Overview

Dataset statistics

Number of variables9
Number of observations27
Missing cells12
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory80.9 B

Variable types

Numeric4
DateTime1
Text3
Categorical1

Dataset

Description석유판매업일반·용제대리점등록현황2016년7월말기준
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202798

Alerts

연번 is highly overall correlated with 등록 번호 and 2 other fieldsHigh correlation
등록 번호 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
수송장비 (대) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
비 고 is highly overall correlated with 연번High correlation
저장시설 (㎘l) has 4 (14.8%) missing valuesMissing
수송장비 (대) has 4 (14.8%) missing valuesMissing
수송장비 (㎘l) has 4 (14.8%) missing valuesMissing
연번 has unique valuesUnique
등록 번호 has unique valuesUnique
업체명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 00:58:00.858853
Analysis finished2024-03-14 00:58:02.663671
Duration1.8 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T09:58:02.727267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2024-03-14T09:58:02.823484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

등록 번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.18519
Minimum101
Maximum195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T09:58:02.930965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103.3
Q1146
median175
Q3187
95-th percentile193.4
Maximum195
Range94
Interquartile range (IQR)41

Descriptive statistics

Standard deviation29.841939
Coefficient of variation (CV)0.18287162
Kurtosis-0.25296694
Mean163.18519
Median Absolute Deviation (MAD)16
Skewness-0.94549453
Sum4406
Variance890.54131
MonotonicityStrictly increasing
2024-03-14T09:58:03.037868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
101 1
 
3.7%
103 1
 
3.7%
195 1
 
3.7%
194 1
 
3.7%
192 1
 
3.7%
191 1
 
3.7%
190 1
 
3.7%
189 1
 
3.7%
188 1
 
3.7%
186 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
101 1
3.7%
103 1
3.7%
104 1
3.7%
118 1
3.7%
136 1
3.7%
138 1
3.7%
141 1
3.7%
151 1
3.7%
154 1
3.7%
155 1
3.7%
ValueCountFrequency (%)
195 1
3.7%
194 1
3.7%
192 1
3.7%
191 1
3.7%
190 1
3.7%
189 1
3.7%
188 1
3.7%
186 1
3.7%
185 1
3.7%
184 1
3.7%

등록일
Date

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum1976-06-01 00:00:00
Maximum2016-07-19 00:00:00
2024-03-14T09:58:03.129928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:03.226239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

업체명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-03-14T09:58:03.366051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.9259259
Min length4

Characters and Unicode

Total characters187
Distinct characters63
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

Unique27 ?
Unique (%)100.0%

Sample

1st row전인석유㈜
2nd row동일유업㈜
3rd row에스제이오일㈜
4th row대림석유㈜
5th row㈜케이원에너지
ValueCountFrequency (%)
주식회사 7
 
20.0%
전인석유㈜ 1
 
2.9%
㈜오일랜드 1
 
2.9%
솔로몬에너지 1
 
2.9%
케이디석유 1
 
2.9%
지에스엠비즈 1
 
2.9%
전국석유 1
 
2.9%
주)제이에스상사 1
 
2.9%
글로벌에너지㈜ 1
 
2.9%
강동석유 1
 
2.9%
Other values (19) 19
54.3%
2024-03-14T09:58:03.620607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 16
 
8.6%
유 13
 
7.0%
에 9
 
4.8%
8
 
4.3%
일 8
 
4.3%
사 8
 
4.3%
주 8
 
4.3%
오 7
 
3.7%
회 7
 
3.7%
식 7
 
3.7%
Other values (53) 96
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157
84.0%
Other Symbol 16
 
8.6%
Space Separator 8
 
4.3%
Open Punctuation 3
 
1.6%
Close Punctuation 3
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 13
 
8.3%
에 9
 
5.7%
일 8
 
5.1%
사 8
 
5.1%
주 8
 
5.1%
오 7
 
4.5%
회 7
 
4.5%
식 7
 
4.5%
석 6
 
3.8%
지 6
 
3.8%
Other values (49) 78
49.7%
Other Symbol
ValueCountFrequency (%)
㈜ 16
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 173
92.5%
Common 14
 
7.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 16
 
9.2%
유 13
 
7.5%
에 9
 
5.2%
일 8
 
4.6%
사 8
 
4.6%
주 8
 
4.6%
오 7
 
4.0%
회 7
 
4.0%
식 7
 
4.0%
석 6
 
3.5%
Other values (50) 84
48.6%
Common
ValueCountFrequency (%)
8
57.1%
( 3
 
21.4%
) 3
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 157
84.0%
None 16
 
8.6%
ASCII 14
 
7.5%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 16
100.0%
Hangul
ValueCountFrequency (%)
유 13
 
8.3%
에 9
 
5.7%
일 8
 
5.1%
사 8
 
5.1%
주 8
 
5.1%
오 7
 
4.5%
회 7
 
4.5%
식 7
 
4.5%
석 6
 
3.8%
지 6
 
3.8%
Other values (49) 78
49.7%
ASCII
ValueCountFrequency (%)
8
57.1%
( 3
 
21.4%
) 3
 
21.4%

소재지
Text

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-03-14T09:58:03.814032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length23
Mean length20.555556
Min length11

Characters and Unicode

Total characters555
Distinct characters99
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

Unique21 ?
Unique (%)77.8%

Sample

1st row전주시 덕진구 감수길 39(팔복동4가)
2nd row익산시 평동로 710(동산동)
3rd row군산시 해망로 252(장미동)
4th row완주군 이서면 콩쥐팥쥐로 1068
5th row전주시 완산구 팔달로 291(서노송동)
ValueCountFrequency (%)
전주시 14
 
13.5%
덕진구 8
 
7.7%
군산시 6
 
5.8%
완산구 6
 
5.8%
익산시 4
 
3.8%
장승배기로 2
 
1.9%
91(삼천동1가,5층 2
 
1.9%
3(소룡동 2
 
1.9%
풍전1길 2
 
1.9%
600 2
 
1.9%
Other values (51) 56
53.8%
2024-03-14T09:58:04.116216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
13.9%
시 26
 
4.7%
동 26
 
4.7%
1 25
 
4.5%
) 22
 
4.0%
( 22
 
4.0%
산 21
 
3.8%
로 21
 
3.8%
전 16
 
2.9%
구 15
 
2.7%
Other values (89) 284
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 325
58.6%
Decimal Number 99
 
17.8%
Space Separator 77
 
13.9%
Close Punctuation 22
 
4.0%
Open Punctuation 22
 
4.0%
Other Punctuation 7
 
1.3%
Dash Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 26
 
8.0%
동 26
 
8.0%
산 21
 
6.5%
로 21
 
6.5%
전 16
 
4.9%
구 15
 
4.6%
주 15
 
4.6%
가 10
 
3.1%
진 9
 
2.8%
덕 9
 
2.8%
Other values (74) 157
48.3%
Decimal Number
ValueCountFrequency (%)
1 25
25.3%
2 14
14.1%
4 11
11.1%
3 9
 
9.1%
0 8
 
8.1%
5 8
 
8.1%
9 8
 
8.1%
7 6
 
6.1%
6 6
 
6.1%
8 4
 
4.0%
Space Separator
ValueCountFrequency (%)
77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 325
58.6%
Common 230
41.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 26
 
8.0%
동 26
 
8.0%
산 21
 
6.5%
로 21
 
6.5%
전 16
 
4.9%
구 15
 
4.6%
주 15
 
4.6%
가 10
 
3.1%
진 9
 
2.8%
덕 9
 
2.8%
Other values (74) 157
48.3%
Common
ValueCountFrequency (%)
77
33.5%
1 25
 
10.9%
) 22
 
9.6%
( 22
 
9.6%
2 14
 
6.1%
4 11
 
4.8%
3 9
 
3.9%
0 8
 
3.5%
5 8
 
3.5%
9 8
 
3.5%
Other values (5) 26
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 325
58.6%
ASCII 230
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
33.5%
1 25
 
10.9%
) 22
 
9.6%
( 22
 
9.6%
2 14
 
6.1%
4 11
 
4.8%
3 9
 
3.9%
0 8
 
3.5%
5 8
 
3.5%
9 8
 
3.5%
Other values (5) 26
 
11.3%
Hangul
ValueCountFrequency (%)
시 26
 
8.0%
동 26
 
8.0%
산 21
 
6.5%
로 21
 
6.5%
전 16
 
4.9%
구 15
 
4.6%
주 15
 
4.6%
가 10
 
3.1%
진 9
 
2.8%
덕 9
 
2.8%
Other values (74) 157
48.3%

저장시설 (㎘l)
Text

MISSING 

Distinct20
Distinct (%)87.0%
Missing4
Missing (%)14.8%
Memory size348.0 B
2024-03-14T09:58:04.311463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.6956522
Min length3

Characters and Unicode

Total characters85
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

Unique18 ?
Unique (%)78.3%

Sample

1st row908
2nd row700
3rd row1,260
4th row1,090
5th row780
ValueCountFrequency (%)
700 3
 
13.0%
1,000 2
 
8.7%
908 1
 
4.3%
770 1
 
4.3%
800 1
 
4.3%
493 1
 
4.3%
728 1
 
4.3%
1,080 1
 
4.3%
840 1
 
4.3%
1,202 1
 
4.3%
Other values (10) 10
43.5%
2024-03-14T09:58:04.561028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30
35.3%
7 11
 
12.9%
1 8
 
9.4%
, 8
 
9.4%
8 8
 
9.4%
2 6
 
7.1%
6 4
 
4.7%
9 4
 
4.7%
3 3
 
3.5%
4 2
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77
90.6%
Other Punctuation 8
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
39.0%
7 11
 
14.3%
1 8
 
10.4%
8 8
 
10.4%
2 6
 
7.8%
6 4
 
5.2%
9 4
 
5.2%
3 3
 
3.9%
4 2
 
2.6%
5 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
35.3%
7 11
 
12.9%
1 8
 
9.4%
, 8
 
9.4%
8 8
 
9.4%
2 6
 
7.1%
6 4
 
4.7%
9 4
 
4.7%
3 3
 
3.5%
4 2
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
35.3%
7 11
 
12.9%
1 8
 
9.4%
, 8
 
9.4%
8 8
 
9.4%
2 6
 
7.1%
6 4
 
4.7%
9 4
 
4.7%
3 3
 
3.5%
4 2
 
2.4%

수송장비 (대)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)30.4%
Missing4
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean3.3478261
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T09:58:04.939049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5.9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7479519
Coefficient of variation (CV)0.52211551
Kurtosis3.8793709
Mean3.3478261
Median Absolute Deviation (MAD)1
Skewness1.7048756
Sum77
Variance3.055336
MonotonicityNot monotonic
2024-03-14T09:58:05.032363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 8
29.6%
2 7
25.9%
5 3
 
11.1%
4 2
 
7.4%
6 1
 
3.7%
9 1
 
3.7%
1 1
 
3.7%
(Missing) 4
14.8%
ValueCountFrequency (%)
1 1
 
3.7%
2 7
25.9%
3 8
29.6%
4 2
 
7.4%
5 3
 
11.1%
6 1
 
3.7%
9 1
 
3.7%
ValueCountFrequency (%)
9 1
 
3.7%
6 1
 
3.7%
5 3
 
11.1%
4 2
 
7.4%
3 8
29.6%
2 7
25.9%
1 1
 
3.7%

수송장비 (㎘l)
Real number (ℝ)

MISSING 

Distinct14
Distinct (%)60.9%
Missing4
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean68.608696
Minimum20
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T09:58:05.144405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile50
Q155
median60
Q365
95-th percentile139.2
Maximum180
Range160
Interquartile range (IQR)10

Descriptive statistics

Standard deviation32.814547
Coefficient of variation (CV)0.47828553
Kurtosis6.4843367
Mean68.608696
Median Absolute Deviation (MAD)5
Skewness2.3724958
Sum1578
Variance1076.7945
MonotonicityNot monotonic
2024-03-14T09:58:05.234365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
64 4
14.8%
60 3
11.1%
56 3
11.1%
55 2
7.4%
50 2
7.4%
96 1
 
3.7%
144 1
 
3.7%
180 1
 
3.7%
80 1
 
3.7%
66 1
 
3.7%
Other values (4) 4
14.8%
(Missing) 4
14.8%
ValueCountFrequency (%)
20 1
 
3.7%
50 2
7.4%
52 1
 
3.7%
54 1
 
3.7%
55 2
7.4%
56 3
11.1%
60 3
11.1%
64 4
14.8%
66 1
 
3.7%
72 1
 
3.7%
ValueCountFrequency (%)
180 1
 
3.7%
144 1
 
3.7%
96 1
 
3.7%
80 1
 
3.7%
72 1
 
3.7%
66 1
 
3.7%
64 4
14.8%
60 3
11.1%
56 3
11.1%
55 2
7.4%

비 고
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
<NA>
22 
해상
4 
용제
 
1

Length

Max length4
Median length4
Mean length3.6296296
Min length2

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 22
81.5%
해상 4
 
14.8%
용제 1
 
3.7%

Length

2024-03-14T09:58:05.340518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:58:05.440212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
81.5%
해상 4
 
14.8%
용제 1
 
3.7%

Interactions

2024-03-14T09:58:02.056310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.138942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.419746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.688282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:02.137226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.200159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.487977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.756025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:02.199234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.258465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.543039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.822712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:02.275081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.333102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.618257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:58:01.941443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:58:05.510286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번등록 번호등록일업체명소재지저장시설 (㎘l)수송장비 (대)수송장비 (㎘l)비 고
연번1.0000.8890.9371.0000.0000.9030.0000.0001.000
등록\n번호0.8891.0001.0001.0000.8670.8460.6700.7050.000
등록일0.9371.0001.0001.0000.9780.9430.0000.0001.000
업체명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지0.0000.8670.9781.0001.0000.9750.9830.9801.000
저장시설\n(㎘l)0.9030.8460.9431.0000.9751.0000.9811.000NaN
수송장비\n(대)0.0000.6700.0001.0000.9830.9811.0000.968NaN
수송장비\n(㎘l)0.0000.7050.0001.0000.9801.0000.9681.000NaN
비 고1.0000.0001.0001.0001.000NaNNaNNaN1.000
2024-03-14T09:58:05.613176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번등록 번호수송장비 (대)수송장비 (㎘l)비 고
연번1.0001.000-0.636-0.3971.000
등록\n번호1.0001.000-0.636-0.3970.000
수송장비\n(대)-0.636-0.6361.0000.416NaN
수송장비\n(㎘l)-0.397-0.3970.4161.000NaN
비 고1.0000.000NaNNaN1.000

Missing values

2024-03-14T09:58:02.377362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:58:02.479433image/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.
2024-03-14T09:58:02.588334image/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

연번등록 번호등록일업체명소재지저장시설 (㎘l)수송장비 (대)수송장비 (㎘l)비 고
011011976-06-01전인석유㈜전주시 덕진구 감수길 39(팔복동4가)908555<NA>
121031976-09-26동일유업㈜익산시 평동로 710(동산동)700360<NA>
231041977-10-23에스제이오일㈜군산시 해망로 252(장미동)1,260496<NA>
341181989-10-25대림석유㈜완주군 이서면 콩쥐팥쥐로 10681,0906144<NA>
451362001-06-04㈜케이원에너지전주시 완산구 팔달로 291(서노송동)780550<NA>
561382001-10-30㈜유진오일전주시 덕진구 원만성로 47(팔복동3가)1,3969180<NA>
671412002-10-02㈜해양유업군산시 풍전1길 3(소룡동)<NA><NA><NA>해상
781512005-11-17(유)이레석유전주시 덕진구 동부대로 985(송천동2가)766556<NA>
891542006-08-30㈜필오일군산시 해령1길 140(내흥동)730264<NA>
9101552006-10-10파인오일판매㈜익산시 함열읍 익산대로 1424-31,000360<NA>
연번등록 번호등록일업체명소재지저장시설 (㎘l)수송장비 (대)수송장비 (㎘l)비 고
17181842013-04-16페트로 앰엔에스 주식회사익산시 석암로 122(용제동)840256<NA>
18191852013-06-26주식회사 한영전주시 완산구 장승배기로 91(삼천동1가,5층)700360<NA>
19201862013-07-25강동석유전주시 덕진구 백제대로 600, 2층(금암동)1,080250<NA>
20211882014-03-18글로벌에너지㈜군산시 풍전1길 3(소룡동)<NA><NA><NA>해상
21221892014-04-08(주)제이에스상사전주시 덕진구 감수길 87-1(팔복동4가)728364<NA>
22231902014-11-10전국석유 주식회사익산시 익산대로4길 31(인화동1가)1,000356<NA>
23241912014-12-24지에스엠비즈 주식회사전주시 덕진구 팔과정로 167(동산동)493120용제
24251922014-12-24주식회사 케이디석유전주시 덕진구 백제대로 600, 2층(금암동)800354<NA>
25261942015-07-07솔로몬에너지 주식회사전주시 완산구 신봉1길 14-8(효자동1가)<NA><NA><NA>해상
26271952016-07-19주식회사 한보전주시 완산구 홍산중앙로 41, 402호(효자동3가)820255<NA>