Overview

Dataset statistics

Number of variables12
Number of observations23
Missing cells37
Missing cells (%)13.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory113.7 B

Variable types

Numeric5
Categorical6
Unsupported1

Dataset

Description연도별 일반기타(광재, 종이, 황산 등) 수송실적 입니다.
Author한국철도공사
URLhttps://www.data.go.kr/data/15068404/fileData.do

Alerts

황산(톤) is highly overall correlated with 연도 and 7 other fieldsHigh correlation
비료(톤) is highly overall correlated with 연도 and 7 other fieldsHigh correlation
종이(톤) is highly overall correlated with 연도 and 7 other fieldsHigh correlation
갑종철도차량(톤) is highly overall correlated with 연도 and 7 other fieldsHigh correlation
광재(톤) is highly overall correlated with 연도 and 7 other fieldsHigh correlation
프로필렌(톤) is highly overall correlated with 연도 and 7 other fieldsHigh correlation
연도 is highly overall correlated with 비료-화학비료(톤) and 9 other fieldsHigh correlation
비료-화학비료(톤) is highly overall correlated with 연도 and 3 other fieldsHigh correlation
비료-석비및기타(톤) is highly overall correlated with 연도 and 2 other fieldsHigh correlation
자동차(톤) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
일반기타(톤) is highly overall correlated with 연도 and 9 other fieldsHigh correlation
광재(톤) is highly imbalanced (56.3%)Imbalance
종이(톤) is highly imbalanced (56.3%)Imbalance
황산(톤) is highly imbalanced (56.3%)Imbalance
프로필렌(톤) is highly imbalanced (56.3%)Imbalance
비료(톤) is highly imbalanced (56.3%)Imbalance
갑종철도차량(톤) is highly imbalanced (56.3%)Imbalance
비료-화학비료(톤) has 4 (17.4%) missing valuesMissing
비료-석비및기타(톤) has 9 (39.1%) missing valuesMissing
석고(톤) has 23 (100.0%) missing valuesMissing
자동차(톤) has 1 (4.3%) missing valuesMissing
연도 has unique valuesUnique
일반기타(톤) has unique valuesUnique
석고(톤) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 23:11:42.373714
Analysis finished2023-12-12 23:11:45.146527
Duration2.77 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
2023-12-13T08:11:45.199741image/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
2023-12-13T08:11:45.327490image/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)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
195224
 
1
130332
 
1
68225
 
1
49746
 
1

Length

Max length6
Median length4
Mean length4.2608696
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%
195224 1
 
4.3%
130332 1
 
4.3%
68225 1
 
4.3%
49746 1
 
4.3%

Length

2023-12-13T08:11:45.448646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:11:45.559193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
195224 1
 
4.3%
130332 1
 
4.3%
68225 1
 
4.3%
49746 1
 
4.3%

종이(톤)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
287226
 
1
216813
 
1
172828
 
1
178187
 
1

Length

Max length6
Median length4
Mean length4.3478261
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%
287226 1
 
4.3%
216813 1
 
4.3%
172828 1
 
4.3%
178187 1
 
4.3%

Length

2023-12-13T08:11:45.673267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:11:45.784790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
287226 1
 
4.3%
216813 1
 
4.3%
172828 1
 
4.3%
178187 1
 
4.3%

황산(톤)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
478300
 
1
510550
 
1
570000
 
1
547200
 
1

Length

Max length6
Median length4
Mean length4.3478261
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%
478300 1
 
4.3%
510550 1
 
4.3%
570000 1
 
4.3%
547200 1
 
4.3%

Length

2023-12-13T08:11:45.946647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:11:46.114350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
478300 1
 
4.3%
510550 1
 
4.3%
570000 1
 
4.3%
547200 1
 
4.3%

프로필렌(톤)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
239414
 
1
231506
 
1
223808
 
1
241256
 
1

Length

Max length6
Median length4
Mean length4.3478261
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%
239414 1
 
4.3%
231506 1
 
4.3%
223808 1
 
4.3%
241256 1
 
4.3%

Length

2023-12-13T08:11:46.232738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:11:46.347684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
239414 1
 
4.3%
231506 1
 
4.3%
223808 1
 
4.3%
241256 1
 
4.3%

비료(톤)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
41140
 
1
42551
 
1
26880
 
1
24887
 
1

Length

Max length5
Median length4
Mean length4.173913
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%
41140 1
 
4.3%
42551 1
 
4.3%
26880 1
 
4.3%
24887 1
 
4.3%

Length

2023-12-13T08:11:46.478887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:11:46.580686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
41140 1
 
4.3%
42551 1
 
4.3%
26880 1
 
4.3%
24887 1
 
4.3%

비료-화학비료(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing4
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean339494.68
Minimum28432
Maximum1346606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T08:11:46.703344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28432
5-th percentile82481.5
Q1113412.5
median174374
Q3475849
95-th percentile952860.5
Maximum1346606
Range1318174
Interquartile range (IQR)362436.5

Descriptive statistics

Standard deviation348476.6
Coefficient of variation (CV)1.0264567
Kurtosis2.7385329
Mean339494.68
Median Absolute Deviation (MAD)81580
Skewness1.7239596
Sum6450399
Variance1.2143594 × 1011
MonotonicityNot monotonic
2023-12-13T08:11:46.829942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
909111 1
 
4.3%
28432 1
 
4.3%
92794 1
 
4.3%
107889 1
 
4.3%
118936 1
 
4.3%
146550 1
 
4.3%
88487 1
 
4.3%
160156 1
 
4.3%
126352 1
 
4.3%
1346606 1
 
4.3%
Other values (9) 9
39.1%
(Missing) 4
17.4%
ValueCountFrequency (%)
28432 1
4.3%
88487 1
4.3%
92794 1
4.3%
100916 1
4.3%
107889 1
4.3%
118936 1
4.3%
126352 1
4.3%
146550 1
4.3%
160156 1
4.3%
174374 1
4.3%
ValueCountFrequency (%)
1346606 1
4.3%
909111 1
4.3%
734546 1
4.3%
631362 1
4.3%
560383 1
4.3%
391315 1
4.3%
299180 1
4.3%
233621 1
4.3%
199389 1
4.3%
174374 1
4.3%

비료-석비및기타(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing9
Missing (%)39.1%
Infinite0
Infinite (%)0.0%
Mean213193.07
Minimum1302
Maximum446436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T08:11:47.260244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1302
5-th percentile42267.6
Q198630
median159593.5
Q3368526.5
95-th percentile426988
Maximum446436
Range445134
Interquartile range (IQR)269896.5

Descriptive statistics

Standard deviation150122.42
Coefficient of variation (CV)0.70416184
Kurtosis-1.4824065
Mean213193.07
Median Absolute Deviation (MAD)90818
Skewness0.35220345
Sum2984703
Variance2.2536742 × 1010
MonotonicityNot monotonic
2023-12-13T08:11:47.383018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1302 1
 
4.3%
446436 1
 
4.3%
385466 1
 
4.3%
416516 1
 
4.3%
383450 1
 
4.3%
323756 1
 
4.3%
245962 1
 
4.3%
166975 1
 
4.3%
152212 1
 
4.3%
118244 1
 
4.3%
Other values (4) 4
17.4%
(Missing) 9
39.1%
ValueCountFrequency (%)
1302 1
4.3%
64326 1
4.3%
80522 1
4.3%
97492 1
4.3%
102044 1
4.3%
118244 1
4.3%
152212 1
4.3%
166975 1
4.3%
245962 1
4.3%
323756 1
4.3%
ValueCountFrequency (%)
446436 1
4.3%
416516 1
4.3%
385466 1
4.3%
383450 1
4.3%
323756 1
4.3%
245962 1
4.3%
166975 1
4.3%
152212 1
4.3%
118244 1
4.3%
102044 1
4.3%

석고(톤)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing23
Missing (%)100.0%
Memory size339.0 B

자동차(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean55467.591
Minimum29595
Maximum173453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T08:11:47.490900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29595
5-th percentile31223.65
Q141197.5
median45160
Q348571.25
95-th percentile135434.3
Maximum173453
Range143858
Interquartile range (IQR)7373.75

Descriptive statistics

Standard deviation34552.048
Coefficient of variation (CV)0.62292318
Kurtosis7.3452394
Mean55467.591
Median Absolute Deviation (MAD)3925
Skewness2.7399314
Sum1220287
Variance1.193844 × 109
MonotonicityNot monotonic
2023-12-13T08:11:47.634558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
173453 1
 
4.3%
42450 1
 
4.3%
31046 1
 
4.3%
34599 1
 
4.3%
42864 1
 
4.3%
37368 1
 
4.3%
41310 1
 
4.3%
47412 1
 
4.3%
41160 1
 
4.3%
29595 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
29595 1
4.3%
31046 1
4.3%
34599 1
4.3%
37368 1
4.3%
37785 1
4.3%
41160 1
4.3%
41310 1
4.3%
41850 1
4.3%
42450 1
4.3%
42864 1
4.3%
ValueCountFrequency (%)
173453 1
4.3%
138726 1
4.3%
72892 1
4.3%
68160 1
4.3%
56655 1
4.3%
48720 1
4.3%
48125 1
4.3%
48037 1
4.3%
47760 1
4.3%
47412 1
4.3%

갑종철도차량(톤)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
100335
 
1
108519
 
1
108849
 
1
122033
 
1

Length

Max length6
Median length4
Mean length4.3478261
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%
100335 1
 
4.3%
108519 1
 
4.3%
108849 1
 
4.3%
122033 1
 
4.3%

Length

2023-12-13T08:11:47.805407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:11:47.937673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
100335 1
 
4.3%
108519 1
 
4.3%
108849 1
 
4.3%
122033 1
 
4.3%

일반기타(톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2771634.8
Minimum690131
Maximum8151394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T08:11:48.082120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum690131
5-th percentile725510.5
Q11965191.5
median2352650
Q33034148.5
95-th percentile7569893.1
Maximum8151394
Range7461263
Interquartile range (IQR)1068957

Descriptive statistics

Standard deviation1896094.4
Coefficient of variation (CV)0.68410685
Kurtosis4.0218049
Mean2771634.8
Median Absolute Deviation (MAD)630117
Skewness1.9092425
Sum63747601
Variance3.5951738 × 1012
MonotonicityNot monotonic
2023-12-13T08:11:48.219965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
7925242 1
 
4.3%
8151394 1
 
4.3%
690131 1
 
4.3%
860927 1
 
4.3%
719743 1
 
4.3%
777418 1
 
4.3%
2224046 1
 
4.3%
2178018 1
 
4.3%
1722533 1
 
4.3%
1844661 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
690131 1
4.3%
719743 1
4.3%
777418 1
4.3%
860927 1
4.3%
1722533 1
4.3%
1844661 1
4.3%
2085722 1
4.3%
2165117 1
4.3%
2178018 1
4.3%
2224046 1
4.3%
ValueCountFrequency (%)
8151394 1
4.3%
7925242 1
4.3%
4371753 1
4.3%
3452178 1
4.3%
3192259 1
4.3%
3074746 1
4.3%
2993551 1
4.3%
2978164 1
4.3%
2676509 1
4.3%
2641930 1
4.3%

Interactions

2023-12-13T08:11:44.255885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:42.779843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.122875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.508580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.869597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:44.324793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:42.844897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.196757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.582593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.933768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:44.403274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:42.920856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.283162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.667857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:44.019148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:44.489384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:42.982350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.358180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.726814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:44.087505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:44.574458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.050977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.432657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:43.798065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:11:44.180528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:11:48.323546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도광재(톤)종이(톤)황산(톤)프로필렌(톤)비료(톤)비료-화학비료(톤)비료-석비및기타(톤)자동차(톤)갑종철도차량(톤)일반기타(톤)
연도1.0001.0001.0001.0001.0001.0000.8840.7050.7481.0000.847
광재(톤)1.0001.0001.0001.0001.0001.000NaNNaNNaN1.000NaN
종이(톤)1.0001.0001.0001.0001.0001.000NaNNaNNaN1.000NaN
황산(톤)1.0001.0001.0001.0001.0001.000NaNNaNNaN1.000NaN
프로필렌(톤)1.0001.0001.0001.0001.0001.000NaNNaNNaN1.000NaN
비료(톤)1.0001.0001.0001.0001.0001.000NaNNaNNaN1.000NaN
비료-화학비료(톤)0.884NaNNaNNaNNaNNaN1.0000.7300.826NaN0.866
비료-석비및기타(톤)0.705NaNNaNNaNNaNNaN0.7301.0000.806NaN0.397
자동차(톤)0.748NaNNaNNaNNaNNaN0.8260.8061.000NaN0.764
갑종철도차량(톤)1.0001.0001.0001.0001.0001.000NaNNaNNaN1.000NaN
일반기타(톤)0.847NaNNaNNaNNaNNaN0.8660.3970.764NaN1.000
2023-12-13T08:11:48.483078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
황산(톤)비료(톤)종이(톤)갑종철도차량(톤)광재(톤)프로필렌(톤)
황산(톤)1.0001.0001.0001.0001.0001.000
비료(톤)1.0001.0001.0001.0001.0001.000
종이(톤)1.0001.0001.0001.0001.0001.000
갑종철도차량(톤)1.0001.0001.0001.0001.0001.000
광재(톤)1.0001.0001.0001.0001.0001.000
프로필렌(톤)1.0001.0001.0001.0001.0001.000
2023-12-13T08:11:48.602673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도비료-화학비료(톤)비료-석비및기타(톤)자동차(톤)일반기타(톤)광재(톤)종이(톤)황산(톤)프로필렌(톤)비료(톤)갑종철도차량(톤)
연도1.000-0.939-0.578-0.840-0.9491.0001.0001.0001.0001.0001.000
비료-화학비료(톤)-0.9391.0000.5820.7540.8650.0000.0000.0000.0000.0000.000
비료-석비및기타(톤)-0.5780.5821.0000.4020.5600.0000.0000.0000.0000.0000.000
자동차(톤)-0.8400.7540.4021.0000.8281.0001.0001.0001.0001.0001.000
일반기타(톤)-0.9490.8650.5600.8281.0001.0001.0001.0001.0001.0001.000
광재(톤)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.000
종이(톤)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.000
황산(톤)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.000
프로필렌(톤)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.000
비료(톤)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.000
갑종철도차량(톤)1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T08:11:44.693410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:11:44.891570image/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.
2023-12-13T08:11:45.045054image/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)종이(톤)황산(톤)프로필렌(톤)비료(톤)비료-화학비료(톤)비료-석비및기타(톤)석고(톤)자동차(톤)갑종철도차량(톤)일반기타(톤)
01996<NA><NA><NA><NA><NA>13466061302<NA>173453<NA>7925242
11997<NA><NA><NA><NA><NA>909111446436<NA>138726<NA>8151394
21998<NA><NA><NA><NA><NA>734546385466<NA>48037<NA>4371753
31999<NA><NA><NA><NA><NA>631362416516<NA>48125<NA>2978164
42000<NA><NA><NA><NA><NA>560383383450<NA>56655<NA>3074746
52001<NA><NA><NA><NA><NA>391315323756<NA>68160<NA>3192259
62002<NA><NA><NA><NA><NA>299180245962<NA>72892<NA>3452178
72003<NA><NA><NA><NA><NA>199389166975<NA>47760<NA>2993551
82004<NA><NA><NA><NA><NA>174374152212<NA>48720<NA>2676509
92005<NA><NA><NA><NA><NA>233621118244<NA>37785<NA>2352650
연도광재(톤)종이(톤)황산(톤)프로필렌(톤)비료(톤)비료-화학비료(톤)비료-석비및기타(톤)석고(톤)자동차(톤)갑종철도차량(톤)일반기타(톤)
132009<NA><NA><NA><NA><NA>8848764326<NA>41850<NA>2165117
142010<NA><NA><NA><NA><NA>146550<NA><NA>29595<NA>2085722
152011<NA><NA><NA><NA><NA>118936<NA><NA>41160<NA>1844661
162012<NA><NA><NA><NA><NA>107889<NA><NA>47412<NA>1722533
172013<NA><NA><NA><NA><NA>92794<NA><NA>41310<NA>2178018
182014<NA><NA><NA><NA><NA>28432<NA><NA>37368<NA>2224046
19201519522428722647830023941441140<NA><NA><NA>42864100335777418
20201613033221681351055023150642551<NA><NA><NA>34599108519719743
2120176822517282857000022380826880<NA><NA><NA>31046108849860927
2220184974617818754720024125624887<NA><NA><NA><NA>122033690131