Overview

Dataset statistics

Number of variables9
Number of observations52
Missing cells11
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory80.5 B

Variable types

Categorical2
Text1
Numeric6

Dataset

Description한국석유관리원이 시험 분석한 석유 및 석유대체연료의 시험 항목별 연 평균값 자료입니다. (휘발유 1,2호, 등유, 자동차용 경유, 바이오디젤, 바이오중유, 부생연료유1,2호, 선박용 경유, 액화석유가스, 중유A~C)
Author한국석유관리원
URLhttps://www.data.go.kr/data/15065232/fileData.do

Alerts

2017년 평균 is highly overall correlated with 2018년 평균 and 4 other fieldsHigh correlation
2018년 평균 is highly overall correlated with 2017년 평균 and 4 other fieldsHigh correlation
2019년 평균 is highly overall correlated with 2017년 평균 and 5 other fieldsHigh correlation
2020년 평균 is highly overall correlated with 2017년 평균 and 5 other fieldsHigh correlation
2021년 평균 is highly overall correlated with 2017년 평균 and 5 other fieldsHigh correlation
2022년 평균 is highly overall correlated with 2017년 평균 and 5 other fieldsHigh correlation
항목 is highly overall correlated with 2019년 평균 and 3 other fieldsHigh correlation
품질기준 has 1 (1.9%) missing valuesMissing
2017년 평균 has 5 (9.6%) missing valuesMissing
2018년 평균 has 5 (9.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:11:11.446732
Analysis finished2023-12-12 13:11:15.313812
Duration3.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

석유 및 석유대체연료
Categorical

Distinct15
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
바이오디젤
7 
바이오중유
5 
자동차용휘발유1호
4 
자동차용휘발유2호
4 
자동차용경유
4 
Other values (10)
28 

Length

Max length12
Median length9
Mean length6.7115385
Min length2

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st row자동차용휘발유1호
2nd row자동차용휘발유1호
3rd row자동차용휘발유1호
4th row자동차용휘발유1호
5th row자동차용휘발유2호

Common Values

ValueCountFrequency (%)
바이오디젤 7
13.5%
바이오중유 5
9.6%
자동차용휘발유1호 4
 
7.7%
자동차용휘발유2호 4
 
7.7%
자동차용경유 4
 
7.7%
부생연료유1호(등유형) 4
 
7.7%
선박용경유 3
 
5.8%
중유A 3
 
5.8%
중유B 3
 
5.8%
중유C 3
 
5.8%
Other values (5) 12
23.1%

Length

2023-12-12T22:11:15.376102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
바이오디젤 7
11.9%
액화석유가스 7
11.9%
바이오중유 5
 
8.5%
자동차용휘발유1호 4
 
6.8%
자동차용휘발유2호 4
 
6.8%
자동차용경유 4
 
6.8%
부생연료유1호(등유형 4
 
6.8%
선박용경유 3
 
5.1%
중유a 3
 
5.1%
중유b 3
 
5.1%
Other values (6) 15
25.4%

항목
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Memory size548.0 B
인화점(℃)
8 
황분(무게%)
7 
황분(mg/kg)
5 
동점도(40℃, ㎟/s)
4 
조성(C4 탄화수소) (mol%)
3 
Other values (16)
25 

Length

Max length18
Median length14.5
Mean length10.865385
Min length6

Unique

Unique8 ?
Unique (%)15.4%

Sample

1st row옥탄값(리서어치법)
2nd row증기압(37.8℃, kPa)
3rd row황분(mg/kg)
4th row벤젠 함량(부피%)
5th row옥탄값(리서어치법)

Common Values

ValueCountFrequency (%)
인화점(℃) 8
15.4%
황분(무게%) 7
13.5%
황분(mg/kg) 5
 
9.6%
동점도(40℃, ㎟/s) 4
 
7.7%
조성(C4 탄화수소) (mol%) 3
 
5.8%
동점도(50℃, ㎟/s) 3
 
5.8%
밀도(15℃, kg/㎥) 2
 
3.8%
벤젠 함량(부피%) 2
 
3.8%
인화점(Tag, ℃) 2
 
3.8%
황함량(mg/kg) 2
 
3.8%
Other values (11) 14
26.9%

Length

2023-12-12T22:11:15.502782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인화점(℃ 8
 
9.9%
황분(무게 7
 
8.6%
㎟/s 7
 
8.6%
황분(mg/kg 5
 
6.2%
탄화수소 5
 
6.2%
mol 5
 
6.2%
동점도(40℃ 4
 
4.9%
조성(c4 3
 
3.7%
동점도(50℃ 3
 
3.7%
kg/㎥ 3
 
3.7%
Other values (21) 31
38.3%

품질기준
Text

MISSING 

Distinct41
Distinct (%)80.4%
Missing1
Missing (%)1.9%
Memory size548.0 B
2023-12-12T22:11:15.703822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length7.254902
Min length3

Characters and Unicode

Total characters370
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)68.6%

Sample

1st row91이상~94미만
2nd row44~82(여름용:44~60,겨울용:44~96)
3rd row10이하
4th row0.7이하
5th row94이상
ValueCountFrequency (%)
10이하 4
 
7.8%
40이상 4
 
7.8%
0.7이하 2
 
3.9%
44~82(여름용:44~60,겨울용:44~96 2
 
3.9%
30이하 2
 
3.9%
70이상 2
 
3.9%
860이상~900이하 1
 
2.0%
0.2이하 1
 
2.0%
91이상~94미만 1
 
2.0%
6이상 1
 
2.0%
Other values (31) 31
60.8%
2023-12-12T22:11:16.087531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 60
16.2%
0 49
13.2%
하 32
 
8.6%
상 28
 
7.6%
. 22
 
5.9%
4 21
 
5.7%
5 18
 
4.9%
1 16
 
4.3%
~ 16
 
4.3%
9 15
 
4.1%
Other values (19) 93
25.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160
43.2%
Other Letter 148
40.0%
Other Punctuation 32
 
8.6%
Math Symbol 16
 
4.3%
Open Punctuation 7
 
1.9%
Close Punctuation 7
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 60
40.5%
하 32
21.6%
상 28
18.9%
용 8
 
5.4%
름 4
 
2.7%
여 4
 
2.7%
겨 4
 
2.7%
울 4
 
2.7%
무 1
 
0.7%
게 1
 
0.7%
Other values (2) 2
 
1.4%
Decimal Number
ValueCountFrequency (%)
0 49
30.6%
4 21
13.1%
5 18
 
11.2%
1 16
 
10.0%
9 15
 
9.4%
8 11
 
6.9%
6 11
 
6.9%
2 8
 
5.0%
3 7
 
4.4%
7 4
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 22
68.8%
, 5
 
15.6%
: 4
 
12.5%
% 1
 
3.1%
Math Symbol
ValueCountFrequency (%)
~ 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 222
60.0%
Hangul 148
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49
22.1%
. 22
9.9%
4 21
9.5%
5 18
 
8.1%
1 16
 
7.2%
~ 16
 
7.2%
9 15
 
6.8%
8 11
 
5.0%
6 11
 
5.0%
2 8
 
3.6%
Other values (7) 35
15.8%
Hangul
ValueCountFrequency (%)
이 60
40.5%
하 32
21.6%
상 28
18.9%
용 8
 
5.4%
름 4
 
2.7%
여 4
 
2.7%
겨 4
 
2.7%
울 4
 
2.7%
무 1
 
0.7%
게 1
 
0.7%
Other values (2) 2
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 222
60.0%
Hangul 148
40.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 60
40.5%
하 32
21.6%
상 28
18.9%
용 8
 
5.4%
름 4
 
2.7%
여 4
 
2.7%
겨 4
 
2.7%
울 4
 
2.7%
무 1
 
0.7%
게 1
 
0.7%
Other values (2) 2
 
1.4%
ASCII
ValueCountFrequency (%)
0 49
22.1%
. 22
9.9%
4 21
9.5%
5 18
 
8.1%
1 16
 
7.2%
~ 16
 
7.2%
9 15
 
6.8%
8 11
 
5.0%
6 11
 
5.0%
2 8
 
3.6%
Other values (7) 35
15.8%

2017년 평균
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)89.4%
Missing5
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean88.892353
Minimum0.0006
Maximum879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T22:11:16.230332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0006
5-th percentile0.03
Q11.47
median13.6
Q382.5
95-th percentile625.7
Maximum879
Range878.9994
Interquartile range (IQR)81.03

Descriptive statistics

Standard deviation203.28627
Coefficient of variation (CV)2.2868815
Kurtosis10.688012
Mean88.892353
Median Absolute Deviation (MAD)13.57
Skewness3.3913498
Sum4177.9406
Variance41325.309
MonotonicityNot monotonic
2023-12-12T22:11:16.352344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
98.0 2
 
3.8%
59.0 2
 
3.8%
0.03 2
 
3.8%
2.0 2
 
3.8%
5.0 2
 
3.8%
14.0 1
 
1.9%
1.3 1
 
1.9%
1.6 1
 
1.9%
819.0 1
 
1.9%
47.9 1
 
1.9%
Other values (32) 32
61.5%
(Missing) 5
 
9.6%
ValueCountFrequency (%)
0.0006 1
1.9%
0.02 1
1.9%
0.03 2
3.8%
0.08 1
1.9%
0.3 1
1.9%
0.4 1
1.9%
0.61 1
1.9%
0.83 1
1.9%
1.0 1
1.9%
1.3 1
1.9%
ValueCountFrequency (%)
879.0 1
1.9%
819.0 1
1.9%
818.0 1
1.9%
177.0 1
1.9%
169.0 1
1.9%
103.0 1
1.9%
100.0 1
1.9%
99.0 1
1.9%
98.0 2
3.8%
91.0 1
1.9%

2018년 평균
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)87.2%
Missing5
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean88.552138
Minimum0.0005
Maximum878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T22:11:16.474064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.023
Q11.38
median9
Q380.5
95-th percentile622.2
Maximum878
Range877.9995
Interquartile range (IQR)79.12

Descriptive statistics

Standard deviation202.87008
Coefficient of variation (CV)2.2909676
Kurtosis10.663869
Mean88.552138
Median Absolute Deviation (MAD)8.98
Skewness3.3863531
Sum4161.9505
Variance41156.268
MonotonicityNot monotonic
2023-12-12T22:11:16.603897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
5.0 3
 
5.8%
98.0 2
 
3.8%
0.02 2
 
3.8%
4.3 2
 
3.8%
2.0 2
 
3.8%
1.3 1
 
1.9%
17.0 1
 
1.9%
25.0 1
 
1.9%
0.08 1
 
1.9%
878.0 1
 
1.9%
Other values (31) 31
59.6%
(Missing) 5
 
9.6%
ValueCountFrequency (%)
0.0005 1
1.9%
0.02 2
3.8%
0.03 1
1.9%
0.08 1
1.9%
0.3 1
1.9%
0.32 1
1.9%
0.4 1
1.9%
0.52 1
1.9%
1.0 1
1.9%
1.3 1
1.9%
ValueCountFrequency (%)
878.0 1
1.9%
818.0 1
1.9%
813.0 1
1.9%
177.0 1
1.9%
174.0 1
1.9%
105.0 1
1.9%
100.0 1
1.9%
99.0 1
1.9%
98.0 2
3.8%
91.0 1
1.9%

2019년 평균
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.64174
Minimum0.0006
Maximum9473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T22:11:16.726486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0006
5-th percentile0.01
Q11.4375
median20.5
Q392.75
95-th percentile846.65
Maximum9473
Range9472.9994
Interquartile range (IQR)91.3125

Descriptive statistics

Standard deviation1318.7014
Coefficient of variation (CV)4.6491795
Kurtosis48.831176
Mean283.64174
Median Absolute Deviation (MAD)20.49
Skewness6.9005579
Sum14749.371
Variance1738973.3
MonotonicityNot monotonic
2023-12-12T22:11:16.841056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.01 3
 
5.8%
6.0 2
 
3.8%
98.0 2
 
3.8%
2.0 2
 
3.8%
91.0 1
 
1.9%
0.07 1
 
1.9%
810.0 1
 
1.9%
49.6 1
 
1.9%
1.45 1
 
1.9%
99.0 1
 
1.9%
Other values (37) 37
71.2%
ValueCountFrequency (%)
0.0006 1
 
1.9%
0.01 3
5.8%
0.03 1
 
1.9%
0.07 1
 
1.9%
0.3 1
 
1.9%
0.36 1
 
1.9%
0.4 1
 
1.9%
0.73 1
 
1.9%
1.1 1
 
1.9%
1.3 1
 
1.9%
ValueCountFrequency (%)
9473.0 1
1.9%
921.0 1
1.9%
878.0 1
1.9%
821.0 1
1.9%
810.0 1
1.9%
178.0 1
1.9%
167.0 1
1.9%
150.0 1
1.9%
109.0 1
1.9%
100.0 1
1.9%

2020년 평균
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.40386
Minimum0.0005
Maximum9445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T22:11:16.959644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.0155
Q11.415
median21.5
Q393.5
95-th percentile848.2
Maximum9445
Range9444.9995
Interquartile range (IQR)92.085

Descriptive statistics

Standard deviation1315.0207
Coefficient of variation (CV)4.6565254
Kurtosis48.805983
Mean282.40386
Median Absolute Deviation (MAD)21.49
Skewness6.8982092
Sum14685
Variance1729279.5
MonotonicityNot monotonic
2023-12-12T22:11:17.085335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5.0 3
 
5.8%
98.0 3
 
5.8%
1.4 2
 
3.8%
0.01 2
 
3.8%
0.02 2
 
3.8%
879.0 1
 
1.9%
816.0 1
 
1.9%
49.9 1
 
1.9%
1.42 1
 
1.9%
176.0 1
 
1.9%
Other values (35) 35
67.3%
ValueCountFrequency (%)
0.0005 1
1.9%
0.01 2
3.8%
0.02 2
3.8%
0.08 1
1.9%
0.22 1
1.9%
0.4 1
1.9%
0.42 1
1.9%
0.5 1
1.9%
1.0 1
1.9%
1.4 2
3.8%
ValueCountFrequency (%)
9445.0 1
1.9%
919.0 1
1.9%
879.0 1
1.9%
823.0 1
1.9%
816.0 1
1.9%
176.0 1
1.9%
162.0 1
1.9%
112.0 1
1.9%
106.0 1
1.9%
100.0 1
1.9%

2021년 평균
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.04174
Minimum0.0003
Maximum9458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T22:11:17.228011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0003
5-th percentile0.02
Q11.375
median21
Q387.25
95-th percentile847.2
Maximum9458
Range9457.9997
Interquartile range (IQR)85.875

Descriptive statistics

Standard deviation1316.8478
Coefficient of variation (CV)4.6689819
Kurtosis48.820277
Mean282.04174
Median Absolute Deviation (MAD)20.98
Skewness6.8996203
Sum14666.17
Variance1734088
MonotonicityNot monotonic
2023-12-12T22:11:17.376406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5.0 3
 
5.8%
98.0 3
 
5.8%
0.02 3
 
5.8%
79.0 2
 
3.8%
0.4 2
 
3.8%
3.0 2
 
3.8%
91.0 1
 
1.9%
49.0 1
 
1.9%
1.28 1
 
1.9%
99.0 1
 
1.9%
Other values (33) 33
63.5%
ValueCountFrequency (%)
0.0003 1
 
1.9%
0.02 3
5.8%
0.03 1
 
1.9%
0.08 1
 
1.9%
0.23 1
 
1.9%
0.4 2
3.8%
0.43 1
 
1.9%
1.0 1
 
1.9%
1.28 1
 
1.9%
1.3 1
 
1.9%
ValueCountFrequency (%)
9458.0 1
 
1.9%
921.0 1
 
1.9%
878.0 1
 
1.9%
822.0 1
 
1.9%
812.0 1
 
1.9%
177.0 1
 
1.9%
162.0 1
 
1.9%
105.0 1
 
1.9%
99.0 1
 
1.9%
98.0 3
5.8%

2022년 평균
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.86789
Minimum0.0003
Maximum9426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T22:11:17.501933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0003
5-th percentile0.02
Q11.3
median20
Q385.75
95-th percentile848.2
Maximum9426
Range9425.9997
Interquartile range (IQR)84.45

Descriptive statistics

Standard deviation1312.5628
Coefficient of variation (CV)4.6732391
Kurtosis48.79832
Mean280.86789
Median Absolute Deviation (MAD)19.98
Skewness6.8975053
Sum14605.13
Variance1722821.2
MonotonicityNot monotonic
2023-12-12T22:11:17.645990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.02 3
 
5.8%
98.0 3
 
5.8%
73.0 2
 
3.8%
5.0 2
 
3.8%
4.0 2
 
3.8%
3.0 2
 
3.8%
1.3 2
 
3.8%
0.07 1
 
1.9%
813.0 1
 
1.9%
48.6 1
 
1.9%
Other values (33) 33
63.5%
ValueCountFrequency (%)
0.0003 1
 
1.9%
0.02 3
5.8%
0.03 1
 
1.9%
0.07 1
 
1.9%
0.2 1
 
1.9%
0.22 1
 
1.9%
0.25 1
 
1.9%
0.5 1
 
1.9%
0.6 1
 
1.9%
1.0 1
 
1.9%
ValueCountFrequency (%)
9426.0 1
 
1.9%
917.0 1
 
1.9%
879.0 1
 
1.9%
823.0 1
 
1.9%
813.0 1
 
1.9%
174.0 1
 
1.9%
171.0 1
 
1.9%
102.0 1
 
1.9%
100.0 1
 
1.9%
98.0 3
5.8%

Interactions

2023-12-12T22:11:14.537145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:11.806152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.302121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.798115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.601518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.069598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.606540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:11.887298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.384172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.186106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.673819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.158263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.691924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:11.969715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.463714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.278656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.745364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.230292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.761445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.064244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.535725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.372897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.810620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.302198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.835598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.142902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.635840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.450524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.875945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.380695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.921094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.220431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:12.708761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.531197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:13.977660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:14.467183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:11:17.761686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
석유 및 석유대체연료항목품질기준2017년 평균2018년 평균2019년 평균2020년 평균2021년 평균2022년 평균
석유 및 석유대체연료1.0000.0000.7450.0000.0000.0000.0000.0000.000
항목0.0001.0000.9960.7580.7581.0001.0001.0001.000
품질기준0.7450.9961.0001.0001.0001.0001.0001.0001.000
2017년 평균0.0000.7581.0001.0001.000NaNNaNNaNNaN
2018년 평균0.0000.7581.0001.0001.000NaNNaNNaNNaN
2019년 평균0.0001.0001.000NaNNaN1.0000.6790.6790.679
2020년 평균0.0001.0001.000NaNNaN0.6791.0000.6790.679
2021년 평균0.0001.0001.000NaNNaN0.6790.6791.0000.679
2022년 평균0.0001.0001.000NaNNaN0.6790.6790.6791.000
2023-12-12T22:11:17.903938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
항목석유 및 석유대체연료
항목1.0000.000
석유 및 석유대체연료0.0001.000
2023-12-12T22:11:18.321097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2017년 평균2018년 평균2019년 평균2020년 평균2021년 평균2022년 평균석유 및 석유대체연료항목
2017년 평균1.0000.9980.9980.9950.9880.9870.0000.420
2018년 평균0.9981.0000.9970.9960.9910.9900.0000.420
2019년 평균0.9980.9971.0000.9980.9920.9910.0000.787
2020년 평균0.9950.9960.9981.0000.9950.9930.0000.787
2021년 평균0.9880.9910.9920.9951.0000.9970.0000.787
2022년 평균0.9870.9900.9910.9930.9971.0000.0000.787
석유 및 석유대체연료0.0000.0000.0000.0000.0000.0001.0000.000
항목0.4200.4200.7870.7870.7870.7870.0001.000

Missing values

2023-12-12T22:11:15.028824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:11:15.156842image/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.
2023-12-12T22:11:15.262043image/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

석유 및 석유대체연료항목품질기준2017년 평균2018년 평균2019년 평균2020년 평균2021년 평균2022년 평균
0자동차용휘발유1호옥탄값(리서어치법)91이상~94미만91.091.091.092.091.091.0
1자동차용휘발유1호증기압(37.8℃, kPa)44~82(여름용:44~60,겨울용:44~96)59.059.060.071.073.073.0
2자동차용휘발유1호황분(mg/kg)10이하5.05.06.05.05.05.0
3자동차용휘발유1호벤젠 함량(부피%)0.7이하0.40.40.40.50.40.5
4자동차용휘발유2호옥탄값(리서어치법)94이상100.0100.0100.0100.098.0100.0
5자동차용휘발유2호증기압(37.8℃, kPa)44~82(여름용:44~60,겨울용:44~96)54.054.054.053.054.054.0
6자동차용휘발유2호황분(mg/kg)10이하5.05.05.05.05.04.0
7자동차용휘발유2호벤젠 함량(부피%)0.7이하0.30.30.30.40.40.2
8등유인화점(℃)38이상43.043.043.044.044.043.0
9등유황분(무게%)0.01이하0.00060.00050.00060.00050.00030.0003
석유 및 석유대체연료항목품질기준2017년 평균2018년 평균2019년 평균2020년 평균2021년 평균2022년 평균
42바이오중유황분(무게%)0.05이하<NA><NA>0.010.020.020.02
43바이오중유밀도(15℃, kg/㎥)991이하<NA><NA>921.0919.0921.0917.0
44바이오중유총발열량(kcal/kg)9,000이상<NA><NA>9473.09445.09458.09426.0
45액화석유가스 1호조성(C3 탄화수소) (mol%)90이상98.098.098.098.098.098.0
46액화석유가스 1호조성(C4 탄화수소) (mol%)<NA>1.31.31.41.41.31.0
47액화석유가스 1호황함량(mg/kg)30이하2.02.02.03.03.03.0
48액화석유가스 2호조성(C3 탄화수소) (mol%)10이하(여름용),25이상~35이하(겨울용)14.017.013.016.014.016.0
49액화석유가스 2호조성(C4 탄화수소) (mol%)85이상(여름용),60이상(겨울용)86.083.087.084.086.084.0
50액화석유가스 2호황함량(mg/kg)30이하7.09.09.08.08.08.0
51액화석유가스 3호조성(C4 탄화수소) (mol%)85이상98.098.098.098.098.098.0