Overview

Dataset statistics

Number of variables15
Number of observations242
Missing cells354
Missing cells (%)9.8%
Duplicate rows1
Duplicate rows (%)0.4%
Total size in memory29.4 KiB
Average record size in memory124.5 B

Variable types

Text9
Categorical1
Numeric4
DateTime1

Dataset

Description대전광역시 유성구 관내 공장등록 현황에 대한 데이터로 회사명, 유형, 사업자등록번호, 업종번호, 업종명, 최초등록일, 지번주소, 도로명주소, 위도, 경도, 전화번호, 팩스번호, 대표자이름, 종업원수, 홈페이지주소 등의 항목을 제공합니다.
Author대전광역시 유성구
URLhttps://www.data.go.kr/data/15028076/fileData.do

Alerts

Dataset has 1 (0.4%) duplicate rowsDuplicates
사업자등록번호 is highly overall correlated with 유형High correlation
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
유형 is highly overall correlated with 사업자등록번호High correlation
사업자등록번호 has 56 (23.1%) missing valuesMissing
도로명주소 has 3 (1.2%) missing valuesMissing
전화번호 has 23 (9.5%) missing valuesMissing
팩스번호 has 65 (26.9%) missing valuesMissing
종업원수 has 13 (5.4%) missing valuesMissing
홈페이지주소 has 192 (79.3%) missing valuesMissing
종업원수 has 4 (1.7%) zerosZeros

Reproduction

Analysis started2024-03-14 23:10:45.873978
Analysis finished2024-03-14 23:10:52.743519
Duration6.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회사명
Text

Distinct235
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-15T08:10:53.519659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length16
Mean length7.9669421
Min length2

Characters and Unicode

Total characters1928
Distinct characters313
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

Unique228 ?
Unique (%)94.2%

Sample

1st row(복)성재원(성세재활자립원)
2nd row(주) 계룡
3rd row(주) 에네시스
4th row(주)가경건설산업
5th row(주)광진통신
ValueCountFrequency (%)
주식회사 66
 
20.4%
농업회사법인 3
 
0.9%
주)백상 2
 
0.6%
월드에너시스 2
 
0.6%
인트테크놀로지(주 2
 
0.6%
주 2
 
0.6%
오비기획 2
 
0.6%
주)엔텍코아 2
 
0.6%
엘케이펩티모 2
 
0.6%
스페셜원 2
 
0.6%
Other values (239) 239
73.8%
2024-03-15T08:10:54.823362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 186
 
9.6%
( 115
 
6.0%
) 115
 
6.0%
82
 
4.3%
사 80
 
4.1%
회 77
 
4.0%
식 74
 
3.8%
스 51
 
2.6%
이 50
 
2.6%
아 30
 
1.6%
Other values (303) 1068
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1555
80.7%
Open Punctuation 115
 
6.0%
Close Punctuation 115
 
6.0%
Space Separator 82
 
4.3%
Lowercase Letter 29
 
1.5%
Uppercase Letter 26
 
1.3%
Other Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 186
 
12.0%
사 80
 
5.1%
회 77
 
5.0%
식 74
 
4.8%
스 51
 
3.3%
이 50
 
3.2%
아 30
 
1.9%
에 28
 
1.8%
리 26
 
1.7%
텍 23
 
1.5%
Other values (268) 930
59.8%
Uppercase Letter
ValueCountFrequency (%)
C 4
15.4%
N 3
11.5%
M 2
 
7.7%
T 2
 
7.7%
A 2
 
7.7%
L 2
 
7.7%
P 2
 
7.7%
D 1
 
3.8%
F 1
 
3.8%
G 1
 
3.8%
Other values (6) 6
23.1%
Lowercase Letter
ValueCountFrequency (%)
e 5
17.2%
o 5
17.2%
c 3
10.3%
n 3
10.3%
t 2
 
6.9%
p 2
 
6.9%
d 2
 
6.9%
i 2
 
6.9%
g 1
 
3.4%
a 1
 
3.4%
Other values (3) 3
10.3%
Other Punctuation
ValueCountFrequency (%)
& 3
50.0%
. 2
33.3%
, 1
 
16.7%
Open Punctuation
ValueCountFrequency (%)
( 115
100.0%
Close Punctuation
ValueCountFrequency (%)
) 115
100.0%
Space Separator
ValueCountFrequency (%)
82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1555
80.7%
Common 318
 
16.5%
Latin 55
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 186
 
12.0%
사 80
 
5.1%
회 77
 
5.0%
식 74
 
4.8%
스 51
 
3.3%
이 50
 
3.2%
아 30
 
1.9%
에 28
 
1.8%
리 26
 
1.7%
텍 23
 
1.5%
Other values (268) 930
59.8%
Latin
ValueCountFrequency (%)
e 5
 
9.1%
o 5
 
9.1%
C 4
 
7.3%
N 3
 
5.5%
c 3
 
5.5%
n 3
 
5.5%
t 2
 
3.6%
p 2
 
3.6%
M 2
 
3.6%
T 2
 
3.6%
Other values (19) 24
43.6%
Common
ValueCountFrequency (%)
( 115
36.2%
) 115
36.2%
82
25.8%
& 3
 
0.9%
. 2
 
0.6%
, 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1555
80.7%
ASCII 373
 
19.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 186
 
12.0%
사 80
 
5.1%
회 77
 
5.0%
식 74
 
4.8%
스 51
 
3.3%
이 50
 
3.2%
아 30
 
1.9%
에 28
 
1.8%
리 26
 
1.7%
텍 23
 
1.5%
Other values (268) 930
59.8%
ASCII
ValueCountFrequency (%)
( 115
30.8%
) 115
30.8%
82
22.0%
e 5
 
1.3%
o 5
 
1.3%
C 4
 
1.1%
& 3
 
0.8%
N 3
 
0.8%
c 3
 
0.8%
n 3
 
0.8%
Other values (25) 35
 
9.4%

유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
법인
186 
개인
56 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
법인 186
76.9%
개인 56
 
23.1%

Length

2024-03-15T08:10:55.231862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:10:55.540935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인 186
76.9%
개인 56
 
23.1%

사업자등록번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct177
Distinct (%)95.2%
Missing56
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean3.7161393 × 109
Minimum1.0281474 × 109
Maximum8.9787005 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T08:10:56.127012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0281474 × 109
5-th percentile1.7536014 × 109
Q13.0706263 × 109
median3.1483805 × 109
Q33.881126 × 109
95-th percentile7.3737004 × 109
Maximum8.9787005 × 109
Range7.950553 × 109
Interquartile range (IQR)8.104997 × 108

Descriptive statistics

Standard deviation1.6124362 × 109
Coefficient of variation (CV)0.43390091
Kurtosis1.779687
Mean3.7161393 × 109
Median Absolute Deviation (MAD)89756382
Skewness1.4600661
Sum6.9120191 × 1011
Variance2.5999506 × 1018
MonotonicityNot monotonic
2024-03-15T08:10:56.595367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2758600893 2
 
0.8%
3588700555 2
 
0.8%
6908800586 2
 
0.8%
5058148852 2
 
0.8%
7548800876 2
 
0.8%
2398800149 2
 
0.8%
3148648509 2
 
0.8%
3148616744 2
 
0.8%
5668801088 2
 
0.8%
2558800724 1
 
0.4%
Other values (167) 167
69.0%
(Missing) 56
 
23.1%
ValueCountFrequency (%)
1028147450 1
0.4%
1118192793 1
0.4%
1148725261 1
0.4%
1198682955 1
0.4%
1238701259 1
0.4%
1308637082 1
0.4%
1308655116 1
0.4%
1588500359 1
0.4%
1678101728 1
0.4%
1748601250 1
0.4%
ValueCountFrequency (%)
8978700496 1
0.4%
8948100608 1
0.4%
8838700013 1
0.4%
8648801191 1
0.4%
8108701218 1
0.4%
7578100750 1
0.4%
7548800876 2
0.8%
7448701323 1
0.4%
7448700101 1
0.4%
7148701211 1
0.4%

업종번호
Text

Distinct191
Distinct (%)79.3%
Missing1
Missing (%)0.4%
Memory size2.0 KiB
2024-03-15T08:10:57.223640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length77
Median length71
Mean length13.788382
Min length5

Characters and Unicode

Total characters3323
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

Unique160 ?
Unique (%)66.4%

Sample

1st row18111+13222+13229+13409+17122+17901+18113+18122+18129+22231+33910
2nd row26421+26519+26529+28123+28423
3rd row27211+27216
4th row25111
5th row26519+26421+26429
ValueCountFrequency (%)
26429 8
 
3.3%
25929 5
 
2.1%
10791 4
 
1.7%
26421 4
 
1.7%
25111 3
 
1.2%
22299 3
 
1.2%
26310 3
 
1.2%
28901 3
 
1.2%
28422 3
 
1.2%
26410 3
 
1.2%
Other values (181) 202
83.8%
2024-03-15T08:10:58.227852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 971
29.2%
1 541
16.3%
9 386
 
11.6%
+ 353
 
10.6%
3 207
 
6.2%
6 186
 
5.6%
0 182
 
5.5%
4 167
 
5.0%
8 113
 
3.4%
7 110
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2970
89.4%
Math Symbol 353
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 971
32.7%
1 541
18.2%
9 386
 
13.0%
3 207
 
7.0%
6 186
 
6.3%
0 182
 
6.1%
4 167
 
5.6%
8 113
 
3.8%
7 110
 
3.7%
5 107
 
3.6%
Math Symbol
ValueCountFrequency (%)
+ 353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3323
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 971
29.2%
1 541
16.3%
9 386
 
11.6%
+ 353
 
10.6%
3 207
 
6.2%
6 186
 
5.6%
0 182
 
5.5%
4 167
 
5.0%
8 113
 
3.4%
7 110
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 971
29.2%
1 541
16.3%
9 386
 
11.6%
+ 353
 
10.6%
3 207
 
6.2%
6 186
 
5.6%
0 182
 
5.5%
4 167
 
5.0%
8 113
 
3.4%
7 110
 
3.3%

업종명
Text

Distinct171
Distinct (%)71.0%
Missing1
Missing (%)0.4%
Memory size2.0 KiB
2024-03-15T08:10:59.503828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length25
Mean length17.443983
Min length6

Characters and Unicode

Total characters4204
Distinct characters230
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

Unique125 ?
Unique (%)51.9%

Sample

1st row경 인쇄업 외 10 종
2nd row방송장비 제조업 외 4 종
3rd row레이더, 항행용 무선기기 및 측량기구 제조업 외 1 종
4th row금속 문, 창, 셔터 및 관련제품 제조업
5th row비디오 및 기타 영상기기 제조업 외 2 종
ValueCountFrequency (%)
제조업 213
 
15.4%
외 160
 
11.5%
종 125
 
9.0%
및 86
 
6.2%
기타 74
 
5.3%
1 47
 
3.4%
그 35
 
2.5%
2 27
 
1.9%
3 20
 
1.4%
통신장비 20
 
1.4%
Other values (242) 579
41.8%
2024-03-15T08:11:01.182701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1145
27.2%
제 265
 
6.3%
업 249
 
5.9%
조 236
 
5.6%
기 185
 
4.4%
외 164
 
3.9%
종 126
 
3.0%
장 87
 
2.1%
및 86
 
2.0%
타 76
 
1.8%
Other values (220) 1585
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2887
68.7%
Space Separator 1145
 
27.2%
Decimal Number 129
 
3.1%
Other Punctuation 35
 
0.8%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 265
 
9.2%
업 249
 
8.6%
조 236
 
8.2%
기 185
 
6.4%
외 164
 
5.7%
종 126
 
4.4%
장 87
 
3.0%
및 86
 
3.0%
타 76
 
2.6%
비 54
 
1.9%
Other values (206) 1359
47.1%
Decimal Number
ValueCountFrequency (%)
1 52
40.3%
2 28
21.7%
3 20
 
15.5%
4 11
 
8.5%
5 5
 
3.9%
7 4
 
3.1%
6 3
 
2.3%
0 2
 
1.6%
8 2
 
1.6%
9 2
 
1.6%
Space Separator
ValueCountFrequency (%)
1145
100.0%
Other Punctuation
ValueCountFrequency (%)
, 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2887
68.7%
Common 1317
31.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 265
 
9.2%
업 249
 
8.6%
조 236
 
8.2%
기 185
 
6.4%
외 164
 
5.7%
종 126
 
4.4%
장 87
 
3.0%
및 86
 
3.0%
타 76
 
2.6%
비 54
 
1.9%
Other values (206) 1359
47.1%
Common
ValueCountFrequency (%)
1145
86.9%
1 52
 
3.9%
, 35
 
2.7%
2 28
 
2.1%
3 20
 
1.5%
4 11
 
0.8%
5 5
 
0.4%
7 4
 
0.3%
( 4
 
0.3%
) 4
 
0.3%
Other values (4) 9
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2884
68.6%
ASCII 1317
31.3%
Compat Jamo 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1145
86.9%
1 52
 
3.9%
, 35
 
2.7%
2 28
 
2.1%
3 20
 
1.5%
4 11
 
0.8%
5 5
 
0.4%
7 4
 
0.3%
( 4
 
0.3%
) 4
 
0.3%
Other values (4) 9
 
0.7%
Hangul
ValueCountFrequency (%)
제 265
 
9.2%
업 249
 
8.6%
조 236
 
8.2%
기 185
 
6.4%
외 164
 
5.7%
종 126
 
4.4%
장 87
 
3.0%
및 86
 
3.0%
타 76
 
2.6%
비 54
 
1.9%
Other values (205) 1356
47.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 3
100.0%

최초등록일
Date

Distinct221
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum1981-05-11 00:00:00
Maximum2024-01-09 00:00:00
2024-03-15T08:11:01.433926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:11:01.781043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

지번주소
Text

Distinct240
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-15T08:11:02.829498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length38
Mean length25.247934
Min length17

Characters and Unicode

Total characters6110
Distinct characters192
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

Unique238 ?
Unique (%)98.3%

Sample

1st row대전광역시 유성구 용계동 319-1번지
2nd row대전광역시 유성구 갑동 387-169번지 2층
3rd row대전광역시 유성구 구암동 328번지
4th row대전광역시 유성구 구암동 476-0
5th row대전광역시 유성구 장대동 341-3번지 자연인빌딩 4층
ValueCountFrequency (%)
대전광역시 242
19.6%
유성구 242
19.6%
1층 21
 
1.7%
장대동 20
 
1.6%
복용동 20
 
1.6%
원내동 19
 
1.5%
2층 19
 
1.5%
노은동 18
 
1.5%
구암동 18
 
1.5%
학하동 17
 
1.4%
Other values (384) 596
48.4%
2024-03-15T08:11:04.336633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
999
 
16.4%
대 283
 
4.6%
구 262
 
4.3%
동 261
 
4.3%
1 251
 
4.1%
성 249
 
4.1%
유 247
 
4.0%
시 246
 
4.0%
전 244
 
4.0%
역 242
 
4.0%
Other values (182) 2826
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3520
57.6%
Decimal Number 1287
 
21.1%
Space Separator 999
 
16.4%
Dash Punctuation 219
 
3.6%
Uppercase Letter 39
 
0.6%
Open Punctuation 19
 
0.3%
Close Punctuation 19
 
0.3%
Other Punctuation 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 283
 
8.0%
구 262
 
7.4%
동 261
 
7.4%
성 249
 
7.1%
유 247
 
7.0%
시 246
 
7.0%
전 244
 
6.9%
역 242
 
6.9%
광 242
 
6.9%
지 171
 
4.9%
Other values (156) 1073
30.5%
Uppercase Letter
ValueCountFrequency (%)
F 20
51.3%
S 5
 
12.8%
K 3
 
7.7%
D 2
 
5.1%
B 2
 
5.1%
P 2
 
5.1%
T 1
 
2.6%
I 1
 
2.6%
N 1
 
2.6%
M 1
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 251
19.5%
2 158
12.3%
3 148
11.5%
5 138
10.7%
6 137
10.6%
4 124
9.6%
0 101
7.8%
7 96
 
7.5%
9 74
 
5.7%
8 60
 
4.7%
Space Separator
ValueCountFrequency (%)
999
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 219
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3520
57.6%
Common 2551
41.8%
Latin 39
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 283
 
8.0%
구 262
 
7.4%
동 261
 
7.4%
성 249
 
7.1%
유 247
 
7.0%
시 246
 
7.0%
전 244
 
6.9%
역 242
 
6.9%
광 242
 
6.9%
지 171
 
4.9%
Other values (156) 1073
30.5%
Common
ValueCountFrequency (%)
999
39.2%
1 251
 
9.8%
- 219
 
8.6%
2 158
 
6.2%
3 148
 
5.8%
5 138
 
5.4%
6 137
 
5.4%
4 124
 
4.9%
0 101
 
4.0%
7 96
 
3.8%
Other values (5) 180
 
7.1%
Latin
ValueCountFrequency (%)
F 20
51.3%
S 5
 
12.8%
K 3
 
7.7%
D 2
 
5.1%
B 2
 
5.1%
P 2
 
5.1%
T 1
 
2.6%
I 1
 
2.6%
N 1
 
2.6%
M 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3520
57.6%
ASCII 2590
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
999
38.6%
1 251
 
9.7%
- 219
 
8.5%
2 158
 
6.1%
3 148
 
5.7%
5 138
 
5.3%
6 137
 
5.3%
4 124
 
4.8%
0 101
 
3.9%
7 96
 
3.7%
Other values (16) 219
 
8.5%
Hangul
ValueCountFrequency (%)
대 283
 
8.0%
구 262
 
7.4%
동 261
 
7.4%
성 249
 
7.1%
유 247
 
7.0%
시 246
 
7.0%
전 244
 
6.9%
역 242
 
6.9%
광 242
 
6.9%
지 171
 
4.9%
Other values (156) 1073
30.5%

도로명주소
Text

MISSING 

Distinct238
Distinct (%)99.6%
Missing3
Missing (%)1.2%
Memory size2.0 KiB
2024-03-15T08:11:05.410067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length40
Mean length31.736402
Min length21

Characters and Unicode

Total characters7585
Distinct characters224
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

Unique237 ?
Unique (%)99.2%

Sample

1st row대전광역시 유성구 유성대로298번길 175 (용계동, 대전장애자종합복지관)
2nd row대전광역시 유성구 갑동로 61, 2층 (갑동)
3rd row대전광역시 유성구 박산로140번길 100, 1층 (구암동)
4th row대전광역시 유성구 박산로 62 (구암동)
5th row대전광역시 유성구 유성대로 800, 4층 (장대동, 자연인빌딩)
ValueCountFrequency (%)
대전광역시 239
 
16.8%
유성구 239
 
16.8%
복용동 20
 
1.4%
2층 19
 
1.3%
구암동 18
 
1.3%
원내동 18
 
1.3%
1층 17
 
1.2%
노은동 14
 
1.0%
장대동 14
 
1.0%
학하동 14
 
1.0%
Other values (473) 809
56.9%
2024-03-15T08:11:06.894907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1182
 
15.6%
대 361
 
4.8%
동 299
 
3.9%
성 279
 
3.7%
유 273
 
3.6%
구 264
 
3.5%
1 256
 
3.4%
( 251
 
3.3%
) 251
 
3.3%
시 243
 
3.2%
Other values (214) 3926
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4386
57.8%
Decimal Number 1265
 
16.7%
Space Separator 1182
 
15.6%
Open Punctuation 251
 
3.3%
Close Punctuation 251
 
3.3%
Other Punctuation 154
 
2.0%
Dash Punctuation 56
 
0.7%
Uppercase Letter 40
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 361
 
8.2%
동 299
 
6.8%
성 279
 
6.4%
유 273
 
6.2%
구 264
 
6.0%
시 243
 
5.5%
전 242
 
5.5%
광 240
 
5.5%
역 239
 
5.4%
로 236
 
5.4%
Other values (188) 1710
39.0%
Uppercase Letter
ValueCountFrequency (%)
F 20
50.0%
S 6
 
15.0%
K 3
 
7.5%
B 2
 
5.0%
D 2
 
5.0%
P 2
 
5.0%
T 1
 
2.5%
I 1
 
2.5%
M 1
 
2.5%
N 1
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 256
20.2%
2 196
15.5%
3 132
10.4%
0 129
10.2%
4 120
9.5%
5 106
8.4%
6 96
 
7.6%
7 86
 
6.8%
9 83
 
6.6%
8 61
 
4.8%
Space Separator
ValueCountFrequency (%)
1182
100.0%
Open Punctuation
ValueCountFrequency (%)
( 251
100.0%
Close Punctuation
ValueCountFrequency (%)
) 251
100.0%
Other Punctuation
ValueCountFrequency (%)
, 154
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4386
57.8%
Common 3159
41.6%
Latin 40
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 361
 
8.2%
동 299
 
6.8%
성 279
 
6.4%
유 273
 
6.2%
구 264
 
6.0%
시 243
 
5.5%
전 242
 
5.5%
광 240
 
5.5%
역 239
 
5.4%
로 236
 
5.4%
Other values (188) 1710
39.0%
Common
ValueCountFrequency (%)
1182
37.4%
1 256
 
8.1%
( 251
 
7.9%
) 251
 
7.9%
2 196
 
6.2%
, 154
 
4.9%
3 132
 
4.2%
0 129
 
4.1%
4 120
 
3.8%
5 106
 
3.4%
Other values (5) 382
 
12.1%
Latin
ValueCountFrequency (%)
F 20
50.0%
S 6
 
15.0%
K 3
 
7.5%
B 2
 
5.0%
D 2
 
5.0%
P 2
 
5.0%
T 1
 
2.5%
I 1
 
2.5%
M 1
 
2.5%
N 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4386
57.8%
ASCII 3199
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1182
36.9%
1 256
 
8.0%
( 251
 
7.8%
) 251
 
7.8%
2 196
 
6.1%
, 154
 
4.8%
3 132
 
4.1%
0 129
 
4.0%
4 120
 
3.8%
5 106
 
3.3%
Other values (16) 422
 
13.2%
Hangul
ValueCountFrequency (%)
대 361
 
8.2%
동 299
 
6.8%
성 279
 
6.4%
유 273
 
6.2%
구 264
 
6.0%
시 243
 
5.5%
전 242
 
5.5%
광 240
 
5.5%
역 239
 
5.4%
로 236
 
5.4%
Other values (188) 1710
39.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct210
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.33258
Minimum127.27423
Maximum127.40961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T08:11:07.320873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.27423
5-th percentile127.30114
Q1127.31555
median127.32307
Q3127.34646
95-th percentile127.38525
Maximum127.40961
Range0.1353855
Interquartile range (IQR)0.030914925

Descriptive statistics

Standard deviation0.026069108
Coefficient of variation (CV)0.00020473243
Kurtosis0.27889341
Mean127.33258
Median Absolute Deviation (MAD)0.01360885
Skewness0.92936335
Sum30814.483
Variance0.00067959837
MonotonicityNot monotonic
2024-03-15T08:11:07.812022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.3217016 9
 
3.7%
127.3010537 4
 
1.7%
127.3051391 4
 
1.7%
127.379126 3
 
1.2%
127.3623135 2
 
0.8%
127.3464624 2
 
0.8%
127.3273554 2
 
0.8%
127.3257866 2
 
0.8%
127.3172649 2
 
0.8%
127.32158 2
 
0.8%
Other values (200) 210
86.8%
ValueCountFrequency (%)
127.2742294 1
 
0.4%
127.2899729 1
 
0.4%
127.2927563 1
 
0.4%
127.2936268 1
 
0.4%
127.2940507 1
 
0.4%
127.2941714 1
 
0.4%
127.2973607 1
 
0.4%
127.2985853 1
 
0.4%
127.2994428 1
 
0.4%
127.3010537 4
1.7%
ValueCountFrequency (%)
127.4096149 1
0.4%
127.4084308 1
0.4%
127.4051855 1
0.4%
127.4044948 1
0.4%
127.3963033 1
0.4%
127.3930221 1
0.4%
127.3909968 1
0.4%
127.3907663 2
0.8%
127.3890027 1
0.4%
127.3859068 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct210
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.363656
Minimum36.292955
Maximum36.47524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T08:11:08.242209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.292955
5-th percentile36.301384
Q136.338272
median36.358805
Q336.379612
95-th percentile36.46154
Maximum36.47524
Range0.18228452
Interquartile range (IQR)0.041340183

Descriptive statistics

Standard deviation0.042764542
Coefficient of variation (CV)0.0011760243
Kurtosis0.8461977
Mean36.363656
Median Absolute Deviation (MAD)0.020738315
Skewness0.97184474
Sum8800.0047
Variance0.001828806
MonotonicityNot monotonic
2024-03-15T08:11:08.699595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.33794471 9
 
3.7%
36.35115541 4
 
1.7%
36.34526702 4
 
1.7%
36.38007403 3
 
1.2%
36.46134941 2
 
0.8%
36.36091999 2
 
0.8%
36.34891774 2
 
0.8%
36.30721711 2
 
0.8%
36.29992278 2
 
0.8%
36.36742377 2
 
0.8%
Other values (200) 210
86.8%
ValueCountFrequency (%)
36.29295548 1
0.4%
36.29885243 1
0.4%
36.29982832 1
0.4%
36.29992278 2
0.8%
36.30005528 1
0.4%
36.30022428 1
0.4%
36.30059598 1
0.4%
36.30070759 1
0.4%
36.30105052 1
0.4%
36.30112099 1
0.4%
ValueCountFrequency (%)
36.47524 2
0.8%
36.47485883 1
0.4%
36.47475513 1
0.4%
36.47469756 1
0.4%
36.47468189 1
0.4%
36.47459303 1
0.4%
36.47458775 1
0.4%
36.47398147 1
0.4%
36.47261648 1
0.4%
36.47189989 1
0.4%

전화번호
Text

MISSING 

Distinct206
Distinct (%)94.1%
Missing23
Missing (%)9.5%
Memory size2.0 KiB
2024-03-15T08:11:10.110315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.077626
Min length9

Characters and Unicode

Total characters2645
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

Unique193 ?
Unique (%)88.1%

Sample

1st row042-540-3300
2nd row042-824-4440
3rd row042-825-3861
4th row042-824-7776
5th row042-625-6629
ValueCountFrequency (%)
070-8670-3151 2
 
0.9%
042-826-3654 2
 
0.9%
042-861-0600 2
 
0.9%
042-543-5485 2
 
0.9%
042-935-0290 2
 
0.9%
042-823-5239 2
 
0.9%
070-7722-6559 2
 
0.9%
042-823-9690 2
 
0.9%
042-823-6073 2
 
0.9%
042-825-7759 2
 
0.9%
Other values (196) 199
90.9%
2024-03-15T08:11:11.687652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 435
16.4%
0 414
15.7%
2 396
15.0%
4 336
12.7%
8 199
7.5%
7 165
 
6.2%
5 157
 
5.9%
6 150
 
5.7%
3 150
 
5.7%
1 126
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2210
83.6%
Dash Punctuation 435
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 414
18.7%
2 396
17.9%
4 336
15.2%
8 199
9.0%
7 165
 
7.5%
5 157
 
7.1%
6 150
 
6.8%
3 150
 
6.8%
1 126
 
5.7%
9 117
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 435
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2645
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 435
16.4%
0 414
15.7%
2 396
15.0%
4 336
12.7%
8 199
7.5%
7 165
 
6.2%
5 157
 
5.9%
6 150
 
5.7%
3 150
 
5.7%
1 126
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 435
16.4%
0 414
15.7%
2 396
15.0%
4 336
12.7%
8 199
7.5%
7 165
 
6.2%
5 157
 
5.9%
6 150
 
5.7%
3 150
 
5.7%
1 126
 
4.8%

팩스번호
Text

MISSING 

Distinct170
Distinct (%)96.0%
Missing65
Missing (%)26.9%
Memory size2.0 KiB
2024-03-15T08:11:12.599788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.146893
Min length11

Characters and Unicode

Total characters2150
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

Unique163 ?
Unique (%)92.1%

Sample

1st row042-543-2133
2nd row042-824-4441
3rd row042-825-3866
4th row042-824-7774
5th row042-826-5090
ValueCountFrequency (%)
042-861-0601 2
 
1.1%
042-823-6009 2
 
1.1%
042-528-6559 2
 
1.1%
042-824-4872 2
 
1.1%
042-634-9598 2
 
1.1%
042-826-2496 2
 
1.1%
0504-250-3699 2
 
1.1%
042-367-1214 1
 
0.6%
02-3397-0034 1
 
0.6%
0504-318-5166 1
 
0.6%
Other values (160) 160
90.4%
2024-03-15T08:11:14.053124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 354
16.5%
2 318
14.8%
0 306
14.2%
4 299
13.9%
8 168
7.8%
3 135
 
6.3%
6 133
 
6.2%
5 131
 
6.1%
7 110
 
5.1%
1 106
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1796
83.5%
Dash Punctuation 354
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 318
17.7%
0 306
17.0%
4 299
16.6%
8 168
9.4%
3 135
7.5%
6 133
7.4%
5 131
7.3%
7 110
 
6.1%
1 106
 
5.9%
9 90
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 354
16.5%
2 318
14.8%
0 306
14.2%
4 299
13.9%
8 168
7.8%
3 135
 
6.3%
6 133
 
6.2%
5 131
 
6.1%
7 110
 
5.1%
1 106
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 354
16.5%
2 318
14.8%
0 306
14.2%
4 299
13.9%
8 168
7.8%
3 135
 
6.3%
6 133
 
6.2%
5 131
 
6.1%
7 110
 
5.1%
1 106
 
4.9%

대표자이름
Text

Distinct231
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-15T08:11:15.422139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.107438
Min length2

Characters and Unicode

Total characters752
Distinct characters147
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique220 ?
Unique (%)90.9%

Sample

1st row윤여웅
2nd row주귀애
3rd row한병섭
4th row김태준
5th row김홍열
ValueCountFrequency (%)
엄태건 2
 
0.8%
이시권 2
 
0.8%
이흥균 2
 
0.8%
강봉수 2
 
0.8%
안병주 2
 
0.8%
길준경 2
 
0.8%
성창윤 2
 
0.8%
노백남 2
 
0.8%
김영희 2
 
0.8%
김태욱 2
 
0.8%
Other values (226) 227
91.9%
2024-03-15T08:11:17.180393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 53
 
7.0%
이 47
 
6.2%
정 23
 
3.1%
성 19
 
2.5%
영 17
 
2.3%
박 15
 
2.0%
태 14
 
1.9%
수 14
 
1.9%
기 12
 
1.6%
상 12
 
1.6%
Other values (137) 526
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 741
98.5%
Other Punctuation 6
 
0.8%
Space Separator 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 53
 
7.2%
이 47
 
6.3%
정 23
 
3.1%
성 19
 
2.6%
영 17
 
2.3%
박 15
 
2.0%
태 14
 
1.9%
수 14
 
1.9%
기 12
 
1.6%
상 12
 
1.6%
Other values (135) 515
69.5%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 741
98.5%
Common 11
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 53
 
7.2%
이 47
 
6.3%
정 23
 
3.1%
성 19
 
2.6%
영 17
 
2.3%
박 15
 
2.0%
태 14
 
1.9%
수 14
 
1.9%
기 12
 
1.6%
상 12
 
1.6%
Other values (135) 515
69.5%
Common
ValueCountFrequency (%)
, 6
54.5%
5
45.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 741
98.5%
ASCII 11
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 53
 
7.2%
이 47
 
6.3%
정 23
 
3.1%
성 19
 
2.6%
영 17
 
2.3%
박 15
 
2.0%
태 14
 
1.9%
수 14
 
1.9%
기 12
 
1.6%
상 12
 
1.6%
Other values (135) 515
69.5%
ASCII
ValueCountFrequency (%)
, 6
54.5%
5
45.5%

종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct37
Distinct (%)16.2%
Missing13
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean8.8034934
Minimum0
Maximum122
Zeros4
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T08:11:17.592372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q310
95-th percentile28
Maximum122
Range122
Interquartile range (IQR)8

Descriptive statistics

Standard deviation13.352534
Coefficient of variation (CV)1.5167313
Kurtosis28.380275
Mean8.8034934
Median Absolute Deviation (MAD)3
Skewness4.4913262
Sum2016
Variance178.29016
MonotonicityNot monotonic
2024-03-15T08:11:18.027698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2 32
13.2%
1 30
12.4%
3 29
12.0%
4 26
10.7%
6 13
 
5.4%
5 13
 
5.4%
8 9
 
3.7%
7 9
 
3.7%
11 6
 
2.5%
9 6
 
2.5%
Other values (27) 56
23.1%
(Missing) 13
 
5.4%
ValueCountFrequency (%)
0 4
 
1.7%
1 30
12.4%
2 32
13.2%
3 29
12.0%
4 26
10.7%
5 13
5.4%
6 13
5.4%
7 9
 
3.7%
8 9
 
3.7%
9 6
 
2.5%
ValueCountFrequency (%)
122 1
0.4%
86 1
0.4%
62 1
0.4%
59 1
0.4%
48 1
0.4%
43 1
0.4%
40 1
0.4%
39 1
0.4%
35 1
0.4%
30 1
0.4%

홈페이지주소
Text

MISSING 

Distinct50
Distinct (%)100.0%
Missing192
Missing (%)79.3%
Memory size2.0 KiB
2024-03-15T08:11:18.867609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length16.06
Min length1

Characters and Unicode

Total characters803
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowwww.enesys.co.kr
2nd rowmght.co.kr
3rd rowwww.botem-e.com
4th rowwww.blsoft.co.kr
5th rowwww.safe-drive.com
ValueCountFrequency (%)
www.pr-zone.com 1
 
2.0%
bonosoo.co.kr 1
 
2.0%
www.artone.co.kr 1
 
2.0%
www.jainco.co.kr 1
 
2.0%
www.makerstec.com 1
 
2.0%
www.dolbomdream.com 1
 
2.0%
www.duretek.com 1
 
2.0%
www.k-doit.com 1
 
2.0%
www.edevicesolution.com 1
 
2.0%
www.remoshot.com 1
 
2.0%
Other values (39) 39
79.6%
2024-03-15T08:11:20.118386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 133
16.6%
. 116
14.4%
o 89
11.1%
c 60
 
7.5%
e 45
 
5.6%
r 43
 
5.4%
k 36
 
4.5%
m 35
 
4.4%
n 31
 
3.9%
i 29
 
3.6%
Other values (17) 186
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 679
84.6%
Other Punctuation 116
 
14.4%
Dash Punctuation 7
 
0.9%
Space Separator 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 133
19.6%
o 89
13.1%
c 60
 
8.8%
e 45
 
6.6%
r 43
 
6.3%
k 36
 
5.3%
m 35
 
5.2%
n 31
 
4.6%
i 29
 
4.3%
t 28
 
4.1%
Other values (14) 150
22.1%
Other Punctuation
ValueCountFrequency (%)
. 116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 679
84.6%
Common 124
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 133
19.6%
o 89
13.1%
c 60
 
8.8%
e 45
 
6.6%
r 43
 
6.3%
k 36
 
5.3%
m 35
 
5.2%
n 31
 
4.6%
i 29
 
4.3%
t 28
 
4.1%
Other values (14) 150
22.1%
Common
ValueCountFrequency (%)
. 116
93.5%
- 7
 
5.6%
1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 133
16.6%
. 116
14.4%
o 89
11.1%
c 60
 
7.5%
e 45
 
5.6%
r 43
 
5.4%
k 36
 
4.5%
m 35
 
4.4%
n 31
 
3.9%
i 29
 
3.6%
Other values (17) 186
23.2%

Interactions

2024-03-15T08:10:50.579603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:47.180559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:48.175066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:49.385563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:50.880096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:47.387399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:48.487257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:49.704452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:51.152840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:47.567375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:48.778777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:49.978765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:51.445492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:47.872178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:49.092863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:10:50.280947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:11:20.388366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형사업자등록번호위도경도종업원수홈페이지주소
유형1.000NaN0.2460.2210.0001.000
사업자등록번호NaN1.0000.0430.0990.0001.000
위도0.2460.0431.0000.8880.0001.000
경도0.2210.0990.8881.0000.3661.000
종업원수0.0000.0000.0000.3661.0001.000
홈페이지주소1.0001.0001.0001.0001.0001.000
2024-03-15T08:11:20.681223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업자등록번호위도경도종업원수유형
사업자등록번호1.000-0.030-0.120-0.1251.000
위도-0.0301.0000.5360.0670.185
경도-0.1200.5361.0000.1770.166
종업원수-0.1250.0670.1771.0000.000
유형1.0000.1850.1660.0001.000

Missing values

2024-03-15T08:10:51.770450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:10:52.146520image/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.
2024-03-15T08:10:52.438563image/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

회사명유형사업자등록번호업종번호업종명최초등록일지번주소도로명주소위도경도전화번호팩스번호대표자이름종업원수홈페이지주소
0(복)성재원(성세재활자립원)법인314820004018111+13222+13229+13409+17122+17901+18113+18122+18129+22231+33910경 인쇄업 외 10 종1994-04-30대전광역시 유성구 용계동 319-1번지대전광역시 유성구 유성대로298번길 175 (용계동, 대전장애자종합복지관)127.32095536.321204042-540-3300042-543-2133윤여웅24<NA>
1(주) 계룡법인311811562526421+26519+26529+28123+28423방송장비 제조업 외 4 종2007-04-03대전광역시 유성구 갑동 387-169번지 2층대전광역시 유성구 갑동로 61, 2층 (갑동)127.29417136.361948042-824-4440042-824-4441주귀애7<NA>
2(주) 에네시스법인314817782427211+27216레이더, 항행용 무선기기 및 측량기구 제조업 외 1 종2007-12-10대전광역시 유성구 구암동 328번지대전광역시 유성구 박산로140번길 100, 1층 (구암동)127.32100936.349756042-825-3861042-825-3866한병섭4www.enesys.co.kr
3(주)가경건설산업법인314812722725111금속 문, 창, 셔터 및 관련제품 제조업2015-02-11대전광역시 유성구 구암동 476-0대전광역시 유성구 박산로 62 (구암동)127.31727636.354917042-824-7776042-824-7774김태준5<NA>
4(주)광진통신법인312814357526519+26421+26429비디오 및 기타 영상기기 제조업 외 2 종2014-09-01대전광역시 유성구 장대동 341-3번지 자연인빌딩 4층대전광역시 유성구 유성대로 800, 4층 (장대동, 자연인빌딩)127.33781836.363252042-625-6629<NA>김홍열3<NA>
5(주)그린텍법인314862021522229+28410+28422+28423+28429기타 건축용 플라스틱 조립제품 제조업 외 4 종2011-10-25대전광역시 유성구 덕명동 산 16-1 번지 한밭대학교 S9동 403호대전광역시 유성구 덕명동 산 16-1 번지 한밭대학교 S9동 403호127.30105436.351155042-826-4080042-826-5090김선배3<NA>
6(주)기린에스아이법인314816275333910+25113+25114+33932간판 및 광고물 제조업 외 3 종2008-11-11대전광역시 유성구 상대동 441-7번지대전광역시 유성구 월드컵대로308번길 12 (상대동)127.33418236.347919042-824-5700042-824-0085최병구5<NA>
7(주)나노하이테크법인314813968329191일반저울 제조업2021-07-14대전광역시 유성구 둔곡동 405-7<NA>127.36306636.460714042-862-0220<NA>김병순<NA><NA>
8(주)누에보컴퍼니법인321860010530201+30121+30202차체 및 특장차 제조업 외 2 종2019-05-03대전광역시 유성구 교촌동 644-6번지대전광역시 유성구 교촌로6번길 2 (교촌동)127.31521536.300055042-621-4260<NA>황진우3<NA>
9(주)다올법인221870118829133+27213+27215+27216탭, 밸브 및 유사장치 제조업 외 3 종2018-11-02대전광역시 유성구 장대동 327-10번지 201호대전광역시 유성구 장대로80번길 36-1, 201호 (장대동)127.33513336.363255<NA><NA>김상재2<NA>
회사명유형사업자등록번호업종번호업종명최초등록일지번주소도로명주소위도경도전화번호팩스번호대표자이름종업원수홈페이지주소
232피엔피에너지텍주식회사법인314816892727212+27213+27215+28111+28201+28202+28909전자기 측정, 시험 및 분석기구 제조업 외 6 종2021-01-14대전광역시 유성구 신동 657-2 피엔피에너지텍주식회사대전광역시 유성구 국제과학2로 28 (신동) 피엔피에너지텍주식회사127.36556436.474588042-932-7731042-932-7732이용현35<NA>
233피카소 블라인드개인<NA>13223+16299+22223커튼 및 유사제품 제조업 외 2 종2018-10-30대전광역시 유성구 구암동 629-21번지 101호대전광역시 유성구 월드컵대로 243, 101호 (구암동)127.32735536.348918042-477-7754042-824-7763박경희1<NA>
234하람비개인<NA>20422+20423치약, 비누 및 기타 세제 제조업 외 1 종2017-01-24대전광역시 유성구 노은동 507-16번지 101호대전광역시 유성구 노은서로96번길 41, 101호 (노은동)127.31626636.368583042-826-1134042-826-1184임숙경<NA><NA>
235하이네트 주식회사법인314860766226421+26295+26299+26321+26329방송장비 제조업 외 4 종2014-10-23대전광역시 유성구 대정동 516-4번지 2층대전광역시 유성구 원계산로77번길 16, 2층 (대정동)127.30582536.320649042-822-7020<NA>최영삼3<NA>
236한국솔라파워(주)법인608870106522299그 외 기타 플라스틱 제품 제조업2018-11-20대전광역시 유성구 하기동 330-1번지대전광역시 유성구 노은로367번길 42 (하기동)127.32576536.392637042-825-5335042-826-5995최영재1www.hankuksp.com
237한동개인<NA>25112구조용 금속 판제품 및 공작물 제조업2019-06-18대전광역시 유성구 봉산동 469-1번지대전광역시 유성구 금남구즉로 1379-35 (봉산동)127.38590736.44626<NA><NA>김보한1<NA>
238한백전자개인<NA>26329+26291+26299+26429+27212+29280기타 주변기기 제조업 외 5 종2019-02-26대전광역시 유성구 봉명동 665-2번지 701호대전광역시 유성구 한밭대로492번길 16-15, 701호 (봉명동)127.35215136.358878042-610-1122050-4225-3838진은성3<NA>
239해동판지개인<NA>17211+17212골판지 제조업 외 1 종1990-07-21대전광역시 유성구 구암동 527-35번지대전광역시 유성구 유성대로709번길 90 (구암동)127.32878636.357515042-823-2696042-822-1697김정열23<NA>
240해뜰개인<NA>10619기타 곡물 가공품 제조업2017-01-17대전광역시 유성구 학하동 722-4번지대전광역시 유성구 학하중앙로128번길 23-10 (학하동)127.30633436.339866042-936-7507042-936-7506김해만1<NA>
241훼미리푸드개인<NA>10301+10302김치류 제조업 외 1 종2013-09-11대전광역시 유성구 원내동 87-11번지대전광역시 유성구 진잠로160번길 31 (원내동)127.31974736.302614042-545-3007042-545-3008정철재2<NA>

Duplicate rows

Most frequently occurring

회사명유형사업자등록번호업종번호업종명최초등록일지번주소도로명주소위도경도전화번호팩스번호대표자이름종업원수홈페이지주소# duplicates
0인트테크놀로지(주)법인314864850923992연마재 제조업2015-11-03대전광역시 유성구 덕명동 16-1번지 S9동 306호대전광역시 유성구 동서대로 125, S9동 306호 (덕명동)127.30513936.345267042-823-9690<NA>노백남1<NA>2