Overview

Dataset statistics

Number of variables8
Number of observations23
Missing cells8
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory77.7 B

Variable types

Numeric3
Categorical5

Dataset

Description연도별 석탄(발전용무연탄, 민수용무연탄 등) 수송실적 입니다.
Author한국철도공사
URLhttps://www.data.go.kr/data/15068514/fileData.do

Alerts

민수용무연탄(톤-키로) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
발전용유연탄(톤-키로) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
시멘트용유연탄(톤-키로) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
발전용무연탄(톤-키로) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
경석(톤-키로) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
연도 is highly overall correlated with 국내(톤-키로) and 5 other fieldsHigh correlation
국내(톤-키로) is highly overall correlated with 연도High correlation
발전용무연탄(톤-키로) is highly imbalanced (56.3%)Imbalance
민수용무연탄(톤-키로) is highly imbalanced (56.3%)Imbalance
발전용유연탄(톤-키로) is highly imbalanced (61.7%)Imbalance
시멘트용유연탄(톤-키로) is highly imbalanced (56.3%)Imbalance
경석(톤-키로) is highly imbalanced (56.3%)Imbalance
국내(톤-키로) has 4 (17.4%) missing valuesMissing
수입(톤-키로) has 4 (17.4%) missing valuesMissing
연도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:56:48.961878
Analysis finished2023-12-12 04:56:50.918292
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007
Minimum1996
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T13:56:50.997680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile1997.1
Q12001.5
median2007
Q32012.5
95-th percentile2016.9
Maximum2018
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.0033793373
Kurtosis-1.2
Mean2007
Median Absolute Deviation (MAD)6
Skewness0
Sum46161
Variance46
MonotonicityStrictly increasing
2023-12-12T13:56:51.160604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1996 1
 
4.3%
1997 1
 
4.3%
2018 1
 
4.3%
2017 1
 
4.3%
2016 1
 
4.3%
2015 1
 
4.3%
2014 1
 
4.3%
2013 1
 
4.3%
2012 1
 
4.3%
2011 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1996 1
4.3%
1997 1
4.3%
1998 1
4.3%
1999 1
4.3%
2000 1
4.3%
2001 1
4.3%
2002 1
4.3%
2003 1
4.3%
2004 1
4.3%
2005 1
4.3%
ValueCountFrequency (%)
2018 1
4.3%
2017 1
4.3%
2016 1
4.3%
2015 1
4.3%
2014 1
4.3%
2013 1
4.3%
2012 1
4.3%
2011 1
4.3%
2010 1
4.3%
2009 1
4.3%

국내(톤-키로)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing4
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean6.5897776 × 108
Minimum1.8771971 × 108
Maximum9.1365961 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T13:56:51.344033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8771971 × 108
5-th percentile2.5200843 × 108
Q16.1103377 × 108
median7.0938147 × 108
Q37.9025103 × 108
95-th percentile9.0976229 × 108
Maximum9.1365961 × 108
Range7.2593991 × 108
Interquartile range (IQR)1.7921726 × 108

Descriptive statistics

Standard deviation2.1427002 × 108
Coefficient of variation (CV)0.32515516
Kurtosis0.3259741
Mean6.5897776 × 108
Median Absolute Deviation (MAD)84706074
Skewness-1.1337183
Sum1.2520577 × 1010
Variance4.5911642 × 1016
MonotonicityNot monotonic
2023-12-12T13:56:51.470805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
782682062.4 1
 
4.3%
187719706.5 1
 
4.3%
259151626.7 1
 
4.3%
442301472.8 1
 
4.3%
292080209.1 1
 
4.3%
564222494.6 1
 
4.3%
657845051.9 1
 
4.3%
706053170.1 1
 
4.3%
703545523.0 1
 
4.3%
909329250.9 1
 
4.3%
Other values (9) 9
39.1%
(Missing) 4
17.4%
ValueCountFrequency (%)
187719706.5 1
4.3%
259151626.7 1
4.3%
292080209.1 1
4.3%
442301472.8 1
4.3%
564222494.6 1
4.3%
657845051.9 1
4.3%
674551416.4 1
4.3%
703545523.0 1
4.3%
706053170.1 1
4.3%
709381470.9 1
4.3%
ValueCountFrequency (%)
913659613.6 1
4.3%
909329250.9 1
4.3%
840725271.1 1
4.3%
796861127.7 1
4.3%
794087545.0 1
4.3%
786414522.7 1
4.3%
782682062.4 1
4.3%
772260843.5 1
4.3%
727705114.4 1
4.3%
709381470.9 1
4.3%

수입(톤-키로)
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)100.0%
Missing4
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean5.695417 × 108
Minimum4.0952107 × 108
Maximum7.9932788 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T13:56:51.649455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.0952107 × 108
5-th percentile4.4033643 × 108
Q15.0012398 × 108
median5.4362242 × 108
Q36.4156171 × 108
95-th percentile7.384323 × 108
Maximum7.9932788 × 108
Range3.8980682 × 108
Interquartile range (IQR)1.4143773 × 108

Descriptive statistics

Standard deviation1.0646444 × 108
Coefficient of variation (CV)0.18693002
Kurtosis-0.27147406
Mean5.695417 × 108
Median Absolute Deviation (MAD)56036346
Skewness0.64903862
Sum1.0821292 × 1010
Variance1.1334677 × 1016
MonotonicityNot monotonic
2023-12-12T13:56:52.120392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
642480500.2 1
 
4.3%
731666123.2 1
 
4.3%
726497783.8 1
 
4.3%
409521068.3 1
 
4.3%
799327884.0 1
 
4.3%
640642915.5 1
 
4.3%
586279268.6 1
 
4.3%
543622417.4 1
 
4.3%
537445776.2 1
 
4.3%
669546924.8 1
 
4.3%
Other values (9) 9
39.1%
(Missing) 4
17.4%
ValueCountFrequency (%)
409521068.3 1
4.3%
443760353.7 1
4.3%
449550899.9 1
4.3%
487586071.4 1
4.3%
489790527.8 1
4.3%
510457425.9 1
4.3%
515128173.9 1
4.3%
523134262.6 1
4.3%
537445776.2 1
4.3%
543622417.4 1
4.3%
ValueCountFrequency (%)
799327884.0 1
4.3%
731666123.2 1
4.3%
726497783.8 1
4.3%
669546924.8 1
4.3%
642480500.2 1
4.3%
640642915.5 1
4.3%
586279268.6 1
4.3%
565884974.0 1
4.3%
548969000.2 1
4.3%
543622417.4 1
4.3%

발전용무연탄(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
91555093.0
 
1
122339350.4
 
1
29685097.4
 
1
11124389.0
 
1

Length

Max length11
Median length4
Mean length5.0869565
Min length4

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 19
82.6%
91555093.0 1
 
4.3%
122339350.4 1
 
4.3%
29685097.4 1
 
4.3%
11124389.0 1
 
4.3%

Length

2023-12-12T13:56:52.313239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:56:52.438699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
91555093.0 1
 
4.3%
122339350.4 1
 
4.3%
29685097.4 1
 
4.3%
11124389.0 1
 
4.3%

민수용무연탄(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
108630526.0
 
1
83958862.8
 
1
94571986.4
 
1
72912680.5
 
1

Length

Max length11
Median length4
Mean length5.0869565
Min length4

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 19
82.6%
108630526.0 1
 
4.3%
83958862.8 1
 
4.3%
94571986.4 1
 
4.3%
72912680.5 1
 
4.3%

Length

2023-12-12T13:56:52.585010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:56:52.725441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
108630526.0 1
 
4.3%
83958862.8 1
 
4.3%
94571986.4 1
 
4.3%
72912680.5 1
 
4.3%

발전용유연탄(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
20 
173709325.1
 
1
99390695.2
 
1
62587148.8
 
1

Length

Max length11
Median length4
Mean length4.826087
Min length4

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 20
87.0%
173709325.1 1
 
4.3%
99390695.2 1
 
4.3%
62587148.8 1
 
4.3%

Length

2023-12-12T13:56:52.870912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:56:53.021557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
87.0%
173709325.1 1
 
4.3%
99390695.2 1
 
4.3%
62587148.8 1
 
4.3%

시멘트용유연탄(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
422703576.9
 
1
380833776.0
 
1
394941835.6
 
1
344118048.5
 
1

Length

Max length11
Median length4
Mean length5.2173913
Min length4

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 19
82.6%
422703576.9 1
 
4.3%
380833776.0 1
 
4.3%
394941835.6 1
 
4.3%
344118048.5 1
 
4.3%

Length

2023-12-12T13:56:53.159224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:56:53.302029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
422703576.9 1
 
4.3%
380833776.0 1
 
4.3%
394941835.6 1
 
4.3%
344118048.5 1
 
4.3%

경석(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
8097537.9
 
1
7077393.4
 
1
10684455.1
 
1
2951503.5
 
1

Length

Max length10
Median length4
Mean length4.9130435
Min length4

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 19
82.6%
8097537.9 1
 
4.3%
7077393.4 1
 
4.3%
10684455.1 1
 
4.3%
2951503.5 1
 
4.3%

Length

2023-12-12T13:56:53.427376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:56:53.554596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
8097537.9 1
 
4.3%
7077393.4 1
 
4.3%
10684455.1 1
 
4.3%
2951503.5 1
 
4.3%

Interactions

2023-12-12T13:56:50.003566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:56:49.338357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:56:49.653981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:56:50.136783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:56:49.416661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:56:49.753626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:56:50.293336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:56:49.544735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:56:49.900515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:56:53.641672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도국내(톤-키로)수입(톤-키로)발전용무연탄(톤-키로)민수용무연탄(톤-키로)발전용유연탄(톤-키로)시멘트용유연탄(톤-키로)경석(톤-키로)
연도1.0000.3110.1381.0001.0001.0001.0001.000
국내(톤-키로)0.3111.0000.852NaNNaNNaNNaNNaN
수입(톤-키로)0.1380.8521.000NaNNaNNaNNaNNaN
발전용무연탄(톤-키로)1.000NaNNaN1.0001.0001.0001.0001.000
민수용무연탄(톤-키로)1.000NaNNaN1.0001.0001.0001.0001.000
발전용유연탄(톤-키로)1.000NaNNaN1.0001.0001.0001.0001.000
시멘트용유연탄(톤-키로)1.000NaNNaN1.0001.0001.0001.0001.000
경석(톤-키로)1.000NaNNaN1.0001.0001.0001.0001.000
2023-12-12T13:56:53.822378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민수용무연탄(톤-키로)발전용유연탄(톤-키로)시멘트용유연탄(톤-키로)발전용무연탄(톤-키로)경석(톤-키로)
민수용무연탄(톤-키로)1.0001.0001.0001.0001.000
발전용유연탄(톤-키로)1.0001.0001.0001.0001.000
시멘트용유연탄(톤-키로)1.0001.0001.0001.0001.000
발전용무연탄(톤-키로)1.0001.0001.0001.0001.000
경석(톤-키로)1.0001.0001.0001.0001.000
2023-12-12T13:56:53.953322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도국내(톤-키로)수입(톤-키로)발전용무연탄(톤-키로)민수용무연탄(톤-키로)발전용유연탄(톤-키로)시멘트용유연탄(톤-키로)경석(톤-키로)
연도1.000-0.8020.2351.0001.0001.0001.0001.000
국내(톤-키로)-0.8021.000-0.3680.0000.0000.0000.0000.000
수입(톤-키로)0.235-0.3681.0000.0000.0000.0000.0000.000
발전용무연탄(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.000
민수용무연탄(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.000
발전용유연탄(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.000
시멘트용유연탄(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.000
경석(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T13:56:50.439480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:56:50.630744image/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.
2023-12-12T13:56:50.810047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도국내(톤-키로)수입(톤-키로)발전용무연탄(톤-키로)민수용무연탄(톤-키로)발전용유연탄(톤-키로)시멘트용유연탄(톤-키로)경석(톤-키로)
01996909329250.9669546924.8<NA><NA><NA><NA><NA>
11997782682062.4642480500.2<NA><NA><NA><NA><NA>
21998786414522.7487586071.4<NA><NA><NA><NA><NA>
31999709381470.9510457425.9<NA><NA><NA><NA><NA>
42000794087545.0523134262.6<NA><NA><NA><NA><NA>
52001840725271.1515128173.9<NA><NA><NA><NA><NA>
62002727705114.4548969000.2<NA><NA><NA><NA><NA>
72003796861127.7565884974.0<NA><NA><NA><NA><NA>
82004674551416.4489790527.8<NA><NA><NA><NA><NA>
92005772260843.5443760353.7<NA><NA><NA><NA><NA>
연도국내(톤-키로)수입(톤-키로)발전용무연탄(톤-키로)민수용무연탄(톤-키로)발전용유연탄(톤-키로)시멘트용유연탄(톤-키로)경석(톤-키로)
132009657845051.9586279268.6<NA><NA><NA><NA><NA>
142010564222494.6640642915.5<NA><NA><NA><NA><NA>
152011292080209.1799327884.0<NA><NA><NA><NA><NA>
162012442301472.8409521068.3<NA><NA><NA><NA><NA>
172013259151626.7726497783.8<NA><NA><NA><NA><NA>
182014187719706.5731666123.2<NA><NA><NA><NA><NA>
192015<NA><NA>91555093.0108630526.0173709325.1422703576.98097537.9
202016<NA><NA>122339350.483958862.899390695.2380833776.07077393.4
212017<NA><NA>29685097.494571986.462587148.8394941835.610684455.1
222018<NA><NA>11124389.072912680.5<NA>344118048.52951503.5