Overview

Dataset statistics

Number of variables6
Number of observations213
Missing cells16
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory49.6 B

Variable types

Numeric1
Text5

Dataset

Description부산광역시남구_공장등록현황_20200820
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15022991

Alerts

전화번호 has 16 (7.5%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:18:36.409224
Analysis finished2023-12-10 16:18:37.148462
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct213
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107
Minimum1
Maximum213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T01:18:37.292980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.6
Q154
median107
Q3160
95-th percentile202.4
Maximum213
Range212
Interquartile range (IQR)106

Descriptive statistics

Standard deviation61.631972
Coefficient of variation (CV)0.57599974
Kurtosis-1.2
Mean107
Median Absolute Deviation (MAD)53
Skewness0
Sum22791
Variance3798.5
MonotonicityStrictly increasing
2023-12-11T01:18:37.492014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
135 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
140 1
 
0.5%
141 1
 
0.5%
142 1
 
0.5%
143 1
 
0.5%
144 1
 
0.5%
Other values (203) 203
95.3%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
213 1
0.5%
212 1
0.5%
211 1
0.5%
210 1
0.5%
209 1
0.5%
208 1
0.5%
207 1
0.5%
206 1
0.5%
205 1
0.5%
204 1
0.5%

회사명
Text

Distinct209
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T01:18:37.757930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length6.8638498
Min length1

Characters and Unicode

Total characters1462
Distinct characters286
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)96.2%

Sample

1st row(유)피피지코리아
2nd row(주)겐츠 베이커리
3rd row(주)경양엔지니어링
4th row(주)광덕창호산업
5th row(주)광명생약
ValueCountFrequency (%)
주식회사 17
 
7.1%
주)인터오션 2
 
0.8%
주)화림제약 2
 
0.8%
주)인들디자인 2
 
0.8%
케이시스 2
 
0.8%
알파마린 1
 
0.4%
씨오션전자주식회사 1
 
0.4%
씨엔스프링연구소 1
 
0.4%
썸원 1
 
0.4%
신우팩토리 1
 
0.4%
Other values (210) 210
87.5%
2023-12-11T01:18:38.161778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 116
 
7.9%
( 97
 
6.6%
) 97
 
6.6%
스 54
 
3.7%
이 48
 
3.3%
사 38
 
2.6%
27
 
1.8%
식 24
 
1.6%
회 23
 
1.6%
에 20
 
1.4%
Other values (276) 918
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1196
81.8%
Open Punctuation 97
 
6.6%
Close Punctuation 97
 
6.6%
Uppercase Letter 31
 
2.1%
Space Separator 27
 
1.8%
Lowercase Letter 10
 
0.7%
Other Punctuation 3
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 116
 
9.7%
스 54
 
4.5%
이 48
 
4.0%
사 38
 
3.2%
식 24
 
2.0%
회 23
 
1.9%
에 20
 
1.7%
지 19
 
1.6%
산 19
 
1.6%
어 19
 
1.6%
Other values (244) 816
68.2%
Uppercase Letter
ValueCountFrequency (%)
E 4
12.9%
D 3
 
9.7%
T 3
 
9.7%
N 2
 
6.5%
C 2
 
6.5%
F 2
 
6.5%
U 2
 
6.5%
B 2
 
6.5%
P 2
 
6.5%
O 1
 
3.2%
Other values (8) 8
25.8%
Lowercase Letter
ValueCountFrequency (%)
e 2
20.0%
c 2
20.0%
d 1
10.0%
n 1
10.0%
i 1
10.0%
r 1
10.0%
o 1
10.0%
h 1
10.0%
Other Punctuation
ValueCountFrequency (%)
& 2
66.7%
/ 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 97
100.0%
Close Punctuation
ValueCountFrequency (%)
) 97
100.0%
Space Separator
ValueCountFrequency (%)
27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1196
81.8%
Common 225
 
15.4%
Latin 41
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 116
 
9.7%
스 54
 
4.5%
이 48
 
4.0%
사 38
 
3.2%
식 24
 
2.0%
회 23
 
1.9%
에 20
 
1.7%
지 19
 
1.6%
산 19
 
1.6%
어 19
 
1.6%
Other values (244) 816
68.2%
Latin
ValueCountFrequency (%)
E 4
 
9.8%
D 3
 
7.3%
T 3
 
7.3%
N 2
 
4.9%
C 2
 
4.9%
e 2
 
4.9%
c 2
 
4.9%
F 2
 
4.9%
U 2
 
4.9%
B 2
 
4.9%
Other values (16) 17
41.5%
Common
ValueCountFrequency (%)
( 97
43.1%
) 97
43.1%
27
 
12.0%
& 2
 
0.9%
/ 1
 
0.4%
- 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1196
81.8%
ASCII 266
 
18.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 116
 
9.7%
스 54
 
4.5%
이 48
 
4.0%
사 38
 
3.2%
식 24
 
2.0%
회 23
 
1.9%
에 20
 
1.7%
지 19
 
1.6%
산 19
 
1.6%
어 19
 
1.6%
Other values (244) 816
68.2%
ASCII
ValueCountFrequency (%)
( 97
36.5%
) 97
36.5%
27
 
10.2%
E 4
 
1.5%
D 3
 
1.1%
T 3
 
1.1%
N 2
 
0.8%
C 2
 
0.8%
e 2
 
0.8%
c 2
 
0.8%
Other values (22) 27
 
10.2%

공장대표주소(도로명)
Text

Distinct205
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T01:18:38.532459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0516432
Min length2

Characters and Unicode

Total characters650
Distinct characters141
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

Unique197 ?
Unique (%)92.5%

Sample

1st row조성삼
2nd row정호연
3rd row윤호경
4th row김필권
5th row김연만
ValueCountFrequency (%)
천병민 2
 
0.9%
허 2
 
0.9%
채재익 2
 
0.9%
전흔수 2
 
0.9%
김종하,박행자 2
 
0.9%
유재태 2
 
0.9%
김정만 2
 
0.9%
박동규 2
 
0.9%
이선학 2
 
0.9%
정영수 1
 
0.5%
Other values (200) 200
91.3%
2023-12-11T01:18:39.042772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 55
 
8.5%
이 28
 
4.3%
영 23
 
3.5%
정 16
 
2.5%
성 16
 
2.5%
재 14
 
2.2%
박 13
 
2.0%
수 12
 
1.8%
종 12
 
1.8%
동 11
 
1.7%
Other values (131) 450
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 641
98.6%
Space Separator 6
 
0.9%
Other Punctuation 2
 
0.3%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 55
 
8.6%
이 28
 
4.4%
영 23
 
3.6%
정 16
 
2.5%
성 16
 
2.5%
재 14
 
2.2%
박 13
 
2.0%
수 12
 
1.9%
종 12
 
1.9%
동 11
 
1.7%
Other values (128) 441
68.8%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 641
98.6%
Common 9
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 55
 
8.6%
이 28
 
4.4%
영 23
 
3.6%
정 16
 
2.5%
성 16
 
2.5%
재 14
 
2.2%
박 13
 
2.0%
수 12
 
1.9%
종 12
 
1.9%
동 11
 
1.7%
Other values (128) 441
68.8%
Common
ValueCountFrequency (%)
6
66.7%
, 2
 
22.2%
2 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 641
98.6%
ASCII 9
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 55
 
8.6%
이 28
 
4.4%
영 23
 
3.6%
정 16
 
2.5%
성 16
 
2.5%
재 14
 
2.2%
박 13
 
2.0%
수 12
 
1.9%
종 12
 
1.9%
동 11
 
1.7%
Other values (128) 441
68.8%
ASCII
ValueCountFrequency (%)
6
66.7%
, 2
 
22.2%
2 1
 
11.1%

업종명
Text

Distinct148
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T01:18:39.462762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length25
Mean length16.835681
Min length5

Characters and Unicode

Total characters3586
Distinct characters203
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

Unique116 ?
Unique (%)54.5%

Sample

1st row일반용 도료 및 관련제품 제조업
2nd row빵류 제조업
3rd row액체 펌프 제조업 외1 종
4th row플라스틱 창호 제조업 외1 종
5th row한의약품 제조업
ValueCountFrequency (%)
제조업 199
17.8%
종 124
 
11.1%
및 93
 
8.3%
기타 63
 
5.6%
외1 58
 
5.2%
그 25
 
2.2%
외 25
 
2.2%
셔츠 22
 
2.0%
블라우스 22
 
2.0%
외2 20
 
1.8%
Other values (211) 469
41.9%
2023-12-11T01:18:40.021086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
907
25.3%
제 239
 
6.7%
업 229
 
6.4%
조 213
 
5.9%
외 150
 
4.2%
종 126
 
3.5%
기 118
 
3.3%
및 93
 
2.6%
1 66
 
1.8%
타 64
 
1.8%
Other values (193) 1381
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2519
70.2%
Space Separator 907
 
25.3%
Decimal Number 131
 
3.7%
Other Punctuation 27
 
0.8%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 239
 
9.5%
업 229
 
9.1%
조 213
 
8.5%
외 150
 
6.0%
종 126
 
5.0%
기 118
 
4.7%
및 93
 
3.7%
타 64
 
2.5%
장 53
 
2.1%
품 53
 
2.1%
Other values (181) 1181
46.9%
Decimal Number
ValueCountFrequency (%)
1 66
50.4%
2 21
 
16.0%
3 18
 
13.7%
5 8
 
6.1%
4 6
 
4.6%
6 5
 
3.8%
7 5
 
3.8%
8 2
 
1.5%
Space Separator
ValueCountFrequency (%)
907
100.0%
Other Punctuation
ValueCountFrequency (%)
, 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2519
70.2%
Common 1067
29.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 239
 
9.5%
업 229
 
9.1%
조 213
 
8.5%
외 150
 
6.0%
종 126
 
5.0%
기 118
 
4.7%
및 93
 
3.7%
타 64
 
2.5%
장 53
 
2.1%
품 53
 
2.1%
Other values (181) 1181
46.9%
Common
ValueCountFrequency (%)
907
85.0%
1 66
 
6.2%
, 27
 
2.5%
2 21
 
2.0%
3 18
 
1.7%
5 8
 
0.7%
4 6
 
0.6%
6 5
 
0.5%
7 5
 
0.5%
8 2
 
0.2%
Other values (2) 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2519
70.2%
ASCII 1067
29.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
907
85.0%
1 66
 
6.2%
, 27
 
2.5%
2 21
 
2.0%
3 18
 
1.7%
5 8
 
0.7%
4 6
 
0.6%
6 5
 
0.5%
7 5
 
0.5%
8 2
 
0.2%
Other values (2) 2
 
0.2%
Hangul
ValueCountFrequency (%)
제 239
 
9.5%
업 229
 
9.1%
조 213
 
8.5%
외 150
 
6.0%
종 126
 
5.0%
기 118
 
4.7%
및 93
 
3.7%
타 64
 
2.5%
장 53
 
2.1%
품 53
 
2.1%
Other values (181) 1181
46.9%

전화번호
Text

MISSING 

Distinct190
Distinct (%)96.4%
Missing16
Missing (%)7.5%
Memory size1.8 KiB
2023-12-11T01:18:40.277598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.010152
Min length9

Characters and Unicode

Total characters2366
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

Unique183 ?
Unique (%)92.9%

Sample

1st row051-624-8221
2nd row1899-6539
3rd row051-418-6628
4th row051-628-0087
5th row051-611-2257
ValueCountFrequency (%)
051-642-7800 2
 
1.0%
051-622-5665 2
 
1.0%
051-626-7726 2
 
1.0%
051-621-6003 2
 
1.0%
051-647-0083 2
 
1.0%
051-627-9870 2
 
1.0%
051-626-6277 2
 
1.0%
051-612-0510 1
 
0.5%
051-624-8221 1
 
0.5%
051-324-5655 1
 
0.5%
Other values (180) 180
91.4%
2023-12-11T01:18:40.697822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 393
16.6%
0 344
14.5%
1 336
14.2%
5 293
12.4%
6 258
10.9%
2 196
8.3%
3 139
 
5.9%
7 127
 
5.4%
4 121
 
5.1%
8 82
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1973
83.4%
Dash Punctuation 393
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 344
17.4%
1 336
17.0%
5 293
14.9%
6 258
13.1%
2 196
9.9%
3 139
7.0%
7 127
 
6.4%
4 121
 
6.1%
8 82
 
4.2%
9 77
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2366
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 393
16.6%
0 344
14.5%
1 336
14.2%
5 293
12.4%
6 258
10.9%
2 196
8.3%
3 139
 
5.9%
7 127
 
5.4%
4 121
 
5.1%
8 82
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 393
16.6%
0 344
14.5%
1 336
14.2%
5 293
12.4%
6 258
10.9%
2 196
8.3%
3 139
 
5.9%
7 127
 
5.4%
4 121
 
5.1%
8 82
 
3.5%

생산품
Text

Distinct197
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T01:18:40.927757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length30
Mean length8.2065728
Min length1

Characters and Unicode

Total characters1748
Distinct characters336
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique185 ?
Unique (%)86.9%

Sample

1st row페인트
2nd row제빵, 제과
3rd row선박용펌프
4th row플라스틱제 창문
5th row광명 당귀외 한약제
ValueCountFrequency (%)
및 9
 
2.5%
등 9
 
2.5%
외 7
 
1.9%
근무복 6
 
1.7%
의류 5
 
1.4%
제조 5
 
1.4%
티셔츠 4
 
1.1%
화장품 4
 
1.1%
잠수복 4
 
1.1%
학생복 3
 
0.8%
Other values (279) 306
84.5%
2023-12-11T01:18:41.288158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
151
 
8.6%
, 100
 
5.7%
기 57
 
3.3%
장 40
 
2.3%
복 38
 
2.2%
제 27
 
1.5%
전 27
 
1.5%
조 25
 
1.4%
스 24
 
1.4%
용 24
 
1.4%
Other values (326) 1235
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1391
79.6%
Space Separator 151
 
8.6%
Other Punctuation 100
 
5.7%
Uppercase Letter 72
 
4.1%
Lowercase Letter 15
 
0.9%
Open Punctuation 8
 
0.5%
Close Punctuation 8
 
0.5%
Decimal Number 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 57
 
4.1%
장 40
 
2.9%
복 38
 
2.7%
제 27
 
1.9%
전 27
 
1.9%
조 25
 
1.8%
스 24
 
1.7%
용 24
 
1.7%
비 23
 
1.7%
수 23
 
1.7%
Other values (291) 1083
77.9%
Uppercase Letter
ValueCountFrequency (%)
E 10
13.9%
D 10
13.9%
L 9
12.5%
C 8
11.1%
T 6
8.3%
S 6
8.3%
P 5
6.9%
V 4
 
5.6%
I 3
 
4.2%
A 2
 
2.8%
Other values (8) 9
12.5%
Lowercase Letter
ValueCountFrequency (%)
c 3
20.0%
t 2
13.3%
s 2
13.3%
i 2
13.3%
p 1
 
6.7%
v 1
 
6.7%
f 1
 
6.7%
y 1
 
6.7%
e 1
 
6.7%
m 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
4 1
33.3%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
151
100.0%
Other Punctuation
ValueCountFrequency (%)
, 100
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1391
79.6%
Common 270
 
15.4%
Latin 87
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 57
 
4.1%
장 40
 
2.9%
복 38
 
2.7%
제 27
 
1.9%
전 27
 
1.9%
조 25
 
1.8%
스 24
 
1.7%
용 24
 
1.7%
비 23
 
1.7%
수 23
 
1.7%
Other values (291) 1083
77.9%
Latin
ValueCountFrequency (%)
E 10
11.5%
D 10
11.5%
L 9
 
10.3%
C 8
 
9.2%
T 6
 
6.9%
S 6
 
6.9%
P 5
 
5.7%
V 4
 
4.6%
c 3
 
3.4%
I 3
 
3.4%
Other values (18) 23
26.4%
Common
ValueCountFrequency (%)
151
55.9%
, 100
37.0%
( 8
 
3.0%
) 8
 
3.0%
3 1
 
0.4%
4 1
 
0.4%
2 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1391
79.6%
ASCII 357
 
20.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
151
42.3%
, 100
28.0%
E 10
 
2.8%
D 10
 
2.8%
L 9
 
2.5%
( 8
 
2.2%
) 8
 
2.2%
C 8
 
2.2%
T 6
 
1.7%
S 6
 
1.7%
Other values (25) 41
 
11.5%
Hangul
ValueCountFrequency (%)
기 57
 
4.1%
장 40
 
2.9%
복 38
 
2.7%
제 27
 
1.9%
전 27
 
1.9%
조 25
 
1.8%
스 24
 
1.7%
용 24
 
1.7%
비 23
 
1.7%
수 23
 
1.7%
Other values (291) 1083
77.9%

Interactions

2023-12-11T01:18:36.884106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-11T01:18:36.991945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:18:37.106751image/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

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