Overview

Dataset statistics

Number of variables9
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory77.9 B

Variable types

Numeric3
Text4
Categorical2

Dataset

Description영천시 소재 면직물, 직물 생산 공장 현황
Author경상북도 영천시
URLhttps://www.data.go.kr/data/15062485/fileData.do

Alerts

대표업종번호 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 대표업종번호High correlation
용지면적 is highly overall correlated with 건축면적High correlation
건축면적 is highly overall correlated with 용지면적High correlation
순번 has unique valuesUnique
용지면적 has 3 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-12 18:59:49.091074
Analysis finished2023-12-12 18:59:52.138726
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T03:59:52.275313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.4
Q118
median35
Q352
95-th percentile65.6
Maximum69
Range68
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.062403
Coefficient of variation (CV)0.5732115
Kurtosis-1.2
Mean35
Median Absolute Deviation (MAD)17
Skewness0
Sum2415
Variance402.5
MonotonicityStrictly increasing
2023-12-13T03:59:52.559257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
45 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
46 1
 
1.4%
44 1
 
1.4%
53 1
 
1.4%
Other values (59) 59
85.5%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%
61 1
1.4%
60 1
1.4%

회사명
Text

Distinct68
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-13T03:59:52.947111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length5.173913
Min length2

Characters and Unicode

Total characters357
Distinct characters103
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)97.1%

Sample

1st row(주)금강텍
2nd row(주)동산
3rd row(주)리유산업
4th row(주)세인무극
5th row(주)세진
ValueCountFrequency (%)
대원섬유 2
 
2.9%
세운섬유공업사 1
 
1.4%
비봉섬유 1
 
1.4%
삼광산업사 1
 
1.4%
삼성직물(주 1
 
1.4%
서광산업 1
 
1.4%
성동산업 1
 
1.4%
보광섬유(주 1
 
1.4%
송이텍스 1
 
1.4%
주)일승섬유 1
 
1.4%
Other values (58) 58
84.1%
2023-12-13T03:59:53.545827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
섬 27
 
7.6%
유 27
 
7.6%
( 22
 
6.2%
주 22
 
6.2%
) 22
 
6.2%
산 17
 
4.8%
업 17
 
4.8%
텍 8
 
2.2%
성 8
 
2.2%
스 8
 
2.2%
Other values (93) 179
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 308
86.3%
Open Punctuation 22
 
6.2%
Close Punctuation 22
 
6.2%
Decimal Number 3
 
0.8%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
섬 27
 
8.8%
유 27
 
8.8%
주 22
 
7.1%
산 17
 
5.5%
업 17
 
5.5%
텍 8
 
2.6%
성 8
 
2.6%
스 8
 
2.6%
이 8
 
2.6%
동 7
 
2.3%
Other values (88) 159
51.6%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
1 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 308
86.3%
Common 49
 
13.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
섬 27
 
8.8%
유 27
 
8.8%
주 22
 
7.1%
산 17
 
5.5%
업 17
 
5.5%
텍 8
 
2.6%
성 8
 
2.6%
스 8
 
2.6%
이 8
 
2.6%
동 7
 
2.3%
Other values (88) 159
51.6%
Common
ValueCountFrequency (%)
( 22
44.9%
) 22
44.9%
2 2
 
4.1%
. 2
 
4.1%
1 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 308
86.3%
ASCII 49
 
13.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
섬 27
 
8.8%
유 27
 
8.8%
주 22
 
7.1%
산 17
 
5.5%
업 17
 
5.5%
텍 8
 
2.6%
성 8
 
2.6%
스 8
 
2.6%
이 8
 
2.6%
동 7
 
2.3%
Other values (88) 159
51.6%
ASCII
ValueCountFrequency (%)
( 22
44.9%
) 22
44.9%
2 2
 
4.1%
. 2
 
4.1%
1 1
 
2.0%

전화번호
Text

Distinct58
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-13T03:59:53.921321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters828
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

Unique55 ?
Unique (%)79.7%

Sample

1st row054-337-8169
2nd row054-000-0000
3rd row054-338-8818
4th row054-337-0809
5th row054-335-3559
ValueCountFrequency (%)
054-000-0000 10
 
14.5%
054-335-6856 2
 
2.9%
054-336-6500 2
 
2.9%
054-333-6223 1
 
1.4%
054-333-0661 1
 
1.4%
054-332-4139 1
 
1.4%
054-337-9757 1
 
1.4%
054-338-1473 1
 
1.4%
054-334-7111 1
 
1.4%
054-331-4970 1
 
1.4%
Other values (48) 48
69.6%
2023-12-13T03:59:54.438899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 173
20.9%
3 147
17.8%
- 138
16.7%
5 116
14.0%
4 82
9.9%
6 44
 
5.3%
1 37
 
4.5%
7 26
 
3.1%
8 25
 
3.0%
9 22
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 690
83.3%
Dash Punctuation 138
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
25.1%
3 147
21.3%
5 116
16.8%
4 82
11.9%
6 44
 
6.4%
1 37
 
5.4%
7 26
 
3.8%
8 25
 
3.6%
9 22
 
3.2%
2 18
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 828
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
20.9%
3 147
17.8%
- 138
16.7%
5 116
14.0%
4 82
9.9%
6 44
 
5.3%
1 37
 
4.5%
7 26
 
3.1%
8 25
 
3.0%
9 22
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
20.9%
3 147
17.8%
- 138
16.7%
5 116
14.0%
4 82
9.9%
6 44
 
5.3%
1 37
 
4.5%
7 26
 
3.1%
8 25
 
3.0%
9 22
 
2.7%

생산품
Text

Distinct47
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-13T03:59:54.750410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length6.3913043
Min length2

Characters and Unicode

Total characters441
Distinct characters97
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)52.2%

Sample

1st row원단
2nd rowPP포대
3rd row신소재응용제품(에어매트)
4th row화학직물
5th row화섬직물 유기질비료
ValueCountFrequency (%)
화섬직물 12
 
12.5%
직물 6
 
6.2%
천막지 4
 
4.2%
타포린 4
 
4.2%
폴리에스텔 4
 
4.2%
원단 3
 
3.1%
타포린직물 3
 
3.1%
스크린샤 3
 
3.1%
화학섬유직물 3
 
3.1%
폴리에스텔직물 2
 
2.1%
Other values (49) 52
54.2%
2023-12-13T03:59:55.283387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
물 40
 
9.1%
직 39
 
8.8%
27
 
6.1%
섬 22
 
5.0%
스 19
 
4.3%
화 17
 
3.9%
린 14
 
3.2%
타 12
 
2.7%
포 11
 
2.5%
리 10
 
2.3%
Other values (87) 230
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 391
88.7%
Space Separator 27
 
6.1%
Uppercase Letter 14
 
3.2%
Close Punctuation 4
 
0.9%
Open Punctuation 4
 
0.9%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
물 40
 
10.2%
직 39
 
10.0%
섬 22
 
5.6%
스 19
 
4.9%
화 17
 
4.3%
린 14
 
3.6%
타 12
 
3.1%
포 11
 
2.8%
리 10
 
2.6%
유 10
 
2.6%
Other values (78) 197
50.4%
Uppercase Letter
ValueCountFrequency (%)
P 9
64.3%
A 2
 
14.3%
C 1
 
7.1%
L 1
 
7.1%
E 1
 
7.1%
Space Separator
ValueCountFrequency (%)
27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 391
88.7%
Common 36
 
8.2%
Latin 14
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
물 40
 
10.2%
직 39
 
10.0%
섬 22
 
5.6%
스 19
 
4.9%
화 17
 
4.3%
린 14
 
3.6%
타 12
 
3.1%
포 11
 
2.8%
리 10
 
2.6%
유 10
 
2.6%
Other values (78) 197
50.4%
Latin
ValueCountFrequency (%)
P 9
64.3%
A 2
 
14.3%
C 1
 
7.1%
L 1
 
7.1%
E 1
 
7.1%
Common
ValueCountFrequency (%)
27
75.0%
) 4
 
11.1%
( 4
 
11.1%
, 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 391
88.7%
ASCII 50
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
물 40
 
10.2%
직 39
 
10.0%
섬 22
 
5.6%
스 19
 
4.9%
화 17
 
4.3%
린 14
 
3.6%
타 12
 
3.1%
포 11
 
2.8%
리 10
 
2.6%
유 10
 
2.6%
Other values (78) 197
50.4%
ASCII
ValueCountFrequency (%)
27
54.0%
P 9
 
18.0%
) 4
 
8.0%
( 4
 
8.0%
A 2
 
4.0%
C 1
 
2.0%
L 1
 
2.0%
, 1
 
2.0%
E 1
 
2.0%

용지면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4275.087
Minimum0
Maximum22435
Zeros3
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T03:59:55.469960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile786.6
Q12026
median2985
Q34905
95-th percentile11447
Maximum22435
Range22435
Interquartile range (IQR)2879

Descriptive statistics

Standard deviation4262.4378
Coefficient of variation (CV)0.99704118
Kurtosis8.1124263
Mean4275.087
Median Absolute Deviation (MAD)1161
Skewness2.6841004
Sum294981
Variance18168376
MonotonicityNot monotonic
2023-12-13T03:59:55.646176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
4.3%
2026 1
 
1.4%
2067 1
 
1.4%
2128 1
 
1.4%
1964 1
 
1.4%
4797 1
 
1.4%
20916 1
 
1.4%
3509 1
 
1.4%
2693 1
 
1.4%
4161 1
 
1.4%
Other values (57) 57
82.6%
ValueCountFrequency (%)
0 3
4.3%
495 1
 
1.4%
1224 1
 
1.4%
1323 1
 
1.4%
1402 1
 
1.4%
1419 1
 
1.4%
1477 1
 
1.4%
1545 1
 
1.4%
1638 1
 
1.4%
1738 1
 
1.4%
ValueCountFrequency (%)
22435 1
1.4%
20916 1
1.4%
18100 1
1.4%
11719 1
1.4%
11039 1
1.4%
9637 1
1.4%
9191 1
1.4%
8191 1
1.4%
8123 1
1.4%
6197 1
1.4%

건축면적
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2082.2754
Minimum425
Maximum13259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T03:59:55.817165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum425
5-th percentile573
Q1970
median1281
Q32218
95-th percentile7040.4
Maximum13259
Range12834
Interquartile range (IQR)1248

Descriptive statistics

Standard deviation2120.1099
Coefficient of variation (CV)1.0181698
Kurtosis11.684896
Mean2082.2754
Median Absolute Deviation (MAD)450
Skewness3.0840238
Sum143677
Variance4494866.1
MonotonicityNot monotonic
2023-12-13T03:59:56.098052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1209 2
 
2.9%
425 1
 
1.4%
970 1
 
1.4%
6714 1
 
1.4%
13259 1
 
1.4%
937 1
 
1.4%
953 1
 
1.4%
2255 1
 
1.4%
809 1
 
1.4%
495 1
 
1.4%
Other values (58) 58
84.1%
ValueCountFrequency (%)
425 1
1.4%
495 1
1.4%
563 1
1.4%
565 1
1.4%
585 1
1.4%
606 1
1.4%
809 1
1.4%
834 1
1.4%
897 1
1.4%
907 1
1.4%
ValueCountFrequency (%)
13259 1
1.4%
7418 1
1.4%
7414 1
1.4%
7258 1
1.4%
6714 1
1.4%
5358 1
1.4%
4342 1
1.4%
4068 1
1.4%
3642 1
1.4%
3445 1
1.4%

공장대표주소(지번)
Text

Distinct67
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-13T03:59:56.510852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length34
Mean length24.347826
Min length18

Characters and Unicode

Total characters1680
Distinct characters69
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

Unique65 ?
Unique (%)94.2%

Sample

1st row경상북도 영천시 청통면 보성리 569번지
2nd row경상북도 영천시 대창면 사리리 23-3번지
3rd row경상북도 영천시 화산면 유성리 200-8번지
4th row경상북도 영천시 청통면 신학리 7-1번지
5th row경상북도 영천시 화산면 유성리 200-9번지
ValueCountFrequency (%)
경상북도 69
18.3%
영천시 69
18.3%
외 20
 
5.3%
금호읍 19
 
5.0%
청통면 15
 
4.0%
1필지 11
 
2.9%
사리리 7
 
1.9%
신대리 7
 
1.9%
오계리 7
 
1.9%
임고면 7
 
1.9%
Other values (102) 146
38.7%
2023-12-13T03:59:57.243755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
312
18.6%
지 89
 
5.3%
천 77
 
4.6%
경 72
 
4.3%
북 72
 
4.3%
상 71
 
4.2%
도 69
 
4.1%
영 69
 
4.1%
시 69
 
4.1%
리 68
 
4.0%
Other values (59) 712
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1061
63.2%
Space Separator 312
 
18.6%
Decimal Number 259
 
15.4%
Dash Punctuation 48
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 89
 
8.4%
천 77
 
7.3%
경 72
 
6.8%
북 72
 
6.8%
상 71
 
6.7%
도 69
 
6.5%
영 69
 
6.5%
시 69
 
6.5%
리 68
 
6.4%
번 68
 
6.4%
Other values (47) 337
31.8%
Decimal Number
ValueCountFrequency (%)
1 46
17.8%
3 39
15.1%
4 34
13.1%
2 34
13.1%
5 28
10.8%
0 18
 
6.9%
9 17
 
6.6%
6 16
 
6.2%
7 14
 
5.4%
8 13
 
5.0%
Space Separator
ValueCountFrequency (%)
312
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1061
63.2%
Common 619
36.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 89
 
8.4%
천 77
 
7.3%
경 72
 
6.8%
북 72
 
6.8%
상 71
 
6.7%
도 69
 
6.5%
영 69
 
6.5%
시 69
 
6.5%
리 68
 
6.4%
번 68
 
6.4%
Other values (47) 337
31.8%
Common
ValueCountFrequency (%)
312
50.4%
- 48
 
7.8%
1 46
 
7.4%
3 39
 
6.3%
4 34
 
5.5%
2 34
 
5.5%
5 28
 
4.5%
0 18
 
2.9%
9 17
 
2.7%
6 16
 
2.6%
Other values (2) 27
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1061
63.2%
ASCII 619
36.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
312
50.4%
- 48
 
7.8%
1 46
 
7.4%
3 39
 
6.3%
4 34
 
5.5%
2 34
 
5.5%
5 28
 
4.5%
0 18
 
2.9%
9 17
 
2.7%
6 16
 
2.6%
Other values (2) 27
 
4.4%
Hangul
ValueCountFrequency (%)
지 89
 
8.4%
천 77
 
7.3%
경 72
 
6.8%
북 72
 
6.8%
상 71
 
6.7%
도 69
 
6.5%
영 69
 
6.5%
시 69
 
6.5%
리 68
 
6.4%
번 68
 
6.4%
Other values (47) 337
31.8%

대표업종번호
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size684.0 B
13213
37 
13219
27 
13229
 
3
13225
 
1
13211
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique2 ?
Unique (%)2.9%

Sample

1st row13213
2nd row13225
3rd row13229
4th row13213
5th row13219

Common Values

ValueCountFrequency (%)
13213 37
53.6%
13219 27
39.1%
13229 3
 
4.3%
13225 1
 
1.4%
13211 1
 
1.4%

Length

2023-12-13T03:59:57.491839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:59:57.704050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13213 37
53.6%
13219 27
39.1%
13229 3
 
4.3%
13225 1
 
1.4%
13211 1
 
1.4%

업종명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size684.0 B
화학섬유직물 직조업
36 
특수 직물 및 기타 직물 직조업
20 
특수 직물 및 기타 직물 직조업 외 2종
6 
기타 직물제품 제조업
 
3
직물포대 제조업
 
1
Other values (3)
 
3

Length

Max length22
Median length10
Mean length13.289855
Min length7

Unique

Unique4 ?
Unique (%)5.8%

Sample

1st row화학섬유직물 직조업
2nd row직물포대 제조업
3rd row기타 직물제품 제조업
4th row화학섬유직물 직조업
5th row특수 직물 및 기타 직물 직조업 외 2종

Common Values

ValueCountFrequency (%)
화학섬유직물 직조업 36
52.2%
특수 직물 및 기타 직물 직조업 20
29.0%
특수 직물 및 기타 직물 직조업 외 2종 6
 
8.7%
기타 직물제품 제조업 3
 
4.3%
직물포대 제조업 1
 
1.4%
특수 직물 및 기타 직물 직조업 외 1종 1
 
1.4%
화학섬유직물 직조업 외 1종 1
 
1.4%
면직물 직조업 1
 
1.4%

Length

2023-12-13T03:59:57.948582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:59:58.247139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직조업 65
24.5%
직물 54
20.4%
화학섬유직물 37
14.0%
기타 30
11.3%
특수 27
10.2%
및 27
10.2%
외 8
 
3.0%
2종 6
 
2.3%
제조업 4
 
1.5%
직물제품 3
 
1.1%
Other values (3) 4
 
1.5%

Interactions

2023-12-13T03:59:51.264034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:50.202945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:50.767953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:51.433961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:50.396111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:50.954949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:51.572137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:50.575747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:51.100183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:59:58.453564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번회사명전화번호생산품용지면적건축면적공장대표주소(지번)대표업종번호업종명
순번1.0001.0000.7450.3420.3040.2230.8540.1350.242
회사명1.0001.0000.9890.9951.0000.9830.9940.9730.990
전화번호0.7450.9891.0000.0000.0000.9180.9990.0000.739
생산품0.3420.9950.0001.0000.9180.9270.9960.9830.993
용지면적0.3041.0000.0000.9181.0000.7820.9800.0000.698
건축면적0.2230.9830.9180.9270.7821.0000.9910.0000.697
공장대표주소(지번)0.8540.9940.9990.9960.9800.9911.0001.0001.000
대표업종번호0.1350.9730.0000.9830.0000.0001.0001.0001.000
업종명0.2420.9900.7390.9930.6980.6971.0001.0001.000
2023-12-13T03:59:58.677511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대표업종번호업종명
대표업종번호1.0000.976
업종명0.9761.000
2023-12-13T03:59:58.833875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번용지면적건축면적대표업종번호업종명
순번1.0000.058-0.0420.0300.106
용지면적0.0581.0000.8190.0000.296
건축면적-0.0420.8191.0000.0000.463
대표업종번호0.0300.0000.0001.0000.976
업종명0.1060.2960.4630.9761.000

Missing values

2023-12-13T03:59:51.778242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:59:52.041091image/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

순번회사명전화번호생산품용지면적건축면적공장대표주소(지번)대표업종번호업종명
01(주)금강텍054-337-8169원단2026809경상북도 영천시 청통면 보성리 569번지13213화학섬유직물 직조업
12(주)동산054-000-0000PP포대30631627경상북도 영천시 대창면 사리리 23-3번지13225직물포대 제조업
23(주)리유산업054-338-8818신소재응용제품(에어매트)01186경상북도 영천시 화산면 유성리 200-8번지13229기타 직물제품 제조업
34(주)세인무극054-337-0809화학직물29851080경상북도 영천시 청통면 신학리 7-1번지13213화학섬유직물 직조업
45(주)세진054-335-3559화섬직물 유기질비료181007414경상북도 영천시 화산면 유성리 200-9번지13219특수 직물 및 기타 직물 직조업 외 2종
56(주)수인산업054-335-3311천막지59842236경상북도 영천시 청통면 신학리 520-4번지 외 3필지13219특수 직물 및 기타 직물 직조업
67(주)신흥054-335-4352폴리에스텔 직물1638897경상북도 영천시 금호읍 오계리 91-1번지 외 3필지13213화학섬유직물 직조업
78(주)우정텍스054-337-8633원단60911780경상북도 영천시 망정동 11-8번지 외 1필지13213화학섬유직물 직조업
89(주)일승섬유054-334-9550폴리에스텔 직물81235358경상북도 영천시 언하동 474-3번지 외 1필지13213화학섬유직물 직조업
910(주)자오053-337-8293타포린직물 각종겉덮개 등의 천막직물제품58563445경상북도 영천시 임고면 매호리 893번지 외 2필지13219특수 직물 및 기타 직물 직조업 외 2종
순번회사명전화번호생산품용지면적건축면적공장대표주소(지번)대표업종번호업종명
5960조원산업2공장054-335-9105타포린49052044경상북도 영천시 금호읍 대곡리 734-5번지13219특수 직물 및 기타 직물 직조업
6061조흥섬유054-335-9353화섬직물49413642경상북도 영천시 금호읍 신월리 236-3번지13213화학섬유직물 직조업
6162진명섬유054-334-0055섬유준비38792118경상북도 영천시 금호읍 신대리 333-7번지13213화학섬유직물 직조업
6263창성섬유054-334-8296섬유제조(정경)61972241경상북도 영천시 망정동 8-9번지13229기타 직물제품 제조업
6364창신섬유054-333-2239직물34021787경상북도 영천시 금호읍 신대리 333-4번지13213화학섬유직물 직조업
6465태경산업054-333-6223천막지1784940경상북도 영천시 금호읍 오계리 56-15번지13219특수 직물 및 기타 직물 직조업
6566태림054-000-0000타포린 직물110392439경상북도 영천시 북안면 신리리 544번지13213화학섬유직물 직조업
6667태영타올054-338-0855타올3207912경상북도 영천시 고경면 용전리 385번지 외 1필지 외 1필지13211면직물 직조업
6768형제섬유054-332-4139인조섬유직물23461040경상북도 영천시 신녕면 화성리 207번지13213화학섬유직물 직조업
6869혜성(주)영천지점054-334-3651폴리에스텔직물61692218경상북도 영천시 망정동 8-10번지13213화학섬유직물 직조업