Overview

Dataset statistics

Number of variables6
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory55.9 B

Variable types

Numeric6

Dataset

Description주요한 경제지표인 수출입 동향 지표에 대한 시의적절한 분석 및 공표를 통해 산업활동, 경제정책 수립과 정책대응에 기여 (연도, 수출금액, 수출증감률 등)
Author산업통상자원부
URLhttps://www.data.go.kr/data/3039964/fileData.do

Alerts

연도 is highly overall correlated with 수출금액(천불) and 2 other fieldsHigh correlation
수출금액(천불) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
수출증감률 is highly overall correlated with 연도 and 2 other fieldsHigh correlation
수입금액(천불) is highly overall correlated with 연도 and 2 other fieldsHigh correlation
수입증감률 is highly overall correlated with 수출증감률High correlation
연도 has unique valuesUnique
수출금액(천불) has unique valuesUnique
수입금액(천불) has unique valuesUnique
수지(천불) has unique valuesUnique
수출증감률 has 1 (1.5%) zerosZeros
수입증감률 has 2 (2.9%) zerosZeros

Reproduction

Analysis started2024-03-15 00:33:06.703341
Analysis finished2024-03-15 00:33:15.759277
Duration9.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1989.5
Minimum1956
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2024-03-15T09:33:16.022607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1956
5-th percentile1959.35
Q11972.75
median1989.5
Q32006.25
95-th percentile2019.65
Maximum2023
Range67
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation19.77372
Coefficient of variation (CV)0.0099390399
Kurtosis-1.2
Mean1989.5
Median Absolute Deviation (MAD)17
Skewness0
Sum135286
Variance391
MonotonicityStrictly increasing
2024-03-15T09:33:16.500245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1956 1
 
1.5%
2000 1
 
1.5%
2006 1
 
1.5%
2005 1
 
1.5%
2004 1
 
1.5%
2003 1
 
1.5%
2002 1
 
1.5%
2001 1
 
1.5%
1999 1
 
1.5%
1991 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
1956 1
1.5%
1957 1
1.5%
1958 1
1.5%
1959 1
1.5%
1960 1
1.5%
1961 1
1.5%
1962 1
1.5%
1963 1
1.5%
1964 1
1.5%
1965 1
1.5%
ValueCountFrequency (%)
2023 1
1.5%
2022 1
1.5%
2021 1
1.5%
2020 1
1.5%
2019 1
1.5%
2018 1
1.5%
2017 1
1.5%
2016 1
1.5%
2015 1
1.5%
2014 1
1.5%

수출금액(천불)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7778844 × 108
Minimum16451
Maximum6.8358476 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2024-03-15T09:33:16.972465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16451
5-th percentile27476.2
Q12824790.8
median63696452
Q33.3498203 × 108
95-th percentile5.9395182 × 108
Maximum6.8358476 × 108
Range6.8356831 × 108
Interquartile range (IQR)3.3215724 × 108

Descriptive statistics

Standard deviation2.2280142 × 108
Coefficient of variation (CV)1.2531829
Kurtosis-0.52564838
Mean1.7778844 × 108
Median Absolute Deviation (MAD)63648607
Skewness1.0278802
Sum1.2089614 × 1010
Variance4.9640474 × 1016
MonotonicityNot monotonic
2024-03-15T09:33:17.480937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24595 1
 
1.5%
172267510 1
 
1.5%
325464848 1
 
1.5%
284418743 1
 
1.5%
253844672 1
 
1.5%
193817443 1
 
1.5%
162470528 1
 
1.5%
150439144 1
 
1.5%
143685459 1
 
1.5%
71870122 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
16451 1
1.5%
19812 1
1.5%
22202 1
1.5%
24595 1
1.5%
32827 1
1.5%
40878 1
1.5%
54813 1
1.5%
86802 1
1.5%
119058 1
1.5%
175082 1
1.5%
ValueCountFrequency (%)
683584760 1
1.5%
644400368 1
1.5%
632225824 1
1.5%
604859657 1
1.5%
573694421 1
1.5%
572664607 1
1.5%
559632434 1
1.5%
555213656 1
1.5%
547869792 1
1.5%
542232610 1
1.5%

수출증감률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.830882
Minimum-26
Maximum99
Zeros1
Zeros (%)1.5%
Negative11
Negative (%)16.2%
Memory size740.0 B
2024-03-15T09:33:18.146679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-26
5-th percentile-10
Q14
median15.5
Q328.5
95-th percentile52
Maximum99
Range125
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation21.018934
Coefficient of variation (CV)1.1787938
Kurtosis2.3465408
Mean17.830882
Median Absolute Deviation (MAD)12.5
Skewness0.97763569
Sum1212.5
Variance441.7956
MonotonicityNot monotonic
2024-03-15T09:33:18.624622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
14.0 4
 
5.9%
28.0 4
 
5.9%
20.0 3
 
4.4%
4.0 3
 
4.4%
12.0 2
 
2.9%
52.0 2
 
2.9%
5.0 2
 
2.9%
-10.0 2
 
2.9%
3.0 2
 
2.9%
16.0 2
 
2.9%
Other values (35) 42
61.8%
ValueCountFrequency (%)
-26.0 1
1.5%
-14.0 1
1.5%
-13.0 1
1.5%
-10.0 2
2.9%
-8.0 1
1.5%
-7.5 1
1.5%
-6.0 2
2.9%
-3.0 1
1.5%
-1.0 1
1.5%
0.0 1
1.5%
ValueCountFrequency (%)
99.0 1
1.5%
66.0 1
1.5%
58.0 1
1.5%
52.0 2
2.9%
47.0 1
1.5%
43.0 1
1.5%
42.0 1
1.5%
38.0 1
1.5%
37.0 2
2.9%
36.0 1
1.5%

수입금액(천불)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6717069 × 108
Minimum303807
Maximum7.3136966 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2024-03-15T09:33:19.073827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum303807
5-th percentile380929.3
Q13810708.2
median65654225
Q33.128081 × 108
95-th percentile5.3181166 × 108
Maximum7.3136966 × 108
Range7.3106585 × 108
Interquartile range (IQR)3.089974 × 108

Descriptive statistics

Standard deviation2.0832837 × 108
Coefficient of variation (CV)1.2462015
Kurtosis-0.14470668
Mean1.6717069 × 108
Median Absolute Deviation (MAD)65222247
Skewness1.1077087
Sum1.1367607 × 1010
Variance4.3400709 × 1016
MonotonicityNot monotonic
2024-03-15T09:33:19.379586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
386063 1
 
1.5%
160481018 1
 
1.5%
309382632 1
 
1.5%
261238264 1
 
1.5%
224462687 1
 
1.5%
178826657 1
 
1.5%
152126153 1
 
1.5%
141097821 1
 
1.5%
119752282 1
 
1.5%
81524858 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
303807 1
1.5%
316142 1
1.5%
343527 1
1.5%
378165 1
1.5%
386063 1
1.5%
404351 1
1.5%
421782 1
1.5%
442174 1
1.5%
463442 1
1.5%
560273 1
1.5%
ValueCountFrequency (%)
731369657 1
1.5%
642572126 1
1.5%
615093447 1
1.5%
535202428 1
1.5%
525514506 1
1.5%
524413090 1
1.5%
519584473 1
1.5%
515585515 1
1.5%
503342947 1
1.5%
478478296 1
1.5%

수입증감률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.380882
Minimum-36
Maximum68
Zeros2
Zeros (%)2.9%
Negative16
Negative (%)23.5%
Memory size740.0 B
2024-03-15T09:33:19.750490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-36
5-th percentile-18.95
Q10
median15
Q325.25
95-th percentile44.2
Maximum68
Range104
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation20.287172
Coefficient of variation (CV)1.5161311
Kurtosis0.42705962
Mean13.380882
Median Absolute Deviation (MAD)13
Skewness0.10180585
Sum909.9
Variance411.56933
MonotonicityNot monotonic
2024-03-15T09:33:20.069158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
18.0 3
 
4.4%
32.0 3
 
4.4%
15.0 3
 
4.4%
-7.0 3
 
4.4%
17.0 3
 
4.4%
0.0 2
 
2.9%
21.0 2
 
2.9%
-1.0 2
 
2.9%
22.0 2
 
2.9%
19.0 2
 
2.9%
Other values (37) 43
63.2%
ValueCountFrequency (%)
-36.0 1
 
1.5%
-28.0 1
 
1.5%
-26.0 1
 
1.5%
-20.0 1
 
1.5%
-17.0 1
 
1.5%
-15.0 1
 
1.5%
-12.1 1
 
1.5%
-12.0 1
 
1.5%
-8.0 1
 
1.5%
-7.0 3
4.4%
ValueCountFrequency (%)
68.0 1
 
1.5%
62.0 1
 
1.5%
55.0 1
 
1.5%
47.0 1
 
1.5%
39.0 2
2.9%
36.0 1
 
1.5%
34.0 1
 
1.5%
33.0 2
2.9%
32.0 3
4.4%
30.0 1
 
1.5%

수지(천불)
Real number (ℝ)

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10617750
Minimum-47784897
Maximum95216125
Zeros0
Zeros (%)0.0%
Negative41
Negative (%)60.3%
Memory size740.0 B
2024-03-15T09:33:20.335662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-47784897
5-th percentile-10246427
Q1-1858729.8
median-364341.5
Q317856782
95-th percentile61779734
Maximum95216125
Range1.4300102 × 108
Interquartile range (IQR)19715512

Descriptive statistics

Standard deviation25657878
Coefficient of variation (CV)2.4165082
Kurtosis3.0463444
Mean10617750
Median Absolute Deviation (MAD)4849108.5
Skewness1.5608124
Sum7.2200697 × 108
Variance6.5832672 × 1014
MonotonicityNot monotonic
2024-03-15T09:33:20.616389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-361468 1
 
1.5%
11786492 1
 
1.5%
16082216 1
 
1.5%
23180479 1
 
1.5%
29381985 1
 
1.5%
14990786 1
 
1.5%
10344375 1
 
1.5%
9341323 1
 
1.5%
23933177 1
 
1.5%
-9654736 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
-47784897 1
1.5%
-20623963 1
1.5%
-13267409 1
1.5%
-10346302 1
1.5%
-10060945 1
1.5%
-9654736 1
1.5%
-8452170 1
1.5%
-6334938 1
1.5%
-5283158 1
1.5%
-5143742 1
1.5%
ValueCountFrequency (%)
95216125 1
1.5%
90257530 1
1.5%
89233053 1
1.5%
69657229 1
1.5%
47150101 1
1.5%
44865275 1
1.5%
44046919 1
1.5%
41171602 1
1.5%
40449040 1
1.5%
39031389 1
1.5%

Interactions

2024-03-15T09:33:13.437405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:06.967074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:08.344029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:09.424910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:10.549017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:11.891490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:13.696128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:07.121502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:08.510371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:09.624087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:10.710196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:12.125100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:13.968492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:07.283165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:08.775613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:09.884333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:10.879825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:12.386392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:14.224307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:07.641261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:08.941647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:10.058923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:11.137380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:12.630510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:14.522855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:07.907372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:09.105471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:10.228516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:11.404282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:12.943604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:14.777894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:08.154614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:09.258193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:10.372568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:11.654030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:33:13.182909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T09:33:20.910190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도수출금액(천불)수출증감률수입금액(천불)수입증감률수지(천불)
연도1.0000.9090.6010.9100.4820.655
수출금액(천불)0.9091.0000.5220.9830.0000.792
수출증감률0.6010.5221.0000.0000.6730.000
수입금액(천불)0.9100.9830.0001.0000.0000.797
수입증감률0.4820.0000.6730.0001.0000.000
수지(천불)0.6550.7920.0000.7970.0001.000
2024-03-15T09:33:21.202937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도수출금액(천불)수출증감률수입금액(천불)수입증감률수지(천불)
연도1.0000.996-0.5080.992-0.1660.395
수출금액(천불)0.9961.000-0.4910.997-0.1400.390
수출증감률-0.508-0.4911.000-0.5040.662-0.212
수입금액(천불)0.9920.997-0.5041.000-0.1280.363
수입증감률-0.166-0.1400.662-0.1281.000-0.207
수지(천불)0.3950.390-0.2120.363-0.2071.000

Missing values

2024-03-15T09:33:15.221650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:33:15.615535image/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

연도수출금액(천불)수출증감률수입금액(천불)수입증감률수지(천불)
01956245950.03860630.0-361468
1195722202-10.044217415.0-419972
2195816451-26.0378165-15.0-361714
319591981220.0303807-20.0-283995
419603282766.034352713.0-310700
519614087825.0316142-8.0-275264
619625481334.042178233.0-366969
719638680258.056027333.0-473471
8196411905837.0404351-28.0-285293
9196517508247.046344215.0-288360
연도수출금액(천불)수출증감률수입금액(천불)수입증감률수지(천불)
5820145726646072.05255145062.047150101
592015526756503-8.0436498973-17.090257530
602016495425940-6.0406192887-7.089233053
61201757369442116.047847829618.095216125
6220186048596575.053520242812.069657229
632019542232610-10.0503342947-6.038889663
642020512498038-6.0467632763-7.044865275
65202164440036826.061509344732.029306921
6620226835847606.073136965719.0-47784897
672023632225824-7.5642572126-12.1-10346302