Overview

Dataset statistics

Number of variables12
Number of observations108
Missing cells241
Missing cells (%)18.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory99.2 B

Variable types

Numeric2
Text5
Categorical4
DateTime1

Dataset

Description대구광역시 서구 건축업체 업종별 현황입니다. (회사명, 공장대표주소지,업종,전화번호,팩스번호,종업원수,공장홈페이지 등)
Author대구광역시 서구
URLhttps://www.data.go.kr/data/15126654/fileData.do

Alerts

담당자 has constant value ""Constant
담당자연락처 has constant value ""Constant
주력업종 is highly overall correlated with 신청 명세 업종High correlation
신청 명세 업종 is highly overall correlated with 주력업종High correlation
전화번호 has 75 (69.4%) missing valuesMissing
팩스번호 has 71 (65.7%) missing valuesMissing
공장홈페이지 has 95 (88.0%) missing valuesMissing
연번 has unique valuesUnique
종업원수 has 5 (4.6%) zerosZeros

Reproduction

Analysis started2024-03-14 15:48:57.518845
Analysis finished2024-03-14 15:48:59.992885
Duration2.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.5
Minimum1
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T00:49:00.210416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.35
Q127.75
median54.5
Q381.25
95-th percentile102.65
Maximum108
Range107
Interquartile range (IQR)53.5

Descriptive statistics

Standard deviation31.32092
Coefficient of variation (CV)0.57469577
Kurtosis-1.2
Mean54.5
Median Absolute Deviation (MAD)27
Skewness0
Sum5886
Variance981
MonotonicityStrictly increasing
2024-03-15T00:49:00.535781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
70 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
Other values (98) 98
90.7%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
108 1
0.9%
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%

회사명
Text

Distinct102
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size992.0 B
2024-03-15T00:49:01.598188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length6.7037037
Min length2

Characters and Unicode

Total characters724
Distinct characters177
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

Unique97 ?
Unique (%)89.8%

Sample

1st row(주)사라미데코
2nd row금빛커텐
3rd row다성
4th row보강산업
5th row주식회사 성진기업
ValueCountFrequency (%)
주식회사 13
 
9.6%
서연유니폼 4
 
3.0%
대구봉제 3
 
2.2%
주)에이치티인테크 3
 
2.2%
금빛커텐 2
 
1.5%
한솔블라인드 2
 
1.5%
제2공장 2
 
1.5%
주)사라미데코 2
 
1.5%
대동산업 1
 
0.7%
shine 1
 
0.7%
Other values (102) 102
75.6%
2024-03-15T00:49:03.095984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 41
 
5.7%
) 34
 
4.7%
( 34
 
4.7%
28
 
3.9%
이 22
 
3.0%
유 20
 
2.8%
사 20
 
2.8%
회 14
 
1.9%
식 14
 
1.9%
대 14
 
1.9%
Other values (167) 483
66.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 589
81.4%
Close Punctuation 34
 
4.7%
Open Punctuation 34
 
4.7%
Space Separator 28
 
3.9%
Uppercase Letter 26
 
3.6%
Lowercase Letter 8
 
1.1%
Decimal Number 3
 
0.4%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 41
 
7.0%
이 22
 
3.7%
유 20
 
3.4%
사 20
 
3.4%
회 14
 
2.4%
식 14
 
2.4%
대 14
 
2.4%
인 14
 
2.4%
니 14
 
2.4%
스 13
 
2.2%
Other values (140) 403
68.4%
Uppercase Letter
ValueCountFrequency (%)
I 4
15.4%
C 3
11.5%
O 2
 
7.7%
N 2
 
7.7%
F 2
 
7.7%
E 2
 
7.7%
S 2
 
7.7%
K 1
 
3.8%
D 1
 
3.8%
W 1
 
3.8%
Other values (6) 6
23.1%
Lowercase Letter
ValueCountFrequency (%)
e 2
25.0%
n 2
25.0%
h 2
25.0%
i 2
25.0%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
1 1
33.3%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
. 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 589
81.4%
Common 101
 
14.0%
Latin 34
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 41
 
7.0%
이 22
 
3.7%
유 20
 
3.4%
사 20
 
3.4%
회 14
 
2.4%
식 14
 
2.4%
대 14
 
2.4%
인 14
 
2.4%
니 14
 
2.4%
스 13
 
2.2%
Other values (140) 403
68.4%
Latin
ValueCountFrequency (%)
I 4
 
11.8%
C 3
 
8.8%
e 2
 
5.9%
O 2
 
5.9%
N 2
 
5.9%
n 2
 
5.9%
F 2
 
5.9%
E 2
 
5.9%
S 2
 
5.9%
h 2
 
5.9%
Other values (10) 11
32.4%
Common
ValueCountFrequency (%)
) 34
33.7%
( 34
33.7%
28
27.7%
2 2
 
2.0%
& 1
 
1.0%
. 1
 
1.0%
1 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 589
81.4%
ASCII 135
 
18.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 41
 
7.0%
이 22
 
3.7%
유 20
 
3.4%
사 20
 
3.4%
회 14
 
2.4%
식 14
 
2.4%
대 14
 
2.4%
인 14
 
2.4%
니 14
 
2.4%
스 13
 
2.2%
Other values (140) 403
68.4%
ASCII
ValueCountFrequency (%)
) 34
25.2%
( 34
25.2%
28
20.7%
I 4
 
3.0%
C 3
 
2.2%
e 2
 
1.5%
O 2
 
1.5%
N 2
 
1.5%
n 2
 
1.5%
F 2
 
1.5%
Other values (17) 22
16.3%

공장대표주소
Text

Distinct101
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size992.0 B
2024-03-15T00:49:04.400692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length36
Mean length27.5
Min length21

Characters and Unicode

Total characters2970
Distinct characters124
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

Unique95 ?
Unique (%)88.0%

Sample

1st row대구광역시 서구 문화로 192-3 (평리동, 사라미테코)
2nd row대구광역시 서구 서대구로10길 53 (내당동)
3rd row대구광역시 서구 국채보상로 405, 4층 (비산동)
4th row대구광역시 서구 북비산로 108-15 (이현동)
5th row대구광역시 서구 문화로 192-3, (지하1,지상1층) (평리동)
ValueCountFrequency (%)
대구광역시 108
18.2%
서구 108
18.2%
중리동 32
 
5.4%
이현동 24
 
4.0%
평리동 16
 
2.7%
비산동 11
 
1.9%
1필지 8
 
1.3%
국채보상로 8
 
1.3%
외 8
 
1.3%
내당동 7
 
1.2%
Other values (175) 263
44.4%
2024-03-15T00:49:06.250383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
485
 
16.3%
구 226
 
7.6%
서 126
 
4.2%
대 125
 
4.2%
동 116
 
3.9%
( 111
 
3.7%
) 111
 
3.7%
광 110
 
3.7%
시 109
 
3.7%
역 108
 
3.6%
Other values (114) 1343
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1738
58.5%
Space Separator 485
 
16.3%
Decimal Number 453
 
15.3%
Open Punctuation 111
 
3.7%
Close Punctuation 111
 
3.7%
Other Punctuation 41
 
1.4%
Dash Punctuation 24
 
0.8%
Uppercase Letter 7
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 226
 
13.0%
서 126
 
7.2%
대 125
 
7.2%
동 116
 
6.7%
광 110
 
6.3%
시 109
 
6.3%
역 108
 
6.2%
로 106
 
6.1%
길 70
 
4.0%
리 65
 
3.7%
Other values (93) 577
33.2%
Decimal Number
ValueCountFrequency (%)
1 91
20.1%
2 85
18.8%
3 56
12.4%
9 38
8.4%
0 37
8.2%
6 34
 
7.5%
5 31
 
6.8%
4 29
 
6.4%
7 28
 
6.2%
8 24
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
28.6%
P 1
14.3%
R 1
14.3%
C 1
14.3%
O 1
14.3%
K 1
14.3%
Space Separator
ValueCountFrequency (%)
485
100.0%
Open Punctuation
ValueCountFrequency (%)
( 111
100.0%
Close Punctuation
ValueCountFrequency (%)
) 111
100.0%
Other Punctuation
ValueCountFrequency (%)
, 41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1738
58.5%
Common 1225
41.2%
Latin 7
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 226
 
13.0%
서 126
 
7.2%
대 125
 
7.2%
동 116
 
6.7%
광 110
 
6.3%
시 109
 
6.3%
역 108
 
6.2%
로 106
 
6.1%
길 70
 
4.0%
리 65
 
3.7%
Other values (93) 577
33.2%
Common
ValueCountFrequency (%)
485
39.6%
( 111
 
9.1%
) 111
 
9.1%
1 91
 
7.4%
2 85
 
6.9%
3 56
 
4.6%
, 41
 
3.3%
9 38
 
3.1%
0 37
 
3.0%
6 34
 
2.8%
Other values (5) 136
 
11.1%
Latin
ValueCountFrequency (%)
A 2
28.6%
P 1
14.3%
R 1
14.3%
C 1
14.3%
O 1
14.3%
K 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1738
58.5%
ASCII 1232
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
485
39.4%
( 111
 
9.0%
) 111
 
9.0%
1 91
 
7.4%
2 85
 
6.9%
3 56
 
4.5%
, 41
 
3.3%
9 38
 
3.1%
0 37
 
3.0%
6 34
 
2.8%
Other values (11) 143
 
11.6%
Hangul
ValueCountFrequency (%)
구 226
 
13.0%
서 126
 
7.2%
대 125
 
7.2%
동 116
 
6.7%
광 110
 
6.3%
시 109
 
6.3%
역 108
 
6.2%
로 106
 
6.1%
길 70
 
4.0%
리 65
 
3.7%
Other values (93) 577
33.2%

주력업종
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size992.0 B
커튼 및 유사제품 제조업
43 
근무복, 작업복 및 유사 의복 제조업
17 
침구 및 관련제품 제조업
9 
셔츠 및 블라우스 제조업
6 
근무복, 작업복 및 유사의복 제조업
 
4
Other values (20)
29 

Length

Max length22
Median length13
Mean length14.805556
Min length6

Unique

Unique14 ?
Unique (%)13.0%

Sample

1st row기타 건축용 플라스틱 조립제품 제조업
2nd row커튼 및 유사 제품 제조업
3rd row커튼 및 유사 제품 제조업
4th row그 외 기타 특수 목적용 기계 제조업
5th row그 외 기타 특수 목적용 기계 제조업

Common Values

ValueCountFrequency (%)
커튼 및 유사제품 제조업 43
39.8%
근무복, 작업복 및 유사 의복 제조업 17
 
15.7%
침구 및 관련제품 제조업 9
 
8.3%
셔츠 및 블라우스 제조업 6
 
5.6%
근무복, 작업복 및 유사의복 제조업 4
 
3.7%
그 외 기타 특수 목적용 기계 제조업 3
 
2.8%
그 외 기타 봉제의복 제조업 3
 
2.8%
커튼 및 유사 제품 제조업 3
 
2.8%
일반 제재업 2
 
1.9%
속옷 및 잠옷 제조업 2
 
1.9%
Other values (15) 16
 
14.8%

Length

2024-03-15T00:49:06.764585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제조업 104
21.1%
및 91
18.5%
커튼 46
9.3%
유사제품 43
8.7%
유사 22
 
4.5%
근무복 21
 
4.3%
작업복 21
 
4.3%
의복 18
 
3.7%
기타 11
 
2.2%
침구 9
 
1.8%
Other values (44) 107
21.7%

신청 명세 업종
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size992.0 B
커튼 및 유사 제품 제조업
50 
근무복, 작업복 및 유사 의복 제조업
41 
천막, 텐트 및 유사 제품 제조업
 
3
근무복, 작업복 및 유사 의복 제조업,
 
2
일반 제재업
 
2
Other values (9)
10 

Length

Max length40
Median length39
Mean length17.462963
Min length6

Unique

Unique8 ?
Unique (%)7.4%

Sample

1st row커튼 및 유사 제품 제조업
2nd row커튼 및 유사 제품 제조업
3rd row커튼 및 유사 제품 제조업
4th row커튼 및 유사 제품 제조업
5th row커튼 및 유사 제품 제조업

Common Values

ValueCountFrequency (%)
커튼 및 유사 제품 제조업 50
46.3%
근무복, 작업복 및 유사 의복 제조업 41
38.0%
천막, 텐트 및 유사 제품 제조업 3
 
2.8%
근무복, 작업복 및 유사 의복 제조업, 2
 
1.9%
일반 제재업 2
 
1.9%
합성수지 및 기타 플라스틱 물질 제조업 2
 
1.9%
근무복, 작업복 및 유사 의복 제조업, 천막, 텐트 및 유사 제품 제조업 1
 
0.9%
카펫, 마루덮개 및 유사 제품 제조업 1
 
0.9%
무복, 작업복 및 유사 의복 제조업 1
 
0.9%
근무복, 작업복 및 유사 의복 제조업,천막, 텐트 및 유사 제품 제조업 1
 
0.9%
Other values (4) 4
 
3.7%

Length

2024-03-15T00:49:07.292833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제조업 108
17.9%
및 108
17.9%
유사 103
17.0%
제품 56
9.3%
커튼 50
8.3%
작업복 47
7.8%
의복 47
7.8%
근무복 45
7.4%
플라스틱 6
 
1.0%
텐트 5
 
0.8%
Other values (21) 30
 
5.0%

최초등록일
Date

Distinct101
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size992.0 B
Minimum1990-11-12 00:00:00
Maximum2024-02-06 00:00:00
2024-03-15T00:49:07.823277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:49:08.250825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct32
Distinct (%)97.0%
Missing75
Missing (%)69.4%
Memory size992.0 B
2024-03-15T00:49:09.596380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.939394
Min length8

Characters and Unicode

Total characters394
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

Unique31 ?
Unique (%)93.9%

Sample

1st row053-557-1420
2nd row053-566-1114
3rd row053-476-0090
4th row053-565-5554
5th row031-3726-6980
ValueCountFrequency (%)
053-657-0042 2
 
6.1%
053-566-1114 1
 
3.0%
053-526-2952 1
 
3.0%
053-589-5510 1
 
3.0%
053-589-5500 1
 
3.0%
053-557-8565 1
 
3.0%
053-1588-9806 1
 
3.0%
053-573-5050 1
 
3.0%
053-589-5511 1
 
3.0%
053-557-1420 1
 
3.0%
Other values (22) 22
66.7%
2024-03-15T00:49:11.070709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 86
21.8%
- 64
16.2%
0 55
14.0%
3 52
13.2%
6 24
 
6.1%
2 20
 
5.1%
8 20
 
5.1%
7 19
 
4.8%
9 19
 
4.8%
1 18
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
83.8%
Dash Punctuation 64
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 86
26.1%
0 55
16.7%
3 52
15.8%
6 24
 
7.3%
2 20
 
6.1%
8 20
 
6.1%
7 19
 
5.8%
9 19
 
5.8%
1 18
 
5.5%
4 17
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 86
21.8%
- 64
16.2%
0 55
14.0%
3 52
13.2%
6 24
 
6.1%
2 20
 
5.1%
8 20
 
5.1%
7 19
 
4.8%
9 19
 
4.8%
1 18
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 86
21.8%
- 64
16.2%
0 55
14.0%
3 52
13.2%
6 24
 
6.1%
2 20
 
5.1%
8 20
 
5.1%
7 19
 
4.8%
9 19
 
4.8%
1 18
 
4.6%

팩스번호
Text

MISSING 

Distinct33
Distinct (%)89.2%
Missing71
Missing (%)65.7%
Memory size992.0 B
2024-03-15T00:49:11.930676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.054054
Min length12

Characters and Unicode

Total characters446
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

Unique30 ?
Unique (%)81.1%

Sample

1st row053-563-1420
2nd row053-566-7805
3rd row053-476-0090
4th row053-551-6088
5th row031-990-6980
ValueCountFrequency (%)
053-951-9485 3
 
8.1%
053-657-0083 2
 
5.4%
053-589-5522 2
 
5.4%
053-589-5512 1
 
2.7%
053-553-4145 1
 
2.7%
053-561-0260 1
 
2.7%
053-557-8564 1
 
2.7%
0507-946-9484 1
 
2.7%
053-552-7308 1
 
2.7%
053-568-0193 1
 
2.7%
Other values (23) 23
62.2%
2024-03-15T00:49:12.994496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 97
21.7%
- 74
16.6%
3 60
13.5%
0 59
13.2%
6 31
 
7.0%
9 28
 
6.3%
4 24
 
5.4%
8 22
 
4.9%
1 18
 
4.0%
7 17
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 372
83.4%
Dash Punctuation 74
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 97
26.1%
3 60
16.1%
0 59
15.9%
6 31
 
8.3%
9 28
 
7.5%
4 24
 
6.5%
8 22
 
5.9%
1 18
 
4.8%
7 17
 
4.6%
2 16
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 446
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 97
21.7%
- 74
16.6%
3 60
13.5%
0 59
13.2%
6 31
 
7.0%
9 28
 
6.3%
4 24
 
5.4%
8 22
 
4.9%
1 18
 
4.0%
7 17
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 97
21.7%
- 74
16.6%
3 60
13.5%
0 59
13.2%
6 31
 
7.0%
9 28
 
6.3%
4 24
 
5.4%
8 22
 
4.9%
1 18
 
4.0%
7 17
 
3.8%

종업원수
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.018519
Minimum0
Maximum101
Zeros5
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T00:49:13.233246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7.5
Q317.25
95-th percentile43.05
Maximum101
Range101
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation15.472606
Coefficient of variation (CV)1.1885074
Kurtosis11.433491
Mean13.018519
Median Absolute Deviation (MAD)4.5
Skewness2.9528671
Sum1406
Variance239.40152
MonotonicityNot monotonic
2024-03-15T00:49:13.607219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3 12
 
11.1%
6 11
 
10.2%
4 8
 
7.4%
5 6
 
5.6%
20 6
 
5.6%
8 5
 
4.6%
0 5
 
4.6%
7 5
 
4.6%
2 5
 
4.6%
10 4
 
3.7%
Other values (24) 41
38.0%
ValueCountFrequency (%)
0 5
4.6%
1 2
 
1.9%
2 5
4.6%
3 12
11.1%
4 8
7.4%
5 6
5.6%
6 11
10.2%
7 5
4.6%
8 5
4.6%
9 4
 
3.7%
ValueCountFrequency (%)
101 1
 
0.9%
71 1
 
0.9%
60 1
 
0.9%
56 1
 
0.9%
50 1
 
0.9%
49 1
 
0.9%
32 1
 
0.9%
31 1
 
0.9%
30 3
2.8%
29 1
 
0.9%

공장홈페이지
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing95
Missing (%)88.0%
Memory size992.0 B
2024-03-15T00:49:14.348080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19
Mean length17.153846
Min length11

Characters and Unicode

Total characters223
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st rowwww.quardk.com
2nd rowwww.cheongpodo.com
3rd rowwww.ssene.co.kr
4th rowsy. www.syuniform.co.kr
5th rowwww.hot-han.com
ValueCountFrequency (%)
www.quardk.com 1
 
7.1%
www.cheongpodo.com 1
 
7.1%
www.ssene.co.kr 1
 
7.1%
sy 1
 
7.1%
www.syuniform.co.kr 1
 
7.1%
www.hot-han.com 1
 
7.1%
www.anywinkorea.net 1
 
7.1%
www.aatex.com 1
 
7.1%
www.bogwangint.com 1
 
7.1%
http://sehwaapparel.co.kr 1
 
7.1%
Other values (4) 4
28.6%
2024-03-15T00:49:15.370580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 37
16.6%
. 29
13.0%
o 21
 
9.4%
a 13
 
5.8%
c 13
 
5.8%
e 11
 
4.9%
n 11
 
4.9%
m 10
 
4.5%
r 9
 
4.0%
s 9
 
4.0%
Other values (18) 60
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 189
84.8%
Other Punctuation 32
 
14.3%
Space Separator 1
 
0.4%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 37
19.6%
o 21
11.1%
a 13
 
6.9%
c 13
 
6.9%
e 11
 
5.8%
n 11
 
5.8%
m 10
 
5.3%
r 9
 
4.8%
s 9
 
4.8%
h 8
 
4.2%
Other values (13) 47
24.9%
Other Punctuation
ValueCountFrequency (%)
. 29
90.6%
/ 2
 
6.2%
: 1
 
3.1%
Space Separator
ValueCountFrequency (%)
1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 189
84.8%
Common 34
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 37
19.6%
o 21
11.1%
a 13
 
6.9%
c 13
 
6.9%
e 11
 
5.8%
n 11
 
5.8%
m 10
 
5.3%
r 9
 
4.8%
s 9
 
4.8%
h 8
 
4.2%
Other values (13) 47
24.9%
Common
ValueCountFrequency (%)
. 29
85.3%
/ 2
 
5.9%
1
 
2.9%
- 1
 
2.9%
: 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 37
16.6%
. 29
13.0%
o 21
 
9.4%
a 13
 
5.8%
c 13
 
5.8%
e 11
 
4.9%
n 11
 
4.9%
m 10
 
4.5%
r 9
 
4.0%
s 9
 
4.0%
Other values (18) 60
26.9%

담당자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
김부현
108 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
김부현 108
100.0%

Length

2024-03-15T00:49:15.777352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:49:16.086540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
김부현 108
100.0%

담당자연락처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
053-663-2655
108 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row053-663-2655
2nd row053-663-2655
3rd row053-663-2655
4th row053-663-2655
5th row053-663-2655

Common Values

ValueCountFrequency (%)
053-663-2655 108
100.0%

Length

2024-03-15T00:49:16.293312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:49:16.461044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
053-663-2655 108
100.0%

Interactions

2024-03-15T00:48:58.559924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:48:58.176680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:48:58.718146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:48:58.420897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:49:16.579818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주력업종신청 명세 업종전화번호팩스번호종업원수공장홈페이지
연번1.0000.7420.5190.9110.9160.1141.000
주력업종0.7421.0000.9541.0000.9930.3291.000
신청 명세 업종0.5190.9541.0000.0000.9030.0001.000
전화번호0.9111.0000.0001.0001.0001.0001.000
팩스번호0.9160.9930.9031.0001.0000.3601.000
종업원수0.1140.3290.0001.0000.3601.0001.000
공장홈페이지1.0001.0001.0001.0001.0001.0001.000
2024-03-15T00:49:16.767718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신청 명세 업종주력업종
신청 명세 업종1.0000.673
주력업종0.6731.000
2024-03-15T00:49:16.912155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번종업원수주력업종신청 명세 업종
연번1.0000.1180.3360.232
종업원수0.1181.0000.1160.000
주력업종0.3360.1161.0000.673
신청 명세 업종0.2320.0000.6731.000

Missing values

2024-03-15T00:48:58.987713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:48:59.534446image/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.
2024-03-15T00:48:59.870852image/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

연번회사명공장대표주소주력업종신청 명세 업종최초등록일전화번호팩스번호종업원수공장홈페이지담당자담당자연락처
01(주)사라미데코대구광역시 서구 문화로 192-3 (평리동, 사라미테코)기타 건축용 플라스틱 조립제품 제조업커튼 및 유사 제품 제조업2007-01-17053-557-1420053-563-14206<NA>김부현053-663-2655
12금빛커텐대구광역시 서구 서대구로10길 53 (내당동)커튼 및 유사 제품 제조업커튼 및 유사 제품 제조업2016-03-15<NA><NA>2<NA>김부현053-663-2655
23다성대구광역시 서구 국채보상로 405, 4층 (비산동)커튼 및 유사 제품 제조업커튼 및 유사 제품 제조업2019-06-20053-566-1114053-566-78053<NA>김부현053-663-2655
34보강산업대구광역시 서구 북비산로 108-15 (이현동)그 외 기타 특수 목적용 기계 제조업커튼 및 유사 제품 제조업2015-07-07053-476-0090053-476-00904<NA>김부현053-663-2655
45주식회사 성진기업대구광역시 서구 문화로 192-3, (지하1,지상1층) (평리동)그 외 기타 특수 목적용 기계 제조업커튼 및 유사 제품 제조업2015-10-30<NA><NA>4<NA>김부현053-663-2655
56포그난대구광역시 서구 당산로67길 26 (중리동, 대일빌딩)그 외 기타 특수 목적용 기계 제조업커튼 및 유사 제품 제조업2012-11-16053-565-5554053-551-60888<NA>김부현053-663-2655
67주식회사 가드케이대구광역시 서구 원대로13길 23, 2층(원대동1가)섬유제품 기타 정리 및 마무리 가공업천막, 텐트 및 유사 제품 제조업2022-01-20031-3726-6980031-990-69804www.quardk.com김부현053-663-2655
78주식회사 대구봉제대구광역시 서구 서대구로 129, 5층 의료봉제 지원센타 (평리동)근무복, 작업복 및 유사 의복 제조업근무복, 작업복 및 유사 의복 제조업, 천막, 텐트 및 유사 제품 제조업2018-04-05053-657-0042053-657-00836<NA>김부현053-663-2655
89대명상사대구광역시 서구 국채보상로 413 (비산동, 대명상사)카펫, 마루덮개 및 유사 제품 제조업카펫, 마루덮개 및 유사 제품 제조업2020-03-19<NA>053-561-99385<NA>김부현053-663-2655
910(주) 제이드 청포도대구광역시 서구 서대구로 351(비산동)근무복, 작업복 및 유사 의복 제조업근무복, 작업복 및 유사 의복 제조업,2006-08-28053-357-0987053-357-689619www.cheongpodo.com김부현053-663-2655
연번회사명공장대표주소주력업종신청 명세 업종최초등록일전화번호팩스번호종업원수공장홈페이지담당자담당자연락처
9899태람대구광역시 서구 국채보상로14길 29-13 (중리동)커튼 및 유사제품 제조업커튼 및 유사 제품 제조업2013-04-17<NA><NA>14<NA>김부현053-663-2655
99100태림대구광역시 서구 문화로 28 (이현동)커튼 및 유사제품 제조업커튼 및 유사 제품 제조업2016-06-28<NA><NA>1<NA>김부현053-663-2655
100101태백목재사대구광역시 서구 평리로21길 7 (중리동, 태백목재)일반 제재업일반 제재업1990-11-12<NA><NA>6<NA>김부현053-663-2655
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