남 840
 
4.5%
도 836
 
4.5%
경 833
 
4.4%
Other values (221) 7339
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10363
55.3%
Decimal Number 4312
23.0%
Space Separator 3249
 
17.3%
Dash Punctuation 676
 
3.6%
Close Punctuation 54
 
0.3%
Open Punctuation 54
 
0.3%
Uppercase Letter 22
 
0.1%
Lowercase Letter 9
 
< 0.1%
Other Punctuation 9
 
< 0.1%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 979
 
9.4%
동 929
 
9.0%
제 895
 
8.6%
거 889
 
8.6%
시 851
 
8.2%
남 840
 
8.1%
도 836
 
8.1%
경 833
 
8.0%
현 262
 
2.5%
포 258
 
2.5%
Other values (193) 2791
26.9%
Decimal Number
ValueCountFrequency (%)
1 1114
25.8%
2 486
11.3%
0 452
10.5%
3 389
 
9.0%
5 376
 
8.7%
9 335
 
7.8%
4 321
 
7.4%
6 291
 
6.7%
7 290
 
6.7%
8 258
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
B 12
54.5%
A 3
 
13.6%
K 1
 
4.5%
R 1
 
4.5%
P 1
 
4.5%
I 1
 
4.5%
C 1
 
4.5%
J 1
 
4.5%
G 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 8
88.9%
@ 1
 
11.1%
Space Separator
ValueCountFrequency (%)
3249
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 676
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10363
55.3%
Common 8359
44.6%
Latin 32
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 979
 
9.4%
동 929
 
9.0%
제 895
 
8.6%
거 889
 
8.6%
시 851
 
8.2%
남 840
 
8.1%
도 836
 
8.1%
경 833
 
8.0%
현 262
 
2.5%
포 258
 
2.5%
Other values (193) 2791
26.9%
Common
ValueCountFrequency (%)
3249
38.9%
1 1114
 
13.3%
- 676
 
8.1%
2 486
 
5.8%
0 452
 
5.4%
3 389
 
4.7%
5 376
 
4.5%
9 335
 
4.0%
4 321
 
3.8%
6 291
 
3.5%
Other values (7) 670
 
8.0%
Latin
ValueCountFrequency (%)
B 12
37.5%
e 9
28.1%
A 3
 
9.4%
K 1
 
3.1%
R 1
 
3.1%
P 1
 
3.1%
I 1
 
3.1%
C 1
 
3.1%
J 1
 
3.1%
Ⅱ 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10363
55.3%
ASCII 8390
44.7%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3249
38.7%
1 1114
 
13.3%
- 676
 
8.1%
2 486
 
5.8%
0 452
 
5.4%
3 389
 
4.6%
5 376
 
4.5%
9 335
 
4.0%
4 321
 
3.8%
6 291
 
3.5%
Other values (17) 701
 
8.4%
Hangul
ValueCountFrequency (%)
상 979
 
9.4%
동 929
 
9.0%
제 895
 
8.6%
거 889
 
8.6%
시 851
 
8.2%
남 840
 
8.1%
도 836
 
8.1%
경 833
 
8.0%
현 262
 
2.5%
포 258
 
2.5%
Other values (193) 2791
26.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

위도
Real number (ℝ)

Distinct669
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.8855
Minimum34.732601
Maximum35.007888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-12-11T08:24:33.449098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.732601
5-th percentile34.855627
Q134.876485
median34.889626
Q334.893827
95-th percentile34.899349
Maximum35.007888
Range0.275287
Interquartile range (IQR)0.0173425

Descriptive statistics

Standard deviation0.020893265
Coefficient of variation (CV)0.0005989097
Kurtosis10.910748
Mean34.8855
Median Absolute Deviation (MAD)0.006442
Skewness0.76737658
Sum28989.851
Variance0.00043652851
MonotonicityNot monotonic
2023-12-11T08:24:33.591212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.890862 10
 
1.2%
34.896917 10
 
1.2%
34.893827 6
 
0.7%
34.914257 5
 
0.6%
34.897509 5
 
0.6%
34.892167 5
 
0.6%
34.860489 4
 
0.5%
34.898125 4
 
0.5%
34.886258 4
 
0.5%
34.88981 4
 
0.5%
Other values (659) 774
93.1%
ValueCountFrequency (%)
34.732601 1
0.1%
34.822618 1
0.1%
34.823536 1
0.1%
34.824262 1
0.1%
34.825433 1
0.1%
34.827432 1
0.1%
34.827611 1
0.1%
34.828902 1
0.1%
34.829026 1
0.1%
34.829088 1
0.1%
ValueCountFrequency (%)
35.007888 1
0.1%
34.990128 2
0.2%
34.9895 1
0.1%
34.987854 1
0.1%
34.987374 2
0.2%
34.98708 1
0.1%
34.986816 1
0.1%
34.969806 1
0.1%
34.958273 1
0.1%
34.956722 1
0.1%

경도
Real number (ℝ)

Distinct670
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.65181
Minimum128.47691
Maximum128.73699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-12-11T08:24:33.717207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.47691
5-th percentile128.60448
Q1128.62383
median128.63774
Q3128.69028
95-th percentile128.72976
Maximum128.73699
Range0.26008
Interquartile range (IQR)0.0664475

Descriptive statistics

Standard deviation0.043173383
Coefficient of variation (CV)0.00033558318
Kurtosis1.3358961
Mean128.65181
Median Absolute Deviation (MAD)0.024649
Skewness-0.37132473
Sum106909.65
Variance0.001863941
MonotonicityNot monotonic
2023-12-11T08:24:33.838313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.689927 10
 
1.2%
128.613105 10
 
1.2%
128.63009 6
 
0.7%
128.635588 5
 
0.6%
128.655403 5
 
0.6%
128.641876 5
 
0.6%
128.623655 4
 
0.5%
128.695323 4
 
0.5%
128.642386 4
 
0.5%
128.685431 4
 
0.5%
Other values (660) 774
93.1%
ValueCountFrequency (%)
128.476909 1
0.1%
128.476926 1
0.1%
128.477555 1
0.1%
128.477775 2
0.2%
128.477898 1
0.1%
128.478692 1
0.1%
128.479306 1
0.1%
128.506822 1
0.1%
128.523841 1
0.1%
128.524134 1
0.1%
ValueCountFrequency (%)
128.736989 1
0.1%
128.736532 1
0.1%
128.735038 1
0.1%
128.734078 1
0.1%
128.733651 2
0.2%
128.733438 1
0.1%
128.732967 1
0.1%
128.732457 1
0.1%
128.732413 1
0.1%
128.732408 1
0.1%

소재지전화번호
Text

MISSING 

Distinct684
Distinct (%)97.4%
Missing129
Missing (%)15.5%
Memory size6.6 KiB
2023-12-11T08:24:34.065128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.249288
Min length12

Characters and Unicode

Total characters8599
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

Unique668 ?
Unique (%)95.2%

Sample

1st row055-635-3588
2nd row055-635-0952
3rd row055-636-3030
4th row055-636-7225
5th row055-687-3281
ValueCountFrequency (%)
0507-1412-8871 3
 
0.4%
055-637-1192 3
 
0.4%
055-687-5602 2
 
0.3%
055-633-6426 2
 
0.3%
055-632-7688 2
 
0.3%
055-637-0709 2
 
0.3%
055-682-0822 2
 
0.3%
055-638-2723 2
 
0.3%
055-637-3993 2
 
0.3%
055-687-7796 2
 
0.3%
Other values (674) 680
96.9%
2023-12-11T08:24:34.436825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1669
19.4%
- 1404
16.3%
0 1177
13.7%
6 921
10.7%
3 795
9.2%
8 633
 
7.4%
7 551
 
6.4%
1 455
 
5.3%
2 411
 
4.8%
4 326
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7195
83.7%
Dash Punctuation 1404
 
16.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1669
23.2%
0 1177
16.4%
6 921
12.8%
3 795
11.0%
8 633
 
8.8%
7 551
 
7.7%
1 455
 
6.3%
2 411
 
5.7%
4 326
 
4.5%
9 257
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 1404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1669
19.4%
- 1404
16.3%
0 1177
13.7%
6 921
10.7%
3 795
9.2%
8 633
 
7.4%
7 551
 
6.4%
1 455
 
5.3%
2 411
 
4.8%
4 326
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1669
19.4%
- 1404
16.3%
0 1177
13.7%
6 921
10.7%
3 795
9.2%
8 633
 
7.4%
7 551
 
6.4%
1 455
 
5.3%
2 411
 
4.8%
4 326
 
3.8%

영업자시작일자
Date

Distinct753
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
Minimum1963-04-23 00:00:00
Maximum2021-08-20 00:00:00
2023-12-11T08:24:34.592435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:24:34.755207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
Minimum2021-09-03 00:00:00
Maximum2021-09-03 00:00:00
2023-12-11T08:24:34.853475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:24:34.940905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T08:24:30.524762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:24:30.356960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:24:30.601884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:24:30.447089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:24:35.028398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명위도경도
업종명1.0000.1770.136
위도0.1771.0000.541
경도0.1360.5411.000
2023-12-11T08:24:35.110746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도업종명
위도1.0000.0690.062
경도0.0691.0000.058
업종명0.0620.0581.000

Missing values

2023-12-11T08:24:30.706205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:24:30.835416image/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

업종명업소명영업소 주소(도로명)영업소 주소(지번)위도경도소재지전화번호영업자시작일자기준일자
0이용업현대이용원경상남도 거제시 거제중앙로11길 2 (고현동)경상남도 거제시 고현동 17334.881914128.626142055-635-35881963-04-232021-09-03
1이용업제일이용원경상남도 거제시 장목면 거제북로 1170, 1층경상남도 거제시 장목면 241-234.987374128.681316055-635-09521963-12-042021-09-03
2이용업공신이용원경상남도 거제시 하청면 연하해안로 1707경상남도 거제시 하청면 하청리 704-434.954747128.654081055-636-30301969-03-082021-09-03
3이용업신신이용원경상남도 거제시 하청면 하청로 13경상남도 거제시 하청면 하청리 659-734.955592128.654337055-636-72251970-12-182021-09-03
4이용업일신이용원경상남도 거제시 옥포로10길 24-1 (옥포동)경상남도 거제시 옥포동 420-234.891197128.694136055-687-32811970-12-212021-09-03
5이용업제일이용원경상남도 거제시 거제면 읍내로 80, 1층경상남도 거제시 거제면 서정리 670-134.987374128.681316055-632-17071973-02-192021-09-03
6이용업청탑이용원경상남도 거제시 고현로 96 (고현동)경상남도 거제시 고현동 100-1834.886543128.623318055-637-28381973-03-172021-09-03
7이용업장평이용원경상남도 거제시 장평1로10길 9 (장평동)경상남도 거제시 장평동 346-12434.893181128.60694055-635-98692015-05-112021-09-03
8이용업중앙이용원경상남도 거제시 장승포로 36 (장승포동)경상남도 거제시 장승포동 697-434.867949128.728206055-681-44231976-12-152021-09-03
9이용업광명이용원경상남도 거제시 사등면 성포로3길 1경상남도 거제시 사등면 성포리 351-1534.920928128.523841055-634-60191977-12-302021-09-03
업종명업소명영업소 주소(도로명)영업소 주소(지번)위도경도소재지전화번호영업자시작일자기준일자
821일반미용업, 피부미용업, 화장ㆍ분장 미용업위즈헤어경상남도 거제시 하청면 거제북로 554경상남도 거제시 하청면 하청리 50234.958273128.6581940507-1306-29032015-12-282021-09-03
822일반미용업, 네일미용업, 화장ㆍ분장 미용업휘오레헤어샵경상남도 거제시 거제중앙로 1860 (고현동,(1층))경상남도 거제시 고현동 80-5 (1층)34.884464128.625287055-636-25662016-10-122021-09-03
823일반미용업, 네일미용업, 화장ㆍ분장 미용업아우라헤어앤네일경상남도 거제시 아주로 50 (아주동)경상남도 거제시 아주동 300-1034.865018128.689341055-681-72482019-01-092021-09-03
824피부미용업, 네일미용업, 화장ㆍ분장 미용업고은손네일경상남도 거제시 거제중앙로19길 34-1, 1층 (고현동)경상남도 거제시 고현동 751-1234.884623128.620961055-635-88512017-07-112021-09-03
825피부미용업, 네일미용업, 화장ㆍ분장 미용업네일이끌림경상남도 거제시 옥포로 250, 옥현시장 1층 145,146,159,160호 (옥포동)경상남도 거제시 옥포동 1293-1 옥현시장34.896917128.689927<NA>2017-07-192021-09-03
826피부미용업, 네일미용업, 화장ㆍ분장 미용업홍쓰네일앤스파경상남도 거제시 중곡로 40, 20동 1층 105호 (고현동, 덕산베스트타운)경상남도 거제시 고현동 104034.896259128.630407070-4642-75892019-10-222021-09-03
827피부미용업, 네일미용업, 화장ㆍ분장 미용업림에스테틱경상남도 거제시 아주1로 66, 2층 (아주동)경상남도 거제시 아주동 1685-934.866694128.685754055-682-72722021-01-252021-09-03
828피부미용업, 네일미용업, 화장ㆍ분장 미용업꽃썸뷰티경상남도 거제시 중곡2로4길 7, 2층 (고현동)경상남도 거제시 고현동 993 2층34.894783128.626987<NA>2018-02-212021-09-03
829피부미용업, 네일미용업, 화장ㆍ분장 미용업공주뷰티아카데미경상남도 거제시 거제중앙로15길 13, 2층 (고현동)경상남도 거제시 고현동 13034.883668128.6240020507-1305-69782019-11-122021-09-03
830피부미용업, 네일미용업, 화장ㆍ분장 미용업오늘도반하다(Vanhada)경상남도 거제시 거제중앙로29길 9, 2층 (고현동)경상남도 거제시 고현동 970-3534.889799128.6212790507-1449-02352021-07-122021-09-03