Overview

Dataset statistics

Number of variables12
Number of observations23
Missing cells13
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory113.7 B

Variable types

Numeric3
Categorical9

Dataset

Description연도별 일반기타(광재, 종이, 황산 등) 수송실적 입니다.
Author한국철도공사
URLhttps://www.data.go.kr/data/15068523/fileData.do

Alerts

프로필렌(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
갑종철도차량(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
황산(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
자동차(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
석고(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
비료(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
종이(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
일반기타(톤-키로) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
연도 is highly overall correlated with 비료-화학비료(톤-키로) and 10 other fieldsHigh correlation
비료-화학비료(톤-키로) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
비료-석비및기타(톤-키로) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
광재(톤-키로) is highly imbalanced (56.3%)Imbalance
종이(톤-키로) is highly imbalanced (56.3%)Imbalance
황산(톤-키로) is highly imbalanced (56.3%)Imbalance
프로필렌(톤-키로) is highly imbalanced (56.3%)Imbalance
비료(톤-키로) is highly imbalanced (56.3%)Imbalance
자동차(톤-키로) is highly imbalanced (61.7%)Imbalance
갑종철도차량(톤-키로) is highly imbalanced (56.3%)Imbalance
일반기타(톤-키로) is highly imbalanced (56.3%)Imbalance
비료-화학비료(톤-키로) has 4 (17.4%) missing valuesMissing
비료-석비및기타(톤-키로) has 9 (39.1%) missing valuesMissing
연도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:51:44.556459
Analysis finished2023-12-12 16:51:47.397751
Duration2.84 seconds
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
2023-12-13T01:51:47.459806image/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
2023-12-13T01:51:47.595945image/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%

광재(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
49599409.4
 
1
33428100.0
 
1
16607602.0
 
1
11958938.4
 
1

Length

Max length10
Median length4
Mean length5.0434783
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%
49599409.4 1
 
4.3%
33428100.0 1
 
4.3%
16607602.0 1
 
4.3%
11958938.4 1
 
4.3%

Length

2023-12-13T01:51:47.782265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:51:47.916704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
49599409.4 1
 
4.3%
33428100.0 1
 
4.3%
16607602.0 1
 
4.3%
11958938.4 1
 
4.3%

종이(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
106640144.4
 
1
82691352.9
 
1
78183534.1
 
1
80606224.1
 
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%
106640144.4 1
 
4.3%
82691352.9 1
 
4.3%
78183534.1 1
 
4.3%
80606224.1 1
 
4.3%

Length

2023-12-13T01:51:48.057042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:51:48.206163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
106640144.4 1
 
4.3%
82691352.9 1
 
4.3%
78183534.1 1
 
4.3%
80606224.1 1
 
4.3%

황산(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
147555550.0
 
1
157504675.0
 
1
175845000.0
 
1
168811200.1
 
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%
147555550.0 1
 
4.3%
157504675.0 1
 
4.3%
175845000.0 1
 
4.3%
168811200.1 1
 
4.3%

Length

2023-12-13T01:51:48.360486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:51:48.549136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
147555550.0 1
 
4.3%
157504675.0 1
 
4.3%
175845000.0 1
 
4.3%
168811200.1 1
 
4.3%

프로필렌(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
40030020.8
 
1
38707803.2
 
1
59941971.4
 
1
71537357.4
 
1

Length

Max length10
Median length4
Mean length5.0434783
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%
40030020.8 1
 
4.3%
38707803.2 1
 
4.3%
59941971.4 1
 
4.3%
71537357.4 1
 
4.3%

Length

2023-12-13T01:51:48.703324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:51:48.828984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
40030020.8 1
 
4.3%
38707803.2 1
 
4.3%
59941971.4 1
 
4.3%
71537357.4 1
 
4.3%

비료(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
9504589.9
 
1
9517180.0
 
1
5385444.3
 
1
4741981.0
 
1

Length

Max length9
Median length4
Mean length4.8695652
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%
9504589.9 1
 
4.3%
9517180.0 1
 
4.3%
5385444.3 1
 
4.3%
4741981.0 1
 
4.3%

Length

2023-12-13T01:51:48.970893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:51:49.112958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
9504589.9 1
 
4.3%
9517180.0 1
 
4.3%
5385444.3 1
 
4.3%
4741981.0 1
 
4.3%

비료-화학비료(톤-키로)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing4
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean94457923
Minimum6647769.3
Maximum3.6047302 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T01:51:49.234412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6647769.3
5-th percentile21924150
Q131104291
median48169530
Q31.3973238 × 108
95-th percentile2.5913401 × 108
Maximum3.6047302 × 108
Range3.5382525 × 108
Interquartile range (IQR)1.0862809 × 108

Descriptive statistics

Standard deviation94943672
Coefficient of variation (CV)1.0051425
Kurtosis2.1391292
Mean94457923
Median Absolute Deviation (MAD)21382861
Skewness1.5881523
Sum1.7947005 × 109
Variance9.0143009 × 1015
MonotonicityNot monotonic
2023-12-13T01:51:49.370274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
247874124.8 1
 
4.3%
6647769.3 1
 
4.3%
28451343.4 1
 
4.3%
26786668.5 1
 
4.3%
32702688.3 1
 
4.3%
39168441.0 1
 
4.3%
23621525.8 1
 
4.3%
42384999.7 1
 
4.3%
34800131.4 1
 
4.3%
360473016.0 1
 
4.3%
Other values (9) 9
39.1%
(Missing) 4
17.4%
ValueCountFrequency (%)
6647769.3 1
4.3%
23621525.8 1
4.3%
26786668.5 1
4.3%
28451343.4 1
4.3%
29505893.5 1
4.3%
32702688.3 1
4.3%
34800131.4 1
4.3%
39168441.0 1
4.3%
42384999.7 1
4.3%
48169529.7 1
4.3%
ValueCountFrequency (%)
360473016.0 1
4.3%
247874124.8 1
4.3%
206485953.2 1
4.3%
177782380.6 1
4.3%
161075618.3 1
4.3%
118389143.4 1
4.3%
87892948.6 1
4.3%
65501079.9 1
4.3%
56987290.6 1
4.3%
48169529.7 1
4.3%

비료-석비및기타(톤-키로)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing9
Missing (%)39.1%
Infinite0
Infinite (%)0.0%
Mean57246146
Minimum388940.4
Maximum1.150736 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T01:51:49.500427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum388940.4
5-th percentile11071320
Q126205229
median43845606
Q396831010
95-th percentile1.1318333 × 108
Maximum1.150736 × 108
Range1.1468466 × 108
Interquartile range (IQR)70625780

Descriptive statistics

Standard deviation39238460
Coefficient of variation (CV)0.68543409
Kurtosis-1.5059172
Mean57246146
Median Absolute Deviation (MAD)26305767
Skewness0.29154505
Sum8.0144604 × 108
Variance1.5396567 × 1015
MonotonicityNot monotonic
2023-12-13T01:51:49.641064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
388940.4 1
 
4.3%
115073604.9 1
 
4.3%
102664349.6 1
 
4.3%
112165491.7 1
 
4.3%
100594960.2 1
 
4.3%
85539157.8 1
 
4.3%
69434903.8 1
 
4.3%
44964801.5 1
 
4.3%
42726410.8 1
 
4.3%
34096097.6 1
 
4.3%
Other values (4) 4
17.4%
(Missing) 9
39.1%
ValueCountFrequency (%)
388940.4 1
4.3%
16823369.8 1
4.3%
23766450.9 1
4.3%
25806706.6 1
4.3%
27400797.1 1
4.3%
34096097.6 1
4.3%
42726410.8 1
4.3%
44964801.5 1
4.3%
69434903.8 1
4.3%
85539157.8 1
4.3%
ValueCountFrequency (%)
115073604.9 1
4.3%
112165491.7 1
4.3%
102664349.6 1
4.3%
100594960.2 1
4.3%
85539157.8 1
4.3%
69434903.8 1
4.3%
44964801.5 1
4.3%
42726410.8 1
4.3%
34096097.6 1
4.3%
27400797.1 1
4.3%

석고(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

2023-12-13T01:51:49.767626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:51:49.877432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
87.0%
0 3
 
13.0%

자동차(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
20 
19814594.6
 
1
15954446.3
 
1
14426696.0
 
1

Length

Max length10
Median length4
Mean length4.7826087
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%
19814594.6 1
 
4.3%
15954446.3 1
 
4.3%
14426696.0 1
 
4.3%

Length

2023-12-13T01:51:50.008589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:51:50.118615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
87.0%
19814594.6 1
 
4.3%
15954446.3 1
 
4.3%
14426696.0 1
 
4.3%

갑종철도차량(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
21176340.0
 
1
25362621.1
 
1
25918466.2
 
1
30317266.2
 
1

Length

Max length10
Median length4
Mean length5.0434783
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%
21176340.0 1
 
4.3%
25362621.1 1
 
4.3%
25918466.2 1
 
4.3%
30317266.2 1
 
4.3%

Length

2023-12-13T01:51:50.237152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:51:50.348905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
21176340.0 1
 
4.3%
25362621.1 1
 
4.3%
25918466.2 1
 
4.3%
30317266.2 1
 
4.3%

일반기타(톤-키로)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
136409720.8
 
1
127095437.7
 
1
157777870.2
 
1
137983985.9
 
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%
136409720.8 1
 
4.3%
127095437.7 1
 
4.3%
157777870.2 1
 
4.3%
137983985.9 1
 
4.3%

Length

2023-12-13T01:51:50.495928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:51:50.626251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
136409720.8 1
 
4.3%
127095437.7 1
 
4.3%
157777870.2 1
 
4.3%
137983985.9 1
 
4.3%

Interactions

2023-12-13T01:51:46.197688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:45.424750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:45.783088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:46.321684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:45.527019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:45.936536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:46.426155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:45.657228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:46.077122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:51:50.705139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도광재(톤-키로)종이(톤-키로)황산(톤-키로)프로필렌(톤-키로)비료(톤-키로)비료-화학비료(톤-키로)비료-석비및기타(톤-키로)자동차(톤-키로)갑종철도차량(톤-키로)일반기타(톤-키로)
연도1.0001.0001.0001.0001.0001.0000.7680.7961.0001.0001.000
광재(톤-키로)1.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.000
종이(톤-키로)1.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.000
황산(톤-키로)1.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.000
프로필렌(톤-키로)1.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.000
비료(톤-키로)1.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.000
비료-화학비료(톤-키로)0.768NaNNaNNaNNaNNaN1.0000.927NaNNaNNaN
비료-석비및기타(톤-키로)0.796NaNNaNNaNNaNNaN0.9271.000NaNNaNNaN
자동차(톤-키로)1.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.000
갑종철도차량(톤-키로)1.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.000
일반기타(톤-키로)1.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.000
2023-12-13T01:51:50.858365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
프로필렌(톤-키로)갑종철도차량(톤-키로)황산(톤-키로)광재(톤-키로)자동차(톤-키로)석고(톤-키로)비료(톤-키로)종이(톤-키로)일반기타(톤-키로)
프로필렌(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
갑종철도차량(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
황산(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
자동차(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
석고(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
비료(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
종이(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
일반기타(톤-키로)1.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T01:51:50.985111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도비료-화학비료(톤-키로)비료-석비및기타(톤-키로)광재(톤-키로)종이(톤-키로)황산(톤-키로)프로필렌(톤-키로)비료(톤-키로)석고(톤-키로)자동차(톤-키로)갑종철도차량(톤-키로)일반기타(톤-키로)
연도1.000-0.947-0.5781.0001.0001.0001.0001.0001.0001.0001.0001.000
비료-화학비료(톤-키로)-0.9471.0000.5820.0000.0000.0000.0000.0000.0000.0000.0000.000
비료-석비및기타(톤-키로)-0.5780.5821.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
광재(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
종이(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
황산(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
프로필렌(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
비료(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
석고(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
자동차(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
갑종철도차량(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
일반기타(톤-키로)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T01:51:46.582046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:51:46.767874image/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.
2023-12-13T01:51:47.259508image/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

연도광재(톤-키로)종이(톤-키로)황산(톤-키로)프로필렌(톤-키로)비료(톤-키로)비료-화학비료(톤-키로)비료-석비및기타(톤-키로)석고(톤-키로)자동차(톤-키로)갑종철도차량(톤-키로)일반기타(톤-키로)
01996<NA><NA><NA><NA><NA>360473016.0388940.4<NA><NA><NA><NA>
11997<NA><NA><NA><NA><NA>247874124.8115073604.9<NA><NA><NA><NA>
21998<NA><NA><NA><NA><NA>206485953.2102664349.6<NA><NA><NA><NA>
31999<NA><NA><NA><NA><NA>177782380.6112165491.7<NA><NA><NA><NA>
42000<NA><NA><NA><NA><NA>161075618.3100594960.2<NA><NA><NA><NA>
52001<NA><NA><NA><NA><NA>118389143.485539157.8<NA><NA><NA><NA>
62002<NA><NA><NA><NA><NA>87892948.669434903.8<NA><NA><NA><NA>
72003<NA><NA><NA><NA><NA>56987290.644964801.5<NA><NA><NA><NA>
82004<NA><NA><NA><NA><NA>48169529.742726410.8<NA><NA><NA><NA>
92005<NA><NA><NA><NA><NA>65501079.934096097.6<NA><NA><NA><NA>
연도광재(톤-키로)종이(톤-키로)황산(톤-키로)프로필렌(톤-키로)비료(톤-키로)비료-화학비료(톤-키로)비료-석비및기타(톤-키로)석고(톤-키로)자동차(톤-키로)갑종철도차량(톤-키로)일반기타(톤-키로)
132009<NA><NA><NA><NA><NA>23621525.816823369.8<NA><NA><NA><NA>
142010<NA><NA><NA><NA><NA>39168441.0<NA><NA><NA><NA><NA>
152011<NA><NA><NA><NA><NA>32702688.3<NA><NA><NA><NA><NA>
162012<NA><NA><NA><NA><NA>26786668.5<NA><NA><NA><NA><NA>
172013<NA><NA><NA><NA><NA>28451343.4<NA><NA><NA><NA><NA>
182014<NA><NA><NA><NA><NA>6647769.3<NA><NA><NA><NA><NA>
19201549599409.4106640144.4147555550.040030020.89504589.9<NA><NA>019814594.621176340.0136409720.8
20201633428100.082691352.9157504675.038707803.29517180.0<NA><NA>015954446.325362621.1127095437.7
21201716607602.078183534.1175845000.059941971.45385444.3<NA><NA>014426696.025918466.2157777870.2
22201811958938.480606224.1168811200.171537357.44741981.0<NA><NA><NA><NA>30317266.2137983985.9