Overview

Dataset statistics

Number of variables9
Number of observations172
Missing cells25
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.6 KiB
Average record size in memory74.8 B

Variable types

Categorical3
Text4
Numeric2

Dataset

Description강원특별자치도 골재업 정보(업체명, 대표자명, 사업장업종명, 소재지주소, 위치의 위도 및 경도 등) 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15033676/fileData.do

Alerts

시도명 has constant value ""Constant
경도 is highly overall correlated with 시군구명High correlation
위도 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
연락처 has 25 (14.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:20:04.187002
Analysis finished2023-12-12 17:20:05.330941
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
강원특별자치도
172 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원특별자치도 172
100.0%

Length

2023-12-13T02:20:05.411022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:20:05.526799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원특별자치도 172
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
강릉시
35 
원주시
21 
삼척시
20 
고성군
20 
양양군
12 
Other values (11)
64 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강릉시 35
20.3%
원주시 21
12.2%
삼척시 20
11.6%
고성군 20
11.6%
양양군 12
 
7.0%
평창군 8
 
4.7%
정선군 8
 
4.7%
양구군 8
 
4.7%
춘천시 6
 
3.5%
철원군 6
 
3.5%
Other values (6) 28
16.3%

Length

2023-12-13T02:20:05.616359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강릉시 35
20.3%
원주시 21
12.2%
삼척시 20
11.6%
고성군 20
11.6%
양양군 12
 
7.0%
평창군 8
 
4.7%
정선군 8
 
4.7%
양구군 8
 
4.7%
춘천시 6
 
3.5%
철원군 6
 
3.5%
Other values (6) 28
16.3%

업체명
Text

Distinct170
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T02:20:05.827294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length5.6976744
Min length3

Characters and Unicode

Total characters980
Distinct characters170
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)97.7%

Sample

1st row(주)동서산업
2nd row(합)신한산업
3rd row㈜테라스톤
4th row(합)대룡물산
5th row이수이앤씨㈜
ValueCountFrequency (%)
합)진성산업 2
 
1.2%
대철개발㈜ 2
 
1.2%
㈜다성실업 1
 
0.6%
합)진흥골재 1
 
0.6%
주)동서산업 1
 
0.6%
㈜케이씨씨글라스 1
 
0.6%
삼척블루파워㈜ 1
 
0.6%
합)한서개발 1
 
0.6%
㈜반석 1
 
0.6%
㈜창해 1
 
0.6%
Other values (160) 160
93.0%
2023-12-13T02:20:06.208033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 136
 
13.9%
산 62
 
6.3%
업 58
 
5.9%
개 32
 
3.3%
발 32
 
3.3%
( 29
 
3.0%
) 29
 
3.0%
성 24
 
2.4%
대 18
 
1.8%
미 18
 
1.8%
Other values (160) 542
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 785
80.1%
Other Symbol 136
 
13.9%
Open Punctuation 29
 
3.0%
Close Punctuation 29
 
3.0%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 62
 
7.9%
업 58
 
7.4%
개 32
 
4.1%
발 32
 
4.1%
성 24
 
3.1%
대 18
 
2.3%
미 18
 
2.3%
재 17
 
2.2%
합 17
 
2.2%
이 17
 
2.2%
Other values (156) 490
62.4%
Other Symbol
ValueCountFrequency (%)
㈜ 136
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 921
94.0%
Common 59
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 136
 
14.8%
산 62
 
6.7%
업 58
 
6.3%
개 32
 
3.5%
발 32
 
3.5%
성 24
 
2.6%
대 18
 
2.0%
미 18
 
2.0%
재 17
 
1.8%
합 17
 
1.8%
Other values (157) 507
55.0%
Common
ValueCountFrequency (%)
( 29
49.2%
) 29
49.2%
1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 785
80.1%
None 136
 
13.9%
ASCII 59
 
6.0%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 136
100.0%
Hangul
ValueCountFrequency (%)
산 62
 
7.9%
업 58
 
7.4%
개 32
 
4.1%
발 32
 
4.1%
성 24
 
3.1%
대 18
 
2.3%
미 18
 
2.3%
재 17
 
2.2%
합 17
 
2.2%
이 17
 
2.2%
Other values (156) 490
62.4%
ASCII
ValueCountFrequency (%)
( 29
49.2%
) 29
49.2%
1
 
1.7%

대표자명
Text

Distinct157
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T02:20:06.521767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0174419
Min length2

Characters and Unicode

Total characters519
Distinct characters129
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

Unique143 ?
Unique (%)83.1%

Sample

1st row홍순희
2nd row이충수
3rd row송병호
4th row한윤섭
5th row서정미
ValueCountFrequency (%)
김기언 3
 
1.7%
박영준 2
 
1.2%
문병연 2
 
1.2%
정달수 2
 
1.2%
최종원 2
 
1.2%
김재향 2
 
1.2%
박대성 2
 
1.2%
김군환 2
 
1.2%
우형호 2
 
1.2%
김희권 2
 
1.2%
Other values (148) 152
87.9%
2023-12-13T02:20:06.967747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 51
 
9.8%
이 16
 
3.1%
수 14
 
2.7%
성 14
 
2.7%
영 14
 
2.7%
남 12
 
2.3%
정 12
 
2.3%
기 11
 
2.1%
박 11
 
2.1%
환 10
 
1.9%
Other values (119) 354
68.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 517
99.6%
Decimal Number 1
 
0.2%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 51
 
9.9%
이 16
 
3.1%
수 14
 
2.7%
성 14
 
2.7%
영 14
 
2.7%
남 12
 
2.3%
정 12
 
2.3%
기 11
 
2.1%
박 11
 
2.1%
환 10
 
1.9%
Other values (117) 352
68.1%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 517
99.6%
Common 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 51
 
9.9%
이 16
 
3.1%
수 14
 
2.7%
성 14
 
2.7%
영 14
 
2.7%
남 12
 
2.3%
정 12
 
2.3%
기 11
 
2.1%
박 11
 
2.1%
환 10
 
1.9%
Other values (117) 352
68.1%
Common
ValueCountFrequency (%)
1 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 517
99.6%
ASCII 2
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 51
 
9.9%
이 16
 
3.1%
수 14
 
2.7%
성 14
 
2.7%
영 14
 
2.7%
남 12
 
2.3%
정 12
 
2.3%
기 11
 
2.1%
박 11
 
2.1%
환 10
 
1.9%
Other values (117) 352
68.1%
ASCII
ValueCountFrequency (%)
1 1
50.0%
1
50.0%

사업장업종명
Categorical

Distinct18
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
육상골재채취업
63 
육상골재채취업, 골재선별파쇄업
33 
골재선별파쇄업
30 
산림골재채취업, 골재선별파쇄업
15 
산림골재채취업, 육상골재채취업, 골재선별파쇄업
 
6
Other values (13)
25 

Length

Max length25
Median length7
Mean length11.069767
Min length5

Unique

Unique8 ?
Unique (%)4.7%

Sample

1st row산림골재채취업, 골재선별파쇄업
2nd row산림골재채취업, 육상골재채취업, 골재선별파쇄업
3rd row골재선별파쇄업
4th row육상골재채취업, 골재선별파쇄업
5th row골재선별파쇄업

Common Values

ValueCountFrequency (%)
육상골재채취업 63
36.6%
육상골재채취업, 골재선별파쇄업 33
19.2%
골재선별파쇄업 30
17.4%
산림골재채취업, 골재선별파쇄업 15
 
8.7%
산림골재채취업, 육상골재채취업, 골재선별파쇄업 6
 
3.5%
산림골재채취업, 세척업 5
 
2.9%
산림골재채취업,골재선별파쇄업 4
 
2.3%
골재선별파쇄업, 육상골재채취업 4
 
2.3%
산림골재채취업 2
 
1.2%
산림골재채취업, 육상골재채취업 2
 
1.2%
Other values (8) 8
 
4.7%

Length

2023-12-13T02:20:07.125011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
육상골재채취업 110
43.7%
골재선별파쇄업 90
35.7%
산림골재채취업 33
 
13.1%
세척업 5
 
2.0%
산림골재채취업,골재선별파쇄업 4
 
1.6%
선별파쇄업 3
 
1.2%
산림골재 2
 
0.8%
선별파쇄 1
 
0.4%
산림골재업 1
 
0.4%
산림 1
 
0.4%
Other values (2) 2
 
0.8%

연락처
Text

MISSING 

Distinct131
Distinct (%)89.1%
Missing25
Missing (%)14.5%
Memory size1.5 KiB
2023-12-13T02:20:07.339045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12
Min length11

Characters and Unicode

Total characters1764
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

Unique119 ?
Unique (%)81.0%

Sample

1st row033-261-9447
2nd row033-262-5272
3rd row033-256-8901
4th row033-244-9930
5th row061-743-7940
ValueCountFrequency (%)
033-802-8850 3
 
2.0%
033-646-7771 3
 
2.0%
033-681-3232 3
 
2.0%
033-651-1337 3
 
2.0%
033-343-3900 2
 
1.4%
033-743-9736 2
 
1.4%
033-462-0489 2
 
1.4%
033-432-8570 2
 
1.4%
033-646-5620 2
 
1.4%
033-575-0350 2
 
1.4%
Other values (121) 123
83.7%
2023-12-13T02:20:07.738045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 418
23.7%
- 294
16.7%
0 260
14.7%
6 141
 
8.0%
2 112
 
6.3%
5 107
 
6.1%
4 105
 
6.0%
7 102
 
5.8%
1 101
 
5.7%
8 83
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
83.3%
Dash Punctuation 294
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 418
28.4%
0 260
17.7%
6 141
 
9.6%
2 112
 
7.6%
5 107
 
7.3%
4 105
 
7.1%
7 102
 
6.9%
1 101
 
6.9%
8 83
 
5.6%
9 41
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1764
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 418
23.7%
- 294
16.7%
0 260
14.7%
6 141
 
8.0%
2 112
 
6.3%
5 107
 
6.1%
4 105
 
6.0%
7 102
 
5.8%
1 101
 
5.7%
8 83
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 418
23.7%
- 294
16.7%
0 260
14.7%
6 141
 
8.0%
2 112
 
6.3%
5 107
 
6.1%
4 105
 
6.0%
7 102
 
5.8%
1 101
 
5.7%
8 83
 
4.7%

소재지주소
Text

Distinct156
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T02:20:08.193651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length31
Mean length24.02907
Min length17

Characters and Unicode

Total characters4133
Distinct characters193
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)82.6%

Sample

1st row강원특별자치도 춘천시 신동면 혈동리 37
2nd row강원특별자치도 춘천시 신동면 혈동리 215
3rd row강원특별자치도 춘천시 사북면 춘화로 919-3
4th row강원특별자치도 춘천시 신동면 증리 산119-16
5th row강원특별자치도 춘천시 사북면 원평리 산117-22
ValueCountFrequency (%)
강원특별자치도 171
 
20.4%
강릉시 35
 
4.2%
원주시 21
 
2.5%
고성군 20
 
2.4%
삼척시 20
 
2.4%
양양군 12
 
1.4%
간성읍 10
 
1.2%
평창군 8
 
1.0%
정선군 8
 
1.0%
양구군 8
 
1.0%
Other values (352) 526
62.7%
2023-12-13T02:20:08.739489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
670
 
16.2%
강 218
 
5.3%
원 217
 
5.3%
자 173
 
4.2%
특 172
 
4.2%
별 172
 
4.2%
도 172
 
4.2%
치 171
 
4.1%
1 128
 
3.1%
시 93
 
2.3%
Other values (183) 1947
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2760
66.8%
Space Separator 670
 
16.2%
Decimal Number 610
 
14.8%
Dash Punctuation 78
 
1.9%
Close Punctuation 6
 
0.1%
Open Punctuation 6
 
0.1%
Other Symbol 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 218
 
7.9%
원 217
 
7.9%
자 173
 
6.3%
특 172
 
6.2%
별 172
 
6.2%
도 172
 
6.2%
치 171
 
6.2%
시 93
 
3.4%
면 83
 
3.0%
군 81
 
2.9%
Other values (167) 1208
43.8%
Decimal Number
ValueCountFrequency (%)
1 128
21.0%
2 90
14.8%
4 69
11.3%
3 63
10.3%
5 49
 
8.0%
8 48
 
7.9%
9 46
 
7.5%
0 39
 
6.4%
6 39
 
6.4%
7 39
 
6.4%
Space Separator
ValueCountFrequency (%)
670
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2762
66.8%
Common 1371
33.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 218
 
7.9%
원 217
 
7.9%
자 173
 
6.3%
특 172
 
6.2%
별 172
 
6.2%
도 172
 
6.2%
치 171
 
6.2%
시 93
 
3.4%
면 83
 
3.0%
군 81
 
2.9%
Other values (168) 1210
43.8%
Common
ValueCountFrequency (%)
670
48.9%
1 128
 
9.3%
2 90
 
6.6%
- 78
 
5.7%
4 69
 
5.0%
3 63
 
4.6%
5 49
 
3.6%
8 48
 
3.5%
9 46
 
3.4%
0 39
 
2.8%
Other values (5) 91
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2760
66.8%
ASCII 1371
33.2%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
670
48.9%
1 128
 
9.3%
2 90
 
6.6%
- 78
 
5.7%
4 69
 
5.0%
3 63
 
4.6%
5 49
 
3.6%
8 48
 
3.5%
9 46
 
3.4%
0 39
 
2.8%
Other values (5) 91
 
6.6%
Hangul
ValueCountFrequency (%)
강 218
 
7.9%
원 217
 
7.9%
자 173
 
6.3%
특 172
 
6.2%
별 172
 
6.2%
도 172
 
6.2%
치 171
 
6.2%
시 93
 
3.4%
면 83
 
3.0%
군 81
 
2.9%
Other values (167) 1208
43.8%
None
ValueCountFrequency (%)
㈜ 2
100.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.49211
Minimum126.88796
Maximum129.34677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T02:20:08.876964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.88796
5-th percentile127.60846
Q1128.0132
median128.55537
Q3128.91119
95-th percentile129.22575
Maximum129.34677
Range2.458803
Interquartile range (IQR)0.897989

Descriptive statistics

Standard deviation0.53585758
Coefficient of variation (CV)0.0041703539
Kurtosis-0.60676611
Mean128.49211
Median Absolute Deviation (MAD)0.377556
Skewness-0.49050544
Sum22100.643
Variance0.28714335
MonotonicityNot monotonic
2023-12-13T02:20:09.020591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.873863 3
 
1.7%
129.163446 3
 
1.7%
128.916109 2
 
1.2%
128.0132 2
 
1.2%
128.914207 2
 
1.2%
128.9084086 2
 
1.2%
128.472882 2
 
1.2%
127.816421 2
 
1.2%
128.201631 2
 
1.2%
128.472568 2
 
1.2%
Other values (147) 150
87.2%
ValueCountFrequency (%)
126.887962 1
0.6%
127.3164001 1
0.6%
127.3304597 1
0.6%
127.3409994 1
0.6%
127.3532338 1
0.6%
127.358588 1
0.6%
127.44044 1
0.6%
127.56268 1
0.6%
127.56573 1
0.6%
127.643418 1
0.6%
ValueCountFrequency (%)
129.346765 1
0.6%
129.338208 1
0.6%
129.333386 1
0.6%
129.294983 1
0.6%
129.290853 1
0.6%
129.2890017 1
0.6%
129.24867 1
0.6%
129.246601 1
0.6%
129.243743 1
0.6%
129.211026 1
0.6%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.703463
Minimum37.096911
Maximum38.382671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T02:20:09.194402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.096911
5-th percentile37.187776
Q137.332814
median37.726165
Q338.084216
95-th percentile38.376047
Maximum38.382671
Range1.28576
Interquartile range (IQR)0.75140125

Descriptive statistics

Standard deviation0.38577911
Coefficient of variation (CV)0.010231928
Kurtosis-1.1657272
Mean37.703463
Median Absolute Deviation (MAD)0.37599175
Skewness0.26462948
Sum6484.9957
Variance0.14882552
MonotonicityNot monotonic
2023-12-13T02:20:09.598120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.226667 3
 
1.7%
37.442676 3
 
1.7%
37.779889 2
 
1.2%
37.29311 2
 
1.2%
37.745284 2
 
1.2%
37.754912 2
 
1.2%
38.379033 2
 
1.2%
37.296811 2
 
1.2%
38.120123 2
 
1.2%
38.382671 2
 
1.2%
Other values (147) 150
87.2%
ValueCountFrequency (%)
37.09691102 1
0.6%
37.127806 1
0.6%
37.132263 1
0.6%
37.133447 1
0.6%
37.14054 1
0.6%
37.1463525 1
0.6%
37.16078738 1
0.6%
37.174967 1
0.6%
37.17696 1
0.6%
37.19662524 1
0.6%
ValueCountFrequency (%)
38.382671 2
1.2%
38.382415 1
0.6%
38.379122 1
0.6%
38.379033 2
1.2%
38.378363 2
1.2%
38.3761806 1
0.6%
38.375937 1
0.6%
38.374855 1
0.6%
38.3667532 1
0.6%
38.366292 1
0.6%

Interactions

2023-12-13T02:20:04.895630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:20:04.679965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:20:04.983572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:20:04.761880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:20:09.712429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명사업장업종명경도위도
시군구명1.0000.7630.9470.911
사업장업종명0.7631.0000.7090.543
경도0.9470.7091.0000.855
위도0.9110.5430.8551.000
2023-12-13T02:20:09.823534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장업종명시군구명
사업장업종명1.0000.356
시군구명0.3561.000
2023-12-13T02:20:09.914704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도시군구명사업장업종명
경도1.000-0.1880.7600.355
위도-0.1881.0000.6610.234
시군구명0.7600.6611.0000.356
사업장업종명0.3550.2340.3561.000

Missing values

2023-12-13T02:20:05.120832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:20:05.271649image/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.

Sample

시도명시군구명업체명대표자명사업장업종명연락처소재지주소경도위도
0강원특별자치도춘천시(주)동서산업홍순희산림골재채취업, 골재선별파쇄업033-261-9447강원특별자치도 춘천시 신동면 혈동리 37127.71871237.766904
1강원특별자치도춘천시(합)신한산업이충수산림골재채취업, 육상골재채취업, 골재선별파쇄업033-262-5272강원특별자치도 춘천시 신동면 혈동리 215127.71184237.776116
2강원특별자치도춘천시㈜테라스톤송병호골재선별파쇄업033-256-8901강원특별자치도 춘천시 사북면 춘화로 919-3127.72926738.003843
3강원특별자치도춘천시(합)대룡물산한윤섭육상골재채취업, 골재선별파쇄업033-244-9930강원특별자치도 춘천시 신동면 증리 산119-16127.72286237.821294
4강원특별자치도춘천시이수이앤씨㈜서정미골재선별파쇄업061-743-7940강원특별자치도 춘천시 사북면 원평리 산117-22127.64341837.993763
5강원특별자치도춘천시㈜영서이앤에스맹은숙육상골재채취업, 골재선별파쇄업033-263-5783강원특별자치도 춘천시 경춘로 2137127.71410137.845662
6강원특별자치도평창군㈜금산산업채희명산림골재채취업, 골재선별파쇄업033-332-4908강원특별자치도 평창군 평창읍 노론리 산40-1128.44981437.351493
7강원특별자치도평창군㈜부일김만겸육상골재채취업, 골재선별파쇄업033-332-7788강원특별자치도 평창군 진부면 간평리 582-2128.58777937.664301
8강원특별자치도평창군㈜태영이엠씨김재문산림골재채취업, 골재선별파쇄업033-333-0323강원특별자치도 평창군 미탄면 수청리 345128.5535737.311174
9강원특별자치도평창군㈜현림레미콘임재호육상골재채취업, 골재선별파쇄업033-333-7020강원특별자치도 평창군 평창읍 상리 48-1128.42562537.366716
시도명시군구명업체명대표자명사업장업종명연락처소재지주소경도위도
162강원특별자치도원주시(자)신성건설김윤기육상골재채취업<NA>강원특별자치도 원주시 지정면 월송리 506-3127.85960837.378309
163강원특별자치도원주시(합)진성산업한광진육상골재채취업033-743-9736강원특별자치도 원주시 문막읍 문막1리 257-4127.81654637.307073
164강원특별자치도원주시㈜한마음전상규육상골재채취업, 골재선별파쇄업033-762-5599강원특별자치도 원주시 문막읍 개나루길 10127.81642137.296811
165강원특별자치도원주시㈜에스에이치개발박신혜육상골재채취업<NA>강원특별자치도 원주시 부론면 노숲길 2127.78112537.259691
166강원특별자치도원주시㈜삼화에코정명숙육상골재채취업033-734-2273강원특별자치도 원주시 문막읍 개나루길 10127.81642137.296811
167강원특별자치도원주시㈜엘케이스톤이윤석산림골재채취업, 골재선별파쇄업033-766-5300강원특별자치도 원주시 귀래면 귀운길 117127.8813137.221001
168강원특별자치도원주시㈜우성기업박길환육상골재채취업<NA>강원특별자치도 원주시 남원로 527번길 8-21127.94511137.332865
169강원특별자치도원주시㈜경기에코이남일육상골재채취업<NA>강원특별자치도 원주시 봉화서부로 6-1127.9205937.356671
170강원특별자치도원주시㈜비젼개발위석환골재선별파쇄업<NA>강원특별자치도 원주시 문막읍 원문로 2568127.77283637.278947
171강원특별자치도원주시㈜에스피골재원연희육상골재채취업, 골재선별파쇄업<NA>강원특별자치도 원주시 양지로 58-1127.98510437.326299