Overview

Dataset statistics

Number of variables8
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory76.3 B

Variable types

Numeric8

Dataset

Description국내 원유수입량, 연간 정제처리량, 월평균 정제처리량, 일평균 정제처리량 및 정유사의 정제능력에 따른 정제가동율 자료
Author한국석유공사
URLhttps://www.data.go.kr/data/15045416/fileData.do

Alerts

년도 is highly overall correlated with 원유수입(천배럴) and 4 other fieldsHigh correlation
원유수입(천배럴) is highly overall correlated with 년도 and 4 other fieldsHigh correlation
연간처리량(천배럴) is highly overall correlated with 년도 and 4 other fieldsHigh correlation
월평균 처리량(천배럴) is highly overall correlated with 년도 and 4 other fieldsHigh correlation
일평균 처리량(천배럴) is highly overall correlated with 년도 and 4 other fieldsHigh correlation
정제능력(천BPSD) is highly overall correlated with 년도 and 4 other fieldsHigh correlation
년도 has unique valuesUnique
원유수입(천배럴) has unique valuesUnique
연간처리량(천배럴) has unique valuesUnique
월평균 처리량(천배럴) has unique valuesUnique
일평균 처리량(천배럴) has unique valuesUnique
증감률(퍼센트) has unique valuesUnique

Reproduction

Analysis started2024-03-14 12:22:39.931225
Analysis finished2024-03-14 12:22:55.374334
Duration15.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5
Minimum1993
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T21:22:55.479080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1993
5-th percentile1994.45
Q12000.25
median2007.5
Q32014.75
95-th percentile2020.55
Maximum2022
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.0043852595
Kurtosis-1.2
Mean2007.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum60225
Variance77.5
MonotonicityStrictly increasing
2024-03-14T21:22:55.689729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1993 1
 
3.3%
2009 1
 
3.3%
2022 1
 
3.3%
2021 1
 
3.3%
2020 1
 
3.3%
2019 1
 
3.3%
2018 1
 
3.3%
2017 1
 
3.3%
2016 1
 
3.3%
2015 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1993 1
3.3%
1994 1
3.3%
1995 1
3.3%
1996 1
3.3%
1997 1
3.3%
1998 1
3.3%
1999 1
3.3%
2000 1
3.3%
2001 1
3.3%
2002 1
3.3%
ValueCountFrequency (%)
2022 1
3.3%
2021 1
3.3%
2020 1
3.3%
2019 1
3.3%
2018 1
3.3%
2017 1
3.3%
2016 1
3.3%
2015 1
3.3%
2014 1
3.3%
2013 1
3.3%

원유수입(천배럴)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean883269.67
Minimum560563
Maximum1118167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T21:22:55.906456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum560563
5-th percentile596388.45
Q1828113.75
median873752.5
Q3956933.25
95-th percentile1099108.1
Maximum1118167
Range557604
Interquartile range (IQR)128819.5

Descriptive statistics

Standard deviation140231.87
Coefficient of variation (CV)0.1587645
Kurtosis0.51646154
Mean883269.67
Median Absolute Deviation (MAD)61801
Skewness-0.52405865
Sum26498090
Variance1.9664978 × 1010
MonotonicityNot monotonic
2024-03-14T21:22:56.218649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
560563 1
 
3.3%
835085 1
 
3.3%
1031283 1
 
3.3%
960147 1
 
3.3%
980259 1
 
3.3%
1071923 1
 
3.3%
1116281 1
 
3.3%
1118167 1
 
3.3%
1078119 1
 
3.3%
1026107 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
560563 1
3.3%
573024 1
3.3%
624945 1
3.3%
721927 1
3.3%
790992 1
3.3%
804809 1
3.3%
819094 1
3.3%
825790 1
3.3%
835085 1
3.3%
843203 1
3.3%
ValueCountFrequency (%)
1118167 1
3.3%
1116281 1
3.3%
1078119 1
3.3%
1071923 1
3.3%
1031283 1
3.3%
1026107 1
3.3%
980259 1
3.3%
960147 1
3.3%
947292 1
3.3%
927524 1
3.3%

연간처리량(천배럴)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean880055.7
Minimum543969
Maximum1117376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T21:22:56.495292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum543969
5-th percentile592461.4
Q1829532
median875568.5
Q3957327
95-th percentile1090725.2
Maximum1117376
Range573407
Interquartile range (IQR)127795

Descriptive statistics

Standard deviation140510.03
Coefficient of variation (CV)0.15966038
Kurtosis0.69658929
Mean880055.7
Median Absolute Deviation (MAD)59636
Skewness-0.61586276
Sum26401671
Variance1.9743069 × 1010
MonotonicityNot monotonic
2024-03-14T21:22:56.969513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
543969 1
 
3.3%
838475 1
 
3.3%
1026121 1
 
3.3%
961382 1
 
3.3%
980379 1
 
3.3%
1064210 1
 
3.3%
1106266 1
 
3.3%
1117376 1
 
3.3%
1071731 1
 
3.3%
1016157 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
543969 1
3.3%
560866 1
3.3%
631078 1
3.3%
720846 1
3.3%
782951 1
3.3%
786805 1
3.3%
825890 1
3.3%
826551 1
3.3%
838475 1
3.3%
852439 1
3.3%
ValueCountFrequency (%)
1117376 1
3.3%
1106266 1
3.3%
1071731 1
3.3%
1064210 1
3.3%
1026121 1
3.3%
1016157 1
3.3%
980379 1
3.3%
961382 1
3.3%
945162 1
3.3%
924441 1
3.3%

월평균 처리량(천배럴)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73338
Minimum45331
Maximum93115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T21:22:57.615053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45331
5-th percentile49371.95
Q169127.5
median72964
Q379777.25
95-th percentile90893.9
Maximum93115
Range47784
Interquartile range (IQR)10649.75

Descriptive statistics

Standard deviation11709.182
Coefficient of variation (CV)0.1596605
Kurtosis0.69652969
Mean73338
Median Absolute Deviation (MAD)4970
Skewness-0.61583462
Sum2200140
Variance1.3710493 × 108
MonotonicityNot monotonic
2024-03-14T21:22:58.080855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
45331 1
 
3.3%
69873 1
 
3.3%
85510 1
 
3.3%
80115 1
 
3.3%
81698 1
 
3.3%
88684 1
 
3.3%
92189 1
 
3.3%
93115 1
 
3.3%
89311 1
 
3.3%
84680 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
45331 1
3.3%
46739 1
3.3%
52590 1
3.3%
60070 1
3.3%
65246 1
3.3%
65567 1
3.3%
68824 1
3.3%
68879 1
3.3%
69873 1
3.3%
71037 1
3.3%
ValueCountFrequency (%)
93115 1
3.3%
92189 1
3.3%
89311 1
3.3%
88684 1
3.3%
85510 1
3.3%
84680 1
3.3%
81698 1
3.3%
80115 1
3.3%
78764 1
3.3%
77037 1
3.3%

일평균 처리량(천배럴)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2411.1333
Minimum1490
Maximum3061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T21:22:58.642994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1490
5-th percentile1623.4
Q12273
median2399
Q32622.75
95-th percentile2988.25
Maximum3061
Range1571
Interquartile range (IQR)349.75

Descriptive statistics

Standard deviation384.92146
Coefficient of variation (CV)0.15964338
Kurtosis0.69770429
Mean2411.1333
Median Absolute Deviation (MAD)163
Skewness-0.61629806
Sum72334
Variance148164.53
MonotonicityNot monotonic
2024-03-14T21:22:59.032466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1490 1
 
3.3%
2297 1
 
3.3%
2811 1
 
3.3%
2634 1
 
3.3%
2686 1
 
3.3%
2916 1
 
3.3%
3031 1
 
3.3%
3061 1
 
3.3%
2936 1
 
3.3%
2784 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1490 1
3.3%
1537 1
3.3%
1729 1
3.3%
1975 1
3.3%
2145 1
3.3%
2156 1
3.3%
2263 1
3.3%
2265 1
3.3%
2297 1
3.3%
2335 1
3.3%
ValueCountFrequency (%)
3061 1
3.3%
3031 1
3.3%
2936 1
3.3%
2916 1
3.3%
2811 1
3.3%
2784 1
3.3%
2686 1
3.3%
2634 1
3.3%
2589 1
3.3%
2533 1
3.3%

증감률(퍼센트)
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5486667
Minimum-8.52
Maximum20.97
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)36.7%
Memory size398.0 B
2024-03-14T21:22:59.253886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.52
5-th percentile-6.7145
Q1-1.9225
median2.64
Q35.645
95-th percentile13.455
Maximum20.97
Range29.49
Interquartile range (IQR)7.5675

Descriptive statistics

Standard deviation6.5166724
Coefficient of variation (CV)2.5568947
Kurtosis1.0462389
Mean2.5486667
Median Absolute Deviation (MAD)3.76
Skewness0.7289603
Sum76.46
Variance42.467019
MonotonicityNot monotonic
2024-03-14T21:22:59.457242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6.53 1
 
3.3%
-3.14 1
 
3.3%
6.73 1
 
3.3%
-1.94 1
 
3.3%
-7.88 1
 
3.3%
-3.8 1
 
3.3%
-0.99 1
 
3.3%
4.26 1
 
3.3%
5.47 1
 
3.3%
10.65 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-8.52 1
3.3%
-7.88 1
3.3%
-5.29 1
3.3%
-4.07 1
3.3%
-3.8 1
3.3%
-3.39 1
3.3%
-3.14 1
3.3%
-1.94 1
3.3%
-1.87 1
3.3%
-0.99 1
3.3%
ValueCountFrequency (%)
20.97 1
3.3%
14.22 1
3.3%
12.52 1
3.3%
10.65 1
3.3%
6.73 1
3.3%
6.53 1
3.3%
5.98 1
3.3%
5.67 1
3.3%
5.57 1
3.3%
5.47 1
3.3%

정제능력(천BPSD)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2707.3667
Minimum1675
Maximum3204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T21:22:59.742656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1675
5-th percentile1753.1
Q12438
median2775
Q33046.5
95-th percentile3204
Maximum3204
Range1529
Interquartile range (IQR)608.5

Descriptive statistics

Standard deviation431.52372
Coefficient of variation (CV)0.15938873
Kurtosis0.59262751
Mean2707.3667
Median Absolute Deviation (MAD)337
Skewness-0.98645432
Sum81221
Variance186212.72
MonotonicityIncreasing
2024-03-14T21:23:00.121550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2438 8
26.7%
3204 5
16.7%
2735 3
 
10.0%
2949 2
 
6.7%
1675 1
 
3.3%
3105 1
 
3.3%
3064 1
 
3.3%
3059 1
 
3.3%
3009 1
 
3.3%
2845 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
1675 1
 
3.3%
1700 1
 
3.3%
1818 1
 
3.3%
2438 8
26.7%
2735 3
 
10.0%
2765 1
 
3.3%
2785 1
 
3.3%
2835 1
 
3.3%
2845 1
 
3.3%
2934 1
 
3.3%
ValueCountFrequency (%)
3204 5
16.7%
3105 1
 
3.3%
3064 1
 
3.3%
3059 1
 
3.3%
3009 1
 
3.3%
2949 2
 
6.7%
2934 1
 
3.3%
2845 1
 
3.3%
2835 1
 
3.3%
2785 1
 
3.3%

가동률(퍼센트)
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.273333
Minimum81.01
Maximum100.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T21:23:00.498451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81.01
5-th percentile81.561
Q184.4625
median87.985
Q394.1525
95-th percentile98.3605
Maximum100.05
Range19.04
Interquartile range (IQR)9.69

Descriptive statistics

Standard deviation5.6628574
Coefficient of variation (CV)0.063432799
Kurtosis-0.9808279
Mean89.273333
Median Absolute Deviation (MAD)3.985
Skewness0.40142782
Sum2678.2
Variance32.067954
MonotonicityNot monotonic
2024-03-14T21:23:00.902869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
84.0 2
 
6.7%
88.97 1
 
3.3%
90.39 1
 
3.3%
87.74 1
 
3.3%
82.21 1
 
3.3%
91.0 1
 
3.3%
94.6 1
 
3.3%
98.59 1
 
3.3%
95.83 1
 
3.3%
91.01 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
81.01 1
3.3%
81.03 1
3.3%
82.21 1
3.3%
82.8 1
3.3%
83.62 1
3.3%
84.0 2
6.7%
84.23 1
3.3%
85.16 1
3.3%
85.39 1
3.3%
86.32 1
3.3%
ValueCountFrequency (%)
100.05 1
3.3%
98.59 1
3.3%
98.08 1
3.3%
97.99 1
3.3%
96.66 1
3.3%
95.83 1
3.3%
95.1 1
3.3%
94.6 1
3.3%
92.81 1
3.3%
91.01 1
3.3%

Interactions

2024-03-14T21:22:53.505385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:40.181828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:42.388363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:44.393419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:46.379668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:48.246578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:49.761063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:51.731205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:53.763047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:40.423745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:42.632524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:44.636652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:46.623099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:48.402083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:49.999354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:51.982128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:53.931662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:40.671243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:42.877208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:44.880311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:46.866187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:48.593656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:50.247685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:52.239413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:54.102660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:41.136298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:43.126903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:45.124899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:47.107548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:48.777317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:50.490807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:52.497295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:54.256671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:41.371277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:43.363766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:45.361942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:47.343408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:48.996625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:50.725786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:52.645675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:54.439804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:41.627619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:43.622942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:45.620617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:47.539283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:49.169353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:50.978092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:52.821971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:54.585841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:41.870454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:43.865001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:45.860525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:47.729106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:49.321496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:51.215050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:52.974519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:54.784996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:42.133526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:44.132281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:46.123930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:48.001539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:49.492647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:51.476744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:22:53.140246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:23:01.172203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도원유수입(천배럴)연간처리량(천배럴)월평균 처리량(천배럴)일평균 처리량(천배럴)증감률(퍼센트)정제능력(천BPSD)가동률(퍼센트)
년도1.0000.8570.8570.8570.8570.0000.9430.520
원유수입(천배럴)0.8571.0000.9990.9990.9990.7590.8880.671
연간처리량(천배럴)0.8570.9991.0001.0001.0000.7550.8820.655
월평균 처리량(천배럴)0.8570.9991.0001.0001.0000.7550.8820.655
일평균 처리량(천배럴)0.8570.9991.0001.0001.0000.7550.8820.655
증감률(퍼센트)0.0000.7590.7550.7550.7551.0000.0000.000
정제능력(천BPSD)0.9430.8880.8820.8820.8820.0001.0000.453
가동률(퍼센트)0.5200.6710.6550.6550.6550.0000.4531.000
2024-03-14T21:23:01.484997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도원유수입(천배럴)연간처리량(천배럴)월평균 처리량(천배럴)일평균 처리량(천배럴)증감률(퍼센트)정제능력(천BPSD)가동률(퍼센트)
년도1.0000.8650.8770.8770.877-0.2680.988-0.247
원유수입(천배럴)0.8651.0000.9960.9960.996-0.0670.8810.162
연간처리량(천배럴)0.8770.9961.0001.0001.000-0.0870.8930.141
월평균 처리량(천배럴)0.8770.9961.0001.0001.000-0.0870.8930.141
일평균 처리량(천배럴)0.8770.9961.0001.0001.000-0.0870.8930.141
증감률(퍼센트)-0.268-0.067-0.087-0.087-0.0871.000-0.2200.169
정제능력(천BPSD)0.9880.8810.8930.8930.893-0.2201.000-0.251
가동률(퍼센트)-0.2470.1620.1410.1410.1410.169-0.2511.000

Missing values

2024-03-14T21:22:55.058089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:22:55.282538image/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

년도원유수입(천배럴)연간처리량(천배럴)월평균 처리량(천배럴)일평균 처리량(천배럴)증감률(퍼센트)정제능력(천BPSD)가동률(퍼센트)
019935605635439694533114906.53167588.97
119945730245608664673915373.11170090.39
2199562494563107852590172912.52181895.1
3199672192772084660070197514.22243881.01
4199787341587197472664238920.97243897.99
51998819094825890688242263-5.29243892.81
619998740908727427272823915.67243898.08
720008939438903047419224392.012438100.05
82001859367860115716762356-3.39243896.66
92002790992786805655672156-8.52243888.42
년도원유수입(천배럴)연간처리량(천배럴)월평균 처리량(천배럴)일평균 처리량(천배럴)증감률(퍼센트)정제능력(천BPSD)가동률(퍼센트)
202013915075906674755562484-4.07294984.23
2120149275249183457652925161.29300983.62
2220151026107101615784680278410.65305991.01
232016107811910717318931129365.47306495.83
242017111816711173769311530614.26310598.59
25201811162811106266921893031-0.99320494.6
26201910719231064210886842916-3.8320491.0
272020980259980379816982686-7.88320484.0
282021960147961382801152634-1.94320482.21
292022103128310261218551028116.73320487.74