Overview

Dataset statistics

Number of variables12
Number of observations430
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 KiB
Average record size in memory107.3 B

Variable types

DateTime1
Numeric10
Categorical1

Dataset

Description한국 무역협회(KITA)에서 매월 업데이트 중인 국가 별 천연가스 수입량 데이터를 기반으로 한국의 아시아 대륙산 LNG 수입량과 단위가격을 산출한다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15102974/fileData.do

Alerts

환율(원달러) is highly overall correlated with 단위가격(Ton당원)_아시아 and 3 other fieldsHigh correlation
금액(백만불)_아시아 is highly overall correlated with 중량(Ton)_아시아 and 8 other fieldsHigh correlation
중량(Ton)_아시아 is highly overall correlated with 금액(백만불)_아시아High correlation
단위가격(Ton당달러)_아시아 is highly overall correlated with 금액(백만불)_아시아 and 7 other fieldsHigh correlation
단위가격(MMBTU당달러)_아시아 is highly overall correlated with 금액(백만불)_아시아 and 7 other fieldsHigh correlation
단위가격(m3당달러)_아시아 is highly overall correlated with 금액(백만불)_아시아 and 7 other fieldsHigh correlation
단위가격(Ton당원)_아시아 is highly overall correlated with 환율(원달러) and 8 other fieldsHigh correlation
단위가격(MMBTU당원)_아시아 is highly overall correlated with 환율(원달러) and 8 other fieldsHigh correlation
단위가격(m3당원)_아시아 is highly overall correlated with 환율(원달러) and 8 other fieldsHigh correlation
단위가격(MJ당원)_아시아 is highly overall correlated with 환율(원달러) and 8 other fieldsHigh correlation
단위가격(MJ당달러)_아시아 is highly overall correlated with 금액(백만불)_아시아 and 7 other fieldsHigh correlation
연월 has unique valuesUnique
중량(Ton)_아시아 has unique valuesUnique
단위가격(Ton당원)_아시아 has unique valuesUnique
단위가격(MMBTU당원)_아시아 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:50:46.838282
Analysis finished2023-12-12 15:51:00.181846
Duration13.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct430
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum1988-01-01 00:00:00
Maximum2023-10-01 00:00:00
2023-12-13T00:51:00.292701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:00.464312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

환율(원달러)
Real number (ℝ)

HIGH CORRELATION 

Distinct427
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1048.6694
Minimum666.56
Maximum1706.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:00.659358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum666.56
5-th percentile714.978
Q1841.735
median1115.68
Q31182.8875
95-th percentile1326.3035
Maximum1706.8
Range1040.24
Interquartile range (IQR)341.1525

Descriptive statistics

Standard deviation202.74276
Coefficient of variation (CV)0.19333335
Kurtosis-0.6408562
Mean1048.6694
Median Absolute Deviation (MAD)105.08
Skewness-0.24066507
Sum450927.83
Variance41104.628
MonotonicityNot monotonic
2023-12-13T00:51:00.864176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1302.6 2
 
0.5%
1125.28 2
 
0.5%
1122.45 2
 
0.5%
787.46 1
 
0.2%
1073.17 1
 
0.2%
1154.27 1
 
0.2%
1135.55 1
 
0.2%
1125.9 1
 
0.2%
1123.35 1
 
0.2%
1145.85 1
 
0.2%
Other values (417) 417
97.0%
ValueCountFrequency (%)
666.56 1
0.2%
666.71 1
0.2%
667.28 1
0.2%
667.36 1
0.2%
668.38 1
0.2%
670.03 1
0.2%
671.27 1
0.2%
672.33 1
0.2%
672.97 1
0.2%
675.17 1
0.2%
ValueCountFrequency (%)
1706.8 1
0.2%
1623.06 1
0.2%
1505.28 1
0.2%
1484.08 1
0.2%
1461.98 1
0.2%
1429.46 1
0.2%
1426.66 1
0.2%
1397.18 1
0.2%
1394.62 1
0.2%
1391.97 1
0.2%

금액(백만불)_아시아
Real number (ℝ)

HIGH CORRELATION 

Distinct309
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.42791
Minimum10
Maximum1338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:01.069748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q1105.75
median225.5
Q3393
95-th percentile729.3
Maximum1338
Range1328
Interquartile range (IQR)287.25

Descriptive statistics

Standard deviation236.77687
Coefficient of variation (CV)0.82665432
Kurtosis2.098749
Mean286.42791
Median Absolute Deviation (MAD)141
Skewness1.3657582
Sum123164
Variance56063.285
MonotonicityNot monotonic
2023-12-13T00:51:01.284884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 5
 
1.2%
216 5
 
1.2%
30 5
 
1.2%
160 4
 
0.9%
292 4
 
0.9%
31 4
 
0.9%
128 3
 
0.7%
231 3
 
0.7%
122 3
 
0.7%
117 3
 
0.7%
Other values (299) 391
90.9%
ValueCountFrequency (%)
10 2
0.5%
11 1
 
0.2%
14 1
 
0.2%
15 1
 
0.2%
18 2
0.5%
19 1
 
0.2%
20 1
 
0.2%
21 3
0.7%
22 1
 
0.2%
26 1
 
0.2%
ValueCountFrequency (%)
1338 1
0.2%
1176 1
0.2%
1167 1
0.2%
1150 1
0.2%
1061 1
0.2%
1026 1
0.2%
1019 1
0.2%
985 1
0.2%
980 1
0.2%
962 1
0.2%

중량(Ton)_아시아
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct430
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean695273.39
Minimum56018
Maximum1759777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:01.528776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56018
5-th percentile168025.7
Q1451865.75
median685563
Q3933708.5
95-th percentile1268568.6
Maximum1759777
Range1703759
Interquartile range (IQR)481842.75

Descriptive statistics

Standard deviation341237.75
Coefficient of variation (CV)0.49079651
Kurtosis-0.19546585
Mean695273.39
Median Absolute Deviation (MAD)235584
Skewness0.30273636
Sum2.9896756 × 108
Variance1.164432 × 1011
MonotonicityNot monotonic
2023-12-13T00:51:01.737905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116512 1
 
0.2%
972893 1
 
0.2%
689858 1
 
0.2%
1029702 1
 
0.2%
663629 1
 
0.2%
995628 1
 
0.2%
1503778 1
 
0.2%
1451075 1
 
0.2%
918440 1
 
0.2%
1689000 1
 
0.2%
Other values (420) 420
97.7%
ValueCountFrequency (%)
56018 1
0.2%
56102 1
0.2%
57566 1
0.2%
59086 1
0.2%
112487 1
0.2%
112603 1
0.2%
113042 1
0.2%
113217 1
0.2%
113468 1
0.2%
114297 1
0.2%
ValueCountFrequency (%)
1759777 1
0.2%
1694047 1
0.2%
1689000 1
0.2%
1647898 1
0.2%
1571835 1
0.2%
1569891 1
0.2%
1541778 1
0.2%
1503778 1
0.2%
1490159 1
0.2%
1451075 1
0.2%

단위가격(Ton당달러)_아시아
Real number (ℝ)

HIGH CORRELATION 

Distinct428
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375.64551
Minimum128.5
Maximum1384.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:01.928603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.5
5-th percentile155.443
Q1192.3325
median327.27
Q3483.0025
95-th percentile786.0105
Maximum1384.82
Range1256.32
Interquartile range (IQR)290.67

Descriptive statistics

Standard deviation213.12302
Coefficient of variation (CV)0.56735143
Kurtosis1.6587736
Mean375.64551
Median Absolute Deviation (MAD)139.365
Skewness1.2319097
Sum161527.57
Variance45421.421
MonotonicityNot monotonic
2023-12-13T00:51:02.118600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.51 2
 
0.5%
420.8 2
 
0.5%
180.24 1
 
0.2%
650.8 1
 
0.2%
750.88 1
 
0.2%
818.68 1
 
0.2%
837.82 1
 
0.2%
680.98 1
 
0.2%
682.28 1
 
0.2%
702.24 1
 
0.2%
Other values (418) 418
97.2%
ValueCountFrequency (%)
128.5 1
0.2%
132.2 1
0.2%
132.82 1
0.2%
135.13 1
0.2%
135.17 1
0.2%
135.93 1
0.2%
138.84 1
0.2%
140.54 1
0.2%
140.7 1
0.2%
142.35 1
0.2%
ValueCountFrequency (%)
1384.82 1
0.2%
1226.92 1
0.2%
1120.56 1
0.2%
1096.58 1
0.2%
1093.75 1
0.2%
979.66 1
0.2%
973.73 1
0.2%
892.74 1
0.2%
846.12 1
0.2%
844.25 1
0.2%

단위가격(MMBTU당달러)_아시아
Real number (ℝ)

HIGH CORRELATION 

Distinct357
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2608372
Minimum2.49
Maximum26.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:02.265244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.49
5-th percentile3.0145
Q13.73
median6.335
Q39.3175
95-th percentile15.173
Maximum26.71
Range24.22
Interquartile range (IQR)5.5875

Descriptive statistics

Standard deviation4.106973
Coefficient of variation (CV)0.56563353
Kurtosis1.6606681
Mean7.2608372
Median Absolute Deviation (MAD)2.69
Skewness1.2329411
Sum3122.16
Variance16.867227
MonotonicityNot monotonic
2023-12-13T00:51:02.408190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.65 4
 
0.9%
3.78 4
 
0.9%
3.51 3
 
0.7%
3.88 3
 
0.7%
3.61 3
 
0.7%
3.46 3
 
0.7%
3.53 3
 
0.7%
3.49 3
 
0.7%
3.42 3
 
0.7%
7.31 3
 
0.7%
Other values (347) 398
92.6%
ValueCountFrequency (%)
2.49 1
0.2%
2.57 1
0.2%
2.58 1
0.2%
2.62 2
0.5%
2.64 1
0.2%
2.69 1
0.2%
2.73 2
0.5%
2.76 1
0.2%
2.77 1
0.2%
2.78 1
0.2%
ValueCountFrequency (%)
26.71 1
0.2%
23.66 1
0.2%
21.61 1
0.2%
21.15 1
0.2%
21.09 1
0.2%
18.89 1
0.2%
18.78 1
0.2%
17.22 1
0.2%
16.32 1
0.2%
16.28 1
0.2%

단위가격(m3당달러)_아시아
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29918605
Minimum0.07
Maximum1.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:02.567088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.1
Q10.16
median0.26
Q30.39
95-th percentile0.63
Maximum1.09
Range1.02
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.17193818
Coefficient of variation (CV)0.57468651
Kurtosis1.3352402
Mean0.29918605
Median Absolute Deviation (MAD)0.11
Skewness1.1364569
Sum128.65
Variance0.029562739
MonotonicityNot monotonic
2023-12-13T00:51:02.736252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15 30
 
7.0%
0.14 24
 
5.6%
0.16 20
 
4.7%
0.21 17
 
4.0%
0.13 14
 
3.3%
0.17 14
 
3.3%
0.34 13
 
3.0%
0.2 13
 
3.0%
0.3 13
 
3.0%
0.1 13
 
3.0%
Other values (56) 259
60.2%
ValueCountFrequency (%)
0.07 1
 
0.2%
0.08 3
 
0.7%
0.09 7
 
1.6%
0.1 13
3.0%
0.11 7
 
1.6%
0.12 8
 
1.9%
0.13 14
3.3%
0.14 24
5.6%
0.15 30
7.0%
0.16 20
4.7%
ValueCountFrequency (%)
1.09 1
 
0.2%
0.97 1
 
0.2%
0.88 1
 
0.2%
0.86 2
0.5%
0.77 2
0.5%
0.7 1
 
0.2%
0.67 4
0.9%
0.66 2
0.5%
0.65 4
0.9%
0.64 3
0.7%

단위가격(MJ당달러)_아시아
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
0.01
252 
0.0
157 
0.02
 
20
0.03
 
1

Length

Max length4
Median length4
Mean length3.6348837
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01 252
58.6%
0.0 157
36.5%
0.02 20
 
4.7%
0.03 1
 
0.2%

Length

2023-12-13T00:51:02.875070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:51:03.016539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01 252
58.6%
0.0 157
36.5%
0.02 20
 
4.7%
0.03 1
 
0.2%

단위가격(Ton당원)_아시아
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct430
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410789.9
Minimum90558.13
Maximum1707362.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:03.152554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90558.13
5-th percentile121975.64
Q1171800.8
median363823.59
Q3535271.43
95-th percentile858581.81
Maximum1707362.8
Range1616804.7
Interquartile range (IQR)363470.63

Descriptive statistics

Standard deviation269094.71
Coefficient of variation (CV)0.65506653
Kurtosis2.9699902
Mean410789.9
Median Absolute Deviation (MAD)186625.65
Skewness1.3877938
Sum1.7663966 × 108
Variance7.2411963 × 1010
MonotonicityNot monotonic
2023-12-13T00:51:03.316863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141930.96 1
 
0.2%
739916.11 1
 
0.2%
858525.2 1
 
0.2%
954183.76 1
 
0.2%
967067.62 1
 
0.2%
773283.7 1
 
0.2%
768180.81 1
 
0.2%
788859.05 1
 
0.2%
716125.06 1
 
0.2%
792820.69 1
 
0.2%
Other values (420) 420
97.7%
ValueCountFrequency (%)
90558.13 1
0.2%
97392.16 1
0.2%
97805.57 1
0.2%
108330.92 1
0.2%
110422.22 1
0.2%
111356.76 1
0.2%
111373.47 1
0.2%
115089.19 1
0.2%
115310.58 1
0.2%
115944.47 1
0.2%
ValueCountFrequency (%)
1707362.79 1
0.2%
1653492.51 1
0.2%
1598656.87 1
0.2%
1421409.06 1
0.2%
1364182.73 1
0.2%
1280808.35 1
0.2%
1217789.7 1
0.2%
1152605.15 1
0.2%
1113093.92 1
0.2%
1038101.46 1
0.2%

단위가격(MMBTU당원)_아시아
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct430
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7937.7838
Minimum1758.11
Maximum32926.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:03.477142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1758.11
5-th percentile2366.411
Q13330.235
median7052.46
Q310322.635
95-th percentile16587.954
Maximum32926.25
Range31168.14
Interquartile range (IQR)6992.4

Descriptive statistics

Standard deviation5186.9072
Coefficient of variation (CV)0.65344526
Kurtosis2.9676328
Mean7937.7838
Median Absolute Deviation (MAD)3576.71
Skewness1.3870269
Sum3413247
Variance26904006
MonotonicityNot monotonic
2023-12-13T00:51:03.918865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2755.47 1
 
0.2%
14295.31 1
 
0.2%
16586.86 1
 
0.2%
18435.0 1
 
0.2%
18683.92 1
 
0.2%
14939.98 1
 
0.2%
14841.39 1
 
0.2%
15240.9 1
 
0.2%
13835.67 1
 
0.2%
15289.44 1
 
0.2%
Other values (420) 420
97.7%
ValueCountFrequency (%)
1758.11 1
0.2%
1890.79 1
0.2%
1898.81 1
0.2%
2103.15 1
0.2%
2143.75 1
0.2%
2158.57 1
0.2%
2162.22 1
0.2%
2230.92 1
0.2%
2238.66 1
0.2%
2250.96 1
0.2%
ValueCountFrequency (%)
32926.25 1
0.2%
31887.37 1
0.2%
30829.87 1
0.2%
27411.67 1
0.2%
26308.07 1
0.2%
24700.21 1
0.2%
23484.9 1
0.2%
22227.83 1
0.2%
21465.86 1
0.2%
20019.64 1
0.2%

단위가격(m3당원)_아시아
Real number (ℝ)

HIGH CORRELATION 

Distinct428
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327.21488
Minimum48.84
Maximum1345.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:04.061620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48.84
5-th percentile76.441
Q1138.765
median292.58
Q3426.7725
95-th percentile685.604
Maximum1345.29
Range1296.45
Interquartile range (IQR)288.0075

Descriptive statistics

Standard deviation215.49809
Coefficient of variation (CV)0.65858278
Kurtosis2.6261819
Mean327.21488
Median Absolute Deviation (MAD)148.75
Skewness1.3005226
Sum140702.4
Variance46439.426
MonotonicityNot monotonic
2023-12-13T00:51:04.234265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
262.72 2
 
0.5%
116.51 2
 
0.5%
76.54 1
 
0.2%
580.5 1
 
0.2%
594.16 1
 
0.2%
685.56 1
 
0.2%
761.95 1
 
0.2%
772.24 1
 
0.2%
617.49 1
 
0.2%
613.42 1
 
0.2%
Other values (418) 418
97.2%
ValueCountFrequency (%)
48.84 1
0.2%
52.52 1
0.2%
52.75 1
0.2%
58.42 1
0.2%
59.55 1
0.2%
60.06 1
0.2%
62.19 1
0.2%
62.53 1
0.2%
62.94 1
0.2%
63.42 1
0.2%
ValueCountFrequency (%)
1345.29 1
0.2%
1302.84 1
0.2%
1259.64 1
0.2%
1119.98 1
0.2%
1074.89 1
0.2%
1009.19 1
0.2%
959.54 1
0.2%
908.18 1
0.2%
877.05 1
0.2%
817.96 1
0.2%

단위가격(MJ당원)_아시아
Real number (ℝ)

HIGH CORRELATION 

Distinct357
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5307209
Minimum1.67
Maximum31.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T00:51:04.366842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.67
5-th percentile2.2445
Q13.16
median6.685
Q39.8
95-th percentile15.7255
Maximum31.21
Range29.54
Interquartile range (IQR)6.64

Descriptive statistics

Standard deviation4.9212771
Coefficient of variation (CV)0.65349349
Kurtosis2.9464863
Mean7.5307209
Median Absolute Deviation (MAD)3.4
Skewness1.3831961
Sum3238.21
Variance24.218968
MonotonicityNot monotonic
2023-12-13T00:51:04.531093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 4
 
0.9%
2.5 3
 
0.7%
2.9 3
 
0.7%
2.57 3
 
0.7%
8.46 3
 
0.7%
5.7 3
 
0.7%
2.58 3
 
0.7%
5.67 3
 
0.7%
8.27 3
 
0.7%
2.56 3
 
0.7%
Other values (347) 399
92.8%
ValueCountFrequency (%)
1.67 1
0.2%
1.79 1
0.2%
1.8 1
0.2%
1.99 1
0.2%
2.03 1
0.2%
2.05 2
0.5%
2.12 2
0.5%
2.13 1
0.2%
2.15 1
0.2%
2.16 1
0.2%
ValueCountFrequency (%)
31.21 1
0.2%
30.23 1
0.2%
29.23 1
0.2%
25.99 1
0.2%
24.94 1
0.2%
23.42 1
0.2%
22.26 1
0.2%
21.07 1
0.2%
20.35 1
0.2%
18.98 1
0.2%

Interactions

2023-12-13T00:50:58.365903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:47.353671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:48.343063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:49.629478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:51.267142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:52.459556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:53.675546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.600843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:55.727484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:56.775796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:58.480687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:47.442738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:48.483383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:49.740274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:51.397404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:52.565716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:53.756387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.701597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:55.846237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:56.890156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:58.606778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:47.550091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:48.615695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:49.878546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:51.528794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:52.694328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:53.858064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.805269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:55.966300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:57.014037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:58.739517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:47.662505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:48.759379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:50.025846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:51.642630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:52.807775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:53.969971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.941043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:56.088800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:57.133545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:58.900994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:47.747295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:48.907990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:50.144340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:51.751490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:52.943699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.053983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:55.067993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:56.198236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:57.252354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:59.048112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:47.858955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:49.043882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:50.558832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:51.882098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:53.064583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.142107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:55.186455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:56.296079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:57.387764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:59.193390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:47.958701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:49.165053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:50.670075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:52.026173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:53.188170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.220857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:55.284597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:56.384961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:57.511244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:59.344031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:48.071498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:49.286791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:50.811939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:52.156356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:53.335110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.304138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:55.394062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:56.501098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:57.660629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:59.474270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:48.159767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:49.387510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:50.948841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:52.257447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:53.449684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.388923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:55.494478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:56.588948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:57.796943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:59.603421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:48.249180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:49.507288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:51.117253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:52.353523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:53.580109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:54.497063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:55.618433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:56.684100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:58.259519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:51:04.661021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환율(원달러)금액(백만불)_아시아중량(Ton)_아시아단위가격(Ton당달러)_아시아단위가격(MMBTU당달러)_아시아단위가격(m3당달러)_아시아단위가격(MJ당달러)_아시아단위가격(Ton당원)_아시아단위가격(MMBTU당원)_아시아단위가격(m3당원)_아시아단위가격(MJ당원)_아시아
환율(원달러)1.0000.6800.7200.7230.7190.7520.6420.7470.7470.7640.745
금액(백만불)_아시아0.6801.0000.7840.9280.9280.9280.8000.8850.8850.8840.885
중량(Ton)_아시아0.7200.7841.0000.5350.5290.5250.4790.5520.5520.5620.553
단위가격(Ton당달러)_아시아0.7230.9280.5351.0001.0000.9950.9830.9630.9630.9570.962
단위가격(MMBTU당달러)_아시아0.7190.9280.5291.0001.0000.9950.9840.9620.9620.9570.962
단위가격(m3당달러)_아시아0.7520.9280.5250.9950.9951.0000.9700.9660.9660.9680.966
단위가격(MJ당달러)_아시아0.6420.8000.4790.9830.9840.9701.0000.8730.8730.8730.873
단위가격(Ton당원)_아시아0.7470.8850.5520.9630.9620.9660.8731.0001.0000.9961.000
단위가격(MMBTU당원)_아시아0.7470.8850.5520.9630.9620.9660.8731.0001.0000.9961.000
단위가격(m3당원)_아시아0.7640.8840.5620.9570.9570.9680.8730.9960.9961.0000.996
단위가격(MJ당원)_아시아0.7450.8850.5530.9620.9620.9660.8731.0001.0000.9961.000
2023-12-13T00:51:04.839434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환율(원달러)금액(백만불)_아시아중량(Ton)_아시아단위가격(Ton당달러)_아시아단위가격(MMBTU당달러)_아시아단위가격(m3당달러)_아시아단위가격(Ton당원)_아시아단위가격(MMBTU당원)_아시아단위가격(m3당원)_아시아단위가격(MJ당원)_아시아단위가격(MJ당달러)_아시아
환율(원달러)1.0000.4110.3990.3440.3430.3640.5310.5310.5300.5310.440
금액(백만불)_아시아0.4111.0000.7790.8690.8690.8810.8840.8840.8870.8840.620
중량(Ton)_아시아0.3990.7791.0000.4000.4000.4250.4650.4650.4700.4650.306
단위가격(Ton당달러)_아시아0.3440.8690.4001.0001.0000.9930.9670.9670.9660.9670.929
단위가격(MMBTU당달러)_아시아0.3430.8690.4001.0001.0000.9930.9670.9670.9650.9670.931
단위가격(m3당달러)_아시아0.3640.8810.4250.9930.9931.0000.9710.9710.9730.9710.905
단위가격(Ton당원)_아시아0.5310.8840.4650.9670.9670.9711.0001.0000.9991.0000.730
단위가격(MMBTU당원)_아시아0.5310.8840.4650.9670.9670.9711.0001.0000.9991.0000.730
단위가격(m3당원)_아시아0.5300.8870.4700.9660.9650.9730.9990.9991.0000.9990.729
단위가격(MJ당원)_아시아0.5310.8840.4650.9670.9670.9711.0001.0000.9991.0000.729
단위가격(MJ당달러)_아시아0.4400.6200.3060.9290.9310.9050.7300.7300.7290.7291.000

Missing values

2023-12-13T00:50:59.814552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:51:00.070886image/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

연월환율(원달러)금액(백만불)_아시아중량(Ton)_아시아단위가격(Ton당달러)_아시아단위가격(MMBTU당달러)_아시아단위가격(m3당달러)_아시아단위가격(MJ당달러)_아시아단위가격(Ton당원)_아시아단위가격(MMBTU당원)_아시아단위가격(m3당원)_아시아단위가격(MJ당원)_아시아
01988-01787.4621116512180.243.50.10.0141930.962755.4776.542.61
11988-02773.4421116282180.63.510.10.0139679.742711.7675.332.57
21988-03753.491059086169.253.290.090.0127524.292475.7868.772.35
31988-04741.833172876190.893.710.10.0141600.922749.0676.362.61
41988-05735.6745231236194.613.780.110.0143166.072779.4577.212.64
51988-06729.4633175143188.423.660.10.0137443.02668.3474.122.53
61988-07725.8121114297183.733.570.10.0133354.422588.9671.922.45
71988-08722.8138228414166.373.230.090.0120249.992334.5564.852.21
81988-09720.2435228291153.312.980.080.0110422.222143.7559.552.03
91988-10709.3518114644157.013.050.090.0111373.472162.2260.062.05
연월환율(원달러)금액(백만불)_아시아중량(Ton)_아시아단위가격(Ton당달러)_아시아단위가격(MMBTU당달러)_아시아단위가격(m3당달러)_아시아단위가격(MJ당달러)_아시아단위가격(Ton당원)_아시아단위가격(MMBTU당원)_아시아단위가격(m3당원)_아시아단위가격(MJ당원)_아시아
4202023-011247.259808959981093.7521.090.860.021364182.7326308.071074.8924.94
4212023-021270.749401150654816.9315.750.640.021038101.4620019.64817.9618.98
4222023-031305.73559769574726.3814.010.570.01948450.7918290.74747.3217.34
4232023-041320.01260540334481.189.280.380.01635167.5112249.12500.4711.61
4242023-051328.21549800983685.4113.220.540.01910365.517556.27717.3116.64
4252023-061296.71393788175498.629.620.390.01646565.8412468.93509.4511.82
4262023-071286.3246534789459.998.870.360.01591690.9311410.68466.2110.82
4272023-081318.47383729644524.9110.120.410.01692082.7313346.72545.3212.65
4282023-091329.47249453134549.5110.60.430.01730552.1814088.6575.6313.36
4292023-101350.69509904826562.5410.850.440.01759815.9314652.94598.6813.89