Overview

Dataset statistics

Number of variables13
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory120.1 B

Variable types

Numeric13

Dataset

Description국내 수입원유의 유질별 수입량(경질유,중(中)질유,중(重)질유) 단위 : 물량(천Bbl), 금액(천$)
URLhttps://www.data.go.kr/data/15054605/fileData.do

Alerts

is highly overall correlated with 경질유_물량 and 11 other fieldsHigh correlation
경질유_물량 is highly overall correlated with and 11 other fieldsHigh correlation
경질유_금액 is highly overall correlated with and 11 other fieldsHigh correlation
경질유_단가 is highly overall correlated with and 10 other fieldsHigh correlation
중(가운데중)질유_물량 is highly overall correlated with and 6 other fieldsHigh correlation
중(가운데중)질유_금액 is highly overall correlated with and 11 other fieldsHigh correlation
중(가운데중)질유_단가 is highly overall correlated with and 10 other fieldsHigh correlation
중(무거울중)질유_물량 is highly overall correlated with and 10 other fieldsHigh correlation
중(무거울중)질유_금액 is highly overall correlated with and 11 other fieldsHigh correlation
중(무거울중)질유_단가 is highly overall correlated with and 10 other fieldsHigh correlation
합계_물량 is highly overall correlated with and 11 other fieldsHigh correlation
합계_금액 is highly overall correlated with and 11 other fieldsHigh correlation
합계_단가 is highly overall correlated with and 10 other fieldsHigh correlation
has unique valuesUnique
경질유_물량 has unique valuesUnique
경질유_금액 has unique valuesUnique
중(가운데중)질유_물량 has unique valuesUnique
중(가운데중)질유_금액 has unique valuesUnique
중(무거울중)질유_물량 has unique valuesUnique
중(무거울중)질유_금액 has unique valuesUnique
합계_물량 has unique valuesUnique
합계_금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:34:51.515515
Analysis finished2023-12-12 06:35:08.753529
Duration17.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001
Minimum1980
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:08.835305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1982.1
Q11990.5
median2001
Q32011.5
95-th percentile2019.9
Maximum2022
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.556539
Coefficient of variation (CV)0.0062751318
Kurtosis-1.2
Mean2001
Median Absolute Deviation (MAD)11
Skewness0
Sum86043
Variance157.66667
MonotonicityStrictly increasing
2023-12-12T15:35:09.280978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1980 1
 
2.3%
1981 1
 
2.3%
2004 1
 
2.3%
2005 1
 
2.3%
2006 1
 
2.3%
2007 1
 
2.3%
2008 1
 
2.3%
2009 1
 
2.3%
2010 1
 
2.3%
2011 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1980 1
2.3%
1981 1
2.3%
1982 1
2.3%
1983 1
2.3%
1984 1
2.3%
1985 1
2.3%
1986 1
2.3%
1987 1
2.3%
1988 1
2.3%
1989 1
2.3%
ValueCountFrequency (%)
2022 1
2.3%
2021 1
2.3%
2020 1
2.3%
2019 1
2.3%
2018 1
2.3%
2017 1
2.3%
2016 1
2.3%
2015 1
2.3%
2014 1
2.3%
2013 1
2.3%

경질유_물량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406405.79
Minimum26584
Maximum701176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:09.450289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26584
5-th percentile49491.9
Q1212852.5
median487289
Q3547533.5
95-th percentile684078.9
Maximum701176
Range674592
Interquartile range (IQR)334681

Descriptive statistics

Standard deviation206920.98
Coefficient of variation (CV)0.50914869
Kurtosis-1.0743715
Mean406405.79
Median Absolute Deviation (MAD)139101
Skewness-0.51861307
Sum17475449
Variance4.2816291 × 1010
MonotonicityNot monotonic
2023-12-12T15:35:09.600952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
26584 1
 
2.3%
39580 1
 
2.3%
477436 1
 
2.3%
482160 1
 
2.3%
497137 1
 
2.3%
518638 1
 
2.3%
487289 1
 
2.3%
473583 1
 
2.3%
513853 1
 
2.3%
525468 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
26584 1
2.3%
39580 1
2.3%
46954 1
2.3%
72333 1
2.3%
88243 1
2.3%
106235 1
2.3%
131400 1
2.3%
132143 1
2.3%
168696 1
2.3%
174874 1
2.3%
ValueCountFrequency (%)
701176 1
2.3%
698507 1
2.3%
687948 1
2.3%
649257 1
2.3%
641291 1
2.3%
636924 1
2.3%
603632 1
2.3%
569032 1
2.3%
562975 1
2.3%
560900 1
2.3%

경질유_금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21967978
Minimum799599
Maximum65741306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:09.742651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum799599
5-th percentile1738364.3
Q14209443
median13362865
Q337164210
95-th percentile60696143
Maximum65741306
Range64941707
Interquartile range (IQR)32954768

Descriptive statistics

Standard deviation20745478
Coefficient of variation (CV)0.94435085
Kurtosis-0.76822746
Mean21967978
Median Absolute Deviation (MAD)11335193
Skewness0.76229875
Sum9.4462305 × 108
Variance4.3037488 × 1014
MonotonicityNot monotonic
2023-12-12T15:35:09.912501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
799599 1
 
2.3%
1422625 1
 
2.3%
17958662 1
 
2.3%
25286006 1
 
2.3%
32170182 1
 
2.3%
36835174 1
 
2.3%
49392402 1
 
2.3%
28972888 1
 
2.3%
40750990 1
 
2.3%
57295093 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
799599 1
2.3%
1422625 1
2.3%
1706219 1
2.3%
2027672 1
2.3%
2240634 1
2.3%
2368988 1
2.3%
2525750 1
2.3%
2635436 1
2.3%
2955046 1
2.3%
3192522 1
2.3%
ValueCountFrequency (%)
65741306 1
2.3%
64323956 1
2.3%
61022070 1
2.3%
57762800 1
2.3%
57295093 1
2.3%
50956872 1
2.3%
49392402 1
2.3%
46189186 1
2.3%
40750990 1
2.3%
38943240 1
2.3%

경질유_단가
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.632326
Minimum14
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:10.056839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15.1
Q120
median31
Q365.435
95-th percentile108.4
Maximum113
Range99
Interquartile range (IQR)45.435

Descriptive statistics

Standard deviation30.893456
Coefficient of variation (CV)0.67700814
Kurtosis-0.32308295
Mean45.632326
Median Absolute Deviation (MAD)14
Skewness0.95138469
Sum1962.19
Variance954.4056
MonotonicityNot monotonic
2023-12-12T15:35:10.175599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
18.0 3
 
7.0%
20.0 3
 
7.0%
30.0 2
 
4.7%
29.0 2
 
4.7%
109.0 2
 
4.7%
15.0 2
 
4.7%
17.0 2
 
4.7%
36.0 2
 
4.7%
103.0 2
 
4.7%
25.0 2
 
4.7%
Other values (20) 21
48.8%
ValueCountFrequency (%)
14.0 1
 
2.3%
15.0 2
4.7%
16.0 1
 
2.3%
17.0 2
4.7%
18.0 3
7.0%
20.0 3
7.0%
21.0 1
 
2.3%
22.0 1
 
2.3%
25.0 2
4.7%
28.0 1
 
2.3%
ValueCountFrequency (%)
113.0 1
2.3%
109.0 2
4.7%
103.0 2
4.7%
101.0 1
2.3%
79.0 1
2.3%
73.0 1
2.3%
71.0 1
2.3%
70.67 1
2.3%
65.87 1
2.3%
65.0 1
2.3%

중(가운데중)질유_물량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136884
Minimum25768
Maximum338568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:10.299110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25768
5-th percentile35868.1
Q154712.5
median154585
Q3184048.5
95-th percentile220798.8
Maximum338568
Range312800
Interquartile range (IQR)129336

Descriptive statistics

Standard deviation70583.907
Coefficient of variation (CV)0.5156476
Kurtosis0.020974319
Mean136884
Median Absolute Deviation (MAD)37460
Skewness0.11083187
Sum5886012
Variance4.9820879 × 109
MonotonicityNot monotonic
2023-12-12T15:35:10.423007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
46049 1
 
2.3%
45121 1
 
2.3%
139805 1
 
2.3%
163094 1
 
2.3%
175973 1
 
2.3%
182475 1
 
2.3%
177020 1
 
2.3%
183878 1
 
2.3%
170681 1
 
2.3%
208611 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
25768 1
2.3%
35168 1
2.3%
35412 1
2.3%
39973 1
2.3%
40799 1
2.3%
41211 1
2.3%
42530 1
2.3%
45121 1
2.3%
46049 1
2.3%
48244 1
2.3%
ValueCountFrequency (%)
338568 1
2.3%
239108 1
2.3%
222153 1
2.3%
208611 1
2.3%
203204 1
2.3%
197898 1
2.3%
195621 1
2.3%
192045 1
2.3%
186261 1
2.3%
185504 1
2.3%

중(가운데중)질유_금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6953603.3
Minimum441484
Maximum22228514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:10.559783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441484
5-th percentile563946.7
Q11641431.5
median3824425
Q311410025
95-th percentile20440729
Maximum22228514
Range21787030
Interquartile range (IQR)9768593.5

Descriptive statistics

Standard deviation6595948.3
Coefficient of variation (CV)0.9485655
Kurtosis-0.2252474
Mean6953603.3
Median Absolute Deviation (MAD)3090823
Skewness0.95684145
Sum2.9900494 × 108
Variance4.3506534 × 1013
MonotonicityNot monotonic
2023-12-12T15:35:10.683807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1492173 1
 
2.3%
1739257 1
 
2.3%
4746493 1
 
2.3%
7797859 1
 
2.3%
10703838 1
 
2.3%
12371777 1
 
2.3%
16669306 1
 
2.3%
11237731 1
 
2.3%
13298663 1
 
2.3%
22228514 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
441484 1
2.3%
501829 1
2.3%
555784 1
2.3%
637411 1
2.3%
733602 1
2.3%
905439 1
2.3%
977595 1
2.3%
1375380 1
2.3%
1431288 1
2.3%
1492173 1
2.3%
ValueCountFrequency (%)
22228514 1
2.3%
22206637 1
2.3%
20659423 1
2.3%
18472479 1
2.3%
17195967 1
2.3%
16669306 1
2.3%
13470895 1
2.3%
13298663 1
2.3%
12690426 1
2.3%
12371777 1
2.3%

중(가운데중)질유_단가
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.811395
Minimum13
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:10.809561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14.1
Q118.5
median32
Q363.595
95-th percentile106.4
Maximum112
Range99
Interquartile range (IQR)45.095

Descriptive statistics

Standard deviation30.410873
Coefficient of variation (CV)0.69413157
Kurtosis-0.25358226
Mean43.811395
Median Absolute Deviation (MAD)16
Skewness0.97549489
Sum1883.89
Variance924.82117
MonotonicityNot monotonic
2023-12-12T15:35:10.925369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
28.0 3
 
7.0%
32.0 2
 
4.7%
18.0 2
 
4.7%
39.0 2
 
4.7%
24.0 2
 
4.7%
20.0 2
 
4.7%
16.0 2
 
4.7%
61.0 2
 
4.7%
17.0 2
 
4.7%
14.0 2
 
4.7%
Other values (21) 22
51.2%
ValueCountFrequency (%)
13.0 1
2.3%
14.0 2
4.7%
15.0 2
4.7%
16.0 2
4.7%
17.0 2
4.7%
18.0 2
4.7%
19.0 1
2.3%
20.0 2
4.7%
24.0 2
4.7%
27.0 1
2.3%
ValueCountFrequency (%)
112.0 1
2.3%
108.0 1
2.3%
107.0 1
2.3%
101.0 1
2.3%
99.0 1
2.3%
94.0 1
2.3%
78.0 1
2.3%
70.52 1
2.3%
69.0 1
2.3%
68.0 1
2.3%

중(무거울중)질유_물량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149546.37
Minimum46247
Maximum283143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:11.051217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46247
5-th percentile51571.1
Q191336.5
median162129
Q3195457
95-th percentile222141.9
Maximum283143
Range236896
Interquartile range (IQR)104120.5

Descriptive statistics

Standard deviation62683.806
Coefficient of variation (CV)0.41915966
Kurtosis-0.79246304
Mean149546.37
Median Absolute Deviation (MAD)38434
Skewness-0.11370144
Sum6430494
Variance3.9292596 × 109
MonotonicityNot monotonic
2023-12-12T15:35:11.194134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
95832 1
 
2.3%
86841 1
 
2.3%
208549 1
 
2.3%
197949 1
 
2.3%
215685 1
 
2.3%
171427 1
 
2.3%
200563 1
 
2.3%
177624 1
 
2.3%
187882 1
 
2.3%
192965 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
46247 1
2.3%
51437 1
2.3%
51511 1
2.3%
52112 1
2.3%
56454 1
2.3%
56910 1
2.3%
63935 1
2.3%
71013 1
2.3%
82357 1
2.3%
85251 1
2.3%
ValueCountFrequency (%)
283143 1
2.3%
269184 1
2.3%
222153 1
2.3%
222042 1
2.3%
218022 1
2.3%
215685 1
2.3%
208549 1
2.3%
208067 1
2.3%
200563 1
2.3%
199553 1
2.3%

중(무거울중)질유_금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7566515.4
Minimum746336
Maximum26946926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:11.365538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum746336
5-th percentile982485
Q12104236
median4033858
Q312141271
95-th percentile20268301
Maximum26946926
Range26200590
Interquartile range (IQR)10037035

Descriptive statistics

Standard deviation7078860.3
Coefficient of variation (CV)0.9355509
Kurtosis-0.086024234
Mean7566515.4
Median Absolute Deviation (MAD)3002188
Skewness1.0088934
Sum3.2536016 × 108
Variance5.0110263 × 1013
MonotonicityNot monotonic
2023-12-12T15:35:11.507794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2869578 1
 
2.3%
2929203 1
 
2.3%
7168527 1
 
2.3%
9519794 1
 
2.3%
12972110 1
 
2.3%
11310432 1
 
2.3%
18933616 1
 
2.3%
10524526 1
 
2.3%
14633887 1
 
2.3%
20593384 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
746336 1
2.3%
823686 1
2.3%
977020 1
2.3%
1031670 1
2.3%
1474590 1
2.3%
1566953 1
2.3%
1740314 1
2.3%
1812058 1
2.3%
1938988 1
2.3%
1982818 1
2.3%
ValueCountFrequency (%)
26946926 1
2.3%
20593384 1
2.3%
20312760 1
2.3%
19868168 1
2.3%
18933616 1
2.3%
17671708 1
2.3%
17390935 1
2.3%
15362634 1
2.3%
14633887 1
2.3%
13925288 1
2.3%

중(무거울중)질유_단가
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.605581
Minimum13
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:11.663015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q118.5
median30
Q361.935
95-th percentile106.311
Maximum113
Range100
Interquartile range (IQR)43.435

Descriptive statistics

Standard deviation30.239654
Coefficient of variation (CV)0.69348127
Kurtosis-0.18123987
Mean43.605581
Median Absolute Deviation (MAD)14
Skewness1.0014241
Sum1875.04
Variance914.43668
MonotonicityNot monotonic
2023-12-12T15:35:11.850690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
34.0 3
 
7.0%
28.0 3
 
7.0%
16.0 3
 
7.0%
17.0 3
 
7.0%
30.0 2
 
4.7%
20.0 2
 
4.7%
15.0 2
 
4.7%
107.0 2
 
4.7%
70.17 1
 
2.3%
43.89 1
 
2.3%
Other values (21) 21
48.8%
ValueCountFrequency (%)
13.0 1
 
2.3%
14.0 1
 
2.3%
15.0 2
4.7%
16.0 3
7.0%
17.0 3
7.0%
18.0 1
 
2.3%
19.0 1
 
2.3%
20.0 2
4.7%
23.0 1
 
2.3%
24.0 1
 
2.3%
ValueCountFrequency (%)
113.0 1
2.3%
107.0 2
4.7%
100.11 1
2.3%
99.0 1
2.3%
94.0 1
2.3%
78.0 1
2.3%
70.17 1
2.3%
69.0 1
2.3%
66.0 1
2.3%
63.87 1
2.3%

합계_물량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692836.14
Minimum168465
Maximum1118168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:12.045546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168465
5-th percentile173953.8
Q1347762.5
median835085
Q3921059
95-th percentile1077499.4
Maximum1118168
Range949703
Interquartile range (IQR)573296.5

Descriptive statistics

Standard deviation319598.7
Coefficient of variation (CV)0.46129045
Kurtosis-1.2028348
Mean692836.14
Median Absolute Deviation (MAD)191021
Skewness-0.57521517
Sum29791954
Variance1.0214333 × 1011
MonotonicityNot monotonic
2023-12-12T15:35:12.212162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
168465 1
 
2.3%
171542 1
 
2.3%
825790 1
 
2.3%
843203 1
 
2.3%
888795 1
 
2.3%
872540 1
 
2.3%
864872 1
 
2.3%
835085 1
 
2.3%
872416 1
 
2.3%
927044 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
168465 1
2.3%
171542 1
2.3%
171841 1
2.3%
192969 1
2.3%
198313 1
2.3%
199482 1
2.3%
213622 1
2.3%
219667 1
2.3%
260180 1
2.3%
292583 1
2.3%
ValueCountFrequency (%)
1118168 1
2.3%
1116281 1
2.3%
1078119 1
2.3%
1071923 1
2.3%
1031283 1
2.3%
1026106 1
2.3%
980259 1
2.3%
960146 1
2.3%
947292 1
2.3%
927523 1
2.3%

합계_금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36488097
Minimum3353187
Maximum1.0684335 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:12.368268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3353187
5-th percentile3931243.3
Q16989332
median21368018
Q360088933
95-th percentile1.0001253 × 108
Maximum1.0684335 × 108
Range1.0349017 × 108
Interquartile range (IQR)53099601

Descriptive statistics

Standard deviation33768427
Coefficient of variation (CV)0.92546421
Kurtosis-0.74277845
Mean36488097
Median Absolute Deviation (MAD)16506415
Skewness0.7846419
Sum1.5689882 × 109
Variance1.1403067 × 1015
MonotonicityNot monotonic
2023-12-12T15:35:12.576224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5161350 1
 
2.3%
6091085 1
 
2.3%
29873682 1
 
2.3%
42603659 1
 
2.3%
55846130 1
 
2.3%
60517383 1
 
2.3%
84995324 1
 
2.3%
50735145 1
 
2.3%
68683540 1
 
2.3%
100116991 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
3353187 1
2.3%
3787492 1
2.3%
3827870 1
2.3%
4861603 1
2.3%
5161350 1
2.3%
5499594 1
2.3%
5767996 1
2.3%
5792390 1
2.3%
5848281 1
2.3%
6091085 1
2.3%
ValueCountFrequency (%)
106843353 1
2.3%
105378658 1
2.3%
100116991 1
2.3%
99072428 1
2.3%
93906987 1
2.3%
84995324 1
2.3%
79790401 1
2.3%
70222972 1
2.3%
68683540 1
2.3%
67690684 1
2.3%

합계_단가
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.825814
Minimum14
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:35:12.747902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15.1
Q119.5
median31
Q364.255
95-th percentile107.418
Maximum113
Range99
Interquartile range (IQR)44.755

Descriptive statistics

Standard deviation30.587213
Coefficient of variation (CV)0.68235712
Kurtosis-0.27658903
Mean44.825814
Median Absolute Deviation (MAD)14
Skewness0.9715876
Sum1927.51
Variance935.57762
MonotonicityNot monotonic
2023-12-12T15:35:12.885539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
17.0 4
 
9.3%
19.0 2
 
4.7%
20.0 2
 
4.7%
108.0 2
 
4.7%
29.0 2
 
4.7%
28.0 2
 
4.7%
15.0 2
 
4.7%
36.0 2
 
4.7%
53.0 2
 
4.7%
113.0 1
 
2.3%
Other values (22) 22
51.2%
ValueCountFrequency (%)
14.0 1
 
2.3%
15.0 2
4.7%
16.0 1
 
2.3%
17.0 4
9.3%
18.0 1
 
2.3%
19.0 2
4.7%
20.0 2
4.7%
21.0 1
 
2.3%
24.0 1
 
2.3%
25.0 1
 
2.3%
ValueCountFrequency (%)
113.0 1
2.3%
108.0 2
4.7%
102.18 1
2.3%
101.0 1
2.3%
98.0 1
2.3%
79.0 1
2.3%
71.0 1
2.3%
70.5 1
2.3%
69.0 1
2.3%
65.51 1
2.3%

Interactions

2023-12-12T15:35:07.286901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:51.895335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.121908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.366554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:55.980537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:57.267636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.633057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.822834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.882580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:02.087075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:03.677948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.811617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.024796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:07.408927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:51.980459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.206189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.459114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.076253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:57.360822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.712297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.901257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.957851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:02.476448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:03.787840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.883241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.135510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:07.518957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.091966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.294432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.547642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.182233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:57.461459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.830224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.012622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.052139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:02.576532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:03.904573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.974891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.231407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:07.622627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.191139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.380777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.633772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.280099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:57.581415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.939548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.102590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.137283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:02.687934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.003734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.065542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.317944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:07.715913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.268809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.472655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.722257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.354671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:57.720522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.042859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.177068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.216632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:02.796185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.077199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.150624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.413465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:07.829349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.353596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.579753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.837684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.443007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:57.811623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.134899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.276855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.323647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:02.901119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.168631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.241694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.530601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:07.922834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.450449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.675242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.939334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.537861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:57.958470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.214856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.357073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.409336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:02.995257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.261257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.368254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.658391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:08.033422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.546160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.757563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:55.353396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.653099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.066852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.303305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.428945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.497342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:03.083059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.334452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.467165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.734809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:08.108470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.627013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.856061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:55.447660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.761674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.172949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.390405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.505553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.578671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:03.180947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.409600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.553591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.821185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:08.181593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.725565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.955528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:55.559344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.874219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.296073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.470603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.585722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.662528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:03.299165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.490229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.640792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:06.919946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:08.246982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.850541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.056416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:55.663275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:56.966298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.383059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.548360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.660038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.750245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:03.397981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.565218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.723828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:07.016805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:08.319494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:52.947409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.159734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:55.775425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:57.061227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.464549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.639171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.736546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.841403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:03.496686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.643694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.808388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:07.110960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:08.395557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:53.046239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:54.266124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:55.883156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:57.177157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:58.550361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:59.735712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:00.813220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:01.953887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:03.590765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:04.727147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:05.916042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:07.197364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:35:12.995410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경질유_물량경질유_금액경질유_단가중(가운데중)질유_물량중(가운데중)질유_금액중(가운데중)질유_단가중(무거울중)질유_물량중(무거울중)질유_금액중(무거울중)질유_단가합계_물량합계_금액합계_단가
1.0000.9360.7940.7540.6920.7080.7800.8140.6350.7770.9400.8110.777
경질유_물량0.9361.0000.4420.2000.5150.4080.2620.7500.0000.2680.9730.0000.268
경질유_금액0.7940.4421.0000.7810.3930.8800.8170.4870.8010.7900.0000.9810.790
경질유_단가0.7540.2000.7811.0000.3050.8930.9930.6620.9470.9970.0000.8480.997
중(가운데중)질유_물량0.6920.5150.3930.3051.0000.5290.0000.6740.0000.0000.7020.4730.000
중(가운데중)질유_금액0.7080.4080.8800.8930.5291.0000.9020.5920.8650.8960.3600.8980.896
중(가운데중)질유_단가0.7800.2620.8170.9930.0000.9021.0000.6080.9330.9990.0000.8010.999
중(무거울중)질유_물량0.8140.7500.4870.6620.6740.5920.6081.0000.7670.6440.7950.3200.644
중(무거울중)질유_금액0.6350.0000.8010.9470.0000.8650.9330.7671.0000.9430.2410.8390.943
중(무거울중)질유_단가0.7770.2680.7900.9970.0000.8960.9990.6440.9431.0000.0000.7931.000
합계_물량0.9400.9730.0000.0000.7020.3600.0000.7950.2410.0001.0000.0000.000
합계_금액0.8110.0000.9810.8480.4730.8980.8010.3200.8390.7930.0001.0000.793
합계_단가0.7770.2680.7900.9970.0000.8960.9990.6440.9431.0000.0000.7931.000
2023-12-12T15:35:13.167055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경질유_물량경질유_금액경질유_단가중(가운데중)질유_물량중(가운데중)질유_금액중(가운데중)질유_단가중(무거울중)질유_물량중(무거울중)질유_금액중(무거울중)질유_단가합계_물량합계_금액합계_단가
1.0000.9320.9410.6960.6960.8680.6940.8220.8170.6900.9540.9160.696
경질유_물량0.9321.0000.8760.5630.7740.8160.5570.7200.7170.5550.9830.8480.560
경질유_금액0.9410.8761.0000.7920.7320.9450.7870.7910.8860.7820.9010.9830.789
경질유_단가0.6960.5630.7921.0000.4940.8440.9970.6280.8910.9960.6160.8420.998
중(가운데중)질유_물량0.6960.7740.7320.4941.0000.8340.4870.4980.6100.4790.7960.7480.486
중(가운데중)질유_금액0.8680.8160.9450.8440.8341.0000.8420.7120.8990.8330.8500.9690.840
중(가운데중)질유_단가0.6940.5570.7870.9970.4870.8421.0000.6310.8970.9970.6100.8390.998
중(무거울중)질유_물량0.8220.7200.7910.6280.4980.7120.6311.0000.8590.6150.7600.8090.631
중(무거울중)질유_금액0.8170.7170.8860.8910.6100.8990.8970.8591.0000.8850.7580.9360.893
중(무거울중)질유_단가0.6900.5550.7820.9960.4790.8330.9970.6150.8851.0000.6100.8300.997
합계_물량0.9540.9830.9010.6160.7960.8500.6100.7600.7580.6101.0000.8770.614
합계_금액0.9160.8480.9830.8420.7480.9690.8390.8090.9360.8300.8771.0000.838
합계_단가0.6960.5600.7890.9980.4860.8400.9980.6310.8930.9970.6140.8381.000

Missing values

2023-12-12T15:35:08.505567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:35:08.682438image/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

경질유_물량경질유_금액경질유_단가중(가운데중)질유_물량중(가운데중)질유_금액중(가운데중)질유_단가중(무거울중)질유_물량중(무거울중)질유_금액중(무거울중)질유_단가합계_물량합계_금액합계_단가
019802658479959930.046049149217332.095832286957830.0168465516135031.0
1198139580142262536.045121173925739.086841292920334.0171542609108536.0
2198246954170621936.042530137538032.082357276668234.0171841584828134.0
3198372333224063431.049623143128829.071013209607430.0192969576799630.0
4198488243263543630.059802168236428.051437147459029.0199482579239029.0
51985106235295504628.03516897759528.056910156695328.0198313549959428.0
61986132143202767215.03541250182914.05211282368616.0219667335318715.0
71987131400236898818.02576844148417.05645497702017.0213622378749218.0
81988168696252575015.03997355578414.05151174633614.0260180382787015.0
91989187437319252217.04121163741115.063935103167016.0292583486160317.0
경질유_물량경질유_금액경질유_단가중(가운데중)질유_물량중(가운데중)질유_금액중(가운데중)질유_단가중(무거울중)질유_물량중(무거울중)질유_금액중(무거울중)질유_단가합계_물량합계_금액합계_단가
33201356090061022070109.019204520659423108.016212917390935107.091507499072428108.0
34201456297557762800103.01862611847247999.01782871767170899.092752393906987101.0
3520156412913506849755.03385681719596751.046247241485852.010261065467932253.0
3620166492572753075242.0239108940729339.0189754735180339.010781194428984841.0
3720176879483749324755.02221531158231952.02080671058491751.011181685966048353.0
3820186985075095687273.01956211347089569.02221531536263469.011162817979040171.0
3920197011764618918665.871527261010849966.192180221392528863.8710719237022297265.51
4020206036322816082246.65154585652109242.18222042974551843.899802594442743245.32
4120215510963894324070.67125907887927670.522831431986816870.179601466769068470.5
42202263692465741306103.012517512690426101.026918426946926100.111031283105378658102.18