Overview

Dataset statistics

Number of variables10
Number of observations126
Missing cells137
Missing cells (%)10.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 KiB
Average record size in memory82.0 B

Variable types

Categorical2
Text6
Numeric1
DateTime1

Dataset

Description경기도 남양주시의 산업단지 내 입주기업체 현황 데이터로 산업단지명, 업체명, 업종명, 종업원 수, 소재지주소, 전화번호, 생산품, 관리기관명에 대한 정보를 제공합니다.
Author경기도 남양주시
URLhttps://www.data.go.kr/data/15126902/fileData.do

Alerts

관리리관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
소재지주소(도로명) has 18 (14.3%) missing valuesMissing
소재지주소(지번) has 108 (85.7%) missing valuesMissing
전화번호 has 11 (8.7%) missing valuesMissing
종업원수 has 4 (3.2%) zerosZeros

Reproduction

Analysis started2024-03-14 20:19:19.338392
Analysis finished2024-03-14 20:19:22.114596
Duration2.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산업단지명
Categorical

Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
진관일반산업단지
62 
광릉테크노밸리
34 
금곡일반산업단지
30 

Length

Max length8
Median length8
Mean length7.7301587
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진관일반산업단지 62
49.2%
광릉테크노밸리 34
27.0%
금곡일반산업단지 30
23.8%

Length

2024-03-15T05:19:22.352340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:19:22.722714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진관일반산업단지 62
49.2%
광릉테크노밸리 34
27.0%
금곡일반산업단지 30
23.8%

업체명
Text

Distinct121
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-15T05:19:23.596449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12.5
Mean length7.7063492
Min length2

Characters and Unicode

Total characters971
Distinct characters221
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

Unique116 ?
Unique (%)92.1%

Sample

1st row(유)우성이엔지
2nd row(주)나우스
3rd row(주)뉴숄더
4th row(주)삼호테크
5th row(주)성창기계
ValueCountFrequency (%)
주식회사 29
 
17.7%
주)대성산업 2
 
1.2%
철마공업사 2
 
1.2%
비엠아이 2
 
1.2%
유봉실업주식회사 2
 
1.2%
주)에이티바이오 2
 
1.2%
부승인터내셔널 2
 
1.2%
맘쿠킹 1
 
0.6%
바라던유통(주 1
 
0.6%
셀론텍(주 1
 
0.6%
Other values (120) 120
73.2%
2024-03-15T05:19:24.999454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 78
 
8.0%
( 49
 
5.0%
) 49
 
5.0%
사 43
 
4.4%
39
 
4.0%
회 38
 
3.9%
식 36
 
3.7%
이 33
 
3.4%
스 30
 
3.1%
에 21
 
2.2%
Other values (211) 555
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 779
80.2%
Open Punctuation 49
 
5.0%
Close Punctuation 49
 
5.0%
Space Separator 39
 
4.0%
Uppercase Letter 34
 
3.5%
Other Symbol 15
 
1.5%
Decimal Number 5
 
0.5%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 78
 
10.0%
사 43
 
5.5%
회 38
 
4.9%
식 36
 
4.6%
이 33
 
4.2%
스 30
 
3.9%
에 21
 
2.7%
우 13
 
1.7%
성 12
 
1.5%
업 12
 
1.5%
Other values (189) 463
59.4%
Uppercase Letter
ValueCountFrequency (%)
E 7
20.6%
K 4
11.8%
L 3
8.8%
C 3
8.8%
P 2
 
5.9%
R 2
 
5.9%
S 2
 
5.9%
O 2
 
5.9%
N 2
 
5.9%
T 2
 
5.9%
Other values (4) 5
14.7%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
1 1
 
20.0%
3 1
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 15
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 794
81.8%
Common 143
 
14.7%
Latin 34
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 78
 
9.8%
사 43
 
5.4%
회 38
 
4.8%
식 36
 
4.5%
이 33
 
4.2%
스 30
 
3.8%
에 21
 
2.6%
㈜ 15
 
1.9%
우 13
 
1.6%
성 12
 
1.5%
Other values (190) 475
59.8%
Latin
ValueCountFrequency (%)
E 7
20.6%
K 4
11.8%
L 3
8.8%
C 3
8.8%
P 2
 
5.9%
R 2
 
5.9%
S 2
 
5.9%
O 2
 
5.9%
N 2
 
5.9%
T 2
 
5.9%
Other values (4) 5
14.7%
Common
ValueCountFrequency (%)
( 49
34.3%
) 49
34.3%
39
27.3%
2 3
 
2.1%
1 1
 
0.7%
3 1
 
0.7%
& 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 779
80.2%
ASCII 177
 
18.2%
None 15
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 78
 
10.0%
사 43
 
5.5%
회 38
 
4.9%
식 36
 
4.6%
이 33
 
4.2%
스 30
 
3.9%
에 21
 
2.7%
우 13
 
1.7%
성 12
 
1.5%
업 12
 
1.5%
Other values (189) 463
59.4%
ASCII
ValueCountFrequency (%)
( 49
27.7%
) 49
27.7%
39
22.0%
E 7
 
4.0%
K 4
 
2.3%
L 3
 
1.7%
2 3
 
1.7%
C 3
 
1.7%
P 2
 
1.1%
R 2
 
1.1%
Other values (11) 16
 
9.0%
None
ValueCountFrequency (%)
㈜ 15
100.0%

업종명
Text

Distinct95
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-15T05:19:26.333658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length23
Mean length18.190476
Min length6

Characters and Unicode

Total characters2292
Distinct characters172
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)58.7%

Sample

1st row주형 및 금형 제조업 외 2 종
2nd row종이 포대 및 가방 제조업
3rd row그 외 기타 분류 안된 섬유제품 제조업 외 1 종
4th row액체 펌프 제조업 외 6 종
5th row음ㆍ식료품 및 담배 가공기계 제조업 외 6 종
ValueCountFrequency (%)
제조업 118
 
15.5%
외 83
 
10.9%
및 63
 
8.3%
종 62
 
8.1%
기타 38
 
5.0%
1 29
 
3.8%
그 21
 
2.8%
2 13
 
1.7%
제품 12
 
1.6%
플라스틱 12
 
1.6%
Other values (148) 311
40.8%
2024-03-15T05:19:28.236411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
636
27.7%
제 161
 
7.0%
조 132
 
5.8%
업 132
 
5.8%
외 87
 
3.8%
기 75
 
3.3%
종 71
 
3.1%
및 63
 
2.7%
용 50
 
2.2%
품 47
 
2.1%
Other values (162) 838
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1571
68.5%
Space Separator 636
27.7%
Decimal Number 66
 
2.9%
Other Punctuation 13
 
0.6%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 161
 
10.2%
조 132
 
8.4%
업 132
 
8.4%
외 87
 
5.5%
기 75
 
4.8%
종 71
 
4.5%
및 63
 
4.0%
용 50
 
3.2%
품 47
 
3.0%
타 38
 
2.4%
Other values (150) 715
45.5%
Decimal Number
ValueCountFrequency (%)
1 32
48.5%
2 14
21.2%
3 7
 
10.6%
4 6
 
9.1%
6 4
 
6.1%
5 1
 
1.5%
0 1
 
1.5%
7 1
 
1.5%
Space Separator
ValueCountFrequency (%)
636
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1571
68.5%
Common 721
31.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 161
 
10.2%
조 132
 
8.4%
업 132
 
8.4%
외 87
 
5.5%
기 75
 
4.8%
종 71
 
4.5%
및 63
 
4.0%
용 50
 
3.2%
품 47
 
3.0%
타 38
 
2.4%
Other values (150) 715
45.5%
Common
ValueCountFrequency (%)
636
88.2%
1 32
 
4.4%
2 14
 
1.9%
, 13
 
1.8%
3 7
 
1.0%
4 6
 
0.8%
6 4
 
0.6%
) 3
 
0.4%
( 3
 
0.4%
5 1
 
0.1%
Other values (2) 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1568
68.4%
ASCII 721
31.5%
Compat Jamo 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
636
88.2%
1 32
 
4.4%
2 14
 
1.9%
, 13
 
1.8%
3 7
 
1.0%
4 6
 
0.8%
6 4
 
0.6%
) 3
 
0.4%
( 3
 
0.4%
5 1
 
0.1%
Other values (2) 2
 
0.3%
Hangul
ValueCountFrequency (%)
제 161
 
10.3%
조 132
 
8.4%
업 132
 
8.4%
외 87
 
5.5%
기 75
 
4.8%
종 71
 
4.5%
및 63
 
4.0%
용 50
 
3.2%
품 47
 
3.0%
타 38
 
2.4%
Other values (149) 712
45.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 3
100.0%

종업원수
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.5
Minimum0
Maximum285
Zeros4
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T05:19:28.519990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15.25
median13
Q327.75
95-th percentile94
Maximum285
Range285
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation43.565078
Coefficient of variation (CV)1.6439652
Kurtosis19.910241
Mean26.5
Median Absolute Deviation (MAD)9.5
Skewness4.1081405
Sum3339
Variance1897.916
MonotonicityNot monotonic
2024-03-15T05:19:28.806271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3 13
 
10.3%
8 8
 
6.3%
5 6
 
4.8%
13 5
 
4.0%
12 5
 
4.0%
4 5
 
4.0%
0 4
 
3.2%
21 4
 
3.2%
20 4
 
3.2%
6 4
 
3.2%
Other values (39) 68
54.0%
ValueCountFrequency (%)
0 4
 
3.2%
2 4
 
3.2%
3 13
10.3%
4 5
 
4.0%
5 6
4.8%
6 4
 
3.2%
7 3
 
2.4%
8 8
6.3%
9 3
 
2.4%
10 2
 
1.6%
ValueCountFrequency (%)
285 2
1.6%
162 1
0.8%
158 1
0.8%
141 1
0.8%
115 1
0.8%
95 1
0.8%
91 1
0.8%
78 1
0.8%
60 1
0.8%
59 1
0.8%

소재지주소(도로명)
Text

MISSING 

Distinct97
Distinct (%)89.8%
Missing18
Missing (%)14.3%
Memory size1.1 KiB
2024-03-15T05:19:29.479591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length36
Mean length28.37963
Min length21

Characters and Unicode

Total characters3065
Distinct characters68
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

Unique87 ?
Unique (%)80.6%

Sample

1st row경기도 남양주시 진건읍 진관산단로70번길 8 가동 1,2층
2nd row경기도 남양주시 진건읍 진관산단로70번길 12
3rd row경기도 남양주시 진건읍 진관산단로70번길 12
4th row경기도 남양주시 진건읍 진관산단로70번길 13, 진관산단 A4-4
5th row경기도 남양주시 진건읍 진관산단로59번길 57, 1동(1,2층 일부), 2동(2층)
ValueCountFrequency (%)
경기도 108
17.4%
남양주시 108
17.4%
진건읍 63
 
10.1%
진접읍 50
 
8.0%
경복대로바람골길 27
 
4.3%
진관산단로59번길 23
 
3.7%
진관산단로70번길 19
 
3.1%
진관산단로54번길 16
 
2.6%
팔야산단로 10
 
1.6%
1층 10
 
1.6%
Other values (102) 188
30.2%
2024-03-15T05:19:30.925418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
514
 
16.8%
진 177
 
5.8%
경 135
 
4.4%
읍 113
 
3.7%
주 111
 
3.6%
시 108
 
3.5%
기 108
 
3.5%
로 108
 
3.5%
양 108
 
3.5%
남 108
 
3.5%
Other values (58) 1475
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1977
64.5%
Space Separator 514
 
16.8%
Decimal Number 476
 
15.5%
Other Punctuation 38
 
1.2%
Dash Punctuation 36
 
1.2%
Open Punctuation 8
 
0.3%
Close Punctuation 8
 
0.3%
Uppercase Letter 8
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
진 177
 
9.0%
경 135
 
6.8%
읍 113
 
5.7%
주 111
 
5.6%
시 108
 
5.5%
기 108
 
5.5%
로 108
 
5.5%
양 108
 
5.5%
남 108
 
5.5%
도 108
 
5.5%
Other values (41) 793
40.1%
Decimal Number
ValueCountFrequency (%)
1 95
20.0%
2 79
16.6%
5 70
14.7%
4 65
13.7%
0 44
9.2%
7 35
 
7.4%
3 35
 
7.4%
9 30
 
6.3%
8 14
 
2.9%
6 9
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
B 4
50.0%
A 4
50.0%
Space Separator
ValueCountFrequency (%)
514
100.0%
Other Punctuation
ValueCountFrequency (%)
, 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1977
64.5%
Common 1080
35.2%
Latin 8
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
진 177
 
9.0%
경 135
 
6.8%
읍 113
 
5.7%
주 111
 
5.6%
시 108
 
5.5%
기 108
 
5.5%
로 108
 
5.5%
양 108
 
5.5%
남 108
 
5.5%
도 108
 
5.5%
Other values (41) 793
40.1%
Common
ValueCountFrequency (%)
514
47.6%
1 95
 
8.8%
2 79
 
7.3%
5 70
 
6.5%
4 65
 
6.0%
0 44
 
4.1%
, 38
 
3.5%
- 36
 
3.3%
7 35
 
3.2%
3 35
 
3.2%
Other values (5) 69
 
6.4%
Latin
ValueCountFrequency (%)
B 4
50.0%
A 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1977
64.5%
ASCII 1088
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
514
47.2%
1 95
 
8.7%
2 79
 
7.3%
5 70
 
6.4%
4 65
 
6.0%
0 44
 
4.0%
, 38
 
3.5%
- 36
 
3.3%
7 35
 
3.2%
3 35
 
3.2%
Other values (7) 77
 
7.1%
Hangul
ValueCountFrequency (%)
진 177
 
9.0%
경 135
 
6.8%
읍 113
 
5.7%
주 111
 
5.6%
시 108
 
5.5%
기 108
 
5.5%
로 108
 
5.5%
양 108
 
5.5%
남 108
 
5.5%
도 108
 
5.5%
Other values (41) 793
40.1%

소재지주소(지번)
Text

MISSING 

Distinct18
Distinct (%)100.0%
Missing108
Missing (%)85.7%
Memory size1.1 KiB
2024-03-15T05:19:31.689200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length30
Mean length24.888889
Min length22

Characters and Unicode

Total characters448
Distinct characters37
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)100.0%

Sample

1st row경기도 남양주시 진건읍 진관리 986번지 (A6-1블럭)
2nd row경기도 남양주시 진건읍 진관리 982-5번지
3rd row경기도 남양주시 진접읍 금곡리 2056번지
4th row경기도 남양주시 진접읍 금곡리 2068번지
5th row경기도 남양주시 진접읍 금곡리 2050번지
ValueCountFrequency (%)
경기도 18
18.2%
남양주시 18
18.2%
진접읍 16
16.2%
팔야리 13
13.1%
외 4
 
4.0%
금곡리 3
 
3.0%
1필지 3
 
3.0%
진관리 2
 
2.0%
진건읍 2
 
2.0%
831번지 1
 
1.0%
Other values (19) 19
19.2%
2024-03-15T05:19:32.864567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
18.1%
지 21
 
4.7%
진 20
 
4.5%
경 18
 
4.0%
시 18
 
4.0%
기 18
 
4.0%
리 18
 
4.0%
읍 18
 
4.0%
주 18
 
4.0%
양 18
 
4.0%
Other values (27) 200
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 282
62.9%
Space Separator 81
 
18.1%
Decimal Number 72
 
16.1%
Dash Punctuation 10
 
2.2%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 21
 
7.4%
진 20
 
7.1%
경 18
 
6.4%
시 18
 
6.4%
기 18
 
6.4%
리 18
 
6.4%
읍 18
 
6.4%
주 18
 
6.4%
양 18
 
6.4%
남 18
 
6.4%
Other values (13) 97
34.4%
Decimal Number
ValueCountFrequency (%)
8 17
23.6%
2 16
22.2%
3 10
13.9%
1 8
11.1%
6 7
9.7%
9 5
 
6.9%
0 5
 
6.9%
5 3
 
4.2%
7 1
 
1.4%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 282
62.9%
Common 165
36.8%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 21
 
7.4%
진 20
 
7.1%
경 18
 
6.4%
시 18
 
6.4%
기 18
 
6.4%
리 18
 
6.4%
읍 18
 
6.4%
주 18
 
6.4%
양 18
 
6.4%
남 18
 
6.4%
Other values (13) 97
34.4%
Common
ValueCountFrequency (%)
81
49.1%
8 17
 
10.3%
2 16
 
9.7%
3 10
 
6.1%
- 10
 
6.1%
1 8
 
4.8%
6 7
 
4.2%
9 5
 
3.0%
0 5
 
3.0%
5 3
 
1.8%
Other values (3) 3
 
1.8%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 282
62.9%
ASCII 166
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81
48.8%
8 17
 
10.2%
2 16
 
9.6%
3 10
 
6.0%
- 10
 
6.0%
1 8
 
4.8%
6 7
 
4.2%
9 5
 
3.0%
0 5
 
3.0%
5 3
 
1.8%
Other values (4) 4
 
2.4%
Hangul
ValueCountFrequency (%)
지 21
 
7.4%
진 20
 
7.1%
경 18
 
6.4%
시 18
 
6.4%
기 18
 
6.4%
리 18
 
6.4%
읍 18
 
6.4%
주 18
 
6.4%
양 18
 
6.4%
남 18
 
6.4%
Other values (13) 97
34.4%

전화번호
Text

MISSING 

Distinct106
Distinct (%)92.2%
Missing11
Missing (%)8.7%
Memory size1.1 KiB
2024-03-15T05:19:33.845092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.973913
Min length9

Characters and Unicode

Total characters1377
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

Unique98 ?
Unique (%)85.2%

Sample

1st row031-567-0609
2nd row031-527-6761
3rd row031-551-8533
4th row031-527-3800
5th row031-529-9609
ValueCountFrequency (%)
031-527-6761 3
 
2.6%
031-573-7620 2
 
1.7%
031-573-8500 2
 
1.7%
031-571-3843 2
 
1.7%
031-567-0609 2
 
1.7%
031-573-5006 2
 
1.7%
031-841-7887 2
 
1.7%
031-572-8883 2
 
1.7%
031-563-5637 1
 
0.9%
031-575-0886 1
 
0.9%
Other values (96) 96
83.5%
2024-03-15T05:19:35.351955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 229
16.6%
0 210
15.3%
3 166
12.1%
5 164
11.9%
1 160
11.6%
7 119
8.6%
2 103
7.5%
8 70
 
5.1%
6 63
 
4.6%
4 55
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1148
83.4%
Dash Punctuation 229
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 210
18.3%
3 166
14.5%
5 164
14.3%
1 160
13.9%
7 119
10.4%
2 103
9.0%
8 70
 
6.1%
6 63
 
5.5%
4 55
 
4.8%
9 38
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 229
16.6%
0 210
15.3%
3 166
12.1%
5 164
11.9%
1 160
11.6%
7 119
8.6%
2 103
7.5%
8 70
 
5.1%
6 63
 
4.6%
4 55
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 229
16.6%
0 210
15.3%
3 166
12.1%
5 164
11.9%
1 160
11.6%
7 119
8.6%
2 103
7.5%
8 70
 
5.1%
6 63
 
4.6%
4 55
 
4.0%

생산품
Text

Distinct121
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-15T05:19:36.228609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length29
Mean length12.769841
Min length3

Characters and Unicode

Total characters1609
Distinct characters306
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)92.9%

Sample

1st row금형제작, 텐트 및 마스크
2nd row쇼핑백외
3rd row펠트, 숄더패트
4th row액체펌프, 기체펌프, 액체여과기, LED조명장치, 공기청정기
5th row분쇄기, 포장기, 볼밀, 음식료품가공기계 등
ValueCountFrequency (%)
등 18
 
5.7%
및 8
 
2.5%
플라스틱 6
 
1.9%
마스크 5
 
1.6%
용기 4
 
1.3%
pcb 3
 
1.0%
쇼핑백 3
 
1.0%
레미콘 3
 
1.0%
금형 3
 
1.0%
전선릴 3
 
1.0%
Other values (243) 259
82.2%
2024-03-15T05:19:37.647709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
194
 
12.1%
, 120
 
7.5%
기 51
 
3.2%
스 45
 
2.8%
용 40
 
2.5%
품 29
 
1.8%
제 27
 
1.7%
조 26
 
1.6%
장 26
 
1.6%
이 24
 
1.5%
Other values (296) 1027
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1224
76.1%
Space Separator 194
 
12.1%
Other Punctuation 120
 
7.5%
Uppercase Letter 40
 
2.5%
Open Punctuation 14
 
0.9%
Close Punctuation 14
 
0.9%
Lowercase Letter 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 51
 
4.2%
스 45
 
3.7%
용 40
 
3.3%
품 29
 
2.4%
제 27
 
2.2%
조 26
 
2.1%
장 26
 
2.1%
이 24
 
2.0%
등 23
 
1.9%
라 19
 
1.6%
Other values (279) 914
74.7%
Uppercase Letter
ValueCountFrequency (%)
L 9
22.5%
D 8
20.0%
E 8
20.0%
C 4
10.0%
P 4
10.0%
B 3
 
7.5%
T 1
 
2.5%
G 1
 
2.5%
I 1
 
2.5%
S 1
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
f 1
33.3%
q 1
33.3%
i 1
33.3%
Space Separator
ValueCountFrequency (%)
194
100.0%
Other Punctuation
ValueCountFrequency (%)
, 120
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1223
76.0%
Common 342
 
21.3%
Latin 43
 
2.7%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 51
 
4.2%
스 45
 
3.7%
용 40
 
3.3%
품 29
 
2.4%
제 27
 
2.2%
조 26
 
2.1%
장 26
 
2.1%
이 24
 
2.0%
등 23
 
1.9%
라 19
 
1.6%
Other values (278) 913
74.7%
Latin
ValueCountFrequency (%)
L 9
20.9%
D 8
18.6%
E 8
18.6%
C 4
9.3%
P 4
9.3%
B 3
 
7.0%
f 1
 
2.3%
q 1
 
2.3%
i 1
 
2.3%
T 1
 
2.3%
Other values (3) 3
 
7.0%
Common
ValueCountFrequency (%)
194
56.7%
, 120
35.1%
( 14
 
4.1%
) 14
 
4.1%
Han
ValueCountFrequency (%)
外 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1223
76.0%
ASCII 385
 
23.9%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
194
50.4%
, 120
31.2%
( 14
 
3.6%
) 14
 
3.6%
L 9
 
2.3%
D 8
 
2.1%
E 8
 
2.1%
C 4
 
1.0%
P 4
 
1.0%
B 3
 
0.8%
Other values (7) 7
 
1.8%
Hangul
ValueCountFrequency (%)
기 51
 
4.2%
스 45
 
3.7%
용 40
 
3.3%
품 29
 
2.4%
제 27
 
2.2%
조 26
 
2.1%
장 26
 
2.1%
이 24
 
2.0%
등 23
 
1.9%
라 19
 
1.6%
Other values (278) 913
74.7%
CJK
ValueCountFrequency (%)
外 1
100.0%

관리리관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
경기도 남양주시
126 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도 남양주시
2nd row경기도 남양주시
3rd row경기도 남양주시
4th row경기도 남양주시
5th row경기도 남양주시

Common Values

ValueCountFrequency (%)
경기도 남양주시 126
100.0%

Length

2024-03-15T05:19:38.068401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:19:38.376214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 126
50.0%
남양주시 126
50.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2024-02-22 00:00:00
Maximum2024-02-22 00:00:00
2024-03-15T05:19:38.626886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:19:38.926200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T05:19:20.507983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:19:39.147031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산업단지명업종명종업원수소재지주소(도로명)소재지주소(지번)
산업단지명1.0000.8670.3211.0001.000
업종명0.8671.0000.0000.9891.000
종업원수0.3210.0001.0000.9981.000
소재지주소(도로명)1.0000.9890.9981.000NaN
소재지주소(지번)1.0001.0001.000NaN1.000
2024-03-15T05:19:39.428082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종업원수산업단지명
종업원수1.0000.224
산업단지명0.2241.000

Missing values

2024-03-15T05:19:20.954246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:19:21.452760image/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.
2024-03-15T05:19:21.929049image/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

산업단지명업체명업종명종업원수소재지주소(도로명)소재지주소(지번)전화번호생산품관리리관명데이터기준일자
0진관일반산업단지(유)우성이엔지주형 및 금형 제조업 외 2 종0경기도 남양주시 진건읍 진관산단로70번길 8 가동 1,2층<NA>031-567-0609금형제작, 텐트 및 마스크경기도 남양주시2024-02-22
1진관일반산업단지(주)나우스종이 포대 및 가방 제조업3경기도 남양주시 진건읍 진관산단로70번길 12<NA>031-527-6761쇼핑백외경기도 남양주시2024-02-22
2진관일반산업단지(주)뉴숄더그 외 기타 분류 안된 섬유제품 제조업 외 1 종23경기도 남양주시 진건읍 진관산단로70번길 12<NA>031-551-8533펠트, 숄더패트경기도 남양주시2024-02-22
3진관일반산업단지(주)삼호테크액체 펌프 제조업 외 6 종23경기도 남양주시 진건읍 진관산단로70번길 13, 진관산단 A4-4<NA>031-527-3800액체펌프, 기체펌프, 액체여과기, LED조명장치, 공기청정기경기도 남양주시2024-02-22
4진관일반산업단지(주)성창기계음ㆍ식료품 및 담배 가공기계 제조업 외 6 종17경기도 남양주시 진건읍 진관산단로59번길 57, 1동(1,2층 일부), 2동(2층)<NA>031-529-9609분쇄기, 포장기, 볼밀, 음식료품가공기계 등경기도 남양주시2024-02-22
5진관일반산업단지(주)세진엠에스그 외 기타 전자부품 제조업 외 3 종8경기도 남양주시 진건읍 진관산단로54번길 2<NA>031-568-7336컨트롤박스(PLC), 합지기, 타발기경기도 남양주시2024-02-22
6진관일반산업단지(주)영우산업일반철물 제조업 외 3 종18경기도 남양주시 진건읍 진관산단로59번길 54<NA>031-529-0342건설안전시설물, 금속구조물, 시민안전시설물, 조립식 구조물, 교육훈련장비경기도 남양주시2024-02-22
7진관일반산업단지(주)우아헬스케어기타 직물제품 제조업0경기도 남양주시 진건읍 진관산단로70번길 12<NA>031-527-2558부직포 마스크경기도 남양주시2024-02-22
8진관일반산업단지(주)장원레미콘레미콘 제조업31경기도 남양주시 진건읍 진관산단로59번길 13<NA>031-572-9300레미콘경기도 남양주시2024-02-22
9진관일반산업단지(주)정현통상근무복, 작업복 및 유사의복 제조업 외 3 종8경기도 남양주시 진건읍 진관산단로70번길 16<NA>031-566-0995여가복 등 의류경기도 남양주시2024-02-22
산업단지명업체명업종명종업원수소재지주소(도로명)소재지주소(지번)전화번호생산품관리리관명데이터기준일자
116광릉테크노밸리주식회사 신우금속그 외 기타 금속가공업 외 10 종48경기도 남양주시 진접읍 팔야산단로32번길 15 외 2필지<NA>031-575-6253금속파이프,판재가공,구조금속공작물경기도 남양주시2024-02-22
117광릉테크노밸리주식회사 우리들행복가게트레일러 및 세미트레일러 제조업5경기도 남양주시 진접읍 팔야산단로41번길 31<NA>031-529-7497캠핑용,여행용 트레일러경기도 남양주시2024-02-22
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