Overview

Dataset statistics

Number of variables13
Number of observations3316
Missing cells5896
Missing cells (%)13.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory353.1 KiB
Average record size in memory109.0 B

Variable types

Categorical3
Text5
Numeric5

Dataset

Description전문연구요원/산업기능요원 지정업체 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=E5X7V72KYXUVBE2KCL7O28587035&infSeq=1

Alerts

배정인원수 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
우편번호 is highly overall correlated with WGS84위도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 배정인원수 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 우편번호 and 3 other fieldsHigh correlation
업종구분 is highly overall correlated with 기업규모High correlation
기업규모 is highly overall correlated with 배정인원수 and 3 other fieldsHigh correlation
기업규모 is highly imbalanced (79.4%)Imbalance
배정인원수 has 43 (1.3%) missing valuesMissing
도로명주소 has 1388 (41.9%) missing valuesMissing
우편번호 has 1142 (34.4%) missing valuesMissing
홈페이지주소 has 645 (19.5%) missing valuesMissing
WGS84위도 has 1339 (40.4%) missing valuesMissing
WGS84경도 has 1339 (40.4%) missing valuesMissing
배정인원수 has 348 (10.5%) zerosZeros

Reproduction

Analysis started2024-04-19 05:48:33.737936
Analysis finished2024-04-19 05:48:38.400710
Duration4.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
화성시
521 
성남시
414 
안산시
296 
용인시
202 
시흥시
201 
Other values (26)
1682 

Length

Max length4
Median length3
Mean length3.0220145
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
화성시 521
15.7%
성남시 414
12.5%
안산시 296
 
8.9%
용인시 202
 
6.1%
시흥시 201
 
6.1%
수원시 196
 
5.9%
안양시 177
 
5.3%
김포시 153
 
4.6%
평택시 148
 
4.5%
부천시 144
 
4.3%
Other values (21) 864
26.1%

Length

2024-04-19T14:48:38.469240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화성시 521
15.7%
성남시 414
12.5%
안산시 296
 
8.9%
용인시 202
 
6.1%
시흥시 201
 
6.1%
수원시 196
 
5.9%
안양시 177
 
5.3%
김포시 153
 
4.6%
평택시 148
 
4.5%
부천시 144
 
4.3%
Other values (21) 864
26.1%

업체명
Text

Distinct3304
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
2024-04-19T14:48:38.771365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length29
Mean length9.1981303
Min length2

Characters and Unicode

Total characters30501
Distinct characters639
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3292 ?
Unique (%)99.3%

Sample

1st row(주)옥서스
2nd row(주)지엠로드
3rd row경기가평군농기계수리
4th row경기가평군농기계운전
5th row경기가평군농어민후계
ValueCountFrequency (%)
기업부설연구소 159
 
4.0%
부설연구소 66
 
1.7%
기술연구소 60
 
1.5%
주식회사 47
 
1.2%
연구소 44
 
1.1%
center 26
 
0.7%
r&d 22
 
0.6%
성균관대학교 15
 
0.4%
중앙연구소 13
 
0.3%
부설 6
 
0.2%
Other values (3367) 3492
88.4%
2024-04-19T14:48:39.223947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 2746
 
9.0%
) 2581
 
8.5%
( 2580
 
8.5%
이 1019
 
3.3%
스 927
 
3.0%
기 683
 
2.2%
소 659
 
2.2%
연 654
 
2.1%
구 652
 
2.1%
642
 
2.1%
Other values (629) 17358
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23963
78.6%
Close Punctuation 2581
 
8.5%
Open Punctuation 2580
 
8.5%
Space Separator 643
 
2.1%
Lowercase Letter 266
 
0.9%
Uppercase Letter 199
 
0.7%
Other Symbol 165
 
0.5%
Other Punctuation 50
 
0.2%
Decimal Number 50
 
0.2%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 2746
 
11.5%
이 1019
 
4.3%
스 927
 
3.9%
기 683
 
2.9%
소 659
 
2.8%
연 654
 
2.7%
구 652
 
2.7%
에 510
 
2.1%
업 405
 
1.7%
부 362
 
1.5%
Other values (571) 15346
64.0%
Lowercase Letter
ValueCountFrequency (%)
e 73
27.4%
r 35
13.2%
t 33
12.4%
n 31
11.7%
a 22
 
8.3%
i 8
 
3.0%
l 8
 
3.0%
m 8
 
3.0%
s 7
 
2.6%
b 7
 
2.6%
Other values (10) 34
12.8%
Uppercase Letter
ValueCountFrequency (%)
R 45
22.6%
D 43
21.6%
C 36
18.1%
T 16
 
8.0%
S 8
 
4.0%
E 7
 
3.5%
A 6
 
3.0%
I 6
 
3.0%
M 5
 
2.5%
N 4
 
2.0%
Other values (10) 23
11.6%
Decimal Number
ValueCountFrequency (%)
1 21
42.0%
2 14
28.0%
3 7
 
14.0%
9 3
 
6.0%
4 1
 
2.0%
7 1
 
2.0%
5 1
 
2.0%
8 1
 
2.0%
6 1
 
2.0%
Space Separator
ValueCountFrequency (%)
642
99.8%
　 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
& 40
80.0%
. 10
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 2581
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2580
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 165
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24128
79.1%
Common 5907
 
19.4%
Latin 466
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 2746
 
11.4%
이 1019
 
4.2%
스 927
 
3.8%
기 683
 
2.8%
소 659
 
2.7%
연 654
 
2.7%
구 652
 
2.7%
에 510
 
2.1%
업 405
 
1.7%
부 362
 
1.5%
Other values (572) 15511
64.3%
Latin
ValueCountFrequency (%)
e 73
15.7%
R 45
 
9.7%
D 43
 
9.2%
C 36
 
7.7%
r 35
 
7.5%
t 33
 
7.1%
n 31
 
6.7%
a 22
 
4.7%
T 16
 
3.4%
i 8
 
1.7%
Other values (31) 124
26.6%
Common
ValueCountFrequency (%)
) 2581
43.7%
( 2580
43.7%
642
 
10.9%
& 40
 
0.7%
1 21
 
0.4%
2 14
 
0.2%
. 10
 
0.2%
3 7
 
0.1%
9 3
 
0.1%
- 3
 
0.1%
Other values (6) 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23963
78.6%
ASCII 6371
 
20.9%
None 166
 
0.5%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 2746
 
11.5%
이 1019
 
4.3%
스 927
 
3.9%
기 683
 
2.9%
소 659
 
2.8%
연 654
 
2.7%
구 652
 
2.7%
에 510
 
2.1%
업 405
 
1.7%
부 362
 
1.5%
Other values (571) 15346
64.0%
ASCII
ValueCountFrequency (%)
) 2581
40.5%
( 2580
40.5%
642
 
10.1%
e 73
 
1.1%
R 45
 
0.7%
D 43
 
0.7%
& 40
 
0.6%
C 36
 
0.6%
r 35
 
0.5%
t 33
 
0.5%
Other values (45) 263
 
4.1%
None
ValueCountFrequency (%)
㈜ 165
99.4%
　 1
 
0.6%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
100.0%

업종구분
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
기계
772 
전기
314 
벤처기업부설연구소
311 
전자
302 
화학
271 
Other values (34)
1346 

Length

Max length11
Median length2
Mean length3.9047045
Min length2

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st row의료의약
2nd row철강
3rd row농기계수리
4th row농기계운전
5th row후계농업민

Common Values

ValueCountFrequency (%)
기계 772
23.3%
전기 314
9.5%
벤처기업부설연구소 311
9.4%
전자 302
 
9.1%
화학 271
 
8.2%
철강 240
 
7.2%
중소기업부설연구소 203
 
6.1%
통신기기 148
 
4.5%
정보처리 130
 
3.9%
생활용품 96
 
2.9%
Other values (29) 529
16.0%

Length

2024-04-19T14:48:39.361272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기계 772
23.2%
전기 314
9.4%
벤처기업부설연구소 311
9.3%
전자 302
 
9.1%
화학 271
 
8.1%
철강 240
 
7.2%
중소기업부설연구소 203
 
6.1%
통신기기 148
 
4.4%
정보처리 130
 
3.9%
생활용품 96
 
2.9%
Other values (31) 543
16.3%

배정인원수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)0.9%
Missing43
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean834.04919
Minimum0
Maximum1255
Zeros348
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2024-04-19T14:48:39.473505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1323
median1086
Q31251
95-th percentile1252
Maximum1255
Range1255
Interquartile range (IQR)928

Descriptive statistics

Standard deviation500.72894
Coefficient of variation (CV)0.600359
Kurtosis-1.3739121
Mean834.04919
Median Absolute Deviation (MAD)166
Skewness-0.65615883
Sum2729843
Variance250729.47
MonotonicityNot monotonic
2024-04-19T14:48:39.602084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1251 906
27.3%
1085 466
14.1%
1252 419
12.6%
0 348
 
10.5%
272 324
 
9.8%
323 246
 
7.4%
1086 183
 
5.5%
1253 82
 
2.5%
1 81
 
2.4%
324 78
 
2.4%
Other values (20) 140
 
4.2%
(Missing) 43
 
1.3%
ValueCountFrequency (%)
0 348
10.5%
1 81
 
2.4%
2 18
 
0.5%
3 1
 
< 0.1%
4 2
 
0.1%
5 2
 
0.1%
10 4
 
0.1%
40 25
 
0.8%
99 10
 
0.3%
272 324
9.8%
ValueCountFrequency (%)
1255 1
 
< 0.1%
1254 13
 
0.4%
1253 82
 
2.5%
1252 419
12.6%
1251 906
27.3%
1088 2
 
0.1%
1087 33
 
1.0%
1086 183
 
5.5%
1085 466
14.1%
999 2
 
0.1%

도로명주소
Text

MISSING 

Distinct1741
Distinct (%)90.3%
Missing1388
Missing (%)41.9%
Memory size26.0 KiB
2024-04-19T14:48:39.848867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length20.130187
Min length13

Characters and Unicode

Total characters38811
Distinct characters310
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

Unique1638 ?
Unique (%)85.0%

Sample

1st row경기도 가평군 상면 청군로 1579-100
2nd row경기도 가평군 가평읍 아랫마장길 59
3rd row경기도 가평군 가평읍 아랫마장길 59
4th row경기도 가평군 가평읍 아랫마장길 59
5th row경기도 고양시 일산동구 일산로 142
ValueCountFrequency (%)
경기도 1928
 
21.8%
화성시 476
 
5.4%
시흥시 199
 
2.3%
김포시 150
 
1.7%
부천시 143
 
1.6%
평택시 138
 
1.6%
군포시 102
 
1.2%
안성시 90
 
1.0%
파주시 88
 
1.0%
광주시 87
 
1.0%
Other values (2124) 5423
61.5%
2024-04-19T14:48:40.264591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6896
 
17.8%
시 2147
 
5.5%
도 2036
 
5.2%
기 2005
 
5.2%
경 1986
 
5.1%
로 1600
 
4.1%
1 1522
 
3.9%
2 1169
 
3.0%
길 991
 
2.6%
3 864
 
2.2%
Other values (300) 17595
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23491
60.5%
Decimal Number 7766
 
20.0%
Space Separator 6896
 
17.8%
Dash Punctuation 650
 
1.7%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2147
 
9.1%
도 2036
 
8.7%
기 2005
 
8.5%
경 1986
 
8.5%
로 1600
 
6.8%
길 991
 
4.2%
성 668
 
2.8%
면 609
 
2.6%
번 600
 
2.6%
화 523
 
2.2%
Other values (287) 10326
44.0%
Decimal Number
ValueCountFrequency (%)
1 1522
19.6%
2 1169
15.1%
3 864
11.1%
4 697
9.0%
5 687
8.8%
6 641
8.3%
8 588
 
7.6%
7 549
 
7.1%
0 531
 
6.8%
9 518
 
6.7%
Space Separator
ValueCountFrequency (%)
6896
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 650
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23491
60.5%
Common 15320
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2147
 
9.1%
도 2036
 
8.7%
기 2005
 
8.5%
경 1986
 
8.5%
로 1600
 
6.8%
길 991
 
4.2%
성 668
 
2.8%
면 609
 
2.6%
번 600
 
2.6%
화 523
 
2.2%
Other values (287) 10326
44.0%
Common
ValueCountFrequency (%)
6896
45.0%
1 1522
 
9.9%
2 1169
 
7.6%
3 864
 
5.6%
4 697
 
4.5%
5 687
 
4.5%
- 650
 
4.2%
6 641
 
4.2%
8 588
 
3.8%
7 549
 
3.6%
Other values (3) 1057
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23491
60.5%
ASCII 15320
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6896
45.0%
1 1522
 
9.9%
2 1169
 
7.6%
3 864
 
5.6%
4 697
 
4.5%
5 687
 
4.5%
- 650
 
4.2%
6 641
 
4.2%
8 588
 
3.8%
7 549
 
3.6%
Other values (3) 1057
 
6.9%
Hangul
ValueCountFrequency (%)
시 2147
 
9.1%
도 2036
 
8.7%
기 2005
 
8.5%
경 1986
 
8.5%
로 1600
 
6.8%
길 991
 
4.2%
성 668
 
2.8%
면 609
 
2.6%
번 600
 
2.6%
화 523
 
2.2%
Other values (287) 10326
44.0%

지번주소
Text

Distinct3230
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
2024-04-19T14:48:40.566082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length81
Mean length34.381484
Min length10

Characters and Unicode

Total characters114009
Distinct characters505
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3171 ?
Unique (%)95.6%

Sample

1st row경기도 가평군 설악면 246
2nd row경기도 가평군 상면 원흥리 526-20번지
3rd row경기도 가평군 가평읍 승안리 100 농업기술센터
4th row경기도 가평군 가평읍 승안리 100 농업기술센터
5th row경기도 가평군 가평읍 승안리 100번지
ValueCountFrequency (%)
경기도 4563
 
18.7%
성남시 813
 
3.3%
안산시 588
 
2.4%
단원구 525
 
2.2%
화성시 524
 
2.1%
분당구 473
 
1.9%
수원시 383
 
1.6%
용인시 343
 
1.4%
안양시 337
 
1.4%
동안구 260
 
1.1%
Other values (5092) 15571
63.9%
2024-04-19T14:48:41.043744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21106
 
18.5%
시 5062
 
4.4%
기 4909
 
4.3%
도 4698
 
4.1%
경 4613
 
4.0%
동 3758
 
3.3%
1 3668
 
3.2%
2 2719
 
2.4%
구 2672
 
2.3%
번 2296
 
2.0%
Other values (495) 58508
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68826
60.4%
Space Separator 21106
 
18.5%
Decimal Number 18369
 
16.1%
Dash Punctuation 1762
 
1.5%
Other Punctuation 1398
 
1.2%
Uppercase Letter 807
 
0.7%
Open Punctuation 761
 
0.7%
Close Punctuation 761
 
0.7%
Math Symbol 158
 
0.1%
Lowercase Letter 57
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5062
 
7.4%
기 4909
 
7.1%
도 4698
 
6.8%
경 4613
 
6.7%
동 3758
 
5.5%
구 2672
 
3.9%
번 2296
 
3.3%
지 2193
 
3.2%
성 1758
 
2.6%
안 1619
 
2.4%
Other values (435) 35248
51.2%
Uppercase Letter
ValueCountFrequency (%)
A 133
16.5%
B 121
15.0%
T 79
9.8%
C 58
 
7.2%
I 54
 
6.7%
D 49
 
6.1%
S 44
 
5.5%
V 39
 
4.8%
K 38
 
4.7%
M 34
 
4.2%
Other values (14) 158
19.6%
Lowercase Letter
ValueCountFrequency (%)
e 13
22.8%
n 10
17.5%
r 7
12.3%
t 6
10.5%
k 4
 
7.0%
c 4
 
7.0%
s 4
 
7.0%
o 2
 
3.5%
b 2
 
3.5%
m 1
 
1.8%
Other values (4) 4
 
7.0%
Decimal Number
ValueCountFrequency (%)
1 3668
20.0%
2 2719
14.8%
0 1983
10.8%
3 1920
10.5%
4 1745
9.5%
5 1578
8.6%
6 1472
8.0%
7 1186
 
6.5%
8 1131
 
6.2%
9 967
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 1354
96.9%
/ 18
 
1.3%
& 16
 
1.1%
. 8
 
0.6%
: 2
 
0.1%
Space Separator
ValueCountFrequency (%)
21106
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1762
100.0%
Open Punctuation
ValueCountFrequency (%)
( 761
100.0%
Close Punctuation
ValueCountFrequency (%)
) 761
100.0%
Math Symbol
ValueCountFrequency (%)
~ 158
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68826
60.4%
Common 44317
38.9%
Latin 866
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5062
 
7.4%
기 4909
 
7.1%
도 4698
 
6.8%
경 4613
 
6.7%
동 3758
 
5.5%
구 2672
 
3.9%
번 2296
 
3.3%
지 2193
 
3.2%
성 1758
 
2.6%
안 1619
 
2.4%
Other values (435) 35248
51.2%
Latin
ValueCountFrequency (%)
A 133
15.4%
B 121
14.0%
T 79
 
9.1%
C 58
 
6.7%
I 54
 
6.2%
D 49
 
5.7%
S 44
 
5.1%
V 39
 
4.5%
K 38
 
4.4%
M 34
 
3.9%
Other values (29) 217
25.1%
Common
ValueCountFrequency (%)
21106
47.6%
1 3668
 
8.3%
2 2719
 
6.1%
0 1983
 
4.5%
3 1920
 
4.3%
- 1762
 
4.0%
4 1745
 
3.9%
5 1578
 
3.6%
6 1472
 
3.3%
, 1354
 
3.1%
Other values (11) 5010
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68826
60.4%
ASCII 45181
39.6%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21106
46.7%
1 3668
 
8.1%
2 2719
 
6.0%
0 1983
 
4.4%
3 1920
 
4.2%
- 1762
 
3.9%
4 1745
 
3.9%
5 1578
 
3.5%
6 1472
 
3.3%
, 1354
 
3.0%
Other values (49) 5874
 
13.0%
Hangul
ValueCountFrequency (%)
시 5062
 
7.4%
기 4909
 
7.1%
도 4698
 
6.8%
경 4613
 
6.7%
동 3758
 
5.5%
구 2672
 
3.9%
번 2296
 
3.3%
지 2193
 
3.2%
성 1758
 
2.6%
안 1619
 
2.4%
Other values (435) 35248
51.2%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%

우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct677
Distinct (%)31.1%
Missing1142
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean15314.14
Minimum10003
Maximum18635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2024-04-19T14:48:41.191113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10003
5-th percentile10048
Q112814
median15657
Q317843
95-th percentile18583
Maximum18635
Range8632
Interquartile range (IQR)5029

Descriptive statistics

Standard deviation2813.4106
Coefficient of variation (CV)0.18371327
Kurtosis-1.0525368
Mean15314.14
Median Absolute Deviation (MAD)2446
Skewness-0.48216216
Sum33292940
Variance7915279.5
MonotonicityNot monotonic
2024-04-19T14:48:41.325251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15657 112
 
3.4%
18487 41
 
1.2%
13449 39
 
1.2%
16508 39
 
1.2%
10048 31
 
0.9%
15117 27
 
0.8%
15850 21
 
0.6%
17704 20
 
0.6%
10049 20
 
0.6%
18449 19
 
0.6%
Other values (667) 1805
54.4%
(Missing) 1142
34.4%
ValueCountFrequency (%)
10003 2
 
0.1%
10005 1
 
< 0.1%
10008 2
 
0.1%
10009 1
 
< 0.1%
10010 4
0.1%
10011 6
0.2%
10012 1
 
< 0.1%
10014 2
 
0.1%
10015 2
 
0.1%
10016 5
0.2%
ValueCountFrequency (%)
18635 4
0.1%
18634 1
 
< 0.1%
18633 6
0.2%
18631 2
 
0.1%
18630 4
0.1%
18629 1
 
< 0.1%
18628 7
0.2%
18627 8
0.2%
18626 4
0.1%
18625 7
0.2%

전화번호
Text

Distinct3157
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
2024-04-19T14:48:41.568611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length11.103739
Min length7

Characters and Unicode

Total characters36820
Distinct characters18
Distinct categories7 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3058 ?
Unique (%)92.2%

Sample

1st row031-477-5252
2nd row031-594-8308
3rd row031-580-2872
4th row031-580-2872
5th row0315802872
ValueCountFrequency (%)
40
 
1.2%
0234759811 6
 
0.2%
0011 6
 
0.2%
031-8018-9439 6
 
0.2%
031-789-2179 4
 
0.1%
031-828-2311 3
 
0.1%
73 3
 
0.1%
031-940-4601 3
 
0.1%
031-860-2312 3
 
0.1%
031-820-5607 3
 
0.1%
Other values (3164) 3274
97.7%
2024-04-19T14:48:41.985908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6297
17.1%
3 5214
14.2%
1 5058
13.7%
- 3169
8.6%
7 2594
7.0%
2 2543
6.9%
4 2498
 
6.8%
6 2293
 
6.2%
8 2293
 
6.2%
5 2191
 
6.0%
Other values (8) 2670
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33119
89.9%
Dash Punctuation 3169
 
8.6%
Other Punctuation 443
 
1.2%
Space Separator 83
 
0.2%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6297
19.0%
3 5214
15.7%
1 5058
15.3%
7 2594
7.8%
2 2543
7.7%
4 2498
 
7.5%
6 2293
 
6.9%
8 2293
 
6.9%
5 2191
 
6.6%
9 2138
 
6.5%
Other Punctuation
ValueCountFrequency (%)
* 440
99.3%
, 3
 
0.7%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 3169
100.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6297
17.1%
3 5214
14.2%
1 5058
13.7%
- 3169
8.6%
7 2594
7.0%
2 2543
6.9%
4 2498
 
6.8%
6 2293
 
6.2%
8 2293
 
6.2%
5 2191
 
6.0%
Other values (8) 2670
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6297
17.1%
3 5214
14.2%
1 5058
13.7%
- 3169
8.6%
7 2594
7.0%
2 2543
6.9%
4 2498
 
6.8%
6 2293
 
6.2%
8 2293
 
6.2%
5 2191
 
6.0%
Other values (8) 2670
7.3%

홈페이지주소
Text

MISSING 

Distinct2482
Distinct (%)92.9%
Missing645
Missing (%)19.5%
Memory size26.0 KiB
2024-04-19T14:48:42.227459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length38
Mean length16.770498
Min length1

Characters and Unicode

Total characters44794
Distinct characters152
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2386 ?
Unique (%)89.3%

Sample

1st rowwww.oxus.co.kr
2nd rowgmroad.kr
3rd rowwww.vrware.us
4th rowwww.neostack.co.kr
5th rowwww.daier.co.kr
ValueCountFrequency (%)
20
 
0.8%
www.samsung.com/sec 6
 
0.2%
http://www.ajucorporation.co.kr 5
 
0.2%
0 3
 
0.1%
http 3
 
0.1%
www.ncsoft.com 3
 
0.1%
없음 3
 
0.1%
www.sp-t.com 2
 
0.1%
www.iwta.co.kr 2
 
0.1%
http://www.isseco.com 2
 
0.1%
Other values (2469) 2558
98.1%
2024-04-19T14:48:42.625175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 6893
15.4%
. 5981
13.4%
o 3871
 
8.6%
c 3364
 
7.5%
r 2128
 
4.8%
t 2108
 
4.7%
e 1995
 
4.5%
m 1923
 
4.3%
k 1812
 
4.0%
n 1633
 
3.6%
Other values (142) 13086
29.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36086
80.6%
Other Punctuation 7834
 
17.5%
Decimal Number 320
 
0.7%
Dash Punctuation 233
 
0.5%
Space Separator 128
 
0.3%
Other Letter 122
 
0.3%
Uppercase Letter 63
 
0.1%
Math Symbol 2
 
< 0.1%
Connector Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 5
 
4.1%
스 4
 
3.3%
자 3
 
2.5%
금 3
 
2.5%
음 3
 
2.5%
프 3
 
2.5%
테 3
 
2.5%
없 3
 
2.5%
메 2
 
1.6%
일 2
 
1.6%
Other values (76) 91
74.6%
Lowercase Letter
ValueCountFrequency (%)
w 6893
19.1%
o 3871
10.7%
c 3364
 
9.3%
r 2128
 
5.9%
t 2108
 
5.8%
e 1995
 
5.5%
m 1923
 
5.3%
k 1812
 
5.0%
n 1633
 
4.5%
s 1526
 
4.2%
Other values (16) 8833
24.5%
Uppercase Letter
ValueCountFrequency (%)
O 10
15.9%
T 6
9.5%
C 6
9.5%
R 5
 
7.9%
W 5
 
7.9%
N 5
 
7.9%
E 4
 
6.3%
K 3
 
4.8%
S 3
 
4.8%
M 3
 
4.8%
Other values (8) 13
20.6%
Decimal Number
ValueCountFrequency (%)
1 86
26.9%
2 86
26.9%
0 38
11.9%
4 23
 
7.2%
3 16
 
5.0%
9 16
 
5.0%
6 15
 
4.7%
8 14
 
4.4%
7 13
 
4.1%
5 13
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 5981
76.3%
/ 1084
 
13.8%
: 451
 
5.8%
* 314
 
4.0%
, 4
 
0.1%
Space Separator
ValueCountFrequency (%)
127
99.2%
　 1
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 233
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36149
80.7%
Common 8523
 
19.0%
Hangul 122
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 5
 
4.1%
스 4
 
3.3%
자 3
 
2.5%
금 3
 
2.5%
음 3
 
2.5%
프 3
 
2.5%
테 3
 
2.5%
없 3
 
2.5%
메 2
 
1.6%
일 2
 
1.6%
Other values (76) 91
74.6%
Latin
ValueCountFrequency (%)
w 6893
19.1%
o 3871
10.7%
c 3364
 
9.3%
r 2128
 
5.9%
t 2108
 
5.8%
e 1995
 
5.5%
m 1923
 
5.3%
k 1812
 
5.0%
n 1633
 
4.5%
s 1526
 
4.2%
Other values (34) 8896
24.6%
Common
ValueCountFrequency (%)
. 5981
70.2%
/ 1084
 
12.7%
: 451
 
5.3%
* 314
 
3.7%
- 233
 
2.7%
127
 
1.5%
1 86
 
1.0%
2 86
 
1.0%
0 38
 
0.4%
4 23
 
0.3%
Other values (12) 100
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44671
99.7%
Hangul 122
 
0.3%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 6893
15.4%
. 5981
13.4%
o 3871
 
8.7%
c 3364
 
7.5%
r 2128
 
4.8%
t 2108
 
4.7%
e 1995
 
4.5%
m 1923
 
4.3%
k 1812
 
4.1%
n 1633
 
3.7%
Other values (55) 12963
29.0%
Hangul
ValueCountFrequency (%)
이 5
 
4.1%
스 4
 
3.3%
자 3
 
2.5%
금 3
 
2.5%
음 3
 
2.5%
프 3
 
2.5%
테 3
 
2.5%
없 3
 
2.5%
메 2
 
1.6%
일 2
 
1.6%
Other values (76) 91
74.6%
None
ValueCountFrequency (%)
　 1
100.0%

선정년도
Real number (ℝ)

Distinct46
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.0793
Minimum1973
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2024-04-19T14:48:42.787329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1973
5-th percentile1993
Q12013
median2017
Q32020
95-th percentile2021
Maximum2022
Range49
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.4328622
Coefficient of variation (CV)0.0041869564
Kurtosis1.8735364
Mean2014.0793
Median Absolute Deviation (MAD)3
Skewness-1.6369011
Sum6678687
Variance71.113165
MonotonicityNot monotonic
2024-04-19T14:48:43.267871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2020 469
14.1%
2021 420
12.7%
2019 361
10.9%
2017 302
 
9.1%
2018 278
 
8.4%
2016 235
 
7.1%
2015 161
 
4.9%
2013 115
 
3.5%
2014 112
 
3.4%
1993 106
 
3.2%
Other values (36) 757
22.8%
ValueCountFrequency (%)
1973 1
 
< 0.1%
1974 1
 
< 0.1%
1975 1
 
< 0.1%
1978 2
0.1%
1981 1
 
< 0.1%
1982 3
0.1%
1983 1
 
< 0.1%
1984 1
 
< 0.1%
1985 3
0.1%
1986 3
0.1%
ValueCountFrequency (%)
2022 56
 
1.7%
2021 420
12.7%
2020 469
14.1%
2019 361
10.9%
2018 278
8.4%
2017 302
9.1%
2016 235
7.1%
2015 161
 
4.9%
2014 112
 
3.4%
2013 115
 
3.5%

기업규모
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
<NA>
3154 
해당사항없음
 
106
농어민후계 등
 
56

Length

Max length7
Median length4
Mean length4.1145959
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row농어민후계 등
4th row농어민후계 등
5th row농어민후계 등

Common Values

ValueCountFrequency (%)
<NA> 3154
95.1%
해당사항없음 106
 
3.2%
농어민후계 등 56
 
1.7%

Length

2024-04-19T14:48:43.412444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:48:43.512522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3154
93.5%
해당사항없음 106
 
3.1%
농어민후계 56
 
1.7%
등 56
 
1.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1795
Distinct (%)90.8%
Missing1339
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean37.362152
Minimum36.929396
Maximum38.101224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2024-04-19T14:48:43.635085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.929396
5-th percentile37.035638
Q137.165894
median37.329571
Q337.523177
95-th percentile37.839254
Maximum38.101224
Range1.1718279
Interquartile range (IQR)0.35728324

Descriptive statistics

Standard deviation0.25028625
Coefficient of variation (CV)0.0066989249
Kurtosis-0.59755215
Mean37.362152
Median Absolute Deviation (MAD)0.18221321
Skewness0.58499751
Sum73864.974
Variance0.062643207
MonotonicityNot monotonic
2024-04-19T14:48:43.778870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3496648085 9
 
0.3%
37.5231773138 9
 
0.3%
37.5535037868 8
 
0.2%
37.4017965187 7
 
0.2%
37.2110217235 6
 
0.2%
37.3484253669 5
 
0.2%
37.3569986562 5
 
0.2%
37.1998386132 4
 
0.1%
37.3804189876 4
 
0.1%
37.6165068253 4
 
0.1%
Other values (1785) 1916
57.8%
(Missing) 1339
40.4%
ValueCountFrequency (%)
36.929396231 1
< 0.1%
36.9329566526 1
< 0.1%
36.9494726346 1
< 0.1%
36.9539581621 1
< 0.1%
36.9566383107 1
< 0.1%
36.9572349191 1
< 0.1%
36.9577187057 1
< 0.1%
36.9581089825 1
< 0.1%
36.9588000358 1
< 0.1%
36.9591143422 1
< 0.1%
ValueCountFrequency (%)
38.1012241704 1
< 0.1%
38.0965166652 2
0.1%
38.0357689216 1
< 0.1%
38.0168123706 1
< 0.1%
37.9880733279 1
< 0.1%
37.9714394185 1
< 0.1%
37.9699514527 1
< 0.1%
37.9685175484 1
< 0.1%
37.9682669281 1
< 0.1%
37.9678954218 1
< 0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1795
Distinct (%)90.8%
Missing1339
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean126.96783
Minimum126.53893
Maximum127.69439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2024-04-19T14:48:43.931896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.53893
5-th percentile126.6176
Q1126.78051
median126.95516
Q3127.09774
95-th percentile127.37882
Maximum127.69439
Range1.1554531
Interquartile range (IQR)0.31722519

Descriptive statistics

Standard deviation0.23018358
Coefficient of variation (CV)0.0018129284
Kurtosis-0.32077414
Mean126.96783
Median Absolute Deviation (MAD)0.17090908
Skewness0.43487229
Sum251015.39
Variance0.052984479
MonotonicityNot monotonic
2024-04-19T14:48:44.104707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9535005762 9
 
0.3%
126.7670606162 9
 
0.3%
127.1946802895 8
 
0.2%
126.9910176434 7
 
0.2%
127.0895177988 6
 
0.2%
126.9525159902 5
 
0.2%
126.9547233857 5
 
0.2%
126.8326588839 4
 
0.1%
126.8040662779 4
 
0.1%
126.621303491 4
 
0.1%
Other values (1785) 1916
57.8%
(Missing) 1339
40.4%
ValueCountFrequency (%)
126.538933301 1
< 0.1%
126.539927111 1
< 0.1%
126.5448109059 1
< 0.1%
126.5471841376 1
< 0.1%
126.5481715952 1
< 0.1%
126.548315156 1
< 0.1%
126.5515262607 1
< 0.1%
126.5533792377 1
< 0.1%
126.5536154463 1
< 0.1%
126.55587178 1
< 0.1%
ValueCountFrequency (%)
127.694386361 1
< 0.1%
127.6889302466 1
< 0.1%
127.6667361303 1
< 0.1%
127.6608517574 1
< 0.1%
127.6598487958 1
< 0.1%
127.6591590048 1
< 0.1%
127.6377657647 1
< 0.1%
127.6366282067 2
0.1%
127.6219608253 1
< 0.1%
127.6107740257 1
< 0.1%

Interactions

2024-04-19T14:48:37.384484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:35.520094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:35.991908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.470611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.926387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:37.484615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:35.616230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.088663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.567740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:37.040838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:37.620921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:35.702618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.190237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.660184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:37.126843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:37.709774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:35.793772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.289267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.745039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:37.209012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:37.804834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:35.891423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.378396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:36.837497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:48:37.298167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T14:48:44.200876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명업종구분배정인원수우편번호선정년도기업규모WGS84위도WGS84경도
시군명1.0000.6340.7890.9930.2930.7290.9530.942
업종구분0.6341.0000.7430.5030.6740.7240.3660.322
배정인원수0.7890.7431.0000.7040.1910.5270.6880.569
우편번호0.9930.5030.7041.0000.2550.8190.9040.896
선정년도0.2930.6740.1910.2551.0000.6090.1700.188
기업규모0.7290.7240.5270.8190.6091.0000.6370.413
WGS84위도0.9530.3660.6880.9040.1700.6371.0000.766
WGS84경도0.9420.3220.5690.8960.1880.4130.7661.000
2024-04-19T14:48:44.312344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업규모업종구분시군명
기업규모1.0000.6650.575
업종구분0.6651.0000.175
시군명0.5750.1751.000
2024-04-19T14:48:44.406832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배정인원수우편번호선정년도WGS84위도WGS84경도시군명업종구분기업규모
배정인원수1.0000.493-0.013-0.5470.1930.4750.4140.789
우편번호0.4931.0000.079-0.8850.1800.9390.1960.623
선정년도-0.0130.0791.000-0.076-0.0200.1050.3120.452
WGS84위도-0.547-0.885-0.0761.000-0.2400.7400.1360.472
WGS84경도0.1930.180-0.020-0.2401.0000.7030.1180.397
시군명0.4750.9390.1050.7400.7031.0000.1750.575
업종구분0.4140.1960.3120.1360.1180.1751.0000.665
기업규모0.7890.6230.4520.4720.3970.5750.6651.000

Missing values

2024-04-19T14:48:37.933470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:48:38.110138image/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.
2024-04-19T14:48:38.299284image/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

시군명업체명업종구분배정인원수도로명주소지번주소우편번호전화번호홈페이지주소선정년도기업규모WGS84위도WGS84경도
0가평군(주)옥서스의료의약323<NA>경기도 가평군 설악면 24612470031-477-5252www.oxus.co.kr2002<NA>37.650181127.497053
1가평군(주)지엠로드철강323경기도 가평군 상면 청군로 1579-100경기도 가평군 상면 원흥리 526-20번지12442031-594-8308gmroad.kr2020<NA>37.824287127.319104
2가평군경기가평군농기계수리농기계수리323경기도 가평군 가평읍 아랫마장길 59경기도 가평군 가평읍 승안리 100 농업기술센터12408031-580-2872<NA>1993농어민후계 등37.845967127.498647
3가평군경기가평군농기계운전농기계운전323경기도 가평군 가평읍 아랫마장길 59경기도 가평군 가평읍 승안리 100 농업기술센터12408031-580-2872<NA>1993농어민후계 등37.845967127.498647
4가평군경기가평군농어민후계후계농업민324경기도 가평군 가평읍 아랫마장길 59경기도 가평군 가평읍 승안리 100번지124080315802872<NA>1993농어민후계 등37.845967127.498647
5고양시(주)글로브포인트정보처리324<NA>경기도 고양시 일산동구 장항동 경기도 고양시 일산동구 태극로 60 빛마루 1603호<NA>031-911-0601www.vrware.us2018<NA><NA><NA>
6고양시(주)네오스텍 기업부설연구소벤처기업부설연구소40<NA>경기도 고양시 일산서구 대화동 경기도 고양시 일산서구 고양대로 건설기술연구원 507호 (대화동)<NA>070-4228-1811www.neostack.co.kr2020<NA><NA><NA>
7고양시(주)다이어 기술연구소벤처기업부설연구소0<NA>경기도 고양시 일산서구 대화동 경기도 고양시 일산서구 고양대로 스마트건설지원센터 510호<NA>01042181322www.daier.co.kr2020<NA><NA><NA>
8고양시(주)대한전광전자323<NA>경기도 고양시 일산동구 장항동 경기도 고양시 일산동구 장대길 42-80<NA>025931491www.daehandisplay.co.kr2001<NA><NA><NA>
9고양시(주)데이타존화학323<NA>경기도 고양시 일산동구 설문동 경기도 고양시 일산동구 고봉로 735<NA>0319774181www.datazone.co.kr2000<NA><NA><NA>
시군명업체명업종구분배정인원수도로명주소지번주소우편번호전화번호홈페이지주소선정년도기업규모WGS84위도WGS84경도
3306화성시한보일렉트(주)철강1251경기도 화성시 양감면 초록로 706경기도 화성시 양감면 284-318628031-8059-2510www.hbjoint.com2007<NA>37.11415126.978047
3307화성시현대자동차(주)파워트레인개발센터대기업부설연구소0경기도 화성시 남양읍 현대연구소로 150경기도 화성시 남양읍 장덕리 772-1번지 파워트레인동,환경선행연구동,배기2,3동,환경차개발시험1동,제어연비동,무향1,2,3동,시작동18280031-368-6017<NA>2001<NA>37.159401126.813508
3308화성시현대자동차차량개발센터대기업부설연구소0경기도 화성시 남양읍 현대연구소로 150경기도 화성시 남양읍 장덕리 772-1번지 외18280031-368-6017<NA>1994<NA>37.159401126.813508
3309화성시효동기계공업㈜기계1253경기도 화성시 향남읍 발안공단로4길 97-17경기도 화성시 향남읍 구문천리 933-8번지 총 2필지1862307071195969www.hyodongmachine.co.kr2013<NA>37.082259126.900116
3310화성시효준정밀(주)기계1251경기도 화성시 팔탄면 고주동방길 40경기도 화성시 팔탄면 고주리 94-1번지18534031-354-1351gohyojun.co.kr2016<NA>37.135116126.883948
3311화성시후성정공(주)기계1251경기도 화성시 팔탄면 현대기아로 60경기도 화성시 팔탄면 449-618576031-352-9111www.foosung.com1996<NA>37.160303126.852074
3312화성시휘일라이팅전기1251경기도 화성시 양감면 은행나무로62번길 66경기도 화성시 양감면 요당리 103-26번지18633031-354-4951www.wheellighting.co.kr2019<NA>37.063804126.941936
3313화성시휘일화성공장철강1251경기도 화성시 양감면 은행나무로62번길 66경기도 화성시 양감면 요당리 103-26번지18633031-354-5171<NA>2019<NA>37.063804126.941936
3314화성시휴비오(주)기계1252경기도 화성시 동탄대로21길 10경기도 화성시 영천동 652-5번지 더퍼스트타워 7층 706호18471031-373-8302www.huvio.co.kr2020<NA>37.207913127.097739
3315화성시흥광테크(주)철강1251경기도 화성시 마도면 마도공단로5길 16경기도 화성시 마도면 678-3 마도지방산업단지 9B-8L18542031-366-8595www.hk-tech.co.kr2009<NA>37.180862126.787347