Overview

Dataset statistics

Number of variables7
Number of observations63
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory64.1 B

Variable types

DateTime1
Numeric6

Dataset

Description기업이 세관에 수출입 신고하는 과정에서 생산된 운임정보를 화물정보와 연계하여 컨테이너 당 평균수출 운송비용을 산출·공표하는 데이터로서, 수출입 기업, 물류업계 등에서 참고자료로 활용 가능. 단위는 천원/2TEU항로 : 항구 단위가 아닌 국가·지역 단위거래조건 : 운임을 포함하는 대표적인 정형거래조건( 국제상업회의소 제정)인 CIF(Cost, Insurance and Freight)와 CFR(Cost and Frieght)을 거래조건으로 하는 수출신고 건적재형태 : 단일 화주의 물품만 적재되는 FCL(Full Container Load) 형태만 선별컨테이너 종류 : 40피트(2TEU) 일반화물 운송용(GP, Genaral Purpose) 컨테이너
Author관세청
URLhttps://www.data.go.kr/data/15116850/fileData.do

Alerts

미국서부 is highly overall correlated with 미국동부 and 3 other fieldsHigh correlation
미국동부 is highly overall correlated with 미국서부 and 3 other fieldsHigh correlation
유럽연합 is highly overall correlated with 미국서부 and 3 other fieldsHigh correlation
중국 is highly overall correlated with 미국서부 and 3 other fieldsHigh correlation
베트남 is highly overall correlated with 미국서부 and 3 other fieldsHigh correlation
기간 has unique valuesUnique
미국서부 has unique valuesUnique
유럽연합 has unique valuesUnique
베트남 has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:25:55.928741
Analysis finished2024-04-21 02:26:01.176291
Duration5.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기간
Date

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
Minimum2019-01-01 00:00:00
Maximum2024-03-01 00:00:00
2024-04-21T11:26:01.244297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:26:01.375671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

미국서부
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6611.1111
Minimum2971
Maximum15671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-04-21T11:26:01.551173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2971
5-th percentile3141.6
Q13509.5
median4603
Q39363
95-th percentile14103.8
Maximum15671
Range12700
Interquartile range (IQR)5853.5

Descriptive statistics

Standard deviation4015.0043
Coefficient of variation (CV)0.60731158
Kurtosis-0.55100636
Mean6611.1111
Median Absolute Deviation (MAD)1156
Skewness1.0285388
Sum416500
Variance16120260
MonotonicityNot monotonic
2024-04-21T11:26:01.730951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3225 1
 
1.6%
3447 1
 
1.6%
12445 1
 
1.6%
15671 1
 
1.6%
14106 1
 
1.6%
14253 1
 
1.6%
13338 1
 
1.6%
13628 1
 
1.6%
14463 1
 
1.6%
14084 1
 
1.6%
Other values (53) 53
84.1%
ValueCountFrequency (%)
2971 1
1.6%
2990 1
1.6%
3135 1
1.6%
3141 1
1.6%
3147 1
1.6%
3184 1
1.6%
3225 1
1.6%
3283 1
1.6%
3364 1
1.6%
3369 1
1.6%
ValueCountFrequency (%)
15671 1
1.6%
14463 1
1.6%
14253 1
1.6%
14106 1
1.6%
14084 1
1.6%
13908 1
1.6%
13864 1
1.6%
13628 1
1.6%
13338 1
1.6%
12922 1
1.6%

미국동부
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6621.127
Minimum3238
Maximum15341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-04-21T11:26:01.878324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3238
5-th percentile3283.4
Q13548.5
median4649
Q38973
95-th percentile15178.6
Maximum15341
Range12103
Interquartile range (IQR)5424.5

Descriptive statistics

Standard deviation4102.3918
Coefficient of variation (CV)0.61959116
Kurtosis-0.35140644
Mean6621.127
Median Absolute Deviation (MAD)1225
Skewness1.1146292
Sum417131
Variance16829618
MonotonicityNot monotonic
2024-04-21T11:26:02.006858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3407 2
 
3.2%
3287 1
 
1.6%
5879 1
 
1.6%
15202 1
 
1.6%
13525 1
 
1.6%
13559 1
 
1.6%
15341 1
 
1.6%
15254 1
 
1.6%
15186 1
 
1.6%
15112 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
3238 1
1.6%
3257 1
1.6%
3264 1
1.6%
3283 1
1.6%
3287 1
1.6%
3295 1
1.6%
3371 1
1.6%
3407 2
3.2%
3410 1
1.6%
3424 1
1.6%
ValueCountFrequency (%)
15341 1
1.6%
15254 1
1.6%
15202 1
1.6%
15186 1
1.6%
15112 1
1.6%
13658 1
1.6%
13652 1
1.6%
13559 1
1.6%
13525 1
1.6%
12965 1
1.6%

유럽연합
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5117
Minimum1837
Maximum13599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-04-21T11:26:02.156441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1837
5-th percentile1899.6
Q12016.5
median2768
Q37942.5
95-th percentile13112.2
Maximum13599
Range11762
Interquartile range (IQR)5926

Descriptive statistics

Standard deviation4040.2607
Coefficient of variation (CV)0.78957606
Kurtosis-0.53156823
Mean5117
Median Absolute Deviation (MAD)872
Skewness1.0429249
Sum322371
Variance16323707
MonotonicityNot monotonic
2024-04-21T11:26:02.308020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1942 1
 
1.6%
1939 1
 
1.6%
10841 1
 
1.6%
10610 1
 
1.6%
11994 1
 
1.6%
13096 1
 
1.6%
13182 1
 
1.6%
13114 1
 
1.6%
13251 1
 
1.6%
13599 1
 
1.6%
Other values (53) 53
84.1%
ValueCountFrequency (%)
1837 1
1.6%
1862 1
1.6%
1894 1
1.6%
1896 1
1.6%
1932 1
1.6%
1938 1
1.6%
1939 1
1.6%
1942 1
1.6%
1946 1
1.6%
1976 1
1.6%
ValueCountFrequency (%)
13599 1
1.6%
13251 1
1.6%
13182 1
1.6%
13114 1
1.6%
13096 1
1.6%
12784 1
1.6%
12356 1
1.6%
11994 1
1.6%
11346 1
1.6%
11096 1
1.6%

중국
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean796.98413
Minimum470
Maximum1386
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-04-21T11:26:02.433409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum470
5-th percentile483.7
Q1582.5
median720
Q31002
95-th percentile1231
Maximum1386
Range916
Interquartile range (IQR)419.5

Descriptive statistics

Standard deviation261.99486
Coefficient of variation (CV)0.32873284
Kurtosis-0.99489891
Mean796.98413
Median Absolute Deviation (MAD)185
Skewness0.56721199
Sum50210
Variance68641.306
MonotonicityNot monotonic
2024-04-21T11:26:02.564478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
613 2
 
3.2%
746 1
 
1.6%
949 1
 
1.6%
1386 1
 
1.6%
1213 1
 
1.6%
1081 1
 
1.6%
1080 1
 
1.6%
1182 1
 
1.6%
1134 1
 
1.6%
1205 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
470 1
1.6%
476 1
1.6%
478 1
1.6%
483 1
1.6%
490 1
1.6%
493 1
1.6%
494 1
1.6%
518 1
1.6%
521 1
1.6%
522 1
1.6%
ValueCountFrequency (%)
1386 1
1.6%
1253 1
1.6%
1247 1
1.6%
1233 1
1.6%
1213 1
1.6%
1210 1
1.6%
1205 1
1.6%
1196 1
1.6%
1185 1
1.6%
1182 1
1.6%

일본
Real number (ℝ)

Distinct59
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.9048
Minimum632
Maximum1385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-04-21T11:26:02.688177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum632
5-th percentile660.6
Q1910.5
median1030
Q31085.5
95-th percentile1304.5
Maximum1385
Range753
Interquartile range (IQR)175

Descriptive statistics

Standard deviation181.6225
Coefficient of variation (CV)0.18091607
Kurtosis-0.25350971
Mean1003.9048
Median Absolute Deviation (MAD)94
Skewness-0.2341012
Sum63246
Variance32986.733
MonotonicityNot monotonic
2024-04-21T11:26:02.845881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1081 2
 
3.2%
970 2
 
3.2%
1046 2
 
3.2%
1045 2
 
3.2%
1051 1
 
1.6%
801 1
 
1.6%
1074 1
 
1.6%
997 1
 
1.6%
945 1
 
1.6%
1065 1
 
1.6%
Other values (49) 49
77.8%
ValueCountFrequency (%)
632 1
1.6%
653 1
1.6%
655 1
1.6%
658 1
1.6%
684 1
1.6%
694 1
1.6%
714 1
1.6%
745 1
1.6%
766 1
1.6%
787 1
1.6%
ValueCountFrequency (%)
1385 1
1.6%
1336 1
1.6%
1334 1
1.6%
1306 1
1.6%
1291 1
1.6%
1242 1
1.6%
1239 1
1.6%
1219 1
1.6%
1191 1
1.6%
1185 1
1.6%

베트남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1166.2698
Minimum583
Maximum2423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-04-21T11:26:03.172491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum583
5-th percentile596.8
Q1679
median819
Q31534
95-th percentile2242.5
Maximum2423
Range1840
Interquartile range (IQR)855

Descriptive statistics

Standard deviation598.30676
Coefficient of variation (CV)0.51300886
Kurtosis-0.81998893
Mean1166.2698
Median Absolute Deviation (MAD)225
Skewness0.81174994
Sum73475
Variance357970.97
MonotonicityNot monotonic
2024-04-21T11:26:03.319713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
626 1
 
1.6%
684 1
 
1.6%
1541 1
 
1.6%
2214 1
 
1.6%
2074 1
 
1.6%
2105 1
 
1.6%
2131 1
 
1.6%
2095 1
 
1.6%
2229 1
 
1.6%
2346 1
 
1.6%
Other values (53) 53
84.1%
ValueCountFrequency (%)
583 1
1.6%
588 1
1.6%
592 1
1.6%
594 1
1.6%
622 1
1.6%
624 1
1.6%
626 1
1.6%
631 1
1.6%
642 1
1.6%
647 1
1.6%
ValueCountFrequency (%)
2423 1
1.6%
2346 1
1.6%
2294 1
1.6%
2244 1
1.6%
2229 1
1.6%
2214 1
1.6%
2153 1
1.6%
2131 1
1.6%
2105 1
1.6%
2095 1
1.6%

Interactions

2024-04-21T11:26:00.307969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:57.478947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.217434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.744971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.249866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.746417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:26:00.395938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:57.650178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.287971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.823635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.331616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.833560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:26:00.474679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:57.871659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.369584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.910091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.414981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.918109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:26:00.565034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:57.969416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.466434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.992359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.499160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:26:00.028867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:26:00.641594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.049974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.563696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.069045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.567467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:26:00.114864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:26:00.906876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.137510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:58.656117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.163301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:25:59.651232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:26:00.214467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:26:03.431685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기간미국서부미국동부유럽연합중국일본베트남
기간1.0001.0001.0001.0001.0001.0001.000
미국서부1.0001.0000.8680.8860.8840.5600.747
미국동부1.0000.8681.0000.9300.6720.4000.834
유럽연합1.0000.8860.9301.0000.7640.3020.906
중국1.0000.8840.6720.7641.0000.5900.754
일본1.0000.5600.4000.3020.5901.0000.513
베트남1.0000.7470.8340.9060.7540.5131.000
2024-04-21T11:26:03.544947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미국서부미국동부유럽연합중국일본베트남
미국서부1.0000.9580.9010.717-0.1190.852
미국동부0.9581.0000.9010.671-0.1490.829
유럽연합0.9010.9011.0000.710-0.0510.807
중국0.7170.6710.7101.0000.3070.827
일본-0.119-0.149-0.0510.3071.0000.030
베트남0.8520.8290.8070.8270.0301.000

Missing values

2024-04-21T11:26:01.025422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:26:01.133415image/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

기간미국서부미국동부유럽연합중국일본베트남
02019-013225328719427461051626
12019-023447342819397631242684
22019-033448355118947201081699
32019-043531357219466451001656
42019-053751357620256901191711
52019-063364337120466711124701
62019-073488329520116171081665
72019-08347534102009662970670
82019-093369342419387531185702
92019-103381344720016601041631
기간미국서부미국동부유럽연합중국일본베트남
532023-06460347402768649787819
542023-07422645312584542745880
552023-08407645702594527766753
562023-09412446172686561653703
572023-10428345152490476714675
582023-11415744622417494694683
592023-12423446732521518632716
602024-01444048384121483658594
612024-02518653494554493655652
622024-03492852354252521684624