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 중남미산 LNG 수입량과 단위가격을 산출한다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15102982/fileData.do

Alerts

금액(백만불)_중남미 is highly overall correlated with 중량(Ton)_중남미 and 8 other fieldsHigh correlation
중량(Ton)_중남미 is highly overall correlated with 금액(백만불)_중남미 and 8 other fieldsHigh correlation
단위가격(Ton당달러)_중남미 is highly overall correlated with 금액(백만불)_중남미 and 8 other fieldsHigh correlation
단위가격(MMBTU당달러)_중남미 is highly overall correlated with 금액(백만불)_중남미 and 8 other fieldsHigh correlation
단위가격(m3당달러)_중남미 is highly overall correlated with 금액(백만불)_중남미 and 8 other fieldsHigh correlation
단위가격(Ton당원)_중남미 is highly overall correlated with 금액(백만불)_중남미 and 8 other fieldsHigh correlation
단위가격(MMBTU당원)_중남미 is highly overall correlated with 금액(백만불)_중남미 and 8 other fieldsHigh correlation
단위가격(m3당원)_중남미 is highly overall correlated with 금액(백만불)_중남미 and 8 other fieldsHigh correlation
단위가격(MJ당원)_중남미 is highly overall correlated with 금액(백만불)_중남미 and 8 other fieldsHigh correlation
단위가격(MJ당달러)_중남미 is highly overall correlated with 금액(백만불)_중남미 and 8 other fieldsHigh correlation
단위가격(MJ당달러)_중남미 is highly imbalanced (52.3%)Imbalance
연월 has unique valuesUnique
금액(백만불)_중남미 has 319 (74.2%) zerosZeros
중량(Ton)_중남미 has 319 (74.2%) zerosZeros
단위가격(Ton당달러)_중남미 has 319 (74.2%) zerosZeros
단위가격(MMBTU당달러)_중남미 has 319 (74.2%) zerosZeros
단위가격(m3당달러)_중남미 has 319 (74.2%) zerosZeros
단위가격(Ton당원)_중남미 has 319 (74.2%) zerosZeros
단위가격(MMBTU당원)_중남미 has 319 (74.2%) zerosZeros
단위가격(m3당원)_중남미 has 319 (74.2%) zerosZeros
단위가격(MJ당원)_중남미 has 319 (74.2%) zerosZeros

Reproduction

Analysis started2023-12-12 21:44:23.556078
Analysis finished2023-12-12 21:44:35.365002
Duration11.81 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
2023-12-13T06:44:35.461932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:35.658476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

환율(원달러)
Real number (ℝ)

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
2023-12-13T06:44:35.830478image/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
2023-12-13T06:44:35.985224image/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)_중남미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.697674
Minimum0
Maximum279
Zeros319
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T06:44:36.144795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316.75
95-th percentile117.65
Maximum279
Range279
Interquartile range (IQR)16.75

Descriptive statistics

Standard deviation40.940479
Coefficient of variation (CV)2.1896027
Kurtosis8.4688788
Mean18.697674
Median Absolute Deviation (MAD)0
Skewness2.7650098
Sum8040
Variance1676.1228
MonotonicityNot monotonic
2023-12-13T06:44:36.316566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 319
74.2%
21 3
 
0.7%
35 3
 
0.7%
45 3
 
0.7%
108 3
 
0.7%
44 3
 
0.7%
37 3
 
0.7%
36 3
 
0.7%
38 3
 
0.7%
33 2
 
0.5%
Other values (70) 85
 
19.8%
ValueCountFrequency (%)
0 319
74.2%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
19 1
 
0.2%
21 3
 
0.7%
22 2
 
0.5%
23 2
 
0.5%
25 2
 
0.5%
26 1
 
0.2%
ValueCountFrequency (%)
279 1
0.2%
211 1
0.2%
198 1
0.2%
191 1
0.2%
179 1
0.2%
173 1
0.2%
168 1
0.2%
164 2
0.5%
154 1
0.2%
150 1
0.2%

중량(Ton)_중남미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31781.826
Minimum0
Maximum316704
Zeros319
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T06:44:36.460594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q348710.5
95-th percentile199967.35
Maximum316704
Range316704
Interquartile range (IQR)48710.5

Descriptive statistics

Standard deviation65671.088
Coefficient of variation (CV)2.0663095
Kurtosis4.5687441
Mean31781.826
Median Absolute Deviation (MAD)0
Skewness2.2769586
Sum13666185
Variance4.3126918 × 109
MonotonicityNot monotonic
2023-12-13T06:44:36.611961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 319
74.2%
188075 1
 
0.2%
286867 1
 
0.2%
215020 1
 
0.2%
71346 1
 
0.2%
140214 1
 
0.2%
142876 1
 
0.2%
73240 1
 
0.2%
59761 1
 
0.2%
132895 1
 
0.2%
Other values (102) 102
 
23.7%
ValueCountFrequency (%)
0 319
74.2%
44486 1
 
0.2%
46836 1
 
0.2%
48202 1
 
0.2%
48880 1
 
0.2%
49245 1
 
0.2%
49798 1
 
0.2%
50661 1
 
0.2%
50682 1
 
0.2%
50875 1
 
0.2%
ValueCountFrequency (%)
316704 1
0.2%
306069 1
0.2%
302604 1
0.2%
286867 1
0.2%
280503 1
0.2%
268305 1
0.2%
260517 1
0.2%
257420 1
0.2%
249344 1
0.2%
230850 1
0.2%

단위가격(Ton당달러)_중남미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.808
Minimum0
Maximum1461.18
Zeros319
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T06:44:36.766371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3204.1225
95-th percentile721.48
Maximum1461.18
Range1461.18
Interquartile range (IQR)204.1225

Descriptive statistics

Standard deviation277.43127
Coefficient of variation (CV)1.8275142
Kurtosis1.9973849
Mean151.808
Median Absolute Deviation (MAD)0
Skewness1.6648827
Sum65277.44
Variance76968.112
MonotonicityNot monotonic
2023-12-13T06:44:36.918022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 319
74.2%
398.78 1
 
0.2%
515.92 1
 
0.2%
502.28 1
 
0.2%
518.6 1
 
0.2%
513.5 1
 
0.2%
475.94 1
 
0.2%
573.46 1
 
0.2%
585.67 1
 
0.2%
481.58 1
 
0.2%
Other values (102) 102
 
23.7%
ValueCountFrequency (%)
0.0 319
74.2%
170.81 1
 
0.2%
177.58 1
 
0.2%
181.39 1
 
0.2%
211.7 1
 
0.2%
284.57 1
 
0.2%
324.07 1
 
0.2%
328.41 1
 
0.2%
349.68 1
 
0.2%
355.6 1
 
0.2%
ValueCountFrequency (%)
1461.18 1
0.2%
1254.89 1
0.2%
1184.34 1
0.2%
1117.36 1
0.2%
1037.53 1
0.2%
950.35 1
0.2%
847.28 1
0.2%
846.22 1
0.2%
832.57 1
0.2%
822.25 1
0.2%

단위가격(MMBTU당달러)_중남미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9292093
Minimum0
Maximum28.18
Zeros319
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T06:44:37.078574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.935
95-th percentile13.925
Maximum28.18
Range28.18
Interquartile range (IQR)3.935

Descriptive statistics

Standard deviation5.3531603
Coefficient of variation (CV)1.8275103
Kurtosis1.9937356
Mean2.9292093
Median Absolute Deviation (MAD)0
Skewness1.6645232
Sum1259.56
Variance28.656325
MonotonicityNot monotonic
2023-12-13T06:44:37.206515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 319
74.2%
9.69 3
 
0.7%
10.93 2
 
0.5%
12.44 2
 
0.5%
10.45 2
 
0.5%
11.57 2
 
0.5%
9.01 1
 
0.2%
9.57 1
 
0.2%
9.95 1
 
0.2%
10.0 1
 
0.2%
Other values (96) 96
 
22.3%
ValueCountFrequency (%)
0.0 319
74.2%
3.29 1
 
0.2%
3.43 1
 
0.2%
3.5 1
 
0.2%
4.08 1
 
0.2%
5.5 1
 
0.2%
6.26 1
 
0.2%
6.33 1
 
0.2%
6.74 1
 
0.2%
6.86 1
 
0.2%
ValueCountFrequency (%)
28.18 1
0.2%
24.2 1
0.2%
22.84 1
0.2%
21.55 1
0.2%
20.01 1
0.2%
18.33 1
0.2%
16.37 1
0.2%
16.35 1
0.2%
16.06 1
0.2%
15.89 1
0.2%

단위가격(m3당달러)_중남미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12125581
Minimum0
Maximum1.15
Zeros319
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T06:44:37.344053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.165
95-th percentile0.581
Maximum1.15
Range1.15
Interquartile range (IQR)0.165

Descriptive statistics

Standard deviation0.22144641
Coefficient of variation (CV)1.8262746
Kurtosis1.9217534
Mean0.12125581
Median Absolute Deviation (MAD)0
Skewness1.6555575
Sum52.14
Variance0.049038512
MonotonicityNot monotonic
2023-12-13T06:44:37.472580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 319
74.2%
0.4 6
 
1.4%
0.46 5
 
1.2%
0.31 5
 
1.2%
0.53 5
 
1.2%
0.59 5
 
1.2%
0.41 5
 
1.2%
0.47 4
 
0.9%
0.39 4
 
0.9%
0.45 4
 
0.9%
Other values (36) 68
 
15.8%
ValueCountFrequency (%)
0.0 319
74.2%
0.14 2
 
0.5%
0.15 1
 
0.2%
0.17 1
 
0.2%
0.23 1
 
0.2%
0.26 1
 
0.2%
0.27 1
 
0.2%
0.28 2
 
0.5%
0.3 1
 
0.2%
0.31 5
 
1.2%
ValueCountFrequency (%)
1.15 1
 
0.2%
0.99 1
 
0.2%
0.96 1
 
0.2%
0.88 1
 
0.2%
0.82 1
 
0.2%
0.75 1
 
0.2%
0.68 3
0.7%
0.66 1
 
0.2%
0.64 3
0.7%
0.61 2
0.5%

단위가격(MJ당달러)_중남미
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
0.0
323 
0.01
94 
0.02
 
12
0.03
 
1

Length

Max length4
Median length3
Mean length3.2488372
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 (%)
0.0 323
75.1%
0.01 94
 
21.9%
0.02 12
 
2.8%
0.03 1
 
0.2%

Length

2023-12-13T06:44:37.636076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:44:37.734085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 323
75.1%
0.01 94
 
21.9%
0.02 12
 
2.8%
0.03 1
 
0.2%

단위가격(Ton당원)_중남미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175077.46
Minimum0
Maximum1910342.4
Zeros319
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T06:44:37.869524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3237347.11
95-th percentile815328.88
Maximum1910342.4
Range1910342.4
Interquartile range (IQR)237347.11

Descriptive statistics

Standard deviation326140.25
Coefficient of variation (CV)1.8628341
Kurtosis3.5529535
Mean175077.46
Median Absolute Deviation (MAD)0
Skewness1.8811549
Sum75283307
Variance1.0636746 × 1011
MonotonicityNot monotonic
2023-12-13T06:44:38.018076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 319
74.2%
452520.27 1
 
0.2%
615898.38 1
 
0.2%
584793.23 1
 
0.2%
609790.04 1
 
0.2%
599486.5 1
 
0.2%
563571.49 1
 
0.2%
686743.58 1
 
0.2%
708058.77 1
 
0.2%
566009.56 1
 
0.2%
Other values (102) 102
 
23.7%
ValueCountFrequency (%)
0.0 319
74.2%
169842.74 1
 
0.2%
177082.59 1
 
0.2%
200733.99 1
 
0.2%
249551.49 1
 
0.2%
338131.45 1
 
0.2%
359267.23 1
 
0.2%
366706.84 1
 
0.2%
383070.61 1
 
0.2%
419230.98 1
 
0.2%
ValueCountFrequency (%)
1910342.41 1
0.2%
1627097.77 1
0.2%
1565167.47 1
0.2%
1524187.3 1
0.2%
1238820.16 1
0.2%
1160409.93 1
0.2%
1053738.34 1
0.2%
974935.08 1
0.2%
950502.01 1
0.2%
948304.59 1
0.2%

단위가격(MMBTU당원)_중남미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3377.9935
Minimum0
Maximum36840.68
Zeros319
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T06:44:38.161849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34577.21
95-th percentile15723.502
Maximum36840.68
Range36840.68
Interquartile range (IQR)4577.21

Descriptive statistics

Standard deviation6292.2851
Coefficient of variation (CV)1.8627286
Kurtosis3.5457276
Mean3377.9935
Median Absolute Deviation (MAD)0
Skewness1.8802915
Sum1452537.2
Variance39592851
MonotonicityNot monotonic
2023-12-13T06:44:38.303905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 319
74.2%
8742.77 1
 
0.2%
11877.51 1
 
0.2%
11277.66 1
 
0.2%
11759.72 1
 
0.2%
11561.01 1
 
0.2%
10868.4 1
 
0.2%
13243.75 1
 
0.2%
13654.81 1
 
0.2%
10915.42 1
 
0.2%
Other values (102) 102
 
23.7%
ValueCountFrequency (%)
0.0 319
74.2%
3275.39 1
 
0.2%
3415.01 1
 
0.2%
3871.13 1
 
0.2%
4812.57 1
 
0.2%
6532.76 1
 
0.2%
6964.14 1
 
0.2%
7084.84 1
 
0.2%
7387.46 1
 
0.2%
8084.81 1
 
0.2%
ValueCountFrequency (%)
36840.68 1
0.2%
31378.35 1
0.2%
30184.03 1
0.2%
29393.74 1
0.2%
23890.47 1
0.2%
22378.34 1
0.2%
20321.2 1
0.2%
18801.49 1
0.2%
18363.87 1
0.2%
18287.92 1
0.2%

단위가격(m3당원)_중남미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.54444
Minimum0
Maximum1505.22
Zeros319
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T06:44:38.467361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3188.1725
95-th percentile654.5285
Maximum1505.22
Range1505.22
Interquartile range (IQR)188.1725

Descriptive statistics

Standard deviation259.8047
Coefficient of variation (CV)1.8618061
Kurtosis3.474845
Mean139.54444
Median Absolute Deviation (MAD)0
Skewness1.8721908
Sum60004.11
Variance67498.48
MonotonicityNot monotonic
2023-12-13T06:44:38.637526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 319
74.2%
361.35 1
 
0.2%
485.29 1
 
0.2%
460.78 1
 
0.2%
480.47 1
 
0.2%
472.36 1
 
0.2%
444.06 1
 
0.2%
541.11 1
 
0.2%
557.9 1
 
0.2%
445.98 1
 
0.2%
Other values (102) 102
 
23.7%
ValueCountFrequency (%)
0.0 319
74.2%
137.74 1
 
0.2%
143.62 1
 
0.2%
162.8 1
 
0.2%
196.63 1
 
0.2%
270.01 1
 
0.2%
290.18 1
 
0.2%
292.83 1
 
0.2%
310.67 1
 
0.2%
330.33 1
 
0.2%
ValueCountFrequency (%)
1505.22 1
0.2%
1319.59 1
0.2%
1233.25 1
0.2%
1200.96 1
0.2%
976.11 1
0.2%
914.33 1
0.2%
854.59 1
0.2%
790.68 1
0.2%
759.01 1
0.2%
747.2 1
0.2%

단위가격(MJ당원)_중남미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct107
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2056744
Minimum0
Maximum34.92
Zeros319
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-13T06:44:38.791623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.34
95-th percentile14.961
Maximum34.92
Range34.92
Interquartile range (IQR)4.34

Descriptive statistics

Standard deviation5.9709629
Coefficient of variation (CV)1.862623
Kurtosis3.539885
Mean3.2056744
Median Absolute Deviation (MAD)0
Skewness1.8795869
Sum1378.44
Variance35.652398
MonotonicityNot monotonic
2023-12-13T06:44:38.944339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 319
74.2%
7.87 2
 
0.5%
7.94 2
 
0.5%
10.96 2
 
0.5%
8.62 2
 
0.5%
12.77 2
 
0.5%
13.22 1
 
0.2%
11.15 1
 
0.2%
10.3 1
 
0.2%
12.56 1
 
0.2%
Other values (97) 97
 
22.6%
ValueCountFrequency (%)
0.0 319
74.2%
3.12 1
 
0.2%
3.25 1
 
0.2%
3.68 1
 
0.2%
4.56 1
 
0.2%
6.19 1
 
0.2%
6.6 1
 
0.2%
6.72 1
 
0.2%
7.03 1
 
0.2%
7.66 1
 
0.2%
ValueCountFrequency (%)
34.92 1
0.2%
29.86 1
0.2%
28.61 1
0.2%
27.86 1
0.2%
22.65 1
0.2%
21.21 1
0.2%
19.34 1
0.2%
17.89 1
0.2%
17.41 1
0.2%
17.34 1
0.2%

Interactions

2023-12-13T06:44:33.582684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.041408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.882069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.885123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.901832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:27.994259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:29.305237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.304597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:31.380165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:32.589208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.681288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.116527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.968780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.970258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.987264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:28.088030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:29.414536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.398468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:31.486902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:32.691419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.792814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.205508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.043083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.094072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:27.079846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:28.184308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:29.513211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.498647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:31.609592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:32.785061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.902020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.285411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.122741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.184229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:27.189943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:28.276722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:29.620271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.590247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:31.748091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:32.908806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.991732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.370249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.223541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.283681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:27.295453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:28.367031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:29.711981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.697179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:31.865205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.019271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:34.080236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.446895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.322377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.371523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:27.412147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:28.450584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:29.808602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.796102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:31.965946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.107275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:34.184978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.535126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.410222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.480522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:27.556664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:28.557861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:29.902373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.916711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:32.073968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.200155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:34.310354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.638172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.513502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.596848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:27.681912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:28.691014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.009941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:31.039638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:32.228513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.296187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:34.426575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.720033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.638103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.707293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:27.799980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:29.118571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.108191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:31.156943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:32.336078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.389361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:34.549327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:24.801774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:25.783836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:26.814385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:27.903240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:29.210895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:30.204470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:31.262195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:32.443215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:33.491325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:44:39.056677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환율(원달러)금액(백만불)_중남미중량(Ton)_중남미단위가격(Ton당달러)_중남미단위가격(MMBTU당달러)_중남미단위가격(m3당달러)_중남미단위가격(MJ당달러)_중남미단위가격(Ton당원)_중남미단위가격(MMBTU당원)_중남미단위가격(m3당원)_중남미단위가격(MJ당원)_중남미
환율(원달러)1.0000.1970.2880.3420.3420.3260.3500.4280.4280.4070.427
금액(백만불)_중남미0.1971.0000.8150.8190.8190.8160.8880.8140.8140.8110.815
중량(Ton)_중남미0.2880.8151.0000.8310.8310.8380.7730.8280.8280.8200.827
단위가격(Ton당달러)_중남미0.3420.8190.8311.0001.0001.0000.9830.9840.9840.9840.984
단위가격(MMBTU당달러)_중남미0.3420.8190.8311.0001.0001.0000.9830.9840.9840.9840.984
단위가격(m3당달러)_중남미0.3260.8160.8381.0001.0001.0000.9820.9840.9840.9840.984
단위가격(MJ당달러)_중남미0.3500.8880.7730.9830.9830.9821.0000.9750.9750.9770.975
단위가격(Ton당원)_중남미0.4280.8140.8280.9840.9840.9840.9751.0001.0001.0001.000
단위가격(MMBTU당원)_중남미0.4280.8140.8280.9840.9840.9840.9751.0001.0001.0001.000
단위가격(m3당원)_중남미0.4070.8110.8200.9840.9840.9840.9771.0001.0001.0001.000
단위가격(MJ당원)_중남미0.4270.8150.8270.9840.9840.9840.9751.0001.0001.0001.000
2023-12-13T06:44:39.616018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환율(원달러)금액(백만불)_중남미중량(Ton)_중남미단위가격(Ton당달러)_중남미단위가격(MMBTU당달러)_중남미단위가격(m3당달러)_중남미단위가격(Ton당원)_중남미단위가격(MMBTU당원)_중남미단위가격(m3당원)_중남미단위가격(MJ당원)_중남미단위가격(MJ당달러)_중남미
환율(원달러)1.0000.2350.2350.2310.2310.2300.2440.2440.2430.2440.215
금액(백만불)_중남미0.2351.0000.9970.9810.9810.9820.9810.9810.9820.9810.796
중량(Ton)_중남미0.2350.9971.0000.9690.9700.9700.9700.9700.9700.9700.583
단위가격(Ton당달러)_중남미0.2310.9810.9691.0001.0001.0000.9990.9990.9990.9990.930
단위가격(MMBTU당달러)_중남미0.2310.9810.9701.0001.0001.0000.9990.9990.9990.9990.930
단위가격(m3당달러)_중남미0.2300.9820.9701.0001.0001.0000.9990.9990.9990.9990.928
단위가격(Ton당원)_중남미0.2440.9810.9700.9990.9990.9991.0001.0001.0001.0000.916
단위가격(MMBTU당원)_중남미0.2440.9810.9700.9990.9990.9991.0001.0001.0001.0000.916
단위가격(m3당원)_중남미0.2430.9820.9700.9990.9990.9991.0001.0001.0001.0000.918
단위가격(MJ당원)_중남미0.2440.9810.9700.9990.9990.9991.0001.0001.0001.0000.916
단위가격(MJ당달러)_중남미0.2150.7960.5830.9300.9300.9280.9160.9160.9180.9161.000

Missing values

2023-12-13T06:44:35.037500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:44:35.271674image/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)_중남미중량(Ton)_중남미단위가격(Ton당달러)_중남미단위가격(MMBTU당달러)_중남미단위가격(m3당달러)_중남미단위가격(MJ당달러)_중남미단위가격(Ton당원)_중남미단위가격(MMBTU당원)_중남미단위가격(m3당원)_중남미단위가격(MJ당원)_중남미
01988-01787.46000.00.00.00.00.00.00.00.0
11988-02773.44000.00.00.00.00.00.00.00.0
21988-03753.49000.00.00.00.00.00.00.00.0
31988-04741.8000.00.00.00.00.00.00.00.0
41988-05735.67000.00.00.00.00.00.00.00.0
51988-06729.46000.00.00.00.00.00.00.00.0
61988-07725.81000.00.00.00.00.00.00.00.0
71988-08722.81000.00.00.00.00.00.00.00.0
81988-09720.24000.00.00.00.00.00.00.00.0
91988-10709.35000.00.00.00.00.00.00.00.0
연월환율(원달러)금액(백만불)_중남미중량(Ton)_중남미단위가격(Ton당달러)_중남미단위가격(MMBTU당달러)_중남미단위가격(m3당달러)_중남미단위가격(MJ당달러)_중남미단위가격(Ton당원)_중남미단위가격(MMBTU당원)_중남미단위가격(m3당원)_중남미단위가격(MJ당원)_중남미
4202023-011247.251911522041254.8924.20.990.021565167.4730184.031233.2528.61
4212023-021270.7480107201746.2614.390.590.01948304.5918287.92747.217.34
4222023-031305.73134195478685.513.220.540.01895076.7917261.43705.2616.36
4232023-041320.013056081534.9410.320.420.01706126.8513617.56556.3812.91
4242023-051328.21000.00.00.00.00.00.00.00.0
4252023-061296.714568350658.3812.70.520.01853722.7516463.92672.6815.61
4262023-071286.3128200542638.2712.310.50.01821007.0715833.01646.915.01
4272023-081318.47000.00.00.00.00.00.00.00.0
4282023-091329.47000.00.00.00.00.00.00.00.0
4292023-101350.69000.00.00.00.00.00.00.00.0