Overview

Dataset statistics

Number of variables11
Number of observations383
Missing cells70
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.5 KiB
Average record size in memory92.3 B

Variable types

Text5
Categorical2
Numeric4

Dataset

Description충청남도 당진시 관내 산업단지내에 등록된 공장 현황입니다.(기업명, 설립일자, 종업원수, 전화번호 등)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=438&beforeMenuCd=DOM_000000201001001000&publicdatapk=15052052

Alerts

남종업원 is highly overall correlated with 여종업원 and 1 other fieldsHigh correlation
여종업원 is highly overall correlated with 남종업원High correlation
단지명 is highly overall correlated with 남종업원 and 1 other fieldsHigh correlation
공장우편번호 is highly overall correlated with 단지명High correlation
전화번호 has 25 (6.5%) missing valuesMissing
팩스번호 has 45 (11.7%) missing valuesMissing
남종업원 has 8 (2.1%) zerosZeros
여종업원 has 80 (20.9%) zerosZeros
외국인(남) has 323 (84.3%) zerosZeros
외국인(여) has 375 (97.9%) zerosZeros

Reproduction

Analysis started2024-01-09 22:16:15.410382
Analysis finished2024-01-09 22:16:17.276880
Duration1.87 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회사명
Text

Distinct356
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-01-10T07:16:17.448625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length7.7780679
Min length2

Characters and Unicode

Total characters2979
Distinct characters289
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

Unique333 ?
Unique (%)86.9%

Sample

1st row그린에어(주)
2nd row현대그린파워(주)
3rd row현대로템(주)
4th row현대제철(주)
5th row현대하이스코(주)당진공장
ValueCountFrequency (%)
현대제철(주 5
 
1.3%
당진공장 3
 
0.8%
주)에스에이 3
 
0.8%
주식회사 3
 
0.8%
주)씨아이티 2
 
0.5%
주)새한공업 2
 
0.5%
태성몰드산업(주 2
 
0.5%
우신공업(주 2
 
0.5%
영진철강(주 2
 
0.5%
주)휴스틸 2
 
0.5%
Other values (358) 373
93.5%
2024-01-10T07:16:17.780713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 334
 
11.2%
( 327
 
11.0%
) 327
 
11.0%
이 91
 
3.1%
스 83
 
2.8%
에 67
 
2.2%
업 53
 
1.8%
진 41
 
1.4%
텍 39
 
1.3%
산 37
 
1.2%
Other values (279) 1580
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2278
76.5%
Open Punctuation 327
 
11.0%
Close Punctuation 327
 
11.0%
Uppercase Letter 18
 
0.6%
Space Separator 16
 
0.5%
Other Punctuation 7
 
0.2%
Decimal Number 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 334
 
14.7%
이 91
 
4.0%
스 83
 
3.6%
에 67
 
2.9%
업 53
 
2.3%
진 41
 
1.8%
텍 39
 
1.7%
산 37
 
1.6%
아 36
 
1.6%
지 35
 
1.5%
Other values (258) 1462
64.2%
Uppercase Letter
ValueCountFrequency (%)
N 3
16.7%
G 3
16.7%
E 2
11.1%
A 2
11.1%
T 1
 
5.6%
M 1
 
5.6%
C 1
 
5.6%
B 1
 
5.6%
V 1
 
5.6%
F 1
 
5.6%
Other values (2) 2
11.1%
Other Punctuation
ValueCountFrequency (%)
. 5
71.4%
& 1
 
14.3%
, 1
 
14.3%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
2 2
33.3%
3 1
 
16.7%
Open Punctuation
ValueCountFrequency (%)
( 327
100.0%
Close Punctuation
ValueCountFrequency (%)
) 327
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2278
76.5%
Common 683
 
22.9%
Latin 18
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 334
 
14.7%
이 91
 
4.0%
스 83
 
3.6%
에 67
 
2.9%
업 53
 
2.3%
진 41
 
1.8%
텍 39
 
1.7%
산 37
 
1.6%
아 36
 
1.6%
지 35
 
1.5%
Other values (258) 1462
64.2%
Latin
ValueCountFrequency (%)
N 3
16.7%
G 3
16.7%
E 2
11.1%
A 2
11.1%
T 1
 
5.6%
M 1
 
5.6%
C 1
 
5.6%
B 1
 
5.6%
V 1
 
5.6%
F 1
 
5.6%
Other values (2) 2
11.1%
Common
ValueCountFrequency (%)
( 327
47.9%
) 327
47.9%
16
 
2.3%
. 5
 
0.7%
1 3
 
0.4%
2 2
 
0.3%
3 1
 
0.1%
& 1
 
0.1%
, 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2278
76.5%
ASCII 701
 
23.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 334
 
14.7%
이 91
 
4.0%
스 83
 
3.6%
에 67
 
2.9%
업 53
 
2.3%
진 41
 
1.8%
텍 39
 
1.7%
산 37
 
1.6%
아 36
 
1.6%
지 35
 
1.5%
Other values (258) 1462
64.2%
ASCII
ValueCountFrequency (%)
( 327
46.6%
) 327
46.6%
16
 
2.3%
. 5
 
0.7%
N 3
 
0.4%
1 3
 
0.4%
G 3
 
0.4%
E 2
 
0.3%
2 2
 
0.3%
A 2
 
0.3%
Other values (11) 11
 
1.6%

단지명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
아산국가산업단지(고대부곡지구)
124 
석문국가산업단지
95 
당진합덕지방산업단지
43 
당진합덕농공단지
25 
당진송산2일반산업단지
24 
Other values (11)
72 

Length

Max length16
Median length15
Mean length11.125326
Min length8

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row당진1철강산업단지
2nd row당진1철강산업단지
3rd row당진1철강산업단지
4th row당진1철강산업단지
5th row당진1철강산업단지

Common Values

ValueCountFrequency (%)
아산국가산업단지(고대부곡지구) 124
32.4%
석문국가산업단지 95
24.8%
당진합덕지방산업단지 43
 
11.2%
당진합덕농공단지 25
 
6.5%
당진송산2일반산업단지 24
 
6.3%
당진송악농공단지 22
 
5.7%
당진당진농공단지 11
 
2.9%
당진신평농공단지 11
 
2.9%
당진면천농공단지 9
 
2.3%
당진1철강산업단지 5
 
1.3%
Other values (6) 14
 
3.7%

Length

2024-01-10T07:16:17.898609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아산국가산업단지(고대부곡지구 124
32.4%
석문국가산업단지 95
24.8%
당진합덕지방산업단지 43
 
11.2%
당진합덕농공단지 25
 
6.5%
당진송산2일반산업단지 24
 
6.3%
당진송악농공단지 22
 
5.7%
당진당진농공단지 11
 
2.9%
당진신평농공단지 11
 
2.9%
당진면천농공단지 9
 
2.3%
당진1철강산업단지 5
 
1.3%
Other values (6) 14
 
3.7%

최초승인일
Text

Distinct290
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-01-10T07:16:18.112423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3830
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

Unique258 ?
Unique (%)67.4%

Sample

1st row2009-12-22
2nd row2014-04-21
3rd row2008-09-09
4th row2001-08-29
5th row2004-10-07
ValueCountFrequency (%)
2009-10-08 20
 
5.2%
2008-12-22 11
 
2.9%
2005-01-05 11
 
2.9%
2009-10-19 8
 
2.1%
2005-03-04 7
 
1.8%
2004-11-30 6
 
1.6%
2009-11-17 5
 
1.3%
2003-07-19 4
 
1.0%
2016-08-31 4
 
1.0%
2016-08-09 3
 
0.8%
Other values (280) 304
79.4%
2024-01-10T07:16:18.423569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1001
26.1%
- 766
20.0%
2 640
16.7%
1 632
16.5%
9 142
 
3.7%
7 118
 
3.1%
8 111
 
2.9%
5 110
 
2.9%
4 107
 
2.8%
3 106
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3064
80.0%
Dash Punctuation 766
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1001
32.7%
2 640
20.9%
1 632
20.6%
9 142
 
4.6%
7 118
 
3.9%
8 111
 
3.6%
5 110
 
3.6%
4 107
 
3.5%
3 106
 
3.5%
6 97
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3830
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1001
26.1%
- 766
20.0%
2 640
16.7%
1 632
16.5%
9 142
 
3.7%
7 118
 
3.1%
8 111
 
2.9%
5 110
 
2.9%
4 107
 
2.8%
3 106
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1001
26.1%
- 766
20.0%
2 640
16.7%
1 632
16.5%
9 142
 
3.7%
7 118
 
3.1%
8 111
 
2.9%
5 110
 
2.9%
4 107
 
2.8%
3 106
 
2.8%

전화번호
Text

MISSING 

Distinct329
Distinct (%)91.9%
Missing25
Missing (%)6.5%
Memory size3.1 KiB
2024-01-10T07:16:18.631335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.002793
Min length11

Characters and Unicode

Total characters4297
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

Unique303 ?
Unique (%)84.6%

Sample

1st row041-680-7582
2nd row041-680-7781
3rd row041-680-0617
4th row041-680-0114
5th row041-680-9132
ValueCountFrequency (%)
041-358-7805 4
 
1.1%
041-357-4671 3
 
0.8%
041-363-3415 2
 
0.6%
031-667-8141 2
 
0.6%
041-363-3521 2
 
0.6%
031-491-4408 2
 
0.6%
041-363-6884 2
 
0.6%
041-351-3100 2
 
0.6%
041-534-8840 2
 
0.6%
041-353-2922 2
 
0.6%
Other values (319) 335
93.6%
2024-01-10T07:16:18.933630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 716
16.7%
0 645
15.0%
1 549
12.8%
3 517
12.0%
4 451
10.5%
5 361
8.4%
2 249
 
5.8%
6 230
 
5.4%
8 220
 
5.1%
7 203
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3581
83.3%
Dash Punctuation 716
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 645
18.0%
1 549
15.3%
3 517
14.4%
4 451
12.6%
5 361
10.1%
2 249
 
7.0%
6 230
 
6.4%
8 220
 
6.1%
7 203
 
5.7%
9 156
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 716
16.7%
0 645
15.0%
1 549
12.8%
3 517
12.0%
4 451
10.5%
5 361
8.4%
2 249
 
5.8%
6 230
 
5.4%
8 220
 
5.1%
7 203
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 716
16.7%
0 645
15.0%
1 549
12.8%
3 517
12.0%
4 451
10.5%
5 361
8.4%
2 249
 
5.8%
6 230
 
5.4%
8 220
 
5.1%
7 203
 
4.7%

팩스번호
Text

MISSING 

Distinct302
Distinct (%)89.3%
Missing45
Missing (%)11.7%
Memory size3.1 KiB
2024-01-10T07:16:19.139184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.008876
Min length11

Characters and Unicode

Total characters4059
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

Unique274 ?
Unique (%)81.1%

Sample

1st row041-680-7586
2nd row041-680-7639
3rd row041-680-0634
4th row041-680-1199
5th row041-680-9997
ValueCountFrequency (%)
041-357-4674 5
 
1.5%
041-358-7807 4
 
1.2%
041-680-1198 3
 
0.9%
031-8041-1849 3
 
0.9%
041-354-5905 3
 
0.9%
041-363-3520 2
 
0.6%
031-667-6180 2
 
0.6%
041-357-8515 2
 
0.6%
041-363-8848 2
 
0.6%
041-357-0239 2
 
0.6%
Other values (292) 310
91.7%
2024-01-10T07:16:19.499239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 676
16.7%
0 547
13.5%
3 489
12.0%
1 473
11.7%
4 466
11.5%
5 337
8.3%
8 222
 
5.5%
6 214
 
5.3%
9 214
 
5.3%
7 212
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3383
83.3%
Dash Punctuation 676
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 547
16.2%
3 489
14.5%
1 473
14.0%
4 466
13.8%
5 337
10.0%
8 222
6.6%
6 214
 
6.3%
9 214
 
6.3%
7 212
 
6.3%
2 209
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
- 676
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4059
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 676
16.7%
0 547
13.5%
3 489
12.0%
1 473
11.7%
4 466
11.5%
5 337
8.3%
8 222
 
5.5%
6 214
 
5.3%
9 214
 
5.3%
7 212
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4059
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 676
16.7%
0 547
13.5%
3 489
12.0%
1 473
11.7%
4 466
11.5%
5 337
8.3%
8 222
 
5.5%
6 214
 
5.3%
9 214
 
5.3%
7 212
 
5.2%

남종업원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.778068
Minimum0
Maximum3300
Zeros8
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-01-10T07:16:19.618354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median13
Q330.5
95-th percentile116.8
Maximum3300
Range3300
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation179.97859
Coefficient of variation (CV)4.5245685
Kurtosis284.84965
Mean39.778068
Median Absolute Deviation (MAD)9
Skewness16.013314
Sum15235
Variance32392.294
MonotonicityNot monotonic
2024-01-10T07:16:19.729776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 29
 
7.6%
3 26
 
6.8%
4 25
 
6.5%
1 18
 
4.7%
20 15
 
3.9%
15 15
 
3.9%
10 12
 
3.1%
8 12
 
3.1%
14 12
 
3.1%
6 12
 
3.1%
Other values (78) 207
54.0%
ValueCountFrequency (%)
0 8
 
2.1%
1 18
4.7%
2 10
 
2.6%
3 26
6.8%
4 25
6.5%
5 29
7.6%
6 12
3.1%
7 11
 
2.9%
8 12
3.1%
9 6
 
1.6%
ValueCountFrequency (%)
3300 1
0.3%
897 1
0.3%
491 1
0.3%
390 1
0.3%
328 1
0.3%
218 1
0.3%
213 1
0.3%
210 1
0.3%
200 1
0.3%
196 1
0.3%

여종업원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3968668
Minimum0
Maximum250
Zeros80
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-01-10T07:16:19.838067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile29
Maximum250
Range250
Interquartile range (IQR)4

Descriptive statistics

Standard deviation24.42017
Coefficient of variation (CV)3.3014208
Kurtosis49.50438
Mean7.3968668
Median Absolute Deviation (MAD)2
Skewness6.5778377
Sum2833
Variance596.3447
MonotonicityNot monotonic
2024-01-10T07:16:20.184983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 90
23.5%
0 80
20.9%
2 70
18.3%
3 30
 
7.8%
5 22
 
5.7%
10 13
 
3.4%
6 12
 
3.1%
4 11
 
2.9%
8 9
 
2.3%
9 8
 
2.1%
Other values (23) 38
9.9%
ValueCountFrequency (%)
0 80
20.9%
1 90
23.5%
2 70
18.3%
3 30
 
7.8%
4 11
 
2.9%
5 22
 
5.7%
6 12
 
3.1%
7 6
 
1.6%
8 9
 
2.3%
9 8
 
2.1%
ValueCountFrequency (%)
250 1
 
0.3%
200 2
0.5%
120 3
0.8%
118 1
 
0.3%
100 1
 
0.3%
95 1
 
0.3%
80 1
 
0.3%
52 1
 
0.3%
50 2
0.5%
44 1
 
0.3%

외국인(남)
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2245431
Minimum0
Maximum50
Zeros323
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-01-10T07:16:20.273804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.4104497
Coefficient of variation (CV)3.6017105
Kurtosis50.493524
Mean1.2245431
Median Absolute Deviation (MAD)0
Skewness6.1952321
Sum469
Variance19.452066
MonotonicityNot monotonic
2024-01-10T07:16:20.362983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 323
84.3%
5 12
 
3.1%
2 9
 
2.3%
3 8
 
2.1%
10 5
 
1.3%
1 5
 
1.3%
8 3
 
0.8%
7 2
 
0.5%
9 2
 
0.5%
4 2
 
0.5%
Other values (11) 12
 
3.1%
ValueCountFrequency (%)
0 323
84.3%
1 5
 
1.3%
2 9
 
2.3%
3 8
 
2.1%
4 2
 
0.5%
5 12
 
3.1%
6 1
 
0.3%
7 2
 
0.5%
8 3
 
0.8%
9 2
 
0.5%
ValueCountFrequency (%)
50 1
0.3%
30 1
0.3%
29 1
0.3%
22 1
0.3%
21 1
0.3%
20 1
0.3%
18 1
0.3%
15 1
0.3%
13 1
0.3%
12 2
0.5%

외국인(여)
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22715405
Minimum0
Maximum30
Zeros375
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-01-10T07:16:20.446508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0790891
Coefficient of variation (CV)9.1527716
Kurtosis138.73839
Mean0.22715405
Median Absolute Deviation (MAD)0
Skewness11.256705
Sum87
Variance4.3226115
MonotonicityNot monotonic
2024-01-10T07:16:20.527518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 375
97.9%
20 1
 
0.3%
1 1
 
0.3%
15 1
 
0.3%
2 1
 
0.3%
10 1
 
0.3%
4 1
 
0.3%
30 1
 
0.3%
5 1
 
0.3%
ValueCountFrequency (%)
0 375
97.9%
1 1
 
0.3%
2 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
10 1
 
0.3%
15 1
 
0.3%
20 1
 
0.3%
30 1
 
0.3%
ValueCountFrequency (%)
30 1
 
0.3%
20 1
 
0.3%
15 1
 
0.3%
10 1
 
0.3%
5 1
 
0.3%
4 1
 
0.3%
2 1
 
0.3%
1 1
 
0.3%
0 375
97.9%

공장우편번호
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
343-823
80 
343-856
61 
343-903
55 
343-827
49 
343-853
33 
Other values (11)
105 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row343-831
2nd row343-831
3rd row343-823
4th row343-823
5th row343-830

Common Values

ValueCountFrequency (%)
343-823 80
20.9%
343-856 61
15.9%
343-903 55
14.4%
343-827 49
12.8%
343-853 33
8.6%
343-822 22
 
5.7%
343-831 21
 
5.5%
343-808 11
 
2.9%
343-905 10
 
2.6%
343-830 9
 
2.3%
Other values (6) 32
 
8.4%

Length

2024-01-10T07:16:20.621728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
343-823 80
20.9%
343-856 61
15.9%
343-903 55
14.4%
343-827 49
12.8%
343-853 33
8.6%
343-822 22
 
5.7%
343-831 21
 
5.5%
343-808 11
 
2.9%
343-905 10
 
2.6%
343-830 9
 
2.3%
Other values (6) 32
 
8.4%

공장대표주소
Text

Distinct320
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-01-10T07:16:20.801777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length45
Mean length27.099217
Min length12

Characters and Unicode

Total characters10379
Distinct characters173
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

Unique278 ?
Unique (%)72.6%

Sample

1st row충청남도 당진시 송산면 북부산업로 1526 (총 3 필지)
2nd row충청남도 당진시 송산면 북부산업로 1526, (당진1철강산업단지)
3rd row충청남도 당진시 송악읍 고대리 315번지 외 2필지 외 2필지
4th row충청남도 당진시 송악읍 북부산업로 1480 (송악읍)
5th row충청남도 당진시 송산면 0번지 당진1철강산업단지 A4블럭 외 1필지
ValueCountFrequency (%)
충청남도 383
17.8%
당진시 383
17.8%
송악읍 169
 
7.9%
석문면 98
 
4.6%
부곡공단4길 78
 
3.6%
합덕읍 74
 
3.4%
송산면 31
 
1.4%
삼봉리 29
 
1.4%
통정리 28
 
1.3%
부곡공단로 27
 
1.3%
Other values (428) 848
39.5%
2024-01-10T07:16:21.097217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1792
 
17.3%
시 407
 
3.9%
진 399
 
3.8%
당 393
 
3.8%
도 384
 
3.7%
충 383
 
3.7%
청 383
 
3.7%
남 383
 
3.7%
1 284
 
2.7%
2 271
 
2.6%
Other values (163) 5300
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6378
61.5%
Space Separator 1792
 
17.3%
Decimal Number 1586
 
15.3%
Open Punctuation 184
 
1.8%
Close Punctuation 184
 
1.8%
Dash Punctuation 178
 
1.7%
Other Punctuation 43
 
0.4%
Uppercase Letter 34
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 407
 
6.4%
진 399
 
6.3%
당 393
 
6.2%
도 384
 
6.0%
충 383
 
6.0%
청 383
 
6.0%
남 383
 
6.0%
읍 245
 
3.8%
단 238
 
3.7%
송 235
 
3.7%
Other values (136) 2928
45.9%
Decimal Number
ValueCountFrequency (%)
1 284
17.9%
2 271
17.1%
3 236
14.9%
4 191
12.0%
5 143
9.0%
0 121
7.6%
8 105
 
6.6%
6 99
 
6.2%
7 85
 
5.4%
9 51
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
A 19
55.9%
L 5
 
14.7%
B 5
 
14.7%
R 1
 
2.9%
S 1
 
2.9%
C 1
 
2.9%
O 1
 
2.9%
F 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 41
95.3%
& 1
 
2.3%
/ 1
 
2.3%
Open Punctuation
ValueCountFrequency (%)
( 183
99.5%
[ 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 183
99.5%
] 1
 
0.5%
Space Separator
ValueCountFrequency (%)
1792
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6378
61.5%
Common 3967
38.2%
Latin 34
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 407
 
6.4%
진 399
 
6.3%
당 393
 
6.2%
도 384
 
6.0%
충 383
 
6.0%
청 383
 
6.0%
남 383
 
6.0%
읍 245
 
3.8%
단 238
 
3.7%
송 235
 
3.7%
Other values (136) 2928
45.9%
Common
ValueCountFrequency (%)
1792
45.2%
1 284
 
7.2%
2 271
 
6.8%
3 236
 
5.9%
4 191
 
4.8%
( 183
 
4.6%
) 183
 
4.6%
- 178
 
4.5%
5 143
 
3.6%
0 121
 
3.1%
Other values (9) 385
 
9.7%
Latin
ValueCountFrequency (%)
A 19
55.9%
L 5
 
14.7%
B 5
 
14.7%
R 1
 
2.9%
S 1
 
2.9%
C 1
 
2.9%
O 1
 
2.9%
F 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6378
61.5%
ASCII 4001
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1792
44.8%
1 284
 
7.1%
2 271
 
6.8%
3 236
 
5.9%
4 191
 
4.8%
( 183
 
4.6%
) 183
 
4.6%
- 178
 
4.4%
5 143
 
3.6%
0 121
 
3.0%
Other values (17) 419
 
10.5%
Hangul
ValueCountFrequency (%)
시 407
 
6.4%
진 399
 
6.3%
당 393
 
6.2%
도 384
 
6.0%
충 383
 
6.0%
청 383
 
6.0%
남 383
 
6.0%
읍 245
 
3.8%
단 238
 
3.7%
송 235
 
3.7%
Other values (136) 2928
45.9%

Interactions

2024-01-10T07:16:16.706640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:15.894264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.165499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.430147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.772827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:15.961104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.234797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.500041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.858053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.029220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.298014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.572672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.942414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.095164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.363576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:16.641114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:16:21.171461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단지명남종업원여종업원외국인(남)외국인(여)공장우편번호
단지명1.0000.8870.7430.0000.0000.980
남종업원0.8871.0000.3220.0000.0000.000
여종업원0.7430.3221.0000.3710.5490.354
외국인(남)0.0000.0000.3711.0000.8560.221
외국인(여)0.0000.0000.5490.8561.0000.063
공장우편번호0.9800.0000.3540.2210.0631.000
2024-01-10T07:16:21.247789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공장우편번호단지명
공장우편번호1.0000.694
단지명0.6941.000
2024-01-10T07:16:21.314789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남종업원여종업원외국인(남)외국인(여)단지명공장우편번호
남종업원1.0000.6890.0850.0970.6050.000
여종업원0.6891.0000.0570.1600.4570.172
외국인(남)0.0850.0571.0000.3690.0000.000
외국인(여)0.0970.1600.3691.0000.0000.028
단지명0.6050.4570.0000.0001.0000.694
공장우편번호0.0000.1720.0000.0280.6941.000

Missing values

2024-01-10T07:16:17.044563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:16:17.160567image/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.
2024-01-10T07:16:17.239881image/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

회사명단지명최초승인일전화번호팩스번호남종업원여종업원외국인(남)외국인(여)공장우편번호공장대표주소
0그린에어(주)당진1철강산업단지2009-12-22041-680-7582041-680-758622000343-831충청남도 당진시 송산면 북부산업로 1526 (총 3 필지)
1현대그린파워(주)당진1철강산업단지2014-04-21041-680-7781041-680-7639122800343-831충청남도 당진시 송산면 북부산업로 1526, (당진1철강산업단지)
2현대로템(주)당진1철강산업단지2008-09-09041-680-0617041-680-06342101000343-823충청남도 당진시 송악읍 고대리 315번지 외 2필지 외 2필지
3현대제철(주)당진1철강산업단지2001-08-29041-680-0114041-680-11998971300343-823충청남도 당진시 송악읍 북부산업로 1480 (송악읍)
4현대하이스코(주)당진공장당진1철강산업단지2004-10-07041-680-9132041-680-99974100343-830충청남도 당진시 송산면 0번지 당진1철강산업단지 A4블럭 외 1필지
5(주)고려산전당진당진농공단지2012-02-13041-355-5598041-362-55904200343-808충청남도 당진시 시곡로 323, 당진농공단지 (시곡동)
6(주)다인이엔지당진당진농공단지2005-10-17<NA>041-355-01992100343-808충청남도 당진시 시곡로 323 (시곡동, (주)이엔이텍)
7(주)벽우당진당진농공단지2013-04-26041-357-4444041-357-04404200343-808충청남도 당진시 시곡로 327 (시곡동)
8(주)보승전기당진당진농공단지2017-05-22041-355-5711041-355-5712244400343-808충청남도 당진시 시곡로 326 (시곡동)
9(주)삼정에스에프당진당진농공단지2009-09-08041-363-2525041-363-25115100343-808충청남도 당진시 시곡로 323 (시곡동, (주)이엔이텍)
회사명단지명최초승인일전화번호팩스번호남종업원여종업원외국인(남)외국인(여)공장우편번호공장대표주소
373티에이치정공아산국가산업단지(고대부곡지구)2014-09-22<NA>041-357-46823000343-823충청남도 당진시 송악읍 부곡공단4길 27-44
374푸양푸내고온재료그룹(주)아산국가산업단지(고대부곡지구)2009-10-05041-358-7241041-358-72438000343-823충청남도 당진시 송악읍 부곡공단4길 53-49
375한국유니콤밸브(주)아산국가산업단지(고대부곡지구)2005-02-28041-350-6000041-350-6009431100343-827충청남도 당진시 송악읍 부곡공단4길 28-175
376한국전력공사아산국가산업단지(고대부곡지구)2012-12-21042-717-4531<NA>10000343-827충청남도 당진시 송악읍 부곡리 564-2번지
377한영강재공업(주)아산국가산업단지(고대부곡지구)2017-03-14070-4613-4929070-4339-71994300343-827충청남도 당진시 송악읍 부곡공단4길 28-252 (하이스틸 당진공장)
378현대비앤지스틸(주)아산국가산업단지(고대부곡지구)2017-10-30055-268-4061055-268-413425200343-827충청남도 당진시 송악읍 부곡공단4길 28-51
379화빈기계(주)아산국가산업단지(고대부곡지구)2007-09-12032-589-6287032-589-628914100343-823충청남도 당진시 송악읍 부곡공단4길 27-38 (총 2 필지)
380효성펌프판매(주)아산국가산업단지(고대부곡지구)2016-11-10041-362-2500041-362-2503141000343-823충청남도 당진시 송악읍 부곡공단로 353 (현진스틸)
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