Overview

Dataset statistics

Number of variables10
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory95.7 B

Variable types

Numeric2
Categorical8

Dataset

Description연도별 컨테이너(일반, 중량, 석탄류 등) 수송실적 입니다.
Author한국철도공사
URLhttps://www.data.go.kr/data/15068510/fileData.do

Alerts

기타중량품(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
석탄류(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
페로니켈(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
JR컨테이너(톤-키로) is highly overall correlated with 일반(톤-키로) and 7 other fieldsHigh correlation
크링카(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
중량(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
택배컨테이너(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
광재(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
연도 is highly overall correlated with 일반(톤-키로) and 7 other fieldsHigh correlation
일반(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
중량(톤-키로) is highly imbalanced (56.3%)Imbalance
석탄류(톤-키로) is highly imbalanced (56.3%)Imbalance
크링카(톤-키로) is highly imbalanced (56.3%)Imbalance
광재(톤-키로) is highly imbalanced (61.7%)Imbalance
기타중량품(톤-키로) is highly imbalanced (56.3%)Imbalance
페로니켈(톤-키로) is highly imbalanced (56.3%)Imbalance
JR컨테이너(톤-키로) is highly imbalanced (57.2%)Imbalance
연도 has unique valuesUnique
일반(톤-키로) has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:16:54.654583
Analysis finished2023-12-12 15:16:55.815299
Duration1.16 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
2023-12-13T00:16:55.876406image/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
2023-12-13T00:16:56.002625image/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%

일반(톤-키로)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1735129 × 109
Minimum2.2190087 × 109
Maximum4.0715882 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T00:16:56.135954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.2190087 × 109
5-th percentile2.4199518 × 109
Q12.8032417 × 109
median3.0388245 × 109
Q33.611647 × 109
95-th percentile4.0245949 × 109
Maximum4.0715882 × 109
Range1.8525794 × 109
Interquartile range (IQR)8.0840531 × 108

Descriptive statistics

Standard deviation5.3099967 × 108
Coefficient of variation (CV)0.16732236
Kurtosis-0.8190867
Mean3.1735129 × 109
Median Absolute Deviation (MAD)3.1265203 × 108
Skewness0.23936842
Sum7.2990796 × 1010
Variance2.8196065 × 1017
MonotonicityNot monotonic
2023-12-13T00:16:56.266173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2219008741.9 1
 
4.3%
2404057484.6 1
 
4.3%
2923813061.5 1
 
4.3%
2945994899.8 1
 
4.3%
2986757287.8 1
 
4.3%
3343505863.9 1
 
4.3%
3579984010.7 1
 
4.3%
4025551788.5 1
 
4.3%
4015982998.4 1
 
4.3%
3793862982.1 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2219008741.9 1
4.3%
2404057484.6 1
4.3%
2563000926.4 1
4.3%
2716718365.6 1
4.3%
2726172422.3 1
4.3%
2772177442.5 1
4.3%
2834305972.3 1
4.3%
2923813061.5 1
4.3%
2945994899.8 1
4.3%
2986757287.8 1
4.3%
ValueCountFrequency (%)
4071588191.8 1
4.3%
4025551788.5 1
4.3%
4015982998.4 1
4.3%
3799182608.9 1
4.3%
3793862982.1 1
4.3%
3643310023.0 1
4.3%
3579984010.7 1
4.3%
3343505863.9 1
4.3%
3286679041.8 1
4.3%
3173224977.5 1
4.3%

중량(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
52287954.0
 
1
43537934.7
 
1
45111369.0
 
1
179371102.8
 
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%
52287954.0 1
 
4.3%
43537934.7 1
 
4.3%
45111369.0 1
 
4.3%
179371102.8 1
 
4.3%

Length

2023-12-13T00:16:56.417797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:56.527331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
52287954.0 1
 
4.3%
43537934.7 1
 
4.3%
45111369.0 1
 
4.3%
179371102.8 1
 
4.3%

석탄류(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
12270938.0
 
1
12035700.0
 
1
8921750.0
 
1
64415754.7
 
1

Length

Max length10
Median length4
Mean length5
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%
12270938.0 1
 
4.3%
12035700.0 1
 
4.3%
8921750.0 1
 
4.3%
64415754.7 1
 
4.3%

Length

2023-12-13T00:16:56.719951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:56.825097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
12270938.0 1
 
4.3%
12035700.0 1
 
4.3%
8921750.0 1
 
4.3%
64415754.7 1
 
4.3%

크링카(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
6224800
 
1
5090110
 
1
7750050
 
1
3305260
 
1

Length

Max length7
Median length4
Mean length4.5217391
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%
6224800 1
 
4.3%
5090110 1
 
4.3%
7750050 1
 
4.3%
3305260 1
 
4.3%

Length

2023-12-13T00:16:56.942877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:57.054715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
6224800 1
 
4.3%
5090110 1
 
4.3%
7750050 1
 
4.3%
3305260 1
 
4.3%

광재(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
20 
5741756
 
1
4676940
 
1
351540
 
1

Length

Max length7
Median length4
Mean length4.3478261
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%
5741756 1
 
4.3%
4676940 1
 
4.3%
351540 1
 
4.3%

Length

2023-12-13T00:16:57.187775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:57.294020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
87.0%
5741756 1
 
4.3%
4676940 1
 
4.3%
351540 1
 
4.3%

기타중량품(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
8532550
 
1
9287330
 
1
46736160
 
1
4743340
 
1

Length

Max length8
Median length4
Mean length4.5652174
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%
8532550 1
 
4.3%
9287330 1
 
4.3%
46736160 1
 
4.3%
4743340 1
 
4.3%

Length

2023-12-13T00:16:57.432422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:57.555553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
8532550 1
 
4.3%
9287330 1
 
4.3%
46736160 1
 
4.3%
4743340 1
 
4.3%

페로니켈(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
9295152.1
 
1
14660539.2
 
1
14895907.6
 
1
15599968.2
 
1

Length

Max length10
Median length4
Mean length5
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%
9295152.1 1
 
4.3%
14660539.2 1
 
4.3%
14895907.6 1
 
4.3%
15599968.2 1
 
4.3%

Length

2023-12-13T00:16:57.701251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:57.817840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
9295152.1 1
 
4.3%
14660539.2 1
 
4.3%
14895907.6 1
 
4.3%
15599968.2 1
 
4.3%

JR컨테이너(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
20 
0
 
2
61560
 
1

Length

Max length5
Median length4
Mean length3.7826087
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 20
87.0%
0 2
 
8.7%
61560 1
 
4.3%

Length

2023-12-13T00:16:57.951781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:58.062124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
87.0%
0 2
 
8.7%
61560 1
 
4.3%

택배컨테이너(톤-키로)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
20 
0
3 

Length

Max length4
Median length4
Mean length3.6086957
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 20
87.0%
0 3
 
13.0%

Length

2023-12-13T00:16:58.169785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:58.357651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
87.0%
0 3
 
13.0%

Interactions

2023-12-13T00:16:55.370478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:55.198521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:55.459942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:55.282761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:16:58.448131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도일반(톤-키로)중량(톤-키로)석탄류(톤-키로)크링카(톤-키로)광재(톤-키로)기타중량품(톤-키로)페로니켈(톤-키로)JR컨테이너(톤-키로)
연도1.0000.8031.0001.0001.0001.0001.0001.0000.000
일반(톤-키로)0.8031.0001.0001.0001.0001.0001.0001.0001.000
중량(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
석탄류(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
크링카(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
광재(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
기타중량품(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
페로니켈(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
JR컨테이너(톤-키로)0.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T00:16:58.610924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기타중량품(톤-키로)석탄류(톤-키로)페로니켈(톤-키로)JR컨테이너(톤-키로)크링카(톤-키로)중량(톤-키로)택배컨테이너(톤-키로)광재(톤-키로)
기타중량품(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.000
석탄류(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.000
페로니켈(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.000
JR컨테이너(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.000
크링카(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.000
중량(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.000
택배컨테이너(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.000
광재(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T00:16:58.760565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도일반(톤-키로)중량(톤-키로)석탄류(톤-키로)크링카(톤-키로)광재(톤-키로)기타중량품(톤-키로)페로니켈(톤-키로)JR컨테이너(톤-키로)택배컨테이너(톤-키로)
연도1.0000.5051.0001.0001.0001.0001.0001.0000.0001.000
일반(톤-키로)0.5051.0001.0001.0001.0001.0001.0001.0001.0001.000
중량(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
석탄류(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
크링카(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
광재(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기타중량품(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
페로니켈(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
JR컨테이너(톤-키로)0.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
택배컨테이너(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T00:16:55.609326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:16:55.752879image/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

연도일반(톤-키로)중량(톤-키로)석탄류(톤-키로)크링카(톤-키로)광재(톤-키로)기타중량품(톤-키로)페로니켈(톤-키로)JR컨테이너(톤-키로)택배컨테이너(톤-키로)
019962219008741.9<NA><NA><NA><NA><NA><NA><NA><NA>
119972404057484.6<NA><NA><NA><NA><NA><NA><NA><NA>
219982563000926.4<NA><NA><NA><NA><NA><NA><NA><NA>
319992772177442.5<NA><NA><NA><NA><NA><NA><NA><NA>
420003112792576.4<NA><NA><NA><NA><NA><NA><NA><NA>
520012726172422.3<NA><NA><NA><NA><NA><NA><NA><NA>
620022834305972.3<NA><NA><NA><NA><NA><NA><NA><NA>
720033014299701.7<NA><NA><NA><NA><NA><NA><NA><NA>
820043038824454.9<NA><NA><NA><NA><NA><NA><NA><NA>
920053286679041.8<NA><NA><NA><NA><NA><NA><NA><NA>
연도일반(톤-키로)중량(톤-키로)석탄류(톤-키로)크링카(톤-키로)광재(톤-키로)기타중량품(톤-키로)페로니켈(톤-키로)JR컨테이너(톤-키로)택배컨테이너(톤-키로)
1320092716718365.6<NA><NA><NA><NA><NA><NA><NA><NA>
1420103173224977.5<NA><NA><NA><NA><NA><NA><NA><NA>
1520113793862982.1<NA><NA><NA><NA><NA><NA><NA><NA>
1620124015982998.4<NA><NA><NA><NA><NA><NA><NA><NA>
1720134025551788.5<NA><NA><NA><NA><NA><NA><NA><NA>
1820143579984010.7<NA><NA><NA><NA><NA><NA><NA><NA>
1920153343505863.952287954.012270938.06224800574175685325509295152.1615600
2020162986757287.843537934.712035700.050901104676940928733014660539.200
2120172945994899.845111369.08921750.077500503515404673616014895907.600
2220182923813061.5179371102.864415754.73305260<NA>474334015599968.2<NA><NA>