Overview

Dataset statistics

Number of variables18
Number of observations565
Missing cells549
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.0 KiB
Average record size in memory152.2 B

Variable types

Numeric8
Text8
Boolean1
Categorical1

Dataset

Description탄소산업 공동활용 플랫폼을 이용하는 사용자들에게 탄소관련 기업 간 기술활용 및 교류를 위한 탄소 수요·공급 기업DB 서비스를 제공합니다.
URLhttps://www.data.go.kr/data/15118605/fileData.do

Alerts

사업자등록번호 is highly overall correlated with 소재지High correlation
설립년도 is highly overall correlated with 총 종업원 수 - 2019년 합계 and 3 other fieldsHigh correlation
총 종업원 수 - 2019년 합계 is highly overall correlated with 설립년도 and 3 other fieldsHigh correlation
총 종업원 수 - 2020년 합계 is highly overall correlated with 설립년도 and 3 other fieldsHigh correlation
매출액 - 2019년 is highly overall correlated with 설립년도 and 3 other fieldsHigh correlation
매출액 - 2020년 is highly overall correlated with 설립년도 and 3 other fieldsHigh correlation
소재지 is highly overall correlated with 사업자등록번호High correlation
모기업(그룹)명 has 382 (67.6%) missing valuesMissing
홈페이지 주소 has 165 (29.2%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:19:56.916147
Analysis finished2023-12-12 04:20:07.591318
Duration10.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct565
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283
Minimum1
Maximum565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-12T13:20:07.688795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29.2
Q1142
median283
Q3424
95-th percentile536.8
Maximum565
Range564
Interquartile range (IQR)282

Descriptive statistics

Standard deviation163.24572
Coefficient of variation (CV)0.57684002
Kurtosis-1.2
Mean283
Median Absolute Deviation (MAD)141
Skewness0
Sum159895
Variance26649.167
MonotonicityStrictly increasing
2023-12-12T13:20:07.886725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
381 1
 
0.2%
375 1
 
0.2%
376 1
 
0.2%
377 1
 
0.2%
378 1
 
0.2%
379 1
 
0.2%
380 1
 
0.2%
382 1
 
0.2%
373 1
 
0.2%
Other values (555) 555
98.2%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
565 1
0.2%
564 1
0.2%
563 1
0.2%
562 1
0.2%
561 1
0.2%
560 1
0.2%
559 1
0.2%
558 1
0.2%
557 1
0.2%
556 1
0.2%

사업체(기업)명
Text

Distinct562
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T13:20:08.218111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length8.120354
Min length3

Characters and Unicode

Total characters4588
Distinct characters328
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique559 ?
Unique (%)98.9%

Sample

1st row효림산업(주)
2nd row현대자동차(주)
3rd row한국항공우주산업(주)
4th row(주)성우하이텍
5th row기아(주)
ValueCountFrequency (%)
주식회사 113
 
16.5%
주)티포엘 2
 
0.3%
주)그린활성탄소 2
 
0.3%
주)카보랩 2
 
0.3%
주)제이앤티지 2
 
0.3%
엠에스 1
 
0.1%
더블유앤에스코리아(주 1
 
0.1%
주)대림기공 1
 
0.1%
카텍에이치 1
 
0.1%
주)이지컴퍼지트 1
 
0.1%
Other values (558) 558
81.6%
2023-12-12T13:20:08.713134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 531
 
11.6%
( 418
 
9.1%
) 418
 
9.1%
이 168
 
3.7%
134
 
2.9%
사 126
 
2.7%
회 114
 
2.5%
식 114
 
2.5%
스 109
 
2.4%
에 85
 
1.9%
Other values (318) 2371
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3583
78.1%
Open Punctuation 418
 
9.1%
Close Punctuation 418
 
9.1%
Space Separator 134
 
2.9%
Control 15
 
0.3%
Other Symbol 13
 
0.3%
Other Punctuation 5
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 531
 
14.8%
이 168
 
4.7%
사 126
 
3.5%
회 114
 
3.2%
식 114
 
3.2%
스 109
 
3.0%
에 85
 
2.4%
지 62
 
1.7%
아 52
 
1.5%
씨 48
 
1.3%
Other values (309) 2174
60.7%
Other Punctuation
ValueCountFrequency (%)
. 4
80.0%
& 1
 
20.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
50.0%
C 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 418
100.0%
Close Punctuation
ValueCountFrequency (%)
) 418
100.0%
Space Separator
ValueCountFrequency (%)
134
100.0%
Control
ValueCountFrequency (%)
15
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3596
78.4%
Common 990
 
21.6%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 531
 
14.8%
이 168
 
4.7%
사 126
 
3.5%
회 114
 
3.2%
식 114
 
3.2%
스 109
 
3.0%
에 85
 
2.4%
지 62
 
1.7%
아 52
 
1.4%
씨 48
 
1.3%
Other values (310) 2187
60.8%
Common
ValueCountFrequency (%)
( 418
42.2%
) 418
42.2%
134
 
13.5%
15
 
1.5%
. 4
 
0.4%
& 1
 
0.1%
Latin
ValueCountFrequency (%)
F 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3583
78.1%
ASCII 992
 
21.6%
None 13
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 531
 
14.8%
이 168
 
4.7%
사 126
 
3.5%
회 114
 
3.2%
식 114
 
3.2%
스 109
 
3.0%
에 85
 
2.4%
지 62
 
1.7%
아 52
 
1.5%
씨 48
 
1.3%
Other values (309) 2174
60.7%
ASCII
ValueCountFrequency (%)
( 418
42.1%
) 418
42.1%
134
 
13.5%
15
 
1.5%
. 4
 
0.4%
F 1
 
0.1%
& 1
 
0.1%
C 1
 
0.1%
None
ValueCountFrequency (%)
㈜ 13
100.0%

사업자등록번호
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean334.67788
Minimum101
Maximum896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-12T13:20:08.918091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile109.2
Q1133
median305
Q3513
95-th percentile621
Maximum896
Range795
Interquartile range (IQR)380

Descriptive statistics

Standard deviation203.61817
Coefficient of variation (CV)0.60840046
Kurtosis-0.93512729
Mean334.67788
Median Absolute Deviation (MAD)180
Skewness0.50256286
Sum189093
Variance41460.361
MonotonicityNot monotonic
2023-12-12T13:20:09.136454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124 19
 
3.4%
418 15
 
2.7%
134 15
 
2.7%
402 14
 
2.5%
513 13
 
2.3%
621 13
 
2.3%
615 13
 
2.3%
125 13
 
2.3%
514 12
 
2.1%
123 11
 
1.9%
Other values (154) 427
75.6%
ValueCountFrequency (%)
101 3
 
0.5%
102 2
 
0.4%
104 5
0.9%
105 6
1.1%
106 1
 
0.2%
107 9
1.6%
109 3
 
0.5%
110 1
 
0.2%
113 5
0.9%
114 2
 
0.4%
ValueCountFrequency (%)
896 1
0.2%
889 1
0.2%
873 1
0.2%
864 1
0.2%
857 1
0.2%
845 1
0.2%
836 1
0.2%
833 1
0.2%
809 1
0.2%
789 1
0.2%

모기업 여부
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size697.0 B
True
380 
False
185 
ValueCountFrequency (%)
True 380
67.3%
False 185
32.7%
2023-12-12T13:20:09.284814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

모기업(그룹)명
Text

MISSING 

Distinct160
Distinct (%)87.4%
Missing382
Missing (%)67.6%
Memory size4.5 KiB
2023-12-12T13:20:09.896006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length4.4371585
Min length2

Characters and Unicode

Total characters812
Distinct characters194
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)78.1%

Sample

1st row효림산업
2nd row현대자동차
3rd row한국항공우주산업
4th row성우홀딩스
5th row현대자동차
ValueCountFrequency (%)
한국카본 4
 
2.2%
일진홀딩스 4
 
2.2%
강남 3
 
1.6%
한국몰드 3
 
1.6%
불스원 2
 
1.1%
평화홀딩스 2
 
1.1%
애경 2
 
1.1%
삼우기업 2
 
1.1%
비츠로테크 2
 
1.1%
유니온씨티 2
 
1.1%
Other values (150) 157
85.8%
2023-12-12T13:20:10.370072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 42
 
5.2%
이 40
 
4.9%
아 20
 
2.5%
한 18
 
2.2%
홀 18
 
2.2%
딩 18
 
2.2%
업 18
 
2.2%
지 16
 
2.0%
에 16
 
2.0%
국 15
 
1.8%
Other values (184) 591
72.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 807
99.4%
Uppercase Letter 5
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 42
 
5.2%
이 40
 
5.0%
아 20
 
2.5%
한 18
 
2.2%
홀 18
 
2.2%
딩 18
 
2.2%
업 18
 
2.2%
지 16
 
2.0%
에 16
 
2.0%
국 15
 
1.9%
Other values (180) 586
72.6%
Uppercase Letter
ValueCountFrequency (%)
M 2
40.0%
H 1
20.0%
K 1
20.0%
S 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 807
99.4%
Latin 5
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 42
 
5.2%
이 40
 
5.0%
아 20
 
2.5%
한 18
 
2.2%
홀 18
 
2.2%
딩 18
 
2.2%
업 18
 
2.2%
지 16
 
2.0%
에 16
 
2.0%
국 15
 
1.9%
Other values (180) 586
72.6%
Latin
ValueCountFrequency (%)
M 2
40.0%
H 1
20.0%
K 1
20.0%
S 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 807
99.4%
ASCII 5
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 42
 
5.2%
이 40
 
5.0%
아 20
 
2.5%
한 18
 
2.2%
홀 18
 
2.2%
딩 18
 
2.2%
업 18
 
2.2%
지 16
 
2.0%
에 16
 
2.0%
국 15
 
1.9%
Other values (180) 586
72.6%
ASCII
ValueCountFrequency (%)
M 2
40.0%
H 1
20.0%
K 1
20.0%
S 1
20.0%

대표자명
Text

Distinct553
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T13:20:10.759300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.7238938
Min length2

Characters and Unicode

Total characters2104
Distinct characters225
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique542 ?
Unique (%)95.9%

Sample

1st row현형주
2nd row정의선/하언태/장재훈
3rd row안현호
4th row이명근/이문용
5th row최준영/송호성
ValueCountFrequency (%)
김영수 3
 
0.5%
심임수 2
 
0.4%
박명식 2
 
0.4%
박현규 2
 
0.4%
김우석 2
 
0.4%
김준현 2
 
0.4%
김태년 2
 
0.4%
천진성 2
 
0.4%
하상헌 2
 
0.4%
이상훈 2
 
0.4%
Other values (543) 544
96.3%
2023-12-12T13:20:11.276074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 142
 
6.7%
/ 96
 
4.6%
이 91
 
4.3%
정 64
 
3.0%
성 51
 
2.4%
박 47
 
2.2%
호 45
 
2.1%
영 44
 
2.1%
상 37
 
1.8%
현 36
 
1.7%
Other values (215) 1451
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2006
95.3%
Other Punctuation 96
 
4.6%
Space Separator 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 142
 
7.1%
이 91
 
4.5%
정 64
 
3.2%
성 51
 
2.5%
박 47
 
2.3%
호 45
 
2.2%
영 44
 
2.2%
상 37
 
1.8%
현 36
 
1.8%
수 33
 
1.6%
Other values (213) 1416
70.6%
Other Punctuation
ValueCountFrequency (%)
/ 96
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2006
95.3%
Common 98
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 142
 
7.1%
이 91
 
4.5%
정 64
 
3.2%
성 51
 
2.5%
박 47
 
2.3%
호 45
 
2.2%
영 44
 
2.2%
상 37
 
1.8%
현 36
 
1.8%
수 33
 
1.6%
Other values (213) 1416
70.6%
Common
ValueCountFrequency (%)
/ 96
98.0%
2
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2006
95.3%
ASCII 98
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 142
 
7.1%
이 91
 
4.5%
정 64
 
3.2%
성 51
 
2.5%
박 47
 
2.3%
호 45
 
2.2%
영 44
 
2.2%
상 37
 
1.8%
현 36
 
1.8%
수 33
 
1.6%
Other values (213) 1416
70.6%
ASCII
ValueCountFrequency (%)
/ 96
98.0%
2
 
2.0%

설립년도
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2000.4708
Minimum1924
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-12T13:20:11.465417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1924
5-th percentile1974
Q11994
median2002
Q32011
95-th percentile2017
Maximum2020
Range96
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.948663
Coefficient of variation (CV)0.00697269
Kurtosis2.7472806
Mean2000.4708
Median Absolute Deviation (MAD)9
Skewness-1.3368138
Sum1130266
Variance194.56519
MonotonicityNot monotonic
2023-12-12T13:20:11.637925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 27
 
4.8%
1999 27
 
4.8%
2010 26
 
4.6%
2011 24
 
4.2%
2000 21
 
3.7%
2006 21
 
3.7%
2012 21
 
3.7%
2001 20
 
3.5%
2002 19
 
3.4%
1998 17
 
3.0%
Other values (54) 342
60.5%
ValueCountFrequency (%)
1924 1
0.2%
1944 1
0.2%
1946 1
0.2%
1950 1
0.2%
1952 1
0.2%
1954 1
0.2%
1955 1
0.2%
1956 2
0.4%
1958 1
0.2%
1961 2
0.4%
ValueCountFrequency (%)
2020 1
 
0.2%
2019 9
 
1.6%
2018 17
3.0%
2017 13
2.3%
2016 15
2.7%
2015 16
2.8%
2014 27
4.8%
2013 11
1.9%
2012 21
3.7%
2011 24
4.2%

설립월
Real number (ℝ)

Distinct12
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.339823
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-12T13:20:11.780872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5145982
Coefficient of variation (CV)0.55436851
Kurtosis-1.2030569
Mean6.339823
Median Absolute Deviation (MAD)3
Skewness-0.065026584
Sum3582
Variance12.352401
MonotonicityNot monotonic
2023-12-12T13:20:11.909755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 77
13.6%
7 63
11.2%
10 52
9.2%
8 51
9.0%
9 44
7.8%
11 42
7.4%
5 42
7.4%
6 42
7.4%
12 40
7.1%
3 40
7.1%
Other values (2) 72
12.7%
ValueCountFrequency (%)
1 77
13.6%
2 36
6.4%
3 40
7.1%
4 36
6.4%
5 42
7.4%
6 42
7.4%
7 63
11.2%
8 51
9.0%
9 44
7.8%
10 52
9.2%
ValueCountFrequency (%)
12 40
7.1%
11 42
7.4%
10 52
9.2%
9 44
7.8%
8 51
9.0%
7 63
11.2%
6 42
7.4%
5 42
7.4%
4 36
6.4%
3 40
7.1%

대표전화-전화번호
Text

Distinct555
Distinct (%)98.6%
Missing2
Missing (%)0.4%
Memory size4.5 KiB
2023-12-12T13:20:12.196190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length12.024867
Min length9

Characters and Unicode

Total characters6770
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)97.2%

Sample

1st row053-851-8600
2nd row02-3464-1114
3rd row055-851-1000
4th row070-7477-5450
5th row02-3464-1114
ValueCountFrequency (%)
055-350-8888 2
 
0.4%
031-680-0212 2
 
0.4%
031-8059-5391-4 2
 
0.4%
02-3464-1114 2
 
0.4%
063-212-9520 2
 
0.4%
031-353-5285 2
 
0.4%
063-212-0830 2
 
0.4%
054-475-6970 2
 
0.4%
051-832-2071 1
 
0.2%
051-831-1453-6 1
 
0.2%
Other values (545) 545
96.8%
2023-12-12T13:20:12.677425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1142
16.9%
- 1137
16.8%
3 731
10.8%
1 664
9.8%
5 592
8.7%
2 566
8.4%
4 450
 
6.6%
7 428
 
6.3%
6 405
 
6.0%
8 358
 
5.3%
Other values (2) 297
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5631
83.2%
Dash Punctuation 1137
 
16.8%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1142
20.3%
3 731
13.0%
1 664
11.8%
5 592
10.5%
2 566
10.1%
4 450
 
8.0%
7 428
 
7.6%
6 405
 
7.2%
8 358
 
6.4%
9 295
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 1137
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6770
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1142
16.9%
- 1137
16.8%
3 731
10.8%
1 664
9.8%
5 592
8.7%
2 566
8.4%
4 450
 
6.6%
7 428
 
6.3%
6 405
 
6.0%
8 358
 
5.3%
Other values (2) 297
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1142
16.9%
- 1137
16.8%
3 731
10.8%
1 664
9.8%
5 592
8.7%
2 566
8.4%
4 450
 
6.6%
7 428
 
6.3%
6 405
 
6.0%
8 358
 
5.3%
Other values (2) 297
 
4.4%

홈페이지 주소
Text

MISSING 

Distinct395
Distinct (%)98.8%
Missing165
Missing (%)29.2%
Memory size4.5 KiB
2023-12-12T13:20:12.995573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length26
Mean length16.645
Min length8

Characters and Unicode

Total characters6658
Distinct characters43
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)97.5%

Sample

1st rowwww.hyolimindustrial.com
2nd rowwww.hyundai.com
3rd rowwww.koreaaero.com
4th rowwww.swhitech.com
5th rowwww.kia.com
ValueCountFrequency (%)
www.thejnt.com 2
 
0.5%
www.carbolab.co.kr 2
 
0.5%
greenecarbon.com 2
 
0.5%
www.t4l.co.kr 2
 
0.5%
www.iljindisplay.co.kr 2
 
0.5%
www.daiyangmetal.co.kr 1
 
0.2%
www.hyolimindustrial.com 1
 
0.2%
www.iljinm.co.kr 1
 
0.2%
www.daesangtape.com 1
 
0.2%
www.scen.co.kr 1
 
0.2%
Other values (385) 385
96.2%
2023-12-12T13:20:13.592735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 1145
17.2%
. 947
14.2%
o 641
9.6%
c 573
 
8.6%
r 337
 
5.1%
m 321
 
4.8%
e 312
 
4.7%
n 305
 
4.6%
k 281
 
4.2%
a 261
 
3.9%
Other values (33) 1535
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5613
84.3%
Other Punctuation 961
 
14.4%
Decimal Number 40
 
0.6%
Dash Punctuation 37
 
0.6%
Other Letter 7
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 1145
20.4%
o 641
11.4%
c 573
10.2%
r 337
 
6.0%
m 321
 
5.7%
e 312
 
5.6%
n 305
 
5.4%
k 281
 
5.0%
a 261
 
4.6%
i 193
 
3.4%
Other values (16) 1244
22.2%
Other Letter
ValueCountFrequency (%)
드 1
14.3%
삼 1
14.3%
우 1
14.3%
기 1
14.3%
업 1
14.3%
로 1
14.3%
지 1
14.3%
Decimal Number
ValueCountFrequency (%)
1 17
42.5%
2 16
40.0%
0 3
 
7.5%
4 2
 
5.0%
8 1
 
2.5%
3 1
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 947
98.5%
/ 13
 
1.4%
: 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5613
84.3%
Common 1038
 
15.6%
Hangul 7
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 1145
20.4%
o 641
11.4%
c 573
10.2%
r 337
 
6.0%
m 321
 
5.7%
e 312
 
5.6%
n 305
 
5.4%
k 281
 
5.0%
a 261
 
4.6%
i 193
 
3.4%
Other values (16) 1244
22.2%
Common
ValueCountFrequency (%)
. 947
91.2%
- 37
 
3.6%
1 17
 
1.6%
2 16
 
1.5%
/ 13
 
1.3%
0 3
 
0.3%
4 2
 
0.2%
8 1
 
0.1%
3 1
 
0.1%
: 1
 
0.1%
Hangul
ValueCountFrequency (%)
드 1
14.3%
삼 1
14.3%
우 1
14.3%
기 1
14.3%
업 1
14.3%
로 1
14.3%
지 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6651
99.9%
Hangul 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 1145
17.2%
. 947
14.2%
o 641
9.6%
c 573
 
8.6%
r 337
 
5.1%
m 321
 
4.8%
e 312
 
4.7%
n 305
 
4.6%
k 281
 
4.2%
a 261
 
3.9%
Other values (26) 1528
23.0%
Hangul
ValueCountFrequency (%)
드 1
14.3%
삼 1
14.3%
우 1
14.3%
기 1
14.3%
업 1
14.3%
로 1
14.3%
지 1
14.3%

소재지
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
경기
149 
서울
67 
전북
65 
경남
49 
경북
47 
Other values (12)
188 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row경북
2nd row서울
3rd row경남
4th row부산
5th row서울

Common Values

ValueCountFrequency (%)
경기 149
26.4%
서울 67
11.9%
전북 65
11.5%
경남 49
 
8.7%
경북 47
 
8.3%
부산 30
 
5.3%
충북 25
 
4.4%
충남 25
 
4.4%
대구 23
 
4.1%
인천 23
 
4.1%
Other values (7) 62
11.0%

Length

2023-12-12T13:20:13.829061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 149
26.4%
서울 67
11.9%
전북 65
11.5%
경남 49
 
8.7%
경북 47
 
8.3%
부산 30
 
5.3%
충북 25
 
4.4%
충남 25
 
4.4%
인천 23
 
4.1%
대구 23
 
4.1%
Other values (7) 62
11.0%

주소(본사)
Text

Distinct552
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T13:20:14.225216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length39
Mean length22.621239
Min length14

Characters and Unicode

Total characters12781
Distinct characters316
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique541 ?
Unique (%)95.8%

Sample

1st row경북 경산시 남산면 전지공단길 22-9  
2nd row서울 서초구 헌릉로 12  
3rd row경남 사천시 사남면 공단1로 78  
4th row부산 기장군 정관읍 농공길 2-9  
5th row서울 서초구 헌릉로 12  
ValueCountFrequency (%)
경기 148
 
5.3%
서울 67
 
2.4%
전북 65
 
2.3%
경남 49
 
1.7%
경북 47
 
1.7%
화성시 32
 
1.1%
부산 30
 
1.1%
전주시 29
 
1.0%
충남 25
 
0.9%
덕진구 25
 
0.9%
Other values (1256) 2297
81.6%
2023-12-12T13:20:14.919513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2249
 
17.6%
  1128
 
8.8%
1 544
 
4.3%
로 457
 
3.6%
2 344
 
2.7%
시 342
 
2.7%
구 328
 
2.6%
3 275
 
2.2%
경 263
 
2.1%
0 258
 
2.0%
Other values (306) 6593
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6544
51.2%
Space Separator 3377
26.4%
Decimal Number 2520
 
19.7%
Other Punctuation 166
 
1.3%
Dash Punctuation 152
 
1.2%
Uppercase Letter 20
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 457
 
7.0%
시 342
 
5.2%
구 328
 
5.0%
경 263
 
4.0%
산 256
 
3.9%
길 241
 
3.7%
남 188
 
2.9%
북 176
 
2.7%
기 162
 
2.5%
서 144
 
2.2%
Other values (284) 3987
60.9%
Decimal Number
ValueCountFrequency (%)
1 544
21.6%
2 344
13.7%
3 275
10.9%
0 258
10.2%
4 238
9.4%
5 218
8.7%
6 181
 
7.2%
7 170
 
6.7%
8 157
 
6.2%
9 135
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 7
35.0%
B 7
35.0%
D 4
20.0%
C 1
 
5.0%
E 1
 
5.0%
Space Separator
ValueCountFrequency (%)
2249
66.6%
  1128
33.4%
Other Punctuation
ValueCountFrequency (%)
, 165
99.4%
. 1
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6544
51.2%
Common 6217
48.6%
Latin 20
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 457
 
7.0%
시 342
 
5.2%
구 328
 
5.0%
경 263
 
4.0%
산 256
 
3.9%
길 241
 
3.7%
남 188
 
2.9%
북 176
 
2.7%
기 162
 
2.5%
서 144
 
2.2%
Other values (284) 3987
60.9%
Common
ValueCountFrequency (%)
2249
36.2%
  1128
18.1%
1 544
 
8.8%
2 344
 
5.5%
3 275
 
4.4%
0 258
 
4.1%
4 238
 
3.8%
5 218
 
3.5%
6 181
 
2.9%
7 170
 
2.7%
Other values (7) 612
 
9.8%
Latin
ValueCountFrequency (%)
A 7
35.0%
B 7
35.0%
D 4
20.0%
C 1
 
5.0%
E 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6544
51.2%
ASCII 5109
40.0%
None 1128
 
8.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2249
44.0%
1 544
 
10.6%
2 344
 
6.7%
3 275
 
5.4%
0 258
 
5.0%
4 238
 
4.7%
5 218
 
4.3%
6 181
 
3.5%
7 170
 
3.3%
, 165
 
3.2%
Other values (11) 467
 
9.1%
None
ValueCountFrequency (%)
  1128
100.0%
Hangul
ValueCountFrequency (%)
로 457
 
7.0%
시 342
 
5.2%
구 328
 
5.0%
경 263
 
4.0%
산 256
 
3.9%
길 241
 
3.7%
남 188
 
2.9%
북 176
 
2.7%
기 162
 
2.5%
서 144
 
2.2%
Other values (284) 3987
60.9%

총 종업원 수 - 2019년 합계
Real number (ℝ)

HIGH CORRELATION 

Distinct231
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455.74159
Minimum1
Maximum73491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-12T13:20:15.112679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median34
Q3152
95-th percentile996.6
Maximum73491
Range73490
Interquartile range (IQR)142

Descriptive statistics

Standard deviation3532.2793
Coefficient of variation (CV)7.7506187
Kurtosis342.60609
Mean455.74159
Median Absolute Deviation (MAD)29
Skewness17.600092
Sum257494
Variance12476997
MonotonicityNot monotonic
2023-12-12T13:20:15.329745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 20
 
3.5%
9 17
 
3.0%
4 17
 
3.0%
10 15
 
2.7%
11 15
 
2.7%
3 14
 
2.5%
6 14
 
2.5%
8 14
 
2.5%
7 14
 
2.5%
14 13
 
2.3%
Other values (221) 412
72.9%
ValueCountFrequency (%)
1 12
2.1%
2 10
1.8%
3 14
2.5%
4 17
3.0%
5 20
3.5%
6 14
2.5%
7 14
2.5%
8 14
2.5%
9 17
3.0%
10 15
2.7%
ValueCountFrequency (%)
73491 1
0.2%
35766 1
0.2%
9682 1
0.2%
8942 1
0.2%
6798 1
0.2%
5657 1
0.2%
5523 1
0.2%
5128 1
0.2%
4830 1
0.2%
4616 1
0.2%

총 종업원 수 - 2020년 합계
Real number (ℝ)

HIGH CORRELATION 

Distinct237
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446.62832
Minimum1
Maximum71664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-12T13:20:15.503401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median33
Q3150
95-th percentile970.2
Maximum71664
Range71663
Interquartile range (IQR)140

Descriptive statistics

Standard deviation3452.1357
Coefficient of variation (CV)7.7293256
Kurtosis340.22566
Mean446.62832
Median Absolute Deviation (MAD)28
Skewness17.537196
Sum252345
Variance11917241
MonotonicityNot monotonic
2023-12-12T13:20:15.709713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 20
 
3.5%
4 17
 
3.0%
9 17
 
3.0%
11 15
 
2.7%
10 15
 
2.7%
7 14
 
2.5%
3 14
 
2.5%
8 14
 
2.5%
6 14
 
2.5%
14 13
 
2.3%
Other values (227) 412
72.9%
ValueCountFrequency (%)
1 12
2.1%
2 10
1.8%
3 14
2.5%
4 17
3.0%
5 20
3.5%
6 14
2.5%
7 14
2.5%
8 14
2.5%
9 17
3.0%
10 15
2.7%
ValueCountFrequency (%)
71664 1
0.2%
35220 1
0.2%
9560 1
0.2%
8833 1
0.2%
6622 1
0.2%
5548 1
0.2%
5405 1
0.2%
5033 1
0.2%
4696 1
0.2%
4550 1
0.2%

매출액 - 2019년
Real number (ℝ)

HIGH CORRELATION 

Distinct336
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3247.5735
Minimum1
Maximum491557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-12T13:20:15.944814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q122
median92
Q3641
95-th percentile8378.2
Maximum491557
Range491556
Interquartile range (IQR)619

Descriptive statistics

Standard deviation26018.688
Coefficient of variation (CV)8.0117319
Kurtosis269.17408
Mean3247.5735
Median Absolute Deviation (MAD)86
Skewness15.728823
Sum1834879
Variance6.7697212 × 108
MonotonicityNot monotonic
2023-12-12T13:20:16.174933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
3.5%
6 10
 
1.8%
4 9
 
1.6%
2 9
 
1.6%
7 9
 
1.6%
19 9
 
1.6%
77 7
 
1.2%
8 7
 
1.2%
25 7
 
1.2%
21 7
 
1.2%
Other values (326) 471
83.4%
ValueCountFrequency (%)
1 20
3.5%
2 9
1.6%
3 1
 
0.2%
4 9
1.6%
5 6
 
1.1%
6 10
1.8%
7 9
1.6%
8 7
 
1.2%
9 5
 
0.9%
10 5
 
0.9%
ValueCountFrequency (%)
491557 1
0.2%
338578 1
0.2%
84538 1
0.2%
80919 1
0.2%
70988 1
0.2%
46777 1
0.2%
36827 1
0.2%
36263 1
0.2%
31731 1
0.2%
31086 1
0.2%

매출액 - 2020년
Real number (ℝ)

HIGH CORRELATION 

Distinct333
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3212.3292
Minimum1
Maximum506610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-12T13:20:16.371052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q121
median79
Q3664
95-th percentile7313
Maximum506610
Range506609
Interquartile range (IQR)643

Descriptive statistics

Standard deviation26538.742
Coefficient of variation (CV)8.2615262
Kurtosis277.82636
Mean3212.3292
Median Absolute Deviation (MAD)76
Skewness16.028869
Sum1814966
Variance7.0430483 × 108
MonotonicityNot monotonic
2023-12-12T13:20:16.588620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 31
 
5.5%
23 10
 
1.8%
3 10
 
1.8%
6 9
 
1.6%
4 9
 
1.6%
12 8
 
1.4%
8 7
 
1.2%
13 7
 
1.2%
18 7
 
1.2%
2 7
 
1.2%
Other values (323) 460
81.4%
ValueCountFrequency (%)
1 31
5.5%
2 7
 
1.2%
3 10
 
1.8%
4 9
 
1.6%
5 5
 
0.9%
6 9
 
1.6%
7 5
 
0.9%
8 7
 
1.2%
9 2
 
0.4%
10 4
 
0.7%
ValueCountFrequency (%)
506610 1
0.2%
343623 1
0.2%
84975 1
0.2%
76373 1
0.2%
68255 1
0.2%
34897 1
0.2%
34008 1
0.2%
29298 1
0.2%
28624 1
0.2%
28120 1
0.2%

1순위 제품명
Text

Distinct436
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T13:20:16.898772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length6.3150442
Min length1

Characters and Unicode

Total characters3568
Distinct characters361
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique389 ?
Unique (%)68.8%

Sample

1st row자동차부품
2nd row자동차
3rd row비행기부품
4th row차체부품
5th row자동차
ValueCountFrequency (%)
부품 58
 
6.7%
자동차부품 28
 
3.2%
자동차 25
 
2.9%
탄소섬유 22
 
2.5%
반도체 14
 
1.6%
전자부품 13
 
1.5%
장비 10
 
1.1%
플라스틱 10
 
1.1%
활성탄소 8
 
0.9%
산업용 8
 
0.9%
Other values (471) 675
77.5%
2023-12-12T13:20:17.467224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
311
 
8.7%
품 169
 
4.7%
부 121
 
3.4%
기 114
 
3.2%
자 103
 
2.9%
, 94
 
2.6%
차 83
 
2.3%
소 76
 
2.1%
탄 74
 
2.1%
동 73
 
2.0%
Other values (351) 2350
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3008
84.3%
Space Separator 311
 
8.7%
Other Punctuation 98
 
2.7%
Uppercase Letter 82
 
2.3%
Lowercase Letter 34
 
1.0%
Open Punctuation 16
 
0.4%
Close Punctuation 15
 
0.4%
Decimal Number 3
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 169
 
5.6%
부 121
 
4.0%
기 114
 
3.8%
자 103
 
3.4%
차 83
 
2.8%
소 76
 
2.5%
탄 74
 
2.5%
동 73
 
2.4%
유 65
 
2.2%
제 61
 
2.0%
Other values (304) 2069
68.8%
Uppercase Letter
ValueCountFrequency (%)
P 9
11.0%
C 9
11.0%
D 8
9.8%
N 8
9.8%
E 8
9.8%
L 8
9.8%
T 6
7.3%
R 5
 
6.1%
G 4
 
4.9%
A 3
 
3.7%
Other values (9) 14
17.1%
Lowercase Letter
ValueCountFrequency (%)
e 6
17.6%
s 4
11.8%
c 3
8.8%
i 3
8.8%
o 2
 
5.9%
n 2
 
5.9%
r 2
 
5.9%
m 2
 
5.9%
p 2
 
5.9%
f 1
 
2.9%
Other values (7) 7
20.6%
Other Punctuation
ValueCountFrequency (%)
, 94
95.9%
. 2
 
2.0%
/ 1
 
1.0%
& 1
 
1.0%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
1 1
33.3%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
311
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3008
84.3%
Common 444
 
12.4%
Latin 116
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 169
 
5.6%
부 121
 
4.0%
기 114
 
3.8%
자 103
 
3.4%
차 83
 
2.8%
소 76
 
2.5%
탄 74
 
2.5%
동 73
 
2.4%
유 65
 
2.2%
제 61
 
2.0%
Other values (304) 2069
68.8%
Latin
ValueCountFrequency (%)
P 9
 
7.8%
C 9
 
7.8%
D 8
 
6.9%
N 8
 
6.9%
E 8
 
6.9%
L 8
 
6.9%
T 6
 
5.2%
e 6
 
5.2%
R 5
 
4.3%
G 4
 
3.4%
Other values (26) 45
38.8%
Common
ValueCountFrequency (%)
311
70.0%
, 94
 
21.2%
( 16
 
3.6%
) 15
 
3.4%
. 2
 
0.5%
/ 1
 
0.2%
3 1
 
0.2%
& 1
 
0.2%
- 1
 
0.2%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3008
84.3%
ASCII 560
 
15.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
311
55.5%
, 94
 
16.8%
( 16
 
2.9%
) 15
 
2.7%
P 9
 
1.6%
C 9
 
1.6%
D 8
 
1.4%
N 8
 
1.4%
E 8
 
1.4%
L 8
 
1.4%
Other values (37) 74
 
13.2%
Hangul
ValueCountFrequency (%)
품 169
 
5.6%
부 121
 
4.0%
기 114
 
3.8%
자 103
 
3.4%
차 83
 
2.8%
소 76
 
2.5%
탄 74
 
2.5%
동 73
 
2.4%
유 65
 
2.2%
제 61
 
2.0%
Other values (304) 2069
68.8%

세부 업종
Text

Distinct62
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T13:20:17.765460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length56
Mean length39.086726
Min length22

Characters and Unicode

Total characters22084
Distinct characters138
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

Unique16 ?
Unique (%)2.8%

Sample

1st row[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
2nd row[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
3rd row[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_항공우주산업
4th row[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
5th row[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
ValueCountFrequency (%)
및 995
25.7%
부품 880
22.8%
탄소소재 511
13.2%
응용제품]_(탄소섬유 269
 
7.0%
제조장비 117
 
3.0%
응용제품)_자동차 90
 
2.3%
응용제품)_기계장비 76
 
2.0%
응용제품)_전자부품 74
 
1.9%
6대 53
 
1.4%
응용제품)_건자재 43
 
1.1%
Other values (75) 760
19.6%
2023-12-12T13:20:18.234560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3303
15.0%
품 1993
 
9.0%
소 1735
 
7.9%
_ 1130
 
5.1%
탄 1120
 
5.1%
제 1079
 
4.9%
부 1031
 
4.7%
및 995
 
4.5%
용 866
 
3.9%
응 858
 
3.9%
Other values (128) 7974
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14320
64.8%
Space Separator 3303
 
15.0%
Open Punctuation 1167
 
5.3%
Close Punctuation 1166
 
5.3%
Connector Punctuation 1130
 
5.1%
Lowercase Letter 570
 
2.6%
Uppercase Letter 189
 
0.9%
Decimal Number 157
 
0.7%
Private Use 42
 
0.2%
Other Punctuation 40
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 1993
13.9%
소 1735
12.1%
탄 1120
 
7.8%
제 1079
 
7.5%
부 1031
 
7.2%
및 995
 
6.9%
용 866
 
6.0%
응 858
 
6.0%
재 685
 
4.8%
유 399
 
2.8%
Other values (92) 3559
24.9%
Lowercase Letter
ValueCountFrequency (%)
a 104
18.2%
r 74
13.0%
t 52
9.1%
n 48
8.4%
e 45
7.9%
d 36
 
6.3%
o 31
 
5.4%
l 30
 
5.3%
c 28
 
4.9%
v 26
 
4.6%
Other values (6) 96
16.8%
Uppercase Letter
ValueCountFrequency (%)
C 58
30.7%
A 55
29.1%
G 42
22.2%
P 13
 
6.9%
N 9
 
4.8%
T 6
 
3.2%
W 2
 
1.1%
M 2
 
1.1%
F 1
 
0.5%
B 1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 602
51.6%
[ 565
48.4%
Close Punctuation
ValueCountFrequency (%)
) 601
51.5%
] 565
48.5%
Decimal Number
ValueCountFrequency (%)
1 104
66.2%
6 53
33.8%
Space Separator
ValueCountFrequency (%)
3303
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1130
100.0%
Private Use
ValueCountFrequency (%)
42
100.0%
Other Punctuation
ValueCountFrequency (%)
, 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14320
64.8%
Common 6963
31.5%
Latin 759
 
3.4%
Unknown 42
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 1993
13.9%
소 1735
12.1%
탄 1120
 
7.8%
제 1079
 
7.5%
부 1031
 
7.2%
및 995
 
6.9%
용 866
 
6.0%
응 858
 
6.0%
재 685
 
4.8%
유 399
 
2.8%
Other values (92) 3559
24.9%
Latin
ValueCountFrequency (%)
a 104
13.7%
r 74
 
9.7%
C 58
 
7.6%
A 55
 
7.2%
t 52
 
6.9%
n 48
 
6.3%
e 45
 
5.9%
G 42
 
5.5%
d 36
 
4.7%
o 31
 
4.1%
Other values (16) 214
28.2%
Common
ValueCountFrequency (%)
3303
47.4%
_ 1130
 
16.2%
( 602
 
8.6%
) 601
 
8.6%
[ 565
 
8.1%
] 565
 
8.1%
1 104
 
1.5%
6 53
 
0.8%
, 40
 
0.6%
Unknown
ValueCountFrequency (%)
42
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14320
64.8%
ASCII 7722
35.0%
PUA 42
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3303
42.8%
_ 1130
 
14.6%
( 602
 
7.8%
) 601
 
7.8%
[ 565
 
7.3%
] 565
 
7.3%
1 104
 
1.3%
a 104
 
1.3%
r 74
 
1.0%
C 58
 
0.8%
Other values (25) 616
 
8.0%
Hangul
ValueCountFrequency (%)
품 1993
13.9%
소 1735
12.1%
탄 1120
 
7.8%
제 1079
 
7.5%
부 1031
 
7.2%
및 995
 
6.9%
용 866
 
6.0%
응 858
 
6.0%
재 685
 
4.8%
유 399
 
2.8%
Other values (92) 3559
24.9%
PUA
ValueCountFrequency (%)
42
100.0%

Interactions

2023-12-12T13:20:05.872237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:58.366675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:59.203862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:00.313338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:01.754263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:02.787760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:03.785500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:04.823853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:06.046359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:58.480979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:59.311490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:00.444176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:01.880748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:02.915241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:03.896648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:04.946099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:06.182938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:58.576101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:59.441478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:00.577688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:02.024916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:03.044360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:04.047608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:05.065825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:06.295753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:58.664514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:59.566769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:00.711729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:02.160396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:03.157433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:04.184469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:05.194718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:06.427500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:58.762426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:59.735341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:00.851119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:02.274719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:03.272889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:04.323849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:05.304766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:06.553967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:58.877239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:59.887173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:01.390699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:02.416671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:03.404596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:04.463182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:05.453517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:06.699773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:59.002105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:00.037471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:01.523790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:02.556253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:03.548489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:04.593602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:05.597260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:06.812698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:19:59.091648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:00.189739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:01.639614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:02.670818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:03.675272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:04.711249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:05.743460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:20:18.360489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호사업자등록번호모기업 여부설립년도설립월소재지총 종업원 수 - 2019년 합계총 종업원 수 - 2020년 합계매출액 - 2019년매출액 - 2020년세부 업종
번호1.0000.2940.2740.2660.0000.3500.0000.0000.0000.0000.674
사업자등록번호0.2941.0000.1540.2100.0910.8300.0000.0000.0000.0000.556
모기업 여부0.2740.1541.0000.5480.0000.1570.1460.1460.1730.1730.324
설립년도0.2660.2100.5481.0000.0000.1840.6590.6590.6550.6550.148
설립월0.0000.0910.0000.0001.0000.0000.0000.0000.0000.0000.108
소재지0.3500.8300.1570.1840.0001.0000.0000.0000.0000.0000.493
총 종업원 수 - 2019년 합계0.0000.0000.1460.6590.0000.0001.0001.0000.9980.9980.000
총 종업원 수 - 2020년 합계0.0000.0000.1460.6590.0000.0001.0001.0000.9980.9980.000
매출액 - 2019년0.0000.0000.1730.6550.0000.0000.9980.9981.0001.0000.000
매출액 - 2020년0.0000.0000.1730.6550.0000.0000.9980.9981.0001.0000.000
세부 업종0.6740.5560.3240.1480.1080.4930.0000.0000.0000.0001.000
2023-12-12T13:20:18.552108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
모기업 여부소재지
모기업 여부1.0000.139
소재지0.1391.000
2023-12-12T13:20:18.688150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호사업자등록번호설립년도설립월총 종업원 수 - 2019년 합계총 종업원 수 - 2020년 합계매출액 - 2019년매출액 - 2020년모기업 여부소재지
번호1.000-0.1060.025-0.078-0.121-0.121-0.104-0.0630.2090.143
사업자등록번호-0.1061.0000.151-0.025-0.131-0.131-0.168-0.1920.1170.511
설립년도0.0250.1511.000-0.078-0.624-0.623-0.654-0.6400.4130.057
설립월-0.078-0.025-0.0781.0000.0910.0910.0650.0790.0000.000
총 종업원 수 - 2019년 합계-0.121-0.131-0.6240.0911.0001.0000.9080.8950.0970.000
총 종업원 수 - 2020년 합계-0.121-0.131-0.6230.0911.0001.0000.9080.8950.0970.000
매출액 - 2019년-0.104-0.168-0.6540.0650.9080.9081.0000.9570.1140.000
매출액 - 2020년-0.063-0.192-0.6400.0790.8950.8950.9571.0000.1140.000
모기업 여부0.2090.1170.4130.0000.0970.0970.1140.1141.0000.139
소재지0.1430.5110.0570.0000.0000.0000.0000.0000.1391.000

Missing values

2023-12-12T13:20:07.016721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:20:07.332005image/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.
2023-12-12T13:20:07.507918image/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

번호사업체(기업)명사업자등록번호모기업 여부모기업(그룹)명대표자명설립년도설립월대표전화-전화번호홈페이지 주소소재지주소(본사)총 종업원 수 - 2019년 합계총 종업원 수 - 2020년 합계매출액 - 2019년매출액 - 2020년1순위 제품명세부 업종
01효림산업(주)514N효림산업현형주199810053-851-8600www.hyolimindustrial.com경북경북 경산시 남산면 전지공단길 22-9188183975847자동차부품[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
12현대자동차(주)101N현대자동차정의선/하언태/장재훈19671202-3464-1114www.hyundai.com서울서울 서초구 헌릉로 127349171664491557506610자동차[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
23한국항공우주산업(주)110N한국항공우주산업안현호199910055-851-1000www.koreaaero.com경남경남 사천시 사남면 공단1로 78512850333108628120비행기부품[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_항공우주산업
34(주)성우하이텍621N성우홀딩스이명근/이문용198112070-7477-5450www.swhitech.com부산부산 기장군 정관읍 농공길 2-9175817181218111520차체부품[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
45기아(주)119N현대자동차최준영/송호성19441202-3464-1114www.kia.com서울서울 서초구 헌릉로 123576635220338578343623자동차[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
56한국지엠(주)122N한국지엠카허카젬200210080-3000-5000www.gm-korea.co.kr인천인천 부평구 부평대로 233894288338453884975자동차[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
67쌍용자동차(주)125N쌍용자동차예병태196212031-610-1114www.smotor.com경기경기 평택시 동삭로 455-12461645503626329298자동차[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
78타타대우상용차(주)401N타타대우상용차김방신200211063-469-3114www.tata-daewoo.com전북전북 군산시 동장산로 1721172115953785493자동차[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
89한국차체(주)402N한국차체양은오199310063-717-5200www.hankookbody.com전북전북 완주군 봉동읍 완주산단7로 27104103625786자동차부품[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
910한국진공(주)514Y<NA>이인우199812053-591-7720www.kova21c.com대구대구 달성군 구지면 달성2차동3로 808381528433탄소섬유파이프[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_기계장비
번호사업체(기업)명사업자등록번호모기업 여부모기업(그룹)명대표자명설립년도설립월대표전화-전화번호홈페이지 주소소재지주소(본사)총 종업원 수 - 2019년 합계총 종업원 수 - 2020년 합계매출액 - 2019년매출액 - 2020년1순위 제품명세부 업종
555556세종공업 주식회사620N세종공업박정길/김익석/김기홍19766052-219-1699www.sjku.co.kr울산울산 북구 효자로 8272871638423321자동차부품[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_자동차
556557남양부직포 주식회사134N남양부직포채수민19983031-491-3536www.nynonwoven.com경기경기 안산시 단원구 해안로 172143141324713타일블럭[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_건자재
557558삼보씨엠씨 주식회사615Y<NA>김치용20012051-717-0581www.sambocmc.com부산부산 강서구 미음산단4로 653232197131건설기계[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_기계장비
558559서울향료 주식회사114N쌍인조병해/조성용1974702-517-4050www.seoulfnf.com서울서울 서초구 신반포로 306117114427502향료[탄소소재 부품 및 응용제품]_(카본블랙 부품 및 응용제품)_잉크, 도료 및 페인트
559560주식회사 에스지플렉시오307N에스지김상호201011042-931-8599www.nandb.co.kr대전대전 대덕구 산업단지로87번길 71101022반도체, 나노물질[탄소소재 부품 및 응용제품]_(그래핀 부품 및 응용제품)_기계장비
560561주식회사 현진기업413Y<NA>임용택199711062-972-8235www.hjing.net전남전남 장성군 남면 나노산단5로 4515151925탈취제, 공기정화[탄소소재 부품 및 응용제품]_(활성탄소 부품 및 응용제품)_환경필터
561562(주)큐젠바이오텍140N큐젠바이오텍최명숙20065031-497-2660www.quegen.com경기경기 시흥시 마유로92번길 54, 218호27262525화장품원료[6대 탄소소재]_(활성탄소)_입상 활성탄소(Granular Activated Carbon, GAC)
562563주식회사 동양116N유진정진학1955802-6150-7000www.tongyanginc.co.kr서울서울 영등포구 국제금융로2길 2449548838414444시멘트, 레미콘[6대 탄소소재]_(활성탄소)_분말 활성탄소(Powdered Activated Carbon, PAC)
563564주식회사 한국화이어텍140Y<NA>강병도200710031-499-8131www.ihft.co.kr경기경기 안산시 단원구 엠티브이5로18번길 5315152427화학제품[6대 탄소소재]_(활성탄소)_입상 활성탄소(Granular Activated Carbon, GAC)
564565주식회사 단석산업133N단석산업한승욱19847031-488-0720www.dansuk.co.kr경기경기 시흥시 협력로 16536135459385882PVC안정제, 안료[탄소소재 부품 및 응용제품]_(탄소섬유 부품 및 응용제품)_건자재