Overview

Dataset statistics

Number of variables7
Number of observations1591
Missing cells598
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory88.7 KiB
Average record size in memory57.1 B

Variable types

Categorical1
Text5
Numeric1

Dataset

Description부산광역시동래구_공중위생업소현황_20230320
Author부산광역시 동래구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15026507

Alerts

영업소전화번호 has 592 (37.2%) missing valuesMissing

Reproduction

Analysis started2023-12-10 16:18:20.612814
Analysis finished2023-12-10 16:18:22.151066
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct22
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
일반미용업
674 
피부미용업
155 
세탁업
111 
숙박업(일반)
108 
네일미용업
107 
Other values (17)
436 

Length

Max length23
Median length5
Mean length5.7064739
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 674
42.4%
피부미용업 155
 
9.7%
세탁업 111
 
7.0%
숙박업(일반) 108
 
6.8%
네일미용업 107
 
6.7%
이용업 107
 
6.7%
건물위생관리업 97
 
6.1%
목욕장업 55
 
3.5%
숙박업(생활) 33
 
2.1%
화장ㆍ분장 미용업 27
 
1.7%
Other values (12) 117
 
7.4%

Length

2023-12-11T01:18:22.257859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 699
38.9%
피부미용업 201
 
11.2%
네일미용업 169
 
9.4%
세탁업 111
 
6.2%
숙박업(일반 108
 
6.0%
이용업 107
 
6.0%
미용업 101
 
5.6%
건물위생관리업 97
 
5.4%
화장ㆍ분장 94
 
5.2%
목욕장업 55
 
3.1%
Other values (2) 54
 
3.0%

업소명
Text

Distinct1562
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
2023-12-11T01:18:22.580056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length26
Mean length5.8724073
Min length1

Characters and Unicode

Total characters9343
Distinct characters598
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1535 ?
Unique (%)96.5%

Sample

1st row(사)부산장애인기업총연합회복지사업단
2nd row(주)경남종합기업
3rd row(주)남부공영
4th row(주)뉴에버크린
5th row(주)대성실업
ValueCountFrequency (%)
에스테틱 12
 
0.7%
hair 11
 
0.6%
헤어 9
 
0.5%
뷰티 8
 
0.4%
네일 7
 
0.4%
미용실 7
 
0.4%
주식회사 7
 
0.4%
salon 6
 
0.3%
the 5
 
0.3%
퀸즈헤나 4
 
0.2%
Other values (1688) 1750
95.8%
2023-12-11T01:18:23.032433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 405
 
4.3%
헤 390
 
4.2%
235
 
2.5%
이 208
 
2.2%
스 197
 
2.1%
미 175
 
1.9%
( 170
 
1.8%
) 170
 
1.8%
일 156
 
1.7%
네 143
 
1.5%
Other values (588) 7094
75.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7639
81.8%
Lowercase Letter 531
 
5.7%
Uppercase Letter 475
 
5.1%
Space Separator 235
 
2.5%
Open Punctuation 170
 
1.8%
Close Punctuation 170
 
1.8%
Other Punctuation 71
 
0.8%
Decimal Number 45
 
0.5%
Dash Punctuation 6
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 405
 
5.3%
헤 390
 
5.1%
이 208
 
2.7%
스 197
 
2.6%
미 175
 
2.3%
일 156
 
2.0%
네 143
 
1.9%
리 141
 
1.8%
용 129
 
1.7%
아 104
 
1.4%
Other values (518) 5591
73.2%
Lowercase Letter
ValueCountFrequency (%)
a 74
13.9%
e 57
10.7%
i 50
9.4%
n 46
8.7%
o 45
8.5%
l 36
 
6.8%
r 35
 
6.6%
h 28
 
5.3%
s 25
 
4.7%
y 23
 
4.3%
Other values (15) 112
21.1%
Uppercase Letter
ValueCountFrequency (%)
A 46
 
9.7%
O 38
 
8.0%
I 34
 
7.2%
H 34
 
7.2%
S 32
 
6.7%
E 30
 
6.3%
M 30
 
6.3%
N 29
 
6.1%
R 25
 
5.3%
T 23
 
4.8%
Other values (14) 154
32.4%
Decimal Number
ValueCountFrequency (%)
2 11
24.4%
1 6
13.3%
9 6
13.3%
3 6
13.3%
4 5
11.1%
5 4
 
8.9%
6 3
 
6.7%
0 3
 
6.7%
8 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 20
28.2%
& 19
26.8%
, 12
16.9%
# 8
 
11.3%
' 8
 
11.3%
: 3
 
4.2%
% 1
 
1.4%
Space Separator
ValueCountFrequency (%)
235
100.0%
Open Punctuation
ValueCountFrequency (%)
( 170
100.0%
Close Punctuation
ValueCountFrequency (%)
) 170
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7634
81.7%
Latin 1006
 
10.8%
Common 698
 
7.5%
Han 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 405
 
5.3%
헤 390
 
5.1%
이 208
 
2.7%
스 197
 
2.6%
미 175
 
2.3%
일 156
 
2.0%
네 143
 
1.9%
리 141
 
1.8%
용 129
 
1.7%
아 104
 
1.4%
Other values (515) 5586
73.2%
Latin
ValueCountFrequency (%)
a 74
 
7.4%
e 57
 
5.7%
i 50
 
5.0%
A 46
 
4.6%
n 46
 
4.6%
o 45
 
4.5%
O 38
 
3.8%
l 36
 
3.6%
r 35
 
3.5%
I 34
 
3.4%
Other values (39) 545
54.2%
Common
ValueCountFrequency (%)
235
33.7%
( 170
24.4%
) 170
24.4%
. 20
 
2.9%
& 19
 
2.7%
, 12
 
1.7%
2 11
 
1.6%
# 8
 
1.1%
' 8
 
1.1%
1 6
 
0.9%
Other values (11) 39
 
5.6%
Han
ValueCountFrequency (%)
美 3
60.0%
朴 1
 
20.0%
秀 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7634
81.7%
ASCII 1704
 
18.2%
CJK 5
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 405
 
5.3%
헤 390
 
5.1%
이 208
 
2.7%
스 197
 
2.6%
미 175
 
2.3%
일 156
 
2.0%
네 143
 
1.9%
리 141
 
1.8%
용 129
 
1.7%
아 104
 
1.4%
Other values (515) 5586
73.2%
ASCII
ValueCountFrequency (%)
235
 
13.8%
( 170
 
10.0%
) 170
 
10.0%
a 74
 
4.3%
e 57
 
3.3%
i 50
 
2.9%
A 46
 
2.7%
n 46
 
2.7%
o 45
 
2.6%
O 38
 
2.2%
Other values (60) 773
45.4%
CJK
ValueCountFrequency (%)
美 3
60.0%
朴 1
 
20.0%
秀 1
 
20.0%

영업소주소(도로명)
Text

Distinct1530
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
2023-12-11T01:18:23.306660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length54
Mean length31.922062
Min length21

Characters and Unicode

Total characters50788
Distinct characters261
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1472 ?
Unique (%)92.5%

Sample

1st row부산광역시 동래구 안남로 23 (낙민동)
2nd row부산광역시 동래구 충렬대로237번길 90 (복천동)
3rd row부산광역시 동래구 명안로 77 (명장동)
4th row부산광역시 동래구 금강로 107, 4층 414호 (온천동)
5th row부산광역시 동래구 낙민로 14 (낙민동)
ValueCountFrequency (%)
부산광역시 1591
 
16.3%
동래구 1591
 
16.3%
온천동 498
 
5.1%
1층 461
 
4.7%
사직동 283
 
2.9%
안락동 272
 
2.8%
2층 201
 
2.1%
명장동 160
 
1.6%
명륜동 158
 
1.6%
수안동 96
 
1.0%
Other values (1184) 4454
45.6%
2023-12-11T01:18:24.070998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8174
 
16.1%
동 3576
 
7.0%
1 2238
 
4.4%
시 1766
 
3.5%
래 1752
 
3.4%
) 1614
 
3.2%
( 1613
 
3.2%
산 1612
 
3.2%
부 1599
 
3.1%
광 1595
 
3.1%
Other values (251) 25249
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29157
57.4%
Decimal Number 8358
 
16.5%
Space Separator 8174
 
16.1%
Close Punctuation 1614
 
3.2%
Open Punctuation 1613
 
3.2%
Other Punctuation 1275
 
2.5%
Dash Punctuation 298
 
0.6%
Uppercase Letter 282
 
0.6%
Math Symbol 9
 
< 0.1%
Lowercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3576
 
12.3%
시 1766
 
6.1%
래 1752
 
6.0%
산 1612
 
5.5%
부 1599
 
5.5%
광 1595
 
5.5%
역 1593
 
5.5%
구 1591
 
5.5%
로 1582
 
5.4%
길 985
 
3.4%
Other values (212) 11506
39.5%
Uppercase Letter
ValueCountFrequency (%)
K 63
22.3%
S 61
21.6%
B 32
11.3%
A 20
 
7.1%
H 16
 
5.7%
U 15
 
5.3%
Y 15
 
5.3%
E 14
 
5.0%
I 13
 
4.6%
V 12
 
4.3%
Other values (8) 21
 
7.4%
Decimal Number
ValueCountFrequency (%)
1 2238
26.8%
2 1402
16.8%
3 932
11.2%
4 689
 
8.2%
0 651
 
7.8%
7 569
 
6.8%
5 544
 
6.5%
6 483
 
5.8%
8 464
 
5.6%
9 386
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 1271
99.7%
@ 3
 
0.2%
. 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 6
75.0%
s 1
 
12.5%
k 1
 
12.5%
Space Separator
ValueCountFrequency (%)
8174
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1614
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1613
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 298
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29157
57.4%
Common 21341
42.0%
Latin 290
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3576
 
12.3%
시 1766
 
6.1%
래 1752
 
6.0%
산 1612
 
5.5%
부 1599
 
5.5%
광 1595
 
5.5%
역 1593
 
5.5%
구 1591
 
5.5%
로 1582
 
5.4%
길 985
 
3.4%
Other values (212) 11506
39.5%
Latin
ValueCountFrequency (%)
K 63
21.7%
S 61
21.0%
B 32
11.0%
A 20
 
6.9%
H 16
 
5.5%
U 15
 
5.2%
Y 15
 
5.2%
E 14
 
4.8%
I 13
 
4.5%
V 12
 
4.1%
Other values (11) 29
10.0%
Common
ValueCountFrequency (%)
8174
38.3%
1 2238
 
10.5%
) 1614
 
7.6%
( 1613
 
7.6%
2 1402
 
6.6%
, 1271
 
6.0%
3 932
 
4.4%
4 689
 
3.2%
0 651
 
3.1%
7 569
 
2.7%
Other values (8) 2188
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29157
57.4%
ASCII 21631
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8174
37.8%
1 2238
 
10.3%
) 1614
 
7.5%
( 1613
 
7.5%
2 1402
 
6.5%
, 1271
 
5.9%
3 932
 
4.3%
4 689
 
3.2%
0 651
 
3.0%
7 569
 
2.6%
Other values (29) 2478
 
11.5%
Hangul
ValueCountFrequency (%)
동 3576
 
12.3%
시 1766
 
6.1%
래 1752
 
6.0%
산 1612
 
5.5%
부 1599
 
5.5%
광 1595
 
5.5%
역 1593
 
5.5%
구 1591
 
5.5%
로 1582
 
5.4%
길 985
 
3.4%
Other values (212) 11506
39.5%

우편번호(도로명)
Real number (ℝ)

Distinct184
Distinct (%)11.6%
Missing6
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean47798.563
Minimum47701
Maximum47905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 KiB
2023-12-11T01:18:24.265713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47701
5-th percentile47709
Q147743
median47803
Q347848
95-th percentile47892
Maximum47905
Range204
Interquartile range (IQR)105

Descriptive statistics

Standard deviation58.872186
Coefficient of variation (CV)0.0012316727
Kurtosis-1.1926313
Mean47798.563
Median Absolute Deviation (MAD)51
Skewness-0.032952942
Sum75760723
Variance3465.9343
MonotonicityNot monotonic
2023-12-11T01:18:24.484769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47709 46
 
2.9%
47712 43
 
2.7%
47715 29
 
1.8%
47813 29
 
1.8%
47710 28
 
1.8%
47728 26
 
1.6%
47837 23
 
1.4%
47808 23
 
1.4%
47738 20
 
1.3%
47866 19
 
1.2%
Other values (174) 1299
81.6%
ValueCountFrequency (%)
47701 5
 
0.3%
47702 1
 
0.1%
47703 4
 
0.3%
47704 4
 
0.3%
47705 5
 
0.3%
47706 1
 
0.1%
47707 2
 
0.1%
47708 19
1.2%
47709 46
2.9%
47710 28
1.8%
ValueCountFrequency (%)
47905 5
 
0.3%
47904 4
 
0.3%
47901 17
1.1%
47900 9
0.6%
47899 6
 
0.4%
47898 9
0.6%
47897 1
 
0.1%
47896 2
 
0.1%
47895 11
0.7%
47894 8
0.5%

영업소주소(지번)
Text

Distinct1389
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
2023-12-11T01:18:24.814996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length44
Mean length22.782527
Min length17

Characters and Unicode

Total characters36247
Distinct characters237
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1235 ?
Unique (%)77.6%

Sample

1st row부산광역시 동래구 낙민동 84-8
2nd row부산광역시 동래구 복천동 380-5
3rd row부산광역시 동래구 명장동 131-16
4th row부산광역시 동래구 온천동 208-2
5th row부산광역시 동래구 낙민동 186-13
ValueCountFrequency (%)
부산광역시 1591
22.2%
동래구 1591
22.2%
온천동 507
 
7.1%
사직동 288
 
4.0%
안락동 276
 
3.9%
명장동 160
 
2.2%
명륜동 159
 
2.2%
수안동 97
 
1.4%
1층 69
 
1.0%
복천동 54
 
0.8%
Other values (1567) 2365
33.0%
2023-12-11T01:18:25.395030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7060
19.5%
동 3366
 
9.3%
1 1679
 
4.6%
래 1679
 
4.6%
산 1610
 
4.4%
시 1603
 
4.4%
부 1599
 
4.4%
광 1594
 
4.4%
역 1593
 
4.4%
구 1591
 
4.4%
Other values (227) 12873
35.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19775
54.6%
Decimal Number 7613
 
21.0%
Space Separator 7060
 
19.5%
Dash Punctuation 1440
 
4.0%
Uppercase Letter 245
 
0.7%
Close Punctuation 45
 
0.1%
Open Punctuation 45
 
0.1%
Other Punctuation 12
 
< 0.1%
Lowercase Letter 11
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3366
17.0%
래 1679
8.5%
산 1610
 
8.1%
시 1603
 
8.1%
부 1599
 
8.1%
광 1594
 
8.1%
역 1593
 
8.1%
구 1591
 
8.0%
천 582
 
2.9%
온 521
 
2.6%
Other values (187) 4037
20.4%
Uppercase Letter
ValueCountFrequency (%)
K 60
24.5%
S 58
23.7%
B 22
 
9.0%
H 15
 
6.1%
Y 14
 
5.7%
U 14
 
5.7%
I 12
 
4.9%
W 12
 
4.9%
E 12
 
4.9%
V 12
 
4.9%
Other values (6) 14
 
5.7%
Decimal Number
ValueCountFrequency (%)
1 1679
22.1%
2 1024
13.5%
4 968
12.7%
3 765
10.0%
5 684
9.0%
6 580
 
7.6%
7 558
 
7.3%
0 493
 
6.5%
8 446
 
5.9%
9 416
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
e 6
54.5%
t 1
 
9.1%
s 1
 
9.1%
k 1
 
9.1%
a 1
 
9.1%
p 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
@ 7
58.3%
, 3
25.0%
. 2
 
16.7%
Space Separator
ValueCountFrequency (%)
7060
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1440
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19775
54.6%
Common 16216
44.7%
Latin 256
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3366
17.0%
래 1679
8.5%
산 1610
 
8.1%
시 1603
 
8.1%
부 1599
 
8.1%
광 1594
 
8.1%
역 1593
 
8.1%
구 1591
 
8.0%
천 582
 
2.9%
온 521
 
2.6%
Other values (187) 4037
20.4%
Latin
ValueCountFrequency (%)
K 60
23.4%
S 58
22.7%
B 22
 
8.6%
H 15
 
5.9%
Y 14
 
5.5%
U 14
 
5.5%
I 12
 
4.7%
W 12
 
4.7%
E 12
 
4.7%
V 12
 
4.7%
Other values (12) 25
9.8%
Common
ValueCountFrequency (%)
7060
43.5%
1 1679
 
10.4%
- 1440
 
8.9%
2 1024
 
6.3%
4 968
 
6.0%
3 765
 
4.7%
5 684
 
4.2%
6 580
 
3.6%
7 558
 
3.4%
0 493
 
3.0%
Other values (8) 965
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19775
54.6%
ASCII 16472
45.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7060
42.9%
1 1679
 
10.2%
- 1440
 
8.7%
2 1024
 
6.2%
4 968
 
5.9%
3 765
 
4.6%
5 684
 
4.2%
6 580
 
3.5%
7 558
 
3.4%
0 493
 
3.0%
Other values (30) 1221
 
7.4%
Hangul
ValueCountFrequency (%)
동 3366
17.0%
래 1679
8.5%
산 1610
 
8.1%
시 1603
 
8.1%
부 1599
 
8.1%
광 1594
 
8.1%
역 1593
 
8.1%
구 1591
 
8.0%
천 582
 
2.9%
온 521
 
2.6%
Other values (187) 4037
20.4%

우편번호(지번)
Text

Distinct66
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
2023-12-11T01:18:25.673218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters11137
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

Unique6 ?
Unique (%)0.4%

Sample

1st row607-801
2nd row607-020
3rd row607-809
4th row607-833
5th row607-800
ValueCountFrequency (%)
607-826 82
 
5.2%
607-804 81
 
5.1%
607-831 78
 
4.9%
607-833 71
 
4.5%
607-020 53
 
3.3%
607-830 52
 
3.3%
607-837 51
 
3.2%
607-815 50
 
3.1%
607-841 47
 
3.0%
607-809 45
 
2.8%
Other values (56) 981
61.7%
2023-12-11T01:18:26.113979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2224
20.0%
7 1804
16.2%
6 1746
15.7%
- 1591
14.3%
8 1561
14.0%
3 595
 
5.3%
2 538
 
4.8%
1 493
 
4.4%
4 343
 
3.1%
5 132
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9546
85.7%
Dash Punctuation 1591
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2224
23.3%
7 1804
18.9%
6 1746
18.3%
8 1561
16.4%
3 595
 
6.2%
2 538
 
5.6%
1 493
 
5.2%
4 343
 
3.6%
5 132
 
1.4%
9 110
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 1591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11137
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2224
20.0%
7 1804
16.2%
6 1746
15.7%
- 1591
14.3%
8 1561
14.0%
3 595
 
5.3%
2 538
 
4.8%
1 493
 
4.4%
4 343
 
3.1%
5 132
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2224
20.0%
7 1804
16.2%
6 1746
15.7%
- 1591
14.3%
8 1561
14.0%
3 595
 
5.3%
2 538
 
4.8%
1 493
 
4.4%
4 343
 
3.1%
5 132
 
1.2%

영업소전화번호
Text

MISSING 

Distinct988
Distinct (%)98.9%
Missing592
Missing (%)37.2%
Memory size12.6 KiB
2023-12-11T01:18:26.415134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters11988
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

Unique977 ?
Unique (%)97.8%

Sample

1st row051-556-9913
2nd row051-521-3833
3rd row051-556-8400
4th row051-527-6661
5th row051-557-4245
ValueCountFrequency (%)
051-555-7879 2
 
0.2%
051-557-4266 2
 
0.2%
051-556-6896 2
 
0.2%
051-506-9500 2
 
0.2%
051-555-4316 2
 
0.2%
051-555-5737 2
 
0.2%
051-946-0025 2
 
0.2%
051-558-1700 2
 
0.2%
051-531-0655 2
 
0.2%
051-523-7700 2
 
0.2%
Other values (978) 979
98.0%
2023-12-11T01:18:26.884151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2909
24.3%
- 1998
16.7%
0 1800
15.0%
1 1481
12.4%
2 844
 
7.0%
3 554
 
4.6%
8 521
 
4.3%
7 515
 
4.3%
6 495
 
4.1%
4 482
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9990
83.3%
Dash Punctuation 1998
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2909
29.1%
0 1800
18.0%
1 1481
14.8%
2 844
 
8.4%
3 554
 
5.5%
8 521
 
5.2%
7 515
 
5.2%
6 495
 
5.0%
4 482
 
4.8%
9 389
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 1998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11988
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2909
24.3%
- 1998
16.7%
0 1800
15.0%
1 1481
12.4%
2 844
 
7.0%
3 554
 
4.6%
8 521
 
4.3%
7 515
 
4.3%
6 495
 
4.1%
4 482
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2909
24.3%
- 1998
16.7%
0 1800
15.0%
1 1481
12.4%
2 844
 
7.0%
3 554
 
4.6%
8 521
 
4.3%
7 515
 
4.3%
6 495
 
4.1%
4 482
 
4.0%

Interactions

2023-12-11T01:18:21.623883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:18:27.010121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명우편번호(도로명)우편번호(지번)
업종명1.0000.3300.503
우편번호(도로명)0.3301.0000.984
우편번호(지번)0.5030.9841.000
2023-12-11T01:18:27.109423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)업종명
우편번호(도로명)1.0000.130
업종명0.1301.000

Missing values

2023-12-11T01:18:21.811403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:18:21.969934image/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.
2023-12-11T01:18:22.083661image/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

업종명업소명영업소주소(도로명)우편번호(도로명)영업소주소(지번)우편번호(지번)영업소전화번호
0건물위생관리업(사)부산장애인기업총연합회복지사업단부산광역시 동래구 안남로 23 (낙민동)47892부산광역시 동래구 낙민동 84-8607-801<NA>
1건물위생관리업(주)경남종합기업부산광역시 동래구 충렬대로237번길 90 (복천동)47809부산광역시 동래구 복천동 380-5607-020051-556-9913
2건물위생관리업(주)남부공영부산광역시 동래구 명안로 77 (명장동)47773부산광역시 동래구 명장동 131-16607-809051-521-3833
3건물위생관리업(주)뉴에버크린부산광역시 동래구 금강로 107, 4층 414호 (온천동)47705부산광역시 동래구 온천동 208-2607-833<NA>
4건물위생관리업(주)대성실업부산광역시 동래구 낙민로 14 (낙민동)47879부산광역시 동래구 낙민동 186-13607-800051-556-8400
5건물위생관리업(주)대웅시설안전부산광역시 동래구 안락로 90, 2층 (안락동)47791부산광역시 동래구 안락동 454-9 2층607-830051-527-6661
6건물위생관리업(주)더베스트이앤씨부산광역시 동래구 수안로8번길 12, 4층 (수안동)47887부산광역시 동래구 수안동 41-6607-823051-557-4245
7건물위생관리업(주)덕양티지에스부산광역시 동래구 금강공원로20번길 56, 4층 402호 (온천동)47709부산광역시 동래구 온천동 138-3607-831051-714-7200
8건물위생관리업(주)디즈텍부산광역시 동래구 충렬대로237번길 66, 4층 (복천동, 동림빌딩)47810부산광역시 동래구 복천동 295-9 외 4필지(4층)607-020051-552-2722
9건물위생관리업(주)비앤비서비스부산광역시 동래구 명륜로 47, 2층 (수안동)47818부산광역시 동래구 수안동 9-33607-822051-558-3710
업종명업소명영업소주소(도로명)우편번호(도로명)영업소주소(지번)우편번호(지번)영업소전화번호
1581화장ㆍ분장 미용업아이블리부산광역시 동래구 충렬대로218번길 51, 3층 (수안동)47818부산광역시 동래구 수안동 9-4607-822<NA>
1582화장ㆍ분장 미용업언니속눈썹부산광역시 동래구 충렬대로 487, 126(상가)동 1층 120호 (안락동, 안락 SK아파트)47798부산광역시 동래구 안락동 472-57 안락 SK아파트607-826<NA>
1583화장ㆍ분장 미용업예쁜여자부산광역시 동래구 명안로45번길 79, 1층 (안락동)47783부산광역시 동래구 안락동 427-46607-828<NA>
1584화장ㆍ분장 미용업오마이뷰티부산광역시 동래구 아시아드대로 234, 오피스텔동 2층 204-1호 (온천동, 온천동반도보라스카이뷰)47837부산광역시 동래구 온천동 1412-1 온천동반도보라스카이뷰607-842<NA>
1585화장ㆍ분장 미용업이오브로우부산광역시 동래구 금강로106번길 35, 3층 (온천동)47712부산광역시 동래구 온천동 189-82607-833<NA>
1586화장ㆍ분장 미용업자연속눈썹부산광역시 동래구 아시아드대로 234, 오피스텔동 2층 214호 (온천동, 온천동반도보라스카이뷰)47837부산광역시 동래구 온천동 1412-1 온천동반도보라스카이뷰607-842<NA>
1587화장ㆍ분장 미용업채비부산광역시 동래구 아시아드대로 234, 104-1호 (온천동, 온천동반도보라스카이뷰)47837부산광역시 동래구 온천동 1412-1 온천동반도보라스카이뷰607-842<NA>
1588화장ㆍ분장 미용업채움속눈썹부산광역시 동래구 안남로 23, 2층 211-1호 (낙민동, 보림프라자)47892부산광역시 동래구 낙민동 84-8607-801<NA>
1589화장ㆍ분장 미용업티나스타일부산광역시 동래구 중앙대로1381번길 25, 3층 (온천동)47728부산광역시 동래구 온천동 750-69 캣츠607-835051-553-9480
1590화장ㆍ분장 미용업티아라속눈썹부산광역시 동래구 온천장로65번길 9, 2층 228호 (온천동, 동래 3차 SK VIEW)47712부산광역시 동래구 온천동 1850 동래 3차 SK VIEW607-838<NA>