Overview

Dataset statistics

Number of variables16
Number of observations31
Missing cells19
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory148.3 B

Variable types

Numeric16

Dataset

Description국내 석유제품의 생산량(휘발유,등유,경유,경질중유,중유,벙커C유,납사,항공유,아스팔트,기타 제품,용제,윤활유,LPG,부생연료유) 단위 : 천BBL
URLhttps://www.data.go.kr/data/15054601/fileData.do

Alerts

is highly overall correlated with 휘발유 and 11 other fieldsHigh correlation
휘발유 is highly overall correlated with and 12 other fieldsHigh correlation
등유 is highly overall correlated with 경질중유 and 5 other fieldsHigh correlation
경유 is highly overall correlated with and 12 other fieldsHigh correlation
경질중유 is highly overall correlated with and 12 other fieldsHigh correlation
중유 is highly overall correlated with and 11 other fieldsHigh correlation
벙커C유 is highly overall correlated with and 11 other fieldsHigh correlation
납사 is highly overall correlated with and 11 other fieldsHigh correlation
용제 is highly overall correlated with and 7 other fieldsHigh correlation
항공유 is highly overall correlated with and 12 other fieldsHigh correlation
아스팔트 is highly overall correlated with and 12 other fieldsHigh correlation
윤활유 is highly overall correlated with and 11 other fieldsHigh correlation
기타제품 is highly overall correlated with and 11 other fieldsHigh correlation
부생연료유 is highly overall correlated with 휘발유 and 2 other fieldsHigh correlation
합계 is highly overall correlated with and 11 other fieldsHigh correlation
윤활유 has 5 (16.1%) missing valuesMissing
기타제품 has 5 (16.1%) missing valuesMissing
부생연료유 has 9 (29.0%) missing valuesMissing
has unique valuesUnique
휘발유 has unique valuesUnique
등유 has unique valuesUnique
경유 has unique valuesUnique
중유 has unique valuesUnique
벙커C유 has unique valuesUnique
납사 has unique valuesUnique
용제 has unique valuesUnique
항공유 has unique valuesUnique
LPG has unique valuesUnique
아스팔트 has unique valuesUnique
합계 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:48:15.068835
Analysis finished2023-12-12 15:48:44.128389
Duration29.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007
Minimum1992
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:44.206393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993.5
Q11999.5
median2007
Q32014.5
95-th percentile2020.5
Maximum2022
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)0.0045302048
Kurtosis-1.2
Mean2007
Median Absolute Deviation (MAD)8
Skewness0
Sum62217
Variance82.666667
MonotonicityStrictly increasing
2023-12-13T00:48:44.345336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1992 1
 
3.2%
1993 1
 
3.2%
2022 1
 
3.2%
2021 1
 
3.2%
2020 1
 
3.2%
2019 1
 
3.2%
2018 1
 
3.2%
2017 1
 
3.2%
2016 1
 
3.2%
2015 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1992 1
3.2%
1993 1
3.2%
1994 1
3.2%
1995 1
3.2%
1996 1
3.2%
1997 1
3.2%
1998 1
3.2%
1999 1
3.2%
2000 1
3.2%
2001 1
3.2%
ValueCountFrequency (%)
2022 1
3.2%
2021 1
3.2%
2020 1
3.2%
2019 1
3.2%
2018 1
3.2%
2017 1
3.2%
2016 1
3.2%
2015 1
3.2%
2014 1
3.2%
2013 1
3.2%

휘발유
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104282.84
Minimum32348
Maximum169068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:44.483877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32348
5-th percentile45989.5
Q175326.5
median79677
Q3144051.5
95-th percentile167711
Maximum169068
Range136720
Interquartile range (IQR)68725

Descriptive statistics

Standard deviation42308.865
Coefficient of variation (CV)0.40571263
Kurtosis-1.3299112
Mean104282.84
Median Absolute Deviation (MAD)29003
Skewness0.2370159
Sum3232768
Variance1.7900401 × 109
MonotonicityNot monotonic
2023-12-13T00:48:44.628312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
32348 1
 
3.2%
41271 1
 
3.2%
169068 1
 
3.2%
166558 1
 
3.2%
143047 1
 
3.2%
168227 1
 
3.2%
167195 1
 
3.2%
157908 1
 
3.2%
153557 1
 
3.2%
157326 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
32348 1
3.2%
41271 1
3.2%
50708 1
3.2%
60459 1
3.2%
70935 1
3.2%
73048 1
3.2%
73823 1
3.2%
75159 1
3.2%
75494 1
3.2%
76026 1
3.2%
ValueCountFrequency (%)
169068 1
3.2%
168227 1
3.2%
167195 1
3.2%
166558 1
3.2%
157908 1
3.2%
157326 1
3.2%
153557 1
3.2%
145056 1
3.2%
143047 1
3.2%
137387 1
3.2%

등유
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42103
Minimum16746
Maximum90501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:44.795950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16746
5-th percentile19006.5
Q127501
median38681
Q352063.5
95-th percentile81315
Maximum90501
Range73755
Interquartile range (IQR)24562.5

Descriptive statistics

Standard deviation20427.784
Coefficient of variation (CV)0.48518595
Kurtosis0.020398001
Mean42103
Median Absolute Deviation (MAD)12621
Skewness0.85893107
Sum1305193
Variance4.1729436 × 108
MonotonicityNot monotonic
2023-12-13T00:48:44.978411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
29309 1
 
3.2%
31915 1
 
3.2%
46510 1
 
3.2%
52825 1
 
3.2%
43646 1
 
3.2%
20705 1
 
3.2%
21125 1
 
3.2%
19896 1
 
3.2%
19520 1
 
3.2%
18493 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
16746 1
3.2%
18493 1
3.2%
19520 1
3.2%
19896 1
3.2%
20705 1
3.2%
21125 1
3.2%
21499 1
3.2%
26615 1
3.2%
28387 1
3.2%
29309 1
3.2%
ValueCountFrequency (%)
90501 1
3.2%
87797 1
3.2%
74833 1
3.2%
71899 1
3.2%
63338 1
3.2%
62501 1
3.2%
61646 1
3.2%
52825 1
3.2%
51302 1
3.2%
49446 1
3.2%

경유
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264289.61
Minimum152577
Maximum366713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:45.285697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum152577
5-th percentile168503.5
Q1217558
median261714
Q3323961.5
95-th percentile359799
Maximum366713
Range214136
Interquartile range (IQR)106403.5

Descriptive statistics

Standard deviation63952.191
Coefficient of variation (CV)0.24197769
Kurtosis-1.163075
Mean264289.61
Median Absolute Deviation (MAD)49166
Skewness0.088024885
Sum8192978
Variance4.0898827 × 109
MonotonicityNot monotonic
2023-12-13T00:48:45.493793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
152577 1
 
3.2%
167967 1
 
3.2%
360818 1
 
3.2%
336185 1
 
3.2%
350147 1
 
3.2%
366713 1
 
3.2%
358780 1
 
3.2%
344882 1
 
3.2%
338517 1
 
3.2%
333421 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
152577 1
3.2%
167967 1
3.2%
169040 1
3.2%
180908 1
3.2%
202677 1
3.2%
207736 1
3.2%
212548 1
3.2%
216446 1
3.2%
218670 1
3.2%
218888 1
3.2%
ValueCountFrequency (%)
366713 1
3.2%
360818 1
3.2%
358780 1
3.2%
350147 1
3.2%
344882 1
3.2%
338517 1
3.2%
336185 1
3.2%
333421 1
3.2%
314502 1
3.2%
309738 1
3.2%

경질중유
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3254.6774
Minimum1010
Maximum5495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:45.683014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1010
5-th percentile1642
Q12633
median3345
Q33895.5
95-th percentile4736
Maximum5495
Range4485
Interquartile range (IQR)1262.5

Descriptive statistics

Standard deviation1002.4309
Coefficient of variation (CV)0.30799699
Kurtosis0.040540197
Mean3254.6774
Median Absolute Deviation (MAD)703
Skewness-0.12591387
Sum100895
Variance1004867.6
MonotonicityNot monotonic
2023-12-13T00:48:45.866146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4243 2
 
6.5%
3360 1
 
3.2%
2940 1
 
3.2%
1010 1
 
3.2%
1630 1
 
3.2%
1654 1
 
3.2%
1999 1
 
3.2%
2027 1
 
3.2%
2624 1
 
3.2%
3345 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
1010 1
3.2%
1630 1
3.2%
1654 1
3.2%
1999 1
3.2%
2027 1
3.2%
2383 1
3.2%
2401 1
3.2%
2624 1
3.2%
2642 1
3.2%
2940 1
3.2%
ValueCountFrequency (%)
5495 1
3.2%
4755 1
3.2%
4717 1
3.2%
4243 2
6.5%
4149 1
3.2%
4097 1
3.2%
3922 1
3.2%
3869 1
3.2%
3826 1
3.2%
3620 1
3.2%

중유
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean719.14065
Minimum66.04
Maximum1696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:46.050778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66.04
5-th percentile89.66
Q1270
median640
Q31108.5
95-th percentile1582.5
Maximum1696
Range1629.96
Interquartile range (IQR)838.5

Descriptive statistics

Standard deviation512.89427
Coefficient of variation (CV)0.71320439
Kurtosis-0.95323098
Mean719.14065
Median Absolute Deviation (MAD)374
Skewness0.57193753
Sum22293.36
Variance263060.53
MonotonicityNot monotonic
2023-12-13T00:48:46.543704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1696.0 1
 
3.2%
1600.0 1
 
3.2%
95.32 1
 
3.2%
66.04 1
 
3.2%
84.0 1
 
3.2%
125.0 1
 
3.2%
249.0 1
 
3.2%
199.0 1
 
3.2%
262.0 1
 
3.2%
266.0 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
66.04 1
3.2%
84.0 1
3.2%
95.32 1
3.2%
125.0 1
3.2%
199.0 1
3.2%
249.0 1
3.2%
262.0 1
3.2%
266.0 1
3.2%
274.0 1
3.2%
335.0 1
3.2%
ValueCountFrequency (%)
1696.0 1
3.2%
1600.0 1
3.2%
1565.0 1
3.2%
1468.0 1
3.2%
1451.0 1
3.2%
1444.0 1
3.2%
1320.0 1
3.2%
1258.0 1
3.2%
959.0 1
3.2%
841.0 1
3.2%

벙커C유
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146410.42
Minimum52500
Maximum235451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:46.748899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52500
5-th percentile55176
Q175366.5
median174319
Q3203498
95-th percentile228828
Maximum235451
Range182951
Interquartile range (IQR)128131.5

Descriptive statistics

Standard deviation64219.898
Coefficient of variation (CV)0.43862929
Kurtosis-1.6351133
Mean146410.42
Median Absolute Deviation (MAD)51818
Skewness-0.20264229
Sum4538723
Variance4.1241953 × 109
MonotonicityNot monotonic
2023-12-13T00:48:46.949940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
174319 1
 
3.2%
184006 1
 
3.2%
90957 1
 
3.2%
73991 1
 
3.2%
73529 1
 
3.2%
55663 1
 
3.2%
66690 1
 
3.2%
65139 1
 
3.2%
67357 1
 
3.2%
54689 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
52500 1
3.2%
54689 1
3.2%
55663 1
3.2%
65139 1
3.2%
66690 1
3.2%
67357 1
3.2%
73529 1
3.2%
73991 1
3.2%
76742 1
3.2%
90957 1
3.2%
ValueCountFrequency (%)
235451 1
3.2%
232394 1
3.2%
225262 1
3.2%
220976 1
3.2%
207845 1
3.2%
206331 1
3.2%
205654 1
3.2%
203886 1
3.2%
203110 1
3.2%
198615 1
3.2%

납사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189659.23
Minimum65259
Maximum313927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:47.156472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65259
5-th percentile66724.5
Q1156691.5
median173746
Q3234015.5
95-th percentile311515.5
Maximum313927
Range248668
Interquartile range (IQR)77324

Descriptive statistics

Standard deviation72716.967
Coefficient of variation (CV)0.38340854
Kurtosis-0.54281457
Mean189659.23
Median Absolute Deviation (MAD)33358
Skewness0.15717683
Sum5879436
Variance5.2877573 × 109
MonotonicityNot monotonic
2023-12-13T00:48:47.348027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
65259 1
 
3.2%
65480 1
 
3.2%
300608 1
 
3.2%
285600 1
 
3.2%
286740 1
 
3.2%
313163 1
 
3.2%
313927 1
 
3.2%
309868 1
 
3.2%
259814 1
 
3.2%
249936 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
65259 1
3.2%
65480 1
3.2%
67969 1
3.2%
88319 1
3.2%
103128 1
3.2%
148923 1
3.2%
153832 1
3.2%
154319 1
3.2%
159064 1
3.2%
162670 1
3.2%
ValueCountFrequency (%)
313927 1
3.2%
313163 1
3.2%
309868 1
3.2%
300608 1
3.2%
286740 1
3.2%
285600 1
3.2%
259814 1
3.2%
249936 1
3.2%
218095 1
3.2%
207645 1
3.2%

용제
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2732.5806
Minimum429
Maximum5356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:47.556847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum429
5-th percentile477.5
Q1715.5
median3104
Q33948.5
95-th percentile4969
Maximum5356
Range4927
Interquartile range (IQR)3233

Descriptive statistics

Standard deviation1680.2661
Coefficient of variation (CV)0.61490084
Kurtosis-1.4961251
Mean2732.5806
Median Absolute Deviation (MAD)1630
Skewness-0.19979311
Sum84710
Variance2823294.3
MonotonicityNot monotonic
2023-12-13T00:48:47.762092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
452 1
 
3.2%
429 1
 
3.2%
2817 1
 
3.2%
3550 1
 
3.2%
5356 1
 
3.2%
3778 1
 
3.2%
3651 1
 
3.2%
4046 1
 
3.2%
3104 1
 
3.2%
2839 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
429 1
3.2%
452 1
3.2%
503 1
3.2%
626 1
3.2%
629 1
3.2%
630 1
3.2%
671 1
3.2%
695 1
3.2%
736 1
3.2%
790 1
3.2%
ValueCountFrequency (%)
5356 1
3.2%
5061 1
3.2%
4877 1
3.2%
4766 1
3.2%
4734 1
3.2%
4266 1
3.2%
4046 1
3.2%
3972 1
3.2%
3925 1
3.2%
3912 1
3.2%

항공유
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93641.387
Minimum20230
Maximum173856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:47.935804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230
5-th percentile23548.5
Q157316.5
median97180
Q3126440
95-th percentile170820
Maximum173856
Range153626
Interquartile range (IQR)69123.5

Descriptive statistics

Standard deviation46330.551
Coefficient of variation (CV)0.49476575
Kurtosis-0.97723038
Mean93641.387
Median Absolute Deviation (MAD)33540
Skewness0.11963705
Sum2902883
Variance2.1465199 × 109
MonotonicityNot monotonic
2023-12-13T00:48:48.117895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20901 1
 
3.2%
20230 1
 
3.2%
122855 1
 
3.2%
97180 1
 
3.2%
112595 1
 
3.2%
170744 1
 
3.2%
170896 1
 
3.2%
173856 1
 
3.2%
160712 1
 
3.2%
151711 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
20230 1
3.2%
20901 1
3.2%
26196 1
3.2%
34981 1
3.2%
38819 1
3.2%
42438 1
3.2%
53056 1
3.2%
55065 1
3.2%
59568 1
3.2%
63801 1
3.2%
ValueCountFrequency (%)
173856 1
3.2%
170896 1
3.2%
170744 1
3.2%
160712 1
3.2%
151711 1
3.2%
135202 1
3.2%
130720 1
3.2%
130025 1
3.2%
122855 1
3.2%
118172 1
3.2%

LPG
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27960.71
Minimum13982
Maximum40507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:48.333018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13982
5-th percentile14688
Q120448
median29113
Q334724.5
95-th percentile38971
Maximum40507
Range26525
Interquartile range (IQR)14276.5

Descriptive statistics

Standard deviation8432.1423
Coefficient of variation (CV)0.30157111
Kurtosis-1.2239505
Mean27960.71
Median Absolute Deviation (MAD)6020
Skewness-0.32176737
Sum866782
Variance71101023
MonotonicityNot monotonic
2023-12-13T00:48:48.519884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
14666 1
 
3.2%
13982 1
 
3.2%
25253 1
 
3.2%
29113 1
 
3.2%
28279 1
 
3.2%
32893 1
 
3.2%
33900 1
 
3.2%
31612 1
 
3.2%
26026 1
 
3.2%
25366 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
13982 1
3.2%
14666 1
3.2%
14710 1
3.2%
15722 1
3.2%
15747 1
3.2%
17251 1
3.2%
18319 1
3.2%
20042 1
3.2%
20854 1
3.2%
24344 1
3.2%
ValueCountFrequency (%)
40507 1
3.2%
39362 1
3.2%
38580 1
3.2%
38126 1
3.2%
37276 1
3.2%
35953 1
3.2%
35133 1
3.2%
34915 1
3.2%
34534 1
3.2%
33951 1
3.2%

아스팔트
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21932.613
Minimum9591
Maximum41476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:48.718363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9591
5-th percentile10060.5
Q113997
median19854
Q328461.5
95-th percentile38841
Maximum41476
Range31885
Interquartile range (IQR)14464.5

Descriptive statistics

Standard deviation9375.9011
Coefficient of variation (CV)0.42748674
Kurtosis-0.70687531
Mean21932.613
Median Absolute Deviation (MAD)6713
Skewness0.53048813
Sum679911
Variance87907522
MonotonicityNot monotonic
2023-12-13T00:48:48.901741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
9674 1
 
3.2%
9591 1
 
3.2%
17275 1
 
3.2%
15414 1
 
3.2%
25574 1
 
3.2%
31219 1
 
3.2%
35908 1
 
3.2%
41476 1
 
3.2%
40508 1
 
3.2%
37174 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
9591 1
3.2%
9674 1
3.2%
10447 1
3.2%
10803 1
3.2%
12174 1
3.2%
12807 1
3.2%
13141 1
3.2%
13582 1
3.2%
14412 1
3.2%
14945 1
3.2%
ValueCountFrequency (%)
41476 1
3.2%
40508 1
3.2%
37174 1
3.2%
35908 1
3.2%
32212 1
3.2%
31219 1
3.2%
29284 1
3.2%
28915 1
3.2%
28008 1
3.2%
26009 1
3.2%

윤활유
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing5
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean17090.231
Minimum4561
Maximum33095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:49.076782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4561
5-th percentile4861.5
Q110637.5
median19422
Q321778
95-th percentile29487.5
Maximum33095
Range28534
Interquartile range (IQR)11140.5

Descriptive statistics

Standard deviation7850.4929
Coefficient of variation (CV)0.45935558
Kurtosis-0.47332307
Mean17090.231
Median Absolute Deviation (MAD)3519
Skewness-0.083145427
Sum444346
Variance61630239
MonotonicityNot monotonic
2023-12-13T00:48:49.228633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
19449 1
 
3.2%
33095 1
 
3.2%
31502 1
 
3.2%
22373 1
 
3.2%
20536 1
 
3.2%
23444 1
 
3.2%
21974 1
 
3.2%
20550 1
 
3.2%
21190 1
 
3.2%
22937 1
 
3.2%
Other values (16) 16
51.6%
(Missing) 5
 
16.1%
ValueCountFrequency (%)
4561 1
3.2%
4806 1
3.2%
5028 1
3.2%
6701 1
3.2%
6739 1
3.2%
7437 1
3.2%
9785 1
3.2%
13195 1
3.2%
14163 1
3.2%
15899 1
3.2%
ValueCountFrequency (%)
33095 1
3.2%
31502 1
3.2%
23444 1
3.2%
22937 1
3.2%
22373 1
3.2%
22245 1
3.2%
21974 1
3.2%
21190 1
3.2%
21098 1
3.2%
21032 1
3.2%

기타제품
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing5
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean40484.115
Minimum19868
Maximum71856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:49.398992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19868
5-th percentile20509.5
Q123392.75
median30975.5
Q359084.75
95-th percentile67203
Maximum71856
Range51988
Interquartile range (IQR)35692

Descriptive statistics

Standard deviation18740.91
Coefficient of variation (CV)0.46292007
Kurtosis-1.7175508
Mean40484.115
Median Absolute Deviation (MAD)10819.5
Skewness0.29867998
Sum1052587
Variance3.512217 × 108
MonotonicityNot monotonic
2023-12-13T00:48:49.596931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
44963 1
 
3.2%
71856 1
 
3.2%
68226 1
 
3.2%
64134 1
 
3.2%
63131 1
 
3.2%
59182 1
 
3.2%
54004 1
 
3.2%
61365 1
 
3.2%
58793 1
 
3.2%
60216 1
 
3.2%
Other values (16) 16
51.6%
(Missing) 5
 
16.1%
ValueCountFrequency (%)
19868 1
3.2%
20444 1
3.2%
20706 1
3.2%
21256 1
3.2%
21373 1
3.2%
21991 1
3.2%
23361 1
3.2%
23488 1
3.2%
24677 1
3.2%
24844 1
3.2%
ValueCountFrequency (%)
71856 1
3.2%
68226 1
3.2%
64134 1
3.2%
63131 1
3.2%
61365 1
3.2%
60216 1
3.2%
59182 1
3.2%
58793 1
3.2%
56327 1
3.2%
54004 1
3.2%

부생연료유
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing9
Missing (%)29.0%
Infinite0
Infinite (%)0.0%
Mean2407.0455
Minimum916
Maximum3030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:49.795325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum916
5-th percentile1775.9
Q12025
median2576
Q32749.75
95-th percentile2972.6
Maximum3030
Range2114
Interquartile range (IQR)724.75

Descriptive statistics

Standard deviation515.71942
Coefficient of variation (CV)0.21425413
Kurtosis1.7748981
Mean2407.0455
Median Absolute Deviation (MAD)221.5
Skewness-1.2756017
Sum52955
Variance265966.52
MonotonicityNot monotonic
2023-12-13T00:48:49.970275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2603 1
 
3.2%
1774 1
 
3.2%
1977 1
 
3.2%
1846 1
 
3.2%
1812 1
 
3.2%
1899 1
 
3.2%
2169 1
 
3.2%
2973 1
 
3.2%
2762 1
 
3.2%
2680 1
 
3.2%
Other values (12) 12
38.7%
(Missing) 9
29.0%
ValueCountFrequency (%)
916 1
3.2%
1774 1
3.2%
1812 1
3.2%
1846 1
3.2%
1899 1
3.2%
1977 1
3.2%
2169 1
3.2%
2387 1
3.2%
2553 1
3.2%
2560 1
3.2%
ValueCountFrequency (%)
3030 1
3.2%
2973 1
3.2%
2965 1
3.2%
2811 1
3.2%
2784 1
3.2%
2762 1
3.2%
2713 1
3.2%
2680 1
3.2%
2603 1
3.2%
2589 1
3.2%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean946982.59
Minimum504561
Maximum1258873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:48:50.117172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum504561
5-th percentile554218.5
Q1875629.5
median938750
Q31075845
95-th percentile1247349.7
Maximum1258873
Range754312
Interquartile range (IQR)200215.5

Descriptive statistics

Standard deviation202174.36
Coefficient of variation (CV)0.21349321
Kurtosis0.0099860353
Mean946982.59
Median Absolute Deviation (MAD)91343
Skewness-0.4637382
Sum29356460
Variance4.0874471 × 1010
MonotonicityNot monotonic
2023-12-13T00:48:50.276229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
504561.0 1
 
3.2%
539986.0 1
 
3.2%
1243991.32 1
 
3.2%
1163817.04 1
 
3.2%
1159004.0 1
 
3.2%
1250708.0 1
 
3.2%
1258873.0 1
 
3.2%
1229653.0 1
 
3.2%
1157610.0 1
 
3.2%
1116983.0 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
504561.0 1
3.2%
539986.0 1
3.2%
568451.0 1
3.2%
643101.0 1
3.2%
724127.0 1
3.2%
841038.0 1
3.2%
858362.0 1
3.2%
864844.0 1
3.2%
886415.0 1
3.2%
907346.0 1
3.2%
ValueCountFrequency (%)
1258873.0 1
3.2%
1250708.0 1
3.2%
1243991.32 1
3.2%
1229653.0 1
3.2%
1163817.04 1
3.2%
1159004.0 1
3.2%
1157610.0 1
3.2%
1116983.0 1
3.2%
1034707.0 1
3.2%
1030093.0 1
3.2%

Interactions

2023-12-13T00:48:41.853950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:15.633425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:17.390518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.261629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.011642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:22.648517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:24.530306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:26.750272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.522950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.981016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.463579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:33.541007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:35.097284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:36.787647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.567727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.403219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.945646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:15.746353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:17.499980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.347929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.119009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:22.753353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:24.628400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:26.876101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.614730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.056711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.557912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:33.625629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:35.188279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:36.908913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.678420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.494763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:42.046113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:15.864082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:17.615881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.446459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.222232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:22.872360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:24.755864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:27.000916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.709330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.151864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.657044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:33.726090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:35.298862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:37.007708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.789757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.585830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:42.131808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:15.977066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:17.750884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.550305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.328681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:23.000667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:24.882750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:27.116185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.806668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.229331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.761253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:33.837081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:35.428796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:37.258851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.913072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.673645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:42.209349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:16.073506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:17.857867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.665210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.410092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:23.111284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:24.980241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:27.248394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.895245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.295652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.876168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:33.939879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:35.530048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:37.366401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:39.023862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.753075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:42.292594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:16.181474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:17.971406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.778347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.490731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:23.222708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:25.092074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:27.367923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.991322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.378859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.973300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.043705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:35.632477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:37.472495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:39.121599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.838311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:42.396348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:16.323983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:18.087129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.908253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.610056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:23.361301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:25.219294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:27.489500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.097996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.488483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:32.096212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.166874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:35.748004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:37.590271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:39.235639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.951033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:42.525181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:16.464004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:18.176884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:20.046896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.729679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:23.469941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:25.343501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:27.610170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.195773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.580825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:32.211857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.271568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:35.857873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:37.694016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:39.340531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.058295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:42.628213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:16.566762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:18.244550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:20.152912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.829836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:23.552369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:25.470922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:27.723467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.288887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.667649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:32.639986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.362655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:35.952988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:37.799006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:39.765102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.144095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:42.733247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:16.660635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:18.312425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:20.249366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:21.910709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:23.678399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:25.581074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:27.846994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.360793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.803302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:32.752031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.453748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:36.057760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:37.895765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:39.850581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.218488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:42.865544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:16.784068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:18.736473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:20.365962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:22.008344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:23.796191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:26.030697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:27.961740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.467700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:30.906029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:32.880547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.543895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:36.176506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.001771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:39.940765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.304454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:43.037700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:16.887350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:18.840035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:20.473240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:22.110408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:23.901178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:26.117712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.044203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.552814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.011166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:32.992651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.630831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:36.283537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.080585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.023365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.381660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:43.157153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:16.980053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:18.929330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:20.592591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:22.201782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:24.010718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:26.225996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.159491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.642925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.103171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:33.106305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.719878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:36.364638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.180809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.098630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.462804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:43.244766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:17.071107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.008326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:20.698252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:22.285581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:24.149089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:26.325992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.238232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.734415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.191329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:33.213823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.807891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:36.473671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.265827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.178089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.560703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:43.349243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:17.167724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.086907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:20.800627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:22.392316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:24.293420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:26.444180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.314261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.819283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.281265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:33.324977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.897621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:36.560308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.350065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.258041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.664403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:43.459840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:17.265188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:19.167720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:20.905522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:22.520408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:24.407126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:26.571499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:28.397928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:29.901655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:31.373968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:33.420954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:34.998563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:36.657868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:38.448113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:40.329841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:41.760906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:48:50.433346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
휘발유등유경유경질중유중유벙커C유납사용제항공유LPG아스팔트윤활유기타제품부생연료유합계
1.0000.8940.7620.9530.8240.7720.8420.8810.7680.9680.9300.8780.8060.8620.6040.822
휘발유0.8941.0000.4000.9050.4070.7380.7760.7060.6860.6510.5390.6360.5600.8550.7120.809
등유0.7620.4001.0000.6240.6460.6470.8270.7290.7030.6640.4500.6180.8340.7720.0000.679
경유0.9530.9050.6241.0000.7390.6520.7580.7840.5420.9110.7780.7960.5850.7070.7550.897
경질중유0.8240.4070.6460.7391.0000.7750.8580.9050.6460.6790.5600.6190.7470.3490.6350.920
중유0.7720.7380.6470.6520.7751.0000.7690.8120.5330.6190.5090.3350.4810.6820.5450.703
벙커C유0.8420.7760.8270.7580.8580.7691.0000.6370.5280.7000.7530.8130.8070.8850.5930.749
납사0.8810.7060.7290.7840.9050.8120.6371.0000.1680.8030.8470.8270.7630.5350.4860.971
용제0.7680.6860.7030.5420.6460.5330.5280.1681.0000.6850.5540.6900.6630.6110.0000.000
항공유0.9680.6510.6640.9110.6790.6190.7000.8030.6851.0000.9010.8830.6450.6600.0000.743
LPG0.9300.5390.4500.7780.5600.5090.7530.8470.5540.9011.0000.9280.5850.8390.0000.676
아스팔트0.8780.6360.6180.7960.6190.3350.8130.8270.6900.8830.9281.0000.7920.7070.4880.520
윤활유0.8060.5600.8340.5850.7470.4810.8070.7630.6630.6450.5850.7921.0000.6650.0000.628
기타제품0.8620.8550.7720.7070.3490.6820.8850.5350.6110.6600.8390.7070.6651.0000.6660.819
부생연료유0.6040.7120.0000.7550.6350.5450.5930.4860.0000.0000.0000.4880.0000.6661.0000.000
합계0.8220.8090.6790.8970.9200.7030.7490.9710.0000.7430.6760.5200.6280.8190.0001.000
2023-12-13T00:48:50.669511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
휘발유등유경유경질중유중유벙커C유납사용제항공유LPG아스팔트윤활유기타제품부생연료유합계
1.0000.947-0.4440.932-0.793-0.985-0.8100.9470.6400.9070.1750.7920.9190.983-0.4460.949
휘발유0.9471.000-0.4520.968-0.787-0.921-0.7870.9210.5030.9070.0990.7810.8460.899-0.5060.956
등유-0.444-0.4521.000-0.4550.5530.4170.795-0.470-0.351-0.6480.314-0.659-0.647-0.664-0.134-0.457
경유0.9320.968-0.4551.000-0.800-0.892-0.7480.9260.5200.8990.0270.7700.8110.894-0.5090.968
경질중유-0.793-0.7870.553-0.8001.0000.7770.820-0.720-0.402-0.7140.204-0.541-0.833-0.8570.606-0.760
중유-0.985-0.9210.417-0.8920.7771.0000.811-0.912-0.627-0.878-0.206-0.784-0.903-0.9620.400-0.906
벙커C유-0.810-0.7870.795-0.7480.8200.8111.000-0.740-0.443-0.8200.152-0.755-0.846-0.8920.249-0.735
납사0.9470.921-0.4700.926-0.720-0.912-0.7401.0000.6000.9210.1790.7840.7810.879-0.4060.978
용제0.6400.503-0.3510.520-0.402-0.627-0.4430.6001.0000.6330.4080.6960.4390.3810.1190.580
항공유0.9070.907-0.6480.899-0.714-0.878-0.8200.9210.6331.0000.1610.9240.7740.822-0.2350.913
LPG0.1750.0990.3140.0270.204-0.2060.1520.1790.4080.1611.0000.278-0.355-0.4480.1490.115
아스팔트0.7920.781-0.6590.770-0.541-0.784-0.7550.7840.6960.9240.2781.0000.6250.6310.1090.773
윤활유0.9190.846-0.6470.811-0.833-0.903-0.8460.7810.4390.774-0.3550.6251.0000.908-0.4080.815
기타제품0.9830.899-0.6640.894-0.857-0.962-0.8920.8790.3810.822-0.4480.6310.9081.000-0.3820.893
부생연료유-0.446-0.506-0.134-0.5090.6060.4000.249-0.4060.119-0.2350.1490.109-0.408-0.3821.000-0.476
합계0.9490.956-0.4570.968-0.760-0.906-0.7350.9780.5800.9130.1150.7730.8150.893-0.4761.000

Missing values

2023-12-13T00:48:43.638020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:48:43.899478image/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.
2023-12-13T00:48:44.053440image/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

휘발유등유경유경질중유중유벙커C유납사용제항공유LPG아스팔트윤활유기타제품부생연료유합계
01992323482930915257733601696.01743196525945220901146669674<NA><NA><NA>504561.0
11993412713191516796735151600.01840066548042920230139829591<NA><NA><NA>539986.0
21994507083230516904034791468.019143467969695261961471010447<NA><NA><NA>568451.0
31995604594192118090839221444.020388688319736349811572210803<NA><NA><NA>643101.0
41996709355130221644654951565.0207845103128671388191574712174<NA><NA><NA>724127.0
51997786487189926417342431451.0232394154319626424382085414945502820444<NA>911462.0
61998760266250123250031271320.0205654153832503638012750612807456120706<NA>864844.0
71999751599050121867038691258.0235451164447630530563022913141480619868<NA>911085.0
8200076202877972279994243959.0225262167012629595683491513582673921991<NA>926898.0
9200176507748332188883826841.0220976162670790652513936214412670121373916907346.0
휘발유등유경유경질중유중유벙커C유납사용제항공유LPG아스팔트윤활유기타제품부생연료유합계
212013135012214992977792383335.07674220764532781300252004228915193955632726031001980.0
222014145056167463145022401274.05250021809529281352022434432212229376021626801030093.0
232015157326184933334213017266.05468924993628391517112536637174211905879327621116983.0
242016153557195203385173345262.06735725981431041607122602640508205506136529731157610.0
252017157908198963448822624199.06513930986840461738563161241476219745400421691229653.0
262018167195211253587802027249.06669031392736511708963390035908234445918218991258873.0
272019168227207053667131999125.05566331316337781707443289331219205366313118121250708.0
28202014304743646350147165484.07352928674053561125952827925574223736413418461159004.0
29202116655852825336185163066.04739912856003550971802911315414315026822619771163817.04
30202216906846510360818101095.329095730060828171228552525317275330957185617741243991.32