Overview

Dataset statistics

Number of variables6
Number of observations62
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory51.1 B

Variable types

Numeric1
Categorical3
Text2

Dataset

Description전라남도 광양시 고압가스 저장소 현황 정보입니다. 사업구분(제조, 저장), 허가구분(특정, 일반, 냉동 등), 상호, 소재지, 운영구분 정보를 제공합니다.
Author전라남도 광양시
URLhttps://www.data.go.kr/data/15112910/fileData.do

Alerts

허가구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
사업구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 사업구분 and 1 other fieldsHigh correlation
운영구분 is highly imbalanced (88.1%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-23 07:13:10.045922
Analysis finished2024-03-23 07:13:12.458469
Duration2.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.33871
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-03-23T07:13:12.672662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.05
Q117.25
median34.5
Q357.75
95-th percentile69.95
Maximum73
Range72
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation22.060233
Coefficient of variation (CV)0.60707255
Kurtosis-1.2675186
Mean36.33871
Median Absolute Deviation (MAD)20
Skewness0.11993941
Sum2253
Variance486.65389
MonotonicityStrictly increasing
2024-03-23T07:13:13.233770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
59 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
43 1
 
1.6%
45 1
 
1.6%
50 1
 
1.6%
Other values (52) 52
83.9%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
11 1
1.6%
ValueCountFrequency (%)
73 1
1.6%
72 1
1.6%
71 1
1.6%
70 1
1.6%
69 1
1.6%
68 1
1.6%
67 1
1.6%
66 1
1.6%
65 1
1.6%
64 1
1.6%

사업구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size628.0 B
제조
36 
저장
26 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제조 36
58.1%
저장 26
41.9%

Length

2024-03-23T07:13:14.073924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:13:14.360024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조 36
58.1%
저장 26
41.9%

허가구분
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size628.0 B
저장소설치
26 
냉동허가
9 
냉동신고
6 
충전신고
6 
특정
4 
Other values (4)
11 

Length

Max length7
Median length5
Mean length4.3387097
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row특정
2nd row특정
3rd row특정
4th row특정
5th row일반

Common Values

ValueCountFrequency (%)
저장소설치 26
41.9%
냉동허가 9
 
14.5%
냉동신고 6
 
9.7%
충전신고 6
 
9.7%
특정 4
 
6.5%
충전허가 4
 
6.5%
일반 3
 
4.8%
용기,특정설비 3
 
4.8%
특정설비 1
 
1.6%

Length

2024-03-23T07:13:14.810707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:13:15.294860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저장소설치 26
41.9%
냉동허가 9
 
14.5%
냉동신고 6
 
9.7%
충전신고 6
 
9.7%
특정 4
 
6.5%
충전허가 4
 
6.5%
일반 3
 
4.8%
용기,특정설비 3
 
4.8%
특정설비 1
 
1.6%

상호
Text

Distinct51
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-03-23T07:13:16.246234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length10.225806
Min length3

Characters and Unicode

Total characters634
Distinct characters138
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)71.0%

Sample

1st row(주)포스코광양제철소
2nd rowOCI(주)광양공장
3rd row(주)피엠씨텍
4th row(주)포스코케미칼
5th row퓨어캠
ValueCountFrequency (%)
주)포스코케미칼 5
 
7.0%
주)포스코에너지 3
 
4.2%
주)대평 2
 
2.8%
주)피앤오케미칼 2
 
2.8%
포스코에이치와이클린메탈(주 2
 
2.8%
주)포스코모빌리티솔루션 2
 
2.8%
oci(주)광양공장 2
 
2.8%
광양제철소 2
 
2.8%
주)포스코 2
 
2.8%
광양2공장 1
 
1.4%
Other values (48) 48
67.6%
2024-03-23T07:13:17.937224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 57
 
9.0%
) 57
 
9.0%
주 54
 
8.5%
스 30
 
4.7%
코 20
 
3.2%
광 20
 
3.2%
포 19
 
3.0%
양 19
 
3.0%
이 14
 
2.2%
케 10
 
1.6%
Other values (128) 334
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 490
77.3%
Open Punctuation 57
 
9.0%
Close Punctuation 57
 
9.0%
Decimal Number 11
 
1.7%
Uppercase Letter 10
 
1.6%
Space Separator 9
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 54
 
11.0%
스 30
 
6.1%
코 20
 
4.1%
광 20
 
4.1%
포 19
 
3.9%
양 19
 
3.9%
이 14
 
2.9%
케 10
 
2.0%
에 10
 
2.0%
미 9
 
1.8%
Other values (117) 285
58.2%
Uppercase Letter
ValueCountFrequency (%)
C 3
30.0%
O 2
20.0%
N 2
20.0%
I 2
20.0%
S 1
 
10.0%
Decimal Number
ValueCountFrequency (%)
1 7
63.6%
9 3
27.3%
2 1
 
9.1%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 490
77.3%
Common 134
 
21.1%
Latin 10
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 54
 
11.0%
스 30
 
6.1%
코 20
 
4.1%
광 20
 
4.1%
포 19
 
3.9%
양 19
 
3.9%
이 14
 
2.9%
케 10
 
2.0%
에 10
 
2.0%
미 9
 
1.8%
Other values (117) 285
58.2%
Common
ValueCountFrequency (%)
( 57
42.5%
) 57
42.5%
9
 
6.7%
1 7
 
5.2%
9 3
 
2.2%
2 1
 
0.7%
Latin
ValueCountFrequency (%)
C 3
30.0%
O 2
20.0%
N 2
20.0%
I 2
20.0%
S 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 490
77.3%
ASCII 144
 
22.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 57
39.6%
) 57
39.6%
9
 
6.2%
1 7
 
4.9%
9 3
 
2.1%
C 3
 
2.1%
O 2
 
1.4%
N 2
 
1.4%
I 2
 
1.4%
2 1
 
0.7%
Hangul
ValueCountFrequency (%)
주 54
 
11.0%
스 30
 
6.1%
코 20
 
4.1%
광 20
 
4.1%
포 19
 
3.9%
양 19
 
3.9%
이 14
 
2.9%
케 10
 
2.0%
에 10
 
2.0%
미 9
 
1.8%
Other values (117) 285
58.2%

소재지
Text

Distinct54
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-03-23T07:13:18.607411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length29
Mean length18.854839
Min length15

Characters and Unicode

Total characters1169
Distinct characters83
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)79.0%

Sample

1st row전라남도 광양시 폭포사랑길 20-26
2nd row전라남도 광양시 산업로 109
3rd row전라남도 광양시 제철로 2408
4th row전라남도 광양시 율촌산단8로 45
5th row전라남도 광양시 태인3길 14
ValueCountFrequency (%)
광양시 63
24.5%
전라남도 62
24.1%
제철로 9
 
3.5%
세풍리 7
 
2.7%
산업로 5
 
1.9%
폭포사랑길 4
 
1.6%
20-26 4
 
1.6%
광양읍 4
 
1.6%
29 3
 
1.2%
항만7로 3
 
1.2%
Other values (75) 93
36.2%
2024-03-23T07:13:19.940986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
195
16.7%
광 68
 
5.8%
양 67
 
5.7%
남 65
 
5.6%
도 65
 
5.6%
2 64
 
5.5%
시 63
 
5.4%
전 62
 
5.3%
라 62
 
5.3%
1 38
 
3.3%
Other values (73) 420
35.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 708
60.6%
Decimal Number 235
 
20.1%
Space Separator 195
 
16.7%
Dash Punctuation 18
 
1.5%
Open Punctuation 6
 
0.5%
Close Punctuation 6
 
0.5%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 68
 
9.6%
양 67
 
9.5%
남 65
 
9.2%
도 65
 
9.2%
시 63
 
8.9%
전 62
 
8.8%
라 62
 
8.8%
로 35
 
4.9%
길 18
 
2.5%
산 14
 
2.0%
Other values (58) 189
26.7%
Decimal Number
ValueCountFrequency (%)
2 64
27.2%
1 38
16.2%
8 22
 
9.4%
5 20
 
8.5%
0 18
 
7.7%
4 16
 
6.8%
3 16
 
6.8%
6 15
 
6.4%
7 15
 
6.4%
9 11
 
4.7%
Space Separator
ValueCountFrequency (%)
195
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 708
60.6%
Common 461
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 68
 
9.6%
양 67
 
9.5%
남 65
 
9.2%
도 65
 
9.2%
시 63
 
8.9%
전 62
 
8.8%
라 62
 
8.8%
로 35
 
4.9%
길 18
 
2.5%
산 14
 
2.0%
Other values (58) 189
26.7%
Common
ValueCountFrequency (%)
195
42.3%
2 64
 
13.9%
1 38
 
8.2%
8 22
 
4.8%
5 20
 
4.3%
0 18
 
3.9%
- 18
 
3.9%
4 16
 
3.5%
3 16
 
3.5%
6 15
 
3.3%
Other values (5) 39
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 708
60.6%
ASCII 461
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
195
42.3%
2 64
 
13.9%
1 38
 
8.2%
8 22
 
4.8%
5 20
 
4.3%
0 18
 
3.9%
- 18
 
3.9%
4 16
 
3.5%
3 16
 
3.5%
6 15
 
3.3%
Other values (5) 39
 
8.5%
Hangul
ValueCountFrequency (%)
광 68
 
9.6%
양 67
 
9.5%
남 65
 
9.2%
도 65
 
9.2%
시 63
 
8.9%
전 62
 
8.8%
라 62
 
8.8%
로 35
 
4.9%
길 18
 
2.5%
산 14
 
2.0%
Other values (58) 189
26.7%

운영구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size628.0 B
영업
61 
중단
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
영업 61
98.4%
중단 1
 
1.6%

Length

2024-03-23T07:13:20.410371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:13:20.726347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 61
98.4%
중단 1
 
1.6%

Interactions

2024-03-23T07:13:11.400326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:13:20.924123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업구분허가구분상호소재지운영구분
연번1.0000.9960.8820.7210.6780.125
사업구분0.9961.0001.0000.0000.6070.000
허가구분0.8821.0001.0000.5530.1310.000
상호0.7210.0000.5531.0000.9981.000
소재지0.6780.6070.1310.9981.0001.000
운영구분0.1250.0000.0001.0001.0001.000
2024-03-23T07:13:21.263376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
허가구분사업구분운영구분
허가구분1.0000.9400.000
사업구분0.9401.0000.000
운영구분0.0000.0001.000
2024-03-23T07:13:21.747528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업구분허가구분운영구분
연번1.0000.8810.6550.075
사업구분0.8811.0000.9400.000
허가구분0.6550.9401.0000.000
운영구분0.0750.0000.0001.000

Missing values

2024-03-23T07:13:11.813819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:13:12.279579image/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

연번사업구분허가구분상호소재지운영구분
01제조특정(주)포스코광양제철소전라남도 광양시 폭포사랑길 20-26영업
12제조특정OCI(주)광양공장전라남도 광양시 산업로 109영업
23제조특정(주)피엠씨텍전라남도 광양시 제철로 2408영업
34제조특정(주)포스코케미칼전라남도 광양시 율촌산단8로 45영업
45제조일반퓨어캠전라남도 광양시 태인3길 14영업
57제조일반(주)포스코케미칼전라남도 광양시 율촌산단8로 45(세풍리 2228-1)영업
68제조일반(주)SNNC전라남도 광양시 제철로 2148-139영업
79제조냉동허가(주)대평전라남도 광양시 중마로 13(도이동)영업
810제조냉동허가(주)부영스포츠타운전라남도 광양시 순광로 482-7영업
911제조냉동허가(주)포스코케미칼전라남도 광양시 폭포사랑길 20-26영업
연번사업구분허가구분상호소재지운영구분
5264저장저장소설치씨지앤율촌전력(주)전라남도 광양시 인덕로 360-202영업
5365저장저장소설치(주)아이더블유 케이에이치피시전라남도 광양시 항만7로 71-9영업
5466저장저장소설치(주)한영이앤씨전라남도 광양시 항만7로 52(도이동)영업
5567저장저장소설치(주)포스코케미칼전라남도 광양시 광양읍 세풍리 2228-1(율촌제1일반산업단지제2-2)영업
5668저장저장소설치(주)상상인선박기계전라남도 광양시 율촌산단3로 116영업
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