Overview

Dataset statistics

Number of variables7
Number of observations82
Missing cells15
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory58.6 B

Variable types

Numeric1
Categorical2
Text3
DateTime1

Dataset

Description전북특별자치도 진안군에 위치한 산업단지 내 입주기업 현황에 관련된 데이터입니다. 산업단지명, 업체명, 전화번호, 주소 정보를 제공합니다.
Author전북특별자치도 진안군
URLhttps://www.data.go.kr/data/15095759/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
전화번호 has 15 (18.3%) missing valuesMissing
순번 has unique valuesUnique
업체명 has unique valuesUnique

Reproduction

Analysis started2024-03-15 01:17:04.305325
Analysis finished2024-03-15 01:17:05.699634
Duration1.39 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.5
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size866.0 B
2024-03-15T10:17:05.834538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.05
Q121.25
median41.5
Q361.75
95-th percentile77.95
Maximum82
Range81
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation23.815261
Coefficient of variation (CV)0.57386172
Kurtosis-1.2
Mean41.5
Median Absolute Deviation (MAD)20.5
Skewness0
Sum3403
Variance567.16667
MonotonicityStrictly increasing
2024-03-15T10:17:06.108326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
63 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%

산업단지명
Categorical

Distinct3
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size784.0 B
진안제2농공단지
37 
진안홍삼한방농공단지
35 
진안연장농공단지
10 

Length

Max length10
Median length8
Mean length8.8536585
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row진안제2농공단지
2nd row진안제2농공단지
3rd row진안제2농공단지
4th row진안연장농공단지
5th row진안제2농공단지

Common Values

ValueCountFrequency (%)
진안제2농공단지 37
45.1%
진안홍삼한방농공단지 35
42.7%
진안연장농공단지 10
 
12.2%

Length

2024-03-15T10:17:06.760356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:17:07.179660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진안제2농공단지 37
45.1%
진안홍삼한방농공단지 35
42.7%
진안연장농공단지 10
 
12.2%

업체명
Text

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size784.0 B
2024-03-15T10:17:08.431628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length9.7439024
Min length2

Characters and Unicode

Total characters799
Distinct characters189
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

Unique82 ?
Unique (%)100.0%

Sample

1st row(유)나눔푸드
2nd row(유)나눔푸드(제2공장)
3rd row(유)토마토이엔씨
4th row(주)건보
5th row(주)그린파이프
ValueCountFrequency (%)
주식회사 27
 
21.3%
농업회사법인 6
 
4.7%
유한회사 4
 
3.1%
영농조합법인 2
 
1.6%
에스엠테크 1
 
0.8%
승오우드 1
 
0.8%
성안바이오 1
 
0.8%
명성 1
 
0.8%
리버텍 1
 
0.8%
다안 1
 
0.8%
Other values (82) 82
64.6%
2024-03-15T10:17:10.121157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60
 
7.5%
주 55
 
6.9%
사 46
 
5.8%
회 44
 
5.5%
식 36
 
4.5%
( 33
 
4.1%
) 33
 
4.1%
업 19
 
2.4%
농 16
 
2.0%
인 15
 
1.9%
Other values (179) 442
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 660
82.6%
Space Separator 60
 
7.5%
Open Punctuation 33
 
4.1%
Close Punctuation 33
 
4.1%
Uppercase Letter 7
 
0.9%
Decimal Number 3
 
0.4%
Other Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 55
 
8.3%
사 46
 
7.0%
회 44
 
6.7%
식 36
 
5.5%
업 19
 
2.9%
농 16
 
2.4%
인 15
 
2.3%
이 14
 
2.1%
진 13
 
2.0%
법 12
 
1.8%
Other values (166) 390
59.1%
Uppercase Letter
ValueCountFrequency (%)
T 1
14.3%
L 1
14.3%
G 1
14.3%
M 1
14.3%
J 1
14.3%
B 1
14.3%
I 1
14.3%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
60
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Decimal Number
ValueCountFrequency (%)
2 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 660
82.6%
Common 132
 
16.5%
Latin 7
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 55
 
8.3%
사 46
 
7.0%
회 44
 
6.7%
식 36
 
5.5%
업 19
 
2.9%
농 16
 
2.4%
인 15
 
2.3%
이 14
 
2.1%
진 13
 
2.0%
법 12
 
1.8%
Other values (166) 390
59.1%
Latin
ValueCountFrequency (%)
T 1
14.3%
L 1
14.3%
G 1
14.3%
M 1
14.3%
J 1
14.3%
B 1
14.3%
I 1
14.3%
Common
ValueCountFrequency (%)
60
45.5%
( 33
25.0%
) 33
25.0%
2 3
 
2.3%
. 2
 
1.5%
, 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 660
82.6%
ASCII 139
 
17.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
60
43.2%
( 33
23.7%
) 33
23.7%
2 3
 
2.2%
. 2
 
1.4%
, 1
 
0.7%
T 1
 
0.7%
L 1
 
0.7%
G 1
 
0.7%
M 1
 
0.7%
Other values (3) 3
 
2.2%
Hangul
ValueCountFrequency (%)
주 55
 
8.3%
사 46
 
7.0%
회 44
 
6.7%
식 36
 
5.5%
업 19
 
2.9%
농 16
 
2.4%
인 15
 
2.3%
이 14
 
2.1%
진 13
 
2.0%
법 12
 
1.8%
Other values (166) 390
59.1%

전화번호
Text

MISSING 

Distinct64
Distinct (%)95.5%
Missing15
Missing (%)18.3%
Memory size784.0 B
2024-03-15T10:17:11.270985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters804
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

Unique61 ?
Unique (%)91.0%

Sample

1st row063-433-7017
2nd row063-433-7017
3rd row063-433-0133
4th row063-214-9911
5th row063-433-1295
ValueCountFrequency (%)
063-432-4477 2
 
3.0%
063-433-7017 2
 
3.0%
063-432-2522 2
 
3.0%
063-433-2602 1
 
1.5%
063-433-9155 1
 
1.5%
063-442-2927 1
 
1.5%
063-432-7350 1
 
1.5%
063-433-7720 1
 
1.5%
063-433-2111 1
 
1.5%
063-432-5700 1
 
1.5%
Other values (54) 54
80.6%
2024-03-15T10:17:12.970238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 173
21.5%
- 134
16.7%
0 103
12.8%
6 89
11.1%
4 80
10.0%
2 69
 
8.6%
1 44
 
5.5%
9 31
 
3.9%
7 30
 
3.7%
5 29
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 670
83.3%
Dash Punctuation 134
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 173
25.8%
0 103
15.4%
6 89
13.3%
4 80
11.9%
2 69
 
10.3%
1 44
 
6.6%
9 31
 
4.6%
7 30
 
4.5%
5 29
 
4.3%
8 22
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 173
21.5%
- 134
16.7%
0 103
12.8%
6 89
11.1%
4 80
10.0%
2 69
 
8.6%
1 44
 
5.5%
9 31
 
3.9%
7 30
 
3.7%
5 29
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 173
21.5%
- 134
16.7%
0 103
12.8%
6 89
11.1%
4 80
10.0%
2 69
 
8.6%
1 44
 
5.5%
9 31
 
3.9%
7 30
 
3.7%
5 29
 
3.6%

도로명주소
Text

Distinct61
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Memory size784.0 B
2024-03-15T10:17:14.150672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length27
Min length23

Characters and Unicode

Total characters2214
Distinct characters54
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

Unique45 ?
Unique (%)54.9%

Sample

1st row전북특별자치도 진안군 진안읍 거북바위로3길 15-22
2nd row전북특별자치도 진안군 진안읍 거북바위로3길 30
3rd row전북특별자치도 진안군 진안읍 거북바위로3길 15-15
4th row전북특별자치도 진안군 진안읍 거북바위로1길 19-6
5th row전북특별자치도 진안군 진안읍 거북바위로3길 15-6
ValueCountFrequency (%)
전북특별자치도 82
19.5%
진안군 82
19.5%
진안읍 82
19.5%
홍삼한방로 35
 
8.3%
거북바위로3길 23
 
5.5%
거북바위로1길 10
 
2.4%
거북바위로2길 8
 
1.9%
30 4
 
1.0%
18 4
 
1.0%
13 3
 
0.7%
Other values (62) 88
20.9%
2024-03-15T10:17:15.258197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
339
 
15.3%
진 164
 
7.4%
안 164
 
7.4%
북 126
 
5.7%
1 83
 
3.7%
읍 82
 
3.7%
군 82
 
3.7%
전 82
 
3.7%
도 82
 
3.7%
치 82
 
3.7%
Other values (44) 928
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1543
69.7%
Space Separator 339
 
15.3%
Decimal Number 285
 
12.9%
Dash Punctuation 46
 
2.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
진 164
 
10.6%
안 164
 
10.6%
북 126
 
8.2%
읍 82
 
5.3%
군 82
 
5.3%
전 82
 
5.3%
도 82
 
5.3%
치 82
 
5.3%
자 82
 
5.3%
별 82
 
5.3%
Other values (31) 515
33.4%
Decimal Number
ValueCountFrequency (%)
1 83
29.1%
3 58
20.4%
2 56
19.6%
5 24
 
8.4%
6 17
 
6.0%
9 12
 
4.2%
8 10
 
3.5%
0 9
 
3.2%
7 9
 
3.2%
4 7
 
2.5%
Space Separator
ValueCountFrequency (%)
339
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1543
69.7%
Common 671
30.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
진 164
 
10.6%
안 164
 
10.6%
북 126
 
8.2%
읍 82
 
5.3%
군 82
 
5.3%
전 82
 
5.3%
도 82
 
5.3%
치 82
 
5.3%
자 82
 
5.3%
별 82
 
5.3%
Other values (31) 515
33.4%
Common
ValueCountFrequency (%)
339
50.5%
1 83
 
12.4%
3 58
 
8.6%
2 56
 
8.3%
- 46
 
6.9%
5 24
 
3.6%
6 17
 
2.5%
9 12
 
1.8%
8 10
 
1.5%
0 9
 
1.3%
Other values (3) 17
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1543
69.7%
ASCII 671
30.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
339
50.5%
1 83
 
12.4%
3 58
 
8.6%
2 56
 
8.3%
- 46
 
6.9%
5 24
 
3.6%
6 17
 
2.5%
9 12
 
1.8%
8 10
 
1.5%
0 9
 
1.3%
Other values (3) 17
 
2.5%
Hangul
ValueCountFrequency (%)
진 164
 
10.6%
안 164
 
10.6%
북 126
 
8.2%
읍 82
 
5.3%
군 82
 
5.3%
전 82
 
5.3%
도 82
 
5.3%
치 82
 
5.3%
자 82
 
5.3%
별 82
 
5.3%
Other values (31) 515
33.4%

상태
Categorical

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size784.0 B
영업
72 
휴업
10 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업
2nd row휴업
3rd row휴업
4th row영업
5th row휴업

Common Values

ValueCountFrequency (%)
영업 72
87.8%
휴업 10
 
12.2%

Length

2024-03-15T10:17:15.487614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:17:15.678126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 72
87.8%
휴업 10
 
12.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size784.0 B
Minimum2024-01-23 00:00:00
Maximum2024-01-23 00:00:00
2024-03-15T10:17:15.913512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:17:16.076426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T10:17:05.049192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:17:16.211824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번산업단지명업체명전화번호도로명주소상태
순번1.0000.4141.0000.9830.5850.359
산업단지명0.4141.0001.0000.8011.0000.048
업체명1.0001.0001.0001.0001.0001.000
전화번호0.9830.8011.0001.0000.9780.000
도로명주소0.5851.0001.0000.9781.0000.000
상태0.3590.0481.0000.0000.0001.000
2024-03-15T10:17:16.410418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산업단지명상태
산업단지명1.0000.077
상태0.0771.000
2024-03-15T10:17:16.573518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번산업단지명상태
순번1.0000.2590.259
산업단지명0.2591.0000.077
상태0.2590.0771.000

Missing values

2024-03-15T10:17:05.397298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:17:05.621675image/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

순번산업단지명업체명전화번호도로명주소상태데이터기준일자
01진안제2농공단지(유)나눔푸드063-433-7017전북특별자치도 진안군 진안읍 거북바위로3길 15-22영업2024-01-23
12진안제2농공단지(유)나눔푸드(제2공장)063-433-7017전북특별자치도 진안군 진안읍 거북바위로3길 30휴업2024-01-23
23진안제2농공단지(유)토마토이엔씨<NA>전북특별자치도 진안군 진안읍 거북바위로3길 15-15휴업2024-01-23
34진안연장농공단지(주)건보063-433-0133전북특별자치도 진안군 진안읍 거북바위로1길 19-6영업2024-01-23
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