Overview

Dataset statistics

Number of variables11
Number of observations200
Missing cells62
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.9 KiB
Average record size in memory91.7 B

Variable types

Numeric3
Text7
Categorical1

Dataset

Description경상남도 거창군 내 공장등록현황(제조업)에 대한 데이터로 회사명, 대표자명, 전화번호, 팩스번호, 소재지 도로명주소, 소재지 지번주소, 생산품을 제공합니다.
Author경상남도 거창군
URLhttps://www.data.go.kr/data/15044917/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
전화번호 has 28 (14.0%) missing valuesMissing
팩스번호 has 34 (17.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:08:31.514754
Analysis finished2023-12-12 18:08:33.356592
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.5
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T03:08:33.441694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.95
Q150.75
median100.5
Q3150.25
95-th percentile190.05
Maximum200
Range199
Interquartile range (IQR)99.5

Descriptive statistics

Standard deviation57.879185
Coefficient of variation (CV)0.57591228
Kurtosis-1.2
Mean100.5
Median Absolute Deviation (MAD)50
Skewness0
Sum20100
Variance3350
MonotonicityStrictly increasing
2023-12-13T03:08:33.966616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
139 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
Other values (190) 190
95.0%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%
195 1
0.5%
194 1
0.5%
193 1
0.5%
192 1
0.5%
191 1
0.5%

회사명
Text

Distinct198
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-13T03:08:34.174553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length18
Mean length9.01
Min length2

Characters and Unicode

Total characters1802
Distinct characters246
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

Unique196 ?
Unique (%)98.0%

Sample

1st row(사)느티나무경남장애인부모회거창군지부(거창군장애인근로사업장)
2nd row(사)승강기밸리기업협의회
3rd row(재)거창화강석연구센터
4th row(주)거창유기
5th row(주)계진푸드 대평리사업장
ValueCountFrequency (%)
주식회사 38
 
13.7%
농업회사법인 12
 
4.3%
태양광발전소 9
 
3.2%
알루앤텍 3
 
1.1%
농업회사법인(주)얼음골식품 2
 
0.7%
홍덕산업(주 2
 
0.7%
2공장 2
 
0.7%
거창지점 2
 
0.7%
서광 2
 
0.7%
제2공장 2
 
0.7%
Other values (201) 204
73.4%
2023-12-13T03:08:34.545387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 121
 
6.7%
( 86
 
4.8%
) 86
 
4.8%
80
 
4.4%
사 73
 
4.1%
회 65
 
3.6%
식 48
 
2.7%
농 45
 
2.5%
업 44
 
2.4%
이 37
 
2.1%
Other values (236) 1117
62.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1520
84.4%
Open Punctuation 86
 
4.8%
Close Punctuation 86
 
4.8%
Space Separator 80
 
4.4%
Uppercase Letter 16
 
0.9%
Decimal Number 12
 
0.7%
Other Punctuation 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 121
 
8.0%
사 73
 
4.8%
회 65
 
4.3%
식 48
 
3.2%
농 45
 
3.0%
업 44
 
2.9%
이 37
 
2.4%
인 32
 
2.1%
산 31
 
2.0%
법 28
 
1.8%
Other values (220) 996
65.5%
Uppercase Letter
ValueCountFrequency (%)
S 5
31.2%
C 3
18.8%
E 2
 
12.5%
T 2
 
12.5%
G 1
 
6.2%
R 1
 
6.2%
H 1
 
6.2%
L 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 9
75.0%
1 2
 
16.7%
3 1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 86
100.0%
Close Punctuation
ValueCountFrequency (%)
) 86
100.0%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1520
84.4%
Common 266
 
14.8%
Latin 16
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 121
 
8.0%
사 73
 
4.8%
회 65
 
4.3%
식 48
 
3.2%
농 45
 
3.0%
업 44
 
2.9%
이 37
 
2.4%
인 32
 
2.1%
산 31
 
2.0%
법 28
 
1.8%
Other values (220) 996
65.5%
Common
ValueCountFrequency (%)
( 86
32.3%
) 86
32.3%
80
30.1%
2 9
 
3.4%
1 2
 
0.8%
3 1
 
0.4%
? 1
 
0.4%
- 1
 
0.4%
Latin
ValueCountFrequency (%)
S 5
31.2%
C 3
18.8%
E 2
 
12.5%
T 2
 
12.5%
G 1
 
6.2%
R 1
 
6.2%
H 1
 
6.2%
L 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1520
84.4%
ASCII 282
 
15.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 121
 
8.0%
사 73
 
4.8%
회 65
 
4.3%
식 48
 
3.2%
농 45
 
3.0%
업 44
 
2.9%
이 37
 
2.4%
인 32
 
2.1%
산 31
 
2.0%
법 28
 
1.8%
Other values (220) 996
65.5%
ASCII
ValueCountFrequency (%)
( 86
30.5%
) 86
30.5%
80
28.4%
2 9
 
3.2%
S 5
 
1.8%
C 3
 
1.1%
1 2
 
0.7%
E 2
 
0.7%
T 2
 
0.7%
3 1
 
0.4%
Other values (6) 6
 
2.1%

대표자명
Text

Distinct178
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-13T03:08:34.877790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.335
Min length2

Characters and Unicode

Total characters667
Distinct characters141
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)79.5%

Sample

1st row김경회
2nd row신양건
3rd row구인모
4th row이기홍
5th row김태동
ValueCountFrequency (%)
조수현 4
 
1.9%
전용민 3
 
1.4%
김흥수 3
 
1.4%
김태동 2
 
1.0%
최학영 2
 
1.0%
김동한 2
 
1.0%
조창호 2
 
1.0%
허원길 2
 
1.0%
윤효미 2
 
1.0%
정재준 2
 
1.0%
Other values (175) 186
88.6%
2023-12-13T03:08:35.382268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 54
 
8.1%
이 27
 
4.0%
정 26
 
3.9%
성 22
 
3.3%
수 21
 
3.1%
종 17
 
2.5%
영 17
 
2.5%
용 13
 
1.9%
민 11
 
1.6%
진 11
 
1.6%
Other values (131) 448
67.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 632
94.8%
Uppercase Letter 13
 
1.9%
Space Separator 11
 
1.6%
Other Punctuation 11
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 54
 
8.5%
이 27
 
4.3%
정 26
 
4.1%
성 22
 
3.5%
수 21
 
3.3%
종 17
 
2.7%
영 17
 
2.7%
용 13
 
2.1%
민 11
 
1.7%
진 11
 
1.7%
Other values (119) 413
65.3%
Uppercase Letter
ValueCountFrequency (%)
U 2
15.4%
S 2
15.4%
E 2
15.4%
W 1
7.7%
A 1
7.7%
B 1
7.7%
I 1
7.7%
N 1
7.7%
J 1
7.7%
R 1
7.7%
Space Separator
ValueCountFrequency (%)
11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 632
94.8%
Common 22
 
3.3%
Latin 13
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 54
 
8.5%
이 27
 
4.3%
정 26
 
4.1%
성 22
 
3.5%
수 21
 
3.3%
종 17
 
2.7%
영 17
 
2.7%
용 13
 
2.1%
민 11
 
1.7%
진 11
 
1.7%
Other values (119) 413
65.3%
Latin
ValueCountFrequency (%)
U 2
15.4%
S 2
15.4%
E 2
15.4%
W 1
7.7%
A 1
7.7%
B 1
7.7%
I 1
7.7%
N 1
7.7%
J 1
7.7%
R 1
7.7%
Common
ValueCountFrequency (%)
11
50.0%
, 11
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 632
94.8%
ASCII 35
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 54
 
8.5%
이 27
 
4.3%
정 26
 
4.1%
성 22
 
3.5%
수 21
 
3.3%
종 17
 
2.7%
영 17
 
2.7%
용 13
 
2.1%
민 11
 
1.7%
진 11
 
1.7%
Other values (119) 413
65.3%
ASCII
ValueCountFrequency (%)
11
31.4%
, 11
31.4%
U 2
 
5.7%
S 2
 
5.7%
E 2
 
5.7%
W 1
 
2.9%
A 1
 
2.9%
B 1
 
2.9%
I 1
 
2.9%
N 1
 
2.9%
Other values (2) 2
 
5.7%

전화번호
Text

MISSING 

Distinct157
Distinct (%)91.3%
Missing28
Missing (%)14.0%
Memory size1.7 KiB
2023-12-13T03:08:35.653808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.023256
Min length9

Characters and Unicode

Total characters2068
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

Unique144 ?
Unique (%)83.7%

Sample

1st row055-943-2345
2nd row055-943-2270
3rd row055-943-3924
4th row055-943-4949
5th row055-942-5500
ValueCountFrequency (%)
055-945-0386 3
 
1.7%
055-945-5581 3
 
1.7%
055-343-9501 2
 
1.2%
055-943-5805 2
 
1.2%
055-943-5418 2
 
1.2%
055-940-4107 2
 
1.2%
055-945-9921 2
 
1.2%
055-391-6054 2
 
1.2%
055-945-5435 2
 
1.2%
055-808-7060 2
 
1.2%
Other values (147) 150
87.2%
2023-12-13T03:08:36.091200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 443
21.4%
- 343
16.6%
0 303
14.7%
9 218
10.5%
4 206
10.0%
3 124
 
6.0%
1 123
 
5.9%
8 87
 
4.2%
2 85
 
4.1%
7 84
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1725
83.4%
Dash Punctuation 343
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 443
25.7%
0 303
17.6%
9 218
12.6%
4 206
11.9%
3 124
 
7.2%
1 123
 
7.1%
8 87
 
5.0%
2 85
 
4.9%
7 84
 
4.9%
6 52
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 443
21.4%
- 343
16.6%
0 303
14.7%
9 218
10.5%
4 206
10.0%
3 124
 
6.0%
1 123
 
5.9%
8 87
 
4.2%
2 85
 
4.1%
7 84
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 443
21.4%
- 343
16.6%
0 303
14.7%
9 218
10.5%
4 206
10.0%
3 124
 
6.0%
1 123
 
5.9%
8 87
 
4.2%
2 85
 
4.1%
7 84
 
4.1%

팩스번호
Text

MISSING 

Distinct151
Distinct (%)91.0%
Missing34
Missing (%)17.0%
Memory size1.7 KiB
2023-12-13T03:08:36.397254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.036145
Min length11

Characters and Unicode

Total characters1998
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

Unique138 ?
Unique (%)83.1%

Sample

1st row055-943-2335
2nd row055-943-2280
3rd row055-943-3942
4th row055-943-4940
5th row055-944-5510
ValueCountFrequency (%)
055-944-9505 3
 
1.8%
055-945-5580 3
 
1.8%
055-391-6459 2
 
1.2%
055-942-4029 2
 
1.2%
055-943-5859 2
 
1.2%
055-808-7090 2
 
1.2%
055-945-5256 2
 
1.2%
070-4275-5007 2
 
1.2%
055-943-8376 2
 
1.2%
055-943-2381 2
 
1.2%
Other values (141) 144
86.7%
2023-12-13T03:08:36.825090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 418
20.9%
- 332
16.6%
0 271
13.6%
9 211
10.6%
4 208
10.4%
3 129
 
6.5%
2 105
 
5.3%
1 102
 
5.1%
7 89
 
4.5%
8 81
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1666
83.4%
Dash Punctuation 332
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 418
25.1%
0 271
16.3%
9 211
12.7%
4 208
12.5%
3 129
 
7.7%
2 105
 
6.3%
1 102
 
6.1%
7 89
 
5.3%
8 81
 
4.9%
6 52
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1998
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 418
20.9%
- 332
16.6%
0 271
13.6%
9 211
10.6%
4 208
10.4%
3 129
 
6.5%
2 105
 
5.3%
1 102
 
5.1%
7 89
 
4.5%
8 81
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 418
20.9%
- 332
16.6%
0 271
13.6%
9 211
10.6%
4 208
10.4%
3 129
 
6.5%
2 105
 
5.3%
1 102
 
5.1%
7 89
 
4.5%
8 81
 
4.1%

소재지도로명주소
Text

Distinct179
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-13T03:08:37.241711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length20.845
Min length14

Characters and Unicode

Total characters4169
Distinct characters142
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique163 ?
Unique (%)81.5%

Sample

1st row경상남도 거창군 남상면 일반산업길 160
2nd row경상남도 거창군 남상면 일반산업길 276
3rd row경상남도 거창군 위천면 화리골길 15
4th row경상남도 거창군 남하면 가조가야로 308
5th row경상남도 거창군 마리면 거안로 487-81
ValueCountFrequency (%)
경상남도 163
 
16.8%
거창군 160
 
16.5%
남상면 57
 
5.9%
거창읍 38
 
3.9%
위천면 26
 
2.7%
일반산업길 20
 
2.1%
화리골길 15
 
1.5%
밤티재로 13
 
1.3%
홍덕길 13
 
1.3%
가조면 13
 
1.3%
Other values (295) 451
46.5%
2023-12-13T03:08:37.739738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
769
18.4%
남 240
 
5.8%
상 229
 
5.5%
거 217
 
5.2%
창 201
 
4.8%
경 178
 
4.3%
도 178
 
4.3%
군 164
 
3.9%
1 137
 
3.3%
길 129
 
3.1%
Other values (132) 1727
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2698
64.7%
Space Separator 769
 
18.4%
Decimal Number 659
 
15.8%
Dash Punctuation 43
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 240
 
8.9%
상 229
 
8.5%
거 217
 
8.0%
창 201
 
7.4%
경 178
 
6.6%
도 178
 
6.6%
군 164
 
6.1%
길 129
 
4.8%
면 125
 
4.6%
로 93
 
3.4%
Other values (120) 944
35.0%
Decimal Number
ValueCountFrequency (%)
1 137
20.8%
2 97
14.7%
3 94
14.3%
5 67
10.2%
6 55
8.3%
4 48
 
7.3%
7 45
 
6.8%
0 45
 
6.8%
8 36
 
5.5%
9 35
 
5.3%
Space Separator
ValueCountFrequency (%)
769
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2698
64.7%
Common 1471
35.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 240
 
8.9%
상 229
 
8.5%
거 217
 
8.0%
창 201
 
7.4%
경 178
 
6.6%
도 178
 
6.6%
군 164
 
6.1%
길 129
 
4.8%
면 125
 
4.6%
로 93
 
3.4%
Other values (120) 944
35.0%
Common
ValueCountFrequency (%)
769
52.3%
1 137
 
9.3%
2 97
 
6.6%
3 94
 
6.4%
5 67
 
4.6%
6 55
 
3.7%
4 48
 
3.3%
7 45
 
3.1%
0 45
 
3.1%
- 43
 
2.9%
Other values (2) 71
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2698
64.7%
ASCII 1471
35.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
769
52.3%
1 137
 
9.3%
2 97
 
6.6%
3 94
 
6.4%
5 67
 
4.6%
6 55
 
3.7%
4 48
 
3.3%
7 45
 
3.1%
0 45
 
3.1%
- 43
 
2.9%
Other values (2) 71
 
4.8%
Hangul
ValueCountFrequency (%)
남 240
 
8.9%
상 229
 
8.5%
거 217
 
8.0%
창 201
 
7.4%
경 178
 
6.6%
도 178
 
6.6%
군 164
 
6.1%
길 129
 
4.8%
면 125
 
4.6%
로 93
 
3.4%
Other values (120) 944
35.0%

소재지지번주소
Text

Distinct176
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-13T03:08:38.091415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length20.98
Min length14

Characters and Unicode

Total characters4196
Distinct characters129
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)79.0%

Sample

1st row경상남도 거창군 남상면 대산리 1546
2nd row경상남도 거창군 남상면 대산리 1553
3rd row경상남도 거창군 위천면 남산리 105-9
4th row경상남도 거창군 남하면 둔마리 산 79-3
5th row경상남도 거창군 마리면 고학리 산 70
ValueCountFrequency (%)
경상남도 164
 
16.8%
거창군 160
 
16.4%
남상면 59
 
6.0%
대산리 51
 
5.2%
거창읍 37
 
3.8%
위천면 26
 
2.7%
남산리 15
 
1.5%
정장리 13
 
1.3%
가조면 13
 
1.3%
석강리 10
 
1.0%
Other values (296) 429
43.9%
2023-12-13T03:08:38.631014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
777
18.5%
남 246
 
5.9%
상 240
 
5.7%
창 198
 
4.7%
거 198
 
4.7%
도 178
 
4.2%
경 175
 
4.2%
리 173
 
4.1%
1 172
 
4.1%
군 163
 
3.9%
Other values (119) 1676
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2529
60.3%
Decimal Number 786
 
18.7%
Space Separator 777
 
18.5%
Dash Punctuation 104
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 246
 
9.7%
상 240
 
9.5%
창 198
 
7.8%
거 198
 
7.8%
도 178
 
7.0%
경 175
 
6.9%
리 173
 
6.8%
군 163
 
6.4%
면 125
 
4.9%
산 103
 
4.1%
Other values (107) 730
28.9%
Decimal Number
ValueCountFrequency (%)
1 172
21.9%
5 116
14.8%
4 74
9.4%
0 71
9.0%
2 70
8.9%
7 66
 
8.4%
9 63
 
8.0%
3 57
 
7.3%
8 52
 
6.6%
6 45
 
5.7%
Space Separator
ValueCountFrequency (%)
777
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2529
60.3%
Common 1667
39.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 246
 
9.7%
상 240
 
9.5%
창 198
 
7.8%
거 198
 
7.8%
도 178
 
7.0%
경 175
 
6.9%
리 173
 
6.8%
군 163
 
6.4%
면 125
 
4.9%
산 103
 
4.1%
Other values (107) 730
28.9%
Common
ValueCountFrequency (%)
777
46.6%
1 172
 
10.3%
5 116
 
7.0%
- 104
 
6.2%
4 74
 
4.4%
0 71
 
4.3%
2 70
 
4.2%
7 66
 
4.0%
9 63
 
3.8%
3 57
 
3.4%
Other values (2) 97
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2529
60.3%
ASCII 1667
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
777
46.6%
1 172
 
10.3%
5 116
 
7.0%
- 104
 
6.2%
4 74
 
4.4%
0 71
 
4.3%
2 70
 
4.2%
7 66
 
4.0%
9 63
 
3.8%
3 57
 
3.4%
Other values (2) 97
 
5.8%
Hangul
ValueCountFrequency (%)
남 246
 
9.7%
상 240
 
9.5%
창 198
 
7.8%
거 198
 
7.8%
도 178
 
7.0%
경 175
 
6.9%
리 173
 
6.8%
군 163
 
6.4%
면 125
 
4.9%
산 103
 
4.1%
Other values (107) 730
28.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct180
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.826634
Minimum35.098471
Maximum38.1757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T03:08:38.789994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.098471
5-th percentile35.288942
Q135.654241
median35.684356
Q335.739573
95-th percentile37.4756
Maximum38.1757
Range3.077229
Interquartile range (IQR)0.085332182

Descriptive statistics

Standard deviation0.54544318
Coefficient of variation (CV)0.015224516
Kurtosis6.1324215
Mean35.826634
Median Absolute Deviation (MAD)0.03245955
Skewness2.6123864
Sum7165.3269
Variance0.29750827
MonotonicityNot monotonic
2023-12-13T03:08:38.949890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.30693978 3
 
1.5%
35.65452124 3
 
1.5%
37.64604581 3
 
1.5%
35.65166008 3
 
1.5%
35.65558258 3
 
1.5%
35.65621929 2
 
1.0%
35.65574527 2
 
1.0%
35.7413504 2
 
1.0%
37.57298154 2
 
1.0%
35.65620557 2
 
1.0%
Other values (170) 175
87.5%
ValueCountFrequency (%)
35.09847097 1
0.5%
35.10810739 1
0.5%
35.14294725 1
0.5%
35.15635994 1
0.5%
35.16733846 1
0.5%
35.17632018 1
0.5%
35.19944473 1
0.5%
35.21280146 1
0.5%
35.21923777 1
0.5%
35.26231951 1
0.5%
ValueCountFrequency (%)
38.17569999 1
 
0.5%
37.64604581 3
1.5%
37.59207983 1
 
0.5%
37.57675437 1
 
0.5%
37.57298154 2
1.0%
37.57254816 1
 
0.5%
37.47840471 1
 
0.5%
37.47545189 1
 
0.5%
37.41706485 1
 
0.5%
37.30693978 3
1.5%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct179
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.92084
Minimum126.68316
Maximum129.2203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T03:08:39.092156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68316
5-th percentile126.96853
Q1127.85723
median127.9257
Q3127.93412
95-th percentile128.99501
Maximum129.2203
Range2.537143
Interquartile range (IQR)0.076898125

Descriptive statistics

Standard deviation0.43620767
Coefficient of variation (CV)0.0034099812
Kurtosis3.0554352
Mean127.92084
Median Absolute Deviation (MAD)0.02497735
Skewness0.12248157
Sum25584.169
Variance0.19027713
MonotonicityNot monotonic
2023-12-13T03:08:39.239394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6831553 3
 
1.5%
127.9260042 3
 
1.5%
127.9279474 3
 
1.5%
126.8319756 3
 
1.5%
127.9280211 3
 
1.5%
129.0204944 2
 
1.0%
127.908151 2
 
1.0%
127.9333913 2
 
1.0%
128.0301014 2
 
1.0%
127.9196111 2
 
1.0%
Other values (169) 175
87.5%
ValueCountFrequency (%)
126.6831553 3
1.5%
126.8319756 3
1.5%
126.8646949 1
 
0.5%
126.8818744 1
 
0.5%
126.8833586 1
 
0.5%
126.8978977 1
 
0.5%
126.9722441 1
 
0.5%
126.9982495 2
1.0%
127.0724638 1
 
0.5%
127.1219619 1
 
0.5%
ValueCountFrequency (%)
129.2202983 1
0.5%
129.1200736 1
0.5%
129.1131674 1
0.5%
129.111324 1
0.5%
129.1076232 1
0.5%
129.0708229 1
0.5%
129.0377008 1
0.5%
129.0204944 2
1.0%
129.0198972 1
0.5%
128.9936969 1
0.5%

생산품
Text

Distinct165
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-13T03:08:39.530199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length25
Mean length9.24
Min length1

Characters and Unicode

Total characters1848
Distinct characters306
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150 ?
Unique (%)75.0%

Sample

1st row골판지 상자
2nd row승강기부품
3rd row광촉매코팅석
4th row유기
5th row닭고기 가공품(부분육)
ValueCountFrequency (%)
승강기 17
 
4.4%
부품 11
 
2.9%
및 9
 
2.3%
엘리베이터 8
 
2.1%
승강기부품 8
 
2.1%
등 7
 
1.8%
경계석 6
 
1.6%
태양력 6
 
1.6%
전기 5
 
1.3%
경계석.건축석 4
 
1.0%
Other values (275) 304
79.0%
2023-12-13T03:08:39.995015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
186
 
10.1%
, 111
 
6.0%
기 78
 
4.2%
품 49
 
2.7%
석 38
 
2.1%
부 34
 
1.8%
강 33
 
1.8%
승 32
 
1.7%
계 29
 
1.6%
재 26
 
1.4%
Other values (296) 1232
66.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1477
79.9%
Space Separator 186
 
10.1%
Other Punctuation 128
 
6.9%
Close Punctuation 21
 
1.1%
Open Punctuation 21
 
1.1%
Uppercase Letter 12
 
0.6%
Decimal Number 1
 
0.1%
Dash Punctuation 1
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 78
 
5.3%
품 49
 
3.3%
석 38
 
2.6%
부 34
 
2.3%
강 33
 
2.2%
승 32
 
2.2%
계 29
 
2.0%
재 26
 
1.8%
제 25
 
1.7%
리 23
 
1.6%
Other values (277) 1110
75.2%
Uppercase Letter
ValueCountFrequency (%)
B 2
16.7%
F 2
16.7%
P 1
8.3%
X 1
8.3%
O 1
8.3%
R 1
8.3%
G 1
8.3%
D 1
8.3%
E 1
8.3%
L 1
8.3%
Other Punctuation
ValueCountFrequency (%)
, 111
86.7%
. 16
 
12.5%
/ 1
 
0.8%
Space Separator
ValueCountFrequency (%)
186
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1477
79.9%
Common 358
 
19.4%
Latin 13
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 78
 
5.3%
품 49
 
3.3%
석 38
 
2.6%
부 34
 
2.3%
강 33
 
2.2%
승 32
 
2.2%
계 29
 
2.0%
재 26
 
1.8%
제 25
 
1.7%
리 23
 
1.6%
Other values (277) 1110
75.2%
Latin
ValueCountFrequency (%)
B 2
15.4%
F 2
15.4%
P 1
7.7%
X 1
7.7%
O 1
7.7%
R 1
7.7%
G 1
7.7%
D 1
7.7%
E 1
7.7%
L 1
7.7%
Common
ValueCountFrequency (%)
186
52.0%
, 111
31.0%
) 21
 
5.9%
( 21
 
5.9%
. 16
 
4.5%
/ 1
 
0.3%
1 1
 
0.3%
- 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1477
79.9%
ASCII 371
 
20.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
186
50.1%
, 111
29.9%
) 21
 
5.7%
( 21
 
5.7%
. 16
 
4.3%
B 2
 
0.5%
F 2
 
0.5%
P 1
 
0.3%
X 1
 
0.3%
O 1
 
0.3%
Other values (9) 9
 
2.4%
Hangul
ValueCountFrequency (%)
기 78
 
5.3%
품 49
 
3.3%
석 38
 
2.6%
부 34
 
2.3%
강 33
 
2.2%
승 32
 
2.2%
계 29
 
2.0%
재 26
 
1.8%
제 25
 
1.7%
리 23
 
1.6%
Other values (277) 1110
75.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-10-01
200 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-01
2nd row2023-10-01
3rd row2023-10-01
4th row2023-10-01
5th row2023-10-01

Common Values

ValueCountFrequency (%)
2023-10-01 200
100.0%

Length

2023-12-13T03:08:40.119029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:40.208863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-01 200
100.0%

Interactions

2023-12-13T03:08:32.740380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.164193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.463176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.827800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.269737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.564581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.921804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.366257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.658003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:08:40.276791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.2230.218
위도0.2231.0000.824
경도0.2180.8241.000
2023-12-13T03:08:40.354404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.087-0.054
위도0.0871.000-0.577
경도-0.054-0.5771.000

Missing values

2023-12-13T03:08:33.044625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:08:33.193145image/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.
2023-12-13T03:08:33.299921image/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

연번회사명대표자명전화번호팩스번호소재지도로명주소소재지지번주소위도경도생산품데이터기준일자
01(사)느티나무경남장애인부모회거창군지부(거창군장애인근로사업장)김경회055-943-2345055-943-2335경상남도 거창군 남상면 일반산업길 160경상남도 거창군 남상면 대산리 154635.656228127.927014골판지 상자2023-10-01
12(사)승강기밸리기업협의회신양건055-943-2270055-943-2280경상남도 거창군 남상면 일반산업길 276경상남도 거창군 남상면 대산리 155335.65097127.9257승강기부품2023-10-01
23(재)거창화강석연구센터구인모055-943-3924055-943-3942경상남도 거창군 위천면 화리골길 15경상남도 거창군 위천면 남산리 105-935.736997127.842208광촉매코팅석2023-10-01
34(주)거창유기이기홍055-943-4949055-943-4940경상남도 거창군 남하면 가조가야로 308경상남도 거창군 남하면 둔마리 산 79-335.700183127.945004유기2023-10-01
45(주)계진푸드 대평리사업장김태동055-942-5500055-944-5510경상남도 거창군 마리면 거안로 487-81경상남도 거창군 마리면 고학리 산 7035.679641127.829422닭고기 가공품(부분육)2023-10-01
56(주)금강엔지니어링김수자055-945-4585033-637-3585강원특별자치도 속초시 농공단지2길 14강원특별자치도 속초시 대포동 976-338.1757128.598517승강기 부품2023-10-01
67(주)금보엘리베이터김영환051-322-8496051-326-8011부산광역시 사상구 학장로248번길 77부산광역시 사상구 학장동 212-1635.142947128.993697승강기2023-10-01
78(주)금산산기조수현055-343-9501055-944-9505경상남도 거창군 남상면 일반산업길 175경상남도 거창군 남상면 대산리 155935.655583127.927947엘리베이터 부품2023-10-01
89(주)금산산기(2공장)조수현055-343-9501055-944-9505경상남도 거창군 남상면 일반산업길 175경상남도 거창군 남상면 대산리 155935.655583127.927947승강기부품2023-10-01
910(주)남양이엔지김진영053-527-1260053-527-1263경상남도 거창군 남상면 홍덕길 72경상남도 거창군 남상면 대산리 158535.653042127.932258엘리베이터, 주차장치2023-10-01
연번회사명대표자명전화번호팩스번호소재지도로명주소소재지지번주소위도경도생산품데이터기준일자
190191한영대리석변종원055-945-1515055-945-1517경상남도 거창군 거창읍 거열로4길 144-21경상남도 거창군 거창읍 가지리 219-135.695331127.907514경계석.건축석2023-10-01
191192한진종합중기정비박규근055-943-1799055-944-8180경상남도 거창군 거창읍 밤티재로 1321경상남도 거창군 거창읍 정장리 284-535.673112127.931547건설기계부품 재생,수리2023-10-01
192193향기나무박정숙055-943-6066<NA>경상남도 거창군 신원면 청수로 651-5경상남도 거창군 신원면 덕산리 842-635.572405127.894181비누 세재2023-10-01
193194현대석재김점식055-943-1476<NA>경상남도 거창군 위천면 화리골길 99경상남도 거창군 위천면 남산리 105-535.740528127.841312석재가공품2023-10-01
194195형광이노텍형지민<NA><NA>경상남도 거창군 웅양면 왕암길 158-44경상남도 거창군 웅양면 신촌리 64835.878813127.915082LED 바닥신호등2023-10-01
195196형제석재김동현055-944-3812055-944-3814경상남도 거창군 주상면 거기1길 39-6경상남도 거창군 주상면 거기리 93035.756359127.941911경계석.건축석2023-10-01
196197홍덕산업(주) 거창SC 2공장주종대055-949-9400055-949-9499경상남도 거창군 남상면 홍덕길 77경상남도 거창군 남상면 대산리 160235.653255127.9341스틸코드2023-10-01
197198홍덕산업(주) 거창SC공장주종대055-943-7977055-943-5137경상남도 거창군 가조면 석강3길 84경상남도 거창군 가조면 기리 140635.684252128.026042스틸코드2023-10-01
198199화신기업신철수055-945-7555055-945-7556경상남도 거창군 거창읍 새동네2길 40경상남도 거창군 거창읍 대평리 151835.687461127.927801승강기 운전반 및 표시기 접속전기2023-10-01
199200흥보석재최일수055-943-0582<NA>경상남도 거창군 위천면 화리골길 35경상남도 거창군 위천면 남산리 975-7035.737878127.842142석재품2023-10-01