Overview

Dataset statistics

Number of variables7
Number of observations1609
Missing cells652
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.7 KiB
Average record size in memory57.1 B

Variable types

Categorical1
Text5
Numeric1

Dataset

Description부산광역시 동래구에 소재한 공중위생업소 현황에 대한 데이터로 업종명, 업소명, 영업소주소(도로명), 우편번호(도로명), 영업소주소(지번), 우편번호(지번), 영업소전화번호 등에 대한 항목을 제공합니다.
Author부산광역시 동래구
URLhttps://www.data.go.kr/data/15026507/fileData.do

Alerts

영업소전화번호 has 647 (40.2%) missing valuesMissing

Reproduction

Analysis started2024-03-14 23:35:16.702734
Analysis finished2024-03-14 23:35:19.220469
Duration2.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct22
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
일반미용업
669 
피부미용업
163 
네일미용업
112 
숙박업(일반)
105 
세탁업
104 
Other values (17)
456 

Length

Max length23
Median length5
Mean length5.7967682
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 669
41.6%
피부미용업 163
 
10.1%
네일미용업 112
 
7.0%
숙박업(일반) 105
 
6.5%
세탁업 104
 
6.5%
이용업 104
 
6.5%
건물위생관리업 97
 
6.0%
목욕장업 53
 
3.3%
숙박업(생활) 33
 
2.1%
화장ㆍ분장 미용업 32
 
2.0%
Other values (12) 137
 
8.5%

Length

2024-03-15T08:35:19.490245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 696
37.8%
피부미용업 217
 
11.8%
네일미용업 184
 
10.0%
미용업 112
 
6.1%
숙박업(일반 105
 
5.7%
화장ㆍ분장 105
 
5.7%
세탁업 104
 
5.7%
이용업 104
 
5.7%
건물위생관리업 97
 
5.3%
목욕장업 53
 
2.9%
Other values (2) 62
 
3.4%

업소명
Text

Distinct1581
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2024-03-15T08:35:20.719483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length5.9366066
Min length1

Characters and Unicode

Total characters9552
Distinct characters610
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

Unique1555 ?
Unique (%)96.6%

Sample

1st row(사)부산장애인기업총연합회복지사업단
2nd row(주)경남종합기업
3rd row(주)남부공영
4th row(주)뉴에버크린
5th row(주)대성실업
ValueCountFrequency (%)
헤어 14
 
0.7%
에스테틱 13
 
0.7%
hair 10
 
0.5%
뷰티 8
 
0.4%
주식회사 7
 
0.4%
salon 6
 
0.3%
미용실 6
 
0.3%
네일 5
 
0.3%
the 5
 
0.3%
de 4
 
0.2%
Other values (1715) 1790
95.8%
2024-03-15T08:35:22.419009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 418
 
4.4%
헤 402
 
4.2%
260
 
2.7%
이 221
 
2.3%
스 200
 
2.1%
) 179
 
1.9%
( 179
 
1.9%
미 177
 
1.9%
일 162
 
1.7%
네 150
 
1.6%
Other values (600) 7204
75.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7813
81.8%
Lowercase Letter 555
 
5.8%
Uppercase Letter 441
 
4.6%
Space Separator 260
 
2.7%
Close Punctuation 179
 
1.9%
Open Punctuation 179
 
1.9%
Other Punctuation 73
 
0.8%
Decimal Number 47
 
0.5%
Dash Punctuation 4
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 418
 
5.4%
헤 402
 
5.1%
이 221
 
2.8%
스 200
 
2.6%
미 177
 
2.3%
일 162
 
2.1%
네 150
 
1.9%
리 143
 
1.8%
용 123
 
1.6%
아 112
 
1.4%
Other values (527) 5705
73.0%
Lowercase Letter
ValueCountFrequency (%)
a 74
13.3%
e 63
11.4%
i 50
9.0%
o 49
8.8%
n 47
 
8.5%
r 37
 
6.7%
l 35
 
6.3%
h 26
 
4.7%
s 26
 
4.7%
b 26
 
4.7%
Other values (16) 122
22.0%
Uppercase Letter
ValueCountFrequency (%)
A 44
 
10.0%
O 37
 
8.4%
I 36
 
8.2%
H 30
 
6.8%
S 30
 
6.8%
N 30
 
6.8%
M 26
 
5.9%
R 25
 
5.7%
E 24
 
5.4%
B 21
 
4.8%
Other values (15) 138
31.3%
Decimal Number
ValueCountFrequency (%)
2 9
19.1%
1 8
17.0%
3 7
14.9%
9 6
12.8%
4 5
10.6%
0 4
8.5%
5 4
8.5%
6 3
 
6.4%
8 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
& 21
28.8%
. 17
23.3%
, 13
17.8%
# 9
12.3%
' 8
 
11.0%
: 3
 
4.1%
/ 1
 
1.4%
% 1
 
1.4%
Space Separator
ValueCountFrequency (%)
260
100.0%
Close Punctuation
ValueCountFrequency (%)
) 179
100.0%
Open Punctuation
ValueCountFrequency (%)
( 179
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7808
81.7%
Latin 996
 
10.4%
Common 743
 
7.8%
Han 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 418
 
5.4%
헤 402
 
5.1%
이 221
 
2.8%
스 200
 
2.6%
미 177
 
2.3%
일 162
 
2.1%
네 150
 
1.9%
리 143
 
1.8%
용 123
 
1.6%
아 112
 
1.4%
Other values (524) 5700
73.0%
Latin
ValueCountFrequency (%)
a 74
 
7.4%
e 63
 
6.3%
i 50
 
5.0%
o 49
 
4.9%
n 47
 
4.7%
A 44
 
4.4%
O 37
 
3.7%
r 37
 
3.7%
I 36
 
3.6%
l 35
 
3.5%
Other values (41) 524
52.6%
Common
ValueCountFrequency (%)
260
35.0%
) 179
24.1%
( 179
24.1%
& 21
 
2.8%
. 17
 
2.3%
, 13
 
1.7%
2 9
 
1.2%
# 9
 
1.2%
' 8
 
1.1%
1 8
 
1.1%
Other values (12) 40
 
5.4%
Han
ValueCountFrequency (%)
美 3
60.0%
朴 1
 
20.0%
秀 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7808
81.7%
ASCII 1739
 
18.2%
CJK 5
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 418
 
5.4%
헤 402
 
5.1%
이 221
 
2.8%
스 200
 
2.6%
미 177
 
2.3%
일 162
 
2.1%
네 150
 
1.9%
리 143
 
1.8%
용 123
 
1.6%
아 112
 
1.4%
Other values (524) 5700
73.0%
ASCII
ValueCountFrequency (%)
260
 
15.0%
) 179
 
10.3%
( 179
 
10.3%
a 74
 
4.3%
e 63
 
3.6%
i 50
 
2.9%
o 49
 
2.8%
n 47
 
2.7%
A 44
 
2.5%
O 37
 
2.1%
Other values (63) 757
43.5%
CJK
ValueCountFrequency (%)
美 3
60.0%
朴 1
 
20.0%
秀 1
 
20.0%

영업소주소(도로명)
Text

Distinct1553
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2024-03-15T08:35:23.600493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length55
Mean length32.241765
Min length21

Characters and Unicode

Total characters51877
Distinct characters267
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

Unique1500 ?
Unique (%)93.2%

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 1609
 
16.1%
동래구 1609
 
16.1%
온천동 512
 
5.1%
1층 486
 
4.9%
안락동 282
 
2.8%
사직동 278
 
2.8%
2층 222
 
2.2%
명륜동 166
 
1.7%
명장동 155
 
1.5%
수안동 99
 
1.0%
Other values (1195) 4589
45.9%
2024-03-15T08:35:25.424389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8398
 
16.2%
동 3631
 
7.0%
1 2288
 
4.4%
시 1790
 
3.5%
래 1778
 
3.4%
부 1638
 
3.2%
) 1634
 
3.1%
( 1633
 
3.1%
산 1630
 
3.1%
광 1613
 
3.1%
Other values (257) 25844
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29701
57.3%
Decimal Number 8537
 
16.5%
Space Separator 8398
 
16.2%
Close Punctuation 1634
 
3.1%
Open Punctuation 1633
 
3.1%
Other Punctuation 1338
 
2.6%
Uppercase Letter 321
 
0.6%
Dash Punctuation 298
 
0.6%
Lowercase Letter 9
 
< 0.1%
Math Symbol 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3631
 
12.2%
시 1790
 
6.0%
래 1778
 
6.0%
부 1638
 
5.5%
산 1630
 
5.5%
광 1613
 
5.4%
역 1611
 
5.4%
구 1609
 
5.4%
로 1599
 
5.4%
길 989
 
3.3%
Other values (218) 11813
39.8%
Uppercase Letter
ValueCountFrequency (%)
K 71
22.1%
S 70
21.8%
B 34
10.6%
A 20
 
6.2%
E 19
 
5.9%
H 18
 
5.6%
I 17
 
5.3%
U 17
 
5.3%
Y 17
 
5.3%
W 16
 
5.0%
Other values (7) 22
 
6.9%
Decimal Number
ValueCountFrequency (%)
1 2288
26.8%
2 1436
16.8%
3 951
11.1%
4 692
 
8.1%
0 670
 
7.8%
7 577
 
6.8%
5 564
 
6.6%
6 487
 
5.7%
8 462
 
5.4%
9 410
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
e 6
66.7%
b 1
 
11.1%
s 1
 
11.1%
k 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 1334
99.7%
@ 3
 
0.2%
. 1
 
0.1%
Space Separator
ValueCountFrequency (%)
8398
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1634
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1633
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 298
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29701
57.3%
Common 21846
42.1%
Latin 330
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3631
 
12.2%
시 1790
 
6.0%
래 1778
 
6.0%
부 1638
 
5.5%
산 1630
 
5.5%
광 1613
 
5.4%
역 1611
 
5.4%
구 1609
 
5.4%
로 1599
 
5.4%
길 989
 
3.3%
Other values (218) 11813
39.8%
Latin
ValueCountFrequency (%)
K 71
21.5%
S 70
21.2%
B 34
10.3%
A 20
 
6.1%
E 19
 
5.8%
H 18
 
5.5%
I 17
 
5.2%
U 17
 
5.2%
Y 17
 
5.2%
W 16
 
4.8%
Other values (11) 31
9.4%
Common
ValueCountFrequency (%)
8398
38.4%
1 2288
 
10.5%
) 1634
 
7.5%
( 1633
 
7.5%
2 1436
 
6.6%
, 1334
 
6.1%
3 951
 
4.4%
4 692
 
3.2%
0 670
 
3.1%
7 577
 
2.6%
Other values (8) 2233
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29701
57.3%
ASCII 22176
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8398
37.9%
1 2288
 
10.3%
) 1634
 
7.4%
( 1633
 
7.4%
2 1436
 
6.5%
, 1334
 
6.0%
3 951
 
4.3%
4 692
 
3.1%
0 670
 
3.0%
7 577
 
2.6%
Other values (29) 2563
 
11.6%
Hangul
ValueCountFrequency (%)
동 3631
 
12.2%
시 1790
 
6.0%
래 1778
 
6.0%
부 1638
 
5.5%
산 1630
 
5.5%
광 1613
 
5.4%
역 1611
 
5.4%
구 1609
 
5.4%
로 1599
 
5.4%
길 989
 
3.3%
Other values (218) 11813
39.8%

우편번호(도로명)
Real number (ℝ)

Distinct183
Distinct (%)11.4%
Missing5
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean47798.111
Minimum47701
Maximum47905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2024-03-15T08:35:25.851333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47701
5-th percentile47709
Q147743
median47803
Q347846
95-th percentile47892
Maximum47905
Range204
Interquartile range (IQR)103

Descriptive statistics

Standard deviation59.119967
Coefficient of variation (CV)0.0012368683
Kurtosis-1.2000866
Mean47798.111
Median Absolute Deviation (MAD)51
Skewness-0.022149852
Sum76668170
Variance3495.1705
MonotonicityNot monotonic
2024-03-15T08:35:26.328313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47712 48
 
3.0%
47709 46
 
2.9%
47813 30
 
1.9%
47715 30
 
1.9%
47710 29
 
1.8%
47837 27
 
1.7%
47728 26
 
1.6%
47808 25
 
1.6%
47730 22
 
1.4%
47815 22
 
1.4%
Other values (173) 1299
80.7%
ValueCountFrequency (%)
47701 5
 
0.3%
47702 1
 
0.1%
47703 4
 
0.2%
47704 3
 
0.2%
47705 5
 
0.3%
47706 1
 
0.1%
47707 2
 
0.1%
47708 20
1.2%
47709 46
2.9%
47710 29
1.8%
ValueCountFrequency (%)
47905 6
 
0.4%
47904 4
 
0.2%
47901 15
0.9%
47900 9
0.6%
47899 7
0.4%
47898 9
0.6%
47897 1
 
0.1%
47896 2
 
0.1%
47895 11
0.7%
47894 9
0.6%

영업소주소(지번)
Text

Distinct1397
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2024-03-15T08:35:27.569619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length44
Mean length22.869484
Min length17

Characters and Unicode

Total characters36797
Distinct characters241
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

Unique1247 ?
Unique (%)77.5%

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 1609
22.1%
동래구 1609
22.1%
온천동 521
 
7.2%
안락동 286
 
3.9%
사직동 283
 
3.9%
명륜동 167
 
2.3%
명장동 155
 
2.1%
수안동 100
 
1.4%
1층 68
 
0.9%
복천동 49
 
0.7%
Other values (1579) 2421
33.3%
2024-03-15T08:35:29.688005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7174
19.5%
동 3412
 
9.3%
래 1708
 
4.6%
1 1692
 
4.6%
산 1628
 
4.4%
시 1622
 
4.4%
부 1618
 
4.4%
광 1612
 
4.4%
역 1611
 
4.4%
구 1609
 
4.4%
Other values (231) 13111
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20076
54.6%
Decimal Number 7701
 
20.9%
Space Separator 7174
 
19.5%
Dash Punctuation 1453
 
3.9%
Uppercase Letter 288
 
0.8%
Open Punctuation 41
 
0.1%
Close Punctuation 41
 
0.1%
Other Punctuation 11
 
< 0.1%
Lowercase Letter 11
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3412
17.0%
래 1708
8.5%
산 1628
 
8.1%
시 1622
 
8.1%
부 1618
 
8.1%
광 1612
 
8.0%
역 1611
 
8.0%
구 1609
 
8.0%
천 593
 
3.0%
온 538
 
2.7%
Other values (194) 4125
20.5%
Uppercase Letter
ValueCountFrequency (%)
K 70
24.3%
S 69
24.0%
B 23
 
8.0%
H 18
 
6.2%
E 17
 
5.9%
U 17
 
5.9%
Y 17
 
5.9%
V 16
 
5.6%
I 16
 
5.6%
W 16
 
5.6%
Other values (3) 9
 
3.1%
Decimal Number
ValueCountFrequency (%)
1 1692
22.0%
2 1031
13.4%
4 975
12.7%
3 781
10.1%
5 707
9.2%
6 579
 
7.5%
7 560
 
7.3%
0 493
 
6.4%
8 465
 
6.0%
9 418
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
e 6
54.5%
a 1
 
9.1%
p 1
 
9.1%
t 1
 
9.1%
s 1
 
9.1%
k 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
@ 7
63.6%
. 2
 
18.2%
, 2
 
18.2%
Space Separator
ValueCountFrequency (%)
7174
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1453
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20076
54.6%
Common 16422
44.6%
Latin 299
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3412
17.0%
래 1708
8.5%
산 1628
 
8.1%
시 1622
 
8.1%
부 1618
 
8.1%
광 1612
 
8.0%
역 1611
 
8.0%
구 1609
 
8.0%
천 593
 
3.0%
온 538
 
2.7%
Other values (194) 4125
20.5%
Latin
ValueCountFrequency (%)
K 70
23.4%
S 69
23.1%
B 23
 
7.7%
H 18
 
6.0%
E 17
 
5.7%
U 17
 
5.7%
Y 17
 
5.7%
V 16
 
5.4%
I 16
 
5.4%
W 16
 
5.4%
Other values (9) 20
 
6.7%
Common
ValueCountFrequency (%)
7174
43.7%
1 1692
 
10.3%
- 1453
 
8.8%
2 1031
 
6.3%
4 975
 
5.9%
3 781
 
4.8%
5 707
 
4.3%
6 579
 
3.5%
7 560
 
3.4%
0 493
 
3.0%
Other values (8) 977
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20076
54.6%
ASCII 16721
45.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7174
42.9%
1 1692
 
10.1%
- 1453
 
8.7%
2 1031
 
6.2%
4 975
 
5.8%
3 781
 
4.7%
5 707
 
4.2%
6 579
 
3.5%
7 560
 
3.3%
0 493
 
2.9%
Other values (27) 1276
 
7.6%
Hangul
ValueCountFrequency (%)
동 3412
17.0%
래 1708
8.5%
산 1628
 
8.1%
시 1622
 
8.1%
부 1618
 
8.1%
광 1612
 
8.0%
역 1611
 
8.0%
구 1609
 
8.0%
천 593
 
3.0%
온 538
 
2.7%
Other values (194) 4125
20.5%

우편번호(지번)
Text

Distinct66
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2024-03-15T08:35:31.013148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters11263
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

Unique5 ?
Unique (%)0.3%

Sample

1st row607-801
2nd row607-020
3rd row607-809
4th row607-833
5th row607-800
ValueCountFrequency (%)
607-804 90
 
5.6%
607-826 85
 
5.3%
607-831 79
 
4.9%
607-833 75
 
4.7%
607-830 53
 
3.3%
607-837 52
 
3.2%
607-815 51
 
3.2%
607-842 49
 
3.0%
607-020 48
 
3.0%
607-834 47
 
2.9%
Other values (56) 980
60.9%
2024-03-15T08:35:32.308847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2238
19.9%
7 1819
16.2%
6 1766
15.7%
- 1609
14.3%
8 1584
14.1%
3 616
 
5.5%
2 551
 
4.9%
1 486
 
4.3%
4 351
 
3.1%
5 134
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9654
85.7%
Dash Punctuation 1609
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2238
23.2%
7 1819
18.8%
6 1766
18.3%
8 1584
16.4%
3 616
 
6.4%
2 551
 
5.7%
1 486
 
5.0%
4 351
 
3.6%
5 134
 
1.4%
9 109
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 1609
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2238
19.9%
7 1819
16.2%
6 1766
15.7%
- 1609
14.3%
8 1584
14.1%
3 616
 
5.5%
2 551
 
4.9%
1 486
 
4.3%
4 351
 
3.1%
5 134
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2238
19.9%
7 1819
16.2%
6 1766
15.7%
- 1609
14.3%
8 1584
14.1%
3 616
 
5.5%
2 551
 
4.9%
1 486
 
4.3%
4 351
 
3.1%
5 134
 
1.2%

영업소전화번호
Text

MISSING 

Distinct951
Distinct (%)98.9%
Missing647
Missing (%)40.2%
Memory size12.7 KiB
2024-03-15T08:35:33.426596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters11544
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

Unique940 ?
Unique (%)97.7%

Sample

1st row051-556-9913
2nd row051-521-3833
3rd row051-556-8400
4th row051-527-6661
5th row051-557-4245
ValueCountFrequency (%)
051-556-6896 2
 
0.2%
051-555-4316 2
 
0.2%
051-531-0655 2
 
0.2%
051-506-9500 2
 
0.2%
051-555-7879 2
 
0.2%
051-523-7700 2
 
0.2%
051-555-5737 2
 
0.2%
051-946-0025 2
 
0.2%
051-558-1700 2
 
0.2%
051-557-4266 2
 
0.2%
Other values (941) 942
97.9%
2024-03-15T08:35:34.829660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2805
24.3%
- 1924
16.7%
0 1737
15.0%
1 1436
12.4%
2 807
 
7.0%
3 529
 
4.6%
8 495
 
4.3%
7 491
 
4.3%
4 477
 
4.1%
6 472
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9620
83.3%
Dash Punctuation 1924
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2805
29.2%
0 1737
18.1%
1 1436
14.9%
2 807
 
8.4%
3 529
 
5.5%
8 495
 
5.1%
7 491
 
5.1%
4 477
 
5.0%
6 472
 
4.9%
9 371
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 1924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2805
24.3%
- 1924
16.7%
0 1737
15.0%
1 1436
12.4%
2 807
 
7.0%
3 529
 
4.6%
8 495
 
4.3%
7 491
 
4.3%
4 477
 
4.1%
6 472
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2805
24.3%
- 1924
16.7%
0 1737
15.0%
1 1436
12.4%
2 807
 
7.0%
3 529
 
4.6%
8 495
 
4.3%
7 491
 
4.3%
4 477
 
4.1%
6 472
 
4.1%

Interactions

2024-03-15T08:35:17.986078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:35:34.988944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명우편번호(도로명)우편번호(지번)
업종명1.0000.3140.420
우편번호(도로명)0.3141.0000.983
우편번호(지번)0.4200.9831.000
2024-03-15T08:35:35.161745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)업종명
우편번호(도로명)1.0000.123
업종명0.1231.000

Missing values

2024-03-15T08:35:18.369896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:35:18.771073image/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.
2024-03-15T08:35:19.076727image/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
1599화장ㆍ분장 미용업아이블리부산광역시 동래구 충렬대로218번길 51, 3층 (수안동)47818부산광역시 동래구 수안동 9-4607-822<NA>
1600화장ㆍ분장 미용업언니속눈썹부산광역시 동래구 충렬대로 487, 126(상가)동 1층 120호 (안락동, 안락 SK아파트)47798부산광역시 동래구 안락동 472-57 안락 SK아파트607-826<NA>
1601화장ㆍ분장 미용업예쁜여자부산광역시 동래구 명안로45번길 79, 1층 (안락동)47783부산광역시 동래구 안락동 427-46607-828<NA>
1602화장ㆍ분장 미용업오마이뷰티부산광역시 동래구 아시아드대로 234, 오피스텔동 2층 204-1호 (온천동, 온천동반도보라스카이뷰)47837부산광역시 동래구 온천동 1412-1 온천동반도보라스카이뷰607-842<NA>
1603화장ㆍ분장 미용업우블랑부산광역시 동래구 석사북로 33, 1층 (사직동)47857부산광역시 동래구 사직동 19-45607-120<NA>
1604화장ㆍ분장 미용업이오브로우부산광역시 동래구 금강로106번길 35, 3층 (온천동)47712부산광역시 동래구 온천동 189-82607-833<NA>
1605화장ㆍ분장 미용업자연속눈썹부산광역시 동래구 아시아드대로 234, 오피스텔동 2층 214호 (온천동, 온천동반도보라스카이뷰)47837부산광역시 동래구 온천동 1412-1 온천동반도보라스카이뷰607-842<NA>
1606화장ㆍ분장 미용업채움속눈썹부산광역시 동래구 안남로 23, 2층 211-1호 (낙민동, 보림프라자)47892부산광역시 동래구 낙민동 84-8607-801<NA>
1607화장ㆍ분장 미용업티나스타일부산광역시 동래구 중앙대로1381번길 25, 3층 (온천동)47728부산광역시 동래구 온천동 750-69 캣츠607-835051-553-9480
1608화장ㆍ분장 미용업티아라속눈썹부산광역시 동래구 온천장로65번길 9, 2층 228호 (온천동, 동래 3차 SK VIEW)47712부산광역시 동래구 온천동 1850 동래 3차 SK VIEW607-838<NA>