Overview

Dataset statistics

Number of variables7
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory63.2 B

Variable types

Categorical2
Text1
Numeric4

Dataset

Description금속광, 비금속광, 석탄광으로 구분되는 주요 광물자원 61종의 최근 국내 수급현황을 수출중량, 수출금액, 수입중량, 수입금액 자료로 제공합니다
URLhttps://www.data.go.kr/data/3070177/fileData.do

Alerts

연도 has constant value ""Constant
수출중량(톤) is highly overall correlated with 수출금액(천불) and 1 other fieldsHigh correlation
수출금액(천불) is highly overall correlated with 수출중량(톤)High correlation
수입중량(톤) is highly overall correlated with 수출중량(톤) and 1 other fieldsHigh correlation
수입금액(천불) is highly overall correlated with 수입중량(톤)High correlation
광종 has unique valuesUnique
수출중량(톤) has 16 (26.7%) zerosZeros
수출금액(천불) has 15 (25.0%) zerosZeros
수입중량(톤) has 7 (11.7%) zerosZeros
수입금액(천불) has 1 (1.7%) zerosZeros

Reproduction

Analysis started2023-12-12 00:16:55.559750
Analysis finished2023-12-12 00:16:58.095836
Duration2.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2021
60 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 60
100.0%

Length

2023-12-12T09:16:58.170795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:58.283977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 60
100.0%

분류
Categorical

Distinct3
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
비금속광
28 
금속광
27 
석탄광
5 

Length

Max length4
Median length3
Mean length3.4666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금속광
2nd row금속광
3rd row금속광
4th row금속광
5th row금속광

Common Values

ValueCountFrequency (%)
비금속광 28
46.7%
금속광 27
45.0%
석탄광 5
 
8.3%

Length

2023-12-12T09:16:58.403887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:58.517997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비금속광 28
46.7%
금속광 27
45.0%
석탄광 5
 
8.3%

광종
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-12T09:16:58.714043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.0833333
Min length1

Characters and Unicode

Total characters185
Distinct characters86
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st row금광
2nd row은광
3rd row동광
4th row연광
5th row아연광
ValueCountFrequency (%)
기타 3
 
4.8%
금광 1
 
1.6%
홍주석 1
 
1.6%
규선석 1
 
1.6%
고령토류 1
 
1.6%
석회석류 1
 
1.6%
규석 1
 
1.6%
규사 1
 
1.6%
황 1
 
1.6%
규조토 1
 
1.6%
Other values (51) 51
81.0%
2023-12-12T09:16:59.040336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
광 24
 
13.0%
석 20
 
10.8%
연 7
 
3.8%
탄 6
 
3.2%
규 5
 
2.7%
금 5
 
2.7%
토 4
 
2.2%
늄 4
 
2.2%
타 4
 
2.2%
주 3
 
1.6%
Other values (76) 103
55.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 182
98.4%
Space Separator 3
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 24
 
13.2%
석 20
 
11.0%
연 7
 
3.8%
탄 6
 
3.3%
규 5
 
2.7%
금 5
 
2.7%
토 4
 
2.2%
늄 4
 
2.2%
타 4
 
2.2%
주 3
 
1.6%
Other values (75) 100
54.9%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 182
98.4%
Common 3
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 24
 
13.2%
석 20
 
11.0%
연 7
 
3.8%
탄 6
 
3.3%
규 5
 
2.7%
금 5
 
2.7%
토 4
 
2.2%
늄 4
 
2.2%
타 4
 
2.2%
주 3
 
1.6%
Other values (75) 100
54.9%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 182
98.4%
ASCII 3
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
광 24
 
13.2%
석 20
 
11.0%
연 7
 
3.8%
탄 6
 
3.3%
규 5
 
2.7%
금 5
 
2.7%
토 4
 
2.2%
늄 4
 
2.2%
타 4
 
2.2%
주 3
 
1.6%
Other values (75) 100
54.9%
ASCII
ValueCountFrequency (%)
3
100.0%

수출중량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63882.975
Minimum0
Maximum1371973
Zeros16
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T09:16:59.172349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median202.5
Q37022.5
95-th percentile351941.4
Maximum1371973
Range1371973
Interquartile range (IQR)7022.5

Descriptive statistics

Standard deviation212062.79
Coefficient of variation (CV)3.3195509
Kurtosis25.600823
Mean63882.975
Median Absolute Deviation (MAD)202.5
Skewness4.7290263
Sum3832978.5
Variance4.4970626 × 1010
MonotonicityNot monotonic
2023-12-12T09:16:59.295333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 16
26.7%
8.0 2
 
3.3%
57.0 2
 
3.3%
1005.0 1
 
1.7%
554.0 1
 
1.7%
27170.0 1
 
1.7%
6713.0 1
 
1.7%
1371973.0 1
 
1.7%
1223.0 1
 
1.7%
249.0 1
 
1.7%
Other values (33) 33
55.0%
ValueCountFrequency (%)
0.0 16
26.7%
0.489 1
 
1.7%
2.0 1
 
1.7%
5.0 1
 
1.7%
8.0 2
 
3.3%
15.0 1
 
1.7%
41.0 1
 
1.7%
51.0 1
 
1.7%
57.0 2
 
3.3%
63.0 1
 
1.7%
ValueCountFrequency (%)
1371973.0 1
1.7%
623093.0 1
1.7%
560664.0 1
1.7%
340956.0 1
1.7%
302518.0 1
1.7%
302062.0 1
1.7%
70012.0 1
1.7%
60731.0 1
1.7%
43136.0 1
1.7%
37182.0 1
1.7%

수출금액(천불)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28310.667
Minimum0
Maximum1185720
Zeros15
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T09:16:59.423093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.25
median174
Q36287.25
95-th percentile55752.65
Maximum1185720
Range1185720
Interquartile range (IQR)6282

Descriptive statistics

Standard deviation154421.87
Coefficient of variation (CV)5.4545473
Kurtosis56.079526
Mean28310.667
Median Absolute Deviation (MAD)174
Skewness7.3979246
Sum1698640
Variance2.3846114 × 1010
MonotonicityNot monotonic
2023-12-12T09:16:59.599930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 15
25.0%
30 2
 
3.3%
84 2
 
3.3%
1062 1
 
1.7%
10691 1
 
1.7%
921 1
 
1.7%
186997 1
 
1.7%
1631 1
 
1.7%
146 1
 
1.7%
165 1
 
1.7%
Other values (34) 34
56.7%
ValueCountFrequency (%)
0 15
25.0%
7 1
 
1.7%
9 1
 
1.7%
30 2
 
3.3%
42 1
 
1.7%
45 1
 
1.7%
59 1
 
1.7%
78 1
 
1.7%
79 1
 
1.7%
84 2
 
3.3%
ValueCountFrequency (%)
1185720 1
1.7%
186997 1
1.7%
90326 1
1.7%
53933 1
1.7%
34679 1
1.7%
23438 1
1.7%
19255 1
1.7%
17438 1
1.7%
11070 1
1.7%
10691 1
1.7%

수입중량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3601914.9
Minimum0
Maximum1.1710571 × 108
Zeros7
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T09:16:59.760448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142.5
median30606
Q3274311
95-th percentile3346203.5
Maximum1.1710571 × 108
Range1.1710571 × 108
Interquartile range (IQR)274268.5

Descriptive statistics

Standard deviation17707015
Coefficient of variation (CV)4.9160002
Kurtosis33.234003
Mean3601914.9
Median Absolute Deviation (MAD)30606
Skewness5.7171116
Sum2.161149 × 108
Variance3.1353836 × 1014
MonotonicityNot monotonic
2023-12-12T09:16:59.904262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
11.7%
1.0 3
 
5.0%
2.0 2
 
3.3%
122.0 2
 
3.3%
5705.0 1
 
1.7%
1087435.0 1
 
1.7%
43355.0 1
 
1.7%
79835.0 1
 
1.7%
597786.0 1
 
1.7%
23577.0 1
 
1.7%
Other values (40) 40
66.7%
ValueCountFrequency (%)
0.0 7
11.7%
0.02 1
 
1.7%
1.0 3
5.0%
1.238 1
 
1.7%
2.0 2
 
3.3%
26.0 1
 
1.7%
48.0 1
 
1.7%
122.0 2
 
3.3%
827.0 1
 
1.7%
850.0 1
 
1.7%
ValueCountFrequency (%)
117105711.0 1
1.7%
74086964.0 1
1.7%
6474924.0 1
1.7%
3181534.0 1
1.7%
2431643.0 1
1.7%
2097948.0 1
1.7%
2035822.0 1
1.7%
1819759.0 1
1.7%
1325095.0 1
1.7%
1087435.0 1
1.7%

수입금액(천불)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean681406.88
Minimum0
Maximum13485474
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T09:17:00.047476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.85
Q1148.5
median17939.5
Q383360.5
95-th percentile2549161.1
Maximum13485474
Range13485474
Interquartile range (IQR)83212

Descriptive statistics

Standard deviation2431006.4
Coefficient of variation (CV)3.5676281
Kurtosis20.781571
Mean681406.88
Median Absolute Deviation (MAD)17933
Skewness4.544101
Sum40884413
Variance5.9097919 × 1012
MonotonicityNot monotonic
2023-12-12T09:17:00.211476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 2
 
3.3%
67000 1
 
1.7%
10008 1
 
1.7%
58652 1
 
1.7%
36167 1
 
1.7%
46661 1
 
1.7%
8479 1
 
1.7%
19788 1
 
1.7%
30801 1
 
1.7%
72893 1
 
1.7%
Other values (49) 49
81.7%
ValueCountFrequency (%)
0 1
1.7%
1 1
1.7%
2 1
1.7%
5 1
1.7%
6 1
1.7%
7 1
1.7%
12 1
1.7%
15 1
1.7%
17 1
1.7%
23 1
1.7%
ValueCountFrequency (%)
13485474 1
1.7%
12074936 1
1.7%
6017233 1
1.7%
2366631 1
1.7%
2044885 1
1.7%
1047778 1
1.7%
889226 1
1.7%
779334 1
1.7%
340685 1
1.7%
302335 1
1.7%

Interactions

2023-12-12T09:16:57.465037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.178796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.696308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.108401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.566748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.366905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.800026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.196576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.656597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.477455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.909768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.285651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.760950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.586184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.016443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.370376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:17:00.341658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류광종수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)
분류1.0001.0000.0000.0000.5960.339
광종1.0001.0001.0001.0001.0001.000
수출중량(톤)0.0001.0001.0000.7950.3540.542
수출금액(천불)0.0001.0000.7951.0000.0000.699
수입중량(톤)0.5961.0000.3540.0001.0001.000
수입금액(천불)0.3391.0000.5420.6991.0001.000
2023-12-12T09:17:00.434511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)분류
수출중량(톤)1.0000.9190.5500.4750.000
수출금액(천불)0.9191.0000.4640.4980.000
수입중량(톤)0.5500.4641.0000.8790.268
수입금액(천불)0.4750.4980.8791.0000.265
분류0.0000.0000.2680.2651.000

Missing values

2023-12-12T09:16:57.898227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:16:58.031051image/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

연도분류광종수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)
02021금속광금광1005.01165705.067000
12021금속광은광63.07937635.0302335
22021금속광동광560664.011857202097948.06017233
32021금속광연광1102.0670642414.02366631
42021금속광아연광23804.0346791819759.02044885
52021금속광철광302518.01743874086964.012074936
62021금속광텅스텐광8.0841.060
72021금속광몰리브덴광9375.09032643719.0779334
82021금속광망간광0.001325095.0290356
92021금속광주석광2.0781.017
연도분류광종수출중량(톤)수출금액(천불)수입중량(톤)수입금액(천불)
502021비금속광수정0.03048.0178
512021비금속광붕소광57.010513297.05406
522021비금속광금강석0.48984771.238112074
532021비금속광하석5.07122.057
542021비금속광기타 비금속43136.03557148419.047448
552021석탄광무연탄430.02086474924.0889226
562021석탄광유연탄0.00117105711.013485474
572021석탄광갈탄0.0026.026
582021석탄광토탄57.084145052.028756
592021석탄광기타 석탄15.092035822.0159198