Overview

Dataset statistics

Number of variables7
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory56.7 B

Variable types

Categorical3
Text4

Dataset

Description2019년 7월 12일 기준 (빅)데이터, DBMS, 데이터 품질, 데이터모델링, 데이터보안, 인공지능 등 국내 데이터 솔루션 제품 정보를 제공합니다. 해당 데이터가 보유한 컬럼은 다음과 같습니다. 컬럼명 : 대분류, 중분류, 소분류, 제조사, 제품명, 전화번호, 홈페이지URL
URLhttps://www.data.go.kr/data/15037474/fileData.do

Alerts

대분류 is highly overall correlated with 중분류 and 1 other fieldsHigh correlation
중분류 is highly overall correlated with 대분류 and 1 other fieldsHigh correlation
소분류 is highly overall correlated with 대분류 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 14:20:00.010484
Analysis finished2023-12-12 14:20:00.807910
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
(빅)데이터
71 
데이터 보안
55 
DB모니터링·튜닝
16 
메타데이터/데이터모델링
11 
데이터품질
10 
Other values (5)
37 

Length

Max length12
Median length6
Mean length6.43
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(빅)데이터
2nd row(빅)데이터
3rd row(빅)데이터
4th row(빅)데이터
5th row(빅)데이터

Common Values

ValueCountFrequency (%)
(빅)데이터 71
35.5%
데이터 보안 55
27.5%
DB모니터링·튜닝 16
 
8.0%
메타데이터/데이터모델링 11
 
5.5%
데이터품질 10
 
5.0%
인공지능 10
 
5.0%
DB통합/백업·복제 9
 
4.5%
검색엔진 9
 
4.5%
DBMS 7
 
3.5%
업무자동화 2
 
1.0%

Length

2023-12-12T23:20:00.911602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:20:01.079932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
빅)데이터 71
27.8%
데이터 55
21.6%
보안 55
21.6%
db모니터링·튜닝 16
 
6.3%
메타데이터/데이터모델링 11
 
4.3%
데이터품질 10
 
3.9%
인공지능 10
 
3.9%
db통합/백업·복제 9
 
3.5%
검색엔진 9
 
3.5%
dbms 7
 
2.7%

중분류
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
(빅)데이터
71 
DB보안
29 
정보보안
26 
DB모니터링·튜닝
16 
메타데이터/데이터모델링
11 
Other values (6)
47 

Length

Max length13
Median length12
Mean length5.96
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(빅)데이터
2nd row(빅)데이터
3rd row(빅)데이터
4th row(빅)데이터
5th row(빅)데이터

Common Values

ValueCountFrequency (%)
(빅)데이터 71
35.5%
DB보안 29
14.5%
정보보안 26
 
13.0%
DB모니터링·튜닝 16
 
8.0%
메타데이터/데이터모델링 11
 
5.5%
데이터품질 10
 
5.0%
인공지능 10
 
5.0%
DB통합/백업·복제 9
 
4.5%
검색엔진 9
 
4.5%
DBMS 7
 
3.5%

Length

2023-12-12T23:20:01.243545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
빅)데이터 71
34.8%
db보안 29
14.2%
정보보안 26
 
12.7%
db모니터링·튜닝 16
 
7.8%
메타데이터/데이터모델링 11
 
5.4%
데이터품질 10
 
4.9%
인공지능 10
 
4.9%
db통합/백업·복제 9
 
4.4%
검색엔진 9
 
4.4%
dbms 7
 
3.4%
Other values (3) 6
 
2.9%

소분류
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
분석
31 
DB모니터링·튜닝
16 
수집
16 
저장/처리
15 
기업정보보안
15 
Other values (15)
107 

Length

Max length9
Median length6
Mean length4.325
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
분석 31
15.5%
DB모니터링·튜닝 16
 
8.0%
수집 16
 
8.0%
저장/처리 15
 
7.5%
기업정보보안 15
 
7.5%
암호화 14
 
7.0%
개인정보보안 11
 
5.5%
데이터품질 10
 
5.0%
인공지능 10
 
5.0%
접근통제 9
 
4.5%
Other values (10) 53
26.5%

Length

2023-12-12T23:20:01.372545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
분석 31
15.5%
수집 16
 
8.0%
db모니터링·튜닝 16
 
8.0%
저장/처리 15
 
7.5%
기업정보보안 15
 
7.5%
암호화 14
 
7.0%
개인정보보안 11
 
5.5%
데이터품질 10
 
5.0%
인공지능 10
 
5.0%
검색엔진 9
 
4.5%
Other values (10) 53
26.5%

제조사
Text

Distinct62
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T23:20:01.610893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7.5
Mean length5.14
Min length2

Characters and Unicode

Total characters1028
Distinct characters128
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)12.5%

Sample

1st row미닝웨어
2nd row지앤클라우드
3rd row다이퀘스트
4th row비아이매트릭스
5th row코난테크놀로지
ValueCountFrequency (%)
데이터스트림즈 10
 
4.9%
코난테크놀로지 9
 
4.4%
솔트룩스 9
 
4.4%
비아이매트릭스 9
 
4.4%
한컴시큐어 8
 
3.9%
다이퀘스트 8
 
3.9%
와이즈넛 7
 
3.4%
웨어밸리 6
 
2.9%
모니터랩 6
 
2.9%
소만사 6
 
2.9%
Other values (52) 126
61.8%
2023-12-12T23:20:02.005696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 77
 
7.5%
트 63
 
6.1%
스 52
 
5.1%
어 29
 
2.8%
소 25
 
2.4%
아 24
 
2.3%
지 24
 
2.3%
터 23
 
2.2%
웨 23
 
2.2%
크 21
 
2.0%
Other values (118) 667
64.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 979
95.2%
Uppercase Letter 30
 
2.9%
Lowercase Letter 8
 
0.8%
Other Symbol 6
 
0.6%
Space Separator 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 77
 
7.9%
트 63
 
6.4%
스 52
 
5.3%
어 29
 
3.0%
소 25
 
2.6%
아 24
 
2.5%
지 24
 
2.5%
터 23
 
2.3%
웨 23
 
2.3%
크 21
 
2.1%
Other values (106) 618
63.1%
Uppercase Letter
ValueCountFrequency (%)
T 6
20.0%
N 6
20.0%
K 6
20.0%
R 4
13.3%
S 2
 
6.7%
F 2
 
6.7%
O 2
 
6.7%
L 2
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
x 4
50.0%
e 4
50.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 985
95.8%
Latin 38
 
3.7%
Common 5
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 77
 
7.8%
트 63
 
6.4%
스 52
 
5.3%
어 29
 
2.9%
소 25
 
2.5%
아 24
 
2.4%
지 24
 
2.4%
터 23
 
2.3%
웨 23
 
2.3%
크 21
 
2.1%
Other values (107) 624
63.4%
Latin
ValueCountFrequency (%)
T 6
15.8%
N 6
15.8%
K 6
15.8%
x 4
10.5%
R 4
10.5%
e 4
10.5%
S 2
 
5.3%
F 2
 
5.3%
O 2
 
5.3%
L 2
 
5.3%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 979
95.2%
ASCII 43
 
4.2%
None 6
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 77
 
7.9%
트 63
 
6.4%
스 52
 
5.3%
어 29
 
3.0%
소 25
 
2.6%
아 24
 
2.5%
지 24
 
2.5%
터 23
 
2.3%
웨 23
 
2.3%
크 21
 
2.1%
Other values (106) 618
63.1%
ASCII
ValueCountFrequency (%)
T 6
14.0%
N 6
14.0%
K 6
14.0%
5
11.6%
x 4
9.3%
R 4
9.3%
e 4
9.3%
S 2
 
4.7%
F 2
 
4.7%
O 2
 
4.7%
None
ValueCountFrequency (%)
㈜ 6
100.0%

제품명
Text

Distinct181
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T23:20:02.323996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23
Mean length9.89
Min length2

Characters and Unicode

Total characters1978
Distinct characters91
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)84.0%

Sample

1st rowBuzzPlus
2nd rowFastcatSearch
3rd rowI-Spider4
4th rowi-STREAM
5th rowKonan Web Crawler
ValueCountFrequency (%)
wise 12
 
4.0%
konan 7
 
2.3%
sqlcanvas 6
 
2.0%
셜록홈즈 5
 
1.7%
teraone 4
 
1.3%
™ 4
 
1.3%
i-stream 4
 
1.3%
빅센 3
 
1.0%
fastcatsearch 3
 
1.0%
5 3
 
1.0%
Other values (200) 249
83.0%
2023-12-12T23:20:02.801398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 142
 
7.2%
a 141
 
7.1%
109
 
5.5%
r 107
 
5.4%
S 97
 
4.9%
A 91
 
4.6%
t 80
 
4.0%
n 79
 
4.0%
i 66
 
3.3%
o 65
 
3.3%
Other values (81) 1001
50.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 966
48.8%
Uppercase Letter 786
39.7%
Space Separator 109
 
5.5%
Other Letter 64
 
3.2%
Dash Punctuation 26
 
1.3%
Other Symbol 8
 
0.4%
Decimal Number 8
 
0.4%
Other Punctuation 6
 
0.3%
Math Symbol 2
 
0.1%
Close Punctuation 1
 
0.1%
Other values (2) 2
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 97
12.3%
A 91
 
11.6%
D 58
 
7.4%
I 53
 
6.7%
M 51
 
6.5%
E 48
 
6.1%
T 45
 
5.7%
C 39
 
5.0%
B 33
 
4.2%
R 32
 
4.1%
Other values (16) 239
30.4%
Other Letter
ValueCountFrequency (%)
빅 6
 
9.4%
센 6
 
9.4%
즈 5
 
7.8%
홈 5
 
7.8%
록 5
 
7.8%
셜 5
 
7.8%
이 3
 
4.7%
스 3
 
4.7%
메 3
 
4.7%
드 3
 
4.7%
Other values (16) 20
31.2%
Lowercase Letter
ValueCountFrequency (%)
e 142
14.7%
a 141
14.6%
r 107
11.1%
t 80
8.3%
n 79
8.2%
i 66
 
6.8%
o 65
 
6.7%
c 41
 
4.2%
s 32
 
3.3%
u 31
 
3.2%
Other values (14) 182
18.8%
Decimal Number
ValueCountFrequency (%)
5 3
37.5%
4 2
25.0%
1 1
 
12.5%
6 1
 
12.5%
2 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
# 4
66.7%
. 1
 
16.7%
' 1
 
16.7%
Space Separator
ValueCountFrequency (%)
109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Other Symbol
ValueCountFrequency (%)
™ 8
100.0%
Math Symbol
ValueCountFrequency (%)
> 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1752
88.6%
Common 162
 
8.2%
Hangul 64
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 142
 
8.1%
a 141
 
8.0%
r 107
 
6.1%
S 97
 
5.5%
A 91
 
5.2%
t 80
 
4.6%
n 79
 
4.5%
i 66
 
3.8%
o 65
 
3.7%
D 58
 
3.3%
Other values (40) 826
47.1%
Hangul
ValueCountFrequency (%)
빅 6
 
9.4%
센 6
 
9.4%
즈 5
 
7.8%
홈 5
 
7.8%
록 5
 
7.8%
셜 5
 
7.8%
이 3
 
4.7%
스 3
 
4.7%
메 3
 
4.7%
드 3
 
4.7%
Other values (16) 20
31.2%
Common
ValueCountFrequency (%)
109
67.3%
- 26
 
16.0%
™ 8
 
4.9%
# 4
 
2.5%
5 3
 
1.9%
> 2
 
1.2%
4 2
 
1.2%
) 1
 
0.6%
( 1
 
0.6%
² 1
 
0.6%
Other values (5) 5
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1905
96.3%
Hangul 64
 
3.2%
Letterlike Symbols 8
 
0.4%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 142
 
7.5%
a 141
 
7.4%
109
 
5.7%
r 107
 
5.6%
S 97
 
5.1%
A 91
 
4.8%
t 80
 
4.2%
n 79
 
4.1%
i 66
 
3.5%
o 65
 
3.4%
Other values (53) 928
48.7%
Letterlike Symbols
ValueCountFrequency (%)
™ 8
100.0%
Hangul
ValueCountFrequency (%)
빅 6
 
9.4%
센 6
 
9.4%
즈 5
 
7.8%
홈 5
 
7.8%
록 5
 
7.8%
셜 5
 
7.8%
이 3
 
4.7%
스 3
 
4.7%
메 3
 
4.7%
드 3
 
4.7%
Other values (16) 20
31.2%
None
ValueCountFrequency (%)
² 1
100.0%

전화번호
Text

Distinct61
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T23:20:03.040604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.595
Min length9

Characters and Unicode

Total characters2319
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

Unique24 ?
Unique (%)12.0%

Sample

1st row02-2678-0988
2nd row02-508-1151
3rd row02-3470-4300
4th row02-561-4475
5th row02-3469-8555
ValueCountFrequency (%)
02-3473-9077 10
 
5.0%
02-2193-1600 9
 
4.5%
02-561-4475 9
 
4.5%
02-3469-8555 9
 
4.5%
02-3470-4300 8
 
4.0%
031-622-6300 8
 
4.0%
02-3404-6100 7
 
3.5%
02-2636-8300 6
 
3.0%
02-422-8005 6
 
3.0%
02-2027-3983 6
 
3.0%
Other values (51) 122
61.0%
2023-12-12T23:20:03.627300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 484
20.9%
- 394
17.0%
2 288
12.4%
3 196
8.5%
4 158
 
6.8%
7 153
 
6.6%
5 148
 
6.4%
6 147
 
6.3%
1 135
 
5.8%
9 113
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1925
83.0%
Dash Punctuation 394
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
25.1%
2 288
15.0%
3 196
10.2%
4 158
 
8.2%
7 153
 
7.9%
5 148
 
7.7%
6 147
 
7.6%
1 135
 
7.0%
9 113
 
5.9%
8 103
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2319
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
20.9%
- 394
17.0%
2 288
12.4%
3 196
8.5%
4 158
 
6.8%
7 153
 
6.6%
5 148
 
6.4%
6 147
 
6.3%
1 135
 
5.8%
9 113
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
20.9%
- 394
17.0%
2 288
12.4%
3 196
8.5%
4 158
 
6.8%
7 153
 
6.6%
5 148
 
6.4%
6 147
 
6.3%
1 135
 
5.8%
9 113
 
4.9%

홈페이지(URL)
Text

Distinct67
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T23:20:03.904116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length23.625
Min length18

Characters and Unicode

Total characters4725
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)14.5%

Sample

1st rowhttp://www.meaningware.co.kr/
2nd rowhttp://www.fastcat.co/
3rd rowhttp://www.diquest.com/
4th rowhttp://www.bimatrix.co.kr/
5th rowhttp://www.konantech.com/
ValueCountFrequency (%)
http://datastreams.co.kr 10
 
5.0%
http://www.bimatrix.co.kr 9
 
4.5%
http://www.konantech.com 9
 
4.5%
http://www.diquest.com 8
 
4.0%
http://www.hsecure.co.kr 8
 
4.0%
http://www.saltlux.com 8
 
4.0%
http://www.wisenut.com 7
 
3.5%
http://www.wise.co.kr 6
 
3.0%
http://www.monitorapp.com 6
 
3.0%
http://www.r2ware.com 6
 
3.0%
Other values (53) 124
61.7%
2023-12-12T23:20:04.303214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 583
12.3%
t 562
11.9%
/ 512
10.8%
. 441
 
9.3%
o 270
 
5.7%
c 247
 
5.2%
h 231
 
4.9%
p 227
 
4.8%
: 205
 
4.3%
m 192
 
4.1%
Other values (25) 1255
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3509
74.3%
Other Punctuation 1158
 
24.5%
Decimal Number 35
 
0.7%
Space Separator 22
 
0.5%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 583
16.6%
t 562
16.0%
o 270
 
7.7%
c 247
 
7.0%
h 231
 
6.6%
p 227
 
6.5%
m 192
 
5.5%
a 185
 
5.3%
e 166
 
4.7%
r 157
 
4.5%
Other values (15) 689
19.6%
Decimal Number
ValueCountFrequency (%)
2 10
28.6%
0 10
28.6%
1 5
14.3%
4 5
14.3%
3 5
14.3%
Other Punctuation
ValueCountFrequency (%)
/ 512
44.2%
. 441
38.1%
: 205
17.7%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3509
74.3%
Common 1216
 
25.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 583
16.6%
t 562
16.0%
o 270
 
7.7%
c 247
 
7.0%
h 231
 
6.6%
p 227
 
6.5%
m 192
 
5.5%
a 185
 
5.3%
e 166
 
4.7%
r 157
 
4.5%
Other values (15) 689
19.6%
Common
ValueCountFrequency (%)
/ 512
42.1%
. 441
36.3%
: 205
16.9%
22
 
1.8%
2 10
 
0.8%
0 10
 
0.8%
1 5
 
0.4%
4 5
 
0.4%
3 5
 
0.4%
- 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 583
12.3%
t 562
11.9%
/ 512
10.8%
. 441
 
9.3%
o 270
 
5.7%
c 247
 
5.2%
h 231
 
4.9%
p 227
 
4.8%
: 205
 
4.3%
m 192
 
4.1%
Other values (25) 1255
26.6%

Correlations

2023-12-12T23:20:04.433003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류소분류제조사전화번호홈페이지(URL)
대분류1.0001.0001.0000.9130.9140.908
중분류1.0001.0001.0000.9090.9100.909
소분류1.0001.0001.0000.9160.9170.924
제조사0.9130.9090.9161.0001.0001.000
전화번호0.9140.9100.9171.0001.0001.000
홈페이지(URL)0.9080.9090.9241.0001.0001.000
2023-12-12T23:20:04.542956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류소분류
대분류1.0000.9970.973
중분류0.9971.0000.976
소분류0.9730.9761.000
2023-12-12T23:20:04.916170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류소분류
대분류1.0000.9970.973
중분류0.9971.0000.976
소분류0.9730.9761.000

Missing values

2023-12-12T23:20:00.604147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:20:00.759560image/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

대분류중분류소분류제조사제품명전화번호홈페이지(URL)
0(빅)데이터(빅)데이터수집미닝웨어BuzzPlus02-2678-0988http://www.meaningware.co.kr/
1(빅)데이터(빅)데이터수집지앤클라우드FastcatSearch02-508-1151http://www.fastcat.co/
2(빅)데이터(빅)데이터수집다이퀘스트I-Spider402-3470-4300http://www.diquest.com/
3(빅)데이터(빅)데이터수집비아이매트릭스i-STREAM02-561-4475http://www.bimatrix.co.kr/
4(빅)데이터(빅)데이터수집코난테크놀로지Konan Web Crawler02-3469-8555http://www.konantech.com/
5(빅)데이터(빅)데이터수집코난테크놀로지Konan Social Crawler02-3469-8555http://www.konantech.com/
6(빅)데이터(빅)데이터수집KT NexRLean Stream02-555-7313http://www.ktnexr.com/
7(빅)데이터(빅)데이터수집미닝웨어mCrawler02-2678-0988http://www.meaningware.co.kr/
8(빅)데이터(빅)데이터수집KT NexRNDAP02-555-7313http://www.ktnexr.com/
9(빅)데이터(빅)데이터수집사이람NetMiner031-739-8352http://www.cyram.com
대분류중분류소분류제조사제품명전화번호홈페이지(URL)
190인공지능인공지능인공지능㈜다음소프트Contextual CA ™02-565-0531http://www.daumsoft.com
191인공지능인공지능인공지능다이퀘스트Infochatter202-3470-4300http://www.diquest.com/
192인공지능인공지능인공지능코난테크놀로지Konan Bot02-3469-8555http://www.konantech.com/
193인공지능인공지능인공지능㈜다음소프트SoMe Report ™02-565-0531http://www.daumsoft.com
194인공지능인공지능인공지능와이즈넛WISE i Chat02-3404-6100http://www.wisenut.com
195인공지능인공지능인공지능와이즈넛WISE i Desk02-3404-6100http://www.wisenut.com
196업무자동화IT 비즈니스 업무자동화배치스케줄러데이터투테크놀로지JOB-PaSS Workload Automation031-734-1451http://www.data2tech.com/
197업무자동화IT 비즈니스 업무자동화배치스케줄러데이터투테크놀로지JOB-PaSS Operations Automation031-734-1451http://www.data2tech.com/
198(빅)데이터(빅)데이터저장/처리티맥스데이터ZetaData031-8018-1000http://www.tmaxdata.com
199(빅)데이터(빅)데이터저장/처리소프트센빅센 SA02-2027-3983http://www.softcen.co.kr